OCS Study 

MMS 2005-029 

 

Coastal Marine Institute 

Modeling Structure Removal 

Processes in the Gulf of Mexico 

Cooperative Agreement

Coastal Marine Institute 

Louisiana State University 

U.S. Department of the Interior

Minerals Management Service 

Gulf of Mexico OCS Region 
U.S. Department of the Interior Cooperative Agreement 

Minerals Management Service Coastal Marine Institute 

Gulf of Mexico OCS Region Louisiana State University 

OCS Study 

 MMS 2005-029 

 

Coastal Marine Institute 

Modeling Structure Removal 

Processes in the Gulf of Mexico 

Authors 

Mark J. Kaiser 

Dmitry V. Mesyanzhinov 

Allan G. Pulsipher 

May 2005 

Prepared under MMS Contract 

1435-01-99-CA-30951-85246 

by 

Center for Energy Studies 

Louisiana State University 

Baton Rouge, Louisiana 70803 

Published by 
iii

DISCLAIMER

This report was prepared under contract between the Minerals Management Service (MMS) and 

Louisiana State University’s Center for Energy Studies. This report has not been technically 

reviewed by MMS. Approval does not signify that the contents necessarily reflect the view and 

policies of the Service, nor does mention of trade names or commercial products constitute 

endorsement or recommendation for use. It is, however, exempt from review and compliance 

with MMS editorial standards. 

REPORT AVAILABILITY 

Extra copies of the report may be obtained from the Public Information Office (Mail Stop 5034) 

at the following address: 

 

 U.S. Department of the Interior 

 Minerals Management Service 

 Gulf of Mexico OCS Region 

 Public Information Office (MS 5034) 

 1201 Elmwood Park Boulevard 

 New Orleans, Louisiana 70123-2394 

 Telephone Number: 1-800-200-GULF 

 1-504-736-2519 

CITATION 

Suggested citation: 

Kaiser, M.J., D.V. Mesyanzhinov, and A.G. Pulsipher. 2005. Modeling structure removal 

processes in the Gulf of Mexico. U.S. Dept. of the Interior, Minerals Management 

Service, Gulf of Mexico OCS Region, New Orleans, La. OCS Study MMS 2005-029. 

133 pp. 

ACKNOWLEDGMENTS 

The advice and critical comments of Kristen Strellec, Tommy Broussard, Harry Luton, Jeff 

Childs, and Sarah Tsoflias are gratefully acknowledged. Editorial assistance provided by Ric 

Pincomb and Versa Stickle is also acknowledged. This paper was prepared on behalf of the U.S. 

Department of the Interior, Minerals Management Service, Gulf of Mexico OCS Region and has 

not been technically reviewed by the MMS. The opinions, findings, conclusions, or 

recommendations expressed in this paper are those of the authors, and do not necessarily reflect 

the views of the Minerals Management Service. Funding for this research was provided through 

the U.S. Department of the Interior and the Coastal Marine Institute, Louisiana State University. 
v

ABSTRACT 

At the end of 2003, there were nearly 4,000 structures in the federal waters of the Gulf of Mexico 

(GOM) associated with hydrocarbon production: 2,175 active (producing) structures, 1,227 idle 

(non-producing) structures, and 505 auxiliary (never-producing) structures. Since 1947, when 

production in the GOM first began, over 2,200 structures have been removed from federal 

waters, and over the past decade, 125 structures on average have been removed annually. The 

purpose of this report is to describe the operational aspects of removal processes in the GOM and 

to develop a production-based model to forecast the removal of offshore structures. 

In Chapter 1, a statistical description of the explosive removal process is presented. The 

influence of factors such as water depth, planning area, configuration type, and structure age 

upon the application of explosive removal methods is described. Estimates for the number of 

structures that are expected to be removed from the GOM over a 25-year time horizon are 

forecast using a heuristic life expectancy and probabilistic removal model. 

In Chapter 2, the factors involved in the decision to use a specific severance technique are 

described, and the probability that a structure will be removed with explosive technology is 

quantified. A simple predictive model of the decision to use explosive methods is also 

developed. An empirical analysis of GOM structures removed between 1986-2001 provide the 

historic data required to compute the probability of an explosive removal and to estimate binary 

choice models for severance selection. Binomial logit and probit models of severance selection 

are constructed to establish the relationship between structure attributes and the probability that a 

particular severance technique will be employed. 

In Chapter 3, four models of abandonment timing decisions are developed, ranging from a 

resource-based forecast to a risked, net present value approach. Meta-modeling simulation is 

employed to construct functionals that describe how the age of the structure upon abandonment 

is related to the system parameters. The sensitivity of the results to differences in the model 

assumptions, and the practical matter of whether sophisticated models are, in fact, more accurate 

than simple forecasting techniques, is discussed. A generic field development scenario is used to 

illustrate the decommissioning timing models. 

In Chapter 4, the economic limit of offshore structures is estimated using historic data from 

GOM structures removed over the past two decades. This is the first time that threshold limits of 

production near abandonment have been investigated and quantified. 

In Chapter 5, a production-based model to forecast removal rates and costs of offshore structures 

is presented. A stochastic decline model is used to forecast production, and in conjunction with 

estimates of the economic limit, is used to determine the time that a structure is abandoned. The 

expected time a structure is removed is based on federal regulatory requirements which 

determine the latest possible removal scenario. A description of the modeling framework and 

results are presented, along with a discussion of the limitations of analysis. 
vii

TABLE OF CONTENTS 

Page

LIST OF FIGURES ................................................................................................... xi 

LIST OF TABLES..................................................................................................................... xiii 

CHAPTER 1: EXPLOSIVE REMOVALS OF OFFSHORE STRUCTURES 

1.1. Introduction........................................................................................................ 1 

1.2. Statistical Description of Structure Removals ................................................... 2 

 1.2.1. Notation.................................................................................................... 2 

 1.2.2. Structure Installation and Removals by Water Depth.............................. 4 

 1.2.3. Age Distribution of Active and Removed Structures .............................. 5 

 1.2.4. Structure Removals by Configuration Type and Method of Removal .... 5 

 1.2.5. Structure Removals by Year and Configuration Type............................. 7 

 1.2.6. Structure Removals by Age, Water Depth, and Configuration Type ...... 7 

1.3. A Life Expectancy Model of Platform Removal Processes............................... 8 

 1.3.1. A Structure Has at Least Five Lives ........................................................ 8 

 1.3.2. Sources of Uncertainty............................................................................. 9 

 1.3.3. Removal and Severance Models.............................................................. 10 

 1.3.4. Model Results .......................................................................................... 11 

 1.3.5. Model Assumptions ................................................................................. 11 

 1.3.6. Limitations of the Analysis...................................................................... 12 

1.4. Conclusions........................................................................................................ 12 

CHAPTER 2: A BINARY CHOICE SEVERANCE SELECTION MODEL FOR OFFSHORE 

STRUCTURE REMOVAL 

2.1. Introduction........................................................................................................ 15 

2.2. Background Information.................................................................................... 16 

 2.2.1. Gulf of Mexico Infrastructure.................................................................. 16 

 2.2.2. Decommissioning Options and Economic Criteria.................................. 16 

 2.2.3. Decommissioning is Often a Severing Intensive Operation.................... 17 

 2.2.4. Environmental Consequences of Severance Technology........................ 20 

 2.2.5. The Regulatory Structure of Explosive Severance .................................. 21 

2.3. Factors Involved in Severance Selection ........................................................... 23 

 2.3.1. Direct Cost ............................................................................................... 23 

 2.3.2. Cost of Failure.......................................................................................... 24 
viii

TABLE OF CONTENTS (continued)

 Page 

 2.3.3. Human Safety........................................................................................... 24 

 2.3.4. Environmental Issues and Structure Disposition ..................................... 24 

 2.3.5. Operator Experience and Preference ....................................................... 25 

 2.3.6. Operational Scheduling............................................................................ 25 

 2.3.7. Contractor Experience and Preference..................................................... 26 

 2.3.8. Structure Characteristics .......................................................................... 26 

 2.3.9. Structure Age ........................................................................................... 26 

 2.3.10. Cutting Reliability.................................................................................. 26 

 2.3.11. Configuration Type................................................................................ 27 

2.4. The Probability of an Explosive Removal......................................................... 27 

2.5. Operator Practice in the Gulf of Mexico............................................................ 28 

2.6. Binomial Logit and Probit Models of Severance Selection............................... 29 

 2.6.1. Model Development................................................................................. 29 

 2.6.2. Model Results .......................................................................................... 30 

 2.6.3. Interpretation and Application of the Probit and Logit Model ................ 31 

 2.6.4. Limitations of the Analysis...................................................................... 31 

2.7. Conclusions........................................................................................................ 32 

CHAPTER 3: MODELING THE DECOMMISSIONING TIME OF OFFSHORE 

STRUCTURES 

3.1. Introduction........................................................................................................ 33 

3.2. After-Tax Net Cash Flow Analysis.................................................................... 35 

 3.2.1. Units of Analysis...................................................................................... 35 

 3.2.2. After-Tax Net Cash Flow......................................................................... 36 

 3.2.3. Cash Flow Components ........................................................................... 36 

 3.2.4. Economic Indicators ................................................................................ 39 

 3.2.5. Typical Cash Flow Patterns ..................................................................... 40 

3.3. Parameter Forecasting........................................................................................ 40 

 3.3.1. Reserves ................................................................................................... 41 

 3.3.2. Production Profile .................................................................................... 42 

 3.3.3. Hydrocarbon Price and Quality ............................................................... 43 

 3.3.4. Capital Expenditures................................................................................ 43 

 3.3.5. Operating Cost ......................................................................................... 44 

 3.3.6. Decommissioning Cost ............................................................................ 45 
ix

TABLE OF CONTENTS (continued)

 Page 

3.4. Model Development........................................................................................... 45 

 3.4.1. Model I – Resource Recovery.................................................................. 46 

 3.4.2. Model II – Threshold Indicators .............................................................. 46 

 3.4.3. Model III – Negative Cash Flow ............................................................. 47 

 3.4.4. Model IV – Maximum Net Present Value ............................................... 48 

3.5. Meta-Modeling Methodology............................................................................ 49 

3.6. Illustrative Example........................................................................................... 50 

 3.6.1. Development Scenario............................................................................. 51 

 3.6.2. Model I Results........................................................................................ 52 

 3.6.3. Model II Results....................................................................................... 53 

 3.6.4. Model III Results ..................................................................................... 54 

3.7. Limitations of the Analysis................................................................................ 56 

 3.7.1. Private Uncertainty .................................................................................. 56 

 3.7.2. Scale Economies ...................................................................................... 56 

 3.7.3. Regulatory Uncertainty............................................................................ 57 

 3.7.4. Random Events ........................................................................................ 57 

3.8. Conclusions........................................................................................................ 57 

CHAPTER 4: A STATISTICAL ANALYSIS OF THE ECONOMIC LIMIT OF OFFSHORE 

HYDROCARBON PRODUCTION 

4.1. Introduction........................................................................................................ 59 

4.2. Model Development........................................................................................... 60 

 4.2.1. Production Allocation .............................................................................. 60 

 4.2.2. Lease Categorization................................................................................ 60 

 4.2.3. Threshold Limits...................................................................................... 61 

 4.2.4. Factor Description.................................................................................... 61 

4.3. Descriptive Statistics.......................................................................................... 63 

 4.3.1. Data Source.............................................................................................. 63 

4.3.2. Lease Categorization Statistics ................................................................ 63 

 4.3.3. Average Production and Revenue Threshold Levels............................... 64 

4.4. Conclusions........................................................................................................ 65 
x

TABLE OF CONTENTS (continued)

 Page 

CHAPTER 5: FORECASTING THE REMOVAL OF OFFSHORE STRUCTURES IN THE 

OUTER CONTINENTAL SHELF 

5.1. Introduction........................................................................................................ 67 

5.2. Model Framework.............................................................................................. 68 

 5.2.1. General Methodology .............................................................................. 68 

 5.2.2. Production Model..................................................................................... 68 

 5.2.3. Revenue Model ........................................................................................ 68 

 5.2.4. Abandonment Time ................................................................................. 68 

 5.2.5. Removal Time.......................................................................................... 69 

 5.2.6. Removal Cost........................................................................................... 69 

5.3. Model Parameterization..................................................................................... 69 

5.4. Model Results .................................................................................................... 71 

 5.4.1. Model Statistics........................................................................................ 71 

 5.4.2. Removal and Cost Forecast ..................................................................... 71 

 5.4.3. Model Discussion..................................................................................... 72 

5.5. Limitations of the Analysis................................................................................ 72 

5.6. Conclusions........................................................................................................ 73 

 

REFERENCES .......................................................................................................................... 75 

APPENDIX A CHAPTER 1 FIGURES AND TABLES .......................................................... 79 

APPENDIX B CHAPTER 2 FIGURES AND TABLES .......................................................... 97 

APPENDIX C CHAPTER 3 TABLES...................................................................................... 111 

APPENDIX D CHAPTER 4 TABLES...................................................................................... 115 

APPENDIX E CHAPTER 5 FIGURES AND TABLES........................................................... 123 
xi

LIST OF FIGURES 

Figure Description Page 

A.1 Federal Outer Continental Shelf Leasing Program............................................ 81 

A.2 Caisson Structures.............................................................................................. 82 

A.3 Well Protector Structures................................................................................... 83 

A.4 Fixed Platform Structures .................................................................................. 84 

B.1 Conventionally Piled Platform with Wells ........................................................ 99 

B.2 Offshore Oil and Gas Facility Decommissioning Tree...................................... 100 

B.3 Decommissioning Is Often a Severing Intensive Operation.............................. 101 

B.4 The NMFS Observer Program (2002) ............................................................... 103 

B.5 The Abrasive Water Jet Cutting Process ........................................................... 106 

B.6 Number of Structures Removed and Percentage of Explosive Removals by 

 the “Top 36” Companies (1986-2001)......................................................... 107 

B.7 Number of Structures Removed and Percentage of Explosive Removals by 

 the “Bottom 91” Companies (1986-2001) ................................................... 107 

E.1 Central GOM Production Threshold Structure Removal Forecast.................... 125 

E.2 Central GOM Revenue Threshold Structure Removal Forecast........................ 126 

E.3 Central GOM Production Threshold Removal Cost Forecast ........................... 127 

E.4 Central GOM Revenue Threshold Removal Cost Forecast............................... 128 

E.5 Western GOM Structure Removal Forecast Comparison.................................. 129 

E.6 Western GOM Removal Cost Forecast Comparison......................................... 130
xiii

LIST OF TABLES 

Table Description Page 

A.1 Total Number of Structures Installed and Removed by Water Depth and 

 Planning Area in the Gulf of Mexico (1947-2001)...................................... 85 

A.2 Average Annual Number of Structures Installed and Removed in the Gulf 

 of Mexico According to Water Depth and Planning Area (1996-2001)...... 86 

A.3 Average Annual Number of Structures Installed and Removed in the Gulf 

 of Mexico According to Water Depth and Planning Area (1991-2001)...... 87 

A.4 The Age Distribution of Active Structures in Shallow Water (0-60 m) by 

 Configuration Type and Planning Area (1947-2001) .................................. 88 

A.5 Average Age of Structures Upon Removal by Water Depth, Configuration 

 Type, and Planning Area (1947-2001)......................................................... 88 

A.6 Number of Structures Removed (R), Structures Removed by Explosive 

 Technique (RE), and the Percentage of Explosive Removals (pE) as a 

 Function of Water Depth and Planning Area (1986-2001).......................... 89 

A.7 Number of Structures Removed (R), Structures Removed by Explosive 

 Technique (RE), and the Percentage of Explosive Removals (pE) as a 

 Function of Water Depth and Configuration Type for the Gulf of Mexico 

 (1986-2001).................................................................................................. 90 

A.8 Number of Structures Removed (R), Structures Removed by Explosive 

 Technique (RE), and the Percentage of Explosive Removals (pE) as a 

 Function of Time and Configuration Type for the Gulf of Mexico 

 (1986-2001).................................................................................................. 91 

A.9 Number of Structures Removed (R) and Percentage of Structures 

 Removed (p) Grouped According to Age Upon Removal and Planning 

 Area (1986-2001)......................................................................................... 92 

A.10 Number of Structures Removed Using Explosives (RE) and the Percentage 

 of All Structures Removed Using Explosives (pE) Grouped According to 

 Age Upon Removal and Planning Area (1986-2001).................................. 92 

A.11 Number of Structures Removed (R) and Percentage of Structures 

 Removed (p) as a Function of Water Depth and Age Upon Removal 

 (1986-2001).................................................................................................. 93 

A.12 Number of Structures Removed Using Explosives Techniques and the 

 Percentage of Explosives Removal as a Function of Water Depth and 

 Age Upon Removal (1986-2001)................................................................. 93 

A.13 Number of Structures Removed (R), Number of Structures Removed by 

 Explosive Technique (RE), and the Percentage of Structures Removed by 

 Explosives (pE) Categorized According to Age and Configuration Type 

 (1986-2001).................................................................................................. 94 

A.14 Percentage of Structures Removed by Configuration Type and Water Depth 

 in the Gulf of Mexico (1986-2001).............................................................. 94 

A.15 Medium-Term Forecast of the Number of Structures Removed in the Gulf 

 of Mexico by Explosive Technique (Model I)............................................. 95 
xiv

LIST OF TABLES (continued) 

Table Description Page 

A.16 Medium-Term Forecast of the Number of Structures Removed in the Gulf 

 of Mexico by Explosive Technique (Model II) ........................................... 95 

B.1 Gulf of Mexico Active and Removed Structures by Configuration Type, 

 Water Depth and Number of Slots (1947-2001).......................................... 108 

B.2 Number of Structures Removed (R), Structures Removed by Explosive 

 Technique (RE), and the Percentage of Explosive Removals (pE) as a 

 Function of Water Depth and Configuration Type for the Gulf of 

 Mexico (1986-2001) .................................................................................... 109 

B.3 Percentage of Explosive Removals by Configuration Type and Age Upon 

 Removal in the Gulf of Mexico (1986-2001) .............................................. 109 

B.4 A Summary of Operator Involvement in Gulf of Mexico Structure Removals 

 (1986-2001).................................................................................................. 110 

B.5 Probit and Logit Model Results ......................................................................... 110 

C.1 Design Space for Models I and II ...................................................................... 113 

C.2 Model I and II Regression Results..................................................................... 113 

C.3 Design Space for Decommissioning Model III ................................................. 114 

C.4 Model III Regression Results............................................................................. 114 

D.1 Summary Statistics for Structures Removed in the Gulf of Mexico ................. 117 

D.2 Average Production Threshold Levels At/Near the Year of Last Production – 

 Lease Category I .......................................................................................... 118 

D.3 Average Revenue Threshold Levels At/Near the Year of Last Production – 

 Lease Category I .......................................................................................... 118 

D.4 Average Production Threshold Levels At/Near the Year of Last Production – 

 Lease Category II......................................................................................... 119 

D.5 Average Revenue Threshold Levels At/Near the Year of Last Production – 

 Lease Category II......................................................................................... 119 

D.6 Average Production Threshold Levels At/Near the Year of Last Production – 

 Lease Category III........................................................................................ 120 

D.7 Average Revenue Threshold Levels At/Near the Year of Last Production – 

 Lease Category III........................................................................................ 120 

D.8 Average Production Threshold Levels At/Near the Year of Last Production – 

 Lease Category IV ....................................................................................... 121 

D.9 Average Revenue Threshold Levels At/Near the Year of Last Production – 

 Lease Category IV ....................................................................................... 121 

E.1 Number of Structures Removed in the Gulf of Mexico (1973-2002)................ 131 

E.2 Active, Idle, and Auxiliary Structures on Active Leases (2003) ....................... 132 

E.3 Active, Idle, and Auxiliary Structures in the Gulf of Mexico (2003)................ 132 

E.4 Idle and Auxiliary Structures on Inactive Leases in the Gulf of Mexico 

 (2003)........................................................................................................... 133 

E.5 Normalized Annual Production and Revenue Threshold Levels in the 

 Gulf of Mexico............................................................................................. 133 
1

CHAPTER 1: EXPLOSIVE REMOVALS OF OFFSHORE STRUCTURES 

1.1. Introduction

The Outer Continental Shelf (OCS) of the U.S. Gulf of Mexico (GOM) is one of the most highly 

developed and mature basins in the world. Over the last 50 years, the oil and gas industry has 

installed over 6,000 structures and 33,000 miles of interconnecting pipelines in the gulf waters. 

Today, there are about 4,000 active structures installed in federal1 water ranging from less than 

10 feet to over 7,000 feet. There are also a few thousand structures in state waters off the coast 

of Louisiana and Texas, almost all of which are small and installed in less than 35 feet of water. 

Structures need to be constructed, delivered, installed, and equipped prior to production, operated 

and serviced during production, and then eventually decommissioned and removed after 

production. Each of these activities has both a direct and indirect impact on the communities in 

which the service facilities and manufacturing operations are located, and hence induce a “spillover” effect on the economic growth of regions which serve the development. An entire industry 

has been built in the GOM around installing production equipment and structures, servicing 

those structures (maintenance, repairs, supply), and then removing the structures when 

production ceases.

During the life of a lease, the leaseholders apply for permits to place structures on the seafloor to 

aid in drilling, development, and production operations. Near the end of the economic life of the 

lease, when the structures have been fully depreciated and reserves depleted, the structures 

represents a financial and operational liability, and at this point in time a decision is made to 

abandon. Within one year of lease termination, the Minerals Management Service (MMS) 

requires that the lessees remove all structures to a depth of 15 feet below the mudline and that the 

site be returned to prelease conditions. Although multiple techniques may be used to sever the 

structural components, they are generally categorized as either explosive or nonexplosive 

methods. 

Operators wishing to remove an OCS platform or facility are required to submit a structure 

removal permit application to MMS for technical review and the preparation of an environmental 

assessment (EA) under National Environmental Policy Act (NEPA) guidelines. Prior to 

mobilization, additional permits are required for well abandonment (temporary or permanent) 

and/or pipeline decommissioning to ensure that all of the infrastructure components to and from 

the structure are secured. Removal operations proposing explosive severance are currently 

subject to the terms and conditions of a programmatic Biological Opinion (BO)/ Incidental Take 

Statement (ITS) issued by the National Oceanographic and Atmospheric Administration’s 

Fisheries Service (NOAA Fisheries) under an Endangered Species Act (ESA) Consultation with 

MMS. If an operator proposes any activities that fall outside of the BO/ITS severance criteria 

(e.g., 50-lb maximum charge weight, cut depth, 900 msec detonation staggering, etc.), a sitespecific ESA Consultation and new BO/ITS will be required. 

 

1

 Federal jurisdiction in the OCS varies with the Gulf state: Florida and Texas have an extended nine nautical mile 

state jurisdiction, while Alabama, Louisiana, and Mississippi have the standard three nautical mile state jurisdiction. 
2

The NOAA Fisheries Service currently assigns observers to every OCS structure removal 

operation using explosive charges >5 lb. A pre-blast aerial survey is conducted immediately 

prior to the explosive detonation using a helicopter with a NMFS observer on board. If marine 

mammals or sea turtles are found within 1,000 yards of the structure, the detonation is delayed 

until the area is clear. A post-blast aerial survey is conducted after the explosives are detonated 

to assess the impact to the marine life. Underwater detonations have the potential to harass, 

injure, or kill marine mammals and sea turtles; however, since introduction of the NOAA 

Fisheries’ Platform Removal Observer Program (PROP) in 1986, only two sea turtles have been 

killed and three turtles have been injured as a result of explosive severance. 

The purpose of this chapter is to provide a statistical description of structures that have been 

removed in the GOM and the manner of their removal. The influence of factors such as water 

depth, planning area, configuration type, and structure age will be examined, and the relationship 

of these factors with explosive removals will be discussed. Estimates for the number of 

structures that are expected to be removed from the GOM over a 25-year time horizon are 

forecast by configuration type, water depth, and planning area categorization over 5-year time 

blocks beginning from the year 2002. The result of the models and a description of the 

limitations of the analysis is then presented. Conclusions complete the chapter. 

1.2. Statistical Description of Structure Removals

1.2.1. Notation: The GOM is partitioned according to protraction area, water depth, and 

planning area categories. The three planning areas which divide the GOM are denoted the 

Western, Central, and Eastern Gulf of Mexico (WGOM, CGOM, EGOM). See Figure A.1. Each 

planning area is subdivided into smaller regions, called protraction areas, which in turn are 

divided further into numbered blocks. Each block is designated by a number and is normally a 

nine square mile area consisting of 5,760 acres. A single block is the smallest unit that can be 

leased for oil and gas exploration on the OCS. The water depth categorization applied in MMS 

resource assessment evaluations, 

W = { } , , W1 K W5 = {0-200m, 201-800m, 801-1600m, 1601-2400m, 2400+

m}, 

is too broadly defined for platform removal studies since nearly all (96%) of the structures 

removed in the GOM to date have been within a 0-60 meters water depth range. To examine the 

use of explosive methods as a function of water depth a finer level of disaggregation needs to 

be employed. A partition is selected that decomposes the 0-200 meters category into a 0-60 

meters and 61-200 meters category, and then a further partition of the 0-60 meters category into 

subcategories is adopted. Eleven subcategories within the 0-60 meters water depth range 

classified according to feet are applied, and we shall transition between the distance measures as 

convenience dictates. The first five subcategories employ a ten feet range, and then from 50-200 

feet, a 25 feet range is employed: 

 

 W = { , , } W1 K W4 ={0-10, 11-20, 21-30, 31-40, 41-50, 51-75, 76-100, 101-125, 126-150, 151-

175, 176-200, 201-656, 657-2624, 2624+

ft}. 
3

The GOM planning areas are denoted by 

P = { } , , P1 P2 P3 = {WGOM, CGOM, EGOM}, 

and since the Eastern GOM has seen only a very small level of activity, this planning area will 

not be considered further. Since the water depth and planning area schemes are disjoint, the two 

categories can be combined using a Cartesian product as follows: 

{ ( , ) | 1, ,14; 1,2}, W × P = Γi, j = Wi Pj i = K j =

where Γi, j denotes the water depth and planning area category indexed by i and j; e.g., Γ4,2

denotes the 31-40 feet water depth range in the Central GOM. 

 

Structures can be classified through their attributes such as configuration type and age upon 

removal. Configuration type is described using four categories as follows: 

{ } , , , T1 T2 T3 T4 = {caissons, well protectors, fixed, floating}. 

The minimum structure for offshore development of a well is a caisson, a cylindrical or tapered 

tube enclosing the well conductor. A small deck is sometimes provided above the wellhead, but 

no facilities are provided except possibly navigational aides and a small crane (Figure A.2). 

Structures that provide support to one or more wells drilled with a mobile drilling rig are 

normally referred to as well protectors. Well protectors are sized to fit within the drilling slot of a 

mobile drilling rig, and are usually 3- or 4-piled structures with minimum decks and production 

facilities (Figure A.3). Production from caissons and well protectors is usually sent to a 

production platform for treating. Well protectors and other fixed platforms are designed with a 

jacket, a three-dimensional welded frame of tubular members, used as a guide for driving piles 

through its legs. Fixed platforms include drilling, production, drilling/production, and auxiliary 

platforms (Figure A.4). Depending on the design and construction requirements and constraints, 

the number of piles of a fixed platform can vary from three to eight or more and can be as small 

as 24 inches or as large as 96 inches. Four-pile and 8-pile fixed platforms are the most common 

structures in the GOM. 

The age of the structure upon removal is grouped according to 

{ } , , , A1 A2 A3 A4 = {0-10, 11-20, 21-30, 30+

 years}. 

 

The number of structures removed from the water depth and planning area region Γi, j over the 

time interval (t −1,t) is specified in terms of configuration type and age as follows: 

R(Γi, j , Tk, t) = Number of structures removed from region Γi, j of type Tk in year t, 

R(Γi, j , Al, t) = Number of structures removed from region Γi, j that fall within age group 

type Al in year t, 
4

R(Γi, j , Tk, Al, t) = Number of structures removed from region Γi, j of type Tk that fall 

within age group Al in year t. 

The number of structures removed using explosive methods is denoted by the subscript E; e.g., 

RE(Γi, j , Tk, t) = Number of structures removed from region Γi, j of configuration type Tk 

using explosive techniques in year t. 

 

The percentage of structures of a given classification that are removed through explosive 

technology is computed as the ratio of (⋅) RE to R(⋅); e.g., the percentage of structures of 

configuration type Tk removed through explosive technology in year t is computed as 

( , , )

( , , ) ( , , )

,

,

, R T t

R T t

p T t

i j k

E i j k

E i j k Γ

Γ

Γ = , 

and in most cases time will be “integrated out” of the data set: 

∑

∑

Γ

Γ

Γ =

t

i j k

t

E i j k

i j k R T t

R T t

p T

( , , )

( , , ) 

( , )

,

,

, . 

Percentage applications must be employed cautiously, however, since if the number of elements 

in the set R(⋅) or (⋅) RE is “small,” then (⋅) E p cannot be considered a reliable statistic. For 

instance, if there are less than a dozen elements in a given set, then one cannot assign much 

confidence to the values as being “representative” of conditions in the region. The tables of 

summary statistics present raw data as well as the percentage values to convey this information. 

The total number of structures removed from the water depth and planning area region Γi, j is 

denoted by R(Γi, j ) and is equal to any complete summation of the decomposed data over the 

universe of the partition: 

R(Γi, j ) ( , , ) ( , , ) ( , , , ).

4

1

4

1

,

4

1

,

4

1

∑∑ , ∑∑ ∑∑∑ = = = =

= Γ = Γ = Γ t

k i

k l

l i j

t l

k i j

t k

i j R T t R A t R T A t

1.2.2. Structure Installation and Removals by Water Depth: Information on offshore 

structures in federal waters was obtained from the U.S. Minerals Management Service. The 

MMS updates its database on a periodic basis as new information is made available, and so there 

is always a time lag between when the data is reported and entered into the database and when it 

is analyzed. The structure data employed in this chapter was current through November 2001. 

The total number of structures installed and removed since 1947 as a function of water depth and 

planning area is depicted in Table A.1. Structures are defined to include all caissons, well 

protectors, fixed platforms, and floating configurations. 
5

Nearly 6,000 structures have been installed in the GOM through the year 2001 and one-third of 

these structures have now been removed. The vast majority of installations and removals have 

been in shallow water: 90% of all structures installed in the GOM and 96% of all the removals 

have been in less than 200 feet (60 meters) of water. Within the 0-200 feet category, 36% of all 

the structures that have been installed through the year 2001 have been removed, while only 14% 

of structures beyond 200 feet have been removed. Activity levels vary widely as a function of 

water depth. 

 

The average annual number of structures installed and removed per water depth and planning 

area category over a 5-year (1996-2001) and 10-year (1991-2001) time horizon is depicted in 

Table A.2 and Table A.3, respectively. The value of the average annual number of installations 

and removals is surprisingly robust over the 5- and 10-year horizon in the sense that the mean 

and standard deviation of the installation and removal rates do not change appreciably. On the 

other hand, activity levels are highly uncertain throughout most of the water depth categories, 

and so the normal statistical interpretation bounding the mean through a confidence interval 

employing one- or two-standard deviations should also be approached cautiously. 

The CGOM and WGOM planning areas exhibit significantly different activity levels. The 

number of structures installed in the CGOM is roughly five times WGOM activity, and a similar 

level of activity governs the removal rates. In shallow waters, structure removal rates are 

comparable to installation rates across planning area. In deep waters, structure installations 

dominate removals. The historic magnitude of installation and removal activity is also clearly 

dominated by shallow water activity. To date 82% of all WGOM structures and 91% of all 

CGOM structures have been installed in less than 200 feet of water. In terms of structure 

removals, 91% of all WGOM removals and 96% of all CGOM removals have been in 200 feet of 

water or less. 

 

1.2.3. Age Distribution of Active and Removed Structures: In Table A.4 the percentage of 

active structures that fall within each age category is depicted. Infrastructure in the GOM is 

aging and this is clearly indicated among all configuration categories, especially within the 

CGOM region where nearly 40% of the well protectors and over a third of the fixed platforms 

are over 30 years old. 

 

In Table A.5 the average age of structures removed from the GOM is depicted. Structures in the 

WGOM tend to be removed, on average, earlier than their counterparts in the CGOM, which 

could be due to smaller field size, faster production rates, or other geologic-based conditions; 

e.g., most fields in the WGOM are gas fields which exhibit a quick depletion rate. Caissons in 

the CGOM are removed after about 16 years of service while caissons in the WGOM have a 

significantly shorter lifespan of seven years. Observe that the standard deviation values in all 

cases are greater than 50% of the value of the mean, and so it is clear that there is wide 

variability in structure removal ages within categories and across water depth and configuration 

type. Also, with the exception of well protectors in the WGOM, there is not a significant 

difference in the average age of removal across water depth categories. 

1.2.4. Structure Removals by Configuration Type and Method of Removal: The application 

of explosive techniques varies widely throughout Gulf waters. In Table A.6, the number of 
6

structures removed R = R(Γi, j ) and the number removed by explosive techniques (RE = 

RE(Γi, j )) are shown as a function of water depth and planning area beginning from 1986. 

Although multiple techniques may be used to sever conductors and piling, severing is usually 

categorized as either explosive or nonexplosive. If explosives are used in any amount and at any 

stage of the decommissioning project, then the method is considered explosive. Beginning in 

1986 companies planning to remove offshore structures with explosives were required to obtain 

a permit from the MMS, and hence only data from this period of time onward is available. The 

data set represents about 80% of the total structure removals to date. 

The percentage of structures removed using explosive techniques is calculated as 

( )

( )

,

,

i j

E i j

E R

R

p Γ

Γ = . 

The percentage values depicted need to be interpreted carefully, however, since the values 

depend upon the selection of the water depth categories employed. An additional problem in 

interpreting the value of E p is that the percentage calculation may be based on only a handful of 

data, and in such circumstances, one cannot assign much confidence to the values as being 

“representative” of conditions in the region. This is particularly a problem throughout the 

shallow water (0-40 feet) and deepwater (657-2,624 feet) categories of the WGOM where only a 

few structures have been removed. With these exceptions noted, however, there does not appear 

to be a significant difference between the application of explosive techniques over the WGOM 

and CGOM planning area, which is quite reasonable considering there is no rational reason why 

explosive techniques would be different across planning area unless the structure types, age2

, or 

year of removal are dramatically different. The data in Table A.6 supports the assertion that 

planning area dependence on E p is weak, and so we can aggregate over planning area and 

consider the application of explosive removals throughout the GOM as representative of either 

the WGOM or CGOM planning area. 

The description of explosive removals across the GOM as a function of configuration type is 

depicted in Table A.7. It is apparent from Table A.7 that the choice of removal method depends 

to some extent on the configuration type of the structure, but there are no observable trends 

within the 0-200 feet category for any of the configuration types. It is also difficult to explain the 

variability that does exist, and most probably, the variation of E p with water depth is due to 

“noise” that cannot be detected. Recall that structure removal decisions are usually based on a 

few factors that are mostly unobservable: cost, safety, risk of failure, and technical feasibility, 

combined with a wide variety of structure-, site-, and company-specific criteria. The variation 

that exists in E p (Wi Tk , ) across water depth and configuration types leads us to conclude that the 

best indicators of E p are aggregate measures across broad water depth categories that do not 

differentiate between planning area. 

 

2

 In fact, as mentioned previously (recall Table A.5), there is a difference in the average age of structures upon 

removal across planning area, and to the extent that a younger structure is more likely to be removed with 

nonexplosive technology, we would suspect WGOM structures to have a slightly lower probability of being 

removed using explosives as shown in the aggregated categories pE (P1) and pE (P2) at the bottom of Table A.6.
7

Using the categorization shown at the bottom of Table A.7, observe that caissons are the most 

commonly removed structure using nonexplosive methods, and well protectors and fixed 

platforms, if removed using nonexplosive techniques, are more commonly performed in shallow 

waters. Caissons have an equal chance of being removed with either explosive or nonexplosive 

methods, and well protectors and fixed structures realize a greater chance of an explosive 

removal. As the water depth increases the chance of using explosives also increase across all 

configuration types. The percentage values depicted for explosive removals for well protectors in 

the 61-200 meters water depth range is slightly suspect, however, since it is based on only six 

data points. Thus far, no caissons, well protectors, or fixed structures have been removed in 

water depth greater than 200 meters, and the two semisubmersibles that have been removed in 

this water depth range are included for completeness. 

1.2.5. Structure Removals by Year and Configuration Type: The number of structures 

removed by configuration type by year is shown in Table A.8 across all water depths in the Gulf 

of Mexico. There are no noticeable trends in the removal rates across time except caissons and 

fixed structures typically compete for the greatest number of removals in any given year. The 

percentage values E p can be considered a stochastic process, but it is preferable to “average out” 

the time variability by aggregating the (⋅) RE and ) R(⋅ values and calculating 

∑

∑

=

t

k

t

E k

E k R T t

R T t

p T

( , )

( , ) 

( , )

as shown in the last row of Table A.8. The variability of E p across time for a given 

configuration class can be explained to some extent through the age of the structure and the 

water depth. 

1.2.6. Structure Removals by Age, Water Depth, and Configuration Type: Structures that 

have been removed from the GOM according to planning area and age upon removal are 

depicted in Table A.9. All structure types are aggregated within the same category and it is clear 

that a significant variation exists across planning areas. More than 90% of all WGOM structures 

are removed within 20 years of their installation – indicating small reserves, quick production, 

poor geologic prospects, or a combination of all these factors. For the most part, structures in the 

GOM are removed several years after they reach the end of their economic life. A few structures 

are removed early because of structural damage (collision with barge, hurricane event, etc.), with 

fewer still removed because of fatigue. Structures are removed near the time when they are no 

longer economic, and this is not (normally) constrained by the design life of the structure. At the 

opposite end of the spectrum is the relatively long life of many CGOM fields: 15% of all CGOM 

structures for instance were at least 30 years old upon removal. 

In Table A.10 the number of structures removed using explosives is depicted along with the 

percentage of explosive removals categorized according to age. Examine the percentage of 

explosive removals shown on the right side of Table A.10. As the age of a structure increases, so 

does the frequency that explosive methods will be employed. It is interesting to note that when 
8

the data is aggregated according to age upon removal, WGOM structures have a greater 

likelihood of an explosive removal relative to CGOM structures. 

To examine the features of water depth and structure age upon removal method, structure data 

was aggregated and then classified as shown in Table A.11 and Table A.12. Table A.11 depicts 

the number of structures removed as a function of water depth and age upon removal, and it is 

clear that the majority of structures removed from both water depth categories are within 20 

years of their installation date. The data in Table A.12 are more interesting, however, since the 

general trends observed earlier hold here with the same caveats: the percentage of structures 

removed using explosive methods increase as a function of age upon removal for the 0-60 meters 

category and is dominated by the application of explosive removals in the 61-200 meters water 

depth category. The number of structures in the 61-200 meters group, however, especially for 

the 21-30 and 30+ age categories, is too small to draw meaningful conclusions. 

The general trends observed in Table A.7 for the application of explosive techniques also apply 

to individual configuration type and water depth categories as shown in Table A.13 and Table 

A.14. In Table A.13, observe that across all configuration types, the use of nonexplosive 

methods is most common in the 0-10 year category, and as the age of the structure increases, so 

does the likelihood that explosive methods will be applied. In Table A.14, the percentage of 

structures removed using explosives as a function of water depth, age upon removal, and 

configuration type is presented. Blank entries indicate that no structures within the given 

categorization were removed. 

1.3. A Life Expectancy Model of Platform Removal Processes 

1.3.1. A Structure Has at Least Five Lives: An offshore structure is an economic investment 

that has at least five distinct “lives”: (1) the physical life, (2) the service life, (3) the depreciation 

life, (4) the design life, and (5) the economic life. 

The physical life of a structure is the period of time over which the investment is actually used, 

while the service life is the period of time over which the structure is held for a particular 

purpose or level of service. The physical life of a structure is not necessarily identical with the 

service life, since a structure on a lease may cease to produce hydrocarbons but is maintained in 

service to be removed and decommissioned at a later date. 

The depreciation life of a structure is the period of time over which the investment is depreciated 

on the operators accounting books. The depreciation period starts each time the structure is 

placed in service by a new owner and ends when (a) the structure is decommissioned, (b) all of 

the allowable depreciation is deducted, or (c) the structure is no longer used for business 

purposes. 

The design life of a structure depends upon the expected field size and operator development 

plan. In the early years of offshore development, structures were typically over-designed to 

compensate for the uncertainties of a hostile and unknown environment. Today, operators have 

greater experience balancing the cost and risk of field development, and the design life of 

structures usually far exceed the actual field life. The design standards for platforms are 
9

specified according to design loads for specific oceanographic criteria, including wave 

directionality, current velocity, wave period, and wind speed. Structures in the GOM are 

designed to withstand a 100-year return period for hurricane wind, wave, and current 

environment. 

 

The economic life of a structure is defined as the time at which the production cost of the 

structure is equal to the production revenue. At the time a structure reaches its economic limit, 

production will cease and operations will be abandoned. A lease may reach its economic limit 

prematurely when hydrocarbon prices are in a depressed price-demand state, but if the operator 

believes stronger prices will prevail in the future, then an abandonment decision is likely to be 

postponed until the operator can no longer sustain operating losses. 

 

1.3.2. Sources of Uncertainty: Decommissioning represents a liability as opposed to an 

investment, and the pressure for an operator to decommission a structure is not nearly as strong 

as installation activities. There are usually no commercial incentives for early removal and 

operators have no incentive to “fast track” decommissioning unless pushed by regulatory time 

limitations. 

Several sources of uncertainty impact decommissioning decision making: 

• Geologic uncertainty, 

• Production uncertainty, 

• Price uncertainty, 

• Investment uncertainty, 

• Technological uncertainty, and 

• Strategic uncertainty. 

Production engineers estimate the reserve potential of a field based on geologic and geophysical 

data and then use this information to design the capacity of the structure and optimize the 

production schedule. Production profiles are used as a guideline to expected removal times since 

investment activity can dramatically alter the form of the production curve as well as the 

recoverable reserves. Hydrocarbon price, technological improvements, and demand-supply 

relations impact the revenue of the lease which also impact investment planning. When the time 

arrives that the cost to operate a lease (maintenance, operating personnel, transportation, fuel, 

insurance etc.) outstrips the income from production, the structures on the lease exist as liabilities 

instead of assets, and a decision is made to divest the property or abandon the structure subject to 

the strategic objectives of the operator. Strategic objectives are generally unobservable, 

nonquantifiable, and vary over time, region, and operator, further exacerbating the capability of 

forecast models. 
10

1.3.3. Removal and Severance Models:

Life Expectancy Removal Model 

The removal date of a structure is estimated through the relation 

r(s) = i(s) + a(Γ) + kσ (Γ),

 

where, 

r(s) = Year of removal of structure s, 

i = (s) Year of initial production of structure s, 

Γ = Classification category, 

a = (Γ) Average age upon removal for structure s ∈ Γ, 

σ (Γ) = Standard deviation of the age statistic. 

The value for ) a(Γ and ) σ (Γ is defined according to configuration type, water depth and 

planning area, as shown in Table A.3. The value of k is user-defined. 

The primary assumption of the model is that the historical characteristics of structures can be 

used to reasonably predict the removal trends of “similar” active structures, where “similarity” is 

defined for structures that fall within the same general classification category. The assumption is 

restrictive but is considered an acceptable first-order approximation. 

The removal model adopts the approach taken by the National Research Council (NRC) 1985 

report, where values for ) a(Γ were estimated as follows: “Smaller structures in shallow waters, 

such as caissons and well protectors, tend to be removed after 20-25 years; larger structures with 

more wells, such as 4- and 8-pile platforms, have a useful life of 25-30 years, and larger 

structures in deepwater should have a useful life of at least 30 years.” The NRC heuristic 

approach is re-calibrated by computing the values of ) a(Γ and ) σ (Γ based on historic data, and 

then selecting k as a user-defined variable. 

In Model I, set k = 1 and compute r(s). If r(s) ≥ 2002, then “accept” the removal time of 

structure s; otherwise, set k = 3. In Model II, the smallest integer value of k is determined such 

that r(s) ≥ 2002, and for this value “accept” the removal time of the structure. Model I and 

Model II ensure that all installed structures will be removed based on their installation date and 

average age of removal plus a perturbation term. Model I presents a slow removal scenario; 

Model II presents an accelerated removal schedule. 

Explosive Severance Model 

The decision to employ explosive techniques in cutting operations depends upon a number of 

factors, and to the extent that these variables can be proxied by configuration type, water depth, 

and age upon removal, the probability that a structure will be removed using explosive 

techniques is written as p (s). E Structure s belongs to category Γ and is estimated to be removed 
11

at the time r(s). Since the age of the structure being removed is known when r(s) is “accepted,” 

the value of p (s) E is extracted from Table A.14 to determine the probability the structure will be 

removed with explosives. 

1.3.4. Model Results: The forecast output predicts the number of structures expected to be 

removed using explosive technology categorized by configuration type, water depth, and 

planning area across 5-year time blocks, where the block 200X− 200(X+4) is interpreted as 

January 1, 200X − December 31, 2000(X+4). A summary of the number of active structures 

expected to be removed with explosives is depicted in Table A.15 and Table A.16. A reasonable 

planning level suggests that between 94 and 159 structures per year will be removed with 

explosives in the short-term future. Structure composition indicates that major structures will 

play an increasingly important role both in terms of the absolute number of structures that will 

need to be removed as well as the expected cost of removal. 

1.3.5. Model Assumptions: All removal forecasts need to be viewed relative to their structural 

framework. The assumptions that provide the framework to perform a forecast also, to varying 

extent, limit the interpretation of model results. Since operator behavior is too complex to model 

on an aggregate basis without the use of production profiles or private information (e.g., 

nomination schedules, leasehold operational cost, field development plans, strategic objectives, 

etc.), all non-production based forecasts are considered to have comparable levels of uncertainty. 

Within the class of non-production based models, the magnitude of the uncertainty cannot be 

mitigated through the selection of more advanced methodologies. In fact, more “advanced” 

approaches merely disguise and shift the uncertainty rather than actually reduce or mitigate it. 

Heuristic methods have some advantage over sophisticated procedures in such an environment 

relative to ease of implementation and focus on the model drivers. On the other hand, heuristic 

procedures are also rather arbitrary, and it is often desirable to investigate more advanced 

techniques to refine and improve the model structure. 

A life expectancy and probabilistic removal model is considered an appropriate first-order

approximation to predict removal times. Better models exist, but these models are considerably 

more difficult to construct and are subject to their own sources of uncertainty. 

The primary assumptions employed in the life expectancy and explosive severance model are as 

follows: 

A1. Structures are differentiated according to configuration type, water depth, and 

planning area, and are removed based on the time history of their installation and 

their estimated age at the time of decommissioning; 

A2. The removal of structures installed in the future are not considered; 

A3. The probability that an active structure is removed with explosives is assumed to 

depend solely on the configuration type, water depth, and age upon removal; 

A4. The operating cost and production revenue associated with a structure, complex, 

leasehold, or field is not considered; 

 
12

A5. Operator-specific conditions such as investment strategy, field development options, 

removal scheduling, regulatory constraints, and divestiture opportunity that may 

influence structure removal times are not considered. 

1.3.6. Limitations of the Analysis: Since all active structures are differentiated on a 

configuration type, water depth, and planning area basis, and are removed according to the time 

history of their installation (A1), the categorization and removal relation ensures a structured 

methodology. 

Most structures that will be removed over the next two decades will come from the population of 

existing structures. Structures expected to be installed in the future may need to be removed at a 

time which overlaps with the current time horizon, but most structure removals associated with 

future installations is expected to occur outside or near the end of the time horizon of the current 

forecast. Assumption (A2) is thus considered a reasonable assumption over a medium term 

horizon. 

The life expectancy removal relation is regarded as a simple first-order heuristic to approximate 

the expected removal time. The removal relation is a simple, non-production based model, and in 

view of the sources and magnitude of uncertainty governing the problem, is considered 

appropriate. In the absence of company-level economic criteria or engineering estimates of field 

life, the values of the parameters used with this relation are based on historic data for elements 

classified within reasonably homogeneous categories. 

The probability of an explosive removal is characterized using aggregate statistics categorized 

according to configuration type, water depth, and age upon removal. More advanced regressionbased methodologies could be examined to predict the removal method as a function of these 

variables, but data limitations are expected to constrain the viability of this approach. 

Assumption (A3) is considered appropriate relative to the constraints of the model and the 

manner in which decisions are made by operators. 

It is desirable to build a model that mimics the economic decision criteria of the operator, but at 

an aggregate level this procedure is also the source of a large amount of uncertainty. The primary 

factor that drives operator decision-making, namely, profit, is not incorporated within the current 

model framework (A4), and other operator-specific conditions are also not considered (A5). To 

determine the profit of a given structure, the model must account for the expected future values 

of hydrocarbon price, production, operator cost, reserve estimates, and investment decisionmaking. Unfortunately, these variables are uncertain, and in most cases, unobservable. 

1.4. Conclusions

A statistical description of the explosive removal of offshore structures in the GOM has been 

presented. The influence of factors such as water depth, planning area, configuration type, 

structure age, and time upon the application of explosive removals have been examined, and 

generally-held industry beliefs appear to be “valid,” namely, that the application of explosive 

techniques appear to increase with (i) water depth, (ii) structure complexity, and (iii) age upon 

removal. Most of the structures that have been removed from the GOM are in the Central 

planning area (84%) with the remaining removals distributed throughout the Western GOM. 
13

Explosive technology was employed in 954 of the 1,626 structures decommissioned to date – 

representing in aggregate a 59% explosive removal rate. Caissons were equally likely to be 

removed with either explosive or nonexplosive methods, while well protector jackets employed 

explosives 62% of the time and fixed structures were removed with explosives 66% of the time. 

The applications of explosive methods increase with the complexity of the configuration type, 

water depth and age of the structure upon removal. The influence of planning area was not a 

significant factor. 

Removal forecasts were developed using a life expectancy and probabilistic removal modeling 

framework to predict the number of offshore structures that are expected to be removed using 

explosive technology. The categories employed partition the structure data according to 

configuration type, water depth, age, and planning area classification, which was subsequently 

combined with life expectancy and explosive severance modules, to predict removal trends. A 

description of the modeling process and a summary of the results were outlined. There are 

significant uncertainties associated with all structure removal forecasts, and forecasting should 

be viewed relative to the model assumptions and in terms of the “potential” of the likely future 

impact rather than as a predictive indicator of the actual number of structures expected to be 

removed. 

 

 
15

 CHAPTER 2: A BINARY CHOICE SEVERANCE SELECTION MODEL 

FOR OFFSHORE STRUCTURE REMOVAL 

2.1. Introduction 

Decommissioning offshore structures is often a severing intensive operation. Cutting is required 

throughout the structure, above and below the waterline and mudline on braces, pipelines, risers, 

umbilicals, templates, guideposts, chains, deck equipment and modules. More significant cutting 

operations are required on elements that are driven into the seafloor, such as multi-string 

conductors, piling, skirt piling, and stubs which need to be cut 15 feet below the mudline, pulled, 

and removed from the seabed. Cutting piles and conductors is probably the most critical and 

important part of a decommissioning project since if the piles and conductors are not cut 

properly, costly time delays and a potentially dangerous condition can arise during the operation. 

A variety of technologies exist to perform severance operations, and the most common cutting 

methods include abrasive water jet, diamond wire, diver torch, explosive charges, mechanical 

methods and sand cutters. For severing operations that occur above the waterline, the cutting 

technique selected is usually dictated by the potential for an explosion. Cold cut methods are 

used when the potential for an explosion exists; otherwise hot cuts are employed. Cutting in the 

air zone is conventional, but not hazard-free, since it involves methods which are regularly used 

for dismantling onshore industrial facilities. Below the waterline, cutting is more specialized. In 

water depths that do not exceed 150 feet or so, divers perform cuts on simple elements such as 

braces and pipeline, and for shallow water structures such as caissons, diver torch is sometimes 

the preferred severance method. In water depths exceeding 150 feet, remotely operated vehicles 

(ROV’s) deployed with abrasive, diamond wire and explosive charges are used for severance 

operations. 

The decision of what cutting method to use will depend on the outcome of a risk-based 

comparative assessment involving cost, safety, technical, environmental, operational and 

managerial considerations. To perform a risk-based cost assessment for decommissioning 

projects after the operation has occurred is clearly an imposing (some would say, impossible) 

task, so we must rely on various proxy variables to estimate the probability that a particular 

severance technique will be applied in a given situation. The scope of this chapter is motivated 

by the desire to predict the removal techniques expected to be deployed in the future. Since 

economic and technical considerations are essentially unobservable, we will rely on a simplified 

decision model to gain insight on the processes involved in severance selection. 

The purpose of this chapter is to describe severance operations within the context of 

decommissioning and to identify the factors involved in the decision to use explosive/ 

nonexplosive methods, to quantify the probability that a structure will be removed with 

explosives, and to establish a relationship between a set of attributes describing a structure and 

the probability that a particular severance technique will be used. 

 

Background information on the offshore structures and the decommissioning options available to 

the operator are first described. Additional background information on the various stages of 

decommissioning and the cutting activities typically performed is presented, along with a 
16

summary of the impact of explosive and nonexplosive severance methods. An outline of the 

regulations associated with the use of explosives is also provided. The factors involved in 

severance selection are described, the frequency of occurrence of explosive removals is 

reviewed, and operator use patterns are summarized. Construction of binomial logit and probit 

models of severance selection conclude the chapter. 

2.2. Background Information 

2.2.1. Gulf of Mexico Infrastructure: Caissons, well protectors, and fixed platforms were first 

installed in shallow water, and as development has shifted farther offshore to deeper water, 

installation types have changed to larger steel structures, tension leg platforms, spars, and subsea 

completions. 

 

If a reservoir is small or isolated, it will normally be completed with a “minimal” structure – a 

caisson, well protector, or subsea completion – with flowlines tied back to shore or an 

accompanying fixed platform. Platforms are classified as major and nonmajor fixed structures. A 

major structure is defined to include at least two pieces of production equipment or six 

completions, and will normally include all braced caissons, conventional piled structures with 

wells, skirt platforms, special platforms, and floating structures (Pulsipher, 1996). Conventional 

piled platforms without wells (quarters platform, flare pile, storage facility, pipeline junction, 

metering facilities), single-well caissons, and well protectors are considered nonmajor structures. 

 

Oil and gas structures in the GOM are presented in Table B.1 according to configuration type, 

water depth, and number of slots available. Major and nonmajor fixed structures comprise 

slightly more than half of all active GOM structures. Most structures removed to date have been 

simple structures in shallow waters, and roughly speaking, for every major structure 

decommissioned, two nonmajor structures have been removed. Over the past decade, the number 

of structures removed has ranged from a low of 75 to a high of 179, and this range continues to 

serve as a good indicator on the bounds of expected decommissioning activity in the future. 

The most common structure in the GOM is the conventionally piled platform with wells as 

shown in Figure B.1. In a conventionally piled platform, the platform is pinned to the seabed by 

long steel tubes called piles which pass through the legs of the structure and act like giant tent 

pegs. The jacket of the structure provides a protective layer around the conductors, which pass 

from the seabed up to the topsides, and serve as the conduit to the reservoir (Graff, 1981; 

McClelland and Reifel, 1986). Piling is sometimes grouted to the jacket leg near the mudline for 

additional stability and support. In many cases the jacket is installed over one or more 

exploratory wells with development wells drilled through conductor slots in a central bay. Fixed 

platforms have been used in the GOM in water depths up to 1,300 feet, but beyond this limit3

floating production structures are required. 

2.2.2. Decommissioning Options and Economic Criteria: Offshore structures normally 

represent a significant investment and are maintained as long as possible unless economic 

conditions force their removal. Engineering fatigue, barge collisions, fire, and the occasional 

 

3

 Shell’s Bullwinkle platform in 1,350 feet water depth stands 1,617 feet tall and is one of the largest fixed structures 

in the world. 
17

natural disaster may take out a few structures unexpectedly, but for the most part, these factors 

do not play a significant role in aggregate removal patterns. Structures are designed to last the 

life of the field. 

Abandonment options that are available to the operator include 

• Relocation for reuse, 

• Removal and scrap, or 

• Relocation to an artificial reef site. 

The topsides removal and disposal options available in decommissioning projects are shown in 

Figure B.2 as a decision tree. Oil and gas processing equipment and piping is sent to shore, 

refurbished and reused, sold for scrap, and/or sent as waste to the landfill. Deck and jacket 

structures have more options for disposal. The deck and jacket may be scrapped onshore, moved 

to a new location and reinstalled, or converted to an artificial reef site (Hakam and Thornton, 

2000; Thornton, 1989). The complete removal of the jacket is the most frequently used technique 

in the GOM, occurring in roughly 90% of the total decommissions to date. The remaining 10% 

of structures that have been decommissioned have been toppled-in-place within an artificial reef 

or towed to an approved reef site. The Texas and Louisiana artificial reef programs currently 

maintain over 200 offshore structures throughout the GOM. 

The economics of decommissioning are usually considered in terms of “least cost liability” as 

opposed to “return on investment.” Decision criteria associated with abandonment options thus 

generally favor minimum cost alternatives as the preferred means of most disposals. The factors 

that determine when a structure will be removed, as well as how it will be removed, are driven by 

engineering, economic and safety criteria that is time, location, and operator specific. 

2.2.3. Decommissioning is Often a Severing Intensive Operation: The basic aim of a 

decommissioning project is to render all wells permanently safe and remove most, if not all, 

surface/seabed signs of production activity. A site should be returned to its “green field” state, 

but how completely the site should be returned remains a subject of discussion between 

government, operator, and the public. Cutting operations occur throughout each stage of 

decommissioning except the first (permitting) and last (site clearance and verification) stage. See 

Figure B.3. For readers requiring additional information on the activities involved throughout a 

decommissioning project, the case studies (Hakam and Thornton, 2000; Kirby, 1999; Thornton, 

1989) and project checklist (Thornton, 1995) are a good starting point, while more detailed 

descriptions of the process can be found in (Manago and Williamson, 1998; Pulsipher, 1996; 

National Research Council, 1985). For a statistical description of GOM activity, see (Kaiser et 

al., 2002; Thornton and Wiseman, 2000), and for a review of regulatory issues, see (Griffen, 

1998; Pulsipher and Daniel, 2000; Shaw, 2000). The phase, timing, and selection of severance 

operations, and in particular, pile and conductor cutting, is planned to maximize the safety of 

workers and to minimize the time of the derrick barge on-site. Cutting activities are performed 

off the critical work path and before the arrival of the barge if the activity can be performed in a 

cost effective manner. 
18

Well Plugging and Abandonment 

A well abandonment program is carried out by injecting cement plugs downhole to seal the 

wellbore to secure it from future leakage while preserving the remaining natural resources. 

Techniques used to accomplish this process are based on industry experience, research, and 

conformance with regulatory standards and requirements (Manago and Williamson, 1998). 

 

A traditional approach begins by “killing” the well with drilling fluids heavy enough to contain 

any open formation pressures. The Christmas tree is then removed and replaced by a blowout 

preventer through which the production tubing is removed. Cement is placed across the open 

perforations and squeezed into the formation to seal off all production intervals and protect 

aquifers. The production casing is then cut and removed above the top of the cement and a 

cement plug positioned over the casing stub. The remaining casing strings are then cut and 

removed close to the surface and a cement plug set across the casing stubs. 

Mechanical methods of cutting and sand cutters are primarily associated with well plugging and 

abandonment (P&A) activities. After wells are plugged and casing tubing cut and pulled, a sand 

cutter or mechanical cutting tool may be run downhole to cut the conductors, or depending on 

the preference of the operator/contractor and configuration of the platform, abrasive or explosive 

severance methods may be applied. In a typical mechanical operation, the tubing and production 

casing is first cut using a jet cutter – a small explosive blast that utilizes less than five pounds 

explosive – and then the strings are cut out from 7 8

5 or 13 85 inches using a mechanical cutter. 

 

All wellheads and casings are required to be removed to a depth of at least 15 feet below the 

mudline, or to a depth approved by the District Supervisor. The requirement for removing subsea 

wellheads or other obstructions may be reduced or eliminated when, in the opinion of the District 

Supervisor, the wellheads would not constitute a hazard to other users of the seafloor. 

Topside Equipment and Deck Preparation 

Topside preparation and deck removal is severing intensive. Cold cuts are generally made with 

pneumatic saws or drills, including diamond wire methods and abrasive techniques. Hot cuts – 

torch cutting and arc gouging – are used to cut steel when there is no risk of explosion. Arc 

gouging is used to remove seal welds between steel connections. Burning torches work on the 

same principle as the arc-gouge, where a burning rod, usually magnesium, is arced with the 

member to be cut. Diamond wire methods have also been occasionally employed in the GOM to 

cut the deck from the jacket. 

Jacket Preparation 

Several severance techniques are used below the waterline. Small cuts made to the jacket bracing 

and trimming, flowlines, umbilicals, and manifolds are typically performed with divers using 

burning torches, or if the water depth exceeds the diver capability, ROV’s with diver torch or 

abrasive technology are employed. Intermediate cuts may be required to separate the jacket into 

vertical sections if the piling extends up through the jacket structure. 
19

Pipeline Abandonment 

Federal regulations allow decommissioned OCS pipelines to be left in place when they do not 

constitute a hazard to navigation, commercial fishing, or other uses of the OCS. Pipelines will 

generally be removed offshore through the surf zone and capped. Onshore pipeline may be 

removed completely, or some sections may be abandoned in place if they transition through a 

sensitive environment. The pipeline end seaward of the surf zone is capped with a steel cap and 

jetted three feet below the mudline. Most pipelines in the GOM are abandoned in place after 

cleaning and cutting its structural connections. 

The methodology for cutting a pipeline depends on the manner the pipeline is to be recovered. 

The protective coatings typical of most pipeline sections must first be removed in order to cut the 

pipe with an arc torch. If a pipeline crosses or is adjacent to an “active” pipeline, chances are it 

will not be disturbed due to the potential damage that would result if complications arise in the 

removal. Diamond wire methods, abrasive water jet, and pneumatic saws deployed with diver or 

ROV are all used to cut pipeline. 

Pile and Conductor Severing 

Pile and conductor severing is the most critical and typically the most expensive of all the 

severance operations. Piles are steel tubes welded together and driven through the legs of the 

jacket and into the seabed to provide stability to the structure, while conductors conduct the oil 

and gas from the reservoir to the surface. Piles and conductors must be cut and removed a 

minimum of 15 feet below the mudline. The physical characteristics that describe piles and 

conductors are important since they determine the technical feasibility of severance options. 

Conductors are cut and pulled, if possible, early in the decommissioning process to avoid delay 

when the barge is on-site. Conductors are configured in various diameters and wall thickness and 

are characterized by the number of inner casing strings, the location of the strings relative to the 

conductor (eccentric vs. concentric), and the application of grout within the annuli. Conductors 

are usually cut with mechanical methods or explosive charges. Grouted annuli are usually easier 

to cut than annuli with voids since voids dissipate the energy/focus of the abrasive and explosive 

cutting mechanisms. Eccentricity may also pose a problem for mechanical cutters (Pulsipher, 

1996). Mechanical methods are commonly applied to cut conductors during P&A activity, while 

if conductors are cut when the barge is on-site, then explosive charges will probably be 

employed. 

 

To sever jacket legs and piles, abrasive cutters and explosive techniques are effective. In 

principle, mechanical cutting could be used to cut piling, but in practice it is rarely used because 

piles are only open when a barge is on-site (after removing the deck from the jacket), and with a 

barge on-site, mechanical cutting is not a cost-effective or efficient way to sever4

. With a barge 

on-site, explosives are deployed down the piling and below the mudline, while abrasive cutters 

can be deployed internally or mounted externally using divers and a track. Obstructions within 

the pile (such as hangers) will necessitate additional operation or deployment of an external cut. 

Internal cutting is usually the preferred approach with water jet technology since it does not 

 

4

 Redeployment of the barge is usually not an option. 
20

require the use of divers to set up the system or jetting operations to access the required mudline 

depth. 

2.2.4. Environmental Consequences of Severance Technology: The use of explosives to cut 

conductors, well casings, and piles was used for many years without regulation, but in 1986 with 

the strandings of numerous sea turtles in Texas, concern5

 was raised on the use and application 

of explosive severance methods. Before 1986, there were no rules or regulations to follow on the 

use of explosives, and the basic rule of thumb was, “if five pounds does a good job, then ten 

pounds does a hell of a good job” (DeMarsh, 2000). Since 1986, several regulations have been 

enacted to help minimize the number of incidental takings6

 and to quantify the impact of using 

explosives on sea turtles and marine mammals. Observers are currently required for all OCS 

removal activities using explosive charges >5 lb, and since introduction of the PROP in 1986, 

only two sea turtles have been killed and three turtles have been injured as a result of explosive 

severance. The injured sea turtles were rehabilitated and released back into the GOM. Since 

1995, more than 750 structures have been removed from federal waters and observers reported 

no indication of injury or death to bottlenose or spotted dolphins or any other marine mammal 

(Federal Register, 2002b). 

The use of explosives to remove offshore platforms does kill and injure fish, but it is important to 

understand the relative magnitude of the kill relative to other sources of fish mortality in the 

GOM. For all practical purposes, it is impossible to predict the mortality of fish at a platform 

during an explosive removal because of the number and uncertain nature of the factors involved. 

The fish density at the structure, the season of the removal, the range of the fish from the blast, 

the amount of explosives detonated, and the types of fish are some of the factors that impact fish 

kill. Between 1993 and 1999, the NOAA Fisheries conducted a study to assess the impact of 

using explosives at platform removals. Ten platforms from various shallow water locations in the 

GOM were examined by divers immediately after the explosives were detonated. Total estimated 

mortality ranged from 2,000-6,000 fish per platform (Gitschlag et al., 2000). The age of the 

structure, platform size, water depth, water temperature and salinity were not considered factors 

relevant to fish mortality. Relative to other sources of fish mortality in the GOM (e.g., shrimp 

trawling), the fish killed by explosive removal are considered negligible. The annual shrimping 

season in Louisiana and Texas consists of 2, 90-day periods, and it has been estimated that the 

trawler by-catch is 1,000 fish/trip. If we assume that 1⁄2 of the 55,000 registered trawlers in 

Louisiana and Texas complete 1 trip/day for 1⁄4 of the shrimping season, then the number of fish 

by-catch on average due to shrimper trawling is roughly 1.25 billion fish per year. On the other 

hand, if 200 structures are removed from the GOM per year with explosives and the fish kill per 

 

5

 The National Marine Fisheries Service (NMFS, currently NOAA Fisheries) sent a letter to the MMS expressing 

concern regarding stranding events in 1985 and 1986. The stranding events in question coincided with a number of 

explosive platform removals that were conducted in the state waters of Texas and NMFS suggested that a correlation 

could exist between the strandings and the use of explosives for decommissioning. 

6

 “Take” is defined to mean “to harass, harm, pursue, hunt, shoot, wound, kill, trap, capture, or collect, or to attempt 

to engage in any such conduct” (16 U.S.C. §1532 (19)). Harass means “an intentional or negligent act or omission 

which creates the likelihood of injury to wildlife by annoying it to such an extent as to significantly disrupt normal 

behavior patterns which include, but not limited to breeding, feeding, or sheltering.” (50 C.F.R. §17.3) Harm means 

“an act which actually kills or injures wildlife. Such an act may include significant habitat modification or 

degradation where it actually kills or injures wildlife by significantly impairing essential behavior patterns, including 

breeding, feeding, or sheltering.” (50 C.F.R. §17.3). 
21

structure is assumed to be an order-of-magnitude greater than the NOAA Fisheries study at 

50,000 fish/removal, then the total number of fish kill associated with structure removals is 10 

million per year – or less than 1% of the expected shrimper by-catch take. 

Nonexplosive cutting methods are considered an ecological and environmentally sensitive 

severance method since the cutting does not create the impulse and shockwave-induced effects 

which accompany explosive detonation (Brandon et al., 2000). In mechanical, abrasive water jet, 

and diamond wire severance technology, a diesel-fueled mechanical motor is employed in the 

operation which results in vibrations, the emissions of CO2 and other gases to the atmosphere, 

and low frequency sound waves into the ocean environment. Abrasive water jet cutting also 

involves using a fluid and garnet/slag for the cutting mechanism, and so there is the question of 

the impact of the fluid and garnet on the marine environment. Since the fluid involved in 

abrasive cutting is water and the garnet is inert, the environmental impact is generally considered 

inconsequential. Further, the noise level of the supersonic cutting jet is safe for divers and is not 

considered harmful to marine life. The direct products of nonexplosive cutting processes are 

water, metal cuttings, and abrasive particles. 

There is also an environmental impact associated with the re-suspension of bottom sediments. If 

the foundation piles are cut below the seabed from the outside, the surrounding sediments will 

have to be dredged away by suction-dredging or jetted. The use of explosives to cut piling will 

likely disturb the sediments in the immediate vicinity of the structure. Both operations will cause 

re-suspension of sediments and contaminants in the cuttings. If the legs/pilings are severed from 

the inside using abrasive techniques, no significant re-suspension of sediments would ensue. 

Impacts resulting from re-suspension of bottom sediments include increased water turbidity and 

mobilization of sediments containing hydrocarbon extraction waste (drill mud, cutting, etc.) in 

the water column. The magnitude and extent of any turbidity increases would depend on the 

hydrographic parameters of the area, nature and duration of the activity, and size and 

composition of the bottom material. The overall impacts to water quality are expected to be 

temporary in nature and limited in scope to the site (Federal Register, 2002a). 

2.2.5. The Regulatory Structure of Explosive Severance: To provide adequate protection for 

marine mammals and sea turtles during OCS decommissionings, explosive-severance activities 

are subject to regulations promulgated under the OCS Lands Act (OCSLA), ESA, and the 

Marine Mammal Protection Act (MMPA). In 1988, MMS consulted with NOAA Fisheries on 

explosive severance and was issued a “generic” or programmatic BO/ITS for sea turtles with 

terms and conditions that are currently still in effect (see 

http://www.gomr.mms.gov/homepg/regulate/environ/generic-consultation.pdf). In 1989, the 

American Petroleum Institute (API) petitioned NOAA Fisheries under Subpart I of the MMPA 

for the incidental take of spotted and bottlenose dolphins during removal operations (both 

explosive and nonexplosive severance). NOAA Fisheries promulgated incidental-take 

regulations under Subpart M of the MMPA regulations in October 1995 with mitigative 

measures similar to those listed in the 1988, ESA BO/ITS. Subpart M expired in November 

2000, after which, NOAA Fisheries released an Interim Subpart M in August 2002, and since its 

expiration on February 2, 2004, operators have continued to follow the similar ESA BO/ITS 

terms and conditions for both turtles and marine mammals. 
22

The MMS has a Notice to Lessees and Operators (NTL), No. 24-G06 

(http://www.gomr.mms.gov/homepg/regulate/regs/ntls/ntl04-g06.html), that summarizes all of 

the current regulations and conditions for explosive-severance activities. For additional 

background information on regulations concerning endangered species, see McKay et al., (2002) 

and Roberts and Hollingshead, (2000). A flowchart of the procedures involved with explosive 

cutting is shown in Figure B.4. 

• Qualified observer(s), as approved by the NOAA Fisheries, must be used to monitor 

the area around the site for a period of 48 hours prior to, during, and after the 

detonation of explosives. 

• If sea turtles are observed in the area and are thought to be resident7

 at the site, predetonation and post-detonation diver surveys must be conducted. 

• On day(s) that blasting operations occur, a 30-minute aerial survey must be conducted 

one hour before and one hour after each blasting episode. To ensure that no marine 

mammals are within the designed 1,000 yards safety zone nor are likely to enter the 

designated safety zone prior to or at the time of detonation, the pre-detonation survey 

must encompass all waters within one nautical mile of the structure. 

• If sea turtles and/or marine mammals are observed within 1,000 yards of the structure 

prior to detonation, blasting must be delayed. The delay must remain in affect until 

the sea turtles and/or marine mammals are beyond 1,000 yards of the platform. The 

aerial survey must be repeated prior to resuming detonation of the charges. 

• If sea turtles and/or marine mammals are observed within 1,000 yards of the structure 

an additional survey must be performed, involving either a diver survey dedicated to 

marine mammals and sea turtles within 24 hours after the detonation event or an 

aerial/vessel survey within 2-7 days after the blast. The aerial/vessel survey must 

concentrate down-current from the structure. 

• The NOAA Fisheries-approved observer may waive post-detonation monitoring 

provided no marine mammal was sighted during either the aerial surveys before 

detonation or during the 48 hour pre-detonation observer monitoring period. 

• Explosives can be detonated no sooner that 1-hour after sunrise and no later than 1 

hour before sunset. Special circumstances may allow for modification of these times 

if permitted by the NOAA Fisheries personnel on-site. 

• During all diving operations, divers are required to scan the area around the platform 

for sea turtles and marine mammals. Any sightings must be reported to the NOAA 

Fisheries personnel of the agent of the holder of the Letter of Authorization upon 

surfacing. 

• In water depth of 150 feet or greater, or in cases where divers are not deployed in the 

course of normal removal operations, a remotely operated vehicle must be deployed 

prior to detonation to scan areas below structures. If marine mammals are sighted, the 

 

7

 The majority of turtle sightings are turtles “in transit”. In cases where turtles are believed to be “resident” to the 

structure, either the turtles are relocated or the severance operation is performed with nonexplosive methods. 
23

ROV operator must inform either the NOAA Fisheries observer or the agent of the 

holder of the Letter of Authorization immediately. 

• In water depth of 328 feet (100 meters) or greater, passive acoustic detection must be 

employed prior to detonation. If marine mammals are detected by the acoustic device, 

the operator must inform either the NOAA Fisheries observer or the agent of the 

holder of the Letter of Authorization. 

• After the explosives are detonated, if sea turtles and/or marine mammals are sighted, 

either dead or injured, attempts should be made to recover the animals. 

• A report summarizing the results of structure removal activities, mitigation measures, 

monitoring efforts, and other information as required by a Letter of Authorization, 

must be submitted to the NOAA Fisheries Regional Administrator within 30 days of 

the removal of the structure. 

 Regulations intended to protect sea turtles and marine mammals provide built-in incentives for 

using nonexplosive techniques. The time, scheduling, and expense of coordinating the NOAA 

Fisheries observers during explosive removals, the restrictions on using explosives, and the 

possible delays associated with the presence of marine life, encourages operators to consider 

alternative severance methods – when alternative methods are feasible. 

2.3. Factors Involved in Severance Selection 

 

A large number of factors are potentially involved in selecting the severance technique for a 

specific job, with cost, safety, risk of failure, and technical feasibility the primary factors that are 

considered when alternative options are available. Many different severance operations are 

required during decommissioning, and depending upon the job, more than one alternative is 

usually available (Figure B.3). In general, cutting techniques are expected to be reliable, flexible, 

adaptable, safe, cost effective and environmentally sensitive (National Research Council, 1986). 

If a cutting technique fails with respect to one or more of these factors, or if an operator has more 

than one “bad experience” with a particular method, then chances are that the technology will not 

gain in popularity or acceptance among GOM contractors. 

Variables that drive the cost and risk associated with a specific severance technique are 

numerous and involve factors such as the location and nature of the site, sensitivity of the marine 

habitat, structural characteristics, the amount of pre-planning involved, salvage/reuse decisions 

of the operator, marine equipment availability, operator experience and preference, contractor 

experience and preference, the number of jobs the contractor is scheduled to perform and the 

schedule of the operation, and market conditions. Some of these variables are observable, but the 

degree of correlation between the observable variables and severance decision factors is expected 

to be weak, and so the extent to which cutting methods can be accurately predicted based on 

these factors remains uncertain. 

2.3.1. Direct Cost: The cost of a derrick barge is at least an order-of-magnitude larger than the 

cost of a cutting spread, and so cutting decisions are not expected to be a primary focus in 

decommissioning operations unless they negatively impact the time on-site of a derrick barge. 

The direct cost of a cutting spread is at most $10,000/day (Kaiser and Pulsipher, 2003), and 
24

when compared to a derrick barge spread of $100,000-$300,000/day, it is clear that cutting 

techniques will not drive decommissioning activities. The cost to sever piles and conductors is 

generally less than 1-3% of the total cost to decommission the structure. 

2.3.2. Cost of Failure: If the cutting operation is not successful on the first attempt, then the 

operator will assume the cost of failure and the additional time required to re-shoot or re-cut the 

tubular element(s). In Figure B.5 the abrasive cutting process is charted. Contractors typically 

charge at work rates that depend upon the critical8

 path crane vessel time. Normally, if “extra 

work” is required that alters the critical path, the contractor charges the operator rates for 

equipment and personnel affected. If extra work is required that does not alter the critical path 

crane vessel time, the operator is charged a different (substantially smaller) hourly composite 

rate. The cost of a failed cut thus depends on the timing of the cut relative to the operational 

activity of the barge. There is a significant difference between a crew preparing for a cutting 

operation, while other barge activities are on-going, versus a crew cutting while other barge 

activities wait for the operation to finish. Failure to cut a conductor off the critical path is not 

nearly as significant as a cutting failure that delays barge activity. The expected cost of failure is 

a primary decision factor in selecting a cutting method (Greca, 1996). 

 

2.3.3. Human Safety: The offshore environment is a potentially hazardous location which 

presents special risk to the personnel involved in the operations. At each stage of 

decommissioning there is the potential for work injury and fatality. Cutting and welding steel 

tubes, setting up rigging, diving, moving cranes, hydraulic equipment, explosive charges, and old 

rusty structures create the potential for a hazardous work environment, and so proper precautions 

are required to ensure that operations are performed in as safe a manner as possible. Fortunately, 

decommissioning activities are fairly standard, of short duration, and relatively safe if properly 

planned and executed. A typical decommissioning operation will take anywhere from 7-14 days 

to complete. 

 

Cutting the piles and conductors is probably the most critical and important part of a 

decommissioning project since if the piles and conductors are not cut properly, a potentially 

dangerous condition could arise during the lift. The bottom cuts on anchor piles and conductors 

must be “clean” and “complete” to allow for a safe operation. Incomplete cuts pose a serious 

danger to the stability of the vessel during lift. 

 

2.3.4. Environmental Issues and Structure Disposition: Under some circumstances, the 

choice of severance method may be determined exclusively by the location of the structure 

and/or the decision to re-use the jacket; e.g., 

• Structure is located in a known turtle habitat, 

• Structure is located in an artificial reef planning area, or 

• Structure jacket will be re-used. 

 

8

 Critical path activities are considered “bottleneck” operations that “use up” barge time and are charged at a 

premium. 
25

These circumstances do not occur frequently – probably in about 10-15% of the structures 

removed from the GOM – but they do occur (e.g., see Ness et al., 1996; O’Connor, 1998) . 

If the jacket is to be re-used or the structure is located in a known turtle habitat, then 

nonexplosive methods will likely be used if technically feasible. Clean cuts are desirable to avoid 

the diver cost/risk associated with flared piles and the possible damage that can occur to the reused jacket with explosive cutting. If a structure is located in an artificial reef planning area and 

it can be toppled-in-place, then the piles and conductors are severed and the jacket is pushed over 

to form the reef (Dauterive, 2001; Reggio, 1989). If the structure does not satisfy the minimum 

85 feet waterline clearance, then the structure will need to be cut in the water column and 

partially removed, that is, the top of the re-used jacket will be cut and placed on its side near the 

bottom of the jacket which will be left in place. In a partial removal, the piles do not need to be 

severed from the bottom structure, and since the use of explosives is prohibited in the water 

column, abrasive water jet, diver torch, or diamond wire methods are used to make the mid-water 

cuts. 

2.3.5. Operator Experience and Preference: The project management team overseeing the 

decommissioning activities, in consultation with the operator, prepares the bid package and 

specifies the work requirements to be performed. This information will include special requests, 

such as platform and jacket disposition, and preference (if any) for the severance method to be 

employed. The operator may also have special concerns or preferences that dictate that a specific 

method be employed. For example, between November 15, 2000 – August 1, 2002 some 

operators (e.g., Shell, El Paso) specifically requested that contractors use nonexplosive methods 

for cutting since the expiration of Subpart M, MMPA took away NOAA Fisheries authority to 

issue Letters of Authorization for removal activities (Guegel, 2001). As a result of Subpart M’s 

expiration, operators were potentially exposed to NOAA Fisheries-assessed penalties and fines 

should a bottlenose or spotted dolphin take be recorded during a structure removal activity 

(either explosive- or nonexplosive severance). 

There are also many other reasons for operator preference. Some operators may consider the 

benefits offered by explosives to be outweighted by public perception, while other operators – 

especially operators who have never decommissioned a structure – may want the first removal to 

use a “standard” (explosive) cutting method to avoid complications. All operators want to avoid 

cost overruns and minimize potential risk to offshore personnel. Unfortunately, unless 

preferences are known or readily identifiable, they are considered unobservable and must be 

accounted for indirectly through proxy variables. 

2.3.6. Operational Scheduling: Conductor severing and recovery may be completed as part of 

well plugging and abandonment activities unless the platform configuration, equipment 

availability or scheduling of the activities prevent the operation. Conductors are cut and pulled, 

if possible, early in the decommissioning process to avoid delay when the barge is on-site. 

Mechanical casing cutters and sand cutters can be used to perform the cut if a crane is available 

on the platform for the deployment of the tool. To verify a complete cut, a jacking spread may be 

used to lift the conductor after the severing attempt. To jack the conductors (prove the cut), the 

platform must have the structural capacity to provide a point to jack against and have a crane 

large enough to set the cutter, jacks, and load spreading beams. If tubing and casing strings have 
26

not been cut prior to the arrival of the derrick barge, then explosive charges will likely be used to 

cut all the elements at once. Mechanical and/or sand cutters are rarely deployed with a derrick 

barge on-site due to the time-consuming and inefficient nature of the operation. 

2.3.7. Contractor Experience and Preference: If the contractor has several removals to make, 

then the preference is to cut as quickly and as safely as possible subject to the technological and 

operational requirements of the job. If explosives are required on one structural element, then a 

preference may arise to blow all the elements at once rather than “mix” explosive and 

nonexplosive severance methods, and as mentioned earlier, if pre-cuts are not performed on the 

conductors, then explosives are more likely to be employed to sever all the elements when the 

barge is on-site. On a few decommissioning projects, abrasive water jet and explosive cutting 

crews have served in a contingency role, but since back-up crews add significantly to the cost of 

the service, cutting redundancy is not standard practice. 

2.3.8. Structure Characteristics: Pile and conductor severing is the most critical and typically 

the most expensive of all the severance operations required on the structure. The physical 

characteristics that describe piles and conductors are important since they allow engineers to 

determine the technical feasibility and potential problems of removal options. 

 

Conductors are configured in various diameters and wall thicknesses and are characterized by the 

number of inner casing strings, the location of the strings relative to the conductor (eccentric vs. 

concentric), and whether or not the annuli are grouted. Conductors typically contain multiple 

strings of casing, eccentric within the well, and grouted with voids. Grouted annuli are usually 

easier to cut than annuli with voids since voids generally dissipate the energy and focus of the 

abrasive cutting mechanisms. The preferred way to cut conductors is with mechanical methods 

or explosives charges, while piles are effectively cut with abrasive methods and explosive 

charges. Since piling cannot be examined before the topsides are lifted off the jacket, bulk 

explosives are usually preferred for piling since they can be sized for unexpected field conditions 

and give a clear indication of a complete cut. 

2.3.9. Structure Age: Contractors select severance methods that are cost effective, reliable, 

efficient, adaptable, safe and environmentally sensitive. If a structure is old, it is less likely to 

have accurate records and drawings available, and if accurate information is not available to the 

cutting crew before the cut is performed, the ability to plan and anticipate potential problems in 

the operation is constrained. Old structures are also less likely to be re-used, and so we would 

expect explosive methods to be more frequently applied as the age of the structure increases. 

2.3.10. Cutting Reliability: The ability of a severance technique to perform a cut, and to 

perform it reliably, is a significant factor and one of the most important criteria contractors 

consider in their selection of a removal method – especially if cutting is to occur near critical 

path barge activities. No cutting technique is 100% reliable and so the operator/contractor’s 

experience with the technology and their perception of reliability is as important as aggregate 

reliability statistics. Unfortunately, it is difficult to acquire reliability statistics that can be 

meaningfully compared across severance methods due to the lack of appropriate data. Industry 

consensus is that explosives remain the most predictable, flexible, and reliable severance 

technique (Kaiser and Pulsipher, 2003; Pulsipher et al., 1996). 
27

2.3.11. Configuration Type: Nonexplosive methods usually carry less financial and operational 

risk with shallow water, simple structures than for complex, deep water structures (National 

Research Council, 1986). Mechanical and sand cutters have been used effectively on shallow 

water caissons and small well protector jackets, and large caissons have been effectively cut by 

divers. As the complexity, size, and water depth of a structure increases, however, the reliability 

of nonexplosive methods decreases while the cost and risk/uncertainty of operations tend to 

increase. On large platforms, especially platforms with wells, the preferred severance method is 

with explosives. There is not a “smooth” transition that occurs as a function of water depth or 

structure complexity, but generally speaking, we would suspect that as the complexity and water 

depth of a structure increases, explosive methods should be applied more frequently, and this is 

borne out by statistical analysis of the removal data. Explosives cut quickly and reliably and 

crew exposure time is minimal. For special structures such as skirt-piled9

 platforms, mechanical, 

abrasive, and diver cuts are usually not feasible and the tubular elements are generally stabbed 

with explosives using an ROV. 

2.4. The Probability of an Explosive Removal 

The choice of which severance technique is used to cut the piles and conductors of a structure 

depends primarily on factors which are unobservable or uncertain, and so it is clear that for a 

given structure we can only seek the probability that explosive methods will be applied. It is 

necessary to proxy the unobservable variables with factors that are accessible and are 

“reasonably” reflective of the offshore environment. Configuration type, water depth, and the 

structure age upon removal are public information, while the characteristics of the piles and 

conductors associated with each structure; e.g., number, size, application of grout, number of 

casing strings, etc. are typically not recorded. It would be preferable to perform the analysis at 

the lowest possible level of aggregation – which in this case is described through the 

characteristics of the structure – and then to “work up” through various aggregation strategies. 

Due to deficiencies in the MMS database, it was not possible to explore the impact of 

aggregation schemes on the probability measures, but for practical purposes due to the nature of 

the problem, additional data would probably contribute only marginally to increased 

understanding. 

The manner in which data is reported also needs to be interpreted carefully since an “explosive 

removal” suggests that all the piles and conductors of a structure are explosively severed, while 

in fact as we have previously discussed, various severance techniques may be used throughout 

the structure to cut the tubular members. The diversity of the severance procedures are not 

captured through the MMS database, and this “information loss” restricts the degree (and 

ultimately limits the ability) to which a decision model can be constructed. The percentage 

values computed thus need to be interpreted as the number of structures in which explosives 

were used at least once during decommissioning. It would be better to compute the frequency of 

application of explosive severance as a function of the number and size and type of tubular 

elements that are cut, but due to deficiencies in data collection, this characterization is not 

possible. 

 

9

 Skirt-piled platforms are predominately used in deepwater with skirt piling driven through sleeves to provide 

additional axial and lateral load bearing support. 
28

Caissons are the most likely to be removed using nonexplosive methods, and well protectors and 

fixed platforms, if removed with nonexplosive technology, is more commonly performed in 

shallow water (Table B.2). As water depth increases, the chance of using explosives increases 

slightly across all configuration types. Refined partitions of the water depth data (e.g., using 3 

meter, 10 meter, and 25 meter increments) indicated no observable “trends,” and so the 

consideration of water depth as a relevant factor is questionable. The percentage of structures 

removed using explosive techniques is depicted in Table B.3 according to age upon removal, 

configuration type, and water depth. The use of nonexplosive methods is most common across 

all configuration types within the 0-10 year category when the structure has the greatest chance 

for re-use, and as the age and water depth of structures increase, roughly speaking, the 

probability of an explosive removal also increases. 

 

2.5. Operator Practice in the Gulf of Mexico 

Since 1986, 1,626 structures operated by 127 companies have been removed in the GOM. A few 

hundred structures were removed before this time, but the use of explosives for decommissioning 

was not documented formally by operators or government agencies. Twelve of the 127 

companies are responsible for half of all structures removed, while the “top 36” companies, each 

removing at least eleven structures, account for 80% of all abandonments (refer to Table B.4). 

Companies that have removed ten structures or less comprise the “bottom 91” category and 

contribute the remaining 20% of decommissioned structures. Summary statistics present a 

complicated picture of operator behavior. 

 

Chevron (now ChevronTexaco) has removed the most structures in the GOM as shown in Figure 

B.6, and when combined with Texaco’s structures, comprise roughly 20% of all removals. 

Company practice in the application of explosives is diverse and centered around a mean 

explosive removal rate of 63% (top 12) and 57% (middle 24). 

 

Companies that have removed ten structures or less are more likely to use only explosive 

technology. Over one-third of the “bottom 91” operators used explosives exclusively, compared 

to only one company out of the “top 36” operators. The “top 36” operators use explosives for 

removal on average 58% of the time compared with a 51% application rate for the “bottom 91” 

companies. The primary statistical difference between the two groups is that the standard 

deviation for the “bottom 91” companies is approximately equal to its mean and twice as large as 

the “top 36.” In other words, there is a wide degree of variability in the application of explosive 

technology among the major players, but among the “bottom 91” firms, the variability is even 

greater (see Figure B.7). 

The percentage measures depicted in Figure B.6 and Figure B.7 are not an especially good 

indicator to compare the use of explosives across operator since structural characteristics are not 

considered in the analysis. Normalization of the data can be performed on a company-wide basis, 

but the results are not expected to provide any additional useful information. 
29

2.6. Binomial Logit and Probit Models of Severance Selection

2.6.1. Model Development: A binary-choice severance selection model assumes that the 

operator is faced with a choice between two alternatives (explosive versus nonexplosive 

severance) and that the choice of which cutting method to select depends on characteristics that 

are identifiable. The requirements of the binary-choice model are quite strong, since as we have 

described previously, many important characteristics of the severance decision are not 

observable, and hence, not possible to incorporate within a model. It is nonetheless useful to 

explore the use of an econometric model since it quantifies the probability of an explosive cut 

and provides additional insight into the data interpretation. 

The purpose of a qualitative choice model is to determine the probability that a structure with a 

given set of attributes will realize a specific removal method. We establish a relationship 

between a set of attributes describing a structure and the probability that the operator will make a 

given choice. A binomial logit and probit model is constructed to model the probability that a 

structure will be explosively severed. Define the dummy variable 

⎩

⎨

⎧ = 0, otherwise.

1, structure is removed with explosives D

If we collect a sample of structures that have been removed from the GOM, it is clear that the 

outcome D is a random variable that will only be known after the sample is drawn. 

 

There are many relevant variables that impact the selection of the severance method, and some of 

the observable characteristics include the configuration type, ST; age upon removal, AGE; and 

water depth of the structure, WD. The general model is thus written 

D= f(ST,AGE,WD), 

where, 

⎪

⎩

⎪

⎨

⎧

=

3, if the structure is a fixed platform,

2, if the structure is a well protector

1, if the structure is a caisson

ST

AGE = Age of the structure upon removal, 

WD = Water depth of the structure. 

The logit model is based on the use of a cumulative logistic probability function which is 

specified as 

l

e

F l P L l − + = ≤ =

1

1 ( ) ( ) , 
30

while the probit model is associated with the cumulative normal probability function which is 

written as 

F z P Z z e du u

z 2 .5

2

1 ( ) ( ) −

−∞

∫ = ≤ = π . 

If the probability of an explosive removal is related to the variables in a linear fashion, such as 

E , ( ) 0 1 2 3 D = β + β ST + β AGE + β WD + ε

then the probability that the observed value D takes the value 1 in the logit model is given by 

P( ) L ≤ ) ( β 0 + β1ST + β 2AGE + β 3

WD = F β 0 + β1ST + β 2AGE + β 3WD 

( ) 0 1 2 3 1

1

ST AGE WD

e − β + β + β + β + = , 

while the probit model expresses the probability as 

P(Z ≤ ) ( β 0 + β1ST + β 2AGE + β 3

WD = F ) β 0 + β1ST + β 2AGE + β 3WD . 

By construction, the value of the probability in the logit and probit models will lie in the interval 

(0, 1) and represent the conditional probability that an event occurs given values of ST, AGE, and 

WD. 

2.6.2. Model Results: The expected signs for the coefficients of the regression model are 

straightforward, since as the complexity, age, and water depth of a structure increases, so does 

the probability that explosives will be used (recall Tables B.2 and B.3). Hence, the coefficients 

of the regression models are expected to be positive. 

The sample set is the universe of structures removed in the GOM between 1986-2002 and 

consists of over 1,500 individual observations. The coefficients of the logit and probit model are 

estimated using maximum likelihood, an iterative estimation technique useful for nonlinear 

equations (Berndt, 1991). 

 

The results of the estimation are shown in Table B.5. The estimated coefficients have the 

expected signs and two coefficients are significantly different from zero. The water depth does 

not appear to be a relevant factor. The logit and probit model results are quite similar since they 

are both based on cumulative distribution functions. The value of 2 R cannot be relied on as a 

measure of the overall fit of the model with a dummy dependent variable, but one alternative is 

to compute 2 Rp , the percentage of the observations that the estimated equation correctly explains. 

To use this approach, compute 

2 Rp = 

total number of observations

number of observations "predicted"correctly . 
31

2 Rp is not used universally, but it is a convenient and easily interpreted measure (Studenmund, 

2001). The 2 Rp indicates that the equation correctly “predicted” over 60% of the sample based on 

three variables – only slightly better than guessing. 

2.6.3. Interpretation and Application of the Probit and Logit Model: The effect of a unit 

change in factor ST holding the factors AGE and WD fixed, on the probability that D = 1, is 

given by 

( ( )

( )

( ) f d ST

dt

dt

dF t

d ST

dP = ⋅ = 0 1 2 3 1 β + β ST + β AGE + β WD)β . 

To predict the probability that a structure will be removed with explosives, we use the 

probability model P= F( ) β 0 + β1ST + β 2AGE + β 3

WD . If we obtain estimates a0,a1,a2, and a3 of 

the unknown parameters, we then estimate the probability to be 

Pˆ = F(a0 + a1ST + a2AGE + a3WD). 

By comparing the probability to a threshold value, the choice of severance method is predicted 

using one-half as the cutoff: 

Dˆ =

⎪⎩

⎪

⎨

⎧

≤

>

0.5. ˆ 0,

0.5 ˆ 1,

P

P

Example 

Consider an 8-year old well protector in the WGOM in 50 feet water depth. The probability the 

structure will be removed with explosives is estimated to be Pˆ =0.61. 

2.6.4. Limitations of the Analysis: The binomial logit and probit models are limited by several 

conditions: 

• Ability to capture the relevant factors involved in decision making, 

• Ability to adequately model the identified factors, and 

• Ability to extract sufficient data to support the modeling effort. 

Each of these issues plays a role in the construction and development of the qualitative choice 

model. 

The large number of factors potentially involved in the selection of a severance method provides 

the first indication that the decisions involved in severance are complicated and difficult to 

model. The proliferation of factors is the first clue that simple models can not provide a complete 

reflection of the decision framework, and in fact, it is unlikely that any model of the decision 

process can incorporate all the relevant factors and most certainly a simple model will not do the 
32

trick. The modeling process in this case is only useful to quantify the data in a more sophisticated 

manner. The model does not reduce or eliminate uncertainty or provide additional information 

that is not already captured through probability tables. Relevant company and site specific 

information (e.g., equipment available at the time of the removal, the amount of pre-planning 

involved in the removal, the contractors preference and the operational scheduling, the terms of 

the contract, the quality of the structure blueprints, etc.) can play an important role in the choice 

of removal method, but because these factors are unobservable, they cannot be statistically 

analyzed. It is thus clear that a significant portion of the decision making framework cannot be 

incorporated within the model. The relationships established should thus be viewed as 

interpretative rather than as causal in nature. 

 

The MMS tracks the number of structures removed, the manner of severance, and the structure 

classification, and this data provides the basis for the model construction. The characteristics of 

the structure, including the number and size of the tubular members, the application of grout, and 

the manner of removal of each tubular element do not form part of the MMS data set, and thus 

also cannot be incorporated within the decision model. It is unlikely that the inclusion of more 

refined data at a lower level of aggregation will provide useful information, however, and so in 

principle, the limitations of the MMS database are not effectual. 

2.7. Conclusions 

Decommissioning activities in the GOM are driven by economics and technological 

requirements and governed by federal regulation. Decisions about when and how a structure is 

decommissioned involve complicated issues of environmental protection, safety, cost, and 

strategic opportunity, and the factors that influence the timing of removal as well as the manner 

in which a structure is removed are complicated and depend as much on the technical 

requirements and cost as on the preferences established by the operator and/or project 

management team overseeing the decommissioning project. 

 

The purpose of this chapter is to describe the issues involved in severance selection and the 

factors involved in the decision to use explosive/nonexplosive severance methods, to quantify 

the probability that a structure will be removed with explosives, and to establish a relationship 

between a set of attributes describing a structure and the probability that the operator will select a 

given severance technique. Models were described to help explain the choice of severance 

technique through a closed-form equation, but because such models are not directly linked to 

structure characteristics, the veracity of the relationships described is purely statistical in nature 

and should not be considered causal. 
33

CHAPTER 3: MODELING THE DECOMMISSIONING TIME OF 

OFFSHORE STRUCTURES 

3.1. Introduction 

Business decisions accompany every stage of oil and gas exploration and production. A company 

acquires a lease or contract area based on geological and geophysical data and conceptual plays, 

and then invests in additional data and manpower to refine their knowledge of the region. If the 

results of the analysis are encouraging, exploratory drilling may result. If drilling is successful 

(and most often it is not), the company will confirm and delineate the field, and if the field is 

judged to be economic, the company will develop and produce the reserves in accord with its 

risk-reward perceptions of development in the area. Enhanced recovery projects may be added 

during the field’s producing life if the incremental economics are positive. Frequently, operators 

will divest their property or form a joint venture/farmout type arrangement before the economic 

limit is reached. When the production revenue of the structure equals the operating costs, 

abandonment follows. 

At any point in time during the life cycle of a field, and depending upon the prevailing and 

expected future economics, technologic development, strategic objectives, political trends, and 

contract terms, the operator has to make short-term operational and long-term strategic planning 

decisions. Four primary options exist: 

• Produce. Hold the asset, produce, and manage the declining reserves. 

• Invest. Invest in the asset to maintain or increase production. 

• Divest. Sell all or a portion of the working interest ownership. 

• Decommission. Stop production and remove the asset in accord with regulatory 

requirements. 

Produce

Early in the life of a field after the development wells have been drilled, the field is produced 

according to equipment capacity and operating constraints. Capital expenditures decline quickly 

after development is complete, and after the field begins to flow, gross revenues turn positive. 

Once the exploration and development costs of the investment have been borne, the variable cost 

of production is usually fairly small, and the operator needs only to produce to achieve cash 

flow. The cumulative net cash flow breaks even at payout, after which the cash flow remains 

positive until such time that additional capital investments are required. 

 

Invest

Investment will alter the production profile and will typically extend the life of the asset. If a 

field requires major new investment such as significant workovers or the introduction of 

secondary techniques to maintain production, then the field is likely to be considered a candidate 

for divestiture or abandonment. Major and large independent operators frequently divest 
34

property before the economic limit is reached if the rate of return does not meet a minimum 

threshold or the strategic goals of the company change; e.g., the operator may redefine their core 

assets or need to raise capital to pursue frontier development. This may lead to the removal of the 

structure, or if the field can still be operated profitably, then it may be purchased and operated by 

another firm. 

Divest

Property divestment is a key feature of offshore operations. Operators regularly “carve up” assets 

and sell or subject them to various joint venture/farmout type arrangements throughout the life 

cycle of the field. This is sometimes referred to colorfully as an asset “moving down the food 

chain,” and in most instances, properties change hands three or more times before the structure is 

finally decommissioned. Companies buy producing properties and then implement a 

comprehensive program to increase production, typically involving drilling new stepout or infill 

wells and recompleting existing wells. Companies specializing in marginal production focus on 

operating mature fields in a geographic region where they already own infrastructure. The fact 

that niche operators can manage marginal assets at a profit is due in part to their lower 

overheads, lower expected rate of return, scale economies, and other strategic factors; e.g., the 

operator may be a subsidiary of a construction company which serves as a feeder to the parent. 

Divestment frequently acts to extend lease life, recover greater quantities of hydrocarbons, and 

ultimately, delay the “expected” decommissioning date of the structure. 

Decommission 

Decommissioning represents a liability as opposed to an investment, and so the pressure for an 

operator to decommission a structure is not nearly as strongly driven as installation activities. 

Delaying decommissioning has an economic value for the firm since it defers expenditure while 

allowing the deferred funds to be invested in productive (profit-generating) activities (Roberts 

and Mitchell, 1997). Operators may maintain uneconomic production to see if new extraction 

technology can increase field life or hold (requalify) infrastructure for new (or marginal) 

development plans. Delaying decommissioning can also play a beneficial role in the extraction of 

natural resources in terms of economic efficiency and optimal resource management. 

Hydrocarbon infrastructure represents a social investment, but once wells are plugged and 

abandoned and infrastructure removed, hydrocarbon resources “left behind” in the field or on 

nearby acreage is either “lost” or only available in the future at a higher cost to society. Various 

proposals have been suggested over the years to delay structure decommissioning to achieve 

better economic efficiency; e.g., allow the operator to pay rent to the government to maintain the 

structure in place after production ceases, but because of liability, maintenance, and 

environmental concerns, these ideas have never received much support. 

The purpose of this chapter is to develop the analytic framework to predict the decommissioning 

time of an offshore structure. The basic economic model and the parameters required to drive the 

models are discussed. The GOM is used as the reference case in discussion, but the framework is 

generally applicable to other offshore regions in the world, when adjusted for the governing 

fiscal regime and legislative requirements. Four models of decommissioning are developed, 

ranging from a simple, production-based forecast to a risked, net present value approach. A 
35

meta-modeling methodology is employed to analyze the simulation results, and a detailed 

example is used to illustrate the approach. The limitations of the analysis are described and 

conclusions complete the chapter. 

3.2. After-Tax Net Cash Flow Analysis 

3.2.1. Units of Analysis: Four units of analysis are typically employed in hydrocarbon 

modeling: well, structure, lease, and field. The unit of categorization employed depends upon the 

requirements of the problem and data availability. Production problems are examined at the 

wellhead, while operators consider development planning and cost allocation on a lease or field 

basis. The U.S. government requires royalty, rent, and bonus bid payment to be paid on a lease 

basis. 

Holes must be drilled into the Earth to search for and produce oil and gas. These holes, or wells, 

produce a mixture of oil, gas, water, and other materials which must be separated and treated 

prior to its transport to market. 

A well produces from a reservoir – a porous, permeable rock body, sort of a sponge – lying 

underneath an impervious layer of rock that traps the resource. Several reservoirs located within 

a “common” geologic feature are called a field and can consist of a single reservoir or multiple 

reservoirs. The pressure on the fluid in a reservoir rock causes the fluids to flow through the 

pores into the well. The reservoir drive comes from fluid expansion, rock expansion, and/or 

gravity. There are four basic types of reservoir drives for oil reservoirs: 1) dissolved gas drive, 2) 

free-gas cap expansion drive, 3) water drive, and 4) gravity. Every oil reservoir has at least one, 

and sometimes two, of these reservoir drives. Gas reservoirs have either an expansion-gas or 

water drive (Hyne, 1995). 

Each well is associated with a structure which is identified by its leasehold and type. Offshore 

structures vary significantly depending on the productivity of the reservoir and the quality of the 

produced hydrocarbons; logistical considerations in moving production to market; and the lead 

time required to acquire or design and construct platforms, drilling rigs, production equipment, 

and other downstream facilities. The basic size and function of an offshore structure result from 

the requirements of the development plan (Gerwich, 2000). Typically, several wells are 

associated with a structure, and more than one structure is located on a lease. 

Lease terms and dimensions vary with the time of the auction and the location of the lease, but 

most give the leaseholder the exclusive right to explore for oil and gas for a period of 5-10 years. 

The terms of the lease extend for as long as the lease is productive or development/drilling 

activities are progressing.

The amount of hydrocarbons produced by well wi in year t is denoted by Q(wi, t). Production is 

expressed separately in terms of barrels (bbl) of oil or cubic feet (cf) of gas, or in terms of a 

single stream as barrels of oil equivalent (BOE). Barrels of oil equivalent are the amount of 
36

natural gas that has the same heat content of an average barrel of oil10. The annual hydrocarbon 

production associated with structure si is the aggregate of its collection of wells, {w1,..., } ni w : 

( , ) ( , )

1

Q s t Q w t

ni

j

i ∑ j

=

= . 

Similarly, the hydrocarbon production on lease l at time t is denoted by Q(l, t), and is determined 

as the collection of all the structures contained on the lease, {s1,..., sm}: 

( , ) ( , )

1

Q l t Q s t

m

i

∑ i

=

= . 

3.2.2. After-Tax Net Cash Flow: The net cash flow vector of an investment is the cash received 

less the cash spent during a given period, usually taken as one year, over the life of the project. 

Using structure s as the basic unit of analysis, the after-tax net cash flow in year t is computed as 

NCF(s,t) = GR(s,t) − ROY(s,t) − CAPEX(s,t) − OPEX(s,t) −TAX (s,t) − OTHER(s,t), 

where, 

NCF(s,t) = After-tax net cash flow of structure s in year t, 

GR(s,t) = Gross revenues of structure s in year t, 

ROY(s,t) = Total royalties paid by structure s in year t, 

CAPEX(s,t) = Total capital expenditures of structure s in year t, 

OPEX(s,t) = Total operating expenditures of structure s in year t, 

TAX(s,t) = Total taxes paid by structure s in year t, 

OTHER(s,t) = Other expenditures of structure s in year t. 

3.2.3. Cash Flow Components: The gross revenues in year t due to the sale of hydrocarbons is 

defined as 

GR(s,t) = go(s,t) P o(s,t) Q o(s,t) +gg(s,t)Pg(s,t) Q g(s,t), 

where, 

g o(s,t), g g(s,t) = Conversion factor of oil (o), gas (g) in year t, 

P o(s,t), P g(s,t) = Average oil, gas benchmark price in year t, 

Q o(s,t), Q g(s,t) = Total oil, gas production in year t. 

 

10 One BOE is about 6 Mcf of gas. 
37

There are four basic types of hydrocarbon molecules, called the hydrocarbon series, in each 

crude oil: paraffins, naphthenes, aromatics, and asphaltics. The relative percentage of each series 

molecule controls the chemical and physical properties of the oil. Natural gas is composed of 

hydrocarbon molecules ranging from one to four carbon atoms in length: methane (CH4), ethane 

(C2H6), propane (C3H8), and butane (C4H10). The conversion factor (or “quality” of the 

production stream) depends on the physical characteristics of the hydrocarbons and is a 

function11 of the API gravity, the sulfur content and the gas-oil ratio (GOR). 

API Gravity 

The API gravity of crude oil is a measure of the density or weight of the oil. Average crude has a 

25o to 35o range, with light oils falling between 35o to 45o and heavy oils below 25o. Light crude 

receives a higher price relative to heavy crudes because they tend to have more gasoline by 

volume. 

Sulfur Content 

The sulfur content for most crude oils falls between 1% and 2.5%, with 1% sulfur content 

considered “sweet” crude and 2.5% sulfur considered “sour.” Sweet crude is priced at a premium 

relative to sour crude. Hydrogen sulfide can occur either mixed with natural gas or by itself. 

Hydrogen sulfide is poisonous, and when it is mixed with natural gas, causes corrosion in the 

well. Sweet gas has no detectable hydrogen sulfide, whereas sour gas has detectable amounts. 

Sweet gas is priced at a premium and sour gas facilities are more expensive to construct and 

operate to handle the corrosive elements. 

Gas-Oil Ratio 

The amount of natural gas dissolved in crude oil at the surface is called the producing gas-oil 

ratio (GOR) and is expressed in cf/bbl. If Qo

(w, t) and Qg(w, t) represent the oil and gas 

production associated with well w, then the producing gas-oil ratio for well w is defined as 

( , )

( , ) ( , ) Q w tQ w t GOR w t o

g

= . 

If GOR(w, t) > 5,000, the well is classified as a “gas” well; GOR(w, t) < 5,000 is classified as an 

“oil” well (Hyne, 1995). Production facility costs are sensitive to the value of GOR and the 

facilities required to handle large volumes of gas can be significantly more expensive than 

low/no gas production. 

The gross revenues adjusted for the cost of basic gathering, compression, dehydration and 

sweetening form the base of the royalty: 

ROY(s,t) = ROY (GR(s,t) – ALLOW(s,t) ). 

 

11 The density, sulfur content, and acidity of a field will usually stabilize after a year or so, but depending on the 

number of producing zones and degree of commingling, variability over the life of the field can occur. 
38

The total allowance cost is denoted by ALLOW(s,t) and the royalty rate ROY, 0 ≤ ROY ≤ 1,

depends upon the location and time the tract was leased and the incentive schemes, if any, in 

effect. The “typical” federal royalty rate in the United States is ROY = 1/8th (12.5%) onshore 

and ROY = 1/6th (16.67%) offshore. In recent years, the suspension or reduction of royalty 

payments in certain offshore areas (and subject to specific conditions) has been introduced. 

Capital expenditures are the expenditures incurred early in the life of a project, often several 

years before any revenue is generated, to develop and produce hydrocarbons. Capital 

expenditures typically consist of geological and geophysical costs, drilling costs, facility costs, 

construction, transportation, and any other cost required to develop the field. Capital cost may 

also occur over the life of the project, such as when wells are recompleted into another formation 

or sidetracked with a horizontal well, artificial lift facilities are installed or facilities are 

upgraded/replaced, etc. Capital expenditures are decomposed into tangible and intangible costs: 

 

CAPEX(s,t) = CAPEX/T(s,t) + CAPEX/I(s,t) , 

where, 

CAPEX/T(s,t) = Tangible capital expenditures of structure s in year t, 

CAPEX/I(s,t) = Intangible capital expenditures of structure s in year t. 

Tangible costs have a useful life greater than one year and a recognizable salvage value, and are 

depreciated according to federal guidelines. Intangible costs are taken as a tax deduction in the 

year of expenditure. 

Operating expenditures represent the money required for the daily operation of the structure to 

operate and maintain the facilities; to lift the oil and gas to the surface; and to gather, treat, and 

transport the hydrocarbons. Many factors influence operating expenditures, including the 

operator, age and type of field, field equipment, location, efficiency of operating labor, fuel 

costs12, wage level, general economic conditions (which affects the cost of oil field services), 

wear and corrosion. Operating costs and intangible capital costs are typically expensed. 

Operating expenditures are frequently described in terms of direct and indirect expense: 

OPEX(s,t) = OPEX / D(s,t) + OPEX / I(s,t) , 

where, 

OPEX / D(s,t) = Direct operating expenditures of structure s in year t, 

OPEX / I(s,t) = Indirect operating expenditures of structure s in year t. 

 

12 Oil production is an energy intensive operation, and when fuel prices increase, so does production costs. Gas 

production is typically more labor intensive with only minor fuel costs. 
39

The general rule for charging costs directly to an operation is that the charges must be for work 

physically performed at the project site or exclusively for that operation. Costs which are 

incurred at a distant location for a number of different operations are considered indirect costs or 

overhead. 

Taxable income is determined as the difference between net revenue and operating cost; 

depreciation, depletion, and amortization; intangible drilling costs; investment credits (if 

allowed), interest in financing (if allowed), and tax loss carry forward (if applicable). In the 

United States, state and federal taxes are determined as a percentage of taxable income, usually 

ranging between 35-50%, and here denoted by the value T, 0 ≤ T ≤ 1: 

 

TAX (s,t) = T(NR(s,t) −CAPEX / I(s,t) − OPEX (s,t) − DEP(s,t) − CF(s,t) − DECOM (s,t)), 

where, 

NR(s,t) = GR(s,t) − ROY(s,t) = Net revenue of structure s in year t, 

CAPEX / I(s,t) = Intangible capital expenditures of structure s in year t,

DEP(s,t) = Depreciation, depletion, and amortization of structure s in year t, 

CF(s,t) = Tax loss carry forward of structure s in year t, 

DECOM (s,t) = Decommissioning cost of structure s in year t. 

The tax and depreciation schedule is normally legislated and will vary across time. In the United 

States, all or most of the intangible drilling and development cost may be expensed as incurred, 

whereas equipment cost must be capitalized and depreciated (Gallun et al., 2001). Tax losses in 

the U.S. may be carried forward for at least three years. 

Decommissioning cost represents the expenditures that occur near the end of the life of a 

structure when the wells are plugged and abandoned; the deck, piles, conductors, and jacket are 

removed; and the site is cleared of debris. There are many factors that influence the time and cost 

to decommission an offshore structure, and the primary factors include the physical 

characteristics and disposition of the structure, the specification of the job, operator preferences, 

the time of removal, market conditions, and the occurrence and duration of exogenous events. 

3.2.4. Economic Indicators: The purpose of economic evaluation is to assess if the revenues 

generated by the project cover the capital investment and expenditures and the return on capital 

is consistent with the risk associated with the project and the strategic objectives of the company 

(Brealey and Myers, 1991). The present value PV(s) and internal rate of return IRR(s) are the 

most common measures in the oil and gas industry (Thompson and Wright, 1984) and are 

computed as 

PV(s) ∑= + = at

t

t D

NCF s t

0 (1 )

( , )

, 

IRR(s) = {D | PV(s)= 0}, 
40

where D is the corporate discount factor and the project is assumed to begin at time t = 0 and end 

at the abandonment time t = ta. The present value provides an evaluation of the project’s net 

worth in absolute terms, while the rate of return is a relative measure used to rank projects for 

capital budgeting. Economic values are not intended to be interpreted on a stand-alone basis and 

should be used in conjunction with other system measures and decision parameters. 

 

3.2.5. Typical Cash Flow Patterns: Oil and gas ventures have a great variety of patterns of 

investment and payout, but most ventures can be decomposed into four basic stages: 

I. Investment/Development 

II. Production 

III. Marginal Production 

IV. Decommissioning 

The cash flows in the early years of development are usually large and negative, and so during 

Stage I, NCF(s,t) < 0. Capital expenditures decline quickly after development is complete, and 

after the field begins to flow, gross revenues turn positive along with NCF(s,t) . The cash flow 

elements peak during the production stage and payout is the point in time when the cumulative 

net cash flow breaks even. The price of hydrocarbon (and production to some extent) is a 

stochastic quantity, and so the gross revenues and NCF(s,t) are uncertain throughout the life 

cycle of the field. The reserves on which the cash flow is derived are finite in nature, and so the 

elements of the cash flow stream will eventually converge to zero. 

Expenses that need to be paid during Stage II and III include royalty, operating cost, and taxes, 

and typically, capital equipment is fully depreciated before the conclusion of Stage II. The onset 

of “marginal” production varies with operator and field, and during the later part of Stage II, 

investment is required to maintain production, but the decision to invest depends on operator 

preference and economic criteria. Properties in Stage II and III are frequently considered for 

divestment and joint agreement activity. During Stage IV, the operator will incur a negative 

outlay when the revenue generating capacity of the structure is exhausted. 

3.3. Parameter Forecasting 

The physics and engineering of oil and gas exploration, development, and production is the same 

worldwide, but the geologic and environmental conditions, fiscal regimes, prospectivity, and 

risk-reward profiles for projects are not homogenous. To construct a model of the time that a 

structure is expected to be decommissioned, a number of forecasts are required, including 

1. Expected reserves, RES (BOE), 

2. Projected production forecast of oil and gas, Qo

(s, t) (bbl) and Qg(s, t) (Mcf), 

3. Projected quality of the oil and gas, go

(s, t) and gg(s, t), 

4. Projected price of oil and gas, Po

(s, t) ($/bbl) and Pg(s, t) ($/Mcf), 

5. Projected capital expenditures, CAPEX(s, t) ($) , 
41

6. Projected tangible and intangible capital expenditures, CAPEX/T(s, t), CAPEX/I(s, t) ($), 

7. Projected operating expenditures, OPEX (s, t) ($), 

8. Projected tax rate, T (%), 

9. Projected decommissioning cost, DECOM(s, t) ($), 

10. Estimated discount rate, D (%). 

Estimates are based upon the best information available at the time the forecast is made and are 

considered under the economic conditions projected for the future, including inflation, 

supply/demand conditions, and technological improvements. 

 

The types of estimates that can be performed depend on the stage of development of the project, 

the experience of the estimator, and the design and planning information available. Initial cost 

and production estimates typically fall between “order-of-magnitude” estimates (on the order of 

25%-50% accuracy) and “conceptual development plan” estimates (on the order of 15%-25% 

accuracy) (Mian, 2002). The uncertainty associated with the value of the parameter forecasts will 

almost always fall within a broad range, and in the worst case, the range itself may be unknown. 

Estimates made after the field is in production (say, during the mid-point of the life cycle of a 

field) are usually more reliable, but other sources of uncertainty which are unobservable, such as 

strategic and technologic factors, enter the analysis at this time. 

3.3.1. Reserves: A detailed and realistic field description is the first and most important estimate 

that is made prior to development. The size, shape, productive zones, fault blocks, drive 

mechanisms, etc. of the reservoir are estimated by company geologists and engineers as 

accurately as possible since these factors determine the design capacity of the equipment and 

structure, the required number and location of wells, and the supporting infrastructure 

requirements. 

Reserves are those quantities of hydrocarbon that are anticipated to be commercially recoverable 

from known accumulations. Proved reserves are known with reasonable certainty because the 

field has been defined by appraisal wells. Developed reserves can be produced from existing 

wells and existing infrastructure, while undeveloped reserves are produced from wells that have 

not yet been drilled or from existing wells that are “beyond the pipe.” Proved reserves are not

fixed, but rather, depend upon the amount of exploration undertaken, technology and economic 

conditions, and thus can vary as a result of changes in the external position of these factors 

(Seba, 2003; Rose, 2001). 

Reserves appreciation refers to the expected increase in estimates of proven reserves as a 

consequence of the extension of known pools or discovery of new pools within existing fields. 

Reserves appreciation, or reserves growth, represents the expected increase in the estimates of 

original proved reserves of an oil and gas field. Field growth can result from several factors, such 

as improvements in recovery, physical expansion of the field, better understanding of the 

reservoir, data re-evaluation, extension drilling, and changes in economic parameters. Changes in 

reserve estimates for a specific field may be negative as well as positive, but on average, reserve 

estimates usually grow over time. Field growth is most rapid the first few years after a field is 
42

discovered, and later tends to level out at a smaller increment, so that near the mid-cycle of a 

field, the recoverable reserves are reasonably well-known. 

3.3.2. Production Profile: Many factors impact the rate at which hydrocarbons are produced, 

but the two primary factors are the geologic conditions and development plan. The geologic 

conditions at the site – the type and characteristics of rock, depth, thickness, fault mechanisms, 

hydrocarbon properties – are essentially “fixed,” while the development plan – well density, 

wellbore size, completion techniques, method of production, equipment capacity – represent 

design parameters. Production rates across fields vary widely because of the variability in these 

factors. 

There is a trade-off in the investment required to produce oil and gas and the production rate 

achieved. A high production rate requires a large capital investment in the form of the number 

and type of wells drilled, structure facilities, and the capacity of production equipment. High 

investment also requires a higher rate of return to justify the increased capital risk, and so the 

preferences of the operator and their perceived risk-reward tradeoff will determine the design 

capacity of the field. 

Most production profiles can be decomposed into three distinct phases: 

Ramp-Up 

Production normally builds up over the first few years of production. Following the installation, 

hookup, and certification of the platform, development drilling is carried out and production 

started after a few wells are completed. Subsea completions may be used to produce from 

appraisal wells before full field development. 

Plateau

The plateau period represents the maximum rate of production the facilities were designed to 

handle, pipeline capacity, or contractual constraints. The duration of the plateau is based upon 

the productivity of the reservoir and the economics of the project. 

Decline 

After peak production, fields will decline due to the geology and pressure loss at a rate 

determined by the reservoir drive, investment, and economic conditions. The nature of the 

decline is characterized through the decline rate. 

A reliable production forecast early in the life of a field can only be developed with knowledge 

of the development plan, reserve estimates and production capacity (Allen and Seba, 2003). 

Limitations on the availability and accuracy of data constrain the reliability of forecasting. 

During the mid-point in the life of a field, a different sort of uncertainty arises, since the 

production profile and the drive mechanisms of the field are now reasonably well understood, 

but the strategic decisions of the operator are unknown. Will the operator invest additional 

capital? Will the operator seek a joint operating agreement or divest the structure? Leases are 
43

held by a wide variety of working interest owners and are inevitably carved up over time and 

sold off or subject to a variety of joint venture/farmout type arrangements. Operators purge their 

portfolios of under-performing and non-core assets on a semi-regular basis, and as properties 

change hands, the capital expenditures and operating cost structures typically change. 

3.3.3. Hydrocarbon Price and Quality: The domestic price of oil and gas is determined by the 

cost of imports and market conditions. Conversion factors for oil and gas adjust the benchmark 

price and depend primarily on the API gravity and sulfur content of the produced hydrocarbon. 

Hydrocarbon prices are a stochastic quantity while production quality is time dependent. 

3.3.4. Capital Expenditures: Capital expenditures typically consist of geological and 

geophysical costs, drilling costs, facility costs, construction, installation, and any other costs 

required to develop the field (Gallun et al., 2001). 

Geological and Geophysical Cost

Geological and geophysical (G&G) costs are pre-drilling exploration costs, and include 

topological, geological, and geophysical studies. G&G costs may occur before or after the 

acquisition of working interest in the lease, and for tax purposes, are usually expensed in the year 

incurred. 

Drilling Cost

Drilling time and costs depend on many technical aspects of the well(s) to be drilled, such as the 

configuration and geometry of the well, type of drilling contract and rig type, well depth and 

formation complexity. Other factors include the preferences of the operator and performance of 

the contractor, the weather encountered, and problems associated with the operation. Drilling and 

development costs are classified as either intangible drilling and development cost or equipment 

costs. 

Development Cost

Three basic options exist to develop reserves: (1) A new platform can be built to drill the 

development wells, (2) a subsea well can be drilled and tied back to a nearby platform, or (3) an 

extended reach well can be drilled from an existing platform. The reserve size, distance from 

existing infrastructure, and time to first production determine which option is the most 

economical. New platforms and subsea wells are the standard development design, but if a high 

level of certainty exists that the reserves are in place and within reach of existing infrastructure, 

then an extended reach well may also be a viable option. 

Many different facilities are required to produce oil and natural gas and the exact specification 

depends, among other things, on the size and distribution of the resource (e.g., broad shallow 

reservoirs, deep compact reservoirs), fluid variations (e.g., gas-oil ratio, nonassociated gas), 

meta-ocean conditions (e.g., hurricane risk, wave height, water depth), pipeline infrastructure, 

local construction infrastructure, and crew accommodation requirements. 
44

Installation Cost

The manufacturing and installation cost of the structure(s) required to develop and produce a 

field is typically the most significant capital expenditure, ranging between 50-75% of the total 

costs of the project. Drilling expenditures usually make up the bulk of the remaining cost. In the 

Gulf of Mexico, total CAPEX is frequently assumed to range between $3/BOE-$4/BOE 

(Johnston, 2000), but these are “zero-type” estimates that are subject to significant uncertainty. 

3.3.5. Operating Cost: Direct operating cost can be expressed in terms of subcategories such as 

production, transportation, maintenance, and other. 

Production Cost

Production cost usually contributes the greatest amount to operating cost, but the percentage 

breakdown varies with the operator, site, and the stage of the project’s life cycle. Production 

costs include the cost to lift and treat (dehydrate and separate) hydrocarbons and to dispose of 

water, which in turn depends on the capacity of the equipment and the throughput. 

Transportation Cost

Transportation costs are related to the transport of oil and/or gas from a field to a refinery or 

processing facility, an export terminal, or any other point of sale. These costs depend on the 

throughput, the distance to be covered, and the means of transport. Transportation cost items 

typically include pump and compressor fuel, tanker rentals (if applicable), pipeline tariffs, and 

terminal cost. 

Maintenance Cost

Maintenance cost is associated with keeping the oilfield equipment and wells in good working 

condition and production. Maintenance covers material and manpower cost and is usually 

subdivided into facility and workover categories. Facility maintenance comprises inspection 

costs, preventative maintenance, and remedial costs. Workover costs occur less frequently and 

include the costs of well stimulation and repair. 

 

Other Cost

For offshore operations, other direct operating cost items typically include supply boats, 

helicopters, standby vessels, docking charges, shore base expense, underwater inspections 

(platforms and pipelines), communications and data transmission, weather services, personnel, 

small tools and supplies, and equipment standby (e.g., wireline, cementing pumps). 

Indirect operating cost items include office expenses, lease supervision, engineering salaries, 

clerical support, warehouse, management salaries, public affairs, and insurance. Administrative 

and general overhead may vary significantly among operators, while insurance varies with the 

cost of replacement and the vulnerability of the insured unit. The method for allocating indirect 

costs is arbitrary, but prorated and percentage rules are commonly employed (Gallun et al., 
45

2001). Ernst & Young LLP surveys operators in the U.S. on their average overhead rates per 

well by producing area and well depth. For offshore wells in the GOM, the monthly median 

overhead rate per well in 2002-2003 was $35,000 for a drilling well and $3,500 for a producing 

well. Full cycle operating cost of $2.5/BOE – $3/BOE is frequently assumed for the GOM, and 

the operating cost in the peak year of production may range from 3-8% total capital expenditures 

(Johnston, 2000). 

3.3.6. Decommissioning Cost: Decommissioning occurs in stages and typically over disjoint 

time frames. Greatly simplified, following project engineering and cost assessment, federal and 

state regulatory permits for well plugging and abandonment, pipeline abandonment, and 

structure removal must be obtained. Approval of the site-clearance methodology (either via 

trawling/diver/ROV/high-resolution sonar) is given during Structure Removal Permit 

Application process. Wells are plugged and the facility is prepared for removal, including 

flushing and cleaning process components, installing padeyes, etc. and then the pipelines are 

pigged and/or flushed riser-to-riser and riser-to-subsea tie-in, detached from the structure, and 

capped. Pipelines are normally left in place with the ends buried 3 feet (1 meter) below the 

mudline. Modules that are to be lifted separate from the deck are removed, the deck is cut and 

removed, and then the conductors and piles are cut 15 feet (5 meters) below the mudline and 

pulled. Heavy lift vessels bring the jacket ashore for recycling, sale, or scrap, and in the GOM, 

the operator may participate in a reefing program. After the structure has been lifted and 

salvaged, any lost/jettisoned/discarded items are removed from the seabed around the removal 

site via trawling contractors, ROVs, or divers in order to verify that the area is returned to “prelease” conditions (Pulsipher, 1996). 

The average total cost to remove 4-pile structures in the GOM has been estimated as $885,000, 

while the average total cost for an 8-pile structure is $1,344,000. Decommissioning cost can be 

estimated across each stage of the operation or an aggregate estimate of total cost can be 

performed based on one or more descriptor variables. For example, if WD(s) represents the water 

depth at the site (in feet) and NP(s) is a count of the number of piles of the structure, then 

empirical cost functions such as 

⎪

⎩

⎪

⎨

⎧

+ ≥

+ < <

+ ≤

=

4.2 ( ) 886, (s) 6,

6.2 ( ) 318, 3 ( ) 6

2.2 ( ) 491, ( ) 3

( )

WD s NP

WD s NP s

WD s NP s

DECOM s

can be employed to estimate decommissioning cost (Kaiser et al., 2003). 

3.4. Model Development 

Four models are developed to predict structure abandonment time. The models range from a 

simple, heuristic-based forecast, to a risked, net present value approach. The choice of which of 

the four models is “best” depends upon the beliefs of the user and the robustness of the model 

assumptions. Factors not considered in the analysis include the strategic objectives of the 

operator, the regulatory flexibility associated with decommissioning, and random acts of nature. 

For a related discussion of decommissioning modeling, see (Kemp and Stephen, 1997). 
46

3.4.1. Model I – Resource Recovery: The simplest “production-based” model of abandonment 

is derived from an estimate of the time when the expected reserves of the field are depleted. The 

expected time of abandonment will occur when the forecasted cumulative production equals the 

reserves expected to be recovered. The resource constraint determines the physical limitation of 

production since, under the assumptions specified, the reserves will be “depleted” at this time. 

The expected time of abandonment is designated formally as 

∑=

= ≥

'

(I) min{ ' | ( , ) }

t

t t

a

o

t t Q s t RES , 

where first production is assumed to start at time to. Production is reported on an annual basis, 

and it is clear that the minimization operator “min” will select the first time when cumulative 

production exceeds the resource base. 

No economic factors influence the result, at least not directly, and both Q(s, t) and RES are 

estimated quantities. Forecasting Q(s,t) is based on assumptions regarding the decline rate, the 

time of peak production, and investment decisions. The resource estimate is based on current 

technology and price levels. After peak production, Q(s, t) is assumed to be a decreasing function 

of time, and for a given value of RES, the time of first passage will be unique. The uncertainty 

associated with the analysis depends on the time relative to the production cycle the forecast is 

performed. If the analysis is performed at the beginning of the life cycle of the field, both Q(s,t) 

and RES will be significantly more uncertain than if the analysis is performed during the midpoint or near the end of the field’s life cycle. 

3.4.2. Model II – Threshold Indicators: It is reasonable to assume that “similar” structures will 

exhibit “similar” conditions13 near the time of abandonment. If the threshold limit of production 

and the adjusted gross revenue for structure s is denoted by Q(s) and GR(s) , then the time of 

abandonment is estimated by 

 

t (IIa) min{t | Q(s,t) Q(s)} a = ≤ , 

t (IIb) min{t | GR(s,t) GR(s)} a = ≤ . 

Hybrid threshold models incorporate the reserves constraint of Model I in the determination of 

abandonment time; i.e., 

(IIc) min{ '| ( , ) ( ), ( , ) }

'

t t Q s t Q s Q s t RES

t

t t

a

o

= ≤ ∑ ≤ =, 

t (IId) min{t'| GR(s,t) GR(s), a = ≤ ( , ) }

'

Q s t RES

t

t to

∑ ≤ =

. 

 

13 This is explored more completely in Chapter 4. 
47

The inclusion of the reserves constraint ensures that the structure cannot extract more than is 

available to produce. The reserve constraint is rarely realized in practice, however, since 

economic and strategic conditions usually dominate removal and divestment decisions. 

A structure may reach its economic limit (“first passage”) when hydrocarbon prices are in a 

depressed price-demand state, but if the operator believes stronger prices will prevail in the 

future, then an abandonment decision is likely to be postponed until the operator can no longer 

sustain operating losses. To reflect an operator’s reluctance to remove a structure at first passage, 

more stringent conditions can be enforced, such as requiring gross revenues to fall below the 

threshold two or three (consecutive) years in a row: 

t (IIe) min{t 1| GR(s,t) GR(s),GR(s,t 1) GR(s)} a = + ≤ + ≤ , 

t (IIf) min{t 2 | GR(s,t) GR(s),GR(s,t 1) GR(s),GR(s,t 2) GR(s)}. a = + ≤ + ≤ + ≤

Although production is assumed to decline after peak production, the price of hydrocarbons is a 

stochastic variable, and thus gross revenues may in fact increase even as production declines. 

 

Model II does not explicitly take into account the capital or operating outlay or the 

decommissioning cost, but it does include costs in an indirect manner since “similar” structures 

are being compared, and it is reasonable to assume that similar structures will exhibit similar 

conditions near abandonment. Model II is considered an improvement over Model I since it 

incorporates site-specific and historical data into the criteria that determines abandonment time. 

Model II is also not dependent on the sequence of divestments that occur over the life cycle of 

the field, since only the abandonment conditions at the end of the life of the structure are 

considered. 

3.4.3. Model III – Negative Cash Flow: The net cash flow model proxies abandonment time 

according to the cash flow of the operator. To compute the net cash flow of an investment 

requires that a detailed life cycle model be developed. Unlike the construction of a cash flow 

model for investment evaluation, where accurate estimates in the early years of production is 

most important, abandonment evaluation requires a reliable estimate of cash flow elements to be 

performed near the end of the production history of the asset where uncertainty is the greatest. 

Abandonment of a structure is expected to occur when the net cash flow of the asset first turns 

negative: 

 

t (IIIa) = min{t | NCF(s,t) ≤ 0} a . 

This is frequently referred in the literature as the “economic limit” of the structure. 

 

Similar to the delay decisions incorporated in Model II, an operator may choose to hold an asset 

for one or more years even if the net cash flow is negative: 

t (IIIb) = min{t +1| NCF(s,t) ≤ 0,NCF(s,t +1) ≤ 0} a , 

t (IIIc) = min{t + 2 | NCF(s,t) ≤ 0,NCF(s,t +1) ≤ 0,NCF(s,t + 2) ≤ 0} a . 
48

More generally, an operator may abandon a property when a threshold limit on the level of cash 

flow is reached, say E > 0, for l > 0 years in a row: 

 

(IIId) min{ | ( , ) }. , 1,...,t t l NCF s t E t t t l a = + ≤ + +I

 

Note that Models IIIa, b, c, are a special case of Model IIId for E = 0 and l = 1,2,3, respectively. 

E is an operator-defined parameter. 

Model III provides a realistic economic assessment from the operator’s perspective since it 

incorporates estimates of the magnitude and timing of future revenue and costs in a standard 

economic framework. There is a trade-off between modeling the capital and operating 

expenditures for the asset directly (as in Model III) versus indirectly (as in Model II). In Model 

II, the structure is compared with historic threshold levels which do not depend on the cash flow 

position of the operator or the divestment sequence. In Model III, the analyst must either be privy 

to private information or must make several additional estimates on the magnitude and timing of 

future costs. 

The accuracy and reliability of Model III is plagued by the uncertainty associated with the 

magnitude and timing of the cost estimates and the uncertainty associated with the strategic 

objectives of the operator. If an operator divests the structure, which is almost certain to occur at 

least two or three times during the life of the asset, the cash flow stream of the asset will need to 

be modified along with updated cost elements for the new operators. Model II and Model III are 

considered roughly equivalent in their uncertainty, and so a preference for the simpler Model II, 

prevails. 

3.4.4. Model IV – Maximum Net Present Value: The net present value model refines the net 

cash flow model by incorporating the time value of money into the relation to establish the 

probable removal time of the structure. Model IV is based on the cost data employed in Model 

III, along with two additional variables, the discount rate and decommissioning cost. The 

discount rate is a user-defined variable, while the decommissioning cost is estimated based on 

the characteristics of the structure to be removed. In the net present value model, abandonment 

time is estimated by maximizing the after-tax net present value of the cash flow. 

The time to abandon a structure is computed from the optimization model: 

(IV) { '| max ( ,[ , '])} ' , 1,t t PV s t t a τ =τ τ + K= , 

where τ denotes the current time and the time of decommissioning is scheduled for some time t

in the future, t ≥ τ. The present value relation is computed as 

∑=

− + =

'

(1 )

( , ) ( ,[ , '])

t

t

t D

NCF s t PV s t τ

τ τ . 
49

The operator selects the time of abandonment to maximize the net present value of the 

investment. 

Application of the net present value model is satisfying from an intellectual perspective since the 

economic criteria is well established and the model incorporates all the essential cash flow 

elements in the analysis, including the discount factor and cost of decommissioning, but from a 

practical perspective, the net present value model will almost always result in an unbounded 

solution. From a present value point-of-view, operators are willing to pay to avoid the cost of 

decommissioning since the economic benefit of deferred abandonment usually far outweigh the 

cost of early removal. Other factors play a role in the decision to abandon – such as liability 

issues, bonding requirements, strategic objectives, and public perception – but these are not 

factors, per se, in the economic model, and are not incorporated in the analysis. 

The cost of decommissioning frequently represents a large negative outlay in the year of 

removal, and so operators have a strong incentive to delay or transfer this liability. If structure s 

is removed in the current year, t = τ, the operator foregoes the benefit of remaining production 

and incurs the cost of decommissioning immediately. The present value of this decision is PV(s, 

[τ, τ]). If the operator produces for one additional year, even if production is marginal and does 

not cover the cost of the operation, and then removes the structure at the end of the year, this 

yields the present value PV(s, [τ, τ +1]), which in most cases will dominate PV(s, [τ, τ]). Since 

the cost of decommissioning is large and at least one or two orders-of-magnitude larger than the 

annual operating cost, and since the discount rate can be considered relatively small, say 10-

15%, the functional PV(s, [τ, t]) will be an increasing function of time over a rather long time 

horizon. Thus, the solution to 

(IV) { '| max ( ,[ , '])} ' , 1,t t PV s t t a τ =τ τ + K= , 

is unbounded, meaning that the time to abandonment will occur far into the future if guided 

strictly by economic criteria. 

 

The primary weakness of Model IV in predicting the expected age upon abandonment is the 

unbounded characteristic of the solution and the fact that structural changes in the cost 

functionals are not considered. The initial development scenario is appropriate for determining 

the economics of the original investment, but near the mid-life cycle of the field, after the 

exploration and development cost have been recovered, the operator will consider options for 

maximizing the value of the field. One of those options is to divest the property or maintain a 

working interest position in a joint venture arrangement. The potential for divestiture occurs 

when another company values the asset more than the operator. A field may be considered a 

candidate for divestment at any time during its life cycle, but the probability of an asset changing 

hands increases significantly after the mid-life of the field. 

 

3.5. Meta-Modeling Methodology 

The impact of changes in system parameters is usually presented as a series of graphs or tables 

that depict the measure under consideration as a function of one or more variables under a “high” 

and “low” case scenario. While useful, these approaches are generally piecemeal and the results 
50

are anchored to the initial conditions employed. The restrictions associated with geometric and 

tabular presentations of multidimensional data are also significant; e.g., on a planar graph at most 

three or four variables can be examined simultaneously. A more general and concise approach to 

sensitivity analysis is now presented. 

 

The abandonment time of a structure varies with the structural and parametric specification in a 

complicated manner, but it is possible to understand the interactions of the variables and their 

relative influence using a constructive modeling approach. The methodology is presented in 

three steps. 

Step 1. For model φ, bound the range of each variable of interest (X1,L, Xk ) = (d(t), P, 

GR ,...) within a design interval, Ai ≤ Xi ≤ Bi, where the values of Ai and Bi are userdefined and account for a reasonable range of the uncertainty associated with each 

parameter. The design space Ω is defined as 

Ω = { ) ( ,..., X 1 Xk | Ai ≤ Xi ≤ Bi, i = 1,..., k}. 

Step 2. Sample the component parameters ) ( , , X1 L Xk over the design space Ω for each 

model φ, and compute the expected abandonment time, ) ta (φ , for each parameter 

selection ) ( , , X1 L Xk . 

Step 3. Using the parameter vector ) ( , , X1 L Xk and computed functional values ) ta (φ ,

construct a regression model based on the system data: 

(φ) at = f( , , ) X1 L Xk = α0 +α1X1 +L+αk Xk , 

where the values of ) ( , , , α0 α1 L α k are determined from the simulation analysis. 

This procedure is sometimes referred to as a “meta” evaluation since a model of the system is 

first constructed, and then meta-data is simulated from the model in accord with the design space 

specifications and system constraints. A good rule of thumb is to sample until the regression 

coefficients “stabilize.” If the regression coefficients do not stabilize, or if the model fits 

deteriorate with increased sampling, then the variables are probably spurious and linearity 

suspect. After the regression model is constructed and the coefficient vector ) ( , , , α0 α1 L αk

determined, if the model fit is reasonable and the coefficients statistically relevant, the value of 

the ) ta (φ functional can be estimated for each model specification for any value of ) ( , , X1 L Xk

within the design space.

3.6. Illustrative Example 

Oil and gas ventures have a great variety of patterns of investment and payout, and unless 

detailed site-specific information is available, cash flow modeling is a generic exercise. The 

example developed is used to illustrate the manner in which models of the abandonment time can 
51

be developed in terms of a meta-evaluation procedure. The field under consideration is labeled 

XYZ, and the project is dated from the beginning of development expenditures, although money 

would have also been required for geologic and geophysical cost, leasing cost, and exploration 

drilling and planning before the decision to proceed with the development was made. 

3.6.1. Development Scenario: At the time of development planning, geologists estimated the 

XYZ field to have between 60-90 million barrels (MMbbl) recoverable reserves spread 

throughout several geologic zones and total depth ranging between 15,000-18,000 feet. After 

eight years of production and reservoir modeling, engineers now believe the ultimate recoverable 

resources to range between 90-110 MMbbl. 

The field was developed at a capital cost of $3.5/bbl based on a 100 MMbbl recoverable reserve 

estimate. The drilling/production facility chosen for development was an 8-pile traditional 

platform structure designed to handle peak production of 12,000 bbl/day. The gas production of 

the field is used to supplement on-site power requirements with the remainder reinjected into the 

field. The hydrocarbons are primarily light, sour oil, with API gravity 42° and 3% sulfur content 

requiring expensive treatment facilities. There are currently six producing wells and one subsea 

tieback with production transported to shore through an existing pipeline. There are no other 

structures on the leasehold. 

Based on historic data from similar fields in the area developed with similar technology, the life 

cycle operating cost are expected to be $3.4/bbl. The capital and operating cost during the first 

eight years of production are known with certainty, and nearly all the tangible capital cost has 

now been depreciated. The field is currently in decline, and because the reservoir is isolated, the 

operator does not anticipate significant additional capital investment. Further, because of the 

potential liability associated with decommissioning, the operator does not intend to divest the 

structure. 

Reservoir dynamics suggest that a percentage decline equation will govern the future production 

profile, described by 

( , ) (1 ( , )) ( ,8), 9 8 = − ≥ − Q s t d s t Q s t t , 

where Q(s, t) is the annual production rate in thousand barrels per year (Mbbl/year), Q(s, 8) = 

9,180 Mbbl, and the decline rate d(t) is assumed to vary with time and fall uniformly between 

8-13%; i.e., d(t) ~ U(0.08, 0.13). 

 

The price of oil is assumed to be described by a Lognormal distribution with mean $25/bbl and 

standard deviation $3/bbl: P ~ LN(25, 3) denotes a flat (time-invariant) price profile over the life 

cycle of the field, while P(t) ~ LN(25, 3) denotes a time-variant price path with prices fluctuating 

on an annual basis. All capital costs are assumed to be tangible and depreciated according to a 

straightline 5-year schedule. The investment is assumed to be completely equity financed so 

interest deductions on the loan need not be considered. No operating taxes besides income tax 

are considered, and the rate of inflation is assumed constant. 
52

3.6.2. Model I Results: In Model I, the expected abandonment time of structure s is determined 

from the relation 

∑

′

=

= ′ ≥

t

t t

a

o

t ( , I) min{t | Q(s,t) RES}

where 1 to = and the ultimate recoverable reserves are assumed Normally distributed with mean 

100,000 Mbbl and standard deviation 10,000 Mbbl; i.e., RES ~ N(100000, 10000). Refer to 

Table C.1. Abandonment is determined when cumulative production first exceeds the resource 

estimate. The expected age of the structure upon abandonment is then computed as 

A(I) = (I) at o − t = ) (I at −1. 

In functional form, the expected age upon abandonment is described by the system parameters 

through the regression model: 

A(I) = α0 +α1d +α2R ES, 

where d is computed as the average value of d(t) over the production profile: 

 

d = ∑− =

at

a t

d t

t 1

( ) 1

1 . 

The signs of the regression model are readily hypothesized. A small average decline rate means 

quick recovery of the reserves; a large decline rate means slow recovery. For a given value of 

RES, a small decline rate translates to a quick abandonment, while as d increases so will the 

abandonment age. Hence, the coefficient of α1 is expected to be positive. If the decline rate is 

held constant, an increase in the reserves RES will extend the field life, and so α 2 > 0. 

For the design space Ω = { ) d(t ~ U(0.08, 0.13), RES ~ N(100000, 10000)} the empirical results 

of the meta-model yield 

A(Ia) = − 90.8 + 362.2d + 0.00070RES, R2

= 0.62, 

consistent with expectation. See Table C.2. For any value of d and RES within the design space, 

the regression model can be used to estimate abandonment age; e.g., if d = 10% and RES = 

100,000 Mbbl, then A(Ia) = 15.4 years. A 1-percentage point increase in the average decline 

parameter (to d = 11%) translates to an abandonment age of 19.0 years, or a 4-year increase in 

the expected age. Similarly, a 10,000 Mbbl increase in reserves translate to an abandonment age 

of 22.4 years, or a 7-year increase. 
53

If the shape or size of the design space is changed, the simulation must be recalibrated and the 

structure equations re-estimated. Adding, deleting, or redefining variables will change the shape 

of the space, while increasing or decreasing the bounds of the parameters will change the size of 

the space. For instance, if the model parameter of RES is revised to reflect greater uncertainty on 

the recoverable reserves, say Ω = { ) d(t ~ U(0.08, 0.13), RES ~ N(100000, 20000)}, then Model 

Ib yields 

A(Ib) = − 81.1+ 382.3d + 0.00066RES, R2

= 0.72. 

In this case, if d = 10% and RES = 100,000 Mbbl, then A(Ib) = 23.1 years. 

3.6.3. Model II Results: In Model II, the gross revenue is used as a threshold indicator on 

abandonment, and so the data requirements are expanded to include information on the price of 

oil and gross revenue. Model II specifies that when the annual gross revenue falls below the 

threshold limit GR(s) , the structure will be removed: 

t (II) min{t | GR(s,t) GR(s)} a = ≤ . 

The value of the threshold level GR(s) is estimated based on inflation-adjusted historical data 

near the end of the life of “similar” assets and is assumed to be Uniformly distributed between 

$15,000 and $30,000: GR (s) ~ U(15000, 30000). 

To compute revenue a price forecast is obviously required. The price of oil can be considered 

constant over the time horizon of production or a stochastic model of price variation can be 

employed; e.g., price can be assumed to follow a stochastic price path. If prices are constant over 

the life cycle of the field, gross revenues will decline in step with production decline; if prices 

fluctuate, so will the gross revenues. 

The functional form of Model II is expressed as 

A(II) = α0 +α1d +α2P +α3GR , 

and again the coefficients of the regression model are readily hypothesized. An increase in the 

average value of d will induce a quicker production decline, thereby decreasing the average age 

of the structure14, and so 0 α1 < . If P increases and all other factors are held constant, gross 

revenues will increase which will extend the economic limit and delay the time of abandonment, 

or α 2 > 0. As the threshold level G R increases, the time of decommissioning will occur sooner, 

and so 0 α3 < . 

 

14 In Model I a fixed reserve base was assumed, implying α1 > 0, while the threshold indicator in Model II implies, 

α1 < 0. 
54

For the design space Ω = { ) d(t ~ U(0.08, 0.13), P ~ LN(25, 3), GR ~ U(15000, 30000)}, the 

results of Model IIa yield 

A(IIa) = 53.4 − 229.8d + 0.37P − 0.00042GR, R2= 0.97. 

All the coefficients yield the expected sign as shown in Table C.2. For d = 10%, P = $22/bbl 

and GR = $20,000, A(IIa) =30.2 years. A $1 increase in the price of oil over the life of the field 

results in an expected age of 30.5 years, or about four additional months of production until 

abandonment. A $1,000 increase in the gross revenue threshold limit decreases the expected age 

to 29.7 years. 

The impact of design space modifications is readily explored. For example, if the gross revenue 

threshold varied on the up-side, indicating that the structure reached its economic limit at a 

higher level of gross revenue, then we would expect that the time of abandonment would occur 

sooner since gross revenues are generally a decreasing function of time after peak production. 

For Model IIb, 

Ω = { ) d ~ (t U(0.08, 0.13), P ~ LN(25, 3), GR ~ U(15000, 50000)}, 

the simulation yields 

A(IIb) = 50.8 − 208.9d + 0.36P − 0.00034GR, R2= 0.96; 

e.g., for d = 10%, P = $22/bbl and GR = $40,000, A(IIb) = 24.2 years. 

The impact of structural modifications on the model can also be explored. For instance, if the 

hydrocarbon price is assumed to vary annually as P(t) ~ LN(25, 3), and the average price over 

the time horizon is denoted P , then the empirical results indicate that the Model IIc coefficients 

remain fairly stable but lose some of their statistical significance – refer to the fixed term and the 

price coefficient in Table C.2 – and compare the results with Model IIb: 

A(IIc) = 51.5 − 206.9d + 0.29P − 0.00034GR, R2= 0.95; 

e.g., for d = 10%, P = $22/bbl and GR = $40,000, A(IIc) = 23.6 years. 

3.6.4. Model III Results: To estimate the net cash flow position of the operator, a host of new 

variables are required. In addition to the parameters ) d(t and P(t), we now also require the 

royalty and tax rate, operating expenditures, capital expenditures, tangible and intangible cost 

decomposition, and the depreciation schedule. As the problem was formulated, the value of these 

parameters is known for the first eight years of production, but during the decline stage 

additional structural assumptions will be required. 

 

The annual operating expenditures for the 8-pile, 6-well structure are assumed to be given by the 

relation 
55

OPEX (s,t) = $10,020 + $1.6Q(s,t),

for ) Q(s,t described in Mbbl/year. This relation is based on historic data of the field and an 

assessment of similar structures in the region. To reflect changes that may occur in the value of 

the operating costs, a perturbation factor k ~ U(0.9, 1.3) is applied to the annual value of 

OPEX (s,t). For k < 1, operating cost would be smaller than the historic relation, while for k > 

1, the operating costs would exceed historic rates. The value of k is assumed constant throughout 

the cash flow cycle, but it is easy to allow k to vary annually, in which case the average measure 

k would be the output variable of interest. The royalty and tax rate are assumed to be Uniformly 

distributed with ROY ~ U(0.10, 0.20) and T ~ U(0.10, 0.20), and the threshold value of the net 

cash flow cut-off is E ~ U(4000, 8000). 

The functional form of Model III is expressed as 

A(III) = α0 +α1d +α 2P +α3ROY +α 4k +α5T +α6E . 

The expected signs of the coefficients α1 < 0 and α 2 > 0 follow from the discussion for Model II. 

As the royalty and tax rate increase, the net cash flow position of the operator will be negatively 

impacted, and so we expect α3 < 0 and α5 < 0. The coefficient α 4 reflects the influence of 

perturbations to the operating expenditures, so that as k increases, operating expenditures 

increase, again negatively impacting the net cash flow position of the operator. Similarly, as the 

value of the net cash flow threshold E is raised, structures will be abandoned earlier, and we 

expect α 4 < 0 and 0 α6 < . 

The net cash flow projection for the field is computed according to the framework previously 

described. The gross revenues are determined as the product of the production and price 

trajectory, and the net revenue is determined after the royalty rate ROY is specified. The values 

for CAPEX and OPEX and the depreciation schedule are known for the first eight years of the 

field’s life and are extrapolated thereafter. The tax is determined after the tax rate T is specified. 

Four different scenarios are considered using the parameter values shown in Table C.3. The 

design space common to each model is given as 

Ω = { d(t) ~ U(0.08, 0.13), P ~ LN(25, 3), ROY ~ U(0.10, 0.20), 

 k ~ U(0.9, 0.13), T ~ U(0.30, 0.50), E ~ U(4000, 8000)}. 

The results of the regression models are depicted in Table C.4. For Model IIIa, 

A(IIIa) = 59.2 − 206.1d + 0.17P −12.6ROY − 4.8k − 7.3T − 0.00071E , R2= 0.95. 

This model is based on a sampling scheme involving 1,000 iterations. For d = 10%, P = 

$25/bbl, ROY = 16.67%, k = 1.1, T = 40%, and E = $8,000, the meta-model yields an expected 

abandonment age A(IIIa) = 26.9 years. For 5,000 model iterations in Model IIb, the regression 
56

coefficients remain fairly stable and generally increase in significance, with the difference in the 

numerical result between the two models imperceptible: A(IIIb) = 26.4 years. 

Alternative decision criteria can be adopted within the analytic framework. Delay can be 

incorporated in the model by adopting the decision rule 

(IIIc,d) min{ | ( , ) }. , 1, 2t t l NCF s t E t t t a = + ≤ + +I

In Model IIIc and Model IIId, the net cash flow elements must fall below E for two and three 

consecutive years before the operator decides to abandon. Obviously, additional constraints on 

the production profile will increase the expected age of the structure, and so the relevant question 

concerns the relative impact of the constraint. If the production decline dominates the 

hydrocarbon price volatility near the time of abandonment, then the incremental impact on the 

average age is expected to be about one year or so per additional constraint. On the other hand, if 

the volatility of the hydrocarbon price is a dominant factor, then we would expect the impact to 

deviate from the one year increment. For d = 10%, P = $25/bbl, ROY = 16.67%, k = 1.1, T = 

40%, and E = $8,000, Model IIIc, d yield 

A(IIIc) = 27.1 years, A(IIId) = 28.2 years, 

suggesting that production decline is the dominating factor.

3.7. Limitations of the Analysis

Significant sources of uncertainty underlie all models of decommissioning, and the framework 

described herein only hints at the complexity involved. Additional sources of uncertainty are 

now described. 

 

3.7.1. Private Uncertainty: The primary sources of private uncertainty include geologic 

uncertainty, production uncertainty, investment uncertainty, and strategic uncertainty. Some 

forms of uncertainty are observable and quantifiable (e.g., price), while other forms are 

quantifiable but unobservable due to their proprietary nature (e.g., geologic). The most difficult 

forms of uncertainty to model are strategic decisions that are neither observable nor readily 

quantifiable. 

3.7.2. Scale Economies: Operators who divest or farm out a structure induce a structural change 

in the operating cost of the asset. If O1 represents the seller and O2 the buyer, then the typical 

structural change would be 

OPEX(s, t, O2) ≤ OPEX(s, t, O1), 

where OPEX(s, t, Oi) represents the operating cost of structure s in year t for operator Oi. Buyers 

reduce the cost to operate a structure through scale economies and by maintaining low overhead. 
57

By bundling structures in a group {s1, ..., sk} and servicing the needs of the group as a unit, scale 

economies can frequently be achieved, such that 

OPEX(s1, ..., sk; t, O) ( , , )

1 1

j

k

i

i

l

j

∑∑OPEX s t O = =

≤ , 

providing the asset a new lease on life. The decision to invest in structure sk+1 when a bundled 

unit {s1, ..., sk} already exists is an economic decision determined by the incremental benefits of 

adding the production of sk+1 versus the incremental costs of operation and decommissioning. If 

structure sk+1 is in the same geographic area as other properties then the scale economies may 

provide residual benefit to the owner. 

Similar to the structural changes that occur under divestment, operators can reduce the overall 

cost to decommission structures on a lease through timing and scale economies. Again, by 

bundling structures in a group {s1, ..., sk} and performing the removal at one time, economies are 

frequently achieved through more favorable contract terms, reduced mobilization/ 

demobilization fees, etc. so that: 

DECOM(s1, ..., sk, t) ( , )

1 1

j

k

i

i

l

j

∑∑ DECOM s t = =

≤ . 

Niche operators can act faster and are more operationally flexible than large independents or 

majors, and this flexibility has value that is expressed in various ways; e.g., niche players can 

wait until the market rates for construction barges are competitive to perform deconstruction 

activities.

3.7.3. Regulatory Uncertainty: Typically, a lease is terminated when production on the lease 

ceases, but if the operator intends to re-work wells or is pursuing drilling activity on the lease, or 

the lease contains an active pipeline, conditions may warrant the MMS to grant an extension of 

the lease termination. Since several structures may be contained on a lease, it is only when 

production from the last productive structure on the lease ceases that all the structures are 

required to be removed. 

3.7.4. Random Events: Random acts of nature (e.g., see (Daniels, 1994)) also influence the 

ability to predict removal times, but because the frequency of such events is relatively small, 

these occurrences do not play a large role in aggregate removal patterns. 

3.8. Conclusions

Four models were developed to model the decommissioning time of an offshore oil and gas 

structure, and the limitations, refinements, and extensions of each model were discussed. The 

models were then implemented on a generic field development plan to illustrate the simulation 

methodology and the manner in which the system variables interact. A meta-modeling 

methodology was used to construct functionals that describe how the age of the structure upon 

abandonment is related to various system parameters. 
58

The high degree of uncertainty and the large number of factors associated with structure removal 

suggest that simple models can capture the essence of a removal forecast in a manner that is 

comparable to more sophisticated methodologies. Academics would probably favor the more 

sophisticated constructions and consider their application to be more “reliable” predictors of 

events, but this is not necessarily the case. Under conditions of unknown uncertainty, the results 

of simple models should not be discounted out-of-hand in favor of more elegant (but just as 

uncertain) “economic” models. When choosing between methodologies that are constrained by 

high levels of uncertainty and factors that cannot be adequately modeled, simple models are the 

preferred approach. 
59

CHAPTER 4: A STATISTICAL ANALYSIS OF THE ECONOMIC LIMIT 

OF OFFSHORE HYDROCARBON PRODUCTION

4.1. Introduction 

The economic life of a structure is defined as the time at which the production cost of the 

structure is equal to the production revenue. Economic life is normally difficult to determine 

directly, since full and accurate economic data are often not available on an individual structure 

basis, and factors such as hydrocarbon price, operational expenditures, investment decisions, and 

strategic objectives contribute to the uncertainty. Toward the end of the lifetime of most 

structures, the capital expenditures and depreciation are generally negligible and the operating 

cost is the primary expense element. When the gross revenue falls below the operating cost, the 

operator will usually shut down production and consider available divestment or 

decommissioning options. 

The economic limit of structure s, te = te(s), is defined as the time when the gross revenue of the 

structure, GR(s, t), equals production cost, C(s, t): 

te = {t| GR(s, t) = C(s, t)}. 

Gross revenue GR(s, t)=P(t)Q(s, t) is an observable quantity, while production cost, C(s, t), is 

generally unobservable. Operators on federal leases are required to report production data to the 

government on a well basis, and so the gross revenue and royalty stream of a structure can be 

estimated, but the revenue stream is only half of the equation and the cost data still needs to be 

inferred. As most casual observers of the oil/gas industry are aware, it is rare indeed when the net 

cash flow from a real asset is available outside the firm, and rarer still if it is made public. Cash 

flow and cost information is proprietary. Fortunately, a proxy of the economic limit of a structure 

can be inferred by observing the production and gross revenue during the structure’s last year of 

production. 

The last year of production of structure s, tlp = tlp(s), 

tlp = min{t| Q(s, t +1) = 0}, 

represents a “snapshot” of the economic conditions specific to the field, structure, operator, 

technology and time of operation. The production and gross revenue at tlp, Q(s, tlp) and GR(s, tlp), 

serves as a proxy of the economic limit and can be used to infer the threshold operating cost of 

the structure. The link between the gross revenue and operating cost threshold is not perfect, but 

if properly normalized and interpreted, is believed to be a reasonably good indicator of the 

economic limit of a structure. 

The purpose of this chapter is to provide a statistical assessment of the economic limit of 

offshore structures in the GOM. The model development is presented, followed by statistical 

relations of the production and revenue threshold levels. Conclusions complete the chapter. 
60

4.2. Model Development 

The economic limit of a structure is estimated following four steps: 

Step 1. Estimate structure production; 

Step 2. Define lease categorization; 

Step 3. Compute production and gross revenue thresholds; and 

Step 4. Define factor variables. 

A description of each stage is now provided. 

4.2.1. Production Allocation: Each structure is identified with a lease, and in theory, each well 

is associated with a (unique) structure. In practice, however, not every well in the MMS database 

is linked to a structure. Wells are grouped into two disjoint categories – assigned {wa

} and 

unassigned {wu

} sets. A well that is a member of { a w } maintains a structure assignment while 

wells in {wu

} require assignment. Unassigned well production is allocated to the nearest lease 

structure. For w l u ∈ , assign u w to structure s ∈l based upon the criteria, 

 

w {s | min d(w ,s)} u

s l

u

∈ ↔ , 

where ) d(w ,s u represents the distance from well u w to structure s and the minimization is 

performed with respect to all structures contained on the same leasehold l as the well u w . The 

annual production of structure s is modified as follows: 

Q(s ,t) Q(s,t) Q(w ,t) a u = + . 

4.2.2. Lease Categorization: Structure data is delineated along two dimensions – the number of 

structures removed and the number of producing structures on a lease at the time of removal. It is 

necessary to disaggregate structures in this manner for two reasons: 

1. Federal regulations allow structures on a lease to remain idle (nonproductive) as long as 

the lease is productive. Producing leases can thus “hold” structures idle for a number of 

years. 

2. Structures that exist in close proximity to other structures (on the same lease or adjacent 

leaseholds) are more likely to exhibit economies of scale in operation and shared 

expenses, and at the time of removal, reduced decommissioning cost. 

Four categories are employed to disaggregate structure/lease data: 

I. One structure removal, no other producing structure on lease at time of removal; 

II. One structure removal, at least one producing structure on lease at time of removal; 
61

III. Two or more structure removals, no other producing structure on lease at time of 

removal; 

 IV. Two or more structure removals, at least one producing structure on lease at time of 

removal. 

Every structure removed in the GOM is an element of one and only one lease category, and since 

the four categories are disjoint, it is clear that the aggregation strategy represents the universe of 

all removals. Category I represent structures with one and only one structure on the leasehold at 

the time of removal. If one structure is removed while one or more structures are active on the 

lease, the structure is classified in category II. Two or more structures may also be removed at 

the “same” time, or nearly the same time, typically within a month or so of one another. 

Structures on leases with two or more structures removed but no other producing structures on 

the lease are classified in category III. Category IV denotes two or more removals on a leasehold 

with at least one producing structure at the time of removal. 

 

4.2.3. Threshold Limits: The annual production and gross revenue streams of structure s are 

computed at time tlp and 1, 2, ..., k years before last production, as 

{Q(s, tlp), Q(s, tlp –1), ..., Q(s, tlp – k)}, 

{GR(s, tlp), GR(s, tlp –1), ..., GR(s, tlp – k)}. 

The production and gross revenue streams at time tlp are referred to as a “threshold” limit, and 

denoted by Q (tlp) = Q(s, tlp) and R (tlp) = GR(s, tlp). Production and gross revenue levels at/near 

the year of last production represents a threshold for economic operations under conditions 

specific to the field, structure, operator, technology, and time of operation. The production and 

gross revenue levels represent a “snapshot” of the structures state in the year of last production, 

and presumably, conditions that approximately describe the economic limit. 

Threshold indicators Q (Γ,t) and R(Γ,t)for category Γ at time t can be computed in several 

ways. An averaging process is the most common: 

∑ Γ ∈Γ

Γ = s

Q s t

t Q t ( , ) # ( )

1 ( , ) , 

R(Γ,t) ∑ Γ ∈Γ

= s

GR s t

t ( , ) # ( )

1 ,

where # Γ(t) denotes the number of elements (structures) in category Γ at time t. 

 

4.2.4. Factor Description: Denote ti = ti(s) as the time structure s is installed and tr = tr(s) as the 

time the structure is removed. The idle time (idle age) of structure s is the amount of time that 

has elapsed between the time the structure was removed and the last year of production, 

IDLE =tr − lp t . 
62

The profile Q(s, t) is used to delineate three phases of production. If peak production, 

 

Q* =Q * (s,t) = maxQ(s,t), 

occurs in year tp, 

t min{t | Q(s,t) Q*}, p = =

then the peak production period is defined as the time interval when production exceeds αQ*, 

where the value of α, 0 < α < 1, is user-defined and selected near the upper bound of the interval; 

e.g., α = 0.8. The plateau production period is defined by the time interval ) [ , c d t t , where 

 

t = min{t | Q(s,t) ≥ αQ*, 0 < α <1} c , 

td = max{t | Q(s,t) ≥ αQ *, 0 < α <1}. 

The ramp-up period is defined by [ti,tc]. The decline period is defined by ] [ , d lp t t . 

The decline rate d(s, t) of structure s in year t, d lp t ≤ t ≤ t , is defined as 

( , )

( , 1) ( , ) ( , ) Q s t

Q s t Q s t d s t − − = . 

Usually, d(s, t) ≥ 0 but investment decisions, maintenance, production problems, weather and 

other events may change the sign of the decline rate for one or more years. The average decline 

rate of structure s over ] [ , d lp t t is computed as 

∑ − =

=

lp

d

t

lp d t t

d s t

t t

DEC s ( , ). 1 ( )

At the time of development, the peak production to expected reserves ratio, Q*/E[RES], serves as 

a proxy for the maximum efficient production rate. To obtain a high Q*/E[RES] ratio, the 

operator will need to have a large number of producing wells and adequate production equipment 

to handle the volumes of oil and gas produced. A low Q*/E[RES] ratio provides an indirect 

indication that an operator has chosen to drill less wells and produce longer. Fewer wells require 

smaller production, processing, and transportation facilities; less operating personnel; reduced 

financing cost, and presumably, lower operating expenditures. At the time of last production, the 

expected value of the recoverable reserves, E[RES], is a deterministic and known quantity, RES, 

computed as 

∑=

=

lp

i

t

t t

RES(s) Q(s,t) . 
63

4.3. Descriptive Statistics 

4.3.1. Data Source: The data for this analysis was obtained through the MMS Technical 

Information Management System database. The sample set contains over 2,000 structures 

removed in the GOM over the past two decades, and after eliminating structures that have never 

produced and structures with missing and/or ambiguous data, the final data set included 1,790 

elements. Only structures that have been removed from operation are under consideration. It is 

clear that production and revenue thresholds can be calculated with a reasonably high level of 

accuracy since production and price forecasting is not required. Uncertainty arises in the 

calculation of Q(s, t) and GR(s, t) because of the nature of well assignments, and since the 

quality of production is not considered, but the exclusion of these factors are not expected to 

have a significant impact on the results. 

4.3.2. Lease Categorization Statistics: Summary statistics for the average production and 

revenue threshold levels (Q (tlp), R (tlp)), idle time (IDLE), design ratio (Q*/RES), and total 

production (RES) are presented in Table D.1 according to lease categorization and structure type. 

The number of elements within each category is denoted by n. 

 

Structures on leases with no other infrastructure at the time of removal cannot be held by lease 

production because federal regulations generally require removal within one year after lease 

production terminates. The average production/revenue thresholds for categorization I structures 

should therefore be the highest among all lease categories because scale economies cannot be 

used to minimize operational cost. The average production and revenue threshold values for 

lease category I, Q (tlp) = $57,000 and R (tlp) = 734,000 BOE, exceed category II, III, and IV 

levels as expected, and this dominance is maintained across each structure type. The average idle 

time of categorization I structures should be the smallest among the four lease categories, and 

indeed this is also the case. The average idle time of structures removed on leases with no 

infrastructure at the time of removal is IDLE = 2.3 years which is nearly three times less than the 

average idle age of categorization II structures. 

For leases with more than one producing structure at the time of removal, we would expect 

production/revenue thresholds to decrease because of the potential for scale economies, while 

average idle times should increase because structures can be held by lease production, and this is 

supported by the empirical data. Leases with two or more structures removed with no other 

producing structures at the time of removal are more “similar” to category I structures, and so the 

difference between the production and revenue thresholds and idle time is expected to be 

smaller. For leases with at least one producing structure at the time of removal, economic theory 

suggests that 

Q (II) ≥ Q (IV), 

R (II) ≥ R (IV), 

IDLE(II) ≤ IDLE(IV). 

In this case, the production/revenue thresholds relations are not supported by the empirical data, 

but the idle time behaves as expected. 
64

Summary statistics for Q*/RES and RES provide a quick description of the field characteristics. 

The value of Q*/RES range broadly between 20-60%, with gas fields produced with Q*/RES as 

high as 40-50%. The average decline rate will often equal or exceed the Q*/RES ratio; i.e.,

DEC ≥ Q*/RES, and this is generally supported by the empirical data. Q*/RES is a decreasing 

function of structure complexity for each lease categorization, while the recoverable reserves is 

an increasing function of structure complexity. In symbolic form, 

Q*/RES(C) > Q*/RES(WP) > Q*/RES(FP), 

RES(C) ≤ RES(WP) ≤ RES(FP). 

Structure complexity serves as a proxy of the development plan, expected size and decline 

characteristics of the field, and this is reflected by the relational forms. 

 

4.3.3. Average Production and Revenue Threshold Levels: Categorization of the data 

according to lease characteristics, structure type, and water depth is presented in Tables D.2-D.9. 

The number of elements within each category in the year of last production is shown in the third 

column of each table. Because some fields have a very short lifetime, the number of elements 

within each category will decrease with the time from last production (so as one moves 

horizontally across each row, the size of the sample set will decrease). 

The value of the average production thresholds is fairly uniform across water depth and structure 

type in Table D.2. Well protectors in the 101-200 feet water depth category appear to be an 

exception due to the presence of a small number of large fields. The average production 

threshold ranges between 43,000 - 57,000 BOE, meaning that on average, when the annual 

production from an offshore structure falls within this range the structure is very near its 

economic limit. The gross revenue thresholds shown in Table D.3 exhibit greater variability than 

the Table D.2 elements since the production data is modified by (inflation-adjusted) hydrocarbon 

prices. Excluding well protectors in the 101-200 feet water depth category, the average revenue 

threshold ranges between $556,000 - $797,000. 

In Tables D.2-D.9, the general trends of the production and revenue thresholds are reasonably 

consistent within each categorization, namely, Q (t t) lp − and R(t t) lp − are increasing functions 

of time and the production/revenue thresholds of fixed platforms generally dominate the 

production of simpler, smaller structures, such as caissons and well protectors, 

Q (C,tlp − t) < Q (WP,t t) lp − <Q (FP,t t) lp − , 

R(C,tlp − t) < R(WP,tlp − t) < R(FP,t t) lp − , 

for t ≥ 1. The dominance of well protectors and fixed platforms is apparent after just a few years, 

while the distinction between well protectors and fixed platforms is not always apparent. 

As the number of elements within each category decrease, inconsistencies arise in lease 

categories III and IV, but this is to be expected considering reductions in the sample size and 

diversity of the fields under consideration. Some indication that the production/revenue 
65

threshold is structure invariant is provided, however, since ( , ) lp Q C t ≈ ( , ) lp Q WP t ≈ ( , ) lp Q FP t

and ( , ) lp R C t ≈ ( , ) lp R WP t ≈ ( , ) lp R FP t , at least approximately, across lease categorizations. 

There is also a weak trend for the production and revenue thresholds to increase with water 

depth: 

Q(ST, 0 −100′) < Q(ST,101− 200′), 

R(ST, 0 −100′) < R(ST,101− 200′) < R(ST, 201− 400′) . 

4.4. Conclusions 

The economic limit of an offshore structure is important from an operational perspective and 

provides insight into the nature of decommissioning activities. No modeling framework is 

perfect, however, and the best a model can do is to provide insight and ensure an interpretation 

supported by empirical data. The unique nature of the economic limit of structures drives the 

observed variability in the data set, and since the category descriptors are constrained and finite, 

the impact of unobservable factors on the model results may be significant. Many factors impact 

the economic limit of a structure and it is not possible to enumerate all the factors in modeling, 

but statistical analysis allows insight to be developed. 
67

CHAPTER 5: FORECASTING THE REMOVAL OF OFFSHORE 

STRUCTURES IN THE OUTER CONTINENTAL SHELF 

5.1. Introduction

Offshore structures combine capital, labor, materials and fuel to produce hydrocarbons, and 

operate under the physical laws and engineering specification of the system, economic principles 

which determine the design and commerciality of production, and man-made rules governing 

operation and decommissioning activities. Significant interrelationships exist between the 

physical laws by which a system operates and the commercial rules and regulations established 

for the system. 

 

Structures are installed to produce and process hydrocarbons, and when the time arrives that the 

cost to operate a structure – or more commonly, a group of structures – exceeds the income from 

the hydrocarbons under production, the structure(s) exists as a liability instead of an asset and 

becomes a candidate for divestiture or decommissioning. Over 2,200 structures have been 

removed from the GOM over the last half-century, and over the past decade, 125 structures on 

average have been removed annually (Table E.1). 

At the end of 2003, there were 2,175 active (producing) structures, 898 idle (non-producing) 

structures, and 440 auxiliary (never-producing) structures on 1,356 active leases; and 329 idle 

structures and 65 auxiliary structures on 273 inactive leases (Table E.2). An active structure 

produced hydrocarbons in the year 2003, while an idle structure once produced hydrocarbons, 

but has not been productive over the past year. An auxiliary structure has never produced 

hydrocarbons, but serves in a support role, as a quarter facility, flare tower, storage platform, etc. 

A total of 2,175 active structures, 1,227 idle structures, and 505 auxiliary structures, or 3,907 

total structures, currently reside in the GOM. More than one-half of producing structures reside 

on leases with one or more active structure. 

 

The distribution of structures according to structure type, water depth, and planning area is 

shown in Table E.3. A few dozen deepwater structures classified as compliant tower, spar, 

tension-less platform, mini tension-leg platform, and semisubmersible are not considered in 

Table E.3 because they are small in number, relatively recent installations, and are expected to 

serve as deepwater hubs for hydrocarbon production for many years to come. 

Federal regulations specify the manner in which structures are removed from the GOM and idle 

iron is maintained. Changes in the regulation, or re-interpretation of removal requirements, will 

change the nature of decommissioning services, changing the number of inputs (i.e., amount of 

service equipment, timing of services) used in decommissioning, which will impact the overall 

cost and development options available to the operator. 

The purpose of this chapter is to develop a general framework to model the removal process that 

governs structure abandonment and decommissioning in the Gulf of Mexico. The outline of the 

chapter is as follows. The general modeling framework is first formalized, followed by model 

parameterization and the results of the analysis. A brief discussion of the limitations of the 

analysis is also presented. Conclusions complete the chapter. 
68

5.2. Model Framework 

5.2.1. General Methodology: The methodology adopted in this paper follows a five-step 

process. For structure s and time t, 

Step 1. Forecast production profile, Q(s, t), 

Step 2. Forecast revenue profile, R(s, t), 

Step 3. Estimate abandonment time, ta(s), 

Step 4. Estimate removal time, tr(s), and 

 Step 5. Estimate removal cost, C(s). 

5.2.2. Production Model: Hydrocarbon production profiles are generated using the iterative 

functional, 

Q(s, t +α(s)) = (Q(s, t + α(s) –1) + β(s)) [1– d(s, t +α(s))], 

where d(s, t) represents the decline parameter selected from a Normal distribution with mean, 

DEC(s), and standard deviation,σ DEC . For structures that have already reached peak production, 

α(s) = β(s) = 0; for structures that have not yet achieved peak production, α(s) and β(s) are used 

to adjust the profile to the expected peak production time and rate. The values of DEC(s), σ DEC , 

α(s), and β(s) are derived from the statistical analysis of structures previously removed from the 

Gulf of Mexico. 

5.2.3. Revenue Model: The gross revenue stream is determined from the relation, 

R(s, t) = g(s)P(t)Q(s, t), 

where the conversion factor, g(s), depends on the gravity and sulfur content of oil, and the 

amount of impurities, condensate, and hydrogen sulfide of natural gas. The hydrocarbon price, 

P(t), is based on a reference benchmark determined from historical data. 

 

5.2.4. Abandonment Time: A structure will cease production when its economic limit is 

reached, that is, when the revenue stream converges to its threshold level: 

( ) min{ | ( , ) ( )} , t s t Q s t s a Q Q = = ε , 

( ) min{ | ( , ) ( )} , t s t R s t s a R R = = ε . 

The abandonment time, ( ) , t s a Q , is associated with the production threshold level, εQ(s); the 

abandonment time, ( ) , t s a R , is associated with the revenue threshold level, εR(s). The threshold 

levels, εQ(s) and εR(s), are empirically derived and vary with characteristics such as water depth, 

structure type, and hydrocarbon production, and the number of structures on the lease. It is 

possible to construct general expressions of the threshold limit as a function of these and other 

factors. 
69

5.2.5. Removal Time: To estimate removal time, a rule-based policy is assumed to govern and 

approximate operator behavior. If structures { k s , ,s 1 L } exist on lease l and are held until 

production from the last structure ceases, then the time in which all the structures on the lease 

are removed is determined from the relation: 

( ) max{ ( )} 1

1,...,

= + = a i i k r i t s t s , i = 1,..., k; 

e.g., if one structure exists on the lease, s∈l , then 

tr (s) = ta (s) +1, 

while if two structures exist on the lease, {s ,s }∈l 1 2 , then 

( ) ( ) max{ tr s1 = tr s2 = ta (s1),ta (s2 )}+1, 

and so on for three or more structures. 

A lease can “hold” structures idle without violating federal guidelines as long as the lease 

remains producing. Operators may remove idle structures on producing leases early, and indeed, 

depending upon decommissioning schedules or other preferences, it may be economic for 

operators to remove select idle structures, but these provisions are not considered. By excluding 

the possibility that idle structures will be removed earlier than required by law, we are modeling 

a latest possible removal scenario. 

5.2.6. Removal Cost: The cost to remove structure s at time t (s) r is determined as the present 

value of the removal cost, 

−τ + = ( ) (1 )

( ) ( ) t s r dC s C s , 

where C(s) denotes the decommissioning cost; d is the discount rate, 0 < d < 1; τ is the 

observation time; and t (s) r is the removal time. The decommissioning cost is described through 

empirically-derived functions and is assumed constant over time. Potential cost savings 

associated with scale economies are not considered, and since decommissioning is generally a 

low-technology, low-margin operation with no significant barriers to entry, technological 

progress is not expected to have a significant impact on future cost drivers. 

5.3. Model Parameterization

Initialization 

The GOM currently contains over 1,356 active leases and 273 inactive leases. The number of 

active, idle and auxiliary structures on active leases was depicted in Table E.2; the number of 

idle and auxiliary structures on inactive leases is shown in Table E.4. Structures on inactive 
70

leases form an inventory of platforms that are expected to be removed in the near future, unless 

they serve a useful economic purpose, or other special circumstance apply. The 245 structures on 

inactive leases are not considered. The 440 auxiliary structures on active leases are assumed to 

be removed when production on the lease on which they reside ceases. The remaining 2,175 

active structures and 898 idle structures on active leases are removed according to the latest 

possible removal scenario. 

Production Model 

For each producing structure in the GOM, the decline parameter, d(s, t), is sampled each year 

from the Normal distribution, N(DEC(s), σ DEC ), to forecast the production profile. The value of 

DEC(s) is computed from historical data of structures removed in the GOM; e.g., 

DEC(s) = −14.8 − 0.035ST − 0.019GORV − 0.0104NW + 0.0017O /G + 0.00771VINT , 

where ST = structure type (ST = 0, caisson; ST = 1, otherwise), GORV = gas-oil ratio variation, 

NW = number of wells, O/G = oil/gas structure (O/G = 0, oil; O/G = 1, gas), and VINT = 

vintage (year structure installed). The values of σ DEC , α(s), and β(s) are similarly derived. 

Revenue Model 

The quality and sulfur content of the hydrocarbon stream is field and time dependent, and for 

convenience, is assumed uniform across GOM fields. The MMS collects field data on API 

gravity, which can be used to derive expressions to adjust the production price, but because the 

correlation between gravity and sulfur content is weak, introduction of a quality-adjusted 

hydrocarbon price does not add materially to the reliability of price forecasting. The adjustment 

is considered of secondary importance to the model results, and only prices derived from historic 

data are employed. 

Abandonment Time 

A structure is abandoned when the production and revenue profile forecast converges to the 

threshold level of the structure. Threshold levels vary with many factors such as the water depth, 

structure type, operator size, hydrocarbon production, number of structures on the lease, etc., but 

by examining the characteristics of structures that have been removed near the time of their 

abandonment, historical threshold limits serve as a guide and predictor of future levels (Table 

E.5). From Table E.5, observe that threshold levels generally increase with water depth and 

structure type, and are dependent on whether oil or gas is being produced. Economies of scale 

are also frequently present, so that threshold levels are slightly lower if more than one producing 

structure exists on a lease; e.g., structures that exist on leases with no other infrastructure at the 

time of removal (lease category I) exhibit an average annual production and revenue threshold of 

57,000 BOE and $734,000; for leases with more than one producing structure at the time of 

removal (lease category II), the average production and revenue thresholds are 33,000 BOE and 

$423,000. For further details, refer to Chapter 4. 
71

Decommissioning Cost 

The total cost to decommission a structure is decomposed according to three categories – 

plugging and abandonment (C1(s)), structure removal (C2(s)), and site clearance and verification 

(C3(s)). Functional expressions for each cost component have previously been derived based on 

market data sampling. Representative functions follow: 

C1(s) = $150,000/well, 

C2(s) = $506.9 + 579.1ST – 0.24WD – 262.5REEF + 53.1CF, 

C3(s) = $1,061+ 66WD + 17,752ST + 405AGE +8,919G, 

where WD = water depth (in feet), ST = structure type (ST = 0, caisson; ST = 1, otherwise), 

REEF = reefing option (REEF = 0, structure not reefed; REEF = 1, structure reefed), CF = 

complexity factor (unitless), AGE = age upon removal (in years), G = Gorilla net application (G 

= 0, Gorilla net not applied; G = 1, Gorilla net applied). 

The complexity factor is defined as the total number of piles and wells associated with the 

structure, and C2(s) needs to be scaled by a factor of 1,000. The reefing option is considered a 

random variable that depends upon water depth and planning area. Application of the Gorilla net 

is assumed to occur in 1-in-4 site clearance and verification operations.

5.4. Model Results 

5.4.1. Model Statistics: Offshore structures can be aggregated according to structure type, 

production type, water depth, planning area, block type, or any other suitable categorization. Let 

the number of structures removed in year t under category Γ be denoted as NR(Γ, t). The time 

variable runs from the observation year, t = τ, until the year the last structure in Γ is removed, t = 

T: 

NR(Γ) = (NR(Γ, τ), NR(Γ, τ+1),L, NR(Γ,T)). 

NR(Γ) is a stochastic process. 

The present value to remove structures is denoted, 

∑∈Γ

− +

Γ =

s

t s r d

C s C d ( ) τ (1 )( ) ( , ) . 

The total undiscounted (d = 0) cost to remove all structures in category Γ is constant. 

5.4.2. Removal and Cost Forecast: The number of structures expected to be removed in the 

CGOM using the production and revenue threshold models is depicted in Figure E.1 (production 

threshold) and Figure E.2 (revenue threshold). The total cost to decommission the CGOM from 

2005-2025 is estimated to range between $4.9 - 5.2 billion as shown in Figure E.3 and Figure 
72

E.4. The net present value of the total cost using a 10% discount factor is computed to range 

between $2.3 – 2.5 billion. 

The number of structures expected to be removed in the WGOM according to the production and 

revenue threshold models is depicted in Figure E.5. The total cost to decommission the WGOM 

from 2005-2020 is shown in Figure E.6. The net present value of the total cost using a 10% 

discount factor is computed to range between $367 – 378 million. 

5.4.3. Model Discussion: Modeling removal processes require a number of structural 

assumptions and parameterizations for decline parameters, economic limits, and removal 

obligations. The model results are closely linked to the model assumptions and parameterization 

so that changes in either of these factors will impact the forecast results. 

 

The production threshold model dominates the revenue threshold model, and is considered less 

robust since hydrocarbon price is not incorporated in the analysis. It is expected that a significant 

number of structures removed over the next few years will remain in inventory on inactive 

leases, and thus the apparent removal rate will be “smoothed” out over the near-term horizon. 

Decommissioning cost patterns reflect the removal forecast and the relative magnitude of 

abandonment. Fixed platforms in deepwater are significantly more costly to remove than shallow 

water caissons, for instance, and this is reflected in the higher proportional cost for fixed 

platform removals. 

5.5. Limitations of the Analysis 

Production and Revenue Model 

A reliable production forecast early in the life of a field can only be developed with knowledge 

of the development plan, reserve estimates and production capacity, and since estimates of these 

parameters are either unknown or uncertain, a large degree of uncertainty exists in forecasting 

production profiles for structures that have yet to reach peak production. During the mid-point in 

the life of a field, a different sort of uncertainty arises since the production profile and the drive 

mechanisms of the field are reasonably well understood, but the strategic decisions of the 

operator are unknown. Will the operator invest additional capital, seek a joint operating 

agreement, or divest the structure? Leases are held by a wide variety of working interest owners 

and are carved up over time and sold off or become subject to a variety of joint venture/farmout 

type arrangements. As properties change hands, the capital expenditures and operating cost 

profiles change, and subsequently, the structure of production profiles can be expected to change. 

The collection of wells in the MMS database associated with a structure is not known 

completely, since not all wells maintain a structure identification code, and so to estimate 

structure production, a correspondence was required to identify unassigned wells with a given 

structure or set of structures. Production from wells with no structure identifiers are assigned 

based upon the criteria 

w {s | min d(w ,s)} u

s l

u

∈ ↔ , 
73

where ) d(w ,s u represents the distance from unassigned well u w to structure s∈l . Model 

uncertainty is introduced into the forecast model since this well-structure assignment is arbitrary. 

Fortunately, the number of wells requiring assignment is reasonably small, and so the error 

associated with the assignment is believed to be reasonably small. 

The quality of production is not considered a primary factor in the forecast model and was 

assumed uniform across the GOM. For a first-order model, this is considered reasonable. 

Abandonment Time 

The link between abandonment and production/revenue threshold levels is not perfect, but is 

believed to provide a reasonably good indicator when an active structure will cease production. 

The economic limit serves to proxy abandonment time, and as with all proxy measures, a degree 

of uncertainty is associated with the inference. 

Removal Time

Delay is the general principle in decommissioning decision making, because 

• Delay transfers liability and the cost of decommissioning to the future, 

• Delay allows economies of scale to be utilized (i.e., remove all the structures on the lease 

at once) which will reduce the cost of the operation, 

• Delay allows structures to be requalified for new or marginal development plans and 

enhance field development options, and 

• Federal regulations specify that an offshore structure can be held offshore as long as the 

lease remains producing, increasing the removal options and opportunities available to 

the operator. 

Operators may remove idle structures early if decommissioning economics are favorable, or as 

strategic objectives dictate, but the input parameters that influence decision-making are generally 

unobservable. 

5.6. Conclusions

The purpose of a forecast model is to gain insight into the nature of the operation, the system 

structure, and the factors that influence the model results. In this chapter, removal processes were 

modeled using a “ground-up” approach employing constructive techniques and a suitable 

parameterization to re-create the economic and behavior conditions that operators apply in 

decision-making. The number of structures removed and cost statistics form the model output. 

The analytic structure of the model was described, and through a suitable parameterization, 

structure removal forecast and cost metrics estimated across each planning area categorization. 
75

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79

APPENDIX A 

CHAPTER 1 FIGURES AND TABLES 
81

Source: Minerals Management Service, 2004 (www.mms.gov). 

Figure A.1: Federal Outer Continental Shelf Leasing Program. 
82

 Source: Twachtman Synder & Byrd, Inc., 2000 (www.tsboffshore.com). 

 Figure A.2: Caisson Structures. 

 
83

 Source: Twachtman Synder & Byrd, Inc., 2000 (www.tsboffshore.com). 

 Figure A.3: Well Protector Structures. 
84

 Source: Twachtman Synder & Byrd, Inc., 2000 (www.tsboffshore.com). 

 Figure A.4: Fixed Platform Structures. 
85

Table A.1 

Total Number of Structures Installed and Removed by Water Depth and 

Planning Area in the Gulf of Mexico (1947-2001) 

Water Depth Installed Removed

Range (ft) WGOM CGOM GOM WGOM CGOM GOM 

0-10 2 103 105 1 37 38 

11-20 0 527 527 0 263 263 

21-30 2 695 697 1 291 292 

31-40 20 660 680 5 190 195 

41-50 67 597 664 33 216 249 

51-75 216 834 1,050 84 305 389 

76-100 123 439 562 40 140 180 

101-125 50 282 332 20 86 106 

126-150 52 242 294 20 64 84 

151-175 48 170 218 19 37 56 

176-200 51 190 241 19 45 64 

Subtotal 631 4,739 5,370 242 1,674 1,916 

201-656 123 447 570 22 63 85 

657-2,624 14 19 33 2 1 3 

2,624+ 2 6 8 0 0 0 

Subtotal 139 472 611 24 64 88 

Total 770 5,211 5,981 266 1,738 2,004 

Footnote: Structures are defined to include all caissons, well-protectors, fixed 

platforms, and floating configurations located within the federal offshore waters of 

the Gulf of Mexico. 
86

Table A.2 

Average Annual Number of Structures Installed and Removed in the Gulf of 

Mexico According to Water Depth and Planning Area (1996-2001) 

Water Depth Installed Removed 

Range (ft) WGOM CGOM GOM WGOM CGOM GOM 

0-10 (0, 0) (2.8, 2.5) (2.8, 2.3) (0.2, 0.4) (2.6, 2.7) (2.8, 2.8)

11-20 (0, 0) (6, 2.9) (6, 2.9) (0, 0) (16.2, 8.6) (16.2, 8.6)

21-30 (0.2, 0.4) (11.2, 5.2) (11.4, 5.4) (0, 0) (11.8, 6.6) (11.8, 6.6)

30-40 (0.8, 0.4) (13.4, 4.0) (14.4, 3.6) (0.6, 1.3) (10, 5.2) (10.6, 6.2)

41-50 (2.8, 1.9) (5.8, 1.3) (8.6, 2.2) (2.6, 1.8) (14.6, 7.7) (17.2, 8.5)

51-75 (7.6, 1.9) (18, 6.4) (25.6, 6.0) (7.8, 6.5) (19.8, 6.4) (26.7, 10.9)

76-100 (2.4, 1.7) (11, 4.3) (13.4, 4.8) (4.6, 4.4) (8, 2.2) (12.6, 3.1)

101-125 (1, 0.7) (8.2, 4.1) (9.2, 4.8) (2, 2) (6.8, 7.4) (8.8, 9.2)

126-150 (1.4, 1.1) (6.6, 2.5) (8, 2.9) (0.8, 1.3) (4.2, 1.5) (5, 2.1)

151-175 (0.8, 0.8) (5.2, 3.3) (6, 3.1) (1.2, 0.8) (2.8, 0.8) (4, 0)

176-200 (1, 1.2) (5, 2.6) (6, 3.8) (0.8, 0.8) (3.8, 1.5) (4.6, 1.8)

Subtotal (18, 3.8) (93, 12.6) (111, 13.2) (20.6, 18.6) (100.6, 17.8) (121.2, 21.3)

201-656 (3.6, 1.5) (16.2, 4.9) (19.8, 6.3) (1.4, 1.1) (5.4, 2.6) (6.8, 3.3)

657-2,624 (1.2, 0.4) (1.4, 1.5) (2.6, 1.9) (04, 0.5) (0, 0) (0.4, 0.5)

2,624+ (0.2, 0.4) (1.2, 0.8) (1.4, 1.1) (0, 0) (0, 0) (0, 0)

Subtotal (5, 1.6) (18.8, 5.2) (21.6, 7.6) (1.8, 1.2) (5.4, 2.6) (7.2, 3.3)

Total (23, 4.1) (111.8, 13.7) (134.8, 14.8) (22.4, 8.7) (106.0, 17.9) (128.4, 21.6)

 Footnote: The data entries are denoted by the coordinate pair μ, σ where μ represents the mean and σ

the standard deviation of the structure data per water depth and planning area category 
87

Table A.3 

Average Annual Number of Structures Installed and Removed in the Gulf of 

Mexico According to Water Depth and Planning Area (1991-2001) 

Water Depth Installed Removed 

Range (ft) WGOM CGOM GOM WGOM CGOM GOM 

0-10 (0.1, 0.3) (2.4, 2.3) (2.5, 2.2) (0.1, 0.3) (2.2, 2.2) (2.3, 2.3)

11-20 (0, 0) (7.2, 3.2) (7.2, 3.2) (0, 0) (17, 9.2) (17, 9.2)

21-30 (0.1, 0.3) (10.9, 4.3) (11, 4.4) (0, 0) (14.6, 8.5) (14.6, 8.5)

30-40 (0.6, 0.5) (10.3, 4.8) (10.9, 4.9) (0.3, 0.9) (10.5, 3.7) (10.8, 4.3)

41-50 (1.8, 1.8) (7.9, 3.5) (9.7, 3.2) (1.9, 1.4) (13, 7.2) (14.9, 7.7)

51-75 (8, 3.9) (19.3, 7.1) (27.3, 5.8) (6.7, 5.3) (20.1, 9.6) (26.8, 12.8)

76-100 (3.9, 3.2) (12.3, 4.3) (16.2, 6.4) (3.6, 3.3) (8.6, 3.8) (12.2, 4.7)

101-125 (1.4, 1.0) (8.8, 3.8) (10.2, 4.5) (1.6, 1.6) (5.5, 6.1) (7.1, 7.5)

126-150 (1.3, 1.1) (6.0, 2.6) (7.3, 3.1) (1.4, 1.8) (4.8, 4.7) (6.2, 5.2)

151-175 (1.6, 1.6) (5.1, 3.3) (6.7, 3.1) (1.5, 1.8) (2.7, 2.5) (4.2, 2.8)

176-200 (0.8, 0.9) (4.8, 2.5) (5.6, 3.2) (1.1, 0.3) (3.6, 1.75) (4.7, 2.3)

Subtotal (19.6, 5.9) (95, 13.3) (114.6, 14.2) (18.2, 7.3) (102.6, 20.1) (120.8, 22.8)

201-656 (3.5, 1.9) (15.2, 5.8) (18.7, 7.4) (1.6, 1.3) (5, 2.1) (6.6, 2.7)

657-2,624 (0.9, 0.6) (1.2, 1.2) (2.1, 1.6) (0.2, 0.4) (0, 0) (0.2, 0.4)

2,624+ (0.2, 0.4) (0.6, 0.8) (0.8, 1.0) (0, 0) (0, 0) (0, 0)

Subtotal (4.6, 2.0) (17, 6.0) (21.6, 7.6) (1.8, 1.4) (5, 2.1) (6.8, 2.7)

Total (24.2, 6.2) (112, 14.6) (136.3, 16.1) (20, 7.4) (107.6, 20.2) (127.6, 23.0)

Footnote: The data entries are denoted by the coordinate pair μ, σ where μ represents the mean and σ

the standard deviation of the structure data per water depth and planning area category 
88

Table A.4 

The Age Distribution of Active Structures in Shallow Water (0-60m) by Configuration 

Type and Planning Area (1947-2001) 

Water Depth Caisson Well Protector Fixed Platform 

Range (m) 

Age 

Distribution 

(Year) 

W C G W C G W C G

0-60 40+ 0 8 7 0 20 18 <1 11 9

 31-40 <1 13 12 11 19 19 4 23 20

 21-30 <1 16 15 13 22 21 23 23 23

 11-20 33 30 30 40 21 23 47 21 25

 1-10 63 33 33 36 17 19 25 23 23

Total Total 79 1,073 1,152 47 355 402 252 1,617 1,800

Footnote: W, C, G denote WGOM, CGOM, and GOM. 

Table A.5 

Average Age of Structures Upon Removal by Water Depth, Configuration Type, and 

Planning Area (1947-2001) 

Water Depth Caisson Well Protector Fixed Platform 

Range (m) W C W C W C 

0-60 (7.1, 4.7) (15.9, 10) (16.3, 1.3) (17.4,10.2) (9.3, 5.3) (16.9, 10.8) 

61-200 (9.5, 1) (18, 13.5) (10.3, 6.4) (12.6, 8.1) 

 Footnote: The data entries are denoted by the coordinate pair (μ, σ), where μ represents the mean and σ is the 

standard deviation of the structure data per water depth and planning area category Γi, j . W, C, G denote 

WGOM, CGOM, and GOM. 
89

Table A.6 

Number of Structures Removed (R), Structures Removed by Explosive Technique (RE), 

and the Percentage of Explosive Removals (pE) as a Function of Water Depth and Planning 

Area (1986-2001) 

Water Depth WGOM CGOM GOM 

Range (ft) R RE pE R RE pE R RE pE

0-10 1 1 100 20 11 55 21 12 54 

11-20 210 71 34 210 71 34 

21-30 1 1 100 208 145 70 209 146 70 

31-40 4 3 75 150 88 59 159 91 59 

41-50 31 22 71 155 107 69 186 129 69 

51-75 78 34 44 238 130 55 316 164 52 

76-100 41 20 71 109 61 56 150 81 54 

101-125 19 12 63 81 45 56 100 57 57 

126-150 20 15 75 60 49 82 80 64 80 

151-175 17 10 59 34 23 68 51 33 65 

176-200 16 13 81 44 26 59 60 39 65 

201-656 23 17 74 64 49 77 87 66 76 

657-2,624 2 1 50 2 1 50 

2,624+ 

Water Depth WGOM CGOM GOM 

Range (m) R RE pE R RE pE R RE pE

0-60 228 131 57 1,309 756 58 1,537 887 58 

61-200 23 17 74 64 49 77 87 66 76 

200+ 2 1 50 2 1 50 

Total 253 149 58 1,373 805 58 1,626 954 59 
90

Table A.7 

Number of Structures Removed (R), Structures Removed by Explosive Technique (RE), 

and the Percentage of Explosive Removals (pE) as a Function of Water Depth and 

Configuration Type for the Gulf of Mexico (1986-2001) 

 Water Depth Range (ft) Caisson Well Protector Fixed Platform All 

R RE pE R RE pE R RE pE R RE pE

0-10 14 5 36 1 1 100 6 6 100 21 12 57 

11-20 170 55 32 11 4 36 29 12 41 210 71 34 

21-30 137 98 71 23 12 52 49 36 73 209 146 70 

31-40 89 49 55 12 6 50 53 36 68 154 91 59 

41-50 99 71 72 28 22 79 59 36 61 186 129 69 

51-75 141 63 44 48 32 67 127 69 54 316 164 52 

76-100 51 19 37 25 12 48 74 50 68 150 81 54 

101-125 25 6 24 14 7 50 61 44 72 100 57 57 

126-150 9 6 67 12 10 83 59 48 81 80 64 80 

151-175 8 5 63 8 7 88 37 22 59 51 33 65 

176-200 11 6 55 41 28 68 60 39 65 

201-656 6 5 83 81 61 75 87 66 76 

657-2,624 2 1 50

2,624+ 

Water Depth Range (m) Caisson Well Protector Fixed Platform All 

R RE pE R RE pE R RE pE R RE pE

0-60 749 381 51 193 119 62 595 387 65 1,537 887 58 

61-200 6 5 83 81 61 75 87 66 76 

200+ 2 1 50 

Total 749 381 51 199 124 62 676 448 66 1,626 954 59 

 
91

Table A.8 

Number of Structures Removed (R), Structures Removed by Explosive Technique (RE), 

and the Percentage of Explosive Removals (pE) as a Function of Time and Configuration 

Type for the Gulf of Mexico (1986-2001) 

Caisson Well Protector Fixed Platform Total 

Year R RE pE (%) R RE pE (%) R RE pE (%) R RE pE (%) 

1986 1 0 0 1 0 0 2 0 0 

1987 10 0 10 2 0 0 7 0 0 19 0 0 

1988 46 5 11 9 2 22 36 19 53 91 26 29 

1989 46 34 74 7 6 86 34 30 88 87 70 80 

1990 53 26 49 9 5 56 36 29 81 98 60 61 

1991 54 26 48 16 11 69 44 36 82 114 73 64 

1992 44 19 43 13 9 99 40 33 83 97 61 63 

1993 77 49 64 30 12 40 61 41 67 168 102 61 

1994 42 22 52 16 14 88 66 51 77 124 87 70 

1995 59 40 68 9 7 78 49 34 69 117 81 69 

1996 48 13 27 15 8 53 56 29 52 119 50 42 

1997 92 54 59 14 11 79 71 38 54 177 63 58 

1998 35 14 40 11 8 73 29 13 45 75 35 47 

1999 72 35 49 17 9 53 45 32 71 134 76 57 

2000 49 37 76 19 13 68 66 42 64 134 92 69 

2001 22 7 32 11 9 82 35 21 60 68 37 54 

Total 749 381 51 199 124 62 676 448 66 1,624 953 59 

 
92

Table A.9 

Number of Structures Removed (R) and Percentage of Structures Removed (p) 

Grouped According to Age Upon Removal and Planning Area (1986-2001) 

Age Number of Structures (R) Percentage p (%) 

WGOM CGOM GOM WGOM CGOM GOM

0-10 159 479 638 63 35 39

 11-20 72 405 477 28 29 29

 21-30 13 282 295 5 21 18

 30+ 9 207 216 4 15 13

Total 253 1,373 1,626 100 100 100

 Footnote: p = R/RT , where RT denotes the total number of structures per planning area. 

Table A.10 

Number of Structures Removed Using Explosives (RE) and the Percentage of All 

Structures Removed Using Explosives (pE) Grouped According to Age Upon Removal 

and Planning Area (1986-2001) 

Number of Structures (RE) Percentage pE (%) 

Age WGOM CGOM GOM WGOM CGOM GOM

0-10 82 229 311 52 48 49 

11-20 50 253 303 69 62 64 

21-30 9 172 181 69 61 61 

30+ 8 151 159 89 73 74 

Total 149 805 954 59 59 59 

 Footnote: p = RE/R , where the R value are obtained from Table A.8. 
93

Table A.11 

Number of Structures Removed (R) and Percentage of Structures 

Removed (p) as a Function of Water Depth and Age Upon Removal 

(1986-2001) 

Water Depth Age Upon Removal (yr) 

Range (m) 0-11 11-20 21-30 30+ Total 

0-60 600 441 283 213 1,537 

61-200 36 36 12 3 87 

200+ 2 0 0 0 2 

Total 638 477 295 216 1,626 

Water Depth Percentage p (%) 

Range (m) 0-11 11-20 21-30 30+ Total 

0-60 39 39 18 14 100 

61-200 41 41 14 3 100 

200+ 100 - - - 100 

Total 638 477 295 216 100 

 Footnote: p = R/RT , where RT denotes the total number of structures per planning area. 

Table A.12 

Number of Structures Removed Using Explosives Techniques 

and the Percentage of Explosives Removal as a Function of 

Water Depth and Age Upon Removal (1986-2001) 

Water Depth Age Upon Removal (yr) 

Range (m) 0-11 11-20 21-30 30+ Total 

0-60 283 276 173 156 888 

61-200 28 27 8 3 66 

200+ 1 0 0 0 1 

Water Depth Percentage pE (%) 

Range (m) 0-11 11-20 21-30 30+ Total 

0-60 47 63 61 73 58 

61-200 78 75 67 100 76 

200+ 50 - - - 50 

 Footnote: pE = RE/R , where the R values are obtained from Table A.8. 
94

Table A.13 

Number of Structures Removed (R), Number of Structures Removed by 

Explosive Technique (RE), and the Percentage of Structures Removed by 

Explosives (pE) Categorized According to Age and Configuration Type 

(1986-2001) 

Age Caisson Well Protector Fixed Platform 

R RE pE (%) R RE pE (%) R RE pE (%)

0-10 295 116 59 75 40 53 266 154 58 

11-20 204 115 56 52 35 67 221 153 69 

21-30 157 83 53 36 21 58 102 77 75 

30+ 93 67 72 36 28 78 87 64 74 

Total 749 381 51 199 124 62 676 448 66 

Table A.14 

Percentage of Structures Removed by Configuration Type 

and Water Depth in the Gulf of Mexico (1986-2001)

Water Depth Caisson 

Range (m) 0-11 11-20 21-30 30+ Total 

0-60 39 56 53 72 51 

61-200 

Water Depth Well Protector 

Range (m) 0-11 11-20 21-30 30+ Total 

0-60 52 67 58 77 62 

61-200 100 67 100 83 

Water Depth Fixed Platform 

Range (m) 0-11 11-20 21-30 30+ Total 

0-60 55 68 77 73 65 

61-200 76 76 67 100 75 
95

Table A.15 

 Medium-Term Forecast of the Number of Structures Removed in the Gulf of Mexico 

by Explosive Technique (Model I) 

Water CAIS WP FP Total 

Depth 

Range (m) 

Forecast 

Horizon W C W C W C W C 

 2002-2006 14 97 4 64 52 155 70 316 

 2007-2011 18 133 4 57 35 255 57 445 

0-60 2012-2016 8 104 5 41 62 222 75 367 

 2017-2021 0 98 7 30 5 215 22 343 

 2022-2026 0 115 9 29 0 203 9 347 

Subtotal 40 547 29 221 165 1,052 234 1,818 

 2002-2006 0 0 5 0 19 62 24 62 

 2007-2011 1 0 2 0 31 65 35 65 

61-200 2012-2016 1 1 0 0 15 83 16 84 

 2017-2021 0 1 0 0 3 53 3 54 

 2022-2026 0 2 0 17 0 17 0 36 

Subtotal 2 4 7 17 69 278 78 301 

 Footnote: W, C denote WGOM and CGOM. 

Table A.16 

 Medium-Term Forecast of the Number of Structures Removed in the Gulf of Mexico 

by Explosive Technique (Model II) 

Water CAIS WP FP Total 

Depth 

Range (m) 

Forecast 

Horizon W C W C W C W C 

 2002-2006 30 168 10 91 89 250 129 509 

0-60 2007-2011 10 221 11 83 62 317 83 621 

 2012-2016 0 132 8 47 14 280 22 459 

 2017-2021 0 26 0 0 0 205 0 231 

Subtotal 40 547 29 221 165 1,052 234 1,820 

 2002-2006 1 0 4 0 48 107 53 104 

61-200 2007-2011 1 0 3 9 26 97 30 106 

 2012-2016 0 2 0 8 0 77 0 87 

 2017-2021 0 2 0 0 0 0 0 2 

Subtotal 2 4 7 17 69 278 83 299 

 Footnote: W, C denote WGOM and CGOM. 

 
97

APPENDIX B 

CHAPTER 2 FIGURES AND TABLES 
99

Figure B.1: Conventionally Piled Platform with Wells. 
100

Offshore oil and 

gas facility 

decommissioning

Oil and gas 

processing 

equipment and 

piping

Deck and jacket 

structure

Onshore scraping

Move to new 

location and 

reinstall

Dispose of in 

deep water

Convert to an 

artificial reef Send to shore

Sell for scrap Refurbish and 

reuse Waste to landfill Recycle steel Waste to landfill Partial removal Topple in place Placement offsite

Figure B.2: Offshore Oil and Gas Facility Decommissioning Tree. 
101

Plug & 

abandonment

 AWJ, Exp, 

Jet, Mech,

Sand

DECOMMISSIONING 

STAGE

CUTTING

TECHNIQUES

Pneumatic, 

Torch

AWJ, DW, 

Torch

Cut casing tubing 

& conductors

Structure 

preparation

Permitting

Cut equipment 

modules

Cut deck

Figure B.3: Decommissioning Is Often a Severing Intensive Operation. 
102

Cut conductors

Cut well stubs

and skirt piles

Pull and remove Pull and remove Pull and remove 

Pull and remove 

jacket

Site clearance

Site verification

DECOMMISSIONING 

STAGE

CUTTING

TECHNIQUES

Cut piles AWJ, Exp

DW, Exp, 

Mech

 Exp, Torch

Complete 

regulatory 

reporting

Pipeline 

abandonment

Remove modules 

from deck

Remove deck 

from jacket

Cut pipe, risers, 

flowlines, etc.

Clean, plug, bury 

pipeline

AWJ, DW, 

Torch

 

Figure B.3: Decommissioning Is Often a Severing Intensive Operation (continued). 
103

Has the 48 hour 

pre-observation 

period passed?

Do weather 

conditions permit air 

survey?

 <B>

Conduct 30-

minute aerial 

survey 1 hour 

before each blast 

episode <C>

Are sea turtles 

observed and thought 

to be resident at the 

site?

Conduct predetonation survey 

around structure

_ DELAY

+

Set charges

<A>

Have divers 

deployed on 

structure?

Is water depth 

> 150 ft?

Deploy an ROV 

prior to detonation 

to scan area 

below structure

Are marine 

mammals sited?

Is water depth 

> 328 ft?

Deploy passive 

acoustic detection

Are marine 

mammals 

detected?

+

+

+

_ _

_

_ _

DELAY

Post-detonation diver 

survey required to be 

performed

Observe and 

report +

DELAY

+

_

_

Observe and 

report DELAY

Observe and 

report DELAY +

Figure B.4: The NMFS Observer Program (2002). 
104

Conduct 30-

minute aerial 

survey 1 hour 

before each blast 

episode <C>

 Are sea turtles and/or 

marine mammals 

observed within 1000 

yards of structure?

 <D>

Is it 1 hour after 

sunrise or 1 hour 

before sunset?

<E>

NMFS permission 

required to 

continue

NMFS 

permission 

granted?

Blow charges

 <F>

Is interval between 

blast episodes > 90 

minutes?

DELAY

(Go to <C>)

All charges 

blown?

Was the 

severance 

successful?

DELAY

(Go to <A>)

+

+

+

_ _

_

_

+

Observe and 

report

Post-detonation diver survey 

required to be performed within 

24 hours after blast or an 

aerial/vessel survey within 2-7 

days after the blast

Is location 

clear?

Go to <B>

DELAY

DELAY

(Go to <A>)

_

+

_

_

+

Figure B.4: The NMFS Observer Program (2002) (continued). 
105

Was the 

severance 

successful?

Severance 

operation 

complete

+

DELAY

_ (Go to <A>)

Are sea turtles or 

marine mammals 

found, dead or 

injured?

Attempt to 

recover +

Were marine mammals 

sited during predetonation survey or 

aerial survey?

Conduct 30 

minute aerial 

survey +

Does NMFS observer 

waive post-detonation 

monitoring?

Conduct 30 

minute aerial 

survey

Conduct 48 hour 

post-detonation 

monitoring

_

_

Report results of removal 

activities, mitigation measures, 

and monitoring efforts to 

NMFS within 30 days

Conduct 48 hour 

post-detonation 

monitoring

+

_

DELAY

Observe and 

report

Observe and 

report

DELAY

DELAY

Figure B.4: The NMFS Observer Program (2002) (continued). 
106

Cut member i

Set up cutting 

equipment

Prove the cut

Is the 

member cut?

All members 

cut?

Is proper depth 

achievable?

Is jetting 

required?

Check mud plug 

depth

Tubular 

member i

Jet

Other options

(DW,Exp, 

External cut)

+

_

_

+

Severance 

operation 

complete

+

+

_

_

Figure B.5: The Abrasive Water Jet Cutting Process. 
107

0

50

100

150

200

250

Chevron

Murphy Kerr-McGee

Texaco

Mobil

Odeco O&G

Unocal

Samedan

Apache Hall-Hstn. 

Newfield

RME

Texaco Inc.

Amoco

Conoco Inc.

Walter 

El Paso

Seagull Dominion

ExxonMobil

Ocean Eng. Amerada

Burlington Devon SFS

Forest Oil

Pennzoil

Shell

Louisiana

McMoRan

Gulfstream

SOCO

Atlantic

OXY USA

Forcenergy Diamond

EOG

Company

Number of Structures

0

20

40

60

80

100

120

Percentage of Explosive Removal 

(%)

Figure B.6: Number of Structures Removed and Percentage of Explosive Removals 

 by the “Top 36” Companies (1986-2001). 

0

2

4

6

8

10

12

Company

Number of Structures

0

20

40

60

80

100

120

Percentage of Explosive 

Removal (%)

Figure B.7: Number of Structures Removed and Percentage of Explosive 

 Removals by the “Bottom 91” Companies (1986-2001).
108

Table B.1 

Gulf of Mexico Active and Removed Structures by Configuration Type, 

Water Depth and Number of Slots (1947-2001) 

Configuration Type Water Depth (feet) Number of Slots Active Removed 

 Caisson 

 0-80 1076 921 

 80-200 117 112 

 200+ 5 1 

Well Protector 

 0-80 

 0-6 271 193 

 7-12 8 8 

 12+ 2 2 

 80-200 

 0-6 97 74 

 7-12 16 12 

 12+ 3 2 

 200+ 

 0-6 26 3 

 7-12 2 2 

 12+ 1 3 

Non-Major Fixed 

Platform 0-80 

 0-6 291 85 

 7-12 5 0 

 12+ 0 0 

 80-200 

 0-6 155 23 

 7-12 7 0 

 12+ 2 1 

 200+ 

 0-6 57 6 

 7-12 9 0 

 12+ 3 0 

Major Fixed 

Platform 0-80 

 0-6 511 272 

 7-12 132 58 

 12+ 85 13 

 80-200 

 0-6 304 168 

 7-12 228 80 

 12+ 178 33 

 200+ 

 0-6 95 33 

 7-12 109 28 

 12+ 212 24 

TOTAL 4,007 2,159 
109

Table B.2 

Number of Structures Removed (R), Structures Removed by Explosive Technique (RE), 

and the Percentage of Explosive Removals (pE) as a Function of Water Depth and 

Configuration Type for the Gulf of Mexico (1986-2001) 

W Caisson Well Protector Fixed Platform All ater Depth Range (m

R RE pE R RE pE R RE pE R RE pE

0-60 749 381 51 193 119 62 595 387 65 1,537 887 58 

61-200 6 5 83 81 61 75 87 66 76 

200+ 2 1 50 

Total 749 381 51 199 124 62 676 448 66 1,626 954 59 

Table B.3 

Percentage of Explosive Removals by Configuration Type and Age Upon Removal in the 

Gulf of Mexico (1986-2001) 

Age Upon Removal Caisson Well Protector Fixed Platform 

 (Year) 0-60m 61-200m 0-60m 61-200m 0-60m 61-200m 

0-10 39 52 100 55 76 

 11-30 55 64 67 71 76 

30+ 72 77 100 73 100 
110

Table B.4 

A Summary of Operator Involvement in Gulf of Mexico Structure Removals (1986-2001) 

Number of 

Operators 

Number of 

Structures 

Removed 

Percentage of 

Total Structure 

Removals (%) 

Percentage of 

Structures 

Removed with 

Explosives (%) 

Number of 

Operators that 

Use Explosives 

Exclusively 

Contribution to 

Subcategory 

Total (%) 

Top 12 R ≥ 28 50 63 0 0 

Middle 24 10 < R <28 30 57 1 4 

Bottom 91 R ≤ 10 20 51 33 55 

Table B.5 

Probit and Logit Model Results 

Factor Probit Logit 

Coefficient (Z-Statistic) Coefficient (Z-Statistic) 

Constant 0.1372 (1.02) 0.0815 (0.97) 

ST 0.0563 (3.21) 0.1121 (3.26) 

AGE 0.0051 (5.42) 0.0167 (5.49) 

WD 0.0005 (0.10) 0.00001 (0.11) 

Rp 0.65 0.63 
111

APPENDIX C 

CHAPTER 3 TABLES
113

Table C.1 

Design Space for Models I and II 

Parameter Model Ia Model Ib Model IIa Model IIb Model IIc 

RES N(100000, 10000)N(100000, 20000) 

d(t) U(0.08, 0.13) U(0.08, 0.13) U(0.08, 0.13) U(0.08, 0.13) U(0.08, 0.13) 

P LN(25, 3) LN(25, 3) 

P(t) LN(25, 3) 

GR U(15000, 30000) U(15000, 50000) U(15000, 50000)

Table C.2 

Model I and II Regression Results 

A(φ) = α0 + α1 d + α2RES + α3P + α4 P + α5GR , φ = {I, II}

Coefficient Ia Ib IIa IIb IIc 

α0 -90.8(-32) -81.1(-32) 53.4(210) 50.8(105) 51.5(19) 

α1 362.2(23) 382.3(20) -229.8(-158) -208.9(-88) -206.9(-77) 

α2 0.0007(33) 0.0006(47) 

α3 0.37(53) 0.36(22) 

α4 0.29(3) 

α5 -0.00042(-88) -0.00034(-115) -0.00034(-102)

Iterations 1,000 1,000 1,000 1,000 1,000 

R2 0.62 0.72 0.97 0.96 0.95 
114

Table C.3 

Design Space for Decommissioning Model III 

Parameter Model IIIa, b Model IIIc Model IIId 

d(t) U(0.08, 0.13) U(0.08, 0.13) U(0.08, 0.13) 

P(t) LN(25, 3) LN(25, 3) LN(25, 3) 

RES U(0.10, 0.20) U(0.10, 0.20) U(0.10, 0.20) 

k U(0.9, 0.13) U(0.9, 0.13) U(0.9, 0.13) 

T U(0.30, 0.50) U(0.30, 0.50) U(0.30, 0.50) 

E U(4000, 8000) U(4000, 8000) U(4000, 8000) 

l 0,0 1 2 

Table C.4 

Model III Regression Results 

A(φ) = α0 + α1 d + α2 P + α3ROY + α4k + α5T + α6E, φ = {III}

Coefficient IIIa IIIb IIIc IIId 

α0 59.2(36) 58.5(78) 51.3(26) 56.8(26) 

α1 -206.1(-127) -208.8(-285) -214.8(-113) -212.9(-101) 

α2 0.17(3) 0.22(8) 0.53(7) 0.34(4) 

α3 -12.6(-16) -11.9(-33) -11.5(-12) -10.7(-10) 

α4 -4.8(-24) -5.3(-57) -4.8(-20) -5.2(-20) 

α5 -7.3(-18) -7.6(-42) -6.8(-14) -6.6(-13) 

α6 -0.00071(-35) -0.00073(-80) -0.00076(-32) -0.00071(-27) 

Iterations 1,000 5,000 1,000 1,000 

R2 0.95 0.96 0.94 0.92 

 

 
115

APPENDIX D 

CHAPTER 4 TABLES 
117

Table D.1 

Summary Statistics for Structures Removed in the Gulf of Mexico 

Lease 

Categorization

Parameters Caisson Well 

Protector 

Fixed 

Platform 

All 

I Q (tlp)(BOE) 50,973 91,584 52,608 57,238 

R (tlp)($) 604,667 1,147,691 698,593 733,805 

IDLE (yr) (2.6, 3.9)a (3.2, 4.6) (1.9, 2.6) (2.3, 3.2) 

 Q*/RES (0.43, 0.23) (0.38, 0.18) (0.38, 0.20) (0.40, 0.20)

RES (MMBOE) 1.02 2.06 4.20 3.10 

 n 170 73 389 632 

II Q (tlp)(BOE) 32,000 30,700 36,174 32,798 

R (tlp)($) 392,006 384,021 522,685 422,867 

IDLE (yr) (6.5, 6.1) (8.0, 7.6) (4.6, 4.8) (6.3, 6.2) 

 Q*/RES (0.34, 0.21) (0.29, 0.19) (0.26, 0.16) (0.31, 0.20)

RES (MMBOE) 1.71 3.00 6.83 3.21 

 n 397 124 171 692 

III Q (tlp)(BOE) 36,693 25,588 39,606 35,035 

R (tlp)($) 531,191 354,210 575,522 507,282 

IDLE (yr) (3.9, 4.2) (4.1, 4.0) (3.6, 4.6) (3.8, 4.3) 

 Q*/RES (0.41, 0.25) (0.35, 0.19) (0.35, 0.18) (0.40, 0.23)

RES (MMBOE) 1.58 3.88 6.16 3.53 

 n 78 35 53 166 

IV Q (tlp)(BOE) 39,061 22,832 39,429 37,495 

R (tlp)($) 528,159 353,362 538,132 512,209 

IDLE (yr) (8.9, 7.3) (6.7, 5.5) (7.1, 7.5) (8.4, 7.2) 

 Q*/RES (0.27, 0.17) (0.19, 0.10) (0.23, 0.13) (0.26, 0.18)

RES (MMBOE) 2.21 2.51 11.83 3.71 

 n 224 30 46 300 

Footnote: (a) Ordered pair (x, y) denotes mean x and standard deviation y. 

 
118

Table D.2 

Average Production Threshold Levels At/Near the Year of Last Production – Lease 

Category I 

Structure Type Water Depth 

(ft) 

n Q (tlp) 

(BOE) 

Q (tlp – 1) 

(BOE) 

Q (tlp –2) 

(BOE) 

Q (tlp – 3) 

(BOE) 

Caisson 0-100 140 50,106 120,809 158,112 144,080 

 101-200 30 53,196 205,617 259,381 165,009 

 

Well Protector 0-100 34 51,749 140,063 211,940 230,572 

 101-200a 39 125,427 244,752 256,661 270,160 

Fixed Platform 0-100 173 48,042 163,978 284,005 294,172 

 101-200 140 56,922 224,423 281,210 322,129 

 201-400 76 42,911 280,082 334,861 397,075 

 Footnote: (a) Includes 5 structures in the 200+ category 

Table D.3 

Average Revenue Threshold Levels At/Near the Year of Last Production – Lease 

Category I 

Structure Type Water Depth 

(ft) 

n R (tlp) 

($) 

R (tlp – 1) 

($) 

R (tlp –2) 

($) 

R (tlp – 3) 

($) 

Caisson 0-100 140 598,846 1,414,225 1,953,555 1,752,930 

 101-200 30 675,836 2,404,673 2,780,803 1,874,705 

 

Well Protector 0-100 34 796,856 1,829,445 2,593,270 2,576,721 

 101-200a 39 1,449,434 3,636,186 3,414,770 3,502,795 

Fixed Platform 0-100 173 637,850 2,162,096 3,822,129 3,759,962 

 101-200 140 739,295 3,069,187 3,971,520 4,630,609 

 201-400 76 556,447 3,674,762 4,527,771 5,225,356 

 Footnote: (a) Includes 5 structures in the 200+ category 
119

Table D.4 

Average Production Threshold Levels At/Near the Year of Last Production – Lease 

Category II 

Structure Type Water Depth 

(ft) 

n Q (tlp) 

(BOE) 

Q (tlp – 1) 

(BOE) 

Q (tlp –2) 

(BOE) 

Q (tlp – 3) 

(BOE) 

Caisson 0-100 376 30,959 96,127 142,145 179,518 

 101-200 14 33,155 53,670 99,175 158,244 

 

Well Protector 0-100 103 23,832 72,639 117,468 146,701 

 101-200 20 67,355 212,480 329,858 539,504 

Fixed Platform 0-100 107 25,197 82,540 104,389 154,998 

 101-200 51 41,349 138,647 162,908 253,433 

 201-400 15 89,712 478,356 527,775 611,107 

Table D.5 

Average Revenue Threshold Levels At/Near the Year of Last Production – Lease 

Category II 

Structure Type Water Depth 

(ft) 

n R (tlp) 

($) 

R (tlp – 1) 

($) 

R (tlp –2) 

($) 

R (tlp – 3) 

($) 

Caisson 0-100 376 385,188 1,183,751 1,815,805 2,244,257 

 101-200 14 457,331 785,508 1,225,973 1,932,231 

 

Well Protector 0-100 103 261,355 884,105 1,554,026 1,812,339 

 101-200 20 1,034,012 2,851,025 4,331,782 5,938,351 

Fixed Platform 0-100 107 350,700 1,179,240 1,698,004 2,529,227 

 101-200 51 675,103 2,069,231 2,509,057 3,605,908 

 201-400 15 1,112,651 5,964,440 6,187,286 7,517,325 
120

Table D.6 

Average Production Threshold Levels At/Near the Year of Last Production – Lease 

Category III 

Structure Type Water Depth 

(ft) 

n Q (tlp) 

(BOE) 

Q (tlp – 1) 

(BOE) 

Q (tlp –2) 

(BOE) 

Q (tlp – 3) 

(BOE) 

Caisson 0-100 75 36,682 182,195 243,703 236,718 

 101-200 4 29,920 153,594 174,716 185,420 

 

Well Protector 0-100 29 30,352 133,058 226,897 354,538 

 101-200 5 3,076 37,216 63,760 81,609 

Fixed Platform 0-100 36 41,678 153,705 279,942 583,044 

 101-200 15 34,685 107,269 135,505 193,528 

 201-400 3 30,176 622,324 435,370 130,086 

Table D.7 

Average Revenue Threshold Levels At/Near the Year of Last Production – Lease 

Category III 

Structure Type Water Depth 

(ft) 

n R (tlp) 

($) 

R (tlp – 1) 

($) 

R (tlp –2) 

($) 

R (tlp – 3) 

($) 

Caisson 0-100 75 584,766 2,483,402 3,160,995 2,914,919 

 101-200 4 348,021 1,825,733 2,205,311 2,635,662 

 

Well Protector 0-100 29 420,353 1,865,306 3,497,969 5,498,238 

 101-200 5 41,416 450,131 803,827 1,058,263 

Fixed Platform 0-100 36 579,338 2,233,477 3,795,996 7,506,140 

 101-200 15 569,607 1,730,695 2,411,147 3,523,914 

 201-400 3 463,694 12,209,740 8,786,409 1,597,371 
121

Table D.8 

Average Production Threshold Levels At/Near the Year of Last Production – Lease 

Category IV 

Structure Type Water Depth 

(ft) 

n Q (tlp) 

(BOE) 

Q (tlp – 1) 

(BOE) 

Q (tlp –2) 

(BOE) 

Q (tlp – 3) 

(BOE) 

Caisson 0-100 221 38,921 124,064 159,916 201,616 

 101-200 2 68,053 223,518 430,632 478,033 

 

Well Protector 0-100 30 22,832 87,589 105,091 133,597 

 101-200 0 

Fixed Platform 0-100 30 37,454 134,922 201,851 279,217 

 101-200 15 45,328 157,210 213,253 221,522 

 201-400 1 10,183 67,671 96,914 94,853 

Table D.9 

Average Revenue Threshold Levels At/Near the Year of Last Production – Lease 

Category IV 

Structure Type Water Depth 

(ft) 

n R (tlp) 

($) 

R (tlp – 1) 

($) 

R (tlp –2) 

($) 

R (tlp – 3) 

($) 

Caisson 0-100 221 527,742 1,558,099 1,933,574 2,237,764 

 101-200 2 813,918 3,334,778 1,826,155 2,420,007 

 

Well Protector 0-100 30 353,362 568,310 1,279,857 1,637,918 

 101-200 0 

Fixed Platform 0-100 30 513,304 1,758,019 2,419,529 3,139,999 

 101-200 15 614,269 2,082,317 3,003,751 3,042,329 

 201-400 1 140,919 907,982 1,446,346 1,503,154 

 
123

APPENDIX E 

CHAPTER 5 FIGURES AND TABLES 
125

0

100

200

300

400

500

600

200520062007200820092010201120122013201420152016201720182019202020212022202320242025

Year

Number of Structures

FP

WP

CAIS

Figure E.1: Central GOM Production Threshold Structure Removal Forecast. 
126

0

100

200

300

400

500

600

200520062007200820092010201120122013201420152016201720182019202020212022202320242025

Year

Number of Structures

FP

WP

CAIS

Figure E.2: Central GOM Revenue Threshold Structure Removal Forecast. 
127

0

100

200

300

400

500

600

700

200520062007200820092010201120122013201420152016201720182019202020212022202320242025

Year

$ Million

FP

WP

CAIS

Figure E.3: Central GOM Production Threshold Removal Cost Forecast. 
128

0

100

200

300

400

500

600

700

200520062007200820092010201120122013201420152016201720182019202020212022202320242025

Year

$ Million

FP

WP

CAIS

Figure E.4: Central GOM Revenue Threshold Removal Cost Forecast. 
129

0

20

40

60

80

100

120

140

200520062007200820092010201120122013201420152016201720182019

Year

Number of Structures

Q-Threshold

R-Threshold

Figure E.5: Western GOM Structure Removal Forecast Comparison. 
130

0

20

40

60

80

100

120

140

160

2005200620072008200920102011201220132014201520162017201820192020

Year

$ Million

Q-Threshold

R-Threshold

Figure E.6: Western GOM Removal Cost Forecast Comparison. 
131

Table E.1 

 Number of Structures Removed in the Gulf of Mexico (1973-2002)

Year Caisson Well Protector Fixed Platform Total 

1973 1 0 0 1 

1974 4 1 0 5 

1975 24 9 3 36 

1976 20 8 2 30 

1977 10 5 2 17 

1978 18 3 5 26 

1979 21 4 10 35 

1980 19 8 9 36 

1981 16 2 6 24 

1982 8 2 5 15 

1983 22 6 10 38 

1984 25 14 14 53 

1985 30 11 14 55 

1986 16 8 10 34 

1987 10 2 11 23 

1988 55 8 36 99 

1989 48 9 37 94 

1990 60 11 37 108 

1991 57 16 44 117 

1992 48 13 45 106 

1993 78 30 64 172 

1994 43 16 66 125 

1995 59 8 46 113 

1996 49 15 55 119 

1997 92 14 71 177 

1998 36 11 29 76 

1999 74 18 46 138 

2000 52 20 69 141 

2001 33 15 58 106 

2002 24 17 54 95 

TOTAL 1,052 304 858 2,214 
132

Table E.2 

 Active, Idle, and Auxiliary Structures on Active Leases (2003) 

k Number of active leases Number of Number of Number of 

with k active structures active structures idle structures auxiliary structures

1 944 944 291 129 

2 245 490 141 79 

3 84 252 96 66 

4 35 140 84 43 

≥ 5 48 348 286 123 

Total 1,356 2,175 898 440 

Table E.3 

Active, Idle, and Auxiliary Structures in the Gulf of Mexico (2003) 

Water Depth WGOM CGOM GOM 

(ft) CAIS WP FP CAIS WP FP Auxiliary 

0-20 1 0 0 200 10 35 79 

21-100 79 25 119 767 268 710 318 

101-200 3 17 82 48 63 490 73 

210-400 1 4 85 1 12 320 31 

400+ 0 0 13 0 3 43 4 

TOTAL 84 46 299 1,016 356 1,598 505 
133

Table E.4 

 Idle and Auxiliary Structures on Inactive Leases in the Gulf of Mexico (2003) 

Water Depth WGOM CGOM 

(ft) CAIS WP FP CAIS WP FP 

0-100 22 7 29 103 20 88 

101-200 3 4 6 13 10 44 

210-400 1 1 9 1 30 

TOTAL 26 12 44 116 31 162 

Table E.5 

 Normalized Annual Production and Revenue Threshold Levels in the Gulf of Mexico 

 Lease Hydrocarbon Water CAIS WP FP 

Threshold Categorization Production Depth (ft) (MBOE) (MBOE) (MBOE)

Production I Oil 0-100 18 21 33 

 101-200 30 30 66 

 201+ 34 

 Gas 0-100 41 41 45 

 101-200 51 47 52 

 201+ 51 

 II Oil 0-100 14 17 23 

 101-200 20 20 44 

 201+ 25 

 Gas 0-100 42 34 29 

 101-200 41 31 31 

 201+ 36 

 Lease Hydrocarbon Water CAIS WP FP 

Threshold Categorization Production Depth (ft) ($1,000) ($1000) ($1,000)

Revenue I Oil 0-100 287 300 540 

 101-200 512 608 623 

 201+ 595 

 Gas 0-100 534 518 578 

 101-200 631 640 697 

 201+ 695 

 II Oil 0-100 221 231 361 

 101-200 488 614 806 

 201+ 545 

 Gas 0-100 516 374 403 

 101-200 546 744 478 

 201+ 1,172 
The Department of the Interior Mission

As the Nation's principal conservation agency, the Department of the Interior has responsibility 

for most of our nationally owned public lands and natural resources. This includes fostering 

sound use of our land and water resources; protecting our fish, wildlife, and biological diversity; 

preserving the environmental and cultural values of our national parks and historical places; 

and providing for the enjoyment of life through outdoor recreation. The Department assesses 

our energy and mineral resources and works to ensure that their development is in the best 

interests of all our people by encouraging stewardship and citizen participation in their care. 

The Department also has a major responsibility for American Indian reservation communities 

and for people who live in island territories under U.S. administration. 

The Minerals Management Service Mission 

As a bureau of the Department of the Interior, the Minerals Management Service's (MMS) 

primary responsibilities are to manage the mineral resources located on the Nation's Outer 

Continental Shelf (OCS), collect revenue from the Federal OCS and onshore Federal and Indian 

lands, and distribute those revenues. 

Moreover, in working to meet its responsibilities, the Offshore Minerals Management Program 

administers the OCS competitive leasing program and oversees the safe and environmentally 

sound exploration and production of our Nation's offshore natural gas, oil and other mineral 

resources. The MMS Minerals Revenue Management meets its responsibilities by ensuring the 

efficient, timely and accurate collection and disbursement of revenue from mineral leasing and 

production due to Indian tribes and allottees, States and the U.S. Treasury. 

The MMS strives to fulfill its responsibilities through the general guiding principles of: (1) being 

responsive to the public's concerns and interests by maintaining a dialogue with all potentially 

affected parties and (2) carrying out its programs with an emphasis on working to enhance the 

quality of life for all Americans by lending MMS assistance and expertise to economic 

development and environmental protection.