OCS Study 

MMS 2004-009 

 

Coastal Marine Institute 

Long-Term Oil and Gas Structure 

Installation and Removal Forecasting 

in the Gulf of Mexico: A Decision- and 

Resource-Based Approach 

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 2004-009 

 

Coastal Marine Institute 

Long-Term Oil and Gas Structure 

Installation and Removal Forecasting 

in the Gulf of Mexico: A Decision- and 

Resource-Based Approach 

Authors 

Mark J. Kaiser 

Dmitry V. Mesyanzhinov 

Allan G. Pulsipher 

May 2004 

Prepared under MMS Contract 

1435-01-99-CA-30951-17806 

by 

The 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 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 or 

 504-736-2519 

CITATION 

Suggested citation: 

Kaiser, M.J., D.V. Mesyanzhinov, and A.G. Pulsipher. 2004. Long-term oil and gas 

structure installation and removal forecasting in the Gulf of Mexico: A decisionand resource-based approach. U.S. Department of the Interior, Minerals 

Management Service, Gulf of Mexico OCS Region, New Orleans, La. OCS Study 

MMS 2004-009. 56 pp. 
v 

ACKNOWLEDGMENTS 

The advice, assistance, and helpful criticisms of Richard Desselles and Thierry DeCort of 

the MMS are gratefully acknowledged. The remarks of an anonymous reviewer are also 

appreciated. This paper was prepared on behalf of the U.S. Department of the Interior, 

Minerals Management Service, Gulf of Mexico OCS Region. 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. 
vii 

ABSTRACT 

The platform forecasting procedures employed by the Resource Evaluation Analysis 

(REA) Unit of the U.S. Minerals Management Service (MMS) is evaluated and quantified 

in a formal analytic framework. The assumptions employed in the REA/MMS 

methodology, the uncertainty associated with the modeling procedures, and the primary 

consequences of the assumption set are examined. Ten recommendations are suggested to 

clarify and maintain the consistency of the approach, and an alternative model is described 

which incorporates the suggestions for improvement. The analytic framework of the 

alternative model is presented and compared to the REA/MMS procedure.

A long-term infrastructure forecast in the Gulf of Mexico is developed in a disaggregated 

decision- and resource-based environment. Models for the installation and removal rates of 

structures are performed across five water depth categories for the Western and Central 

Gulf of Mexico planning areas for structures grouped according to a major and nonmajor 

classification. Master hydrocarbon production schedules are constructed per water depth 

and planning area using a two-parameter decision model, where “bundled” resources are 

recoverable at a given time and at a specific rate. The infrastructure requirements to 

support the expected production are determined by extrapolating historical data. The 

analytic forecasting framework allows for subjective judgment, technological change, 

analogy, and historical trends to be employed in a user-defined manner. Special attention 

to the aggregation procedures employed and the general methodological framework are 

highlighted, including a candid discussion of the limitations of analysis and suggestions for 

further research. 
ix 

TABLE OF CONTENTS

Page 

List of Tables................................................................................................................xi 

Chapter 1: An Assessment of the Infrastructure Forecast Methodology of the U.S. 

 Minerals Management Service 

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

1.2. The MMS Infrastructure Forecast Methodology ...................................... 2 

 1.2.1. The REA/MMS Infrastructure Forecasting Procedure ................... 2 

 1.2.2. Resource Category Definition......................................................... 3 

 1.2.3. The Production Profile Forecast ..................................................... 5 

 1.2.4. The Infrastructure Forecast............................................................. 8 

1.3. Critique of the MMS Methodology .......................................................... 8 

 1.3.1. A Critique of the Production Profile Forecast ................................ 8 

 1.3.2. A Critique of the Infrastructure Forecast...................................... 10 

1.4. Ten Recommendations............................................................................ 11 

1.5. The CES Infrastructure Forecast Methodology ...................................... 14 

 1.5.1. The CES Infrastructure Forecasting Procedure ............................ 14 

 1.5.2. The Cumulative Production Profile .............................................. 14 

 1.5.3. Expression for the Number of Active Platforms........................... 14 

 1.5.4. The CES Methodology ................................................................. 16 

 1.5.5. A Comparison of the CES and REA Forecast Methodologies ..... 17 

1.6. Conclusions............................................................................................. 19 

Chapter 2: A Decision- and Resource-Based Approach to Long-Term Infrastructure 

 Forecasting in the Gulf of Mexico 

2.1. Introduction............................................................................................. 21 

2.2. Background Information......................................................................... 22 

 2.2.1. The Demand for Infrastructure in the Gulf of Mexico ................. 22 

 2.2.2. The Need to Forecast Infrastructure ............................................. 24 

 2.2.3. The Importance of Categorization ................................................ 24 

 2.2.4. Overview of Infrastructure Forecast Methodologies .................... 25 

2.3. General Methodological Issues............................................................... 27 

 2.3.1. Selecting the Appropriate Level of Disaggregation...................... 27 

 2.3.2. Categorization and System Constraints ........................................ 28 
x 

TABLE OF CONTENTS (continued) 

 Page 

 2.3.3. Modeling Principles...................................................................... 29 

 2.3.4. General Methodology ................................................................... 29 

2.4. A Decision- and Resource-Based Model................................................ 31 

 2.4.1. The Infrastructure Forecasting Methodology ............................... 31 

 2.4.2. The Supply Forecast ..................................................................... 33 

 2.4.3. The Infrastructure Ratio Forecast ................................................. 35 

 2.4.4. Model Results and Interpretation.................................................. 36 

2.5. Limitations of the Analysis..................................................................... 38 

2.6. Conclusions............................................................................................. 40 

References ................................................................................................................. 41 

Appendix A Chapter 1 Tables..................................................................................... 45 

 

 Appendix B Chapter 2 Tables ..................................................................................... 49 
xi 

LIST OF TABLES 

Table Description Page 

A.1 Western and Central GOM National Assessment Resource Estimates 

 and REA/MMS Allocation Quantities............................................... 47 

A.2 A Summary of Measures Implied by the REA/MMS Forecast .............. 48 

B.1 Offshore Statistics by Water Depth ........................................................ 51 

B.2 National Assessment Results for the Western and Central Planning 

 Area in the Gulf of Mexico, BOE (Bbbl) .......................................... 51 

B.3.1 Forecast of the Annual Number of Major and Nonmajor Structures 

 Installed and Removed in the CGOM Through 2010 as a Function 

 of Water Depth and Supply Curve Parameter p ................................ 52 

B.3.2 Forecast of the Annual Number of Major and Nonmajor Structures 

 Installed and Removed in the CGOM Through 2020 as a Function 

 of Water Depth and Supply Curve Parameter p ................................ 52 

B.3.3 Forecast of the Annual Number of Major and Nonmajor Structures 

 Installed and Removed in the CGOM Through 2030 as a Function 

 of Water Depth and Supply Curve Parameter p ................................ 53 

B.3.4 Forecast of the Annual Number of Major and Nonmajor Structures 

 Installed and Removed in the CGOM Through 2040 as a Function 

 of Water Depth and Supply Curve Parameter p ................................ 53 

B.4.1 Forecast of the Annual Number of Major and Nonmajor Structures 

 Installed and Removed in the WGOM Through 2010 as a Function 

 of Water Depth and Supply Curve Parameter p ................................ 54 

B.4.2 Forecast of the Annual Number of Major and Nonmajor Structures 

 Installed and Removed in the WGOM Through 2020 as a Function 

 of Water Depth and Supply Curve Parameter p ................................ 54 

B.4.3 Forecast of the Annual Number of Major and Nonmajor Structures 

 Installed and Removed in the WGOM Through 2030 as a Function 

 of Water Depth and Supply Curve Parameter p ................................ 55 

B.4.4 Forecast of the Annual Number of Major and Nonmajor Structures 

 Installed and Removed in the WGOM Through 2040 as a Function 

 of Water Depth and Supply Curve Parameter p ................................ 55 

B.5 Average Annual Number of Major and Nonmajor Structures 

 Installed and Removed in the Western and Central Gulf of Mexico 

 via Water Depth Category (1996-2000) ............................................ 56 
1 

CHAPTER 1: AN ASSESSMENT OF THE INFRASTRUCTURE 

FORECAST METHODOLOGY OF THE U.S. MINERALS 

MANAGEMENT SERVICE

1.1. Introduction

The Resource and Economic Analysis (REA) Unit of the U.S. Minerals Management 

Service (MMS) performs infrastructure forecasting in support of economic and 

environmental impact studies in the Gulf of Mexico (GOM) (Coffman et al., 2001). The 

MMS performs a number of forecasts related to GOM infrastructure, including but not 

necessarily limited to, 

Number of exploratory wells, 

Number of delineation wells, 

Number of development wells, 

Number of production wells, 

Number of workovers, 

Number of structures installed, 

Number of structures removed, 

Number of subsea completions, and 

Miles pipeline. 

Infrastructure forecast is an important input element to decision making at the MMS, and 

because of the scale of activities involved, drilling activity and structure installations are 

closely watched and used as a general guide on resource levels and development 

expectations. 

 

The purpose of exploration is to discover the presence of oil and gas, and the number of 

exploratory wells provides a gauge of interest in the region and the level of development 

activity expected in the future. If hydrocarbons are discovered, delineation wells may be 

drilled to establish the amount of recoverable oil, production mechanism, and structure 

type. The number of wells required to develop reserves depends on a trade-off between 

risked capital and expected production. Development wells are drilled to produce the 

hydrocarbon resources discovered, and production wells are defined (aptly enough) as 

those wells that are currently in production. Development and production wells are tied 

into structures such as caissons, well protectors, and both fixed and floating platforms. If 

the wellhead control values (Christmas tree, Blow Out Preventer, etc.) are located on the 

seabed, the well is referred to as a subsea completion. Workovers are carried out 

periodically to ensure the continued productivity of the wells. The number of structures 

installed is an important statistic since platforms support production, must be manufactured 

prior to development, installed, serviced during production, and finally removed after 

decommissioning. The economic, environmental, and socioeconomic impact of structure 

installations is a significant contribution to the economic vitality of Gulf Coast 

communities and is evaluated and assessed with every five year lease program on offer. 
2 

Infrastructure forecasting is an uncertain and difficult endeavor due to the confluence of a 

number of interrelated factors, such as the uncertainty of the resource base and its future 

development; the uncertain economic and regulatory environment; and the large number of 

operator- and site-specific variables that drive activity trends. To help minimize the 

forecast uncertainty and ensure the accuracy and reliability of input-output models, it is 

important to maintain forecasting procedures that are well documented and established on 

a solid quantitative foundation. 

There is only a limited amount of documentation supporting the REA/MMS forecast 

models, however, and so the first task of this paper is to formalize the REA/MMS 

procedure in a quantitative framework. The REA’s platform forecasting procedure is 

analyzed in an effort to add operational insight into the methodology and to provide 

support for refinements to the approach. The primary purpose of this paper is thus threefold: 

(1) To document and specify the procedures currently employed by the REA in the 

construction of a platform forecast in the Gulf of Mexico, 

(2) To analyze the assumption set employed in the REA forecast and to critique the 

procedure, and 

(3) To use the assessment as a guide to develop an alternative methodology for 

platform forecasting. 

The outline of Chapter 1 is as follows. In Chapter 1.2, a description of the REA/MMS 

platform forecast procedure is detailed. The general methodology is presented in Chapter 

1.2.1 followed by important background information in Chapter 1.2.2 on the definition of 

resource categories. The production profile forecast is described in Chapter 1.2.3 and the 

infrastructure requirements forecast is described in Chapter 1.2.4. A critique of the REA 

methodology is discussed in Chapter 1.3 and a series of procedural recommendations are 

presented in Chapter 1.4. In Chapter 1.5, the analytic framework for an alternative 

infrastructure forecast is presented. The methodology is outlined in Chapter 1.5.1, followed 

in Chapter 1.5.2 and Chapter 1.5.3 by a description of the cumulative production profile 

and the derivation for the expression of the number of active platforms. The alternative 

approach is formalized in Chapter 1.5.4 and compared to the REA approach in Chapter 

1.5.5. Conclusions are presented in Chapter 1.6. 

1.2. The MMS Infrastructure Forecast Methodology

1.2.1. The REA/MMS Infrastructure Forecasting Procedure: Platform forecasting 

was initiated at the REA/MMS in the early 1990’s and has evolved to a sophisticated 

“resource-based” methodology. The infrastructure forecast employed at the REA/MMS is 

summarized as a four-step procedure (Desselles, 2001a): 

Step 1. Hydrocarbon production profiles per planning area are constructed over a 40-

year time horizon. 
3 

Step 2. Annual production per active platform is forecast per water depth and planning 

area category. 

Step 3. The number of active platforms required to meet the annual production 

projections (from Step 1) is determined using the annual production to active 

platform ratio forecast (from Step 2). 

Step 4. Platform installation and removal rates are estimated to achieve the number of 

active platforms computed in Step 3. 

The hydrocarbon production profile outlined in Step 1 will be discussed Chapter 1.2.3 after 

the resource categories are defined. The infrastructure forecast outlined in Steps 2-4 will be 

examined in Chapter 1.2.4. 

1.2.2. Resource Category Definition: The MMS is required by legislative mandate to 

provide assessments of Outer Continental Shelf (OCS) undiscovered oil and gas. Reserve 

estimates are performed frequently – normally every year or so – while undiscovered oil 

and gas assessments are performed every four years or so. 

Reserves are those quantities of hydrocarbon that are anticipated to be commercially 

recoverable from known accumulations. Proved reserves can be thought of as inventory in 

the ground already paid for with investment dollars. The inventory is known with high 

confidence (“reasonable certainty”) and is specified in terms of a given reserves amount. 

Proved reserves are known with reasonable certainty because the field has been defined by 

appraisal wells, and while developed reserves can be produced from existing wells and 

existing infrastructure, undeveloped reserves will have to be 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; e.g., proved reserves increase with successful exploration and decrease by 

the amount of production. 

Unproven reserves are calculated similar to proven reserves, but because of technical, 

contractual, economic, or regulatory uncertainty, their production is not as certain as 

proven reserves. Unproven reserves are producible and economically recoverable, but their 

presence is based more on geologic interpretation than on physical evidence, and hence, 

the quantities are less certain than proved reserves. 

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 may be negative as well as positive, 

but on average reserve estimates have grown over time. Field growth is most rapid the first 

few years after a field is discovered, and later tend to level out at a smaller increment. 
4 

Statistical growth curves based on historic data are commonly used to estimate reserves 

appreciation, and this is the method employed by the MMS (Lore et al., 2001). 

 

Unproved reserves and reserves appreciation are not quite the same as proved reserves 

because for unproved reserves and appreciation we are forecasting how to build inventory, 

and of course, we don't know the required investment or the future prices that will induce 

such investment. Hence, unproved reserves and reserves appreciation are more uncertain 

than remaining proved reserves, but are generally considered reliable and more certain than 

undiscovered resources. 

Undiscovered conventionally recoverable resources (or, ultimately recoverable reserves) is 

oil and gas which has not yet been physically discovered but for which there is “some 

certainty” that it exists and can be extracted with, by definition, conventional technology1

. 

Undiscovered resources are those resources outside of known fields that is postulated to 

exist based on broad geologic knowledge. Undiscovered resources come in two forms – 

conventionally recoverable and economically recoverable. Conventionally recoverable 

resources are that portion of the hydrocarbon potential that is producible, using present or 

reasonably foreseeable technology, without any consideration of economic feasibility. 

Undiscovered conventionally recoverable resources (UCRR) are primary located outside of 

known fields, but undiscovered pools within known fields are also included to the extent 

that they occur within separate plays. The portion of UCRR that is economically 

recoverable under imposed economic and technologic conditions2

 is referred to as 

undiscovered economically recoverable resources (UERR). Resource appraisals are based 

on group assessments and the application of subjective probability estimates of various 

parameters. Undiscovered resources have significant uncertainty associated with its 

expected magnitude and development. 

The following notation is established. Proved reserves located within the Western and 

Central GOM planning area Pi , i=1,2, are denoted by ) ( R1 Pi , unproved reserves by 

( ) R2 Pi , reserves appreciation by ) ( R3 Pi , undiscovered conventionally recoverable 

resources by ) ( R4 Pi , and undiscovered economically recoverable resources by ) ( R5 Pi . The 

magnitude of { ) ( R1 Pi , ) ( R2 Pi , ) ( R3 Pi , ) ( R4 Pi , ) ( R5 Pi } depend upon a number of 

uncertain economic, geologic, and technologic parameters, and are considered stochastic 

quantities; e.g., although the estimate of the magnitude of ) ( R1 Pi is fairly certain, ) ( R5 Pi is 

highly variable, and the uncertainty associated with ) ( R2 Pi , ) ( R3 Pi , and ) ( R4 Pi fall in 

 

1

 While unconventional oil deposits (e.g., tar sand, oil shale) have thus far contributed little to domestic U.S. 

production, unconventional natural gas deposits from low-permeability sandstones, fractured shale, and coal 

beds are making an important contribution, and so undiscovered conventionally recoverable resources may 

be underestimated. The distinction between “conventional” and “unconventional” resources remains a bit 

fuzzy and should be viewed somewhat skeptically. 

2

 The imposed economic scenario is defined to be a 12.5-percent or 16.7 percent royalty rate, 35 percent 

Federal tax rate, three percent inflation rate, and natural gas prices related to oil prices at 66 percent of the 

oil-energy equivalent (Lore et al., 2001). 
5 

between these two classifications. Resource estimates based on the 2000 National 

Assessment data are depicted in Table A.13. 

The elements { ) ( R1 Pi , ) ( R2 Pi , ) ( R3 Pi } are assessed as point estimates in the 2000 

National Assessment and represent average (or expected) values, while { ) ( R4 Pi , ) ( R5 Pi } 

are estimated relative to a “low” (pessimistic) and “high” (optimistic) case scenario 

corresponding to a “pessimistic” and “optimistic” economic environment4. For ) ( R4 Pi , the 

low and high estimates are referred to as F5 (5th percentile) and F95 (95th percentile), 

respectively, while the average estimate is referred to as F50 (50th percentile). The F5 

estimate reflects the resource quantity having a five percent probability that the ultimate 

resource, when found, will equal or exceed the estimated quantity. The F50 and F95 

estimates have similar interpretations. For ) ( R5 Pi , the low and high estimates represent the 

mean resources at $18/bbl oil, $2.11/Mcf and $30/bbl oil, $3.52/Mcf. 

1.2.3. The Production Profile Forecast: The Resource and Economic Analysis Unit of 

the MMS performs a 40-year production profile forecast for oil and natural gas for each 

planning area of the Gulf of Mexico. The REA constructs production profiles by 

specifying annual production rates to recover the resource base within each resource 

category over the time horizon of the forecast. If 

( , , ) = [ , ]

( , ) i i

L H

q o g P t K Annual production rate of (oil, gas) = (o, g) under a [low, high] = [L, 

H] case economic scenario corresponding to planning area Pi at time 

t and resource category Ki , 

then the construction of the supply curve follows by specifying ( , , ) [ , ]

( , ) i i

L H

q o g P t K each year 

over the range (τ, τ+40] subject to the following conditions: 

1

[ , ]

1 1

40

[ , ]

( , ) q (P,t,K ) p K L H

i

t

L H

∑ o g =

+

=

τ

τ

, 

(1) 

2

[ , ]

2 2

40

[ , ]

( , ) q (P,t,K ) p K L H

i

t

L H

∑ o g =

+

=

τ

τ

, 

where τ represents the current time; K =1 { ) ( R1 Pi + ) ( R + 2 Pi ( ) R3 Pi }; K =2 { ) ( R5 Pi }; and 

0 1, [ , ] < ≤ L H

i p i=1,2. Resource category K1 is reserves and appreciation while K2 is 

undiscovered resources. If 1, [ , ] = L H

i p full recovery of the resource category is specified, 

while 1 [ , ] < L H

i p indicates only partial recovery. The value of [L,H ]i p can be viewed as a 

 

3

 The Eastern GOM planning area is not considered due to its low production and activity levels and historic 

restrictions on leasing sales. 

4

 The pessimistic economic scenario corresponds to $18/bbl oil and $2.11/Mcf gas, while the optimistic 

economic scenario corresponds to $30/bbl oil and $3.52/Mcf gas. 
6 

decision variable, and in the present context is inferred from the production profiles 

established by the REA. 

After the production profiles 

( , , ) [ , ]

( , ) i j

L H

q o g P t K , i=1, 2; j=1,2, 

are constructed for each resource class K j , the total hydrocarbon annual production within 

planning area Pi at time t, ) ( , [ , ]

( , ) q P t i

L H

o g , is computed as the sum across the two resource 

categories; i.e., 

( , ) [ , ]

( , ) q P t i

L H

o g = ( , , )1

[ , ] q( , ) Pi t K L H

o g

+ ( , , )2

[ , ] q( , ) Pi t K L H

o g , (2) 

while the cumulative production at time t = x is defined as 

( , ) [ , ]

( , ) Q P x i

L H

o g = ( ( , , )1

[ , ] q( , ) Pi t K L H

o g

x

t

∑=τ

+ ( , , )) 2

[ , ] q( , ) Pi t K L H

o g . (3) 

The REA/MMS constructs production profiles for the WGOM and CGOM planning 

regions, and the following observations are derived from profiles provided by the MMS 

(Desselles, 2001b). 

1. (a) The production profile ) ( , , ( , ) K1 q P t o g i recovers the resource category K1(P1) = 

R1(P1) + R2(P1) + R3(P1) according to an average profile since it is not bound 

through a low and high case scenario. 

 (b) The annual production profile ) ( , , ( , ) K1 q P t o g i recovers proved reserves, 

unproved reserves, and reserves appreciation beginning at time τ = 2001 under 

an exponentially declining and smooth profile. The recovery time of the profile 

is assumed to be essentially the time horizon of the forecast. 

 (c) The production forecast for class K1 recovers 94% of the oil and all of the 

natural gas unproved reserves and reserves appreciation values estimated to exist 

in the WGOM. Similar recovery percentages are achieved in the CGOM. 

 (d) ( ) R2 Pi is recovered according to a low and high case scenario using a bellshaped curve that reaches its peak within roughly seven years and decays to zero 

over a 30-year horizon. The low and high case scenarios represent scaled 

versions of one another which peak at approximately the same time. 

 (e) The annual production curves that recover ) ( R2 Pi are “normalized” in the REA 

procedure. Normalization is used to adjust the production profile to “match” the 

estimate of the resource base. 
7 

2. (a) To recover undiscovered economically recoverable resources, the REA/MMS 

employs a procedure where the production profiles used to recover R5(P1) are 

decomposed into a “proposed” and “future” sales category which begins 

recovery within three and four years, respectively, of the current time. Proposed 

sales represents approximately 7% of the estimated total oil resources and 8% of 

the estimated natural gas resources within the category. 

 (b) The recovery curves ) ( , , 2

[ , ] q( , ) Pi t K L H

o g are smooth and bell-shaped reaching their 

maximum recovery rates near the year 2030 for both oil and gas. The production 

profiles for the low and high case scenario are approximately scaled versions of 

one another; i.e., 

q( , ) (Pi ,t,K2 ) ≈ α H

o g ( , , ) ( , ) K2 q P t i

L

o g , for α >1. 

 (c) The REA hydrocarbon production forecast recovers a fraction of the 

undiscovered resource base in the WGOM. More precisely, (31%, 35%) of the 

oil resources and (46%, 58%) of the gas resources in the K2 category are 

recovered over the time horizon of the forecast. The recovery rates of K3 are 

slightly higher in the CGOM region: (41%, 54%) for oil and (62%, 91%) for 

gas. Refer to Table A.1. 

3. (a) The REA allocates (41%, 45%) of the total oil resources and (66%, 73%) of the 

total gas resources within the WGOM over a 40-year horizon. In the CGOM, 

(61%, 64%) of the total oil resources and (83%, 95%) of the total gas resources 

are recovered. Oil recovery is underestimated relative to gas resources, and gas 

recovery percentages exhibit a larger spread. In terms of BOE recovery rates, the 

percentage values are a weighted average of the oil and gas recovery rates and 

converge in value: (56%, 60%) of the WGOM resources are recovered and 

(73%, 85%) of the CGOM resources are recovered. 

 (b) The cumulative curves for the WGOM imply that the REA “implicit” forecasts 

is that between 63% (low) to 50% (high) of the oil resources, and between 57% 

(low) to 52% (high) of the gas resources, will be recovered within 20 years5

. 

Production profiles constructed for oil and natural gas can be compared to other industry 

supply forecasts, such as those performed by the Gas Research Institute, National 

Petroleum Council, American Gas Association, and U.S. Department of Energy. A direct 

comparison of production forecasts should be approached cautiously, however, since the 

estimates of the resource base and underlying assumptions of the various models are not 

likely to be compatible; e.g., industry forecast generally tend to be “optimistic” relative to 

government assessments, although even government forecast have occasionally been 

criticized as being too optimistic (e.g., see (Attanasi, 2001)). 

 

 

5

 The estimated amount of resources to be recovered varies under the low and high case scenarios, and hence, 

the percentage recovery rates depend upon both the conjectured production profiles and the estimated 

resource base. Since these quantities are independent of one another, it is not necessary that the “high” 

recovery percentage exceed the “low” recovery percentage values. 
8 

1.2.4. The Infrastructure Forecast: The hydrocarbon production profile is one of three 

forecasts required to drive the platform infrastructure requirements. Two additional 

forecasts are required to determine the number of structures installed and removed. In 

reference to the four-step procedure outlined in Chapter 1.2.1, we now focus on Steps 2-4: 

The total annual production profile in planning area Pi in year t is denoted by q( Pi , t) and 

the number of active platforms is designated by A( Pi , t). A forecast is denoted by a 

superscript *, so that for instance, 

q(P,t) *

i = Forecast of the annual production profile in Pi 

*

( , )

( , )

















A P t

q P t

i

i = Forecast of the annual production per active platform ratio in Pi .

The REA employs the forecast * q(P,t) i and 

*

( , )

( , )

















A P t

q P t

i

i to determine the number of active 

platforms that are required to meet the production projection. The number of active 

platforms is established by multiplication: 

 

q(P,t) *

i

*

( , )

( , )

















A P t

q P t

i

i = * A(P,t) i , (4) 

and using the forecasted number of active platforms, * A(P,t) i , and a conjectured relation 

on the ratio of installed-to-removed platforms, ) i(P,t i : ) r(P,t i , to satisfy the relation 

 

A(P,t) *

i = A(P,t −1) *i + * r(P,t) i i(P,t) * − i . (5) 

The annual number of installed and removed platforms required to achieve balance is 

estimated based on historic installed-removed ratio patterns and life-cycle analysis. 

1.3. Critique of the MMS Methodology

1.3.1. A Critique of the Production Profile Forecast: Production forecasts are 

notoriously difficult to perform regardless of the individual, agency, or organization. A 

production forecast is based upon the best information available at the time and must be 

considered under the economic conditions projected for the future, including assumptions 

regarding inflation and technological improvements. It is important to carefully delineate 

all assumptions and decision parameters in a forecasting procedure. Based on REA/MMS 

methodology, the following observations are provided. 

 

1. (a) The National Assessment of the remaining proved reserves in the WGOM is 

stated as a point estimate of 0.495 Bbbl oil and 7.393 Tcf gas (see Table A.1). 
9 

The REA bounds these values through the selection of a low and high case 

scenario which is not compatible with the manner in which the data is 

reported. To employ the point estimates it is suggested that appropriate 

assumptions be specified. 

(b) For the estimated proved reserves, bounds on the recovery rate (not on the 

magnitude) of R1(P1) and R2(P1) may be desirable; e.g., the form of the low 

and high case profiles ) ( , , ( , ) 1 K1 q P t H

o g should not necessarily be scaled versions 

of one other; i.e., 

 ( , , ) ( , ) 1 K1 q P t H

o g ( , , ) ( , ) 1 K1 q P t L ≠ α o g , for α >1. (6) 

The magnitude of the total recovery for the low and high case profiles, 

however, should be identical: 

( , , ) ( , , ). 1 1

40

1 1 ( , )

40

q( , ) P t K q P t K t

H

o g

t

L

∑ o g ∑

+

=

+

=

=

τ

τ

τ

τ

(7) 

(c) Reserves appreciation are reported in terms of a point quantity and the profile 

that recovers the appreciation is reported as a single curve. This is logically 

consistent, but considering the resource class under assignment (and the 

magnitude and uncertainty associated with its recovery and development), 

bounds on the recovery rate of this resource class may be desirable. 

2. (a) R5(P1) is recovered according to scenarios based on “proposed” and “future” 

leasing to account for specific lease sale alternatives required for economic and 

environmental impact assessment. 

(b) In the construction of the production profiles ) ( , , 1 2

[ , ] q( , ) P t K L H

o g , the uncertainty 

associated with the profile appears to be due almost entirely to the uncertainty 

in the estimated magnitude of R5(P1) in the F95 and F5 estimates. Since the 

production profile is also highly uncertain with regard to development time 

and investment activities, this uncertainty should be incorporated, if possible, 

in the forecast scenarios. 

3. (a) For the resource categories K1 and K2, resources should be recovered at the 

amount estimated in the National Assessment or at a specified percentage of 

the value, and this value should be stated explicitly. The percentage values 

may vary as a function of water depth and planning area, but for consistency 

the “decision parameters” should be clearly stated for each category. 

(b) From the cumulative productive profile for RT(P1), a given recovery rate can 

be inferred. This “implied recovery” could be combined with a 2-parameter 

supply model to simplify the construction of the supply profiles while 

incorporating a decision-oriented approach within the modeling framework. 

The uncertainty associated with the construction of a simplified supply profile 

and the REA approach is believed to be of the same order of magnitude. 
10 

(c) It is desirable that production forecasts performed at the MMS should be 

consistent in the sense that the first five years of the REA/MMS 40-year 

production profile should coincide with the MSM short-term forecast profile 

(Melancon et al., 2001) 

1.3.2. A Critique of the Infrastructure Forecast:

1. (a) Platforms are used to process both oil and gas, although in practice, most 

platforms process either oil or gas, and re-inject, store, or flare the unwanted 

component. Platforms characterized as either “oil” or “gas” platforms can be 

used with oil and gas production data, while platforms that are not

disaggregated according to these categories should use production data on a 

“BOE” basis. It is not appropriate to mix categories; i.e., to forecast the 

number of “gas” platforms based on “BOE” production profiles or 

“platforms” based solely on “oil” production. It is convenient and logical in a 

first-order assessment to use platforms and BOE data rather than to 

disaggregate at a lower level, but if separate oil and gas resource forecast are 

employed, then platforms should be distinguished by class. 

(b) A “platform” is very broadly defined and should be disaggregated in a 

manner compatible with the application of the forecast. Although all 

platforms support production, they do so in a number of ways; e.g., there are 

production platforms, quarter platforms, storage platforms, transportation 

platforms, and combinations of these types, as well as caissons and wellprotector jackets which would not generally be considered a “platform” at 

all. It is desirable to distinguish structures into configuration type to 

differentiate the economic and environmental impact of installation and 

removal. One possible categorization is through major and nonmajor 

structure classifications6. Failure to account for the structure type will bias 

the forecast and the resultant levels of economic and environmental activity. 

(c) The water depth and planning area categorization employed in the forecast is 

considered appropriate to group entities into “reasonably” homogeneous 

categories. 

(d) The REA appears to construct supply profiles over a planning area basis 

which is not compatible or logically consistent with the water depth 

categories employed in the infrastructure forecast. A supply profile 

 

6

 Major structures are defined as having at least six completions or two pieces of production equipment, 

while nonmajor structures do not satisfy either of these requirements. Major structures thus include platforms 

or satellites with six completions or more, or at least two pieces of production equipment such as braced 

caissons, conventional piles with wells, skirt platforms, and floating platforms. Caissons and well protector 

jackets surrounding five wells or less and conventional piled platforms without wells (e.g., quarters 

platforms, storage platforms, pipeline junction and metering facilities) would be considered nonmajor 

structures. 
11 

constructed over a planning area basis cannot drive a forecast through depth 

categories unless appropriately disaggregated. 

2. (a) The annual production per active platform is a function of time, and to be 

used across a water depth categorization the functional needs to be computed 

and forecast across each individual water depth category. 

(b) The REA uses a five year moving average to reduce the volatility of the 

annual production per active platform ratio and this is acceptable as long as 

the forecast of the moving average is based on historic trends. Conjectured 

trends are acceptable as long as they are a clearly specified decision 

parameter. 

3. (a) The forecasted number of active platforms is determined through the product 

of the forecasted supply profile and platform-production ratio profile. From 

the forecast of active platforms, the annual platform installation and removal 

rates are determined to achieve balance with this number, but to “achieve 

balance” requires an additional assumption on the installation and removal 

trends in a given region. Determining the normalization factor is difficult 

since the historical trends on installation and removal patterns are dynamic 

quantities with a large standard deviation. The transition from an estimate of 

active platforms to an estimate of installed and removed platforms is subject 

to significant uncertainty. 

 

1.4. Ten Recommendations 

A platform forecast should be performed in a manner compatible with the system data and 

the intended use of the forecast. In economic and environmental impact studies for 

instance, where the number of platforms expected to be installed and removed in a given 

region is the primary input factor to assess the impact on surrounding communities, the 

forecast must be performed at a fairly low level of aggregation for the results to be 

meaningful. Since platforms are diverse by nature, it is not logical to categorize a well 

caisson and a fixed platform as similar GOM “structures.” Separate forecasts must be 

performed over distinct category sets, and it is neither necessary nor desirable7

 to apply the 

same type of forecast to each category. Since several decision parameters are implicitly 

stated throughout the REA procedure, it is also desirable to make these assumptions 

explicit and to try to relate the uncertainty associated with the forecast with the input 

parameters of the decision maker. And since the number of categories and decision 

parameters for this problem is large, an automated procedure is essential. An automated 

decision-oriented framework is possible only if a structured and well-defined environment 

has been created for this purpose. 

 

7

 In fact, requiring the same forecasting methodology over each category imposes an unnecessarily tight 

constraint on the procedure which defeats the purpose of disaggregation. 
12 

Ten recommendations follow. 

1. The notion of being able to recover a fixed resource “bundle” (e.g., R1(P1)+R2

(P2)+ R3(P2)) at a given rate (e.g., ) ( , , 1 K1 q P t L

o ) beginning at a specific time is 

entirely hypothetical. Resources flow between and within categories in ways that 

are not predictable or quantifiable. Reality is better explained in a dynamic manner 

in terms of a flow (Adelman, 1996): 

“Mineral production is a flow from an unknown physical resource, first 

through exploration from “basins” to “plays,” then into identified “fields” 

and “reservoirs,” then through development into current inventories or 

“proved reserves,” to be extracted and sold. Reserves are renewable and 

constantly renewed, if and only if there is enough inducement to invest in 

creating them.” 

Bundling resources is nonetheless a convenient mechanism that helps establish a 

structured environment in which to perform a production forecast, and it is a 

justifiable and viable method in the construction of supply curves. Platform 

activity is driven by trends in discoveries, which is conditional on geologic and 

economic factors. It is recommended that the REA/MMS resource-based approach 

be refined, clarified, and automated in a decision-structured environment. 

2. A more structured presentation of the production profile should be established 

with all assumptions and decision parameters specified, the methodology 

performed in a step-by-step and explicit fashion, and the forecast performed across 

individual water depth categories. Structured approaches do not necessarily imply 

that the resulting forecasts are more accurate, but rather, the results of the forecast 

are consistent with the assumptions and parameters selected for the model. 

Structured models help to avoid mixing assumptions that are inconsistent and/or 

incompatible, clarify uncertainty, and are easier to defend/analyze since the user 

understands the limitations of the methodology and the implications of the 

variability. Assumptions delineate uncertainty but do not (nor can not) eliminate it. 

Structural models are useful to quantify uncertainty and also provide a framework 

to perform sensitivity analysis. 

3. Establish bounds on the rate of recovery of the production curve ) ( , , ( , ) 1 K1 q P t o g to 

recover the resources in category K1 = {R1(Pi) + R2(Pi) + R3(Pi)} under a low and 

high case scenario. 

4. The REA/MMS production profiles are likely to be underestimated due to the 

conservative estimates associated with the magnitude of resource recovery. Full 

recovery of each resource category should be made, or the percentage of the 

resource allocation be declared as a decision variable and specified across each 

water depth category. Allocation levels that are not specified appear arbitrary, 

while variable allocations that are specified appear reasoned; the uncertainty is the 

same in either case, but in the later approach the method is justified. 

 
13 

5. The construction of the REA supply profiles can be simplified in terms of a 2-

parameter decision model where the decision maker specifies the amount of 

( ) RT Pi that is expected to be recovered (the first parameter) within a particular 

time frame (the second parameter). This will help to clarify the exposition and 

simplify the forecast across multiple water depth categories; the downside of the 

approach is erosion of the link between policy variables (e.g., lease sales) and 

production profiles. 

6. The notion of forecasting an entity labeled a “platform” is troublesome since the 

category is too broadly-defined and encompasses widely disparate infrastructure. 

The selection of the appropriate classification category is determined by the 

application of the forecast. For economic impact studies, the platform as a 

physical structure (wells, deck and jacket) as well as the topsides production and 

drilling equipment provides the stimulus for the economic impact. A logical 

classification can be made in terms of “major” and “nonmajor” structures. For 

environmental impact studies, the potential biomass and habitat provided by a 

structure between the waterline and the mudline is the relevant parameter, and so 

the size and platform construction would be the appropriate aggregation category; 

e.g., conventionally-piled platforms with n-piles provides a measure of the 

complexity of the structure. Alternative platform classifications are also desirable 

since “oil” and “gas” structures could be directly linked to oil and gas production 

profiles. 

7. Planning areas need to be disaggregated in terms of water depth categorization 

since the entities of the forecast are not homogeneous across water depth. 

Production profiles are currently performed over a planning area basis, but to drive 

an infrastructure forecast across water depth, it is necessary to construct 

production profiles on a water depth basis. 

8. If resource estimates can be reported over lower levels of categorization; e.g., 0-

60m, 61-200m, then additional categorization of the structure data may enhance 

understanding of the platform installation and removal rates. National Assessment 

resource data are currently reported using categories 0-200m, 201-800m, etc. 

9. Annual short-term and long-term infrastructure forecast should be documented, 

the model assumptions clearly specified, and the forecast results formally 

evaluated so that institutional memory and experience improves forecast 

performance. 

10. The REA method of platform forecasting should be analyzed in conjunction with 

the CES methodology and converged into a “best practice” model. 

 
14 

1.5. The CES Infrastructure Forecast Methodology 

1.5.1. The CES Infrastructure Forecasting Procedure: The infrastructure forecast 

methodology proposed by the CES/LSU is a four-step procedure based in part on the 

structure of the REA/MMS approach. A formal treatment of the methodology is described 

as follows: 

Step 1. Construct cumulative hydrocarbon production profiles on a BOE basis per 

water depth and planning area category over a 40-year time horizon 

Step 2. The cumulative number of major and nonmajor structures installed and 

removed per cumulative BOE production on a water depth and planning 

area basis is forecast over a 40-year time horizon. 

Step 3. The cumulative number of structures installed and removed over each 

water depth and planning area category is determined by the product of 

the profiles determined in Step 1 and Step 2. 

Step 4. The annual number of major and nonmajor structures installed and 

removed are derived from the cumulative forecast in Step 3. 

1.5.2. The Cumulative Production Profile: To perform a resource-based infrastructure 

forecast on a water depth and planning area basis requires the user to construct a 

production profile forecast over a similar water depth and planning area basis. The REA 

currently performs a supply profile forecast over three planning areas; in the CES approach 

this is replaced by nine supply forecasts (one for each planning area and water depth 

category: 0-200m, 201-800m, 800+m). The CES approach employs a decision-oriented 

construction, requesting the user provide the following information: 

What percentage of RT (Γi, j ) will be recovered within the time horizon τ? 

The recovery of the remaining conventionally recoverable resources is thus based on the 

belief of the decision maker. Two input parameters are required: the percentage of RT(Γi, j ) 

to be recovered (the first parameter) within the time horizon τ (the second parameter). The 

application of a 2-parameter supply profile not only simplifies the construction of the 

profiles, but also creates a framework to perform sensitivity analysis. 

1.5.3. Expression for the Number of Active Platforms: The number of active platforms 

operating in the water depth and planning area region Γi, j at time t is denoted A( t i j , Γ, ). 

The number of active platforms is a dynamic quantity and is computed on an annual basis 

in terms of the relation 

A( t i j , Γ, )=A( t i j , Γ, -1) +i(Γi, j , t) – r(Γi, j , t) , (8) 
15 

where i( t i j , Γ, ) and r( t i j , Γ, ) represent the annual number of platforms installed and 

removed in region Γi, j over the time interval (t-1,t]. In words, relation (8) indicates that the 

number of platforms active at time t, A( t i j , Γ, ), is equal to the number of platforms active 

in the previous year, A( t i j , Γ, -1), plus the number of platforms installed minus the number 

of platforms that were removed over the past year (t-1, t], i( t i j , Γ, ) and r( t i j , Γ, ). 

Initially there are no platforms active; i.e., the boundary condition is defined as follows: 

A(Γi, j ,0)= 0; (9) 

 and using the recursive relation (8) we obtain 

A(Γi, j , 1) = i(Γi, j , 1) − r( 1, Γi, j ), (10) 

A(Γi, j , 2) = A(Γi, j , 1) + i(Γi, j , 2) − r( , Γi, j 2) 

= (i(Γi, j , 1) + i(Γi, j , 2) − (r( 1, Γi, j )+r( , Γi, j 2)) (11) 

∑=

=

2

1

(

k

i Γi, j , k) − ∑=

2

1

(

k

r Γi, j , k). (12) 

The general relationship is thus 

Γ = ∑ Γ − =

( , ) ( , )

1

, , A t i k

t

k

i j i j ( , )

1

, r k

t

k

∑ i j

=

Γ . (13) 

If I( t i j , Γ, ) and R( t i j , Γ, ) denote the cumulative number of structure installation and 

removals through time t; i.e., 

I( t i j , Γ, ) = ) ( , 1

, i k

t

k

∑ i j

=

Γ ,

R( t i j , Γ, ) = ) ( , 1

, r k

t

k

∑ i j

=

Γ , 

then relation (13) can be expressed more compactly as 

A(Γi, j ,t) = I( t i j , Γ, ) – R( t i j , Γ, ). (14) 
16 

In words, relation (14) expresses the number of active platforms in region Γi, j at time t, 

A( t i j , Γ, ), as the difference in the cumulative number of platforms installed and removed 

through time t, I( t i j , Γ, ) and R( t i j , Γ, ). 

1.5.4. The CES Methodology: The REA approach to infrastructure forecasting employs 

the number of active platforms to annual production ratio, 

( , )

( , )

,

,

q t

A t

i j

i j

Γ

Γ , (15) 

to determine the number of active platforms required to recover the anticipated production. 

Using relation (14), the ratio (15) is written equivalently as 

( , )

( , )

,

,

q t

A t

i j

i j

Γ

Γ = = ΓΓ − Γ

( , )

( , ) ( , )

,

, ,

q t

I t R t

i j

i j i j − Γ

Γ

( , )

( , )

,

,

q t

I t

i j

i j

( , )

( , )

,

,

q t

R t

i j

i j

Γ

Γ . (16) 

Observe that the numerator and denominator terms in (16) are “mixed” in the sense that the 

numerator is a cumulative measure while the denominator is an annual factor. Formally, 

the measure is well-defined, but methodologically it is desirable to have the numerator and 

denominator of the same type (annual or cumulative, but not both). 

The CES approach to platform forecasting first modifies the denominator in the ratio (15) 

to its cumulative form: 

( , )

( , )

,

,

Q t

A t

i j

i j

Γ

Γ = = ΓΓ − Γ

( , )

( , ) ( , )

,

, ,

Q t

I t R t

i j

i j i j − Γ

Γ

( , )

( , )

,

,

Q t

I t

i j

i j

( , )

( , )

,

,

Q t

R t

i j

i j

Γ

Γ , (17) 

and then decomposes the expression in terms of two separate functionals 

( , )

( , )

,

,

Q t

I t

i j

i j

Γ

Γ , ( , )

( , )

,

,

Q t

R t

i j

i j

Γ

Γ . (18) 

The CES approach then follows the REA procedure and forecasts the supply profile 

Q( t i j , Γ, ) (see Chapter 1.5.2) and the functionals (18) over a 40-year horizon. The forecast 

of each of the functionals are denoted with a superscript * as follows: 

Q( t i j , Γ, )*, 

*

( , )

( , )

,

,

















Γ

Γ

Q t

I t

i j

i j , 

*

( , )

( , )

,

,

















Γ

Γ

Q t

R t

i j

i j . (19) 
17 

The product of the production and ratio forecast provides an estimate of the cumulative 

number of platforms installed and removed; i.e., 

Q( t i j , Γ, )* · 

*

( , )

( , )

,

,

















Γ

Γ

Q t

I t

i j

i j = I( t i j , Γ, )*, 

Q( t i j , Γ, )* · 

*

( , )

( , )

,

,

















Γ

Γ

Q t

R t

i j

i j = R( t i j , Γ, )*. 

(20) 

The output functionals I( t i j , Γ, )* and R( t i j , Γ, )* are then used to compute the annual 

installation and removal rates using backward recursion 

I( t i j , Γ, )* – I( 1 , Γi, j t − )* = i( t i j , Γ, )*, (21) 

R( t i j , Γ, )* – R( 1 , Γi, j t − )* = r( t i j , Γ, )*. (22) 

By starting at the end of the time horizon and working through to the present year-by-year, 

the annual number of installation and removal rates are calculated using (21) and (22). 

1.5.5. A Comparison of the CES and REA Forecast Methodologies: The CES/LSU 

and REA/MMS approach to infrastructure forecasting are both resource-based and driven 

by historical system characteristics and explicitly defined decision parameters. The 

primary distinction between the two approaches is as follows. 

• The CES approach partitions the GOM into water depth and planning area 

categories, Γi, j , and performs a BOE cumulative production forecast over each 

category. The decision parameters (p, θ) are employed to simplify the construction 

of the production profile, where the parameter p indicates the percentage of the 

total resource RT(Γi, j ) to be recovered within a time period θ years from the 

present. The REA approach determines individual production profiles for the 

resource classes R1(Pi), R2(Pi), R3(Pi), and R5(Pi) over the planning area category 

Pi for both oil and natural gas, and then computes the total economically 

recoverable resource profile, RT(Pi)= R1(Pi)+ R2(Pi)+ R3(Pi)+ R5(Pi). 

• The CES approach disaggregates platforms in terms of major and nonmajor 

structures and forecasts each category separately while the REA approach 

employs the general platform categorization. 

• The REA approach develops the ratio of the annual production, q(Pi, t), to the 

annual number of active platforms, A(Pi, t), within planning area Pi: 
18 

( , )

( , )

A P t

q P t

i

i , (23) 

while the CES approach applies the cumulative number of (major, nonmajor) 

structures installed and removed over the cumulative production in region Γi, j : 

 

( , )

( , )

,

,

[ , ]

Q t

I t

i j

i j

m n

Γ

Γ , ( , )

( , )

,

,

[ , ]

Q t

R t

i j

i j

m n

Γ

Γ . 

(24) 

• The REA forecasts q(Pi, t)* and the ratio 

*

( , )

( , )

















A P t

q P t

i

i , (25) 

while the CES approach forecasts each of the functionals 

*

( , )

( , )

,

,

[ , ]

















Γ

Γ

Q t

I t

i j

i j

m n

, 

*

( , )

( , )

,

,

[ , ]

















Γ

Γ

Q t

R t

i j

i j

m n

. 

 

(26) 

separately. 

• The REA forecasts the number of active platforms to support a given production 

profile by multiplying a forecast of the annual production, q(Pi, t)*, by (the 

inverse of) the forecasted ratio (q(Pi, t)/A(Pi, t))*, to yield the number of active 

platforms: 

q(Pi, t)* ·

*

( , )

( , )

,

,

















Γ

Γ

q t

A t

i j

i j = A(Pi, t)*.

 

(27) 

The CES approach forecasts the cumulative supply curve Q(Pi, t)* over the water 

depth and planning area category Γi, j , and then multiplies the cumulative profile 

by a forecast of the infrastructure requirements ratio: 

Q(Γi,j, t)* ·

*

( , )

( , )

,

,

[ , ]

















Γ

Γ

Q t

I t

i j

i j

m n

= [m,n] I (Γi, j ,t)*, 

 

(28) 
19 

Q(Γi,j, t)* ·

*

( , )

( , )

,

,

[ , ]

















Γ

Γ

Q t

R t

i j

i j

m n

= [m,n] R (Γi, j ,t)*,

 to yield the cumulative number of major and nonmajor structures installed and 

removed over Γi, j , [m,n] I (Γi, j ,t)* and [m,n] R (Γi, j ,t)*. 

• The CES approach employs the cumulative installation and removal functionals 

per major and nonmajor structure to derive the annual installation and removal 

rates and active platforms per category Γi, j . The annual future platform 

installation and removal rates, [m,n] i (Γi, j ,t)* and [m,n] r (Γi, j ,t)*, are computed 

directly from [m,n] I (Γi, j ,t)* and [m,n] R (Γi, j ,t)*: 

[m,n] I (Γi,j, t)* – [m,n] I (Γi,j, [ , ] 1)* m n t − = i (Γi, j ,t)*, (29) 

[m,n] R (Γi,j, t)* – [m,n] R (Γi,j, [ , ] 1)* m n t − = r (Γi, j ,t)*. (30) 

Since active platforms are defined in terms of platform installation and removal 

rates, [m,n] i (Γi, j ,t)* and [m,n] r (Γi, j ,t)* are then used to compute [m,n] A (Γi, j ,t)*: 

[m,n] A (Γi,j, t)* = [m,n] A (Γi,j, [ , ] 1) * m n t − +i (Γi, j ,t)* – [m,n] r (Γi, j ,t)*. (31) 

The REA approach employs a conceptually intuitive measure – the annual 

production per active platform – to forecast the number of active platforms, but is 

then required to employ additional assumption to determine the annual installation 

and removal rates. The number of platforms that will be installed and removed to 

achieve the number of active platforms is then estimated based on historic patterns 

and life-cycle analysis. The CES approach employs a more complex and lessintuitive functional – the cumulative installation and removal rate per cumulative 

production – but then derives the annual number of installed and removed 

platforms without the need to invoke additional assumptions. The trade off 

between the two approaches is comparable. 

1.6. Conclusions 

Forecasting is a traditional blend of art and science, and under conditions of unknown 

uncertainty, it is desirable to maintain a consistent, simplistic, and structured methodology 

that explicitly enumerates the assumption set and does not mix scenario assumptions or 

introduce additional uncertainty external to the modeling framework. Infrastructure 

forecast deserve careful attention to detail and deliberate methodological development, and 

should be performed on a regular basis to develop a better understanding of the system 

drivers and limitations of the analysis. The REA/MMS has done a good job in developing 
20 

and promulgating the GOM platform forecast, and as discussed in this paper, the main 

criticism is aimed at refining the methodology, explicitly enumerating the assumption set, 

and ensuring that the solution methodology is consistent within the model framework. A 

set of recommendations was described addressing these concerns. 

The methodology suggested in Chapter 1 is an adaptation of the REA approach that 

formalizes the framework in a consistent manner, modifies some key elements of the 

methodology, and incorporates decision parameters within the procedure. A formal 

development of the proposed methodology was presented and compared with the REA 

approach. Comparison of the two approaches illustrates that the REA and CES procedures 

represent a trade-off between the preferences of the user and the assumptions of the 

methodology. It is suggested that the two procedures evolve into an automated “best 

practice” model. 
21 

CHAPTER 2: A DECISION- AND RESOURCE-BASED APPROACH 

TO LONG-TERM INFRASTRUCTURE FORECASTING IN THE 

GULF OF MEXICO 

2.1. Introduction

Platforms and pipelines are the primary infrastructure used to develop and transport 

hydrocarbons in the Gulf of Mexico (GOM). Platforms represent the visible signatures of 

production with pipelines their invisible partner. A wide variety of platforms, or more 

generally, structures, are used in gulf waters to support the equipment used for drilling 

wells, processing hydrocarbon production, or housing offshore personnel. Offshore 

structures are designed under specific environmental conditions and operator loads, and 

although it is difficult to classify all configurations, most structures may be characterized 

as caissons, well-protector jackets, conventionally piled fixed platforms, and floating 

structures. Knowledge of the number of structures expected to be installed and removed in 

offshore waters is of primary importance in the U.S. Minerals Management Service 

(MMS) responsibility to manage the oil and gas activities on the Outer Continental Shelf 

(OCS) of the GOM. 

 

The purpose of Chapter 2 is to develop a methodological framework to forecast structure 

installation and removal rates in the GOM over a 40-year time horizon. The framework is 

based on a model structure developed by the Resource and Evaluation Analysis Unit 

(REA) of the MMS (Desselles, 2001a and b), but departs from the REA approach in terms 

of the level and type of disaggregation employed and the methodology used to construct 

the forecast. For a description of the REA model and its relation to the CES approach, see 

Chapter 1 for additional information. 

The number of structures required to develop a field is affected by a number of factors, 

including but not necessarily limited to, the expected size of the field, the number of 

productive wells drilled from each platform, the amount of oil/gas each productive well 

produces, environmental factors such as water depth, soil conditions, and storm intensity, 

capital cost, availability of platforms, and strategic opportunity. The emphasis of this 

Chapter is to present a decision- and resource-based framework to an infrastructure 

forecast in a manner subject to an explicit set of assumptions. 

The outline of Chapter 2 is as follows. Background information is provided in Chapter 2.2. 

In Chapter 2.2.1, the demand for infrastructure in the GOM is described, followed in 

Chapter 2.2.2 by a brief discussion for the need for an infrastructure forecast. In Chapter 

2.2.3, the definition and classification of structures is reviewed followed in Chapter 2.2.4 

by a critique of platform forecast methodologies. General methodological issues are 

presented in Chapter 2.3. The motivation for disaggregating data is described in Chapter 

2.3.1 and special reference to the primary constraints of disaggregation is discussed in 

Chapter 2.3.2. In Chapters 2.3.3 and 2.3.4, the basic principles of forecasting are 

enumerated and the general methodology is described. A decision- and resource-based 

forecast model is then presented in Chapter 2.4. In Chapter 2.4.1, the general infrastructure 
22 

forecasting methodology is described, followed by a description in Chapters 2.4.2 and 

2.4.3 of the two key steps of the forecast, namely, constructing the supply profile and 

forecasting the infrastructure requirements ratio. Model results and a general interpretation 

of the procedure are presented in Chapter 2.4.4. Limitations of the analysis and suggestions 

for further research are presented in Chapter 2.5. In Chapter 2.6, conclusions complete the 

paper. 

2.2. Background Information 

2.2.1. The Demand for Infrastructure in the Gulf of Mexico: The oil and gas industry 

has operated along the Gulf Coast for nearly 100 years, beginning with overwater drilling 

at Caddo Lake, Louisiana, in 1905. To be able to drill and produce without direct contact 

to the land, construction engineers at Caddo Lake drove pilings into the bottom of the lake 

bed to provide support for the platforms. Barges transported drilling equipment and 

supplies to the drill site, and underwater pipeline connected the producing wells to 

gathering stations on the lake (Pratt et al., 1997). The first offshore platform was installed 

in the Gulf of Mexico in 1947 in about 5m of water (Kerr-McGee, Ship Shoal Block 32), 

while today, drilling and production companies are moving into the ultradeepwaters of the 

gulf in depths approaching 3,000m (Baud et al., 2000). 

Platforms and pipelines are the primary infrastructure required to develop and transport 

hydrocarbons. Platforms either stand on the sea bottom or float on the sea surface, and hold 

the equipment required to treat and process the produced fluids, including drilling 

equipment, cranes, compressors, power generators, etc. Structures provide the foundation 

for surface facilities and serves as the “ground” in offshore development, while pipelines 

are used in gathering systems, moving fluids between facilities within and between fields, 

and for final transport away from the field to market8

. In deep waters well platforms are 

connected by pipeline to a facilities platform where processing takes place. Wells, 

facilities, and quarters are kept on separate platforms, if possible, for safety reasons, but as 

the water gets deeper and platform jackets become more expensive, well, facility, and 

quarters’ platforms are normally combined into a single super structure. Platform costs 

escalate with increasing water depth and shift the economics away from wells with 

individual jackets toward multiple wells drilled from a single platform and subsea wells 

tied back to a host facility (Conaway, 1999). 

Platform and pipeline infrastructure development in the offshore Gulf of Mexico is closely 

associated with field development but the correlation depends on a complex array of 

factors which are not readily quantified. The shallow waters of the GOM is a mature 

offshore region with the most highly developed infrastructure in the world, while deep 

water development is still considered a frontier region with virtually no supporting 

infrastructure (Baud et al., 2000). The dynamics of deepwater development are also quite 

different than shallow water fields, both in terms of production characteristics, 

development costs, cash flow streams, development time, and technology applications. 

While shallow water wells are commonly associated with individual structures, deepwater 

 

8

 All the natural gas in the GOM, and practically all the oil, is transported via pipeline to shore. 
23 

platforms are normally developed as a “hub” to serve several wells, and increasingly, more 

than one field. The number of producing wells per active platform as shown in Table B.1 is 

one measure that captures this trend. Recent technological advances in field development 

such as extended reach, directional drilling, multilateral and subsea completions continue 

to reduce the number of platforms necessary to develop a field. 

The demand for infrastructure in the Gulf of Mexico, and in particular, the demand for a 

structure is a function of the outcome of the discovery process, when and where the field is 

discovered, and the development plan of the companies involved with the discovery. The 

development plan is a critical factor and closely tied to the expected size of the field, the 

field’s geographic location relative to existing infrastructure, and the potential to support 

future development. These demand variables in turn are influenced by factors such as the 

fields ranking within the company’s portfolio of plans and geologic conditions in the area, 

which in turn depend upon the price and demand for oil and natural gas, the impact of 

regulatory action, and global economic conditions. 

 

Basic economics imply that if a well or collection of wells can produce enough oil or gas 

to cover the cost of completion, operating costs, and a favorable return, then it will be put 

into production and a platform will be installed. Given the enormous front-end investment 

cost oil companies make in bidding for drilling rights, exploration, and development, there 

is considerable financial incentive for rapid field development. On the other hand, near the 

end of the life of a field when the drive mechanism and production decline are well 

understood, a structure will be maintained as long as possible given that it exists as an asset 

and not a liability; i.e., cost are adequately covered. The parameters associated with 

installation decisions are mostly uncertain, however, and so investment opportunities are 

usually risked and individual projects ranked in accordance with a priority list. 

If the field is large enough and falls within the economic-risk criteria of the company 

portfolio and budgetary constraints, then a platform is the usual course of development. 

Smaller fields, or fields sufficiently removed from existing infrastructure, are usually not 

cost-effectively developed with a platform but may be completed with subsea technology 

tied-back to existing infrastructure. In the ultradeepwaters of the GOM, infrastructure is 

sparse and pipeline costs can be a significant factor in the overall cost of development. The 

anticipated use of floating production storage and offloading units may play an important 

role in future deepwater production which will further act to distort “traditional” platformproduction characteristics. 

The impact of technology on field development is pervasive and difficult to quantify. A 

field can now be developed without building a new platform as long as a tie-back to an 

existing structure is economically feasible. Subsea completions, where the wellheads are 

installed at the mudline and connected by pipeline with nearby platforms, increase the 

number of small fields that can be developed without a platform. Additional technological 

advances such as directional drilling, horizontal drilling, slimhole drilling, 3-D and 4-D 

seismic, improved drill bits, advanced synthetic drilling fluids, and corrosion resistant 

alloys improve completions, offshore and deepwater drilling, and provide more efficient 
24 

reservoir management that act to reduce the number of new structures that need to be 

installed. 

2.2.2. The Need to Forecast Infrastructure: Platform forecasting in the GOM is used as 

a guide to activity levels and as input to economic and environmental impact studies of oil 

and gas development (Coffman et al., 2001; Olatubi and Dismukes, 2002; Skolnik and 

Holleyman, 2002; USDOI, MMS, 2001). The need to forecast infrastructure development 

is thus primarily viewed in terms of its economic and environmental impact. In terms of 

economic development, platforms 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 “spill-over” effect on the economic growth and 

vitality of the regions which serve the development; e.g., see (Olatubi and Dismukes, 2002; 

Skolnik and Holleyman, 2002). 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. In terms of 

environmental impact, the installation of structures and pipelines and production of 

hydrocarbon may have an adverse effect on the habitat and surrounding communities in the 

GOM; production activities may result in oil spills, air pollution, and other environmental 

impacts; and during decommissioning, marine habitat is lost and sea turtles, dolphins, and 

sperm whales may be at risk if explosives are used for severance operations. 

 

2.2.3. The Importance of Categorization: Generally speaking, the application of the 

forecast indicates the level of categorization that should be employed. For economic 

impact models the aggregation level should be able to distinguish between the cost of 

structures, and one convenient way to do this is by identifying the structure configuration 

type, its topside inventory, and location (water depth). For environmental impact models 

on the other hand the categorization level will depend on structural characteristics (number 

of piles, number of conductors, area of platform) where the emphasis is on the bottomside 

of the structure and the requirements associated with decommissioning. 

A detailed analysis of data which is not carefully screened and properly understood is of 

little value in modeling and may lead to erroneous conclusions. This is a precautionary 

statement made in regard to the manner in which structure data is classified. If the 

categories of aggregation are so broadly defined that the elements within the categories are 

heterogeneous, then the resultant analysis may not be meaningful. The notion of a 

“platform” for example is an entity that is so broadly defined (since it encompasses well 

protectors, satellites, and fixed platforms) that it will lead to bias if used without careful 

processing. A shallow water platform is not in any sense comparable to a deepwater 

platform, and although both count as one platform in an enumerative sense, there is 

perhaps a billion dollars difference in average development cost and at least a multiple of 

this factor in the resultant cash flow streams and their ultimate economic impact9

. The 

 

9

 Other categorizations are also possible as discussed in Section 6; e.g., gas wells and oil wells are quite 

dissimilar and may be logical to break out. 
25 

application of a water depth categorization partitions configuration type to some extent, but 

unless structures are specifically identified, they cannot be categorized in a useful manner. 

Partitioning data into finely selected categories on the other hand tends to introduce a large 

amount of noise and may also bias the analysis. A degree of balance thus needs to be 

maintained when selecting the level of aggregation. 

The classification strategy employed in this paper is to categorize platforms in terms of 

“major” and “nonmajor” structures decomposed according to planning area and five water 

depth categories: 0-200m, 201-800m, 801-1600m, 1601-2400m, 2400+

m. The major and 

nonmajor structure classification and water depth categorization helps to differentiate 

structures for economic impact studies, while a planning area categorization is useful for 

locational specificity and to maintain compatibility with area-wide leasing sale proposals. 

 

To maintain the consistency and logical cohesion among the component forecast the class 

of structures are aggregated using the MMS assignment of “major” or “nonmajor” 

structure. Major structures are defined as having at least six completions or two pieces of 

production equipment, while nonmajor structures do not satisfy either of these 

requirements. Major structures thus include platforms or satellites with six completions or 

more, or at least two pieces of production equipment. This would normally include all 

braced caissons, conventional piles with wells, skirt platforms, special platforms, and 

floating platforms (Pulsipher, 1996). Caissons and well protector jackets surrounding five 

wells or less and conventional piled platforms without wells (e.g., quarters platforms, 

storage platforms, flare piles, pipeline junction and metering facilities) would be 

considered nonmajor structures. Platforms may be physically connected by walkways, but 

if a structure has its own support base then it is counted as a separate entity. Further, a 

structure may evolve over time from being a caisson, say, to a fixed structure with 

production equipment. These cases generally occur in only very small numbers, however, 

and when they do, the most recent structure (which is typically the most complex) is 

assigned. 

 

2.2.4. Overview of Infrastructure Forecast Methodologies: Infrastructure forecasting 

can be performed in a variety of ways, but care must be taken to properly define the 

entities and categories in a manner that does not destroy the information content of the 

data, induce unforeseen bias in the analysis, or misrepresent the results. 

 

In a previous study performed by the CES (Pulsipher et al., 2001), an econometric model 

for the number of offshore structures operating in the GOM was developed. The total 

number of structures installed and removed from the GOM was estimated based on 

regression analysis to be 

INSt 93 = −113. + 16.2·CFZt + 2.09·Pt + 0.46·INSt-1

REM t = 0.95·INSt-33 + 0.35·INSt-21

where, 

INS t = Number of structures installed at time t, 
26 

REM t = Number of structures removed at time t, 

CFZ t = Cumulative field size at time t, and 

P t = Crude oil price at time t. 

These relations were then combined with a drilling and discovery process model to 

forecast the number of structures through the year 2023. The impact of crude oil price 

variations on the number of active structures was also investigated through price scenarios 

supported by an EIA forecast. The reader can consult (Pulsipher et al., 2001) for a 

description of the procedures and discussion of the model results. 

The MMS also performs platform forecasting (Desselles, 2001a and b) in support of 

economic and environmental impact studies and other regulatory concerns as described in 

(Coffman et al., 2001). The MMS forecasting procedure is a “resource-based” approach, 

meaning that the platform requirements necessary to develop the hydrocarbon resources 

are estimated using hypothetical production profiles that recover an estimated resource 

base. A “master” hydrocarbon production schedule is conjectured and the infrastructure 

trends required to support the production are then estimated using historic data and 

extrapolation techniques. The platform forecast performed by the MMS is at a lower 

aggregation level than the CES study, occurring over separate water depths and planning 

regions in the GOM, but as in the CES approach, the MMS does not aggregate the data 

elements into category types (e.g., major and nonmajor structures). 

 

A review of the advantages, disadvantages, and assumptions of each approach is useful to 

guide and inform the current methodology. Regression-based models as employed 

previously in the CES study develop their relationship based on econometric fundamentals 

and historic data. These relationships are typically determined at a high level of 

aggregation and attempt to incorporate exogenous factors that influence the installation and 

removal rates of platforms. By employing data that does not distinguish between 

geographic location or structure type, however, it is difficult to categorize the model output 

in terms that are directly relevant for impact studies. In essence, the high level of 

aggregation destroys the information content of the data since heterogeneous entities are 

grouped and forecast as similar elements. The data aggregation schemes are easy to revise 

by constructing regression models over appropriate data subcategories, but the “slicing and 

dicing” needs to be performed carefully. The application of a discovery process model is 

also a standard approach to incorporate new field discoveries, but the uncertainty 

associated with discovery models performed over a large geographic region generally 

limits their usefulness in practice (Lynch, 2002; National Academy of Sciences, 1991). 

 

The MMS approach drives the infrastructure forecast with a production profile which 

serves as a substitute for the discovery model. Production profiles are constructed over a 

planning area and are based on recovering the reserve and resource estimates provided in 

the National Assessment (Lore et al., 2001). Production profiles are relatively easy to 

construct, but there is also a high degree of uncertainty in the profiles which is not 

explicitly taken into account. The MMS performs the infrastructure forecast over a lower 
27 

aggregation level than the CES study, employing planning area and water depth categories, 

but the production profiles themselves are not performed across the same water depth 

categories leading to an inconsistent formulation. Recovering the resource base along a 

production curve also limits the application of some exogenous variables – notably price 

and technological change – since the inclusion of such factors would not be logically 

consistent with the form of the resource data10. There is also no attempt to distinguish 

between the type of structures upon which the forecast is based. Since the platform 

forecast is used as input to economic impact studies which rely upon class demarcation 

more than merely a “count” of structures, the disaggregation of structures into type is 

considered essential. 

The purpose of this paper is to combine and refine the CES and MMS approaches to 

platform forecasting to develop a framework that is logically coherent and sensitive to the 

needs of the user. An infrastructure forecast model is developed for major and nonmajor 

structures aggregated over a water depth and planning area category and driven production 

profiles determined in a user decision framework. The impact of technological change is 

considered a user defined parameter incorporated within the decision parameters of the 

model. The MMS approach of driving the forecast with a production profile is maintained 

and careful structure classification allow more meaningful inferences of the model results 

to be made. The development and exposition of a structured forecasting model serves as 

the primary task of this paper. 

2.3. General Methodological Issues 

2.3.1. Selecting the Appropriate Level of Disaggregation: The primary reason to 

aggregate data is based on modeling philosophy. If processes, activities, or entities (such as 

discovery rates, formation structures, production profiles, platforms, etc.) are not 

reasonably uniform within their category of analysis, it is generally agreed that the 

classification category should be subdivided to create more uniform subcategories in the 

belief that the categorization will reduce bias in the analysis11. The classification scheme 

employed in this analysis is based on structure type, water depth, and planning area. 

 

The categorization of structure types ensures that the data is properly balanced for 

economic impact studies. The classification of structures via water depth helps to account 

for the technology factor and increased economic stimulus associated with increased water 

depths. Planning areas are also useful to distinguish activity levels associated with the 

area-wide leasing sale programs. 

The process of aggregation and “slicing and dicing” does not always come for “free,” 

however, nor does it necessarily imply that the resultant forecasts are better predictors of 

the future. Aggregating data over small categories is likely to induce additional variability 

 

10 The MMS does not explicitly incorporate price effects or technological change in their supply model, 

however, and so this restriction is compatible with their approach. 

11 For example, one of the early criticisms from the National Academy of Sciences was specifically directed 

at the aggregation levels employed by the MMS in their resource evaluation assessments. 
28 

since the integrating effects of large data sets are lost. It is generally agreed, however, that 

the imposition of structure through classification schemes – when the classification is 

meaningful – improves understanding and helps to identify the limitations of the analysis. 

In the case of infrastructure forecasting, it is not only logical but necessary to aggregate 

structures according to water depth and configuration type to accurately account for the 

nature of installation and removal rates and to better understand the dynamics of 

development. 

 

2.3.2. Categorization and System Constraints: Aggregation imposes a tight burden on 

the model structure and the data requirements of the problem. If a forecast is to be 

performed at a given level of aggregation then the system data must also be available at the 

same level of aggregation. Unfortunately, this does not usually occur for a variety of 

reasons. 

To perform a resource-based forecast, it is necessary to acquire 

 

1. Resource estimates, 

2. Supply forecast, and 

3. System data, 

at the same level of aggregation. If data is not available at the level of aggregation in which 

the forecast is to be performed, then the data must either be processed and brought “up” 

from a “lower” level or partitioned from “above” into the appropriate categories. Data at a 

low level of aggregation is not always available, however, while reducing high-level data 

usually involves additional estimation. Additional uncertainty can thus be introduced at 

both ends of the spectrum. A simple example illustrates the complications. 

 

If the desired level of data aggregation is a water depth and planning area category Γi, j , 

then because production data is reported at a well level (the “lowest” level possible), the 

production profiles are readily aggregated to Γi, j without consequence. On the other hand, 

supply forecasts are usually performed at a “high” level of analysis (normally, on a 

planning area basis), and hence, to obtain a supply curve over Γi, j it is necessary to 

decompose the planning area in a water depth categorization and to perform a separate 

forecast over each water depth category. The introduction of a water depth categorization 

is not currently compatible with MMS supply forecasting profiles, and so the 

determination of supply profiles over Γi, j introduces uncertainty within the model that is 

difficult to quantify. 

 

The time horizon of the forecast also induces additional system constraints. A long-term 

forecast is commonly required in economic impact studies and logical constraints require 

that forecasted production profiles do not exceed the estimated resource base. The 

production profile across each region is thus constrained by the resource estimates in the 

region. The impact of technological change and the price variability of oil and natural gas 
29 

are not explicit parameters, but are incorporated indirectly in terms of how fast the 

resource base is estimated to be recovered. This will be discussed in more detail in Chapter 

2.4.2. 

2.3.3. Modeling Principles: An infrastructure forecast is developed guided by the 

following criteria: 

• The forecast should be based on a clearly defined structure. 

• The forecast should be supported by a set of explicit assumptions. 

• The forecast should be performed at a meaningful level of aggregation. 

• The model structure and assumptions should be subject to parametric analysis. 

• The model should be easy to calibrate and update, and preferably, performed in 

a step-by-step manner that is easy to understand and modify. 

• Uncertainty should be accounted for based on the form of the data and userdefined parameters. 

• The process should be transparent and well-documented. 

• Short-term and medium-term forecast should be performed separately and 

calibrated against the long-term forecast. 

The philosophy adopted to satisfy these criteria is to maintain simplicity of form and to 

extrapolate historical experience to predict future trends. To a large extent the current REA 

model satisfies most of these objectives, but it is deficient in specifying the model 

uncertainty and allowing user-defined parameters to be incorporated within the procedure. 

It is for these and other reasons (Lore and Batchelder, 1995) that the REA structure is 

maintained but structured to ensure a consistent assumption set to allow decision 

parameters to be applied with scenario analysis. The structural aspects of the proposed 

model should also help to deflect criticism of the methodology since the results are subject 

to an explicit assumption set rather than perceived as absolute in nature. 

 

2.3.4. General Methodology: The general methodology is presented in four stages: 1. 

Pre-Processing, 2. Forecasting, 3. Post-Processing, and 4. Scenario Analysis. The stages 

are described as follows: 

1. Pre-Processing. Develop historic trends of hydrocarbon production and 

infrastructure requirements on a water depth and planning area basis Γi, j . 

 1.1. Report reserves (proved, unproved, appreciation) and resource (undiscovered 

conventionally recoverable, undiscovered economically recoverable) data 

over the category Γi, j , Rk(Γi, j ), k = 1,..., 5. 
30 

1.2. Aggregate historic production profiles for oil and natural gas over Γi, j , 

combine production in terms of a BOE basis, and report a cumulative 

production profile per category Γi, j , Q(Γi, j , t). 

1.3. Compute the cumulative number of major (m) and nonmajor (n) structures 

installed and removed in category Γi, j through time t, [m,n] I (Γi, j ,t) and 

[m,n] R (Γi, j , t). 

1.4. Compute the ratio of cumulative structure installation and removal to 

cumulative production for each classification type per category Γi, j , 

[m,n] I (Γi, j ,t)/Q(Γi, j , t) and [m,n] R (Γi, j , t)/Q(Γi, j , t). 

2. Forecasting. Construct production profiles and determine infrastructure 

requirements over a θ-year time horizon. 

2.1. Forecast hydrocarbon production profiles on a BOE basis to recover p-percent 

of the resource base ) ( RT Γi, j = ) ( R1 Γi, j + ) ( R2 Γi, j + ) ( R3 Γi, j + ) ( R5 Γi, j described 

in Step 1.1 over a θ-year horizon, where the parameters (p, θ) are user-defined 

input. 

2.2. Forecast infrastructure requirements for major and nonmajor structures over the 

category Γi, j based on the extrapolation of the historic ratios computed in Step 

1.4. 

2.3. Forecast the cumulative infrastructure required to recover the anticipated 

production profiles by multiplying the forecast profiles determined in Step 2.1

and Step 2.2.

3. Post-Processing. Determine the annual number of structures installed and removed 

and the number of active structures per region per structure classification. 

3.1. The annual number of structures installed and removed in category Γi, j over the 

time interval (t-1, t], [m,n] i (Γi, j , t) and [m,n] r (Γi, j , t), is determined through the 

algebraic relations: 

[m,n] i (Γi, j , t) = [m,n] I (Γi, j , t) – [m,n] I (Γi, j , t-1), 

[m,n] r (Γi, j , t) = [m,n] R (Γi, j , t) – [m,n] R (Γi, j , t-1). 

 

3.2. The number of active major and nonmajor structures in category Γi, j in year t, 

[m,n] A (Γi, j , t) , is determined through the dynamic recursion: 

[m,n] A (Γi, j , t) = [m,n] A (Γi, j , t-1) + [m,n] i (Γi, j , t) – [m,n] r (Γi, j , t). 
31 

4. Scenario Analysis. Determine the number of structures installed and removed under 

scenarios that incorporate variation in resource magnitude, development profiles, 

and technological improvements. 

4.1. The average values of [m,n] i (Γi, j , t) and [m,n] r (Γi, j , t) are assessed over the time 

horizon θ through resource magnitude and development profile scenarios 

incorporated through the parameters (p, θ) in Step 2.1. 

4.2. The values of [m,n] i (Γi, j , t) and [m,n] r (Γi, j , t) are assessed through technological 

improvement scenarios by bounding the magnitude of the infrastructure 

requirements forecast in Step 2.2. 

2.4. A Decision- and Resource-Based Model 

2.4.1. The Infrastructure Forecasting Methodology: The infrastructure forecast 

methodology proposed is a four-step procedure that is similar in structure to the 

REA/MMS approach, but differs in the level and type of categorization employed and 

development of the functionals implemented. To maintain contact with the baseline model, 

the REA/MMS approach is highlighted in the discussion that follows. 

The methodology is described as follows: 

Step 1. Construct cumulative hydrocarbon production profiles on a BOE basis per 

water depth and planning area category over a 40-year time horizon. 

Step 2. Forecast the cumulative number of major and nonmajor structures installed 

and removed per cumulative BOE production on a water depth and planning 

area basis over a 40-year time horizon. 

Step 3. The cumulative number of structures installed and removed over each water 

depth and planning area category is determined by the product of the 

profiles determined in Step 1 and Step 2. 

Step 4. The annual number of major and nonmajor structures installed and removed 

are derived from the cumulative forecast in Step 3. 

The infrastructure forecast is dependent upon the construction of the hydrocarbon 

production profile (Step 1) and forecast of the infrastructure requirements ratio (Step 2) 

across categories specified on a water depth and planning area basis. The hydrocarbon 

production profile forecast, 

Q(Γi, j , t)*, 

and the infrastructure requirements ratio forecast, 
32 

 ( , )*,

*

( , )

( , ) ,

[ , ]

,

,

[ , ]

t

Q t

I t

i j

m n

I

i j

i j

m n

= Γ 















Γ

Γ

γ 

 

( , )*,

*

( , )

( , ) ,

[ , ]

,

,

[ , ]

t

Q t

R t

i j

m n

R

i j

i j

m n

= Γ 















Γ

Γ

γ

represent the key input parameters to the model. 

Unfortunately, over a long-term horizon no one knows – or should pretend to know for that 

matter – the production profile, Q(Γi, j , t)*, but since this is a required element in all 

resource-based forecasts, uncertainty should be accounted for in some manner. Uncertainty 

can be made explicit through the use of decision parameters which allow the user to 

express in a simple manner their expectations for future production, price, the impact of 

technological change, etc. The infrastructure requirements ratio forecast, * ( , ) ,

[ , ] t i jm n γ I Γ and 

( , ) * ,

[ , ] t i jm n γ R Γ , is less uncertain due to the integrating nature of the functional. Hence, there 

is a greater degree of confidence in extrapolating these trends in the future where sufficient 

data exists to reasonably allow such trending. It is also possible to incorporate a decision 

parameter in the infrastructure ratio to account for technological change specific to well 

productivity per platform, but this was not pursued since it can be incorporated within the 

previous decision parameter. 

The product of the functionals 

Q(Γi, j , t)* ⋅ (Γ, , )* = [ , ] t i j

m n

I γ [m,n] I (Γi, j , t), 

Q(Γi, j , t)* ⋅ (Γ, , )* = [ , ] t i j

m n

R γ [m,n] R (Γi, j , t), 

yields a forecast of the cumulative number of structures installed and removed through 

time t. 

 

The annual number of structures installed and removed over the time horizon, [m,n] i (Γi, j , 

t)* and [m,n] r (Γi, j , t)*, is then derived from the cumulative forecast, and the number of 

active platforms, [m,n] A (Γi, j , t), is calculated from a recursive relation using the annual 

installation and removal rates and a boundary condition on the number of active platforms: 

[m,n] i (Γi, j , t)* = [m,n] I (Γi, j , t)* – [m,n] I (Γi, j , t –1)*, 

[m,n] r (Γi, j , t)* = [m,n] R (Γi, j , t)* – [m,n] R (Γi, j , t –1)*, 

[m,n] A (Γi, j , t)* = [m,n] A (Γi, j , t –1)* + [m,n] i ( * Γi, j , t) – [m,n] r (Γi, j , t )*. 
33 

The output functionals, [m,n] I (Γi, j , t)* and [m,n] R (Γi, j , t)*, are used to compute the annual 

installation and removal rates using backward recursion. By starting at the end of the time 

horizon and working through to the present year-by-year, the annual number of 

installations and removals are calculated. The number of active platforms is then computed 

using the annual installation and removal rates and the value of [m,n] A (Γi, j , t –1)*. 

The uncertainty associated with the output of the model, namely, the uncertainty in the 

forecasted quantities, [m,n] i (Γi, j , t)* and [m,n] r (Γi, j , t)*, is bound through the uncertainty 

associated with the construction of the input functionals and the convolution operations. 

 

2.4.2. The Supply Forecast: A variety of production models can be employed to predict 

hydrocarbon supply, but it is difficult to ascertain the accuracy of one method relative to 

another since the specific methodologies must first be duplicated under the assumption set 

of the models, and then the output must then be compared with data realized in the 

forecast period. Generally speaking, attention is normally devoted to developing models to 

replicate historical data as opposed to evaluating the ultimate success/failure of the 

forecasted results, which is not unusual considering the difficult (some would say 

impossible) nature of forecasting. An exceptionally good introductory discussion on the 

limitations of forecasting is provided by Lynch (Lynch, 2002) and is considered required 

reading. Geophysical, price/regulatory, partial adjustment, discovery, simulation, and 

hybrid models have all been examined at various times with varying degrees of “success” 

(Barouch and Kaufman, 1976; Cleveland and Kaufmann, 1991; Eckbo et al., 1978; Lore 

and Batchelder, 1995; MacDonald et al., 1994; Moroney and Berg, 1999; Pesaran and 

Samiei, 1995; Power and Fuller, 1992; Power and Jewkes, 1992; Smith, 1980; Smith and 

Paddock, 1984; Walls, 1994). Hence, rather than focus on a specific methodology to 

construct a production profile, it is better to view all supply forecasts as highly uncertain 

with the understanding that some researchers have developed a preference for one 

methodological technique over another. The MMS constructs production profiles for oil 

and natural gas using a Hubbert-type model with reserves and resource data provided 

through the National Assessment (Crawford et al., 2000; Lore et al., 2001). This technique 

is one among many alternative methods available and is appealing because of its intuitive 

nature. 

 

Resource recovery models are characterized by considering resources in terms of a 

“bundled” unit recoverable within a given time and at a specific rate. In a very real sense 

this type of assessment is a complete fiction since mineral resources should not be treated 

as a fixed mineral stock, but rather as a flow created by investment (Adelman, 1996). 

Despite the obvious problems and criticism associated with such a model (Lynch, 2002; 

National Academy of Sciences, 1991), recovering bundled units of resource is justified in 

terms of its simplicity and the manner in which the forecast is used. Specification of 

recovery rates and development time leads to the construction of a production path with 

profile parameters which can incorporate technological change and other variables 

specified by the user. The simplest resource-based model is to recover the remaining 
34 

conventionally recoverable resources, ) ( RT Γi, j , according to the belief of the decision 

maker; i.e., the decision maker must address the following question: 

What percentage of ( ) RT Γi, j will be recovered within the time horizon θ? 

The specification of p and θ is user-defined and accounts for the decision makers 

understanding of resource recovery, including the nature of technological change, 

development timing, lease specific conditions, oil price, and supply/demand conditions. It 

is possible to try to account for these variables in separate models, but this was considered 

outside the scope of the project. The supply model is referred to as a 2-parameter recovery 

model since two parameters – p and θ – need to be specified to recover the resource. 

The remaining conventionally recoverable resources of region Γi, j , ) ( RT Γi, j , represents the 

volume of all conventionally recoverable resources that has not yet been produced and 

includes remaining proved reserves )) ( ( R1 Γi, j , unproved reserves )) ( ( R2 Γi, j , reserves 

appreciation )) ( ( R3 Γi, j , and undiscovered economically recoverable resources )) ( ( R5 Γi, j : 

( ) RT Γi, j = ) ( R1 Γi, j + ) ( R2 Γi, j + ) ( R3 Γi, j + ) ( R5 Γi, j . 

For a detailed definition of each resource category, see (Crawford et al., 2000; Lore et al., 

2001; Society of Petroleum Engineers and World Petroleum Congress, 1997) and for the 

current National Assessment estimates, refer to Table B.2. Attanasi (Attanasi, 2001) 

provides one alternative perspective the reader may wish to consult. The value of ) ( RT Γi, j

is bound by a high (optimistic) and low (pessimistic) estimate, referred to as F5 (5th 

percentile) and F95 (95th percentile), respectively. The F5 estimate12 reflects the resource 

quantity having a 5-percent probability that the ultimate resource, when found, will equal 

or exceed the estimated quantity. Similarly, the F95 estimate reflects the resource quantity 

having a 95-percent probability (19 in 20 likelihood) that the ultimate resource will equal 

or exceed the estimate. 

The decision variables of the resource recovery model are denoted by (p, θ), 0 ≤ p ≤ 1, 

where 100p designates the percentage of the resource recovered within the time horizon θ.

The region of aggregation, Γi, j , and resource quantity, ) ( RT Γi, j , is assumed given, and the 

time horizon of the forecast is assumed to be θ years from the current time τ. The system 

variables are denoted as: 

Input: 0< p ≤1, θ and ) ( RT Γi, j

Output: Q(Γi, j , t)*, 

and the procedure to construct the supply forecast is described as follows: 

 

12 The F5 and F95 estimates represent the mean economically recoverable resources using an optimistic 

($30/bbl oil, $3.52/Mcf gas) and pessimistic ($18/bbl oil, $2.11/Mcf gas) scenario. 
35 

Step 1. Plot the historic cumulative production curve Qh(Γi, j , t), t ≤ τ. 

Step 2. The cumulative production forecast Qh(Γi, j , t)*, t > τ, is extrapolated in a 

linear fashion to recover 100p-percentage of RT = RT (Γi, j ) within θ years, 

and all the resources within the 40-year time horizon. The cumulative 

production curve is written analytically as 



















− − + + Γ + ≤ ≤ + −

−

− + Γ ≤ ≤ +

Γ ≤

Γ =

( ) ( , ), 40. 40

(1 )

( ) ( , ), 

( , ), 

( , )*

,

,

,

,

τ θ τ θ τ

θ

τ τ τ θ

θ

τ

t pR Q t t p pR

t Q t t pR

Q t t

Q t

T h i j

T

h i j

T

h i j

i j

The value of RT = RT (Γi, j ) can be selected as the F5, F50, or F95 estimate. The model is 

simple to use, which is its main attraction, and one can argue that the uncertainty involved 

with estimating future production profiles and the impact of technological change is such 

that structurally simple models capture the essence of the forecast in a manner analogous to 

more sophisticated models. Through the selection of p and θ the user can dictate a wide 

variety of scenarios that reflect trends and technological conditions within specific regions 

of the GOM. 

2.4.3. The Infrastructure Ratio Forecast: The MMS approach requires three forecast to 

predict platform installation and removal rates: 

• q(Γi, j , t)* 

• 

*

,

,

( , )

( , )

















Γ

Γ

A t

q t

i j

i j , and 

• i(Γi, j , t)* : r(Γi, j , t)*. 

The CES approach also requires three forecast: 

• Q(Γi, j , t)* 

• [m,n]

I γ (Γi, j , t)*, and 

• [m,n]

R γ (Γi, j , t)*. 

The MMS application of the annual production forecast q(Γi, j , t)* and infrastructure 

forecast ratios ( ) ( , )

( , )

,

,

A t

q t

i j

i j

Γ

Γ * motivated the CES application of cumulative production Q(Γi, j , 

t)* and the ratios [m,n]

I γ (Γi, j , t)* and [m,n]R γ (Γi, j , t)*. In the MMS approach, however, 

another forecast is required to translate the number of active platforms to annual 
36 

installation and removal rates, while in the CES approach application of the cumulative 

infrastructure ratio eliminates the need to forecast the installation and removal ratio. The 

CES methodology incorporates expert judgment in the determination of Q(Γi, j , t)*, and 

although it is not difficult to structure the procedure to allow user-defined input on the 

selection of the ratio forecast, [m,n]

I γ (Γi, j , t)* and [m,n]R γ (Γi, j , t)*, the current methodology 

does not develop this approach. The MMS employs expert judgment to determine the 

infrastructure forecast ratio, and while there are some benefits for a decision maker to 

provide such input, there are also disadvantages since the inclusion of too many parameters 

can confound the analysis. 

 

The forecast [m,n]

I γ (Γi, j , t)* and [m,n]R γ (Γi, j , t)* are based on linear extrapolation of historic 

data within category Γi, j , and in the event that the category Γi, j does not have sufficient 

data on which to base a trend, a constant value is assumed based on the ratio at the current 

time τ; i.e., m

I γ (Γi, j , t)* = mI γ (Γi, j , τ) for t ≥ τ. 

 

2.4.4. Model Results and Interpretation: After the historic data on offshore structures 

has been categorized according to water depth, planning area, and configuration type, and 

after the appropriate resource data has been compiled and tabulated, it is then a matter of 

providing a forecast for 

Q(Γi, j , t)*, 

and 

[m,n]

I γ (Γi, j , t)* 

*

,

,

[ , ]

( , )

( , )

















Γ

Γ = Q tI t

i j

i j

m n

, [m,n]

R γ (Γi, j , t)* 

*

,

,

[ , ]

( , )

( , )

















Γ

Γ = Q tR t

i j

i j

m n

, 

 

to drive the model output. The form of the supply forecast is tied to the decision maker, in 

the selection of the parameter values p and θ, while the infrastructure forecast is automated 

based on linear extrapolation. The product of the functionals yields the cumulative number 

of installed and removed structures which is then processed to determine the annual 

installation and removal rates for the major and nonmajor categories. 

The forecast of the average annual number of major and nonmajor structures installed and 

removed in the CGOM and WGOM is provided in Tables B.3 and Tables B.4. The annual 

installation and removal rates over the time horizon of the forecast are reported in terms of 

an annual average rate over θ which is denoted by 

[m,n] 〈i ( Γi, j , t)* 〉

[m,n] 〈r ( Γi, j , t)* 〉 . 
37 

Average annual rates are normally non-integer, so that for instance if [m,n] 〈i ( Γi, j , t)* 〉 = 

0.5, then one nonmajor structure will be installed inΓi, j every two years. 

 

To interpret the model output, the user selects the time frame and resource recovery 

parameters that he/she believes will reflect the nature of resource recovery, supply/demand 

conditions, and technological change in the future. For instance, if 75% of the hydrocarbon 

resources within 0-200m in the CGOM are believed to be recoverable by 2020, then from 

Table B.3.2 with p = 0.75 the average annual number of major and nonmajor structure 

installations are (10.9, 56.3) with (54.5, 95.3) annual major and nonmajor structure 

removals. The decision maker may have reason to assess the 201-800m and 800+ m 

categories on a different time horizon and recovery rate; e.g., if 50% of the resources 

within the deepwater categories are believed to be recoverable through 2030, then from 

Table B.3.3 with p = 0.50 an expected (1.3, 0.2) major and nonmajor structure installations 

are expected in 201-800m and (3.1, 1.5) installations are expected within 800+ m. No 

major or nonmajor structures are expected to be removed during this time. 

The annualized forecast values in Tables B.3 and Tables B.4 can be compared against the 

average short-term historic installation and removal rates shown in Table B.5. For 

example, using Table B.3.1 and the short-term historic average values shown in Table B.5, 

the model forecast can be calibrated by comparing the 0-200m values for major and 

nonmajor installations and removals, (32, 78) and (37, 69), respectively, with the closest 

approximation in Table B.3.1. In this case the installation rate corresponds to p ≈ 0.5 while 

the removal rate corresponds to p ≈ 0.25. 

In principle, the tables can be used one of two ways: 

I. The user determines the values of p and θ per water depth and planning area 

category, Γi, j , and then employs the table to forecast expected average 

installation and removal rates, [m,n] 〈i ( Γi, j , t)〉 and [m,n] 〈r ( Γi, j , t)〉 . 

II. The user specifies the expected average installation and removal rates per 

category Γi, j , [m,n] 〈i ( Γi, j , t)* 〉 and [m,n] 〈r ( Γi, j , t)* 〉 , and then using the table, 

determines the value of p and θ that correspond to these rates. 

The selection of p and θ is considered independent across water depth categories. 

The results of the model reveal expected behavior. For instance, recovering a greater 

percentage of the available resources within a short time horizon (should) impose greater 

infrastructure requirements; i.e., if a data entry in the table is denoted by f(Γi, j , p, θ), then 

f (Γi, j , p,θ ) ≤ ( , , ) f Γ, p θ′ i j if θ' > θ, 

f (Γi, j , p,θ ) ≤ ( , , ) f Γi, j p′ θ if p' > p. 
38 

2.5. Limitations of the Analysis 

A number of issues in Chapter 2 warrant a more careful treatment. The main issues are as 

follows. 

(1) Decoupling Installation and Removal Methodologies 

The majority of structures that will be removed in the GOM over the next 25 years are 

primarily the structures that are currently active. Structures that are expected to be removed 

in the late future (25 years hence) will be primarily drawn from structures to be installed in 

the future. It is thus desirable to decouple the installation and removal models and to 

develop separate models for installation and removal. While structure installations over a 

long-term horizon are naturally developed with the framework described in this paper, a 

variety of structure removal models (life expectancy, simulation, production-based) are 

considered superior to the model described herein because they can explicitly take into 

account the operating environment, geologic conditions, and structure characteristics. 

(2) Structure Classification 

There are a number of ways in which structures can be classified and further investigation 

of categorization schemes is warranted. Classifying structures within “major” and 

“nonmajor” categories was performed to differentiate heterogeneous structures, but further 

refinement of the methodology which classifies structures according to configuration type 

(caisson, well-protector, fixed, floating) can also be pursued. 

 

(3) Supply Forecast Models 

Forecasting hydrocarbon supply profiles is considered a problem in any modeling 

environment regardless of the application since forecasting oil prices and consumption 

levels is not an endeavor that can ever be done particularly well. The supply forecast is a 

highly uncertain quantity that depends largely on the assumptions and preferences of the 

user or organization performing the forecast. It is necessary to perform a supply forecast 

across region Γi, j since the resource assessment and the infrastructure statistics are 

aggregated at this level, but if the user is not experienced or comfortable performing 

supply profiles at this level, then a re-assessment of the methodology may be required. 

Production profiles based on a 2-parameter decision model were employed to construct the 

supply forecast. The supply model implicitly incorporates technological change and other 

exogeneous variables within the user-defined parameters, but if an econometric model 

were to be developed, it would be desirable to account for these variables directly. The 

benefits of applying discovery models over low aggregation levels versus the additional 

complexity and uncertainty of such models needs to be evaluated. 
39 

(4) Infrastructure Requirement Ratio Forecast 

A variety of techniques can be employed to forecast infrastructure requirements, and it is 

difficult to gauge the accuracy of one technique over another. It is therefore essential that 

the user of a particular approach understand the limitations and implicit assumptions of the 

model and uncertainty inherent to the methodology. Scenario analysis is a useful tool to 

explore some of this uncertainty as proposed in the 2-parameter supply model, and it may 

be desirable to incorporate additional decision parameters to control the infrastructure ratio 

forecast. 

(5) Application of BOE 

A barrel of oil has about 5.6 times the heat value of one thousand cubic feet of gas, and so 

it is popular to express gas in terms of barrels of “oil equivalent.” This was necessary in 

the forecast model since both oil and gas contribute to the need for infrastructure. 

Unfortunately, since neither the price nor the in-ground value of oil and gas track each 

other, and in fact are constantly in flux depending on inventory levels, fuel prices and other 

market conditions, the application of BOE is less desirable than the use of separate oil and 

gas accounting standards in resource estimates and forecast modeling. 

(6) Oil and Gas Wells 

After a well is drilled it is either classified as an “oil” well or a “gas” well depending on 

the value of the gas-oil ratio. Since a collection of wells is typically associated with a given 

structure, it is possible to classify a structure as an “oil” or “gas” structure and then to 

perform the infrastructure forecast at this level of aggregation. One advantage of this 

approach is that it would allow separate oil and gas production profiles to drive the forecast 

which would allow the nature of oil and gas production and their infrastructure 

requirements to be better understood. Unassociated gas wells have significantly different 

productive lifetimes (usually shorter) than oil and associated gas wells, and so there is 

good reason to pursue a disaggregated forecast in this manner. 

(7) Forecast Time Horizons 

Infrastructure forecasting can be considered across three time horizons: the short-term (1-5 

years), the medium-term (6-20 years), and the long-term (21-40 years). Each time horizon 

should be considered separately and the best forecast model applied to each category. The 

short-term installation and removal forecast can be developed using historic trends. The 

medium-term removal forecast can be developed using life-expectancy and/or economic 

limit models, and for the installation forecast, discovery models. A long-term forecast can 

employ a decision and resource framework. The short-term and medium-term forecast 

should also be calibrated against the long-term forecast. 
40 

2.6. Conclusions

The task of Chapter 2 was to develop a unified approach to infrastructure forecasting in the 

Gulf of Mexico and to forecast the number of structures expected to be installed and 

removed per water depth and planning area category over a long-term horizon. The 

consistency of the methodology, the application of a discrete assumption set, and the 

inclusion of a decision-oriented framework within the model represent the central tenants 

of the procedure. The model results should be interpreted as a first-order approximation to 

a very complex reality, and as such, should serve as a guide to infrastructure forecast 

requirements. 

The need to properly select and specify aggregation categories is a critical ingredient in 

any forecast strategy, and the decomposition of the data into appropriate categories is an 

important aspect of pre-processing to ensure that the methodology is consistent and the 

procedure is sufficiently structured. No matter how fine the infrastructure data is 

decomposed and disaggregated within various categories, however, the forecasting 

procedures employed in Chapter 2 rely upon other forecast and judgmental adjustments 

which can differ enormously in scope and magnitude. The uncertainty in these adjustments 

should be understood and clearly communicated to the user group. One of the principal 

tasks of this Chapter was to identify this uncertainty and to employ decision-oriented 

parameters as a means to explore the sensitivity of the model results. 

The infrastructure forecast developed is a decision- and resource-based model similar to 

the methodology employed by the MMS but more broadly defined and executed within an 

analytic and computational framework. The output of the model is derived from the 

forecast of a supply curve and an infrastructure requirement ratio. A decision-oriented 

framework is employed to construct the supply curve which incorporates the beliefs of the 

user and variables such as technology change. The results of the forecast were presented 

under various assumptions on recovery rate and development timing. 
41 

REFERENCES 

Adelman, M.A. 1996. The Genie out of the Bottle. Cambridge, MA.: MIT Press. 

Attanasi, E. 2001. “U.S. gas production: Can we trust the projections?” Public Utilities 

Fortnightly 139(15):12-19. 

Barouch, E. and G.M. Kaufman. 1976. “Probabilistic modeling of oil and gas discovery.” 

In: Roberts, F., ed. Energy, Mathematics, and Models. Society for Industrial and 

Applied Mathematics, Philadelphia, PA. 

Baud, R.D., R.H. Peterson, C. Doyle, and E. Richardson. 2000. “Deepwater Gulf of 

Mexico: America’s emerging frontier.” U.S. Department of the Interior, Minerals 

Management Service, Gulf of Mexico Region, New Orleans, LA. OCS Report MMS 

2000-022. 

Beck, F.W.M. and K.M. Wiig. 1975. The Economics of Offshore Oil and Gas Supplies. 

Lexington, MA.: D. C. Heath and Company. 

Cleveland, C.J. and R. Kaufmann. 1991. “Forecasting ultimate oil recovery and its rate of 

production: Incorporating economic forces into the models of M. King Hubbert.” 

The Energy Journal 12 (2):17-46. 

Coffman, K.F., V. Zatarain, and S. Gambino. 2001. “The new regional economic modeling 

approach for the U.S. Minerals Management Service.” IAEE Newsletter Second 

Quarter:18-22. 

Conaway, C.F. 1999. The Petroleum Industry, A Nontechnical Guide. Tulsa, OK: 

PennWell. 

Crawford, T.G., B.J. Bascle, C.J. Kinler, M.T. Prendergast, and K.M. Ross. 2000. 

“Estimated oil and gas reserves, Gulf of Mexico outer continental shelf, December 

31, 1998.” U.S. Department of the Interior, Minerals Management Service, Gulf of 

Mexico Region, New Orleans, LA. OCS Report MMS 2000-006. 

Crovelli, R.Q. 1984. “Procedures for petroleum resource assessment used by the U.S. 

Geological Survey – Statistical and probabilistic methodology.” In: Masters, C.D., 

ed. Petroleum Resource Assessment: International Union of Geological Sciences 

Publication 17:24-38. 

Desselles, R.P. 2001a. “General methodology for determining platform installation and 

removals in the GOM OCS,” MMS-CES Meeting, April. 

Desselles, R.P. 2001b. “1997 vs. 2002 5-year programs,” U.S. Department of the Interior, 

Minerals Management Service, Gulf of Mexico Region, New Orleans, LA. 

Dodson, J., T. Dodson, and L. LeBlanc. 2001. “Will royalty relief, gas price bail out U.S. 

Gulf shelf production in rapid decline?” Offshore January:32-36. 

Eckbo, P.L., H.D. Jacoby and J. L. Smith. 1978. “Oil supply forecasting: A disaggregated 

process approach.” Bell Journal of Economics 9(1):218-235. 
42 

Hyne, N.J. 1995. Nontechnical Guide to Petroleum Geology, Exploration, Drilling, and 

Production. Tulsa, OK.: PennWell. 

Lore, G.L. and E.C. Batchelder. 1995. “Using production-based plays in the Northern Gulf 

of Mexico as a hydrocarbon exploration tool.” Gulf Coast Association of Geological 

Societies Transactions 45:371-376. 

Lore, G.L., D.A. Marin, E.C. Batchelder, W.C. Courtwright, R.P. Desselles, Jr., and R.J. 

Klazynski. 2001. “2000 Assessment of conventionally recoverable hydrocarbon 

resources of the Gulf of Mexico and Atlantic outer continental shelf as of January 1, 

1999.” U.S. Department of the Interior, Minerals Management Service, Gulf of 

Mexico OCS Region, Office of Resource Evaluation, New Orleans, LA. 

Lore, G.L. 1992. Exploration and discoveries, 1947-1989: An historic perspective. U.S. 

Department of the Interior, Minerals Management Service, Gulf of Mexico OCS 

Region, New Orleans, LA. OCS Report MMS 91-0078. 

Lynch, M. 2002. “Forecasting oil supply: Theory and practice.” The Quarterly Review of 

Economics and Finance 42:373-389. 

MacDonald, D.G., M. Power, and J.D. Fuller. 1994. “A new discovery process approach to 

forecasting hydrocarbon discoveries.” Resource and Energy Economics 16:147-166. 

Melancon, J.M., R. Bongiovanni, and R. D. Baud. 2001. “Gulf of Mexico outer continental 

shelf daily oil and gas production rate projections from 2001 through 2005.” U.S. 

Department of the Interior, Minerals Management Service, Gulf of Mexico Region, 

New Orleans, LA. OCS Report MMS 2001-044. 

Moroney, J.R. and M.D. Berg. 1999. “An integrated model of oil production.” The Energy 

Journal 20(1):105-124. 

National Academy of Sciences. 1991. Undiscovered Oil and Gas Resources: An Evaluation 

of the Department of Interior’s 1989 Assessment Procedures. Washington, DC.: 

National Academy Press. 

Olatubi, W. and D. Dismukes. 2002. “Do deepwater activities create different economic 

impacts to communities surrounding the Gulf OCS.” IAEE Newsletter Second 

Quarter:16-20. 

Pesaran, M. and H. Samiei. 1995. “Forecasting ultimate resource recovery.” International 

Journal of Forecasting 11:543-555. 

Power, M. and J.D. Fuller. 1992. “A comparison of models for forecasting the discovery of 

hydrocarbon deposits.” Journal of Forecasting 11:183-193. 

Power, M. and E. Jewkes. 1992. “Simulating natural gas discoveries.” Interfaces 22:38-51. 

Pratt, J.A., Priest, T. and C. Castaneda. 1997. Offshore pioneers. Brown and Root and the 

history of offshore oil and gas. Houston: Gulf Publishing Company. 

Pulsipher, A.G. (Ed.). 1996. Proceedings: An international workshop on offshore lease 

abandonment and platform disposal: Technology, regulations, and environmental 

effects. New Orleans, LA.: LSU Center for Energy Studies. 
43 

Pulsipher, A.G., O.O. Iledare, D.V. Mesyanzhinov, A. Dupont and Q.L. Zhu. 2001. 

“Forecasting the number of offshore platforms on the Gulf of Mexico OCS to the 

year 2023.” U.S. Department of the Interior, Minerals Management Service, Gulf of 

Mexico OCS Region, New Orleans, LA. OCS Study MMS 2001-013. 

Reggio, V.C. 1989. “Petroleum structures as artificial reefs: A compendium.” U.S. 

Department of the Interior, Minerals Management Service, Gulf of Mexico OCS 

Region, New Orleans, LA. OCS Study MMS 89-0021. 

Skolnik, J. and C.D. Holleyman. 2002. “Modeling the economic impacts of offshore 

activities in the Alaska Arctic.” IAEE Newsletter Third Quarter:16-23. 

Smith, J.L. 1980. “A probabilistic model of oil discovery.” Review of Economics and 

Statistics 62:578-594. 

Smith, J.L. and J.L. Paddock. 1984. “Regional modeling of oil discovery and production.” 

Energy Economics 6:578-594. 

Society of Petroleum Engineers and World Petroleum Congress. 1997. Petroleum Reserves 

Definitions. Dallas: Society of Petroleum Engineers. 

U.S. Department of the Interior, Minerals Management Service. 2001. Outer continental 

shelf oil and gas leasing program: 2002-2007, draft environmental impact statement, 

October 2001, Vol. I. U.S. Department of the Interior, Minerals Management 

Service, Gulf of Mexico OCS Region, New Orleans, LA. OCS EIS/EA MMS 2001-

079. 

Walls, M.A. 1994. “Using a ‘Hybrid’ approach to model oil and gas supply: A case study 

of the Gulf of Mexico outer continental shelf.” Land Economics. 70(1):1-19. 
45 

APPENDIX A 

 CHAPTER 1 TABLES
47 

Table A.1

 Western and Central GOM National Assessment Resource Estimates and 

REA/MMS Allocation Quantities

 Western Gulf of Mexico 

Category2 Notation3, 4

National Assessment1,5 MMS Allocation6

 Percent Recovery7

Oil (Bbbl) Gas (Tcf) Oil (Bbbl) Gas (Tcf) Oil (%) Gas (%) 

Qh 0.559 23.800 0.557 23.4 100 100 

R1 0.495 7.393 

R2 0.067 0.603 

R3 1.091 17.881 

K1 R1+R2 + R3 1.653 25.877 (1.37, 1.669) (24.88, 26.6) (83, 101) (96, 103) 

 R4 (12.11, 14.22) (70.19, 80.36) 

K2 R5 (6.46, 9.87) (38.49, 54.10) (1.98, 3.47) (17.7,31.6) (31, 35) (46, 58) 

 Total (K1+K2) RT (8.11, 11.52) (64.37, 79.98) (3.35, 5.14) (42.6,58.2) (41, 45) (66, 73) 

 Central Gulf of Mexico 

 National Assessment MMS Allocation Percent Recovery6

Qh 10.4 108.9 10.36 109.3 100 100 

R1 2.9 22.6 

R2 0.9 3.8 

R3 6.6 49.6 

K1 R1+ R2 + R3 10.4 76 (8.15, 8.26) (74.4, 76.0) (78, 79) (98, 100) 

R4 (18.5, 23.8) (99.4, 114.0) 

K2 R5 (9.51, 15.37) (54.73, 77.47) (3.90, 8.30) (33.90, 70.3) (41, 54) (62, 91) 

 Total (K1+K2) RT (19.91, 25.77) (130.73,153.47) (12.05, 16.56) (108.3,146.3) (61, 64) (83, 95) 

 Footnote: 

 (1) The National Assessment data is current as of January 1, 1999, and the MMS data is adjusted to 

correspond to this date; i.e., the cumulative production represented by Qh is taken through the year 1998. 

(2) Resource category K1 denotes assessed reserves and appreciation. Resource category K2 denotes assessed 

undiscovered economic resources. 

(3) The resource estimates are designated as remaining proved reserves R1 unproved reserves R2, reserves 

appreciation R3, undiscovered conventionally recoverable resources R4, and undiscovered economically 

recoverable resources R5. RT denotes the remaining conventionally recoverable resources: RT = R1+ R2+ R3+

R5. 

(4) Undiscovered economically recoverable resources R5 are evaluated at the mean of the price-supply curves 

at $18/bbl and $30/bbl oil. 

(5) The National Assessment of the resource estimates R4 are provided at the 95th and 5th percentile 

scenario, (F95, F5). 

(6) The MMS allocation levels for R1 are provided as “low” and “high” estimates, while the allocation levels 

for R5 and RT correspond to mean levels. 

(7) The percent recovery is defined as the MMS allocation divided by the National Assessment estimates: p 

= Q(Pi)/ Rj(Pi). 
48 

Table A.2 

 A Summary of Measures Implied by the REA/MMS Forecast 

Measure WGOM CGOM GOM3 

Total Resource Recovery1 

Oil (41%, 44%) (59%, 64%) (54%, 58%) 

Gas (67%, 73%) (83%, 95%) (77%, 88%) 

BOE (55%, 60%) (72%, 75%) (61%, 73%) 

Implied Recovery1 

Oil (63%, 50%) (65%, 73%) (64%, 73%) 

Gas (57%, 52%) (77%, 93%) (64%, 81%) 

BOE (59%, 51%) (72%, 85%) (64%, 73%) 

Footnote: 

 (1) The total resource recovery percentage is defined as the MMS allocation divided by the National 

Assessment estimates. The values are summarized from Table A.1. 

(2) Implied recovery is determined by plotting the cumulative production profile and then computing how 

much of the resource estimate is recovered θ years from the present time, where for convenience, θ is 

selected as 20 years. The National Assessment estimates and MMS allocation are given under a (low, high) 

case scenario and are computed with respect to this classification. Note that (x, y) does not necessarily 

require x<y since the low and high case calculations are independent estimates of resource and recovery 

rates.

(3) The Gulf of Mexico resource estimates include only the Western and Central planning areas. 
49 

APPENDIX B 

CHAPTER 2 TABLES 
51 

Table B.1 

 Offshore Statistics by Water Depth 

Water Depth (m) Active Platforms Production Wells 

Active Platform

Producing Well

0-200 3,489 3,840 1.1 

201-400 455 1,873 4.1 

401-800 49 285 5.8 

801-1000 4 50 12.5 

1000+ 22 309 14.0 

Table B.2 

 National Assessment Results for the Western and Central Planning Area in the Gulf 

of Mexico, BOE (Bbbl) 

Water Depth R1

a R2 R3 R4b R5 RT

(m) F95 F5 F95 F5 F95 F5 

Western 

0-200 1.16 0.00 2.72 4.32 5.43 3.12 3.63 7.00 7.51 

201-800 0.23 0.02 0.43 3.37 4.47 2.94 3.24 3.62 3.92 

801-1600 0.42 0.15 1.12 7.57 9.42 4.76 6.73 6.45 8.42 

1601-2400 0.00 0.00 0.00 6.54 8.38 1.99 4.66 1.99 4.66 

2400+ 0.00 0.00 0.00 1.73 2.65 0.54 1.29 0.54 1.29 

Total c 1.81 0.17 4.27 24.60 28.52 13.31 19.50 19.56 25.75 

Central 

0-200 4.30 0.11 8.51 6.89 8.21 5.13 6.00 18.05 18.92 

201-800 1.18 0.14 1.69 3.23 4.26 3.24 3.56 6.25 6.57 

801-1600 1.28 0.58 2.98 10.31 12.84 6.70 9.50 11.54 14.34 

1601-2400 0.13 0.79 2.29 11.27 15.07 3.36 7.91 6.57 11.12 

2400+ 0.00 0.00 0.00 3.00 7.02 0.89 2.34 0.89 2.34 

Total c 6.89 1.62 15.47 36.15 44.04 19.25 29.15 43.23 53.13 

Source: (Lore et al., 1999) 

Footnote: (a) The resource estimates are designated as remaining proved reserves ( R1 ), unproved reserves 

( R2 ), reserves appreciation ( R3 ), undiscovered conventionally recoverable resources ( R4 ), and 

undiscovered economically recoverable resources ( R5 ). RT denotes the remaining 

conventionally recoverable resources: RT = R1 + R2 + R3 + R4 . 

 (b) F95 represents the mean economically recoverable resources at $30/bbl ($3.52/Mcf). F5 

represents the mean economically recoverable resources at $18/bbl ($2.11/Mcf). 

 (c) Summation of individual resource values may differ from total values due to independent 

computer runs and rounding. 
52 

Table B.3.1 

 Forecast of the Annual Number of Major and Nonmajor Structures Installed and 

Removed in the CGOM Through 2010 as a Function of Water Depth and Supply 

Curve Parameter p 

Water Depth 

(m) 

p Major 

Installed 

Nonmajor 

Installed 

Major 

Removed 

Nonmajor 

Removed 

0-200 0.25 12.5 35.1 42.9 74.0 

 0.50 32.3 81.2 56.1 100.4 

 0.75 61.8 127.3 69.2 126.9 

 1.00 91.5 173.4 82.4 153.3 

201-800 0.25 1.9 0.4 0.1 - 

 0.50 3.9 0.7 0.3 - 

 0.75 5.8 1.1 0.4 - 

 1.00 7.7 1.4 0.5 - 

800+ 0.25 4.6 2.3 - - 

 0.50 9.2 4.6 - - 

 0.75 13.8 6.9 - - 

 1.00 18.3 9.2 - - 

Table B.3.2 

 Forecast of the Annual Number of Major and Nonmajor Structures Installed and 

Removed in the CGOM Through 2020 as a Function of Water Depth and Supply 

Curve Parameter p 

Water Depth 

(m) 

p Major 

Installed 

Nonmajor 

Installed 

Major 

Removed 

Nonmajor 

Removed 

0-200 0.25 4.2 11.8 37.1 62.1 

 0.50 5.7 34.1 45.8 78.7 

 0.75 10.9 56.3 54.5 95.3 

 1.00 23.6 78.6 63.3 112.0 

201-800 0.25 1.0 0.2 0.1 - 

 0.50 1.9 0.4 0.1 - 

 0.75 2.9 0.5 0.2 - 

 1.00 3.9 0.7 0.2 - 

800+ 0.25 2.3 2.3 - - 

 0.50 4.6 4.6 - - 

 0.75 6.9 6.9 - - 

 1.00 9.2 9.1 - - 
53 

Table B.3.3 

 Forecast of the Annual Number of Major and Nonmajor Structures Installed and 

Removed in the CGOM Through 2030 as a Function of Water Depth and Supply 

Curve Parameter p 

Water Depth 

(m) 

p Major 

Installed 

Nonmajor 

Installed 

Major 

Removed 

Nonmajor 

Removed 

0-200 0.25 1.1 4.0 35.2 58.1 

 0.50 1.4 18.3 42.4 71.4 

 0.75 2.5 32.7 49.6 84.8 

 1.00 6.1 47.0 56.9 98.1 

201-800 0.25 0.6 0.1 - - 

 0.50 1.3 0.2 0.1 - 

 0.75 1.9 0.4 0.1 - 

 1.00 2.6 0.5 0.2 - 

800+ 0.25 1.5 0.8 - - 

 0.50 3.1 1.5 - - 

 0.75 4.6 2.3 - - 

 1.00 6.1 3.1 - - 

Table B.3.4 

 Forecast of the Annual Number of Major and Nonmajor Structures Installed and 

Removed in the CGOM Through 2040 as a Function of Water Depth and Supply 

Curve Parameter p 

Water Depth 

(m) 

p Major 

Installed 

Nonmajor 

Installed 

Major 

Removed 

Nonmajor 

Removed 

0-200 0.25 0.2 0.2 34.2 56.1 

 0.50 0.2 10.5 40.7 67.8 

 0.75 0.6 20.8 47.2 79.5 

 1.00 1.5 31.2 53.7 91.2 

201-800 0.25 0.5 0.1 - - 

 0.50 1.0 0.2 0.1 - 

 0.75 1.4 0.3 0.1 - 

 1.00 1.9 0.4 0.1 - 

800+ 0.25 1.1 0.6 - - 

 0.50 2.3 1.1 - - 

 0.75 3.4 1.7 - - 

 1.00 4.6 2.3 - - 
54 

Table B.4.1 

 Forecast of the Annual Number of Major and Nonmajor Structures Installed and 

Removed in the WGOM Through 2010 as a Function of Water Depth and Supply 

Curve Parameter p 

Water Depth 

(m) 

p Major 

Installed 

Nonmajor 

Installed 

Major 

Removed 

Nonmajor 

Removed 

0-200 0.25 8.2 11.7 15.7 17.1 

 0.50 11.8 24.8 23.7 26.2 

 0.75 23.8 37.9 31.7 35.3 

 1.00 35.7 51.1 39.7 44.4 

201-800 0.25 3.0 0.8 0.3 0.3 

 0.50 6.1 1.7 0.6 0.6 

 0.75 9.1 2.5 0.8 0.8 

 1.00 12.2 3.3 1.1 1.1 

800+ 0.25 1.2 1.1 - - 

 0.50 2.5 2.4 - - 

 0.75 3.7 3.7 - - 

 1.00 4.9 4.9 - - 

Table B.4.2 

 Forecast of the Annual Number of Major and Nonmajor Structures Installed and 

Removed in the WGOM Through 2020 as a Function of Water Depth and Supply 

Curve Parameter p 

Water Depth 

(m) 

p Major 

Installed 

Nonmajor 

Installed 

Major 

Removed 

Nonmajor 

Removed 

0-200 0.25 5.7 4.9 12.8 13.7 

 0.50 10.2 11.2 18.2 19.7 

 0.75 17.1 17.5 23.6 25.7 

 1.00 22.2 23.8 29.0 31.8 

201-800 0.25 1.5 0.4 0.1 0.1 

 0.50 3.0 0.8 0.3 0.3 

 0.75 4.6 1.2 0.4 0.4 

 1.00 6.1 1.7 0.6 0.6 

800+ 0.25 0.6 0.6 - - 

 0.50 1.2 1.2 - - 

 0.75 1.8 1.8 - - 

 1.00 2.5 2.5 - - 
55 

Table B.4.3 

 Forecast of the Annual Number of Major and Nonmajor Structures Installed and 

Removed in the WGOM Through 2030 as a Function of Water Depth and Supply 

Curve Parameter p 

Water Depth 

(m) 

p Major 

Installed 

Nonmajor 

Installed 

Major 

Removed 

Nonmajor 

Removed 

0-200 0.25 3.2 2.7 11.8 12.5 

 0.50 8.8 6.7 16.3 17.5 

 0.75 14.7 10.7 20.9 22.5 

 1.00 17.3 14.7 25.4 27.6 

201-800 0.25 1.0 0.3 0.1 0.1 

 0.50 2.0 0.6 0.2 0.2 

 0.75 3.0 0.8 0.3 0.3 

 1.00 4.1 1.1 0.4 0.4 

800+ 0.25 0.4 0.4 - - 

 0.50 0.8 0.8 - - 

 0.75 1.2 1.2 - - 

 1.00 1.6 1.6 - - 

Table B.4.4 

 Forecast of the Annual Number of Major and Nonmajor Structures Installed and 

Removed in the WGOM Through 2040 as a Function of Water Depth and Supply 

Curve Parameter p 

Water Depth 

(m) 

p Major 

Installed 

Nonmajor 

Installed 

Major 

Removed 

Nonmajor 

Removed 

0-200 0.25 1.1 1.5 11.3 11.9 

 0.50 5.4 4.4 15.4 16.5 

 0.75 10.1 7.3 19.5 20.9 

 1.00 14.4 10.2 23.7 25.4 

201-800 0.25 0.8 0.2 0.1 0.1 

 0.50 1.5 0.4 0.1 0.1 

 0.75 2.3 0.6 0.2 0.2 

 1.00 3.0 0.8 0.3 0.3 

800+ 0.25 0.3 0.3 - - 

 0.50 0.6 0.6 - - 

 0.75 0.9 0.9 - - 

 1.00 1.2 1.2 - - 
56 

Table B.5 

The Average Annual Number of Major and Nonmajor Structures Installed and Removed 

in the Western and Central Gulf of Mexico via Water Depth Category (1996-2000) 

Water Depth Major Structures Nonmajor Structures All Structures 

(m) Installed Removed Installed Removed Installed Removed 

Western 

0-60 (5, 3.9) (9.2, 3.3) (13, 3.1) (11.4, 6.4) (18, 5.2) (20.6, 7.1) 

61-200 (2, 0.7) (1.2, 0.8) (1.6, 1.1) (0.2, 0.5) (3.6, 5.1) (1.4, 1.1) 

0-200 (7, 4.5) (10.4, 3.1) (14.6, 3.2) (11.6, 6.1) (21.6, 6.2) (22, 6.4) 

201-800 (0.6, 0.9) (0.2, 0.5) (0.6, 0.6) (0.2, 0.5) (1.2, 0.5) (0.4, 0.6) 

800+ (0, 0) (0, 0) (0.2, 0.5) (0, 0) (0.2, 0.5) (0, 0) 

Total (7.6, 4.0) (10.6, 2.9) (15.4, 3.2) (11.8, 5.4) (23.0, 5.8) (22.4, 6.2) 

Central 

0-60 (22.8, 14.7) (31.8, 12.3) (70.2, 16.8) (67.8, 22.2) (93.0, 22.4) (99.6, 31.8) 

61-200 (9, 7.5) (5.6, 2.6) (7.4, 4.2) (0.8, 0.8) (16.4, 4.6) (6.4, 3.1) 

0-200 (31.8, 20.7) (37.4, 13.9) (77.6, 17.2) (68.6, 22.1) (109.4, 23.2) (106.0, 33.9) 

201-800 (1.2, 1.6) (0, 0) (0.2, 0.5) (0, 0) (1.4, 1.5) (0, 0) 

800+ (0.8, 0.5) (0, 0) (0.4, 0.6) (0, 0) (1.2, 0.8) (0, 0) 

Total (33.8, 20.4) (37.4, 13.1) (78.2, 16.5) (68.6, 20.8) (112.0, 21.7) (106.0, 32.0) 

GOM 

0-60 (27.8, 17.3) (41.0, 14.5) (83.4, 15.8) (79.2, 24.9) (111.2, 24.0) (120.2, 35.2) 

61-200 (11, 8) (6.8, 2.3) (9, 4) (1, 1.2) (20, 6) (7.8, 3.2) 

0-200 (38.8, 24.2) (47.8, 15.9) (92.4, 14.9) (80.2, 24.6) (131.2, 25.2) (128.0, 36.7) 

201-800 (1.8, 2.4) (0.2, 0.5) (0.8, 0.8) (0.2, 0.5) (2.6, 1.9) (0.4, 0.6) 

800+ (0.8, 0.5) (0, 0) (0.6, 0.9) (0, 0) (1.4, 1.1) (0, 0) 

Total (41.4, 23.7) (48.0, 14.9) (93.8, 13.8) (80.4, 23.2) (135.2, 23.4) (128.4, 34.7) 

 Footnote: The data entries are denoted (μ, σ), where μ is the average and σ is the standard deviation of the 

category data. 
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.