_________

OCS Study 

 MMS 2004-018 

Modeling Exploration, Development and 

Production in the Gulf of Mexico 

Volume III: Technical Appendix 

 U.S. Department of the Interior 

 Minerals Management Service 

 Environmental Studies Program 
Photo credit: © Harald Sund, 2002. Brand X Pictures/PictureQuest. 
 OCS Study 

 MMS 2004-018 

Modeling Exploration, Development and 

Production in the Gulf of Mexico 

Volume III: Technical Appendix 

Authors 

Peter K. Ashton 

Robert A. Speir 

Lee O. Upton III 

Innovation & Information Consultants, Inc. 

Concord, Massachusetts 

This study was funded by the U.S. Department of the Interior, 

Minerals Management Service (MMS), Environmental Studies Program 

Contract No. 0102CT85138

Published by 

U.S. Department of the Interior 

Minerals Management Service 

Environmental Studies Program 

DISCLAIMER 

The opinions, findings, conclusions, or recommendations expressed in this report or product are those of 

the authors and do not necessarily reflect the views of the U.S. Department of the Interior, nor does 

mention of trade names or commercial products constitute endorsement or recommendation for use by 

the Federal Government.

REPORT AVAILABILITY 

Extra copies of this model and user guide may be obtained from: 

 U.S. Department of the Interior 

 Minerals Management Service 

 Environmental Studies Program 

 381 Elden Street 

 Herndon, VA 20170-4817 

CITATION 

Suggested citation: 

Ashton, P.K., R.A. Speir, and L.O.Upton III. 2004. Modeling exploration, development and production 

in the Gulf of Mexico. Volume III: Technical Appendix. U.S. Dept. of the Interior, Minerals 

Management Service,Environmental Studies Program, Herndon, VA. OCS Study MMS 2004-

018. 74 pp. 

III-i

Table of Contents 

1. Introduction ................................................................................................ III-1

Model Structure .....................................................................................................................III-1 

Overview of this Volume....................................................................................................... III-2 

2. Model Structure and Flow ........................................................................ III-3

Exploration.............................................................................................................................III-3 

Development..........................................................................................................................III-3 

In-Field Reserves Growth...................................................................................................... III-4 

Production and Infrastructure ................................................................................................III-5 

Revenue and Cost Estimation ................................................................................................ III-5 

The Forty Year Loop .............................................................................................................III-6 

3. Exploration and Discovery........................................................................ III-9

Model Equations and Logic...................................................................................................III-9 

Discovery Model................................................................................................................III-9 

Exploratory Well Equations.............................................................................................III-11 

Area Well Preference Matrix...........................................................................................III-14 

Data and Input Development ............................................................................................... III-16

Resource Field Estimation ...............................................................................................III-16 

Undiscovered (Resource) Fields......................................................................................III-17 

Exploratory Wells ............................................................................................................III-17 

Discovery Process Inputs.................................................................................................III-19

Well Generation Equation Inputs.....................................................................................III-24 

Fitting the Well Equations ...............................................................................................III-25

Rig Supply, Drilling Rates and Rig Retirement...................................................................III-30 

Parameter Estimation.......................................................................................................III-30 

4. Production and Reserve Growth ............................................................ III-35

Production and Reserve Equations ......................................................................................III-36 

Determination of Production Wells Drilled.........................................................................III-41 

Oil Yield Determination ......................................................................................................III-44 
III-ii

5. The Economic Submodel......................................................................... III-47

Overview..............................................................................................................................III-47 

Revenue Component............................................................................................................ III-48 

Cost Component...................................................................................................................III-51 

Capital Costs ....................................................................................................................III-52 

Operating Costs................................................................................................................III-62 

Depreciation.....................................................................................................................III-63 

Annual Cash Flows..............................................................................................................III-64 

Appendix A. References Cited...................................................................... III-67

Appendix B. Review of Oil and Gas Supply Models .................................. III-69

MMS – Current E&D Model ............................................................................................... III-69 

MMS – P and D Model........................................................................................................ III-70 

MMS – RSVP Model........................................................................................................... III-70 

MMS - IMODEL .................................................................................................................III-71 

DOE – EIA OSS (Offshore Supply Submodule).................................................................III-71 

DOE – TORIS Model .......................................................................................................... III-72 

AGA – TERA Model........................................................................................................... III-73 

DRI Oil and Gas Model .......................................................................................................III-73 

Walls Supply Model ............................................................................................................ III-73 
III-iii

List of Figures

Figure III.1. Field Discoveries – USGS Size 10, 0-60M, in Central Gulf..............................III-19 

Figure III.2. Field Discoveries – All USGS Sizes, 0-60M, in Central Gulf ...........................III-20 

Figure III.3. Search Coefficients – Central Gulf.....................................................................III-21 

Figure III.4. Relative Search Coefficients for the Central GoM.............................................III-23 

Figure III.5. Exploratory Wells – Central GoM, 0-60M Depth..............................................III-29 

Figure III.6. Exploratory Wells – Western GoM, 60-200M Depth ........................................III-29 

Figure III.7. Exploratory Wells – Central GoM, >800M Depth.............................................III-30 

Figure III.8. Jackup Rig Deliveries.........................................................................................III-31 

Figure III.9. Deliveries of Platform Rigs, Semi-Submersible Rigs, and Drill Ships..............III-32 

Figure III-10. Age Distribution of Rigs in Service in 2003....................................................III-33 

Figure III.11. Production Percent of Remaining Reserves Based on Year of Production...... III-38 

Figure III.12. Cumulative Production Wells by Class Size....................................................III-42 

Figure III.13. Cumulative Production Wells – Class 12.........................................................III-43 

Figure III.14. Logic of the Economic Module........................................................................III-48 

Figure III.15. Timing of EDP Process in Cost Modeling .......................................................III-53 

Figure III.16. Drilling Cost Comparison.................................................................................III-55

Figure III.17. Range of Platform Costs by Water Depth ........................................................III-58 
III-iv
III-v

List of Tables

Table III.1. Three Model Area Exploratory Well Example.....................................................III-27 

Table III.2. Rig Construction Response.................................................................................. III-32

Table III.3. Drilling Depths and Rates.................................................................................... III-34 

Table III.4. Area-Specific Production Coefficient Adjustments ............................................III-40 

Table III.5. Production Wells Active in First Production Year ..............................................III-41 

Table III.6. Production Well Trendline Coefficients..............................................................III-42 

Table III.7. Areas 1-3 (CGM 0-60, CGM 60-200, CGM 200-400)........................................III-44 

Table III.8. Areas 4-7 (CGM 400-800, CGM 800-1600, CGM 1600-2400, CGM 2400+) ...III-45 

Table III.9. Western Gulf Oil Yields Areas 8-12 

(0-60, 60-200, 200-400, 400-800, 800-1600) ..................................................................III-45 

Table III.10. Summary of API Gravity Data by WD/Location ..............................................III-50 

Table III.11. Selection of Platform Structures........................................................................III-56 

Table III.12. Number of Slots by Platform Type and Water Depth .......................................III-56 

Table III.13. Additional Non-Original Platform Installations During a Forty-Year Cycle ....III-59 

Table III.14. Platform Abandonment Percentage and Year Abandonment Begins................III-60 

Table III.15. Pipeline Mileage Matrix .................................................................................... III-62 
III-vi
III-1

1. Introduction 

 Innovation & Information Consultants, Inc. (IIC, Inc.) began work on a new Gulf of 

Mexico (GoM) oil and gas production model for the Minerals Management Service (MMS) of 

the U.S. Department of the Interior in the fall of 2002. The principal objectives of the model 

were to be: 

• Providing inputs for the environmental impact statements associated with 5-year 

MMS leasing plans, and 

• Evaluating policy studies such as those associated with changes in MMS royalty 

assessment and collection.

 MMS specifications1

 for the model suggested that an expected value simulation model is 

appropriate. Those requirements further suggested that the most efficient course of action was to 

develop and program a new model, rather than attempt to learn and modify an existing one.2

 

Throughout this volume, as well as Volume I (Summary) and Volume II (User’s Guide) we refer 

to the model we have constructed as the “IIC EDP Model.” In this volume we present the 

technical discussion of the data and methods used in developing the IIC EDP Model. We 

provide detail on the techniques applied, the interpretations of the data and analyses used, and a 

discussion of the various analyses we conducted. It is important to note and reiterate that this 

model was developed using data for the Gulf of Mexico only. Therefore it is intended to be used 

to model Gulf of Mexico exploration, development and production activity only. It should not 

be applied to other regions or planning areas. 

Model Structure 

 The IIC EDP Model is field level in its attention to detail, although we aggregate where 

that detail is not needed. The model’s flexibility is ensured by allowing the user to subdivide the 

GoM into disjoint “areas,” each described by its own discovered and undiscovered field counts, 

exploration search coefficients, exploratory well generation equation parameters, producing field 

counts by age of field, and a number of other parameters. Typically, the areas are defined 

geographically, but with the help of geologists, geologic categorizations can be used instead.3

 

 The model may be described in terms of the normal flow of the exploration and 

development process. Potential profits influence exploratory drilling, and some wells find fields. 

If the fields are themselves profitable, they go into development and subsequent production; if 

not, they wait for some future date when they do become economic to produce. Proven reserves 

for discovered fields grow over time, but production depletes those reserves such that, after a 

number of years, the field is exhausted and its assets are retired. Along the way, various stated 

 

1

 See Volume I for a list of these requirements. 

2

 A synopsis of our model review is included in Appendix III-A to this volume. 

3

 This requires careful consideration of the inputs, as many of the cost parameters derived for our test case are 

geographically based. 
III-2

variables such as production, revenue flows, wells drilled, platforms installed and removed, and 

pipeline miles installed, are tabulated and made available to the analyst. 

Overview of this Volume 

 This volume generally follows the logical description above. In Chapter 2 we provide an 

overview of the model flow.4 Chapter 3 of this volume describes in detail the exploration and 

field discovery portion of the model including logic, equations, data sources, and the analyses we 

performed. Chapter 4 does the same for development and production. Chapter 5 presents the 

logic, data, analyses and equations underlying the economic or net present value (NPV) portion 

of the model. 

 

 

4

 See Volume II, User’s Guide, for a more detailed description of the model in terms of how to create a baseline 

projection using the model, how to create an alternative projection for sensitivity or policy analysis, and how to 

update inputs. 
III-3

2. Model Structure and Flow 

 This section provides an abbreviated overview of the flow of the model. Volume II, the 

User’s Guide, provides more detail. The IIC EDP Model generates simulations following typical 

GoM exploration, development and production behavior. This chapter briefly discusses this 

behavior, which directly impacts how the model performs the simulation. 

Exploration 

 Undiscovered resources are found through a discovery process that begins with 

exploratory well drilling. The first step in the exploration process is the determination of the 

exploratory well “demand” for each user defined “area.” Areas represent the different 

geographical regions specified by the user. The IIC EDP Model defines 21 default areas, based 

on the three geographical planning areas, and seven water depth categories. The well demand, 

calculated on an area basis, is generated by the profitability of previous discoveries and the 

profitability of expected future discoveries, both of which are a function of the net present values 

for fields in each area. After the annual well demand is computed for each area, certain 

constraints must be tested to determine the actual number of exploratory wells that are allocated 

to each area. 

 Drilling constraints include both regulatory limitations and rig availability. If the user 

has imposed a lease drilling constraint for a particular area, the well “demand” is reduced by the 

fraction the user has specified. In addition, if the number of rigs is not sufficient to drill the 

“demand” wells, the model will allocate as many wells as feasible to each area given the rig 

constraints. The model employs a well allocation procedure to ensure that no area is completely 

“shut-out” of wells. The allocation preference for exploratory wells is driven by the expected 

NPV per well. For example, if “Area 1” has an expected NPV value per well of $1,000,000 and 

“Area 2” has an expected NPV value per well of $2,000,000, the model will allocate wells (in 

each allocation cycle) to “Area 2” before “Area 1.” 

 The actual number of exploratory wells allocated to each area, each model year, is the 

number “demanded” less the number of wells “lost” due to regulatory or drilling constraints. 

Once the model has allocated exploratory wells by area, the discovery process can begin. 

Exploratory wells discover fields of various USGS sizes in any area. Exploratory drilling 

success is determined by area and field size-specific search coefficients, total fields in the area of 

each size class, and past discoveries. The process is exponential so that the number of total 

fields input for an area limits the number of fields of any type that will be discovered. The result 

of the annual discovery process employed by the model is a series of field discoveries by area 

and size of field. 

Development 

 The next step is to determine the viability of continuing with the development process for 

the new field discoveries. The model computes an annual net present value of such an 
III-4

investment for each area and size of field. If the net present value (NPV) of a discovered field is 

positive (see Revenue and Cost Estimation in this section), the discovery moves through a 

development process. Development is modeled by placing the field into a queue that delays 

production for the period of time it takes to perform field delineation, development, and 

infrastructure construction. The length of time (in years) the field stays in the queue is a user 

input that can be varied by area and field size. If the net present value is negative, the new 

discovery enters a holding queue, where it remains until conditions change such that the 

expected economic benefits of developing and producing the field are positive. 

In-Field Reserves Growth 

 Exploratory well “demand” is only a portion of the well demand that occurs on an annual 

basis in the GoM. Just as important is the annual well demand generated from fields that are 

currently in development and production. To simulate the development, delineation, and 

subsequent in-field exploration process, fields’ proved reserves grow each year. This reserve 

growth will continue at an exponentially declining rate for 50 years, as defined by the MMS in 

its National Assessment reserve appreciation methodology, unless constrained by rig availability. 

Our analysis shows that there is a continual demand for exploration, delineation, development 

(“ED&D”) and production wells to support the reserve growth for existing fields. 

 The model addresses the in-field growth phenomenon by examining each “cell” to 

determine well requirements.5

 Prior to the first model year, all existing fields are rolled up into 

cells based on the particular area, size, and most importantly DPY. The model examines where 

each existing field is in terms of cumulative production (as a percent of its total grown estimate) 

and imputes what year of production this field should be in under a stylized unconstrained 

reserve growth scenario. Thus, if there are 1,200 existing fields in the underlying Field Input 

table, the model will roll these up into cells and track each cell’s properties, such as area, size, 

DPY, field count, grown reserve estimate, and current imputed reserve estimate, among others. 

It is possible to have more than one field rolled up into a particular cell. For example there 

might be three fields in Area 1, Size 11, DPY 12, and this cell would incorporate the value of 

each field contribution. The model determines the well requirements on a cell basis for the 

purpose of well allocation. Because some ED&D wells, in reality, become production wells, the 

model simply divides these into non-production and production wells. 

 Similar to exploratory wells, the demand for production and non-production wells is also 

subject to constraints. The actual production and non-production wells allocated by area follow 

a similar allocation scheme as the exploratory wells, where wells are allocated based on available 

rigs and drilling rates. However, the in-field wells take precedence over the exploratory wells, 

such that the production and non-production wells get allocated across the GoM before a single 

exploratory well is allocated. If there are not enough rigs to satisfy even the in-field 

requirements, then areas with the highest remaining reserves receive priority. Within this area, 

the younger, larger fields receive the first allocated wells, and the smaller, older fields are the last 

to receive wells. Like the exploratory wells, the production and non-production well allocation 

 

5

 “Cells” are additional categorizations of the fields in an area by their development production year (DPY). 

Therefore, the three primary properties that apply to every field are the area to which it belongs, its size and its DPY. 
III-5

occurs in ten installments, to ensure that no area is entirely eliminated from the well allocation 

process. 

 Absent zero production and non-production wells allocated, each cell will experience a 

growth in its annual reserve estimate. If a well-shortfall is experienced, the reserve growth will 

result in an estimate that is lower than the unconstrained reserve estimate, and an interpolated 

reserve estimate is based on the actual number of wells allocated versus unconstrained well 

allocation behavior. Each model year, the number of production and non-production wells 

drilled are tabulated for each cell, and stored in arrays, where they are ultimately retrieved to 

display in the output results. 

Production and Infrastructure 

 Production and infrastructure concerns are computed based on a cell’s current model year 

DPY, as well as coefficients and inputs specific to the cell’s primary characteristics (area and 

size). To enable development time, the first production year actually corresponds to a DPY of 8. 

If a cell is in DPY 7, initial infrastructure, including platforms and pipelines, is put into place. In 

addition to the initial infrastructure additions, depending on the water depth (specified by the 

area) and size of the field, additional infrastructure may be added and removed. Each model 

year, the number of platforms added and removed, as well as the pipeline miles added, are 

tabulated for each cell, and are stored in arrays, where they are ultimately retrieved to display in 

the output results. 

 Once a cell is producing, the annual production is expressed as a function of the 

remaining reserves in each year. The amount of oil and gas produced depends on the specific 

yield inputs for that particular cell. Cells that exist prior to the model run will follow the yield 

profile dictated by the oil and gas reserve estimates in the Field Input database table. Newly 

discovered fields, which are allocated to cells when the net present value of the field is positive, 

are assigned a yield based on an aggregation of historic data given the area and size of field. As 

the productive life of each field reaches a terminal point, production ceases, and remaining 

infrastructure is removed. Each model year, the amount of oil and gas production for each cell is 

tabulated and stored in arrays, where it is ultimately retrieved to display in the output results. 

Revenue and Cost Estimation 

 A central and appealing feature of the IIC EDP Model is the incorporation of an 

economic component that handles revenue and cost estimation based on user inputs. Each model 

year, a net present value (NPV) for each area and size of field is computed based on the 

particular inputs entered by the user. Such inputs include the model year price, cost escalators, 

and royalty rules or incentives. These NPVs are used as a primary component of the model’s 

simulation of a producer/explorer’s decision process. Rising (falling) NPVs stimulate (reduce) 

exploratory drilling and make smaller fields more (less) attractive to develop and produce. 

 The principal components of the NPV determination are expected revenues, exploration 

and drilling costs, development costs, operating costs, depreciation and user defined inputs such 
III-6

as the tax rate, royalty rate, possible suspension volume, and discount rate. The NPV calculation 

begins with the determination of a typical unconstrained field production profile, and then 

computes the annual costs of exploration, development, and production of the field. Revenues 

are computed based on the production profile, the current model year price, and the decisionmaker’s estimation of where the price will head in the future via a price escalator (which can be 

positive, zero, or negative). In addition, revenues are adjusted for taxes and royalties. Once the 

cost and net revenue streams are determined, the discounted cash flow over a forty-year cycle is 

summed to arrive at the NPV for a particular area and size of field. These values are stored in an 

array, where they are retrieved as needed to determine the economic viability of a field. 

The Forty Year Loop 

 Once the model has created the fundamental objects needed to carry out the complex 

algorithms involved in the modeling process, it begins a forty-year cycle of NPV determination, 

well generation, well allocation, discovery, development and production. At the beginning of 

each model year, NPVs for each possible area and size of field are determined, and the results 

are stored in the cell NPV object. The well generation object steps in and determines how many 

wells each area expects, given the past and future profitability, while the cell output object cycles 

through each active cell and determines the production well drilling requirements. Once the well 

expectations are determined, a well allocation process begins in order to allocate the available 

number of wells given different constraints. 

 The well allocation procedure (handled through the creation of an annual well allocation 

object) begins by handling existing in-field requirements, followed by exploratory well demand. 

In order to allocate wells needed for existing cells, the different areas are ranked based on which 

areas have the most remaining reserves. On a volume basis, these areas offer the highest return, 

and are ranked at the front of the priority list. Similarly, once production wells have been 

allocated, the well allocation routine focuses on satisfying the exploratory well demand. Each 

area undergoes a similar ranking process, where the areas with the highest expected NPV per 

well receive higher priority than areas with low expected NPVs per well. 

 Wells are allocated using rigs available, area-specific drilling rates and rig preferences. 

The model employs a ten cycle process for production well and exploratory well allocation that 

usually ensures that no area is completely “shut-out” from obtaining any wells. As wells are 

allocated to each area, the number of rigs declines based on the drilling rates. If there are not 

enough rigs to satisfy the well requirements, then a rig shortfall is logged, and the particular area 

does not receive all the wells that are demanded. It is important to reiterate that the model 

handles in-field requirements first, and then handles exploratory well requirements. 

 Once in-field wells are allocated, the cell output object retrieves those wells and allocates 

them according to each cell’s demand. Beginning with the larger, younger cells, the cell output 

object allocates wells, thereby stimulating reserve growth. If a well shortfall exists based on a 

rig constraint, the reserve growth of affected cells will be constrained, and the reserve estimate is 

computed based on the number of wells that were actually allocated. This will lead to a lower 

increase in the reserves added, and will result in lower production due to constrained reserve 

growth. Once the additional reserves, if any, are added, the cell production is determined as a 
III-7

function of remaining reserves (last year’s reserve estimate plus the additional reserves gained 

from reserve growth this year, less cumulative production through last year). Depending on the 

reserve estimate profile, the cell output object will impute a DPY, and calculate infrastructure 

output based on this imputed DPY. At the conclusion of the in-field well allocation, the cell 

output object has dutifully updated the reserve estimate, and logged the production and nonproduction wells drilled, production volume, platforms added, platforms removed, and pipeline 

miles added for each cell. 

 Next, the model proceeds to handle new discoveries. The model determines fields 

discovered in this particular model year based on area and size of field-specific search 

coefficients, total fields, and past discoveries (tracked in the discovery object), using exponential 

discovery equations and the number of exploratory wells allocated to each area as a result of the 

well allocation routine. Once these new fields are discovered for a particular area and size of 

field, the model performs an economic feasibility test, by retrieving the NPV for this area and 

size stored in the cell NPV object. If the NPV is positive, a new cell is created. The cell stores 

the relevant properties of the new discovery, and begins the development process in a queue 

designed to delay the onset of production by a set number of years. Alternatively, if the NPV is 

negative, the new discovery is handled by the holding object, which will store it until conditions 

change such that the NPV is positive (e.g., a future price increase). 

 At the conclusion of the discovery and economic feasibility process the model performs 

an end-of-year rig analysis. The model abandons rigs based on the user-specified rig 

abandonment rates, and checks to see if any rig shortfalls were logged in the well allocation 

process. If so, the model will stimulate additional rig construction, relying on user-specified 

inputs concerning response factors and construction times. Finally, new rigs that were 

previously under construction, but ready to come on-line, are added to the next year’s rig count. 

The model then proceeds to the beginning of the forty-year model loop, and cycles through the 

next model year. After forty years, the model will exit the loop, tabulate results, and offer the 

user the ability to view these results. 
III-8
III-9

3. Exploration and Discovery 

 This chapter describes the process by which the model generates exploratory wells and 

field discoveries. After discussing the model logic and mathematics, the remainder of the 

chapter addresses data sources and the development of model inputs. 

Model Equations and Logic 

 The model flow described in the previous chapter is somewhat stylized; nevertheless, it is 

useful for explaining various aspects of the model. The same might be said of our description of 

the exploration and discovery processes. In this simulation, exploratory wells discover fields, 

but exploratory wells are themselves products of past and prospective discoveries. Somewhat 

arbitrarily, we explain the discovery process first. After describing how the model estimates 

field discoveries, we then explain how the model uses field economics to generate subsequent 

exploratory drilling. The last section describes how to develop inputs and other model 

parameters. 

Discovery Model 

 The wells that discover fields in this model can be viewed as “wildcats” in the purest 

sense, as they discover only new fields, not new reservoirs in existing fields. In the data 

development sections of this chapter, we explain how we differentiate those wells from others, 

and why it is necessary to do so. Here at the beginning of this explanation, it is sufficient to view 

the process as one in which wells are drilled in a cell beyond existing field boundaries, and some 

fraction of them discover new fields. Paragraphs below describe how that fraction is 

determined. 

 J. J. Arps and T. G. Roberts (1958) developed a simple model to help understand the 

exploratory process in the Denver-Julesburg Basin.6

 They found that the number of field 

discoveries could be reliably expressed as an exponential function of cumulative “wildcat” wells. 

The model they employed was: 

 Fd = Ft (1- e-caW/A) (Eq. III.1) 

where: 

 Fd is the number of fields that have been discovered by W cumulative wells, 

 Ft is the number of total fields in the basin, 

 a is the plan area of a field, 

 

6

 Their objective was somewhat different from ours here. Once Arps and Roberts estimated the exponential model 

parameters by measurement, estimation and curve fitting, they could solve Equation III-1 for the total number of 

fields present. Subtracting the fields that had been discovered yielded the remaining fields yet to be found in the 

basin – their primary objective. 
III-10

 A is the search area – the basin area in this case, and 

 c is a “search” coefficient derived to make the equation fit the data. 

 Over the nearly 50 years since Arps and Roberts originally used this formulation, many 

analysts have employed variations of their approach. Some introduced complexity in the search 

coefficient, while others found new ways to measure the field and basin areas. U.S. Geological 

Survey Circular (1980) described a special procedure thought necessary to adapt Equation III.1 

to multi-layered reservoir strata of the type seen in the Permian Basin. The same geologic 

phenomenon exists in the GoM, but we found that a reduced form of Equation III.1 fit the GoM 

data in a large enough number of circumstances to justify its use. 

 We employed a geographical description of the GoM to derive search coefficients for our 

test case. First, we defined model areas as water depth ranges within the MMS western, central 

and eastern GoM subdivisions (e.g., Central GoM for water depths between 200 and 400 meters). 

Then we derived coefficients that fit exponential expressions to MMS data showing cumulative 

fields of various USGS size classes discovered versus cumulative wells drilled in that model 

area.7

 That is, for a given area, W exploratory wells drilled would discover fields according to 

Equation III-2: 

 Fdi = Ft

i

 (1- e-ci

W) (Eq. III.2) 

where the i subscript denotes a particular USGS field size. Later sections of this chapter describe 

how we developed the search coefficients. 

 It is useful at this point to derive a few frequently-used relationships that are based on 

discovery Equation III-2. For convenience, we will temporarily drop the field subscript i in the 

equations below. Finding rate (which we also call discovery rate) is simply the derivative of Eq. 

III-2 with respect to wells: 

 ∂Fd/∂W = cFt e-cW (Eq. III.3) 

 The initial finding rate, prior to the first wells having been drilled in an area, is cFt. A 

simpler, general expression for finding rate results by first rearranging Equation III.2 to obtain: 

 e-cW = (1 - Fd / Ft) 

Substituting the right side in Equation III-3 yields: 

 ∂Fd/∂W = c(Ft – Fd) (Eq. III.4) 

 Expressing the finding rate solely as a function of the current state of discovery in an area 

is useful for examining historic data, which is a discrete rather than a continuous process. 

 

7

 MMS field discoveries came from the latest National Assessment; we augmented these with more recent MMS 

discoveries. Well data were derived from the MMS borehole file. 
III-11

 Even a very good statistical fit of fields discovered to historic wells drilled usually will 

not yield the exact number of fields that have been found prior to the first year of the model run. 

This also produces a discontinuity in the finding rate, as shown by inserting the total historic 

wells in Equation III-3 and comparing the results to Equation III.4. In this case, the model could 

either under-, or over-, count fields and produce results that are not consistent with the overall 

MMS resource estimates. To ensure a smooth transition between history and model projections, 

and to stay within MMS resource estimates, we must make certain adjustments. 

 If Equation III.2 does not yield the exact number of actual fields that have been 

discovered by all the historic wells, we correct for this situation by deriving a “well dummy” that 

produces the exact number of fields that have been discovered. This adjustment, which is only 

used for internal calculations, ensures that the model picks up precisely where history left off. 

 Rearranging Equation III.2 to express exploratory wells as a function of fields 

discovered, and inserting the actual total fields discovered produces: 

 Wd = -(1/c) ln(1 – Fd/Ft) (Eq. III-5) 

The model computes a well dummy for each field size category in each model area. The well 

dummies would be zero in areas where no fields have been found. 

 An example may help to clarify the use of Equation III-5. Suppose 150 exploratory wells 

were drilled in a model area thought to contain a total of 120 fields, and 45 fields were found. 

Additionally, assume that when cumulative field discoveries over time were plotted versus 

cumulative wells, a search coefficient of 0.0025 was found to fit the actual field discovery 

historic data acceptably. However, using this search coefficient, Equation III-2 produces an 

estimate of 37 fields discovered, or 8 fields less than the actual situation. This would be, in 

effect, a discontinuity at the history-model interface because the model would “see” that 120 – 

37 = 83 fields remain to be discovered. In reality, only 75 fields remain, so 8 fields might be 

rediscovered if no adjustment is made. Employing Equation III.5, we see that the “well dummy” 

would be about 188. That is, letting W equal 188 in Equation III.2 produces about 45 fields 

discovered. An additional check shows that the finding rate is now continuous. The additional 

wells only adjust for the fact that the discovery equation, although statistically acceptable, is not 

as precise at the history-model interface as we require. Both Equations III.4 and III.5 are 

employed extensively in various parts of the model. 

Exploratory Well Equations 

 Since the mid-1970s, GoM-wide exploratory well drilling has shown a pattern of 

responsiveness to oil and gas price changes, but the relationship has not been stable. The number 

of wells drilled each year tends to increase with rising prices and decrease with falling prices 

after some delay. Standard statistical procedures, however, produce relationships that explain 

little of the year-to-year variation in drilling because the lag period varies in length. Even poorer 

results are obtained when subcategories of GoM drilling are considered (e.g., within a water 

depth range). 

 Our approach was to structure a generic sub-model that resembles a lagged control 

system. It produces exploratory wells (the output) in response to yearly field production 
III-12

economics over a multi-year historic period (the inputs).8

 This modeling takes place for each 

area in our test case. The simulation then uses the sub-model parameters to forecast exploratory 

drilling in the model years in each area. 

 Fitting the model involved estimating coefficients that multiply functions of the fieldlevel net present values (NPVs) in each model area. We used an optimization procedure that 

minimized the sums of the squares of the yearly differences between exploratory wells produced 

by the drilling equations and actual historic drilling. Principal assumptions that contribute to the 

specification of the exploratory equations are: 

• Oil and gas companies primarily react at the model area level to both the 

possibility of future profitable discoveries and to the values of past discoveries by 

increasing or decreasing exploratory drilling in an area. There is at least a oneyear delay in their reaction to an economic change, but the total delay can vary 

from area to area.9

• The drilling reaction in any given year is the result of the cumulative effects of 

fields discovered and the possibilities future discoveries in multiple past years. 

Our optimization procedure derives coefficients that apply to each preceding 

year’s production economics, so exploratory drilling in a given year is not 

dependent on only one specific previous year. 

• Historic data and anecdotal industry trade press stories suggest that industry 

awareness of the value of discoveries tended to occur a few years before MMS 

published its first estimates of field size. Therefore, in fitting drilling equations to 

the field discovery data, we simulated the information flow process by assuming 

that a field’s discovery and initial size becomes public knowledge two years 

before MMS’s reserve estimates for the field totaled at least 25 percent of the 

field’s current reserve estimate.10 We attributed the value of the discovery to this 

year and the following year. For many fields discovered in the 1980s and early 

1990s, this was several years after discovery. In the GoM model, however, we 

assume that future discoveries will be publicized in the years of their discoveries, 

as seems to be the case now.11 

• The full size of a field discovery usually is not known immediately. Consistent 

with MMS reserves growth factors, we assumed a field’s discovery contributes 

about 21 percent of its entire NPV to the value of discoveries in the first year it 

becomes public knowledge. The field adds another 4 percent of NPV in the next 

year, after which it is considered “old news,” and ceases to contribute to NPV. 

 

8

 Typically, the period 1992-2001 was used to derive the drilling coefficients. However, we also considered longer 

periods (as far back as 1975) in some cases. 

9

 For example, we might expect the response to be delayed several years in deepwater cells. 

10 If this rule resulted in a public knowledge date that preceded the field discovery year, the latter was used. 11 In the remainder of this report, for simplicity, we will refer to the year that a field’s NPV initially contributes to 

the exploratory well drilling equation as the discovery year, even though, to be precise, in the historic analysis the 

discovery year may be several years prior to the year we assumed industry first knew about the field. 
III-13

• The sum of the discovered NPV value in a model area for a given year is divided 

by the 3-year moving average of exploratory wells drilled in that area. The 

underlying assumption here is that the level of effort required to find a given 

amount of value in an area is important to those who would explore in the area.12 

It also puts the value discovered on the same per-well basis as the expected value 

for future discoveries (discussed below). 

• The value of future discoveries is modeled as the field finding rates multiplied by 

their current NPVs. This is carried out on a field size-by-field size basis and 

summed over the fields in the cell. It should be interpreted to be the expected 

return for exploratory drilling in a cell at the start of each year. 

The resulting well equation for each cell is: 

 Wi = sNNi-1-jbj + sEEi-1-jgj + k (Eq. III.6)

where: 

Ni-1-j and Ei-1-j are vectors whose components are, respectively, the NPV values of fields 

discovered and the values of expected discoveries in j successive years up through the 

year i-1. 

bj and gj are coefficient vectors [b1,b2,...bj] and [g1, g2,....gj] that multiply the annual 

values discovered and expected values. The components of these vectors are determined 

so that Equation III.6 fits the historic data for this area optimally. These are defined so 

that Σbi = Σgi = 1.0. 

 sN and sE are scalars also determined by the optimizing routine. 

k is an additive constant that, while usually zero, can be used to represent a base level of 

drilling that occurs no matter what the economic conditions. 

 Our historic data analysis showed that the relationship of exploratory drilling to 

profitability varied over time and geography. To insure that this model is applicable to a wide 

range of problems, we allow the user to control the responsiveness of exploratory drilling to 

economic signals by changing inputs. This could be done by simply inputting the coefficient 

vector components [b1,b2,...bj] and [g1, g2,....gj] directly, but it would be difficult to relate those 

inputs to real-world observations and assumptions. In the interest of making the model more 

user-friendly, we programmed the model to use a functional form for the vector components. 

We began with the Weibull density function: 

 C(t) = αβt

α-1e-βt

α 

(Eq. III.7) 

where α and β are parameters that define the shape of the distribution. We let Equation III.7 

generate values that ultimately become the b and g vector components. That is, given α and β, 

 

12 For example, if the NPV discovered in a year is $100 million, it is of less significance if it took 50 wells to 

discover it than would be the case if only 10 wells were drilled. 
III-14

the value of Equation III.7 at t = 1 produces a value that is associated with the coefficient b1; a 

value at t = 2 that is associated with b2 and so on. Different values for α and β in Equation III.7 

produce the components of the g vector. Equation III.7 can represent an exponential, log normal, 

or even a normal distribution by appropriately choosing the α and β parameters appropriately. 

 Inputting Weibull distribution parameters is still too abstract for practical use, so we 

allow the user instead to input two values, Y1 and Y2, for which the cumulative function for 

Equation III.7 takes on the values 0.25 and 0.50, respectively. The cumulative function for 

Equation III.7 is: 

 ∫C(t)dt = 1 – e-βt

α

 (Eq. III.8) 

Letting t alternatively equal Y1 and Y2 produces two non-linear equations that can be solved for 

α and β. 

 α = ln[ln(1-0.25) / ln(1-0.5)] / ln(Y1/Y2) = -0.8794 / ln(Y1/Y2) (Eq. III.9)

and, β = 0.2877 Y1

-α (Eq. III.10) 

 This procedure allows the user place about (but not exactly) 25 percent of the weight in 

the exploratory drilling equation on years up to and including Y1, and another 25 percent on the 

years Y1+1 through Y2. In theory, we could let Y1 and Y2 be very large, thereby allowing 

drilling potentially to be influenced by many years of history. Practically, we are data limited, so 

we restricted the well generation equations to consider only the previous ten years of activity. 

To accomplish this, we: 

• Calculate α and β with the equations above; 

• Calculate preliminary coefficients for 10 previous years using Equation III.7 with 

t = 1, 2, ...., 10; then 

• Normalize these values so that their sum is 1.0. 

 We included the normalization process so that the components of the bj and gj vectors 

would control only the shape of the drilling response and the sN and sE constants would control 

the magnitude of the response. 

Area Well Preference Matrix 

 At this point in our model description, it would seem that the areas in the model operate 

independently. That would be the case if there were no binding constraints on shared resources. 

We have, however, allowed the user to restrict drilling rigs and allocate them through a 

preference table.13 Three user-defined rig types are allowed in the model. Typically, they might 

be defined as shallow water rigs such as jackups and platform rigs; medium depth rigs such as 

platform rigs and semi-submersibles; and deepwater rigs such as drill ships and deepwater rated 

 

13 Description of the preference process is included in several places in this report, so here we will provide a more 

general description of how the logic works. 
III-15

semi-submersibles. Each of the three types has a unique drilling rate (wells per year) for each 

model area in the problem.14 Together, rigs and drilling rates dictate the number of wells that 

can be drilled in an area. Our allocation scheme actually allocates wells, not rigs, to areas. For 

simplicity in this explanation, we abbreviate “wells drilled by rig type X” as “well type X.” 

 In each cycle, the model computes area-by-area requirements for production wells and 

other in-field drilling (ED&D wells) as well as exploratory well demand (using the well 

equations above). Production and ED&D wells take total precedence over explorations wells; 

i.e., exploration receives what is not needed to maintain reserves and production. Model area 

priorities for production and ED&D wells are set in order of declining reserves remaining; for 

exploration wells, the value of future potential discoveries establishes the priority, again with the 

highest value area receiving the highest priority. Although this scheme tends to give the 

advantage to the least developed areas, we employ an allocation process that tends to spread the 

rigs in proportion to the drilling requirements so that most areas receive some drilling. 

 If there are not enough wells available to satisfy production and ED&D requirements, 

reserve growth, and therefore production, is restricted. An insufficient number of wells for 

exploration slows field discoveries, which results in lower production in the years that follow. 

Well shortfalls generate a requirement for more rigs that materialize in subsequent model 

periods. 

 The user inputs a preference order for each model area for each of the three rig types. A 

zero signifies that the rig type cannot be used in that area. In a deepwater area, for example, the 

preference might be 1 for rig type 3 (drill ships and semi-submersibles), 2 for rig type 2 (semisubmersibles and platform rigs), and 0 for rig type 1 (primarily jackups). The well allocation 

process begins each period by ranking each model area in order of its priority measure, as 

described above: then the allocation process starts a 10-cycle process of allocating wells to the 

areas. In each of the cycles, 10 percent of each area’s requirement is addressed. For example, if 

the highest priority model area had a requirement for 50 total production and ED&D wells this 

period, its first cycle requirement would be 5 wells. If its first choice is available, it would 

receive 5 wells of that type. If not, it would get a chance to satisfy its demand from its second 

choice well type. When the first priority area receives its requirement, or applicable wells are 

depleted, the second priority area is considered, and so on. 

 This allocation scheme tends to distribute wells in proportion to overall demand for wells, 

but with some quirks. Suppose, for example, that the highest priority area, call it “A,” has as 

first priority well type 3 and type 2 as second priority. Further, assume that the second highest 

priority area, “B,” has well type 2 as its first choice and well type 3 as its second. Under a pure 

one-step allocation process, A would take all it needed of well type 1 and, if that was not enough, 

it would use some or all of type 2. B would get some of type 2 if there were any left. In our 

process, A would initially fill 10 percent of its requirement with type 1 wells, while B fills 10 

percent of its drilling requirement with type 2 wells. As the allocations continue through the 10 

cycles, it is possible that the second priority area uses all of the type 2 wells before A exhausts 

type 1 wells. In this case, the requirements of the highest priority area might not be satisfied, 

while the second priority area might receive all or most of what it needs. 

 

14 See the data development section for a description of how we derived drilling rates and numbers of rigs available. 
III-16

 This process simulates a situation wherein several drillers plan their capital allocation for 

a given year, then simultaneously seek assets (rigs in this case) according to their preference. In 

the above example, drillers in area A try to contract for rig type 3 at the same time those in area 

B are bidding for rig type 2. By the time the last bidders in area A find they cannot obtain rig 

type 3s, drillers in area B already have under contract all the type 2 rigs. Finally, when wells 

from a given rig type are exhausted in a given year with requirements for that rig type for some 

areas still outstanding, a signal is sent to generate more rigs of that type. This is addressed in the 

next section. 

Data and Input Development 

 This section discusses the data that must be developed for the discovery and exploration 

process. In the comprehensive test case we supplied with the model, we defined areas as 

combinations of MMS planning areas and water depth ranges (three planning areas and 7 water 

depth categories for a total of 21 model areas). If that is satisfactory for a user, only a periodic 

update is needed. On the other hand, if a user wants to restructure the environment definitions 

significantly (for example, employ different water depth categories) then the historic data must 

be re-categorized. At that point, the user should consider whether or not the discovery 

coefficients for the new areas should be adjusted, and whether the well generation equations are 

still appropriate. 

 Also even though oil and gas reserves can be described in three dimensions by including 

subsurface geologic strata to the two-dimensional geographic description, MMS’s leasing 

process is fundamentally two-dimensional. Leases are defined in geographical terms, and lessees 

are free to drill to any depth within the confines of their leases. Further, MMS catalogs fields in 

geographic terms, so a field may contain reserves from several oil and gas plays in different 

geologic strata. The user is cautioned to consider this situation carefully in the problem 

formulation and data development process. 

Resource Field Estimation 

 The first step in problem formulation is to define the model areas that the model will 

consider. In our test case, we have used water depth ranges and planning areas, so we will 

present the data development from that perspective, and leave to the user’s ingenuity other 

formulations. Further, we will describe how we developed historic data and our observations 

thereon, but many users may only need to update our historic data inputs. 

 We began with the latest MMS “National Assessment” (Lore et al. 2001), recognizing 

that it will be a few years out of date even when it is published. We sorted discovered fields 

listed in this study, both proved and unproved, into the model areas where they are located. Then 

they were categorized by USGS size, recording their discovery dates in the process. Our next 

step was to update the data with fields discovered since the data cutoff date of National 
III-17

Assessment data base. We relied on MMS to provide the most recent data, recognizing that 

some of the reserves estimates for unproven fields were very preliminary.15 

Undiscovered (Resource) Fields 

 The current copy of the National Assessment also provides MMS estimates of 

undiscovered fields. Our problem, and the problem of future users of the model, is that the 

resource fields may not be categorized by the same locational scheme as the discovered fields. 

For example, the year 2000 National Assessment listed numbers of undiscovered fields by play – 

a geologic a geologic categorization – while discovered fields were located geographically. 

 We addressed this problem by allocating fields within a play in using the geographical 

proportion of the play that lies within each model area. If 20 percent of a play with 10 fields, for 

example, lies in the central GoM and 80 percent is in the western GoM, 8 of the 10 fields would 

be attributed to the western GoM. Water depth can also be mapped geographically, so an 

analogous process would be employed to further map the fields into the appropriate water depth 

categories. 

 This procedure implicitly assumes a uniform probability distribution for fields within a 

play, but that is not necessary. If geologists can make more accurate estimates of field locations, 

the GoM model can certainly employ the data, however, discovery model parameters depend on 

estimates of the number of undiscovered fields in a model area. Therefore, significantly 

changing the undiscovered resource area-by-area estimates from what we have developed might 

require the user to revisit the discovery model data development process described later in this 

section.16 

Exploratory Wells 

 Our basic source for well data was the “borehole” file that can be downloaded from 

MMS’s website. This large file contains records on more than 40,000 wells drilled in the GoM 

since the late 1940s. Although we focused many of our analyses on the last 25 years, for the 

discovery submodel, we had to go back to early days of GoM exploration. Wells on the borehole 

file can be located geographically by water depth and MMS blocks (which can be translated to 

planning area), as well as the discovered field, if any, to which they were attributed. More 

precise placement is possible with block reference measurements or the latitude and longitude 

information contained on the file for each well. Therefore, placing a given well in a 

geographically defined area is simple; placing a well in a geologic strata would require the help 

of a geologist or more detailed data than are generally publicly available from MMS. 

 

15 If the user is comfortable that industry or trade press data can be related to, and resolved with, MMS field reserve 

and resource estimates, these sources can also be used to update the National Assessment. We did not attempt this, 

however. 

16 Borrowing from an earlier example to make this clearer, suppose 45 fields have been discovered and 188 wells 

drilled in a cell that is thought to contain 75 more undiscovered fields. Assume the discovery process analysis 

resulted in a search coefficient of 0.0025. If a subsequent review changed the estimate from 75 to 100 undiscovered 

fields, Equation III.2 would produce an estimate of 54 fields discovered – about 9 fields too high. Therefore, a 

downward revision to the search coefficient would be necessary. 
III-18

 While it would seem simple to use the wells labeled on the file with an “E,” to designate 

exploratory, we did not want to include wells that are drilled to locate reservoirs within 

previously discovered fields. Further, the capability exists for a leaseholder to change the 

designation of a well’s purpose. For example, we found a significant number of “D” 

(development) wells to have been drilled before the fields to which they were attributed were 

found. For this reason, we included both E and D wells (less than one percent of the wells 

carried a classification other than E or D) in a selection process described below, but otherwise 

ignored their classification. 

 Sidetrack wells17 offer a special challenge. Originally, these were bypass wells that 

started somewhere down-hole in an original well to go around an obstruction such as a broken 

well tool that could not be extracted. By the early 1980s, slant well drilling, and later horizontal 

drilling, somewhat compromised this definition. Now, and during the last 10 to 15 years, a large 

number of the wells classified as sidetracks are actually functional wells that only use the top 

part of an earlier borehole.18 

 Counting both the original parent well and its true bypass wells amounts to doublecounting in our attempts to determine the number of pure exploratory wells. Ultimately, we 

included all original wells, but excluded from consideration all sidetrack wells that did not 

deviate from their parent wells by more than 300 feet laterally. We used longitude/latitude data 

on the wells from the borehole file for this task. 

 To begin the task of extracting exploratory wells from the borehole file, we established a 

cutoff date based on the discovered field data. While the borehole files are very current, field 

discovery data may lag by several years, depending on what information has been provided by 

lease holders. For this reason, we established a data cut-off date of end-2001, or almost two 

years before the time we performed the exploratory well and discovery analyses. Using current 

well data with out-of-date field discovery data makes it seem as if the last two years’ exploratory 

drilling found few, if any fields. Even with a two-year-old cutoff, we found areas in deepwater 

where substantial drilling was taking place but nothing seemed to be discovered. 

 We then created an Access database from data on the borehole file relevant to our task. 

These data included well number, water depth, bottom-hole field, surface block, well spud and 

total depth dates, and bottom-hole geographical coordinates. To this, we added additional field 

tables with discovery dates and planning areas. Then we selected from the borehole file all wells 

attributed to specific fields but spudded before the fields’ discovery dates. The MMS convention 

is to label a well “WILD” in the bottom field category if the well was never associated with a 

field. Since these wells are largely unsuccessful exploratory wells, we added these to the 

candidate exploratory well dataset. 

 Subtracting bypass sidetracks can be accomplished several ways, but we chose to match 

sidetrack wells to their parents by considering the first ten digits of their well identification 

 

17 Sidetrack wells can be identified by the last two digits of the 12-digit well number. The original, or parent, well 

has “00” as the last two digits, while its side tracks have the same first 10 digits, then are successively numbered in 

the last two digits (i.e., “01,” “02,” etc.). 

18 In the last few years, MMS has implemented a way of segregating true by-pass wells from other wells that 

actually have an exploration or development purpose, but that is not helpful if very old data must be examined. 
III-19

number. Then we used the parent’s and its sidetracks’ latitude and longitude coordinates to 

compute their bottom-hole distance. If that was less than 300 feet, we removed the sidetrack 

well. The end result of this process was a file of about 5200 wells drilled through 2001 that we 

considered to be exploratory for field discovery purposes. By comparison, there are about 

15,000 wells on the borehole file labeled “E” wells. Except for a small fraction, the remaining 

25,000-27,000 are labeled “D” wells.19 

Discovery Process Inputs 

 We began with the objective of fitting a relationship of the form of Equation III.2 to each 

field size in each water depth range and MMS planning area – each of the 21 model areas in our 

test case. This was overly optimistic. In areas that have been thoroughly explored, the equation 

fit the data very well, demonstrating results similar to other onshore reserve studies. Figure III.1, 

for example, shows cumulative discoveries of USGS size 10 fields versus cumulative 

exploratory wells drilled in the central GoM planning area in the 0-60 meter water depth range. 

During the 50 years this area has been under development, about 57 percent of the size 10 fields 

in MMS’s latest National Assessment have been discovered (as of end 2001). A discovery 

equation with a search coefficient of 0.00033 fits the data quite acceptably. 

Figure III.1. Field Discoveries – USGS Size 10, 0-60M, in Central Gulf 

0

10

20

30

40

50

60

70

80

90

0 500 1000 1500 2000 2500 3000

Exploratory Wells

Discovered Fields

Actual Fields Discovered Modeled Discoveries

139 Total Fields

 The data in Figure III.2 for all fields in this area taken together exhibit a different trend 

than that for USGS size 10. The discovery rate falls, then begins to rise around the 1000th well, 

 

19 One interesting observation is that on a cell-by-cell basis, field discovery rates tended to lie in the four or five 

exploratory wells per field found using our counting methodology. This is generally consistent with assumptions we 

have seen in other models and less detailed financial analyses. Had we simply used “E” wells, the field discovery 

rates would have been too low. 
III-20

or around 1974-75. Drew, Schuenemeyer, and Baiwiec (1982, 9) first recognized this 

phenomena and attributed it to recognition of smaller fields that, at the time they were originally 

discovered, were not economic to develop due to low natural gas prices. In the GoM, this means 

that the lease holder may have found oil or gas in small quantities, but would not have filed a 

development plan, thereby initiating the MMS review process and the official recognition of a 

field discovery. Subsequently, rising commodity prices made these fields attractive and they 

were “rediscovered.” 

 We model field economics specifically in the overall GoM model, so fields that are not 

profitable will be listed as discovered, but will not be developed until they have positive NPVs. 

This leads us to fit the discovery curve to the data as shown in Figure III.2. A search coefficient 

of 0.00046 produced the fit in the figure. 

Figure III.2. Field Discoveries – All USGS Sizes, 0-60M, in Central Gulf 

0

100

200

300

400

500

600

0 500 1000 1500 2000 2500 3000

Exploratory Wells

Discovered Fields

Actual Fields Discovered Modeled Discoveries

797 Total Fields

 There were originally 797 undiscovered fields in this model, of which 533 had been 

discovered by 2001. Therefore, the finding rate was initially about one field discovered every 2 

to 3 wells. By 2001, the rate had dropped to slightly less than one field for every 9 wells. By 

comparison, the discovery rate for USGS size 10 fields was about 22 wells per size 10 field 

initially, and fell to 55 wells per field by 2001. 

 Finding rates equate to the probability of the next well discovering a field, or of finding a 

field of a given size. Using probabilities derived from the finding rates above, we see that the 

probability of a field discovery being a size 10 field was originally about 0.12. After the larger 

fields in the area have been discovered, by 2001, the absolute probability of discovering a field 

had fallen substantially, but the likelihood that a discovered field is of size class 10 had risen to 

0.17. Simple computations such as this are useful for understanding results of the overall model. 
III-21

 Fitting discovery curves to model areas with sparse exploratory activity is problematic. 

Even if we designed a discovery equation that precisely fit the limited number of data points in 

an area with little history, we would have no guarantee that, ten years later, the entire shape of 

the discovery curve would not change. If one looks carefully at the early stages of field 

discovery in Figure III.1, for example, the curve does not seem to fit the points well. Yet, 

viewed from the perspective of 50 years of history, the curve looks quite a bit better. To mitigate 

this problem, we designed a method that employs the most data available for areas, and then 

adapts it to produce the field-specific search coefficients we needed for the model. 

Figure III.3. Search Coefficients – Central Gulf 

0

0.002

0.004

0.006

0.008

0.01

0.012

5 6 7 8 9 10 11 12 13 14 15

USGS Field Size

Coefficient

CGM 0-60M CGM 2-800M

 Figure III.3 displays search coefficients by field size for fields found in two water depths 

in the central GoM. The 0-60 meter series, which we discussed above, is well-ordered, but it 

shows an interesting phenomena that is more pronounced for the other water depth. From about 

USGS size 10 up, fields tend to have progressively higher search coefficients. This is expected 

– big fields are easier to find than small fields. Below size 10 or so, however, search coefficients 

also increase, seeming to contradict the notion that small fields are harder to find. There is an 

entirely different reason for this. Geologists usually assume fields in an area are distributed by 

size class according to a log normal distribution. Using this methodology, MMS resource 

estimates produce a smaller number of small fields than intermediate size fields (e.g., size 10). 

This approach implicitly includes an assumption that small fields are of questionable economic 

viability. That is, there may be a large number of small fields in an area, but the smaller fields 

are less likely to be producible economically. Drew, Schuenemeyer, and Baiwiec (1982, 17) 

recognized this situation. Since staying within a reserve estimate was not an issue, the authors 

estimated that the number of fields in categories smaller than size 9 was simply 1.65 times the 

number in the next higher category. 

 Inasmuch as oil and gas prices are volatile, and there is a considerable amount of 

variation in individual companies’ situations, a significant number of small fields are discovered 
III-22

and put into production. When the discovery success is then compared to the relatively low 

MMS estimates for the number small fields present, it appears that companies have been able to 

find them relatively easily. This, in turn, leads to a search coefficient that increases with 

declining field size. 

 Other than the odd relationship described above, we were not able to discern a clear 

pattern for search coefficients in various water depths or planning areas. Some have conjectured 

that exploratory success rates have improved dramatically with the advent of 3-D and 4-D 

seismic techniques. This may be true for field delineation and in-field exploration, but it is not 

evident from the field discovery data. Figure III.3 does show a higher set of search coefficients 

for the more recently explored 200-800 meter water depths than for the older 0-60 meter area. 

However, success rates were lower in water depths greater than 800 meters for both central and 

western GoM. Exploration in these areas is even more recent than in the 200-800 meter depths. 

 We elected to express the search coefficient as a polynomial function of field size, 

thereby reflecting the only trend of Figure III.3.20 The process included the following steps: 

• In each model area, we attempted to fit discovery curves to each field size class, 

grouping field classes where necessary to obtain enough data. Then we put each 

size class coefficient in relative terms by dividing it by the coefficient for field 

size 10, picked because it tended to have the lowest value. 

• We then fit two polynomials – one for central and one for western GoM – to all 

the relative coefficients in the two areas. The polynomials yield relative 

coefficients as a function of field size. Figure III.4 shows the result for the central 

GoM. The western GoM discovery history seemed different enough to justify its 

own equation. This may be due to the fact that it is much more gas prone than the 

central planning area.21 

 

20 We cannot lower the search coefficients and raise the number of small fields as did the literature reference, 

because that would run the risk of the model finding more reserves than is in the MMS National Assessment. 21 There is no exploratory history in the eastern Gulf, so we would be inclined to use the same equations as for the 

central area, unless specific information became available to discriminate between the two. 
III-23

Figure III.4. Relative Search Coefficients for the Central GoM 

-

1

2

3

4

5

6

7

8

9

10

6 8 10 12 14 16

USGS Size

Search Coefficient 

Relative Size

• Using these polynomials and the actual search coefficients for field size 10 in 

each area, we make a first estimate of the absolute values for coefficients for each 

field size in each area. The functional relationship derived above allows us to 

estimate a unique search coefficient for each field, rather than rely on groupings 

of field classes as would be the case if we simply used the original discovery data. 

• One more scaling procedure is required to insure that the coefficients produce 

results that agree with the overall discovery results in each area. Making the 

reasonable assumption that the sum of the initial finding rates for all field sizes in 

an area must agree with the overall initial finding rate for the area, we can derive 

a scaling constant that assures the integrity of the coefficients. The first step in 

this process is to fit the discovery equation to all the field discoveries in the area 

taken altogether. Multiplying by the total number of fields in the area produces 

the initial total finding rate. Next we computed the initial finding rates for each 

field size by multiplying the results of the calculation described above by the 

number of fields of each type in the area. Then we divided the all-fields finding 

rate by the sum of the individual field size finding rates. This scalar constant 

should be applied to the results of the polynomial-derived first estimate of search 

coefficients. 

 The following example should clarify this process. A model area has 75 fields of size 1 

and 50 of size 2. We have fit the discovery equation to discoveries for field type 2 and 

determined that a preliminary search coefficient of 0.005 fits the data well. Then we used a 

polynomial equation along with the coefficient derived for field size 2 to make a first estimate 

that the search coefficient for field of type 1 is 0.003. This gives initial finding rates for types 1 

and 2 such that their sum is 0.475 fields found per well drilled in the area. However, when we 

consider a fit of the discovery equation to the overall data (discoveries of field types 1 and 2 
III-24

taken together), we get a coefficient of 0.0045, yielding an initial total finding rate of 0.5625. 

This leads to an upward adjustment of 0.5625/0.475 = 1.1842. Our final estimate becomes 

0.0050 x 1.1842 = 0.0059 for the size class 1 search coefficient. For size class 2 it is 0.0036. 

Multiplying the search coefficients times the initial field numbers and adding produces a 

combined finding rate of 0.5625, as required. This procedure is meant to compensate for the fact 

that individual estimates of the components of a process do not necessarily sum the overall 

estimates for the process taken in the aggregate due to estimating error in the component 

analysis. 

Well Generation Equation Inputs 

 The model produces exploratory wells by applying Equation III.6 to the historic and 

model-generated NPV discovered and expected value of future discoveries. The user must input 

Y1, Y2, sN, sE, and k values for each model area so that the model-generated coefficient vectors bj

and gj, together with the scalars and constant, best represent exploratory drilling in each area of 

the problem. 

 For maximum model flexibility, any of these parameters can take on any positive value 

or zero, with the caveat that Y2 must be greater than Y1 + 0.5. This is to insure that Equations 

III.9 and III.10 produce meaningful results. Users should consider the following points when 

developing the well generation parameters: 

• The bj and gj vectors dictate the timing of the drilling response to previous years’ 

production economics. Therefore, the Y1 and Y2 inputs can be viewed as response 

shaping parameters. Letting Y1 equal 0.5 and Y2 equal 1.5 will allow the previous 

year’s profitability to determine most of the drilling response. Changing Y2 to 3, 

for example, will bring more past years into play in determining exploratory 

drilling. If the user believes that there would be no drilling response for one or 

more years, then that can be simulated by raising the value for Y1. 

• The parameters sN and sE are magnitude inputs that permit the user to adjust the 

drilling response to drilling economics, which are, in turn, measured in financial 

terms. Since the well equation produces wells, the products of the components of 

the bj and gj vectors and sN and sE are expressed in units of wells per dollar 

(actually wells per million dollars). Using two parameters allows a differential 

response to past discoveries and future expectations. As a word of caution, an 

area that has no field discoveries will see not drilling unless sE > 0. 

• k is a constant that can reflect a minimum level of drilling that has occurred in the 

historic data, no matter what the economic conditions. We found it useful, for 

example, to allow k to be greater than zero in modeling the activity in the central 

GoM in water depths less than 60 meters. In practice, it would be inappropriate 

to allow this constant level of drilling to exist for the entire modeled period 

(which might be 40 or 50 years). Therefore, we treat it as a beginning level that 

declines in proportion to the overall yearly field finding rate in a cell. For 

example, if k was input as 30 wells per year and the initial model year finding rate 

was 5 wells per field found, at some future time when the finding rate had fallen 
III-25

to 10 wells per field discovered, the operative constant would have fallen to 15 

wells per year. 

 The overall GoM simulation begins the first model year by applying the well equations to 

the most recent 10 years of historic data (which is input). Each model year, one history year is 

dropped and the most recent model year is added. The “bootstrap” process moves forward so 

that, by model year 11, model outputs alone are determining the drilling process. Therefore, 

depending on the values input for Y1 and Y2, historic events may influence drilling directly for 

as much as 10 years into the model run. A case in point is the dramatic price increases 

experienced in years 2000-2003, which potentially could have a substantial effect on exploration 

through the model year representing 2013. 

 Fitting the Well Equations 

 We calculated the exploratory well equation parameters for the GoM model with a standalone optimizing spreadsheet model.22 We did not include it in the GoM model for two reasons: 

first, that would limit user flexibility, and second, the process requires a certain amount of 

intervention by the analyst.23 We describe the methodology, but the user can use any technique 

consistent with the form described in Equations III.6 through III.10. 

 The well equations employ NPVs in the preceding 10 years to generate wells for the 

current period. However, we employed an extended period of historic data to derive the 

exploratory drilling equation parameters. In most model areas, we began the analysis with 1980, 

but allowed the fitting model to derive separate parameters for the periods 1980-1991 and 1992-

2001 if that resulted in a better optimal fit to the entire period. 

 We derived model parameters that minimize a squared error term, where the error each 

historic year is the difference between the actual exploratory drilling that year and the well 

equation prediction for that year (based on previous years’ production economics). The principal 

economic components of the equation are: 

• Value of previous discoveries. In the model equations section, we described our 

assumption that industry awareness of a discovered field or its potential value 

may have been delayed in the 1980s and early 1990s. Once our algorithm 

recognized a previously discovered field, its NPV, discounted to the recognition 

year, was summed with other fields contributing to that year in the particular 

model area under consideration. The sum is the “discovered” value for that year. 

Dividing by the three-year moving average of exploratory wells drilled in the area 

puts this value on a basis compatible with the value of future discoveries. 

 

22 This spreadsheet has been provided along with the GoM model as a tool to use exogenously to prepare inputs for 

the overall GoM simulation. 

23 Users who accept the model cells as we define them in the test case – by water depth and MMS planning area –

probably will not find it necessary to modify the well equation parameters substantially from those provided with the 

model. On the other hand, redefinition of cells by some other convention should require at least a review of the 

historic data to determine if the equations’ parameters offer a good fit. 
III-26

• Value of future discoveries. At the start of a period, finding rates by field size in 

the area represent the probabilities of the “next” well discovering a field of a 

particular type (see Equation III.4). Multiplying by that field’s NPV and 

summing gives the expected potential for that area. Just as with previous 

discoveries, we assumed that potential future discoveries stimulate a drilling 

response that may be delayed and spread over several years. 

 Separate Y1, Y2, sN, sE and k parameters are developed for these two value components. 

Once these calculations are made for the history years, they are entered into the spreadsheet 

model along with the actual exploratory drilling history over the history period (e.g., 1980-2001). 

The spreadsheet model minimizes the squared-error expression by changing the Y1, Y2, sN, sE

and k parameters for both the value of previous discoveries term and the value of future 

discoveries term subject to the following constraints: 

• all parameters are non-negative; 

• Y2 ≥ Y1 + 0.5; and 

• the sums of the bj and gj vector components each equal 1.0. 

 We used Excel’s “Solver” optimization scheme to produce the optimal solution to the 

problem described above. This is, of course, a very non-linear problem due to the way the bj and 

gj vector components are calculated in Equations III.6 through III.10. We considered forgoing 

the Weibull coefficient representation in favor of a linear model that could derive the vector 

components automatically. Such an internal optimization program could be included in the GoM 

model itself. We rejected that approach due to the possibility of a quirk in the historic data that 

would generate coefficients that produced negative drilling. Nevertheless, it is a concept that 

bears consideration in any follow-on work, as it might ease model use, while maintaining some 

degree of user flexibility. 

 Finally, we note a few characteristics of the optimal parameter solution. First, it is often 

fairly insensitive to changes in certain parameters. Second, often the optimal solution will 

include zero values for either the sN or the sE multipliers. This is, in effect, a conclusion that 

either the value of previous discoveries term or the value of future discoveries term is irrelevant 

to the optimum solution. This warrants revisiting an earlier caution about model behavior. If the 

equations developed for an active area are applied to an unexplored area, no drilling will take 

place if sE is zero. Therefore, in developing equations for unexplored model areas, it may be best 

to further constrain the optimization to force use of the sE term consistent with the furtherconstrained optimal solution being within a small percentage difference from the less constrained 

solution. Fortunately, the insensitivity of the optimal solution often allows the user to perform 

this operation and have the two candidate solutions be different by less than 5 percent or so.24

 Table III.1 gives an example of the type of inputs that must be compiled to fit the 

exploratory well equations. In the example, three water depths for western and central GoM 

 

24 Generally speaking, this technique yields a very good fit to historic data. However, external factors, such as the 

industry’s early 1990s shift from exploration to acquiring reserves through corporate takeovers, are outside the 

scope of the model. 
III-27

define three areas, all with historic drilling. A brief examination of these data shows what type 

of results to anticipate even before the well equations are fit. 

Table III.1. Three Model Area Exploratory Well Example 

Area Central Western Central 

Water 

Depth 0-60M 60-200M >800M 

NPVs 

& Wells Past* Future* 

Historic 

Wells Past* Future* 

Historic 

Wells Past* Future* 

Historic 

Wells 

1980 1.55 2.28 52 2.53 2.37 6 - - - 

1981 5.44 4.22 50 4.15 4.20 6 - - - 

1982 4.53 4.80 72 1.77 5.28 5 - - - 

1983 3.02 4.54 72 6.42 5.43 4 - 257.2 1 

1984 1.57 4.29 87 7.87 5.51 17 - 254.8 2 

1985 1.54 3.67 65 0.45 4.93 15 - 229.1 2 

1986 0.78 1.92 30 0.01 2.76 8 - 105.1 4 

1987 1.14 1.71 46 1.40 2.11 10 - 123.8 5 

1988 0.65 1.53 70 0.05 1.93 17 10.33 102.6 12 

1989 0.49 1.69 44 0.10 1.88 10 0.45 122.9 7 

1990 0.67 1.99 68 0.00 2.10 17 0.02 161.6 8 

1991 1.23 1.61 49 0.25 1.64 9 6.22 128.9 7 

1992 0.24 1.48 25 2.16 1.76 5 1.56 132.0 1 

1993 0.24 1.69 50 1.50 2.38 5 2.35 118.2 1 

1994 0.36 1.29 50 0.95 1.66 13 193.57 94.9 8 

1995 0.07 0.94 43 0.01 0.80 4 17.61 94.9 6 

1996 0.40 1.93 55 0.52 2.55 4 1.32 160.8 7 

1997 0.59 1.97 54 0.38 2.73 6 5.53 149.2 20 

1998 0.09 1.03 48 0.00 1.29 10 23.29 59.7 25 

1999 0.16 1.54 43 0.11 2.01 8 42.60 73.8 30 

2000 2.72 4.09 53 0.02 6.46 5 67.46 125.4 38 

2001 0.25 3.95 49 0.01 7.24 10 4.15 108.8 55 

2002 71 7 56 

2003 105 18 63 

 

*The values of past and future potential discoveries are in millions of dollars per well drilled. 

 The Central GoM area in 0-60 meters has seen a substantial amount of exploration during 

the last 50 years, so the value of reserves discovered and the future potential are both low in 

recent years. Nevertheless, drilling is high compared to the other two areas, at least until the 

upswing in deepwater drilling in the last few years. The Central GoM is an area that lends itself 

to the use of the k constant in well fitting (see Equation III.6 and explanation), representing a 

level of drilling that exists even in poor economic conditions. 

 The expected value of future discoveries in the Western GoM area is higher than in the 

shallow water central area, even though there are 3 to 4 times the undiscovered fields, reserves, 

and reserves value left in the Central GoM area. The reason is that the field discovery rates are 
III-28

much higher in the Western GoM 60-200 meter model area. This may be related to the fact that 

the central GoM area is so accessible that it receives a good bit of exploration that is not well 

researched, or that is really aimed more at investment programs than finding oil and gas. 

 Unlike the two shallow water areas, the deepwater area in the table is in the early stages 

of exploration, so the values for both NPV measures are large by comparison. The value 

discovered per well in the deepwater area over the period shown in the table is about 20 times as 

high as the more mature model area. Because many large fields remain to be found, the potential 

value of future discoveries is much higher also. On the other hand, exploratory wells per year 

number about the same as the 0-60 meter Central GoM area. This can be seen as an indirect and 

implicit result of the capital intensive nature of exploration and development in the deepwater 

area. It follows that exploratory well equations fit to the deepwater history will have lower sN

and sE constants than for the shallow water areas. That is because the actual data show that the 

relatively large reserves value for the deepwater area produce about the same number of wells as 

the low-valued shallow water areas. 

 Figures III.5 through III.7 show how the procedure fits the historic wells drilled between 

1980 and 2001. We added two more years of actual drilling data to show how “history” 

developed after 2001. We found that, typically, the equation parameters for the 1980-1991 

period were different from 1992-2001, thereby suggesting that drilling response has changed. 

For example, Figure III.5 shows that the best fit for the last 10 years was obtained by ignoring 

past NPV discovered and relying on future expectations, but adding in a constant of 37 

exploratory wells drilled per year (i.e., the k factor in Equation III.6). This is consistent with an 

area that has a “level-of-effort” drilling plus a component that tends to be related closely to 

changes in oil and gas prices. The gj vector components are such that the response to the 

increase in potential discovery value shown in Table III.1 for 2000 and 2001 will begin to appear 

and influence drilling in 2003, thereby following what we have seen to happen since the last 

history year considered in the model fits. 

 For the Western GoM area, the fitting procedure produced equations that rely both on 

past discoveries and future expectations, but use no “k” constant. This suggests that the memory 

of recent discoveries acts as a stimulus to drilling, whereas, it apparently did not in the central 

GoM, 0-60M area. Virtually all the weight in the bj, and gj vectors is in the first two 

components, meaning that, again, the up-turn in projected drilling will track the doubling of 

drilling rates seen in Figure III.6 for 2003. 

 Figure III.7 shows the equation fit for the deep water area. The equation-fitting 

procedure placed almost all the weight on past discoveries occurring 5 to 9 years in the past. 

That implies that a best fit to the historic data is obtained with an equation that places the credit 

for drilling in 2001, for example, on discoveries that occurred in 1996 and earlier. Fields 

discovered since 1996 have yet to contribute to the drilling equation, although some would by 

2003-2004. Discoveries since 1996 have been large fields, but drilling in the area has risen 

sharply. Since we estimate the impact in terms of discovered value per well drilled, the 

discovered value contribution is not as large as in the early 1990s when a few large fields were 

discovered and drilling was low. 
III-29

Figure III.5. Exploratory Wells – Central GoM, 0-60M Depth 

0

20

40

60

80

100

120

1980 1985 1990 1995 2000

Wells/Year

Historic Wells Model Fit

cc

Figure III.6. Exploratory Wells – Western GoM, 60-200M Depth 

-

2

4

6

8

10

12

14

16

18

20

1980 1985 1990 1995 2000

Wells/Year

Historic Wells Model Fit
III-30

Figure III.7. Exploratory Wells – Central GoM, >800M Depth 

0

10

20

30

40

50

60

70

1980 1985 1990 1995 2000

Wells/Year

Historic Wells Model Fit

Rig Supply, Drilling Rates and Rig Retirement 

 To determine the supply of rigs, drilling rates and rig retirement, several key parameters 

must be estimated including: the rigs initially available; the response delay between the time that 

a rig shortfall occurs and new construction produces additional rigs; and the number of wells that 

rigs can drill wells per year in various water depths. The most current and readily available 

source of rig counts by type of rig is Rigzone, an online data, information and analysis company. 

Detailed current snapshot data are available at no cost for the Gulf of Mexico and other areas. 

Historic data are available through a subscription. Although we relied on the free information, a 

more in-depth analysis of rig historic availability could be conducted with the subscription data. 

To back-cast current 2003 Rigzone data, we used historic drilling information from the Baker 

Hughes website. We also employed data from the MMS borehole file. 

Parameter Estimation 

 To estimate current rig availability with Rigzone data, we considered rigs in the GoM to 

be eligible to address well demand if they were not “cold stacked” – a category that applies 

predominantly to aging equipment – or doing workovers. The following procedure produced 

estimates for rig availability at the end of 2001: 

• We used Rigzone data for rigs in the GoM with a status classification of 

“Drilling” to represent the actively employed rigs at the end of 2003. 

• Then, we estimated the rigs being utilized at the end of 2001 by taking the ratio of 

Baker Hughes “rigs working” at the end of 2001 to that at the end 2003 (13% 

more rigs were working at end-2001). 
III-31

 For Rigzone’s GoM rigs, we considered the remaining rigs that were not cold-stacked or 

doing workovers at the end of 2003 to be available; then we subtracted those that entered service 

after end-2001. For the same classifications in the Rigzone Latin America area, we considered 

half the drill ships and semi-submersible rigs to be available to the GoM if needed. 

 Figure III.8 shows drilling on the GoM shelf, which began to fall in 1984, bottomed out 

in 1986, then climbed until 1990, a period of four years. In 1990, four years after the initial rise 

in drilling, jackup production increased for four years, then fell to zero by 1996. Meanwhile, 

drilling fell again to new lows by 1992, and then doubled over the 1992-1997 period. This time, 

rig manufacturing did not pick up until 1998, six years later. Similarly, the drilling increase in 

the year 2000 can be associated with the rig building increase in 2002. 

Figure III.8. Jackup Rig Deliveries 

0

1

2

3

4

5

6

7

1985 1990 1995 2000

Rigs Delivered

0

200

400

600

800

1000

1200

Wells per Year

Jackup Deliveries Wells <200M

 It is likely that a large overhang in relatively new but idle rigs delayed the need to build 

more in the early and mid 1990s because it does not take 4 to 6 years to build a jackup drilling 

rig. By the end of the decade, however, the aging rig fleet seems to have reached a state that 

required more responsive rig construction. Taken together, all these observations suggest that a 

reasonable assumption is that jackup rig construction will respond two years after a shortfall is 

evidenced. 

 Figure III.9 displays a similar history for drilling in depths greater than 200M and 

delivery dates for deeper water drilling systems. After hitting a low point in 1992, drilling began 

a sustained climb that is still being maintained. Apparently, platform rig construction responded 

with little delay, while construction of new drill ships and semi-submersible rigs – much more 

complicated systems – increased only four or five years later. Again, it not possible to trace the 

increase in construction of these two types of drilling systems to their precise cause, but the chart 

suggests that a shortfall to delivery response delay in the range of three to five years is 

appropriate. Accordingly, we have chosen to be conservative and use three years as a delay for 

both drill ships and semi-submersibles, while using a delay of only one year for platform rigs. 
III-32

Figure III.9. Deliveries of Platform Rigs, Semi-Submersible Rigs, and Drill Ships 

0

2

4

6

8

10

12

14

1985 1990 1995 2000

Rigs Delivered

0

50

100

150

200

250

300

350

Wells per Year

Semi-subs Drill ships

Platforms Wells >200M

 The multi-year increases and decreases in rig deliveries suggest that drilling companies 

respond to increases in rig demand with some reservation. Clearly, this is well-deserved due to 

the volatility of drilling demand. To model that process of gradualism, we assume here that in 

any one year, the industry begins construction on only half the rigs needed to satisfy that year’s 

shortfall. The model user should be aware that this will perpetuate a rig shortfall if drilling 

demand continues to rise, but ultimately will create more rigs than needed. If drilling demand 

falls, then the overage in rig inventories will be mitigated. Table III.2 summarizes these inputs. 

Table III.2. Rig Construction Response 

 Construction 

Response (years) 

Shortfall 

Response Percentage 

Jackup Rigs 2 50% 

Platform Rigs 2 50% 

Semi-submersible Rigs 3 50% 

Drill Ships 3 50% 

 The Rigzone data suggest that, even though there may be a significant number of rigs in 

inventory, practically speaking, older rigs are not deployed. At this writing (end 2003) drilling 

rates in the GoM are relatively high, particularly in deep water. Yet we see that most of the rigs 

in service today are less than 25 years old. In fact, the age drop-off curve, shown in Figure 

III.10, is very sharp. This figure includes rigs of all types and drill ships, so it is dominated by 
III-33

jackup rigs. Nevertheless, other rig types show similar trends. Few semi-submersible rigs are 

older than 30 years, for example. Of the 172 rigs still in Rigzone’s worldwide inventory, 47 (27 

percent) are not working, and of those, 85 percent are older than 20 years. 

Figure III-10. Age Distribution of Rigs in Service in 2003 

0

0.2

0.4

0.6

0.8

1

0 10 20 30 40

Age (Years)

Percent of Rig Fleet

 These observations suggest that rigs should be taken out of inventory when they reach 

20-25 years old, but we do not keep track of individual rigs in this model version. A reasonable 

approximation, therefore, is to assume that 4 to 5 percent of the rig inventory will retire each 

year. We used MMS’s borehole well file to determine drilling rates. We only considered wells 

drilled after 1974 with records having both vertical and borehole axis drilling distances available. 

Further, we did not consider sidetrack wells, as many sidetracks do not drill as far as the average 

original well in the same area. 

 We began by examining the length of time between the date when wells were spudded 

and when they reached their total depth date for five water depth classes. Then we calculated 

average borehole axis drilling distances for each, thereby allowing us to make available both 

drilling distance by water depth and an estimate for how long it takes to drill the average well in 

at a given water depth. To this we added a reasonable assumption of logistical (move and setup) 

times to produce an annual average drilling rate by water depth. These data are shown in Table 

III.3. 
III-34

Table III.3. Drilling Depths and Rates 

Water Depth 

Drilling Rate 

(ft/day) 

Borehole 

Axis 

Distance (ft) 

Well Drilling 

Period 

(days) 

Logistics 

Delays 

(Days) 

Drilling Rate 

(Wells/Yr.) 

0-60M 428 10715 25.0 7 11.4 

60-200M 397 9004 22.7 7 12.3 

200-400M 370 10195 27.5 14 8.8 

400-800M 269 10331 38.5 14 7.0 

>800M 293 10765 36.8 21 6.3 

 Unfortunately, we found no way to further subdivide these drilling rates by rig type. 

However, it is reasonable to conclude that the shallower water rates are associated primarily with 

jackup rigs, while the deeper water drilling was with semi-submersibles and drilling ships. The 

current Rigzone data show that roughly one-third of the working platform rigs also are in water 

depths between 200 and 400M. 
III-35

4. Production and Reserve Growth 

 A primary output of the model is annual oil and gas production by area. In this chapter 

we describe how the model estimates production, additions to reserves, and reserves growth as 

well as production yields of oil and natural gas. The model relies upon a cell definition, where 

fields are assigned to different areas by planning area, water depth, size of field, and year of 

development or production. During the initial stages of model development, we defined 

production as a function of the total grown reserve estimate, consistent with the current MMS 

methodology. This method allowed us to determine exponentially declining production profiles 

for each field size in the Gulf of Mexico. Furthermore, this methodology allowed us the 

opportunity to compare production profiles across water depth categories. Ample data exist for 

shallow producing areas, but less production data (both in duration and number of fields) exist 

for deeper producing areas. Our investigation showed that although there were minor differences 

in the year-to-year production, in general, the shallow production data did not deviate greatly 

from the deeper water production data in terms of annual production trends. A set production 

profile is appealing in that it is easily applicable to historic and model year discoveries, it fits the 

current MMS methodology, and the data analysis can easily be performed. 

 As model development progressed, it became readily apparent that although appealing, 

computing annual production as a percent of total grown reserves was not feasible. The primary 

drawback in relying upon such a method is the circumvention of the reserves appreciation 

process that occurs in discovered fields. As fields are discovered, there are secondary processes, 

e.g., in-field drilling, which occur to stimulate in-field reserve growth above the initial reserve 

estimate. Alternatively, one can envision reserve growth constraints which hinder reserve 

appreciation. A fixed production profile will always generate the same cumulative production 

level, and it is possible that under a constrained reserve growth scenario, e.g., shutting off infield drilling, the cumulative production after a period of years will exceed the reserve estimate. 

In order to address this counter-intuitive situation, and handle a modified in-field discovery 

model, we elected to express the annual production as a percent of the remaining reserves. By 

recognizing reserve appreciation and its impact on production, we are able to model constrained 

reserve growth scenarios without worrying about overproduction of resources. 

 The production methodology we employ differs from the current MMS methodology. 

The MMS elects to use a fixed oil and gas production profile, applicable across the entire Gulf of 

Mexico. Although this method may “work” in dealing with aggregated Gulf of Mexico data, the 

IIC EDP Model’s field-level approach dictates a more rigorous method to handle the in-field 

issues discussed in the preceding paragraph. Our approach is similar to the methodology 

employed by the Energy Information Administration in its Oil and Gas Supply Model. Both the 

EIA and IIC models incorporate reserve growth, as the IIC model employs the MMS’s reserve 

appreciation method, and the EIA model calculates annual reserve growth based on the 

remaining reserves (proved reserves less production) multiplied by a growth rate multiplier. 

 Our model computes the annual production based on the annual remaining reserve level 

for each field. The EIA model computes annual reserves to production ratios based on the age of 

the field, and applies these ratios to the level of proved-reserves in any given year to determine 

production. 
III-36

Production and Reserve Equations 

 We define annual production in year i as: 

 Pi = α * (REi-1 + RAi – CPi-1) (Eq. III.11)

where: 

 α = production coefficient 

 REi-1 = reserve estimate last year 

 RAi = reserves added this year 

 CPi-1 = cumulative production through last year (i-1) 

 The reserves added component reflects the reserve growth of the field in that given year. 

For a given cell, if all in-field well requirements are met, then the reserves added will equal the 

difference between this year’s unconstrained reserve estimate and last year’s reserve estimate. In 

the following example, assume an initial field size of 100.0 BOE, a year 10 unconstrained 

reserve estimate of 2.50.6697 BOE, and that 3 in-field wells are needed in year 11 to achieve 

unconstrained reserve growth. In addition, assume that 87 BOE have been produced through 

year 10. If all three wells are allocated, the reserve estimate is increased, the additional reserves 

are added, and annual production is computed, assuming a production coefficient of 15 percent: 

In-field In-field Reserve Reserves Cumulative Annual

Year Wells Needed Wells Allocated Estimate Added Production Production

10 250.6697 87

11 3 3 260.6581 9.9884 26.04872

production coefficient alpha = 15%

 Alternatively, if only 2 wells are allocated, then reserve growth is constrained and we can 

not achieve a reserve estimate of 2.606581. Using an interpolated growth estimate based on the 

logarithmic nature of the MMS reserve appreciation methodology, we can determine what 

reserve estimate is expected with only 2 wells, as opposed to our unconstrained requirement of 3: 

In-field In-field Reserve Reserves Cumulative Annual

Year Wells Needed Wells Allocated Estimate Added Production Production

10 250.6697 87

11 3 2 257.4322 6.7625 25.56483

production coefficient alpha = 15%

 The impact on production is readily noticed, as a reserve growth constraint leads to a 

decline in the annual production. Furthermore, the model will log a well shortfall of 1, which 

will be carried into the following year. Again, assuming that 3 wells are required to hit the year 

12 unconstrained reserve estimate, the model will actively seek 4 wells for this cell (shortfall of 1 

plus requirement of 3). If 4 wells are available, the reserve estimate will rise to the 
III-37

unconstrained reserve estimate of year 12, and production will include the increase in reserves 

added: 

In-field In-field Reserve Reserves Cumulative Annual

Year Wells Needed Wells Allocated Estimate Added Production Production

10 250.6697 87

11 3 2 257.4322 6.7625 112.56483 25.56483

12 4 4 269.8386 12.4064 23.59107

production coefficient alpha = 15%

year 12 well requirements include the 3 annual wells and the 1 well

not allocated in year 11

 However, if only 1 well is allocated, then the reserve estimate is determined by the 

interpolated reserve growth. In this case, since we were short 1 well in year 11, if we only have 

1 well in year 12, then the year-end interpolated reserve estimate would equal the unconstrained 

reserve estimate for year 11, even though we are in year 12. We would log the shortfall of 3 

wells and proceed to year 13: 

In-field In-field Reserve Reserves Cumulative Annual

Year Wells Needed Wells Allocated Estimate Added Production Production

10 250.6697 87

11 3 2 257.4322 6.7625 112.56483 25.56483

12 4 1 260.6581 3.2259 22.21399

13 6

production coefficient alpha = 15%

year 12 well requirements include the 3 annual wells and the 1 well

not allocated in year 11

year 13 well requirements include the 3 annual wells and the 3 wells

not allocated in year 12

 Every cell (existing and newly discovered) follows the same reserve growth process and 

relies upon the MMS reserve appreciation methodology. Based on the 2000 National 

Assessment (Lore et al. 2001, 47-53), annual growth is determined by applying an annual growth 

factor (agf) to last year’s reserve estimate. The agf is calculated as: 

 agf = 1.01138 + 0.20027exp(-x/5.63808) (Eq. III.12)

where: 

 x represents the year after the initial reserve estimate (field age). 

 After fifty years of reserve growth, the 2000 National Assessment (Lore et al. 2001, 47-

53) data show that on an aggregated basis, every field will grow to 4.68 times its initial reserve 

estimate. We applied these reserve growth parameters in determining the unconstrained reserve 

growth profiles of each area and size of field. The unconstrained reserve profiles do not vary 

across areas or field sizes, e.g., after ten years of unconstrained growth, every field will be 2.5 

times its initial estimate. Differences in reserve growth and production are reflected through the 
III-38

annual number of in-field wells required and the specific production coefficients applied to the 

annual remaining reserves. 

 Production coefficients were estimated based on actual production and reserve data for 

existing fields in the Gulf of Mexico between 1975 and 2002.25 For each field, we included an 

annual line item entry that tracked field properties, annual production, the end of year reserve 

estimate and the cumulative production. This enabled us to calculate the annual production as a 

percentage of the remaining reserves for each field, each year: 

Field Year Size Age Production Year Ann. Prod. EOY Res. Est. Cum. Prod. Prod Percent

AC025 2000 14 3 0 1,185,566 80,722,835 1,185,566 1%

AC025 2001 14 4 1 8,834,464 61,504,391 10,020,030 15%

BA001A 1975 9 5 2 2,059,557 18,672,857 4,988,141 13%

BA001A 1976 9 6 3 1,876,917 18,672,857 6,865,058 14%

BA001A 1977 9 7 4 1,579,627 23,182,270 8,444,685 10%

Age: Year after discovery

Production Year: 0 is the first production year

 Once we calculated the annual production percents, we aggregated the results by field 

size and production year. In general, we observed that each class size had a build-up period until 

the production percent hit a peak, followed by a relatively stable annual production percent as 

shown in Figure III.11. 

Figure III.11. Production Percent of Remaining Reserves Based on Year of 

Production 

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

1

3

5

7

9

11

13

15

17

19

21

23

25

27

29

31

33

35

37

39

41

Year of Production

Size 14 Size 9

 Smaller and older fields tended to exhibit greater year-to-year variance in the annual 

production percent. In the model we treat reserve growth uniformly, and many of the variances 

 

25 Data obtained from “2002grw.xls,” provided by Richard Desselles, MMS Gulf of Mexico Region, March 12, 

2003, and “NEWIICProd.txt,” provided by Richard Desselles, MMS Gulf of Mexico Region, December 9, 2003. 
III-39

observed correspond to the fact that reserve growth occurs uniformly only in the aggregate Gulf 

of Mexico. Thus, it is conceivable that a reserve estimate may actually decline for a particular 

field, and the resulting production percent can fluctuate greatly. In order to smooth the annual 

production coefficient, we examined the average production coefficient over the duration of 

available production years as shown below: 

 

Class Size Average Coefficient

9 and smaller 20%

10 18%

11 16%

12 15%

13 14%

14 and larger 13%

 We applied these coefficients for newly discovered fields in model years, modifying the 

coefficient only to incorporate a build-up period. In the build-up phase, the coefficient was 

determined as the maximum coefficient linearly scaled to the current production year. For 

example, a class size 12 field has a production coefficient of 15 percent and a build-up period of 

3 years. In year one, the production coefficient would be 5% ((15%/3) * 1). In year two, the 

production coefficient would be 10% ((15%/3) * 2), and in year three plus the production 

coefficient would be 15 percent. 

 The production coefficients are applied based on a reserve growth factor that follows the 

MMS reserve appreciation methodology, and were determined on an aggregate Gulf of Mexico 

basis. In order to model the reserve growth process for existing fields (i.e., the fields that are 

discovered prior to model year one), it was necessary to impute a reserve estimate for each field 

based on the cumulative production of each field. During the calibration of the model it became 

apparent that certain existing fields in certain areas do not exhibit reserve growth and production 

consistent with that observed in the aggregated Gulf of Mexico analysis. To ensure that the 

production levels in each area are consistent with the last history years, we added a production 

adjustment factor that is additive to the production coefficients determined above. The resulting 

production coefficient applied to existing fields would be: 

αtotal = αsize-specific + αarea-specific-for-existing-fields (Eq. III.13)

where the size-specific coefficients are described above, and the area-specific coefficients for 

existing fields are show in Table III.4. 
III-40

 Table III.4. Area-Specific Production Coefficient Adjustments 

Area Planning Area Water Depth Production Adjustment

1 CGM 0-60m 1.50%

2 CGM 60-200m 0.35%

3 CGM 200-400m 5.00%

4 CGM 400-800m 2.30%

5 CGM 800-1600m 6.50%

6 CGM 1600-2400m 2.30%

7 CGM 2400+m 0.00%

8 WGM 0-60m -3.50%

9 WGM 60-200m 0.00%

10 WGM 200-400m 25.00%

11 WGM 400-800m 10.00%

12 WGM 800-1600m 0.50%

13 WGM 1600-2400m 0.00%

14 WGM 2400+m 0.00%

 In particular, two areas, 10 and 11, exhibited unusually large production adjustment 

factors. These are a direct result of relying on uniform reserve appreciation, aggregated 

production coefficients, and the MMS National Assessment reserve estimates. For example, 

there are 18 fields in area 10 (WGM 200-400 meters), with a National Assessment grown reserve 

estimate of approximately 659 million BOE. As of the first model year, cumulative production 

has reached 316.5 million BOE. In 2001, annual production was 31.2 million BOE, which 

would represent a large portion of the currently “known” remaining reserves (recall that fields 

“grow” over a fifty year time span). This is primarily driven by one field, GB171. The National 

Assessment estimates this field at 23.8 mmBOE. However, by year-end 2001 it has produced 

over 21 mmBOE. More recent estimates have shown that the GB171 field is closer to 245.7 

mmBOE. Yet, because the field has not yet exceeded the National Assessment reserve estimate, 

it is perceived by the model as a field that is nearing the end of its productive life, having 

produced approximately 88 percent of its National Assessment estimate. 

 Clearly this is not the case, but is a function of the estimates and reserve appreciation 

methodology. As a result, the model is forcing other Area 10 fields to compensate for the lost 

production that is coming from an understated GB171 estimate, thus requiring a large production 

adjustment to match history. It is the responsibility of the model user to keep a close watch on 

the historic production and expected production transition point. If the user were to update the 

reserve estimate for GB171, than it would be necessary to scale back the Area 10 production 

adjustment, alleviating the “burden” on the other fields to make up for the lack of production the 

model would generate for GB171 under the reduced estimate. 

 In fact, more recent reserve estimates show that area 10 has ultimately grown reserves 

closer to 1 billion BOE, versus the 660 million BOE contained in the national assessment. As 

the user updates the model with new reserve distributions it is imperative to also re-calibrate the 

production adjustment coefficients to ensure an accurate and reasonable transition that takes into 

account the fact certain fields and areas do not exhibit reserve growth or production behavior 

such as in the aggregate Gulf of Mexico analyses. 
III-41

Determination of Production Wells Drilled 

 In-field reserve growth is driven by the annual number of initial and subsequent 

production wells that each field requires. In determining the initial number of production wells, 

we examined MMS data that listed monthly field production back to 1947 on a well basis. We 

determined the number of actively producing wells, per field, in the first production year as 

shown in Table III.5. 

Table III.5. Production Wells Active in First Production Year 

Size Pre-1991 Post-1991 Change Assumed

7 2.19 1.44 (0.75) 1.44

8 2.51 1.55 (0.96) 1.55

9 3.51 1.71 (1.80) 1.71

10 4.73 2.59 (2.14) 2.59

11 5.79 2.59 (3.21) 2.59

12 7.03 2.70 (4.33) 2.70

13 11.74 3.00 (8.74) 3.00

14 11.33 3.14 (8.19) 3.14

15 8.00 5.00 (3.00) 4.00

Source: Monthly Well Production Data

 Interestingly, analysis of the last ten years of data showed a considerably lower number 

of initial production wells than the previous forty years. It is possible this decline in initial 

production wells is due to technological advances which have led to greater production per well., 

as well as the advent of deeper water projects, where the production per well is greater than in 

shallower water. We elected to employ the more recent results in determining the initial 

production wells per field. 

 The second component of in-field requirements was the determination of additional 

production wells drilled over time. Historical data has shown that after the initial production 

wells are drilled, additional production wells continue to be drilled over time. The model treats 

these production wells as necessary to stimulate the reserve growth. We returned to the 

production well data, and analyzed the additional level of production wells generated for each 

field class size. Aggregating across the Gulf of Mexico, we plotted the time series of cumulative 

production wells drilled for each class size six through twelve as shown in Figure III.12. 
III-42

Figure III.12. Cumulative Production Wells by Class Size 

y = 0.3154x + 4.9258

R2

 = 0.9903

0

5

10

15

20

25

30

0 5 10 15 20 25

Year of Production

Wells

Six Seven Eight Nine Ten Eleven Twelve Linear (Ten)

 Each class size exhibited roughly the same pattern: addition of annual production wells in 

a linear fashion. Intuitively the data were consistent with the concept that larger fields would 

require a greater number of additional wells. We added trendlines to each field class scatter, like 

the one for class ten fields, in order to see if a coherent linear solution could be determined. The 

following table summarizes the results and lists the corresponding R-square value, which 

represents a “goodness of fit” measure. In each case, the R-square exceeded 90 percent and for 

most field sizes exceeded 96 percent as shown in Table III.6. 

Table III.6. Production Well Trendline Coefficients 

 

Size Slope Intercept R-Square

6 0.0169 1.3323 0.9241

7 0.0227 1.9283 0.9478

8 0.0908 2.2339 0.9722

9 0.2086 3.0690 0.9872

10 0.3154 4.9258 0.9903

11 0.4857 7.7945 0.9939

12 0.6345 12.5250 0.9650

 One can infer two things from these results. First, the slope represents the annual number 

of production wells that are added in each year after the initial production year. Second, the 

intercept should represent the number of initial production wells. However, our data analysis 
III-43

showed that the initial number of production wells has declined significantly in recent years. 

Therefore, we elected to rely upon our previous analyses to determine initial production wells, 

but accepted the additional production well results generated by this analysis. Thus, a class 

seven field would require .0227 additional production wells each year following the initial 

production year. For field class sizes greater than twelve, we used the following function to 

determine the annual number of production wells required: 

 Production Wells = 0.6345 + (0.15 * (size – 12)) 

 For example, a class 15 field would require 0.6345 + (0.15 + (15-12)) = 1.0845 

production wells. Our initial calibration attempts resulted in an unusually large number of 

production wells being drilled in the Western Gulf, and a below average number of production 

wells being drilled in the Central Gulf. We turned our attention back to the production well 

analysis and split out the Central and Western Gulf. For field class sizes eleven and twelve, we 

noticed a distinct difference between the two planning areas as shown in Figure III.13. 

Figure III.13. Cumulative Production Wells – Class 12 

y = 0.7686x + 12.638

R2

 = 0.9584

y = 0.5274x + 13.066

R2

 = 0.9754

0

10

20

30

40

0 5 10 15 20 25

Wells

Year of Production

CGM WGM Linear (CGM) Linear (WGM)

 The additional number of production wells in the CGM was larger those drilled in the 

WGM. Class 12 fields, shown above, typically drilled 0.7686 additional wells per year in the 

CGM, compared with 0.5274 in the WGM, while class 11 fields drilled 0.5588 and 0.3647 wells 

in the CGM and WGM, respectively. Incorporating these results for class sizes greater than ten 

helped smooth out the aberration discovered in the initial model calibration. Although the 

number of wells drilled in the WGM during the first model year is higher than the previous three 

years (1999-2001) it is still within the range of reasonableness observed over the previous fifteen 

years. 
III-44

Oil Yield Determination 

 Oil yield is defined as the percentage of the total grown reserve estimate that is 

attributable to oil and condensate. For existing fields, the model tracks the actual oil yield, 

determined from the oil and gas estimates included by the user in the “Field Input” table 

contained in the underlying Access database. In order to calculate net present values, as well as 

assign oil yields to model discoveries, we assigned average oil yields based on area (planning 

area and water depth) and in some instances, size of field.26 The Central Gulf of Mexico 

planning area had numerous field discoveries in each area, and our analysis of the oil yield 

indicated significant differences among both water depth categories and field size. As a result, 

we analyzed oil yields based on water depth and size of field, and aggregated the data across 

field sizes where appropriate. The results of the analysis are shown in Tables III.7 and III.8. 

Table III.7. Areas 1-3 (CGM 0-60, CGM 60-200, CGM 200-400) 

Area Size Field Count Yield Simple Avg Weighted Avg Assumed

1 5 14 10%

1 6 37 5%

1 7 58 6%

1 8 86 6% 7% 6% 7%

1 9 83 13%

1 10 71 14%

1 11 68 15% 14% 14% 14%

1 12 51 21% 21% 21% 21%

1 13 42 33%

1 14 22 32% 32% 33% 32%

1 15 6 47% 47% 47% 50%

2 5 3 10%

2 6 4 7%

2 7 24 11%

2 8 19 9% 9% 10% 11%

2 9 24 17%

2 10 34 26%

2 11 31 34%

2 12 21 29% 26% 27% 30%

2 13 22 48%

2 14 7 37%

2 15 1 55% 47% 46% 50%

3 8 9 53%

3 9 3 52% 52% 52% 50%

3 10 6 37%

3 11 3 20%

3 12 2 45% 34% 34% 37%

3 13 4 59%

3 15 1 67% 63% 60% 60%

 

26 Data obtained from “fldna99.xls,” included with the 2000 National Assessment CD provided by Richard 

Desselles, MMS Gulf of Mexico Region, October 2002, and additional reserves data provided June 17, 2003, by 

Richard Desselles, MMS Gulf of Mexico Region. 
III-45

Table III-8. Areas 4-7 (CGM 400-800, CGM 800-1600, CGM 1600-2400, CGM 

2400+) 

Area Size Field Count Yield Simple Avg Weighted Avg Assumed

4 5 1 24%

4 6 1 40%

4 9 4 33%

4 10 1 43% 35% 34% 35%

4 11 4 67%

4 12 8 59%

4 13 4 49%

4 14 4 75%

4 15 1 84%

5 8 1 3%

5 9 3 51%

5 11 6 67%

5 12 8 60%

5 13 3 50%

5 14 5 71%

5 15 1 70%

5 16 2 82%

6 11 2 0%

6 12 7 59%

6 13 1 1%

6 14 2 42%

6 16 1 53%

6 17 2 83% 54% 59% 60%

 We aggregated the data and computed a simple average and a weighted average (by field 

count) of oil yield. We then applied the results of our findings to determine an assumed oil yield 

for each area and size of field, extrapolating data to CGM areas where no data existed. The 

Western Gulf lacked the robust discovery data of the Central Gulf, and as such, we approached 

the oil yield situation from an aggregated area basis. Using the oil and gas reserves incorporated 

in the MMS-provided estimates, we determined the oil yield based on the percentage of oil 

reserve estimate to the total as shown in Table III-9. 

Table III.9. Western Gulf Oil Yields Areas 8-12 (0-60, 60-200, 200-400, 400-800, 

800-1600) 

Area Oil Gas Oil Yield Assumed

8 186.41 3327.86 5% 5%

9 324.43 1755.2 16% 15%

10 104.48 290.17 26% 25%

11 166.94 108.38 61% 60%

12 613.85 469.38 57% 60%

 As new fields are discovered during model years, they are assigned an oil yield based on 

the results of the above analyses. For areas 13 and 14 (WGM 1600+), we applied a 60 percent 

oil yield. For Eastern Gulf discoveries, we applied the default CGM oil yields. 
III-46
III-47

5. The Economic Submodel 

Overview 

 The economic portion of the IIC EDP model is a discounted cash flow (DCF) net present 

value (NPV) model that determines whether it is economic to produce from a given field over the 

assumed life of that field. By “economic,” we mean that the model generates a positive after tax 

net present value for a given field in a given area and water depth. As discussed in previous 

chapters, a positive value acts as a signal to exploration and production activities to commence 

and continue. A negative NPV suggests it is uneconomic to go forward with a plan to explore 

and develop a particular resource. This model is similar to the models used by E&P companies 

to evaluate prospects as well as others who examine and measure the value of oil and gas 

reserves and production in a given area. In essence, the economic model simulates the economic 

activity of an E&P company in making the decisions to explore, develop, produce, and abandon 

an oil and gas field. 

 The economic component of the model is used in two ways. First, it is the primary 

component of the model’s simulation of the producer/explorer’s decision making process of 

when and where to explore (drill) for resources. It is a direct input into the exploration process 

and is the primary means by which the model selects which areas will be explored that will then 

lead to new discoveries. The second way in which the NPV module is used is that once a field 

has been discovered, the NPV is used to prioritize and determine when a field will move into 

production. A negative NPV sends the field into a “holding” inventory until sometime in the 

future when (if) the NPV becomes positive. A positive NPV causes that field to move into a 

development queue that leads to eventual development and production.27

 In developing the economic submodel, we relied extensively on prior modeling work, 

especially as it relates to the cost component. For example, we have utilized various elements of 

the U.S. Department of Energy, Energy Information Administration’s (EIA) Oil and Gas Supply 

Module, particularly the Offshore Supply Submodule (OSS). The OSS is a PC-based modeling 

system that is used to project reserve additions and production from undiscovered resources in 

the offshore Gulf of Mexico. We have also relied on information and data contained in 

numerous other economic modeling efforts.28 

 As with any cash flow model, the model is composed of two parts: a revenue component 

and a cost component with the difference between the two over time reflecting cash flows which 

then must be discounted to present value. The model relies on certain user-generated 

assumptions and model-generated functions and parameters to compute expected revenues and 

expenses, and discount the resulting cash flows to a present value. The logic of the economic or 

NPV module and an overview of its basic components are shown in Figure III.14. 

 

27 These processes are described in more detail in Chapters III and IV. 

28 See, for example, Walls (1994, 1992); Eppink et al. (2001); Kaufman et al. (1981); Vidas, Manger and Woods 

(1985); Eckbo (1978). 
III-48

Figure III.14. Logic of the Economic Module 

NPV = (Revenues – Costs)* [1/(1+r)t] 

 

NPV = Net present Value 

r = Discount rate 

t = Time 

 Revenue Component Cost Component

Oil/gas price * oil/gas quantity E&D Drilling Annual

- less royalties less Field Development = Cash Flows

- less taxes Infrastructure - discounted 

 Operating 

 Removal 

Revenue Component 

 The revenue component of the economic module computes the anticipated revenue that 

production would generate for a given field. Revenue in its simplest form represents the price of 

the commodities produced (oil, gas, NGLs) multiplied by the volume of each commodity. The 

user is permitted to specify a number of inputs including current and future oil prices, and the 

royalty and tax rates and regimes in place. Based on our analysis of historical data on oil yields, 

the model computes the amount of oil and gas produced, and establishes price relationship 

between oil, gas and NGLs. Gas and NGL prices are set in a fixed proportion to oil prices, 

however oil prices may be allowed to remain constant, escalate at a user-determined rate, or 

follow a random process as provided for in the “Price Path Form” of the model. 

 The net present value calculation entails computing revenues received over a certain 

producing life. In any given model year, the decision maker (namely the one computing the net 

present value) must make an assumption regarding his/her future expectation of what the price 

will be in future model years. This is different than what the actual oil price is, determined 

annually by the user in the model. The calculation of net present value in each model year does 

not know what the user has entered for the next model year’s actual price. The difference 

between the actual model oil price and the decision maker’s projected oil price is best illustrated 

using a historical example. Suppose one discovers a class 7 field in 1993, when the average oil 

price was $16.15 per barrel. To determine economic viability, the interested party computes the 

net present value of the investment over a forty-year horizon. As of 1993, this person does not 

know what the actual oil price will be in years 1994-2033. The decision maker (and the model) 

start with the $16.15 per barrel price, and make an assumption as to whether this price will 

decline, remain constant, or increase. 

 In this example, the decision maker assumes that the price will rise by 2 percent per year. 

The revenue determined from production that is expected to occur in the year 2000 would be the 

expected price assuming this constant growth, $18.55 per barrel multiplied by the expected 
III-49

production volume. In actuality, the oil price in 2000 was $27.59 per barrel. This situation is 

analogous to model years. The user may set the model year 4 price to $24 per barrel, the model 

year 5 price to $30 per barrel and finally set the model year 6 price to $15 per barrel. Regardless, 

in model year 5, the calculation of revenue would start with a price of $30 per barrel, which can 

escalate, remain constant, or decline, based on the decision maker’s best estimate of what he or 

she thinks will happen to the price in years 6 and beyond. 

 Royalties may be set at a fixed rate and the model captures the effect of suspension 

volumes on existing production based on deepwater relief. The user may modify the model to 

account for continuing relief (suspension) volumes as well as others changes in royalties such as 

changes in the rate by water depth. This is provided in the “Royalty Rule Entry Form.” The user 

defines the income tax rate with the default set at the statutory 35 percent rate. Taxes may be 

user defined in the “Cost Inputs Form.” 

 Another important element in the determination of revenues in the model is the quality 

characteristics of the oil being produced in a given field. Oil is not homogeneous and differs in 

quality from field to field and area to area. The two most important determinants of a crude oil’s 

quality are its API gravity and its sulfur content. API gravity measures the viscosity of the oil, 

which has an important bearing on its value when it is refined. As a general matter, subject to 

various exceptions, the less viscous a crude oil, the less value it has. This is because refiners 

require more costly means to process the more viscous (or “heavy”) oils and without such costly 

processing refiners are unable to obtain the same proportion of more valuable refined products 

such as gasoline and diesel as with a less viscous, “light” crude oil. Purchasers of crude oil 

utilize API gravity as the primary means of measuring difference in the quality of crude oil and 

adjust the price they are willing to pay based on this measure. Sulfur can also play an important 

role. Generally oils with more sulfur require more costly processing and hence are less valuable 

than low sulfur crude oils. Thus for the model to properly measure the likely revenue from crude 

oils produced in the GoM, we must make certain assumptions about the quality of the crude oil 

being produced in the Gulf. 

 We performed a study to analyze the general quality characteristics including both sulfur 

and API gravity of crude oils that have been and are being produced in the Gulf. We relied on 

data provided to us by MMS. Unfortunately, although MMS collects data on API gravity, it does 

not obtain data on sulfur content. In prior studies relating to GoM production, IIC, Inc. has 

found that sulfur content and API gravity are somewhat correlated. That is to say, lower gravity 

(lower quality), crude oils tend to have a higher sulfur content. Although by no means a hard 

and fast rule, these two measures are correlated, and provided the basis for developing an 

adjustment for differences in crude oil quality. We analyzed two sets of data provided by MMS 

on API gravity by field, planning area and water depth. We relied on data obtained from the 

National Assessment as it was generally more comprehensive and detailed. These data are 

shown in Table III.10. We computed the weighted average gravity in each water depth using 
III-50

reserves as a weighting factor. We then used these API gravity factors as an average measure of 

the quality of crude oil produced from each area and water depth.29

Table III.10. Summary of API Gravity Data by WD/Location 

CGM

Water Depth Assessment

Assessment 

Weighted Average

0-60 34.0 33.9 34.0

60-200 33.5 33.8 33.7

200-800 30.9 31.5 31.2

800-1600 31.4 30.0 30.7

1600-2400 31.4 36.7 34.0

WGM

Water Depth Assessment

Assessment 

Weighted Average

0-60 36.2 37.6 36.9

60-200 35.3 34.4 34.9

200-800 32.4 33.3 32.8

800-1600 34.8 35.4 35.1

 Next, we determined an appropriate monetary adjustment factor to adjust the oil price. 

The oil price input by the user reflects a standard API gravity – 40o

 API. Based on a regression 

analysis of the spot prices of three different Gulf of Mexico crude oils of different gravity and 

sulfur,30 we developed a quality adjustment factor of $0.04 per degree API across the range of 

relevant gravities. Assuming that there is a correlation between gravity and sulfur, this 

adjustment factor accounts for both gravity and sulfur content. This adjustment factor is userdefined so that the user can change it (in the “Gas to Oil Price Ratio” factor) as part of the “Cost 

Inputs Form” in the model. 

 The prices for gas and NGLs are established in the model as a function of the oil price 

and the content of NGLs found in an MCF of natural gas. As noted above the oil price is 

determined by the user as is the rate of escalation in oil prices over time. As a first step we 

established the value relationship between a barrel of crude oil and an MCF of natural gas. The 

model fixes the price of natural gas based on a relationship between these two products, as well 

as the BTU content between a barrel of oil and an MCF of natural gas. The price relationship is 

trended based on history, emphasizing more heavily the recent time period in which natural gas 

prices have increased in value slightly relative to oil prices. We used several price series 

including spot prices, the average wellhead sales values reported to MMS and onshore sales 

prices. The average and median values of these series indicated that the price of natural gas has 

been about 13.5 percent of the price of crude oil. 

 

29 The model currently does not have the ability to update gravity information, although in the future if needed this 

enhancement could be made by expanding the existing field input table. One would not expect, however, that the 

quality characteristics would change significantly over time. 

30 These included LLS (Light Louisiana Sweet), HLS (Heavy Louisiana Sweet), and Eugene Island (a sour crude). 
III-51

 The model also considers the value added by NGLs. To do this, we determined the NGL 

yield per MCF of gas which is approximately 10 percent and then we measured the relationship 

between the value of NGLs and a barrel of crude oil again based on the average wellhead sales 

values as reported to MMS. We then added 10 percent of that value to the 13.5 percent natural 

gas/crude oil value relationship to arrive at an adjusted value of 14.1 percent, thus including the 

average value and average content of NGLs found in an MCF of wet gas. This factor is applied 

to the user-input oil price to determine the natural/NGL value in the model. It may also be 

altered by the user should the user feel that this relationship had changed or will change over 

time as provided for in the “Cost Inputs Form.” 

 Once oil and gas prices and associated quality adjustments are made, we turn to the 

volume part of the revenue equation. The two major items here are to determine the relative 

yields of oil and gas in a particular field and the actual volume of production. These are 

described in detail in Chapter IV. Briefly, the amount of oil and gas produced depends on the 

yield inputs that characterize the area where the field is located, and production is expressed as a 

function of remaining reserves (after an initial build-up period). Production is assumed to 

commence once all development and production activities and infrastructure are completed and 

in place. These are determined in the cost component of the model. There is, however, a lag 

between exploration and development on the one hand, and commencement of production as has 

been observed historically. 

 Expected revenues are computed based on the estimated oil and gas production in each 

year is multiplied by a price for oil and gas. The oil price is adjusted for quality from a base 40o

API price. Revenue produced each year in the field is the sum of oil revenue and gas revenue. 

Royalties and taxes are deducted to determine “net” revenue. 

Cost Component 

 The cost component reflects a time series behavioral simulation model of exploration, 

development, and production activity; as noted above lags between discovery and production 

reflect these activities. The logic of the cost component is to model these activities, recognizing 

that costs of each activity will vary depending on several key factors including: 

• water depth 

• drilling depth 

• field size 

• drilling rig type (function of water depth) 

• number and type of platforms and number of slots per platform/structure (function 

of water depth, field class) 

• drilling success rate and number of development and producing wells 
III-52

 The costs relating to EDP activity in the Gulf of Mexico are driven by choices, 

technology, and the expected size of the prospect. Our model attempts to simulate those choices 

that are most consistent with current practice and projections of future technological options. 

The technology used in deepwater offshore areas of the GoM is significantly different from the 

more mature shallow water areas. Deepwater exploration and development is still a rapidly 

evolving area and technological choices have expanded dramatically in recent years. Our 

approach attempts to model those choices relying on the best and most recent data. However, the 

user must be aware that the data underlying the economic parameters relating to deepwater 

activity is far less robust than in shallow areas and will need to be revised and updated over time. 

Some of the issues related to deepwater that influence technology choices and costs include: 

• deepwater is a frontier exploration area with limited but increasing information 

• deepwater locations are remote and face more extreme conditions 

• deepwater production rates (BoE/well) are considerably higher than in shallow 

water 

 Costs may be divided into three broad categories: capital, operating, and depreciation. 

The model handles capital costs including depreciation under current tax rules. As noted above, 

a number of the cost components were developed from data and models maintained by the 

Energy Information Administration (EIA), MMS, and our own analyses of cost parameters and 

data found in the literature as well as reported by EIA and MMS. 

Capital Costs 

 The model treats capital costs as individual functions or stages of the EDP process as 

shown in Figure III.15. Investments must be made at each stage of the EDP process, using 

available technology and based on the estimated costs of each item. Cost algorithms have been 

developed or utilized from existing models for these investments. It is important to note that the 

model only considers post-discovery costs. Activities such as original exploratory (“wildcat”) 

wells, early field evaluation and the costs associated with the acquisition of the right to explore 

are considered to be “sunk” and do not affect project viability. Nevertheless, the timing 

associated with the activities shown in Figure III.15 is critical to the project’s viability. Our 

objective is to model an in-field exploration, development and production plan in a way that both 

realistically portrays current and anticipated offshore practices and allows the most cost-effective 

development of a field. 
III-53

Figure III.15. Timing of EDP Process in Cost Modeling 

 The first cost element represents the assessment of the geological and geophysical 

attributes of a field or area. These costs are assumed to be 15 percent of the cost of all in-field 

exploration drilling activity. This is based on our analysis of model data from EIA and is a fixed 

parameter in the model. We have included this cost element as the model simulates behavior 

assuming the explorer/producer has already obtained a lease in the field being modeled. Some 

G&G costs would have been expended prior to this point in time and these costs are considered 

“sunk” in the model. However, some additional exploratory G&G costs are assumed to be 

expended after the leasing decision to support local reservoir location and field delineation; 

accordingly, these costs are included in the model. 

 During the in-field exploration phase, the type of drilling rig that is used depends on both 

economic and technical criteria. Offshore exploratory and development drilling is typically 

performed using self-contained rigs which can be moved easily. In deeper water two other types 

of rigs are used. The costs to drill offshore exploration and delineation wells are dependent on 

the water depth and drilling depth. Water depth determines the type of drilling rig that the model 

selects: 

• jack-ups are used in depths of less than 200 meters 

• semi-submersibles are used in water depths between 200 and 900 meters 

• drillships are assumed to be used is water depths greater than 900 meters 

G&G Activity 

In-Field 

Exploratory Delineation Development and Production Drilling 

Drilling Drilling 

 

Production Structures Constructed 

 Start of Production

 

Time (years)

0 1 2 3 4 5 

Discovery 
III-54

 Individual cost functions by rig type were derived from the EIA offshore model and were 

validated by other models of offshore drilling activity. These cost functions include fixed costs 

such as wellhead and borehole equipment; time-related items such as rig rates, labor, and 

services; and well depth-related items such as equipment and materials such as casing, tubing, 

and cementing. The drilling cost functions for each rig type vary with water depth and drilling 

depth. The functional form is shown below: 

 Exploration Drilling Cost = ά + β (WD) + γ (WD+DD) + λ(WD)*(DD)μ (Eq. III.14)

where: 

 WD = water depth 

 DD = drilling depth 

Ά, β, γ, λ, and μ are estimated parameters that differ based on choice of rig type 

 We developed data on the relationship between average drilling depth and water depth 

based on data contained in the National Assessment by field class. We developed statistical 

relationships for each field size that permitted estimation of an equation that predicts the average 

drilling depth for each water depth category. These relationships are then input into the drilling 

cost equations to determine drilling costs on a per well basis. Delineation drilling costs are 

expressed as a fraction of the cost of in-field exploration drilling (85 percent). 

 We validated the drilling costs by making comparisons to other data and information on 

drilling costs including data collected by API and MMS and as well as from our review of 

various other models.31 Figure III.16 illustrates this comparison. As can be seen, the costs 

generated by the IIC, Inc. model are generally in line with these other estimates with the 

exception of the 60-200 meter category for which the IIC model generates somewhat lower 

costs. As can be seen, drilling costs generally decline in the 60-200 meter water depth because 

drilling depths are considerably less than in other areas. 

 The model also estimates the number of producing and non-producing wells that must be 

drilled for a given field size and water depth. The number of in-field exploratory wells drilled in 

the model is based on data from the MMS “borehole file” analyzed after we removed original 

“wildcat” exploratory wells. We categorized these wells by field size and water depth, then we 

computed the total number of producing and non-producing wells. Then we computed the ratio 

of non-producing to producing wells by field size and water depth and applied this “dry hole” 

ratio in the model. For fields with water depths greater than 800 meters the data were not 

sufficient to develop a consistent pattern or ratio; therefore, we averaged these data with 

shallower depths (200-800 meters) to develop realistic dry hole ratios. To attempt to reflect 

modern drilling practices, while not sacrificing too much data, we disregarded wells drilled 

before 1975. This point in time also corresponds to the emergence of natural gas as a commodity 

of interest in the GoM. 

 

31 See, for example, Walls (1994) and Vidas, Manger, and Woods (1985). 
III-55

Figure III.16. Drilling Cost Comparison 

$0

$2,000

$4,000

$6,000

$8,000

$10,000

$12,000

0-60 60-200 200-800 800+

Water Depth

$000/Well

IIC API MMS Others

 The model also simulates the type of development and production structure which the 

field operator is assumed to install. The types of structures that the model “uses” include the 

following: 

• Conventional Fixed Platform (FP) 

• Caisson (Cais) 

• Compliant Tower (CT) 

• Tension Leg Platform (TLP) 

• Floating Production System (FPS) 

• Spar Platform (SPAR) 

• Subsea Tie-In/Manifold System (Subsea) 

 The model selects the type of structure to be used based on water depth and field size as 

shown in Table III.11. 
III-56

Table III.11. Selection of Platform Structures 

FC/WD 0-60 60-200 200-400 400-800 800-1600 1600+

<8 FP FP FP FPS/Subsea FPS/Subsea FPS/Subsea

8 FP FP FP FPS/Subsea FPS/Subsea FPS/Subsea

9 FP FP FP FPS/Subsea FPS/Subsea FPS

10 54% - FP FP FP FPS FPS FPS

11 46% - Cais. FP FP FPS TLP FPS

12 FP FP CT TLP SPAR

13 FP FP CT TLP SPAR

14 FP FP CT TLP SPAR

15+ FP FP CT TLP SPAR

The total cost of the development structure is spread evenly over the time period it is 

assumed to take to construct the facility, typically between two and three years. The cost 

functions for each platform type are dependent on water depth and number of slots. 

 The size of the field determines the number of slots, as larger fields require more drilling 

and more wells. We relied on MMS data on platforms that have been installed in the Gulf of 

Mexico as the basis for determining the number of slots per structure type and field class. We 

estimated the average number of slots by field size and platform type and these data are used in 

the model to compute the number of slots per platform type. In deeper water and with newer 

platform types, the data are less robust, and in the future this set of input parameters should be 

revisited as more data becomes available. Shown below in Table III.12 are the input parameters 

used for the number of slots by platform type and field size. 

Table III.12. Number of Slots by Platform Type and Water Depth 

No. of slots: FP FPS

FC/WD 0-60 60-200 200-400 400-800 800-1600 1600+

<8 2 3 6 5 4 3

8348 7 6 5

9 4 6 10 11 10 9

10 4 8 12 12

11 4 10 14 12

12 4 10 20

13 4 10 25

14 5 13 25

15+ 6 13 25

TLP/CT SPAR

400-800 800-1600

11 4 5

12 9 12

13 14 12

14 19 12

15+ 24 15

Caissons have only 1 slot throughout
III-57

Finally, the number of structures installed in a field will vary over time based on field 

size and production. For fixed platforms, fields with larger production require multiple 

structures. For other structures, only one is assumed. Again we relied on MMS data on 

platforms installed in the GoM to determine the average number of fixed platforms for each field 

class. For fields less than category 8, only one structure was assumed, with the number 

increasing to a maximum of twenty in field class 15 or higher. 

 Once these parameters are known, the platform cost equations were developed based on 

several different models including the EIA offshore model and various academic and trade 

journal articles.32 Fixed structures have a fixed cost component as well. These cost functions are 

shown below: 

 FP = 2,000,000 + 9,000(SLT) + (1,500* WD*SLT) + 40(WD)2 (Eq. III.15)

 Caisson = 1,000,000 (Eq. III.16)

 CT = (SLT + 30) * (1,500,000 + 2,000* (WD - 1,000)) (Eq. III.17)

 TLP = (SLT + 30) * (3,000,000 + 750 * (WD - 1,000) (Eq. III.18)

 SPAR = (SLT + 20) * (5,000,000 + 500 * (WD - 1,000)) (Eq. III.19)

 FPS = (SLT + 20) * (1,500,000 + 250* (WD – 1,000)) (Eq. III.20)

 Subsea = 2,500,000 (Eq. III.21)

where: 

 SLT = number of slots 

 WD = water depth 

 We compared these costs against data maintained by EIA, MMS and other groups 

(Coastal Marine Institute 2003) to validate these costs. The range of platform costs as computed 

by the model is shown in Figure III.17 below. 

 Our review of the platform data indicated a substantial number of non-major structures, 

primarily between 0-200 meters. For fields with initial structures greater than one, we split the 

initial structures between fixed platforms and caissons. The average number of caissons to fixed 

platforms was roughly one to one, and we employed a percentage of 46 percent Caissons with 54 

percent fixed platforms. Thus, in determining the initial platforms costs for fixed platforms in 

the 0-200 meter range with more than one initial structure, the cost would equal the total number 

of structures multiplied by the caisson cost times caisson percentage (46 percent) plus the fixed 

platform cost multiplied by one minus the caisson cost (54 percent). Furthermore, historical data 

 

32 See for example, Johnston (1985); Attanasi and Haynes (1983); and Leffler et al. (2003). 
III-58

shows that additional structures are consistently being placed in the 0-200 meter water depth 

areas. The costs of these non-host structures were assumed to be covered by the additional well 

costs portion of post-production expenses. 

 Alternatively, we encountered areas where significant sub-sea operations were required 

to develop marginal fields. These fields would not actually receive a platform, rather would bear 

infrastructure cost related to installation of sub-sea components. This cost was determined by 

multiplying the sub-sea coefficient ($2.5 million) by the number of initial production wells per 

marginal field (class sizes less than 10 in water depths over 400 meters). In addition we also 

included sub-sea infrastructure costs associated with fixed platforms between 200-400 meters 

water depth. Unlike the fixed platforms in the 0-200 meter range, we did not observe multiple 

structures going in over time, and it appeared more likely that sub-sea infrastructure was being 

employed in developing fields with fixed platforms in the 200-400 meter range. 

Figure III.17. Range of Platform Costs by Water Depth 

0

100

200

300

400

500

600

700

30 130 200 400 600 800 1000 1200 1400 1600 1800 2000 2400 3000

Water Depth

$ Millions

 A final element of the platform structure capital costs is the cost associated with 

abandonment and the timing of such abandonment. Structure abandonment costs are a function 

of the type of platform used and are expressed in the model as a fraction of the initial costs of the 

structure. The costs of abandoning individual structures was derived from EIA’s offshore model 

and validated against data obtained by MMS. We relied on several studies performed for MMS33

as well as data maintained by MMS on platform additions and removals to determine the rate at 

 

33 See, for example, McGinnis et al. (2001); Pulsipher et al. (2001); Kaiser et al. (2003). 
III-59

which structures would be removed. Again, particularly in deepwater areas, the dearth of 

historical data makes this estimation process very difficult. 

 Our investigation and analysis of the platform data led us to conclude that for fields in 

water depths greater than 200 meters, the number of structures was typically one, although there 

are cases where more than one exists. For these structures, and for initial host structures 

installed in the 0-200 meter range, abandonment occurred as a function of declining production. 

As a field approached the end of its production life and cumulative production exceeded ninetyseven percent of the grown reserve estimate, this field went into shut-down and remaining 

infrastructure was removed. In addition to the initial structures, fields in 0-200 meters also 

received secondary structures, installed over the life of the field. These are shown in Table 

III.13. We elected to employ a process by which secondary structures were employed linearly 

over a forty year cycle, with the exception of field sizes 13 and above in 0-60 meters. With these 

fields, we employed a “sum of the years” method, as the data showed a higher percentage of 

installations occurring earlier in the forty year time period. After forty production years, no 

additional structures were installed. Finally, the data for the WGM were not nearly as robust to 

allow us to conclusively determine a forty year installation cycle. However, comparison with the 

CGM data showed that for field sizes 9 and larger, the number of installations was significantly 

reduced, roughly half of what was observed in corresponding time periods in the Central Gulf. 

For these fields, we reduced the number of installations accordingly. 

Table III.13. Additional Non-Original Platform Installations During a Forty-Year 

Cycle 

Size Additional Structures 0-60m Additional Structures 60-200m

<9 0 0

9 5 0.5

10 8 1

11 16 1.5

12 16 3

13 33 4.5

14 33 6

>14 60 6

Based on a time series of platform installations in CGM 0-200

 Associated with these platform installations were assumptions regarding the removal of 

all non-original structures. We did not decline these structures according to production levels, 

like the original structures. Rather, we abandoned these structures after a period of time using a 

specific abandonment percentage applied to the current inventory. The abandonment 

percentages were determined during model calibration to ensure that the magnitude of platform 

abandonment was consistent with historic data. In addition, review of time-series platform 

removals indicated that many larger fields delayed the onset of platform removal until 10-15 

years after the commencement of production, unlike smaller fields who typically exhibited 

abandonment behavior after the first 5 years. The platform abandonment percentages used are 

shown in Table III.14. 
III-60

Table III.14. Platform Abandonment Percentage and Year Abandonment Begins 

Size Percent

Year of Production 

Abandonment Begins

<10 5.00% 5

10 3.50% 5

11 3.00% 5

12 2.50% 5

13 2.50% 5

14 2.50% 10

>14 2.50% 15

Based on a model calibration and analysis of platform removals in 0-200m.

 At the conclusion of the producing life, many fields still had a significant number of nonoriginal structures in place. During the calibration of the model, these structures were assumed 

to decline by ten percent per year, until less than five structures remained. At this point, all 

remaining structures were removed. 

 Once the platform structures are assumed to be in place, then development and 

production drilling occurs from the platform structure and as such a different cost structure for 

drilling is utilized in the model. These drilling costs include dry holes and completion costs; dry 

development drilling costs are a function of water depth and drilling depth as are well 

completion and equipment costs (including casing costs). Dry development costs do not include 

the costs to complete and equip a well. The costs associated with a producing (successful) well 

include the dry development drilling costs plus the completion and equipment costs. Each of 

these sets of costs has a specified cost function. Dry development costs are a function of water 

depth and drilling depth. These per well drilling costs are generally lower than exploration 

drilling costs due to the use of the platform structure already assumed to be in place. These cost 

equations are shown below: 

 $/well = 1,500,000 + (1,500 + 0.04 * DD) * WD + (0.035 *DD – 300) *DD (Eq. III.22)

and in water depth greater than 900 meters: 

 $/well = 5,500,000 + (150 + 0.004 * DD) * WD + (0.035 *DD – 250) *DD (Eq. III.23)

where: 

 WD = water depth 

 DD = drilling depth 

Well completion and equipment costs are also a function of water depth and drilling depth and 

range from $800,000 to as much as $3.3 million per well depending on drilling depth. 

 With regard to development and production wells, the model determines not only the cost 

on a per-well basis, but also the number of wells that must be drilled. We relied on data from the 

“borehole” file provided to us by MMS as the basis to analyze the number of both successful and 
III-61

unsuccessful wells that would be drilled in a given water depth and field class. This file 

contained data, by API Well Number, on all well drilling that has occurred in the Gulf of Mexico 

since the 1940s. The ratio of producing to non-producing wells is expressed in the model as a 

functional relationship between field size and water depth. In water depths less than 800 meters, 

we had sufficient historical data to develop a statistical relationship across different field sizes 

using a polynomial function.34 In water depths greater than 800 meters the model applies fixed 

coefficients representing the ratio of non-producing to producing wells ranging from 2 to 3.5. 

This is based on reasonable estimates drawn from the limited amount of historical data. We 

validated these relationships by comparing actual historical data on the number of wells per field 

class as well as production per well per field class with those generated by the model. The 

model calibration allowed us to determine whether we were accurately accounting for the correct 

ratio of producing to non-producing wells. 

 The model also simulates the installation of production equipment. The cost to install 

production equipment on the development structure is a function of the expected peak production 

of oil/gas and therefore depends on field class size. The production equipment includes 

separation facilities, piping, treating equipment, storage systems, etc. The cost equations for 

these facilities relate to both oil and gas production. The oil equipment equations are expressed 

on a per well basis and vary based on peak production and the number of structures in place. 

The gas production equipment includes both top side costs and processing equipment and is 

related to the peak volume of gas production and the number of structures. These costs are 

assumed to occur over the same period as the structure that is installed. 

 The final element of infrastructure that the model considers is the cost of installing 

pipeline systems, both gathering and trunk lines required to ship crude oil and gas onshore. 

These costs depend both on the number of miles of pipeline that are installed when a new field 

comes on line as well as the per mile construction costs. Construction costs are a function of 

both water depth and field size. Water depth dictates both the absolute level of construction 

costs per mile as well as the number of miles installed. Total costs vary based on water depth 

which affects mileage (deeper water wells are further from shore). 

 The number of miles of pipe installed is based on the MMS “Pipeline Master” database 

and a recent MMS report on pipeline infrastructure (Alvarado 1998). The master database 

includes information on miles of pipe installed by field, water depth, and origin and destination 

points among other data. We analyzed these data to match pipeline mileage to field class and 

water depth and we measured total pipeline miles installed over time for each field and averaged 

these field specific data across water depth and field class. These data are summarized below in 

Table III.15. We analyzed the number of new pipeline miles the model predicted would be built 

compared with the number of miles added historically over the last ten years and the model’s 

results fell well within the range of the historical data. 

 

34 These data were developed from the MMS production file and borehole well files. 
III-62

Table III.15. Pipeline Mileage Matrix 

WD Field Class Category

<8 8 9 10 11 12 13 14+

0-60 1.5 2.1 2.7 4.2 5.7 7.4 12.7 18.0

60-200 2.9 4.75 6.6 7.6 8.6 9.7 14.6 19.5 

200-400 6.7 9.1 11.5 12.7 13.9 15.0 21 27.0 

400-800 5.1 7.2 9.3 10.3 11.4 12.5 17.8 23.0 

800-1600 5.7 9.1 12.5 14.6 16.8 19.0 28.5 38.0 

1600+ 4.5 7.6 10.8 12.2 13.6 15.0 22.5 30.0 

 The construction cost for pipelines is based on MMS estimates of the cost to build new 

pipelines in varying water depths (Coastal Marine Institute 2003). These costs range from 

$250,000 per mile to as much as $4.2 million per mile depending on water depth and size of 

pipe. These costs are a function of both water depth and field class size. Again these data and 

the model’s results were compared with data from EIA to validate their accuracy. 

 All capital costs in the model are in current dollars. The user is given the option to inflate 

or deflate costs to account for anticipated inflation or technological advances that may lower 

such costs in the future. This is found in the “Cost Inputs Form” which contains an item “Capital 

Expenses Escalator”; this can be set at a given percentage rate that the user assumes such costs 

will increase in the future. 

Operating Costs 

 Operating costs refer to the ongoing annual costs to operate various facilities used to 

produce oil and gas in the Gulf. These refer to labor costs, fuel and power, communications and 

safety equipment, supplies, well service and workovers, insurance, transportation of personnel 

and equipment. IIC, Inc. maintains data on oil and gas production operating costs and we 

developed estimates of these operating costs on a per barrel (BoE) basis from these data. These 

costs vary according to water depth and range from $3.25 per BoE to $4.50 per BoE. In 

addition, the user is given the option to inflate these costs over time as the model looks out over 

the future time horizon. The “Cost Inputs Form” contains an item “Operating Expenses 

Escalator” which can be set at a given percentage rate that the user assumes such costs will 

increase in the future. 

 The second category of operating costs is pipeline operating costs. These operating costs 

are based on FERC data35 for various GoM pipelines. We obtained data on the cost per barrel 

mile to move a BoE from federal waters to the first onshore delivery point or terminus of a given 

pipeline over a three-year period and took the average of this figure as the cost to move a BoE 

onshore. It should be noted that the costs to move oil and gas from offshore locations to onshore 

delivery points is significantly higher than for onshore pipelines. For example, offshore costs 

were approximately $0.00672 per barrel mile for offshore shipments whereas onshore costs were 

 

35 These data were derived from annual Form 6 reports filed by various offshore Gulf of Mexico pipeline companies 

for the years 1999-2001. These forms include data on costs and revenues, throughput, and mileage which allowed 

the computation to be expressed on a $/barrel-mile basis. 
III-63

about $0.00213 per barrel mile. We applied this cost figure of $0.00672 per barrel mile and, 

based on the data collected for pipeline miles and annual production rates, assumed for each field 

class the model computes the pipeline operating costs. As a result these costs vary both on the 

basis of water depth and field size. Also as with the other element of operating costs, pipeline 

operating costs may also be indexed and inflated at some factor as input by the user. 

 In addition to the typical operating costs, each lessee must pay rentals on non-producing 

tracts. The net present value calculation incorporates rentals through the use of a rental rate per 

acre, the typical number of acres per tract, an assumption regarding the number of years between 

leasing and exploration, and the assumption that the particular field was discovered on one 

successful lease. Analysis of leasing data has shown that the lease to exploration lag increases 

with water depth. The model includes the followings assumed lags: 

Lease to Exploration Lag in Years

Water Depth (meters) Lag

0-60 1

60-200 1

200-400 2

400-800 4

800+ 5

 The user is allowed to modify the rental rate on an area-specific basis in the sub-drill 

form of the model. Rentals are front-loaded into the cost stream by computing yearly rent as the 

rental rate (e.g., $5 per acre) times the number of acres per lease (5,700) inflated each year of the 

lag. Thus, for a field in 200-400 meters of water, we would include two years’ worth of rentals, 

inflated to adjust for the time value of money. Once we begin our normal cost stream (beginning 

with first year of exploration cost) rentals accrue normally until the first year of production. 

Depreciation 

 The third element of costs is depreciation. Depreciation (and depletion) is treated as a 

separate portion of the cost component as it varies with each stage of the EDP process. Taxable 

income is reduced by depreciation. Two methods are employed: MACRS and unit of 

production. A single MACRS useful lives is used: 7 years. Unit of production method deducts 

the asset cost over the life of the asset in proportion to the expected rate of production. As 

described above, various “capital” costs are included in the IIC cost model, including certain 

geological and geophysical (G&G) costs, exploration, delineation development drilling, platform 

construction, production structures, and pipeline construction. According to the tax code, some 

of these costs are expensed in the year incurred, whereas others are capitalized and deducted for 

tax purposes over an extended period of time. The cash flow model treats these costs 

accordingly to compute the tax benefit of depreciation since we are computing after-tax cash 

flows discounted to present value. 

 G&G costs are split into tangible and intangible costs. The ratio depends on the ratio of 

dry to successful wells drilled. (Dry wells are expensed, productive wells are capitalized.) 

Tangible costs are depreciated using the unit of production method. The units of production 

method depreciates the asset over the producing life of the asset by taking annual depreciation 
III-64

equal to the asset cost times the ratio of the annual production to total estimated reserves in that 

year of the asset (field). 

 Exploration and delineation drilling costs are treated as intangible investments and are 

eligible for expensing in the year incurred. 

 Development and production drilling costs are split into tangible and intangible 

components. Of these costs, 30 percent are treated as tangible investment costs and are 

depreciable using MACRS. Of the remaining 70 percent intangible costs, 70 percent of these 

may be expensed with the remaining 30 percent capitalized. Thus in effect 49 percent of these 

costs are expensed and 51 percent are depreciated. 

 Production structure costs are split into tangible and intangible components. Ninety 

percent of these costs are treated as tangible costs and are depreciated using MACRS. The 

remaining 10 percent of these costs are treated as intangible capital investments and are expensed 

in the year incurred. Sub-sea tie-ins, caissons and well protectors are included in this category. 

 Production facilities and pipeline capital costs are treated as capital investments and 

are depreciated using MACRS. 

 Production and pipeline operating costs are expensed in the year incurred. 

 Structure abandonment costs are treated as intangible investment costs and are 

expensed in the year incurred. 

Annual Cash Flows 

 The model computes a series of cash flows simulating the economic realities of the 

decisions behind the EDP process. These cash flows represent the net of inflows and outflows of 

cash as discussed above in the revenue and cost components of the module. In the early years of 

the exploration and development process prior to the advent of oil and gas production, significant 

outflows of cash are anticipated. In later years after the commencement of production, revenues 

from the sale of oil and gas generate positive cash flows and royalties. As the field becomes 

depleted other costs for abandonment are included and the field eventually is exhausted or 

becomes uneconomic to produce further. All of these annual cash flows, both positive and 

negative, must be discounted to a present value today. This is the heart and soul of a discounted 

net present value analysis. A discount rate must be selected by the user to represent the 

opportunity cost of capital or, alternatively, a required rate of return that the EDP project must 

meet to be considered worth proceeding. A positive NPV under all of the assumptions described 

above would mean a project passes economic muster, whereas a negative NPV suggests it is not 

worth proceeding at the current point in time. 

 The model allows the user discretion in selecting a discount rate to compute the model 

year NPVs. This is found in the Cost Inputs form under “Discount Rate (%).” The default rate 

is set at 10 percent. It is also important for the user to be aware that since taxes are considered in 

this model, this is an after-tax discount rate. The model does not allow the user to modify the 

historic NPVs that are used in the generation of expected model wells. The well generation 
III-65

routine is based upon ten years’ worth of historical data for a given model year. This means in 

the first ten model years, historical NPV and discovery data are being used to generate the 

expected number of exploratory wells in those model years. Furthermore, the historical drilling 

equations and coefficient determination were performed using the historic NPV values. These 

NPVs were determined based on actual historical data, including oil prices, gas prices, tax rates, 

and royalty rules. The discount rate applied in computing the historic NPVs was based on the 

30-year Treasury yield plus a risk premium of 5 percent in each given history year. Through the 

use of the historic NPVs utility, the user is allowed to view how the historic NPVs might change 

based on an interpretation of a uniform discount rate, but the user is not allowed to overwrite the 

actual NPVs used in the underlying historic well-fitting algorithm or determination of historic 

well generation. 

 When the model is run, it generates only a limited amount of information relating to 

economic data in the output files. This is largely due to the stated goal and purpose of the model 

as provided in the Work Statement. One element that is reported with each model run is the 

level of royalties given the assumptions underlying the particular model run. In this way the 

model provides a direct way to measure not only the impact on resource discovery, development, 

and production of different royalty regimes, but also a direct measure of the anticipated royalty 

revenues. 
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III-67

Appendix A. References Cited 

Alvarado, A. 1998. Pipeline regulatory issues and deepwater production. Washington, DC: 

U.S. Department of the Interior, Minerals Management Service. GP-9801. 

Arps, J.J. and T.G. Roberts. 1958. Economics of drilling for cretaceous oil on the east flank of 

the Denver-Julesburg Basin. Bulletin of the American Association of Petroleum 

Geologists, November. 

Attanasi, E.D. and J.L. Haynes. 1983. Future supply of oil and gas from the Gulf of Mexico. 

U.S. Geological Society Professional Paper 1294. 

Coastal Marine Institute. 2003. Modeling the economic impacts of offshore oil and gas 

activities in the Gulf of Mexico: methods and application. Washington, DC: U.S. 

Department of the Interior, Minerals Management Service. MMS 2003-018. 

Drew, L.J., J.H. Schuenemeyer, W. J. Bawiec. 1982. Estimation of the future rates of oil and 

gas discoveries in the western Gulf of Mexico. U.S. Geological Survey Professional 

Paper 1252. 

Eckbo, P. et al. 1978. Oil supply forecasting: a disaggregated process approach. Bell Journal 

of Economics, Spring. 

Eppink, J.F. et al. 2001. Assessment of natural gas and oil supply issues in the deepwater Gulf 

of Mexcio. Presented at Offshore Technology Conference. OTC 13225. 

Johnston, D. 1985. Comparison of alternatives for deepwater development in the Gulf. March. 

SPE 13779. 

Kaiser, M.J. et al. 2003. Long-term infrastructure forecasting in the Gulf of Mexico: a 

decision- and resource-based approach. Washington, DC: U.S. Department of the 

Interior, Minerals Management Service. MMS 2003-000. 

Kaufman, G.M. et al. 1981. Predicting the time rate of supply from a petroleum play. In: 

Ramsey, J.B., ed. The Economics of Exploration for Energy Resources. Greenwich, CT: 

JAI Press. 

Leffler, W. et al. 2003. Deepwater petroleum exploration and production. Tulsa, OK: Pennwell 

Publishing. 

Lore, G.L. et al. 2001. 2000 assessment of conventionally recoverable hydrocarbon resources of 

the Gulf of Mexico and Atlantic OCS as of January 1, 1999. Washington, DC: U.S. 

Department of the Interior, Minerals Management Service. MMS 2001-087. 

McGinnis, M. et al. 2001. The politics, economics and ecology of decommissioning offshore oil 

and gas structures. Washington, DC: U.S. Department of the Interior, Minerals 

Management Service. MMS 2001-006. 
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Melancon, J.M., R. Bongiovanni, R. Baud. 2002. Gulf of Mexico Outer Continental Shelf daily 

oil and gas production rate projections from 2002 through 2006. Washington, DC: U.S. 

Department of the Interior, Minerals Management Service. MMS 2001-044. 

Pulsipher, A. et al. 2001. Forecasting the number of offshore platforms on the Gulf of Mexico 

OCS to the year 2023. Washington, DC: U.S. Department of the Interior, Minerals 

Management Service. MMS 2001-013. 

U.S. Geological Survey. 1980. Future supply of oil and gas from the Permian Basin of West 

Texas and southeastern New Mexico. Circular 828. Washington, DC: U.S. Department 

of the Interior. 

Vidas, E.H., K.C. Manger, and T.J. Woods. 1985. The oil and gas resource potential and 

economics of the Gulf shelf and slope. SPE 14357. 

Walls, M.A. 1992. Modeling and forecasting the supply of oil and gas: a survey of existing 

approaches. Resources and Energy, September. 

__________. 1994. Using a ‘hybrid’ approach to model oil and gas supply: a case study of the 

Gulf of Mexico Outer Continental Shelf. Land Economics, February. 
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Appendix B. Review of Oil and Gas Supply Models 

 The first task of this project was to review various oil and gas supply models to gain a 

thorough understanding of the various approaches that have been taken to model oil and gas 

production and reserve additions in the Gulf of Mexico. The critical review of these models has 

covered both theoretical and practical issues to determine whether “pieces” of these models 

might be incorporated in this modeling effort. 

MMS – Current E&D Model 

 The current MMS E&D model is a spreadsheet-based model that predicts future 

exploration, development, and production activity based largely on past, historical experience in 

the Gulf of Mexico (GoM). The determination of future production is drawn from three different 

categories of reserves: proved reserves, unproved reserves, and undiscovered resources. The 

production of producing proven reserves and non-producing proved and unproved reserves 

follows historical decline curves based on the trends established from historical data. As part of 

this process, the MMS has performed significant field mapping studies, investigated lags 

between lease bids and drilling activity and size of field, and established a series of production 

decline curves applicable. An important consideration in the future production of proved and 

unproved reserves is that they are constrained by the total grown remaining proved and unproved 

reserves from MMS’s latest assessment (2000). 

The undiscovered resources are addressed separately from the proved and unproved reserves and 

are determined by adding the anticipated production from each future lease sale in the GoM. 

Again, the total production is not allowed to exceed the total undiscovered resource base 

provided in the 2000 MMS assessment. The starting point for determining the E&D projections 

of undiscovered resources involves examination of the historical lease sale data. The MMS 

examined the previously undiscovered resources developed for each annual bid after lease sale 

72.36 From this history, the MMS estimated the annual reserve development from each sale. 

Although there was a large variance in the volumes, 200-300 million barrels per sale was 

considered representative. Using this estimate, the MMS projects reserve development from the 

present throughout the planning horizon. Two important constraints apply: 

1. MMS assumes that the reserves development for the first five annual leasing sales in the 

future planning period is constant, in accordance with the assumptions inherent in the 

five-year lease sale environmental impact statement (EIS) and analysis. 

2. The latest MMS resource estimate is considered to be binding. That is, the E&D 

projection of reserve development over the planning period must agree with the official 

MMS estimate of the total oil and gas endowment. 

 

36 MMS considered earlier sales as unrepresentative of the current leasing environment because initial lease sales 

were smaller and more selective than the area wide sales that have been conducted in the recent past. 
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 The final reserve discovery projection is the result of a considerable amount of expert 

judgement being applied within an historical framework. While the reserves assumed to be 

developed for each year of the EIS period roughly approximate the average of previous (historic) 

years, it usually does not equal that average. Generally, the MMS assumes that reserves 

development will decline slowly after the five-year EIS period, consistent with dwindling 

undiscovered resources. Adjustment of the reserves that are developed as a result of sales 

during the EIS period and the rate of decline in reserves development thereafter produces an 

estimate of total reserves developed that matches the MMS total endowment assessment. 

MMS – P and D Model 

 The P and D Model (Melancon et al. 2002) is a short-term (five-year) model that projects 

production and development (not exploration) in the Gulf of Mexico based largely on actual, 

existing production facilities and anticipated new development. The model breaks out shallow 

water production from deepwater production. Shallow-water production (less than 1,000 ft.) is 

forecast based on historical production and decline rates and the rate of reserves replacement. 

 Deepwater production and development is based on a survey conducted by MMS of 

deepwater operators’ new projects and projections of expected production and reserves. The 

model assumes all deepwater fields that have been discovered will commence production prior to 

2007 and production has been accurately forecasted by these operators to within an error range 

of 7 percent for oil and 8 percent for gas. A low case and a high case are presented for both oil 

and gas production for both shallow and deepwater production. 

MMS – RSVP Model 

 RSVP is MMS’s royalty suspension viability program which was developed to perform 

economic calculations of the viability of field development projects in deepwater areas of the 

Gulf of Mexico. The model allows users to compare the viability of development projects with 

and without royalty relief. Users include applicants seeking royalty suspension under the 

Deepwater Royalty Relief Act, and thus the model is available to users outside MMS. 

 The model is comprised of two modules: a resource module that computes recoverable 

resources for a field and a viability module that performs a discounted cash flow analysis on a 

net present value basis of the revenues generated by the resources compared with the costs of 

developing and producing the field.37 The program is spreadsheet-based (Excel) and also utilizes 

the Crystal Ball risk-analysis software. An interesting feature of the model is that critical 

parameters are input as probability distributions, and the program then performs a simulation 

consisting of multiple iterations to provide a distribution of possible outcomes. The mean of the 

distribution is used as the expected value of the simulation. 

 

37 A third module exists, the Economic Module, which is internal to MMS, and computes the full project net present 

value including all royalties and also calculates the suspension volume needed to set the NPV = 0. 
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 The resource module calculates the probability distribution parameters of the resources 

based on inputs of engineering and geologic information and provides this as an input (total 

reserves and oil fraction) into the viability module. The viability module computes the 

prospective net present value of the field based on various economic data and the resource base 

estimated from the resource module. The viability module uses the resource estimate to choose a 

capital expenditure profile, a reserves estimate, and a development and production scenario 

(costs and revenues). 

MMS - IMODEL 

 IMODEL is a computer program developed by the MMS to simulate a series of OCS 

lease sales in order to determine their likely economic outcomes. Users provide as inputs a series 

of assumptions about the number and size of the tracts being offered for sale, prices, taxes, and 

other factors. Using these assumptions, the model evaluates the tracts and applies a gametheoretical bidding model to compute the resulting expected bonuses. The model also computes 

reserve levels, production profiles, royalties, taxes, and profits resulting from the assumed lease 

sales. The model presumes a schedule of exploration and development activity based on 

historical data. 

 Forecasting the result of any auction is notoriously difficult, and IMODEL’s results 

depend entirely on the choice of inputs. IMODEL offers these primary advantages: it 

automatically calculates the effect of price changes and offering terms, freeing the user to 

concentrate on tract inventories and other sensitive inputs. Second, it forecasts production levels, 

royalties, taxes, and profits as well as bonuses, making it easier to consider long-range 

consequences of a proposed schedule in parallel with the more-immediate bonus receipts. And 

finally, it permits tests of policy changes, such as changes in royalty rates, tax rates, minimum 

bids, and lease terms. 

 The simulation depends on evaluation of OCS tracts from the point of view of potential 

bidders. Evaluation starts with a discounted cash flow analysis, assuming a particular schedule 

of exploration. Many tracts also include an “option premium,” which is the extra value resulting 

from the operator’s ability to delay exploration, pending more information. IMODEL calculates 

the option value resulting from possible price changes as well as possible new discoveries in 

frontier areas. 

DOE – EIA OSS (Offshore Supply Submodule) 

 The Offshore Supply Submodule is an “off-line” hybrid module to EIA’s oil and gas 

supply model and is used to generate estimates of reserve additions and production from 

undiscovered resources in the Gulf of Mexico offshore region. There are two components to the 

OSS: an exogenous component that computes undiscovered resource price-supply relationships 

based on a detailed, field-specific DCF method. This module utilizes data on field properties, 

production profiles, technology choices, exploration, and production costs to compute a 

minimum acceptable supply price at which the discounted net present value of a prospect equals 

zero. This module relies on a database from MMS of undiscovered prospects (“plays”) as a 
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starting point. The price and reserve estimates are then aggregated by region and resource type to 

generate resource-specific price-supply curves. 

 Cost components are also estimated and provide inputs into the endogenous component 

of the module. The exploration, development and production schedules are based on an EDP 

“plan” that both realistically portrays existing and/or anticipated offshore activity and allows for 

the most economic development of a given field. Various engineering cost algorithms have been 

developed as the key cost elements in the module and are based on experience, prior analytical 

work and other sources. Finally the financial analysis or DCF component estimates revenues 

and costs over the life of the project and provides the net present value after-tax cash flows. We 

have utilized various parts of the cost component of this model in our own modeling effort. 

 The endogenous component uses the price-supply curves and the cost data to determine 

the exploration, development, and production schedules of the oil and gas resources over a 15-20 

year time frame. Reserves are estimated subject to physical and financial constraints and a 

reserves growth component based on a multiplier is also estimated. Production is based on a 

reserves to production ratio based on historical data. This module also includes the capability to 

estimate the impact of advanced technology on exploration, production, drilling, platform, and 

operating costs. 

DOE – TORIS Model 

 DOE’s Fossil Energy Office (FE) has for years used the TORIS Model to support its 

advanced research and development programs. TORIS is an integrated set of models focused on 

U.S. oil resources and is designed to facilitate the decision-making process integral to the 

formulation of policy affecting the exploration and development of petroleum resources. TORIS 

is capable of evaluating the entire domestic oil resource base by modeling various state-of-the-art 

technologies. It is a hybrid engineering and economic model with a heavy focus on modeling of 

different recovery technologies. TORIS evaluates the technical and economic viability of these 

processes for individual reservoirs and selects the most viable technology. The results for 

individual reservoirs are aggregated to state, regional and national levels as required. The output 

from the model consists primarily of production, reserves, tax, and revenue streams. 

 The model consists of an engineering reservoir database containing data on rock and fluid 

properties of over 2,500 crude oil reservoirs in the United States. It also includes volumetric and 

development data and geologic classification information. The second element of the model is 

the production database which contains data on annual production and well counts for 1,800 

reservoirs. These data are used to assess conventional decline curves of existing production. 

The third component is a variety of process screening models that evaluate the application of 

various technologies to various reservoirs. The fourth component is the predictive models which 

are used to model each specific technology and recovery process and to apply these to particular 

reservoirs to estimate production rates for oil and gas. The fifth component of TORIS is the 

economic analysis model which projects detailed cash flow evaluations at the reservoir level. 

This model may be used to evaluate specific policy issues as well. Finally TORIS also includes 

a comprehensive system for modeling offshore Gulf of Mexico resources. This component of 

the model is reportedly still under development but the intent is evaluate production, reserves, 
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technology and economic viability at the well, platform, and lease level. A hyberbolic decline 

model is being planned for estimated future technically recoverable oil and gas in the OCS. 

AGA – TERA Model 

 Total Energy Resource Analysis (TERA) is a long-term energy modeling system. This 

model is a hybrid model that utilizes an econometric approach to determine drilling activity in 

the lower 48 based on a profitability index. Drilling activity is tied to reserve additions via a 

finding rate based on historical experience. The finding rate is not based on an explicit resource 

assessment, but does reflect a decline in reserve growth given depletion of existing resources and 

cumulative reserve growth. Technology interacts with the model by reducing the number of dry 

holes and thus reduces costs of finding new reserves. 

 The TERA model contains several sub-modules including: 

1. Demand sub-models for residential, commercial, industrial, transportation and 

electricity generation sectors; 

2. Drilling and reserve addition models for onshore and offshore natural gas and oil; 

3. Pricing models that include differential transportation costs and taxes for each 

demand segment; 

4. A natural gas reserve “balances” model. 

Prices and price projections balance the overall supply (including reserve additions) and demand 

of natural gas. 

DRI Oil and Gas Model 

 This model is proprietary, but it has been briefly described to us as follows. It is a longterm supply model of oil and gas that employs a hybrid approach. Lower 48 exploratory drilling 

depends on an economic component that projects net revenues or cash flow. Development 

drilling is a lagged function of exploratory wells. New supplies result from discoveries that are a 

function of an historical finding rate. No explicit resource base is used and technology is treated 

as an exogenous variable. As this model was developed by a private company, few other details 

are publicly available about this model. 

Walls Supply Model 

This model (Walls 1994) takes a “hybrid” approach by combining an econometric model 

of exploration and development activity with a geologic/engineering model of the discovery 

process that explains discoveries as a function of cumulative drilling. Lags are incorporated into 

the model by using a three-year moving average of discovery rates and by discounting the 
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expected net present value variable in the drilling equation by two periods to allow for delays in 

the response of production to drilling. 

 The drilling program consists of exploratory wells and development wells at a later time 

period. Drilling varies with changes in economic variables such as oil and gas prices and costs 

and also with variations in the number of tracts being leased. More wells are drilled as the NPV 

of a discovery increases; likewise, more wells are drilled when more tracts are leased. Drilling is 

assumed to occur in fixed proportions between exploration and development activity. 

 The per-well NPV is a function of several factors including: the after-tax discounted net 

profit per barrel of oil or mcf of gas where all taxes, royalties and operating costs are accounted 

for; the probability of finding oil or gas based on the success ratio; the expected size of the new 

discovery; the after-tax cost of drilling exploratory and development wells. A reserve additions 

component is included as part of the discovery process and production occurs based on a reserve 

to production ratio based on historical 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.