_________

OCS Study 

 MMS 2004-018 

Modeling Exploration, Development and 

Production in the Gulf of Mexico 

Volume I: Summary 

 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 I: Summary 

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 I: Summary. U.S. Dept. of the Interior, Minerals Management 

Service,Environmental Studies Program, Herndon, VA. OCS Study MMS 2004-018. 11 pp. 
I-i

Table of Contents 

1. Introduction ....................................................................................................I-1

1.1 Study Objectives ...........................................................................................................I-1 

1.2 Study Constraints..........................................................................................................I-1 

2. Study Design and Tasks Performed .............................................................I-2

2.1 Task 1. Model Review .................................................................................................I-2 

2.2 Task 2. Defining and Analyzing Variables..................................................................I-4 

2.3 Task 3. Data Collection.................................................................................................I-5

2.4 Task 4. Establish Linkage to MMS Environmental Models........................................I-6 

2.5 Task 5. Build Model Equations and Implement Model................................................I-6 

2.6 Task 6. Validate and Test Model ..................................................................................I-6 

2.7 MMS Guidance and Review.........................................................................................I-7 

3. Model Structure and Overview ....................................................................I-8

4. Overview of the Study Documentation ......................................................I-11
I-ii
I-iii

List of Figures 

Figure I.1. Schematic of IIC EDP Model ....................................................................................I-9 
I-iv
I-v

List of Tables 

Table I.1. List of Model Outputs ...............................................................................................I-10 
I-1

1. Introduction 

1.1 Study Objectives 

 In late 2002, Innovation & Information Consultants, Inc. (IIC, Inc.) contracted with the 

Minerals Management Service (MMS) of the U.S. Department of the Interior to develop a 

computer model that would estimate oil and gas exploration and discovery, development, and 

production in the Gulf of Mexico (GoM). The model is to be used for a variety of purposes, 

including, but not limited to: 

• providing inputs for the environmental impact statements associated with various 

MMS program documents and lease sale analyses, and 

• informing policy evaluation studies such as those associated with changes in 

MMS royalty assessment and collection. 

 Environmental impact forecasts depend directly on activity projections, so the model 

development process included a substantial amount of data examination and statistical analysis to 

quantify relationships that would increase the information available to estimate environmental 

and social impacts. Because the second objective – policy analysis – is so broad, it was 

imperative that the model be flexible to permit addressing a wide range of problems. In 

addition, the model’s projections must be sufficiently detailed by area, water depth, and well 

depth to be useful to these MMS objectives. 

 The contract left open whether IIC, Inc. would create a new model or adapt an existing 

model to MMS requirements. After reviewing several well-known model simulations that could 

address the GoM environment, it was clear that the most efficient course of action was a new 

development effort. The result was the IIC EDP Model. 

1.2 Study Constraints 

 MMS representatives specified several requirements for the model that, in large part, led 

to our decision to construct a new computer model: 

• The model should be a quick-to-iterate, portable computer program that could be 

easily adapted to a wide range of problems. Uncertainty in many of the variable 

inputs leads to the need to have a model that can be run quickly through a large 

number of sensitivity cases. 

• Ease in changing inputs and data availability should be insured by depending 

heavily on data that MMS itself produces. 
I-2

• The model must adhere to the MMS’s long-term forecasts for recoverable 

resources in the GoM. That is, the model should not produce oil and gas 

quantities that exceed MMS estimates of resources in place. 

• The model should affect a smooth transition between history and model 

prediction. Since there have been more than 60 years of drilling, development, 

and production in the GoM, over 1,200 oil and gas fields have been discovered, 

most of which are in the development, delineation, or production stages. For 

those in production, many will soon reach the ends of their normal lives. 

Additionally, a substantial amount of infrastructure is already present and serving 

current activities. The effects that various regulatory and policy actions have on 

these fields and infrastructure in the future will be the focus of some of the more 

common uses of the model. Therefore, it should not only predict future field 

discoveries and their associated development and production, but also model the 

remaining lives of existing fields and major logistical assets. 

 Since we were undertaking a new model development, we were able to rely extensively 

on MMS data and involve MMS representatives in the details of the development to ensure 

compliance with these constraints. Finally, 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. 

2. Study Design and Tasks Performed 

 Research and data analysis consumed roughly two-thirds of the effort spent on this 

project. This period of study produced a series of findings and observations that then guided 

development of the model. IIC, Inc. senior staff met with MMS technical staff several times 

over the course of the project to ensure the model would meet MMS’s needs. Our study design 

included six technical tasks, described below, plus delivery of a report, user’s guide for the 

model, and a technical report on the mathematical detail of the model, analyses performed, and 

the data sources used in developing and implementing the model. The following sections 

summarize the technical tasks that were performed as part of this study design. 

2.1 Task 1. Model Review 

 The objective of this task was to determine whether it would be easier to adapt an 

existing model to address MMS’s specific requirements, or to build a new model. We initially 

considered the following models, some of which were suggested by MMS:1

 

• The Energy Information Administration (EIA) Oil and Gas Supply Model 

(Offshore Supply Module) 

 

1

 See Appendix III-A for a more detailed review of these models. 
I-3

• MMS’s E & D Scenario Model 

• MMS’s IMODEL 

• MMS’s Gulf of Mexico Production and Development (PD) projections 

• MMS RSVP Model 

• American Gas Association (AGA) Model 

• Margaret Walls’ (1994) hybrid econometric approach as described in Land 

Economics. 

• Department of Energy (Office of Fossil Energy) TORIS Model 

 Early in the model review task, we determined it would be more difficult to try to 

acquire, understand, and reprogram an existing model than to develop and program a new one. 

For example, at the time we reviewed its documentation, EIA’s Offshore Model was coded in 

FORTRAN, considered to be outmoded by most contemporary programmers.2

 Additionally, its 

logic did not include a field discovery submodel, a component that MMS users recommended. 

Our approach quickly evolved to examining existing documentation of these models to draw on 

the theory and approach of others where we could, and still answer the broad requirements listed 

above. 

 We found the EIA’s approach to be very useful in a number of areas, particularly for our 

cost submodel. The model developed under this contract would replace MMS’s E & D Scenario 

Model, so we employed its output and some of its methods as guidelines for what we would 

develop. We elected to build an expected value simulation, so MMS’s IMODEL structure was 

not applicable, but we found its approach useful toward understanding how MMS views the 

leasing process and results.3

 Additionally, the model is very “user-friendly,” so we designed a 

similar user interface structure. 

 We also studied closely Walls’ modeling paper (1994), and, more importantly, traced her 

references back to the original U.S. Geological Service (USGS) papers, some of which were 

written almost 50 years ago. Despite their age, we found the theory applicable to the problem at 

hand in the Gulf of Mexico. After considerable data analysis, we independently arrived at 

discovery and exploratory well generation submodels that are similar in form to what Walls 

proposed based in part on the approaches found in these USGS papers. Finally, important 

elements of our review include noting strengths and shortcomings of the methods or models that 

were used in support of each effort (e.g., inability to distinguish by water or drilling depth, 

enough detail either geographically or geologically, inadequate data analysis, etc.). 

 

2

 Since our initial consideration in the fall of 2002, the model apparently has been reprogrammed. 

3

 It is important to recognize that we are not attempting to model the leasing process itself. 
I-4

2.2 Task 2. Defining and Analyzing Variables 

 This phase of the study encompassed identifying variables to be included in the model, 

performing a requirements analysis aimed at determining the MMS’s primary uses of the model, 

and defining the inputs and outputs that are needed to support that use. The work statement 

generated a lengthy list of possible variables to consider. Variables that we considered included 

the following: 

• the number of wells drilled by type and drilling depth (exploration, development, 

production), 

• reserve additions, 

• existing reserves, 

• oil and gas production, 

• platforms installed, added, and removed, 

• miles of pipeline installed, 

• oil and gas prices, 

• royalty regimes, 

• cost variables such as exploration and development drilling, platform costs, 

production drilling, other production and equipment costs, pipeline capital and 

operating costs, platform abandonment costs. 

 We also reviewed various MMS studies and decision documents such as the latest fiveyear leasing program EIS, a multi-sale EIS, and their supporting documents. An important part 

of our requirements review was a two-day, multi-session set of interviews with MMS staff 

members who would either be using the model or the output from it. The sessions began with 

MMS representatives presenting their current methodology, the E & D Scenario Model, and then 

evolved into detailed discussions with staff members who perform the EIS analyses for lease 

sales and five-year leasing programs. Broad issues addressed included: 

• MMS’s views on other models’ shortcomings; 

• what additional capability would MMS like to have; 

• what types of output variables do the users need; 

• MMS staff perspectives on the exploration, development and production process 

in the GoM that should be reflected in the model; 
I-5

• computer and software issues such as programming language, operating system 

environment, input/output method preferences, and how many people will need to 

access the uncompiled version of the program. 

 IIC, Inc. staff also provided a small spreadsheet model of GoM exploration, development 

and production that aimed at focusing consideration of key technical areas. This was a useful 

tool because we received a number of more detailed suggestions than might otherwise be the 

case in a simple discussion forum. Following the meeting, we produced an eight-page detailed 

memorandum to the MMS staff that documented our understanding of MMS requirements and 

comments. This was our initial point of departure for developing the model. However, as noted 

above the development process over the next year or more was highly interactive. 

2.3 Task 3. Data Collection 

 We submitted our first data request at the kickoff meeting and received a major portion of 

the data immediately. This request included 13 items from publicly available files (some were 

downloadable free, but others carried a purchase charge), 18 categories of data on which we 

sought MMS recommendations regarding sources, and 39 reports, some of which had multiple 

volumes and appendices, or multi-year issues. We also relied on data from a number of other 

sources including the Department of Energy, Energy Information Administration, Oil and Gas 

Journal, Rigzone, Baker Hughes, American Petroleum Institute, Platt’s, and various technical 

and economic reports and articles. 

 The data we found most useful in the model development included (but were not limited 

to): 

• Data files associated with the 2000 Assessment of Conventionally Recoverable 

Hydrocarbon Resources of the Gulf of Mexico and Atlantic OCS as of January 1, 

1999 (Lore et al. 2001, often called the “National Assessment”). This was our 

primary source of data on discovered and undiscovered fields. 

• The “well bore” or “borehole” file that can be downloaded from MMS’s website. 

It lists 32 data elements on every well drilled in the GoM. 

• Pipeline master database. 

• MMS database extract files providing yearly reserve estimates for discovered 

fields. We used this to examine reserve growth, analyze production as a function 

of field reserves, and to update the discovered fields in the 2000 National 

Assessment. 

• Platform data extracted from the MMS database. 

• Monthly production data by field, by year, and by well extracted from the MMS 

database. 
I-6

 The last three data sets are not openly available to users outside the federal government. 

IIC, Inc. staff signed confidentiality statements to ensure protection of any proprietary 

information received through these data exchanges. 

2.4 Task 4. Establish Linkage to MMS Environmental Models 

 This task was essentially a requirements review of the particular needs of the MMS 

environmental analysts who perform the EIS studies. Although most of that was accomplished 

in the two-day kick-off meeting, discussion of those requirements frequently took place 

throughout the study as model development proceeded. We interacted with MMS staff to ensure 

that outputs from the IIC EDP model would meet the needs of those at MMS performing 

environmental analyses and modeling exercises. 

2.5 Task 5. Build Model Equations and Implement Model 

 Model equation development was scheduled for the last half of this project, but actually 

began prior to the kickoff meeting and continued throughout the effort. Although we studied 

other models’ approaches to modeling various aspects of the exploration, discovery, 

development and production process, we relied on the data for the Gulf of Mexico to dictate the 

model’s mathematical structure and detail. In the next section, we summarize the approach we 

determined best to model each aspect of the process and present an overview of the model itself. 

Details of the data, mathematics, and model flow are in the Technical Appendix (Volume III of 

the report) and the User’s Guide (Volume II). 

2.6 Task 6. Validate and Test Model 

 Most of the validation of the subcomponents of the IIC EDP Model occurred as they 

were being developed. For example, we fit discovery equations to actual field discoveries in the 

water depth and planning areas used to describe the GoM. A similar procedure was employed to 

construct the submodels that produced exploratory wells, reserves growth, and production as a 

function of known reserves at the field level. Cost equations and their results were verified 

based on historical cost information from various published sources. The model also draws on a 

substantial amount of historic data input as internal databases to insure that the “model years” 

simulated transition smoothly from the historic series. 

 Each model run actually begins by overlapping one or two history years. We began our 

“model year” simulation 2002, although the model was delivered to MMS in early 2004. That is 

because 2001 was the last year in which we had reasonable confidence that the field discovery 

data were current. This practice allows for two years of validation and calibration where data 

sources are current enough to make this possible. 

 Near the end of the development of the IIC EDP Model, IIC, Inc. staff met with MMS 

representatives to run the preliminary model and to receive other guidance MMS thought 

appropriate. This meeting also addressed model calibration and validation. In the process of 
I-7

performing multiple test cases, we were able to validate the reasonableness of the model theory 

and algorithms, and confirmed that test case results were generally consistent with MMS 

expectations. The calibration effort allowed us to isolate certain areas where adjustments were 

needed. We addressed these and other issues with subsequent model modifications to ensure that 

the model accurately portrayed historical precedent. 

2.7 MMS Guidance and Review 

 Although we frequently discussed data and methodology issues with the MMS staff, we 

held several more structured reviews to insure that the model development was on the right track. 

The first, described above, was the kickoff meeting. Three months after the kickoff meeting, we 

provided a detailed 15-page quarterly progress memorandum outlining our findings to date and 

highlighting problem areas, primarily in finding data to support certain aspects of the model 

development. Much of this report recounted our approaches to representing certain processes 

functionally (e.g., field discoveries as functions of exploratory wells, production versus field age 

and/or reserves remaining, etc.). 

 The project’s midterm review in April 2003 at MMS offices in Herndon, Virginia took 

place over a two-day period. It was followed with a summary memorandum describing our 

understanding of the MMS guidance. At this point in the development, the approach was far 

enough along to begin talking about applications of the model, and how they might be addressed. 

Two key issues were the “single sale analysis” and deep gas exploration. 

• The “single sale analysis” issue arose from MMS’s historic approach to preparing 

an evaluation of future production and infrastructure activity that would come 

from a particular lease sale. The question comes from our earlier agreement to 

structure the model as an exploration-discovery-development-production model 

without explicitly modeling the leasing process. Therefore, wells drilled and 

fields found in any given model year could come from a number of lease sales in 

the past five to eight years. We decided that it would be necessary to impute the 

distribution of lease years associated with drilling in any given year to handle the 

problem this way similar to the way MMS has addressed it in the past. 

• Deep gas exploration is more of a data definitional problem than a methodological 

problem. In practice, a lease holder could drill for deep gas within an existing 

lease, and the model could treat it as either reserves growth within an existing 

field, or discovery of new fields in an area. This issue was tabled for the time 

being because no one at the meeting could say how deep gas reserves would be 

treated in MMS’s future reserves and resources estimates. 

 One additional issue that we addressed (which delayed the project completion) was that 

of field redefinition. After decades of specifying field sizes according to a certain lognormal 

classification, MMS had decided to change the base of the lognormal calculation in such a way 

that a one-to-one mapping of fields from old to new size classifications was not possible. Since 

we had developed all our test case data and equations on the old method, we were forced to 

revise all our analyses at about the mid-point of the project. 
I-8

 Our third quarterly review in July of 2003 covered some of the same issues as the 

midterm review: discovery model results, production well data and the method of modeling 

production and reserves, single sale modeling, and deep gas royalty relief. MMS proposed an 

acceptable method tying exploratory wells to lease sales, and we once again deferred the 

question of how best to model deep gas exploration. Much of the meeting centered on 

explaining and running the cost and net present value calculation submodel, which was already 

operational. 

 We met with MMS staff twice more before the February 2004 model delivery meeting in 

New Orleans: once for an informal progress report in October 2003, and then for two days in 

December to demonstrate the full model to the primary users. The first meeting was brief, but 

the December meeting was a two-day process of hands-on model demonstration involving a 

variety of cases and sensitivities that the MMS analysts proposed as the meeting progressed. 

Several model calibration issues developed, but the model seemed to be producing meaningful 

results at this point. 

3. Model Structure and Overview 

As noted earlier, MMS’s specifications suggested that a fast-running expected value 

simulation was appropriate. We programmed it in the C# language with database and 

spreadsheet interfaces. Primary outputs are wells drilled of various types, fields discovered, 

production of oil and gas, infrastructure additions and removals, but the IIC EDP Model makes 

available a myriad of secondary parameters that the user can extract from the status variables 

generated by the model. The detailed output required by some applications of the model 

suggested that an aggregate perspective (e.g., GoM wide or play-level) would either be 

inadequate, or require excessive aggregation of widely disparate variables. Therefore, we 

elected to include a substantial amount of field-level modeling, while retaining the capability to 

aggregate where appropriate. 

 Flexibility is permitted by not tying the model to a precise geographic or geologic 

structural depiction of the GoM. Rather, the analyst may define multiple model areas for which 

discovered and undiscovered field counts are input by field size (according to the latest USGS 

category classification method) along with other state variable inputs such as exploration search 

coefficients, exploratory well generation equation parameters, producing field counts by age of 

field, etc. 

 The lack of a pre-programmed model structure permits the analyst to define the problem 

area arbitrarily as the entire GoM, a particular subset of GoM geography (e.g., the Eastern GoM), 

water depth category, some cross-cutting category subset of these, or even in geologic terms 

(e.g., by play or province). While developing inputs for a very esoteric problem definition might 

require a substantial amount of exogenous analysis, the model structure does not preclude it, and 

is made easier by the fact that the logic is designed to employ relationships defined from readily 

available MMS data. 

 A central feature of the IIC EDP Model is its economic submodel that computes revenue 

from production and estimates costs. Each model year, a net present value (NPV) for each area 
I-9

and size of field is computed. User inputs include a commodity price path, cost escalators that 

apply to internal capital and operating costs, and royalty rules and incentive structures. The 

NPVs are used as primary components 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 

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 based on the production profile, the current model year price, and the decision-maker’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. 

 Figure I.1 presents a stylized overview of the IIC EDP model. The model itself is 

composed of several components or submodels including one which models the process of 

discovering new fields, another which generates economic parameters by which 

exploration/producers are simulated to make decisions affecting both the discovery process and 

the development/production process, and finally a component that estimates field production and 

reserves appreciation. As noted above, the model generates a series of output tables indicating 

number and type of wells drilled, fields discovered, production of oil and gas, infrastructure 

additions and remaining resources. 
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Figure I.1. Schematic of IIC EDP Model 

 The simulation is easiest to describe in terms of the normal flow of the exploration and 

development process as follows: 

• Profitability, both due to recent and prospective discoveries, stimulates 

exploratory “wildcat” well drilling. Factors that influence profitability include oil 

and gas prices, costs of development and production, taxes, royalties, size of field, 

potential fields left to be discovered, and many other factors, both input and 

internally generated. 

• Exploratory wells find new fields. The number of fields found in an area in a 

particular year depends on the total field estimate input for that area and what has 

been found in previous years. 

• If newly discovered fields are economic to produce, i.e., the economic submodule 

generated a positive net present value (NPV), they enter a queue and move toward 

production in a subsequent year if resources (principally in-field well drilling) are 

available for development. 

• After discovery, a field’s reserves appreciate according to a user input schedule 

(simulating the continual in-field exploration and delineation process). 

Production is a function of reserve levels for fields as they mature. 

Discovery Model

new discoveries are 

function of NPV, 

Exploration wells and 

rate of success

Inventory of new proven fields

by field size/class, depth, area Lease sale

NPV/Production Model

expected value simulation to

trigger production

lag between discovery, 

development & production

Inputs:

oil/gas prices/projections

costs (cap, oper, aband.)

field characteristics

reserve growth

policy variables

production data

Constraints:

Rig availability

2000 Assessment

Production Function

by field based on historical

field development data

Production from Existing Reserves

field size

depth

area

Production from New Reserves

field size

depth

area

Outputs:

reserves

production

infrastructure
I-11

• At some point, a field’s reserves are essentially exhausted, so its infrastructure 

assets are retired, and it ceases production. 

• Output variables such as reserves added, production, revenue flows, wells drilled, 

platforms installed and removed, and pipeline miles installed, are tabulated and 

made available to the analyst. 

Table I.1. List of Model Outputs 

Discovered fields 

Exploratory wells drilled 

Reserves discovered 

Oil production 

Gas production 

Production wells drilled 

Non-production wells drilled 

Platforms added 

Platforms removed 

Pipeline miles added 

Royalty revenue 

Total drill depth of exploratory wells 

Total drill depth of production wells 

Total drill depth of non-production wells 

4. Overview of the Study Documentation 

 In accordance with the Work Statement, Volume I describes the work performed, the 

model development process and an overview of the model structure and attributes. Volume II is 

the User’s Guide, which provides more detail on model flow and how to use the model. Volume 

III devotes multiple chapters to describing in detail the model logic, mathematics, and data 

sources, as well as observations resulting from the model data analysis. 
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.