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GEOPLAY, A Knowledge-Based 

Expert System-A Model for 

Exploration Play Analysis 

By Betty M. Miller 

U. S. GEOLOGICAL SURVEY BULLETIN 2118 

A knowledge-based expert system designed to peiform as a diagnostic 

consultant to assist geologists to define potential petroleum plays, 

prospects, and reservoirs as input to an exploration play-analysis 

model used in the assessment of hydrocarbon resources 

UNITED STATES GOVERNMENT PRINTING OFFICE, WASHINGTON : 1995 
U.S. DEPARTMENT OF THE INTERIOR 

BRUCE BABBITT, Secretary 

U.S. GEOLOGICAL SURVEY 

Gordon P. Eaton, Director 

For sale by U.S. Geological Survey, Information Services 

Box 25286, Federal Center 

Denver, CO 80225 

Any use of trade, product, or firm names in this publication is for descriptive purposes only and 

does not imply endorsement by the U.S. Government 

Library of Congress Cataloging-in-Publication Data 

Miller, Betty M. 

GEOPLAY : a knowledge-based expert system-A model for exploration play analysis 

I by Betty M. Miller. 

p. em.- (U.S. Geological Survey Bulletin ; 2118) 

Includes bibliographical references. 

Supt. of Docs. no.: I 19.3:2118 

1. GEOPLAY. 2. Stratigraphic traps (Petroleum geology)-Computer 

simulation. 3. Petroleum-Prospecting-Data processing. 4. Play analysis I. 

Title. II. Series. 

QR75.B9 no. 2118TN870.57 

[QE697] 

557.3 s-dc20 

[622'.1828'0285] 95-3058 

CIP 
CONTENTS 

Abstract. ......................................................................................................................... . 

Introduction ................................................................................................................... . 

Exploration Play Analysis .............................................................................................. 2 

Geologic Model Based on the Play-Analysis Approach........................................ 2 

Problems Inherent in the Application of the Exploration Play-Analysis 

Approach.............................................................................................................. 4 

Expert-Systems Technology........................................................................................... 4 

Knowledge-Based Systems.................................................................................... 4 

Object-Oriented Data Bases .. . . . . . . . . . . . . . . . . . . . .... .. .......... .. . . . . . . . . . ... . ...... .. ... . . . . .......... .. .... 5 

Expert-System Shell for Play Analysis ........................... ....................................... 6 

GEOPLA Y -A Model for Exploration Play Analysis................................................... 7 

Building the Knowledge Base................................................................................ 8 

Object-Oriented Hierarchy..................................................................................... 8 

Object and Attribute Editors........................................................................... 14 

Rule-Based System................................................................................................. 14 

Structure of a Rule.......................................................................................... 16 

Rule Editor...................................................................................................... 17 

Goal-Based Consultation........................................................................................ 17 

Goal Editor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 

Inference Strategies, Consultation, and Conclusions in GEOPLA Y.... .......... .. ...... 17 

User Strategies for Results and Conclusions.................................................. 18 

Consultation Process and Applications . . . . .... .. .... .. . .. . .. . .. . . . . .. . . . . ...... .. . .. . . . . .. . .. . . . . . . . . . .. 18 

A Dynamic System................................................................................................. 25 

New Developments in Expert Systems . .. .. . ... . . . . . . . .. . . . . . . . . . . . . . .. ............. ...... ... . . . . .. .......... .. . 25 

Conclusions ......... .. . ... . .............. ............... .. . . . ... . . . . . .. . . . . . . . . . . . . ..... ....... .. . ...... .. ... . .. . .......... .. .. 26 

References Cited............................................................................................................. 26 

Appendixes-Printouts from the GEOPLAY-MAHOGANY Expert System Program 

1. Object-Attribute-Value Hierarchies for the Superclasses Play Attributes, 

Prospect Attributes, and Hydrocarbon Volume Attributes.................................. 30 

2. IF-THEN Rule-Structures Illustrating 18 Rules in the Object-Attribute-Value 

Format for Play-Specific and Prospect-Specific Evaluations............................. 42 

3. Goal-Structures Illustrating 15 Goals in the Object-Attribute Format for 

Play-Specific and Prospect-Specific Evaluations............................................... 51 

4. Example of the Conclusions Arrived at by GEOPLA Y Using the 

Attribute-Values as Defined for a Highly Favorable Play.................................. 52 

FIGURES 

1. Oil and gas appraisal data form used to assess resources in the play-analysis system for the NPRA study............... 3 

2. Diagram showing the partial hierarchy within an object-oriented sedimentary basin expert system......................... 6 

3. Diagram showing the basic architecture of an expert system..................................................................................... 7 

4. Diagram showing GEOPLAY's hierarchy using the Class-Object-Attribute-Value format to define the 

decision-tree structure . ... . .. . . . . . . ................ ..... ... . . . . . . . .. . .. . .. . .. . . . . . . . . . . . . . . . . . . . ....... .. . . . . . . . ..... .. . .. . . . . ... .. . . . . . . . . . . . . . . . .. ................... 9 

5. Illustrations of the Enter Attribute Value window in GEOPLA Y .............................................................................. 15 

III 
IV CONTENTS 

TABLE 

1. Guide to values for source-bed and reservoir-bed attributes for each of three favorability 

classes for GEOPLAY ................................................................................................................................................. 19 
GEOPLAY, A Knowledge-Based Expert SystemA Model for Exploration Play Analysis 

By Betty M. Miller 

ABSTRACT 

GEOPLA Y is a knowledge-based expert system 

designed to perform as a diagnostic consultant or intelligent 

guide to assist the geologist in the recognition, identification, 

and characterization of the geologic conditions used to 

define potential petroleum plays, prospects, and reservoirs 

as input to an exploration play-analysis model. It also provides the geologist with capabilities to document major basin 

components such as stratigraphy, structural geology, and 

sedimentation in order to evaluate the traditional concepts of 

source, reservoir, and trapping mechanisms. 

GEOPLA Y incorporates three basic sets of unique geologic conditions to characterize play-specific, prospect-specific, and reservoir-specific properties for the play-analysis 

model. These specific geologic conditions are evaluated for 

each identified play within a basin and constitute the basic 

geologic data for the expert-system's model. These geologic 

data are defined in terms of characteristics such as geologic 

age, lithology, environment of deposition, depositional systems, stratigraphy, structural style, organic types, and effective porosity used to evaluate source-bed and reservoir-bed 

conditions and trapping mechanisms. 

GEOPLA Y is a knowledge-based hierarchical system 

that follows the order of geologic reasoning used by an expe-

'fienced geologist to arrive at a logical set of conclusions relative to the favorability of the geologic conditions under 

evaluation for prospective plays and prospects within a basin. 

The knowledge-based structures in GEOPLA Y are 

made up of classes, objects, attributes, values, IF-THEN 

Rules, and Goals, the concepts and constructions of which 

are described in this report. These elements work together to 

represent the knowledge and relationships linking the various geologic criteria within GEOPLA Y's knowledge-based 

system. 

The GEOPLA Y expert system results in a better 

knowledge representation of the geologic relationships 

among basin, play, and prospect characteristics than do 

other methodologies currently available. It also provides a 

better means for the geologist to more effectively incorporate inexact information into the expert system to 

accommodate the uncertainties inherent in the geological 

data used for play analysis. 

INTRODUCTION 

Exploration and assessment of most of the world's 

energy and mineral resources require an understanding of 

their relations to the host strata and to the geologic and paleogeographic evolution of the sedimentary basins in which 

they occur. The most important product of the study of these 

host strata is a comprehensive basin analysis that demands 

an interpretation of data from many specialties, such as sedimentology, stratigraphy, geophysics, structural geology, 

and geochemistry, as well as the ability to assess and relate 

many types of multivariate spatial data. 

The concepts and methods of sedimentary basin analysis and energy resource assessment have evolved from fairly 

simplistic geologic studies that employ primarily qualitative 

and semiquantitative techniques to studies of ever-increasing 

complexity that make use of quantitative evaluations of total 

basin systems within three-dimensional frameworks. One of 

the more popular techniques used for petroleum resource 

assessments is play analysis. In play analysis a basin is 

divided into prospective resource areas called plays. The 

resource-appraisal technique focuses on the play as a basic 

unit of geologic analysis in which one or more prospects are 

present in a common or fairly homogeneous geologic setting. A fundamental assumption is that geologic characteristics are significantly correlated within the play but show 

substantially less correlation between plays. 

Computer assistance is essential in performing integrated basin and play-analysis procedures. New applications 

of knowledge-based expert-systems techniques adapted from 

Artificial Intelligence (AI) can be interfaced with computer 

mapping and data-analysis techniques, known as Geographic 

Information Systems (GIS) (Miller, 1992), to provide the 

tools needed to define new strategies and technologies for 

conducting and automating the complex tasks common to 

sedimentary basin analysis and exploration play analysis. 
2 GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

In this report I discuss current research in the application of expert systems and knowledge-acquisition techniques 

to (1) the design and development of an expert system 

model, called GEOPLAY, for characterizing exploration 

plays and (2) the geologic analysis of exploration plays for 

diagnosing geologic conditions favorable to the occurrence 

of petroleum resources. 

Acknowledgments.-The work described in this report 

was mostly funded by the U.S. Geological Survey Director's 

GIS Sweepstakes award to the author in 1988 from funds to 

support GIS research and related computer techniques for 

geologic applications. 

EXPLORATION PLAY ANALYSIS 

Exploration play-analysis methods described in this 

report were first used by the U.S. Geological Survey in 1979 

to assess the petroleum resources of the National Petroleum 

Reserve in Alaska (NPRA) (U.S. Department of the Interior, 

1979). A Monte Carlo simulation model based on an exploration play-analysis approach was used to produce an 

appraisal of the conventional petroleum resources. 

In the exploration play-analysis method used in the 

NPRA, the oil and gas resource base was simulated by the 

integration of two independent submodels: (1) a geologic 

submodel based on a probabilistic assessment of the most 

important geologic variables or attributes in a province or 

basin related to the occurrence of hydrocarbons, and (2) an 

exploration submodel that simulated the search for oil and 

gas in the basin, producing a sequence of discoveries that 

provided an inventory of pools for subsequent evaluation 

with the petroleum development and economic submodels. 

A detailed explanation of these submodels is given in White 

(1981). 

The geologic submodel produced a list of prospects 

(potential drilling targets) and a resource appraisal of the oil 

and gas in-place by using subjective probability distributions 

for the values of the geologic variables estimated by experts 

familiar with the geology of the area. The original basic geology submodel designed for the NPRA study was modified 

and used for the basic geology model as the framework for 

the expert system GEOPLA Y. 

GEOLOGIC MODEL BASED ON THE 

PLAY-ANALYSIS APPROACH 

The geologic assessment procedure devised specifically for application to the NPRA resource assessment 

focuses on the play as a basic unit of geologic analysis. A 

play is an area within a basin in which the geology and geophysics indicate that any prospects present may have similar 

combinations of the major assessable geologic attributes: 

hydrocarbon sources, reservoir beds, and traps. Equally 

important, but more difficult to assess, are the attributes of 

thermal history and the relative timing of oil generation, 

migration, and entrapment. In brief, a play consists of one or 

more prospects in an common or fairly homogeneous 

geologic setting that can be explored using geologic, 

geochemical, and geophysical techniques. 

The play is a useful analytical concept that allows the 

analysis to be sensitive to the physical processes involved in 

the entrapment of oil and gas and in the discovery of oil and 

gas accumulations. A fundamental assumption is that the 

geologic characteristics are significantly related within a 

play but show substantially less correlation between plays. 

Thus, if all the regional geologic characteristics necessary 

for the occurrence of trapped hydrocarbons are present 

within the play area, the play probably will contain accumulations of oil or gas or both; however, if one or more of the 

significant geologic characteristics is missing or unfavorable, all the prospects within the play probably will be 

unsuccessful. The primary output for the geologic model is a 

probabilistic appraisal of oil and gas resources in-place for 

each play. 

The play approach divides the geologic characteristics 

of a potential deposit into three categories: play-specific, 

prospect-specific, and reservoir-specific (Eckbo and others, 

1978). Therefore, for each play, three sets of probabilistic 

judgments are elicited from experts familiar with the local 

geology. Figure 1 is an example of the data form for recording these judgments used in the geologic model and is 

referred to later in this report for the basic framework used 

in the expert system. 

Play-specific attributes consist of geologic characteristics common to the play as a unit and include favorable 

lithology, hydrocarbon source, timing, migration, reservoir 

rock, and number of drillable prospects. The product of the 

single-value probability estimates for hydrocarbon source, 

timing, migration, and potential reservoir facies is termed the 

marginal-play probability; that is, the joint probability that 

all regional geologic characteristics necessary for the existence of oil or gas deposits or prospects in the play are simultaneously favorable. 

Prospect-specific attributes are the geologic characteristics common to the individual prospects within the play 

and include trapping mechanism, minimum effective porosity, and hydrocarbon accumulation. The probability judgment of the presence or absence of these geologic 

characteristics is made conditional on the favorable 

existence of all four play attributes. The product of the three 

prospect-attribute probabilities is termed the conditional 

deposit probability; that is, the probability that a particular 

prospect is an actual accumulation of oil or gas, given that all 

the play attributes are favorable. 

Reservoir-specific attributes are the reservoir characteristics of an individual deposit of oil or gas in the play and 

include area of closure, reservoir thickness, effective 

porosity, trap fill, reservoir depth, water saturation, and 
EXPLORATION PLAY ANALYSIS 

Oil and Gas Appraisal Data Form 

Evaluator: Play Name ----------------

Date Evaluated: -----------------

Probability of 

Attribute Favorable Comments 

or Present 

Hydrocarbon Source 

(/) 

~ Timing > ::J 1!1.0 

c.. ·s Migration 

~ 

Potential Reservoir Facies 

Marginal Play Probability 

Trapping Mechanism 

.... (/) 

u Q) Effective Porosity (>3%) Q) .... 

~~ Hydrocarbon Accumulation 0 .... 

a':~ 

Conditional Deposit 

Probability 

Sand 

Reservoir Lithology 

Carbonate 

Gas 

Hydrocarbon Oil 

~ Probability of equal to or greater than 

(J) 

~ 

e 

100 95 75 50 25 5 0 ::J 

.0 ·s 

~ Area of Closure Q) (x103 Acres) E 

::J 

0 

> 

c: 

0 Reservoir Thick- .0 

«; ness/vertical u 

0 closure (Ft) 

-o 

> 

::r: 

Effective Porosity 

% 

Trap Fill(%) 

Reservoir Depth 

(x103 Ft) 

No. of drillable prospects 

(a play characteristic) 

Proved Reserves (x 106 Bbl; TCF) (If known production) 

Figure 1. Oil and gas appraisal data form used to assess resources in the play-analysis system for the National Petroleum Reserve 

in Alaska study. From U.S. Department of the Interior (1979, p. 121) and Miller (1993, p. 14). 

3 
4 GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

hydrocarbon type. The reservoir-volume characteristics 

jointly determine the volume of oil or gas present in the 

reservoir or pool for a simulated deposit. 

These three basic sets of subjective judgments-playspecific, prospect-specific, and reservoir-specific-are 

made for each of the identified plays within a basin and constitute the basic geologic data that were entered in the 

geologic submodel used in the 1979 NPRA petroleum 

assessment study (Bird, 1988; Miller, 1988). 

PROBLEMS INHERENT IN THE 

APPLICATION OF THE EXPLORATION 

PLAY-ANALYSIS APPROACH 

Problems inherent in the application of exploration play 

analysis to petroleum resource appraisal were recognized 

during the time of the NPRA study. One problem concerns 

the amount and kinds of available data necessary for credible 

input by the geologists for each attribute. In frontier areas 

where few data are available, the subjective evaluations for 

the attributes are usually based on comparisons with analogs. 

The resulting resource assessments are only as good as the 

geologic analogs or the geologic assumptions made by the 

geologists in arriving at the subjective evaluations for the 

attributes as recorded on the form illustrated in figure 1. In 

addition, there is no means by this method to document the 

geologic assumptions made or the uncertainties involved in 

the decision-making procedures to arrive at the subjective 

evaluations recorded as input to the geologic submodel. 

Another serious problem recognized in the NPRA study 

is that many geologists are unfamiliar with the basic principles relative to probability distributions. Additional concerns 

expressed by geologists relate to their acceptance of the geologic assumptions built into the model and to the uncertainties in arriving at assigning values as input on the data form 

for specific probabilities for the individual attributes. A 

related concern is the assumption that all the attributes in the 

play-specific- and prospect-specific-analysis models are 

independent, when in fact, many of the geologic and reservoir attributes are interrelated. 

Additional problems of concern in the exploration playanalysis procedures used in the NPRA study, but which are 

not applicable to this study, are summarized in Miller 

(1988). Those relative to the play-analysis procedures cited 

above are addressed in this report. Some solutions to these 

problems are discussed in the application of an expert system's model for play-analysis procedures. 

EXPERT -SYSTEMS TECHNOLOGY 

In recent years, research in applied artificial intelligence has achieved considerable success, especially in the 

industrial and business world. Among the most significant of 

these successes-and one that has attracted both business 

and research interests since the mid-1960's-is the development of powerful computer software programs known as 

expert systems or knowledge-based systems. These programs are designed to present and apply factual knowledge 

and rules drawn from experts in specific areas to solve complex problems. This area of artificial intelligence research 

concentrates on constructing high-performance software that 

uses symbolic programming to replicate the knowledge, reasoning, and linguistic skills of people in specialized professional domains. These knowledge-based systems are 

different from conventional programming techniques used to 

create the large data-processing systems that we commonly 

associate with computers. By means of complex algorithms, 

conventional systems collect and process large volumes of 

factual data to build information data bases. In contrast, 

knowledge-based systems combine facts, specialized knowledge, and the expert's subjective judgment, as well as any 

levels of uncertainty relative to the available knowledge. 

Even though numerous uses for expert systems are now 

considered feasible for many domains of expertise, these systems are not currently in wide use nor are they readily available in the public domain. Some better known expert systems 

are used as diagnostic tools in the medical profession, such 

as INTERNIST and MYCIN (Hayes-Roth and others, 1983); 

for chemical analyses, such as DENDRAL (Hayes-Roth and 

others, 1983) and AAexpert (Lahiri and Stillman, 1992); and 

as exploration tools in mineral prospecting, such as PROSPECTOR (Duda and others, 1981). Several prototype expert 

systems have been designed by the USGS for microcomputers, such as muPETROL (Miller, 1986, 1987a, b, 1991) for 

classification of sedimentary basins for petroleum assessment and an abbreviated version of PROSPECTOR, called 

muPROSPECTOR (McCammon, 1986, 1990). In general, 

however, very few expert systems presently being used in the 

earth sciences are available for public use. A more detailed 

discussion of various expert systems and expert-systems 

technology is given in Miller (1993). 

KNOWLEDGE-BASED SYSTEMS 

An expert system is defined as a reasoning system that 

uses a knowledge base to capture and replicate the problemsolving ability of human experts. Knowledge-based systems 

contain structured data and reasoning rules that link the evidence about a problem to derived conclusions or hypotheses. 

The domain knowledge is a data base that contains the facts 

and rules of the problem domain (such as play analysis) as 

described by the human expert. The inference engine, that 

part of a knowledge-based system that contains the general 

problem-solving knowledge or inference and the control 

strategies, solves problems and deduces results or diagnoses 

conditions based on user input and information contained in 

the knowledge base. In expert systems, separation of the 
EXPERT-SYSTEMS TECHNOLOGY 5 

knowledge base from both the inference engine and the user 

interface allows the knowledge base to be maintained, 

updated, and expanded without changing the entire control 

structure. 

Knowledge in most specialties, and particularly in the 

earth sciences, is usually derived from both public and private sources. Public knowledge includes published definitions, facts, and theories typically contained in textbooks, 

references, and maps in the domain of study. Expertise in 

research, government, and industry, however, generally calls 

for access to confidential records, techniques, and methodologies and requires possession of private knowledge that is 

not part of the published literature. This unpublished expert 

knowledge frequently consists of rules of thumb, based on 

experience, that have come to be called heuristic rules, or 

heuristics. 

Heuristics enable the expert to make decisions or educated quesses when necessary, to recognize promising solutions to problems, and to deal effectively with incomplete or 

uncertain data. Techniques now being developed in artificial 

intelligence research are capable of dealing with inexact reasoning in expert systems. These various schemes, with their 

ability to simulate reasoning under uncertain conditions, provide an ideal tool for applications in the geologic sciences. 

The capabilities of these expert systems in dealing with reasoning under uncertainty are essential to the geologist, who 

must work with information that is frequently incomplete, 

inferred or interpretive, often uncertain, and sometimes 

unreliable and who must bridge the unknown with little or no 

information in frontier areas. For a more detailed discussion 

of knowledge-based expert systems and knowledge acquisition techniques see Miller (1993). 

Sound expert systems must also accommodate several 

types of uncertainty inherent in earth-science problem solving. The first type of uncertainty is associated with factual 

knowledge. For example, geologic evidence may be difficult 

to observe or is unavailable. It may be ill-defined or may 

have been measured using unreliable techniques or equipment. Most expert systems associate numeric values such as 

probabilities or certainty levels with factual information to 

account for this type of uncertainty. 

The second type of uncertainty is present in the inference rules or heuristics themselves. For example, inference 

rules represent the experience, judgment, or intuition inherent in the interpretive reasoning of experienced geologists. 

Even the judgment of experts can contain a degree of uncertainty. Most expert systems deal with this type of uncertainty 

by assigning a value to each rule that expresses the degree or 

probability for which an expert believes an inference rule to 

be valid. 

The third type of uncertainty is the user's own measure 

of belief in a particular piece of evidence. An example is a 

geologist's belief in the reliability of his data as to the certainty of occurrence of any essential geologic attribute used 

for the analysis. Some expert systems allow the user to 

personally assign the degree of uncertainty for an answer 

(Lecot and Parker, 1986). 

OBJECT-ORIENTED DATA BASES 

A new concept in data bases is gaining acceptance in 

the application of expert systems. This concept is the objectoriented data base, which can store, retrieve, and manage any 

type of graphic, text, numeric, or functional information. 

Broadly speaking, an object refers to any physical or 

conceptual entity that may have many attributes (properties 

or characteristics) and that is an elementary unit of description of anything-a thing, a concept, or an event (Harmon 

and King, 1985). A collection of objects that usually share 

attributes is called a class. Class, the first fundamental concept of object orientation, is the hierarchical construct most 

commonly used to define abstract data types in objectoriented systems. Figure 2 illustrates part of an expert system 

in which all·interactions with information relative to basin 

stratigraphy are through the protocol or interface operations 

of the class Basin Stratigraphy (Miller, 1993). The objects' 

attributes define the structure or state of the basin's stratigraphy. They correspond to properties or characteristics in relational data bases. 

Inheritance, the second fundamental concept in objectoriented data bases, allows the expert to design and explore 

the properties inherited by objects and classes in relation to 

the rules. Within an existing hierarchy, an ordered network 

of concepts or objects in which some are subordinate to others, the expert can build new classes that can inherit behavior 

(such as operations or methods), attributes, and specific values for variables from existing classes. For example, in figure 2 each class has a set of objects covering various 

geologic characteristics, such as lithology and geologic age. 

All classes in the hierarchy share these objects because they 

all inherit from superclass Basin Types. The third fundamental concept of object orientation is object identity, the property that distinguishes each object from all others. With this 

property, objects can contain or refer to other objects. Object 

identity organizes the objects manipulated by an objectoriented program. 

Object-oriented knowledge representation is an ideal 

methodology for applying expert systems to the earth sciences. It has an intuitive appeal because it provides better 

concepts and tools with which to model and represent the 

real world than does the more simplistic rule-based approach 

to expert systems, which only focuses on representing the 

reasoning steps for the particular problem to be solved (Sacerdoti, 1991). Object-oriented systems offer the means to 

identify the physical and conceptual objects that characterize 

the decision-making process by providing a data-modeling 

method that identifies and documents physical and conceptual entities, events, and their relationships to one another. 

Such systems allow the user to identify classes and objects, 
6 GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

Basin Stratigraphy 

BASIN 

TYPES 

Basin Tectonics 

l SUPERCLASS 

J 

l 

CLASSES 

J 

MAJOR 

STRATIGRAPHIC 

UNITS LITHOLOGY DEPOSITIONAL 

ENVIRONMENT 

REGIONAL 

UNCONFORMITIES 

LITHOLOGIC TYPE GEOLOGIC AGE 

L ____ j 

STRUCTURAL 

MECHANISM 

STRUCTURAL 

TYPE 

OBJECTS 

l ATTRIBUTES 

J 

Figure 2. Partial hierarchy within an object-oriented sedimentary basin expert system. Modified from Miller (1993, p. 5). 

and their attributes and relationships, and to define the 

behavior and interactions between objects and classes. 

Object-oriented systems also reveal the reasoning processes 

used by the experts and many of the rules, procedures, and 

constraints used in the decision-making process. Classobject-attribute relationships are ideal for characterizing the 

geologic conditions used to define decision-making procedures in basin and play analysis. For a more detailed description of object-oriented data-based systems see Miller ( 1993 ). 

EXPERT-SYSTEM SHELL FOR 

PLAY ANALYSIS 

The basic architecture of an expert system can be 

divided into two parts. The knowledge base and working 

memory make up one part of the system, and the inference 

engine and all of the subsystems and interfaces constitute the 

second part (fig. 3). The knowledge base contains the facts, 

rules, and heuristics that embody expert knowledge. The 

inference engine contains the inference strategies and 

controls, explanations, and user-interface subsystems that 

experts employ when they manipulate facts and rules to 

reach a decision or conclusion. 

A major effort in the world of commercial software is 

the development of off-the-shelf expert-system shells--computer programs that provide the framework for developing 

expert systems. Using shells greatly reduces the task of 

developing a new expert system. These products differ from 

programming languages in that they already contain the 

inference engine and various interface and knowledgeacquisition aids that determine how they will apply reason to 

reach a conclusion; however, they lack the rules and facts 

contained in the knowledge base. Thus the primary attention 

in working with an expert-system's shell is focused on the 

acquisition of the knowledge and development of the rules 

that make up the construction of the knowledge base. 

In this report I describe the development and implementation of GEOPLA Y, an expert system for characterizing 

exploration plays and diagnosing the geologic conditions 

favorable for the occurrence of potential plays, prospects, 

and reservoirs within a basin. GEOPLA Y is built using an 
GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 7 

KNOWLEDGE BASE 

• RULES 

• FACTS 

I 

INFERENCE ENGINE 

• INCERENCE 

• CONTROL 

I 

KNOWLEDGE 

ACQUISITION 

SUBSYSTEM 

t EXPERT OR KNOWLEDGE 

ENGINEER 

r----, 

I WORKING I 

l+---_..•1 ~ MEMORY I L. ____ .J 

EXPLANATION 

.... SUBSYSTEM .... 

• ADVICE • REASONS 

INPUT/OUTPUT 

SYSTEM 

USER INTERFACE 

t 

USER 

Figure 3. Basic architecture of an expert system. Modified from 

Harmon and King (1985, p. 34). 

expert-system development tool or shell called MAHOGANY (Emerald Intelligence, Ann Arbor, Mich.). Other 

expert-system shells that have been developed for object representation and that have a rule-based reasoning mechanism 

were reviewed for this project: KEE (Knowledge Engineering Environment, by IntelliCorp, Mountain View, Calif.), 

KEYSTONE (Technology Applications, Jacksonville, Fla.), 

and NEXPERT OBJECT (Neuron Data, Palo Alto, Calif.) 

(Miller, 1993). 

MAHOGANY was selected for this project for many 

reasons. It is a user-friendly system, especially for the geologist who is not a computer programmer in the LISP or C 

programming languages. One of its most promising features 

is that it allows the experts to assign certainties directly to the 

rules and to the data values used within the knowledge base. 

It also allows the geologist as the user to assign certainty values to his or her answers. The geologist may also set priorities for the goals or decision-making sequences. This basic 

feature allows the geologist to deal with the uncertainties 

that are a significant part of the geologic knowledge base. 

MAHOGANY includes the following specific features 

that are essential to the design of GEOPLA Y (Emerald Intelligence, 1990): 

•Dealing with problems that have many variables 

or pieces of information that affect the answer, 

situations that have no single right answer, but 

for which the best solution must be selected, and 

problems that have some contributing factors 

that are unknown or uncertain 

•Representing knowledge using a class-objectattribute hierarchy and multiple inheritance for 

objects and attributes 

•Storing knowledge by the use of a rule-based 

structure composed of an IF, a THEN, an ELSE, 

and an UNKNOWN clause; using AND and OR 

binding in complex multiple IF clauses in 

designing rule structures 

•Reasoning and inferencing strategies for deriving geologic characteristics for play analysis 

based on information provided by the geologist 

• Inference strategies based on forward chaining 

(data driven), backward chaining (goal driven), 

and synergistic inference, which combines both 

backward- and forward-chaining to provide a 

more complete set of conclusions 

•Interfacing with the user (geologist) to acquire 

essential information relative to the characteristics defined in the geologic model. Interface 

options are menu driven and graphics oriented 

•Assigning certainties to the rules and facts within 

the knowledge base and to the geologist's 

answers 

•Selecting options for statistical methods to calculate the certainties or probabilities that conclusions or goals are correct. These methods include 

average, cumulative, highest, lowest, or product 

(joint probability) techniques for calculating certainties 

•Setting probability thresholds below which a rule 

will not be considered in arriving at a conclusion 

•Designing goals to answer the questions or arrive 

at a conclusion, to determine the path of the 

decision-making process, and to retrieve and document specific information from the geologist or 

the knowledge base 

•Editing and creating the knowledge-base elements through specific editors; that is, Rule Editor, Word Editor, Object Editor, and Goal Editor 

In order to use the GEOPLA Y expert system the 

MAHOGANY expert-system shell is needed to provide the 

inference engine that runs the program. 

GEOPLA Y-A MODEL FOR 

EXPLORATION PLAY ANALYSIS 

GEOPLA Y is a diagnostic consultant or intelligent 

guide to assist the geologist in organizing, documenting, and 

analyzing, in a consistent manner, geologic data to differentiate petroleum plays, prospects, and reservoirs. These data 

are defined in terms of the essential geologic characteristics 
8 GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

needed to describe source-bed and reservoir-bed conditions 

and trapping mechanisms and include properties such as 

geologic age, lithology, environments of deposition, depositional systems, stratigraphy, and structural style. 

GEOPLA Y allows the geologist to assign numeric values in terms of certainties or beliefs to record the level of 

confidence in the essential geologic criteria used to characterize the petroleum plays and prospects under evaluation. 

The relations between the essential geologic characteristics 

and the favorability of the play and prospect qualities are set 

by the geologic rules of thumb and may also have associated 

certainty values provided by the geologic experts. Combinations of certainty or belief values in essential criteria, 

together with the relations supplied by the experts in the rule 

base, establish the overall belief in the derived conclusions 

relative to the favorability of the play and prospect characteristics. Less experienced geologists are able to follow the 

play-analysis concepts and understand and focus on the 

essential geologic characteristics needed to arrive at the subjective judgments required in the play-analysis model. 

BUILDING THE KNOWLEDGE BASE 

In GEOPLA Y three basic sets of unique geologic 

conditions are identified to characterize the play-specific, 

prospect-specific, and reservoir-specific properties for the 

play-analysis model. These specific geologic conditions are 

evaluated for each of the identified plays within a basin and 

constitute the basic geologic data in the model. 

To arrive at the subjective judgments elicited to complete the data form shown in figure 1, the geologist must, 

however, call upon considerable background experience and 

additional supportive data relative to the geologic conditions 

within the respective basin or play in order to record the single 

values on the data form expressing probabilities of occurrence or favorability for each of the respective attributes. It 

is in the role as a consultant to the geologist that GEOPLA Y 

provides the means to explore and evaluate the basic geologic 

framework of the basin and plays to arrive at the subjective 

judgments recorded in the play-analysis model. 

GEOPLA Y' s knowledge base is a hierarchical scheme 

based on a multilinked structure or decision tree showing 

the relations between the various geological criteria defining a play, prospects, and reservoir conditions. The hierarchical scheme attempts to follow the order of geologic 

reasoning used by an experienced geologist to arrive at a 

logical set of conclusions relative to the geologic conditions 

under evaluation. 

Knowledge-based structures developed within the 

MAHOGANY system are made up of Classes, Objects, 

Attributes, Values, Rules, and Goals. These elements work 

together to represent the knowledge and the relationships 

linking the various geologic criteria within the knowledgebased system. 

OBJECT-ORIENTED HIERARCHY 

Figure 4 illustrates GEOPLA Y' s hierarchy by means of 

a Class-Object-Attribute-Value decision-tree structure or 

network. Incorporating the terminology used on the data 

form (fig. 1) in designing the hierarchical scheme, there are 

three superclasses: Play Attributes, Prospect Attributes, and 

Hydrocarbon Volume Attributes. The use of the expression 

"attributes" within the superclass names is taken directly 

from the data form and should not be confused with its usage 

in the object-attribute relationship as referred to in the hierarchical structure. In the superclass Play Attributes (fig. 4), 

there are four classes: Hydrocarbon Source, Timing, Migration, and Potential Reservoir Facies, which reflect the design 

of the data form. Under the class Hydrocarbon Source there 

is one major object, Probable Source Beds, and 13 attributes. 

These 13 attributes are: 

Direct Evidence 

Geologic Age 

Lithology 

Environment of Deposition 

Structural Form for Basin Development 

Maturity Level of Source Beds 

Type of General Kerogens 

Percent of Total Organic Carbons (TOC) 

Quality of Source Beds 

Proximity to Reservoir Beds 

Areal Extent of Source Beds 

Average Depth to Source Beds 

Average Total Thickness of Source Beds 

Each of the 13 attributes has its own set of values. 

These values are entered by the geologist either as a single 

numeric value or from an automatic-value selection provided for the geologist to answer the query as presented in a 

text field (see fig. 5). For example, tracing this hierarchical 

structure one level lower, under the attribute Geologic Age 

of Source Beds there are eight age categories for value 

selection by the geologist. 

Geologic age Percentage1 

Quaternary/Oligocene/Upper Cretaceous 15.3 

Middle through Lower Cretaceous 31.6 

Upper Jurassic through Upper Permian 26.2 

Lower Permian/Pennsylvanian/Middle Mississippian 8.4 

Lower Mississippian through Devonian 8.3 

Silurian 9.0 

Ordovician through Cambrian 1.0 

Upper Proterozoic (Vendian) 0.2 

2 Age "* * * distribution of effective source rocks given as a percentage 

of the world's original petroleum reserves generated by these rocks." These 

percentage distributions are used in the construction of the rules to determine source-rock favorability (Klemme and Ulmishek, 1991, p. 1810). 
Key to hierarchical structure 

-- Super class 

.......... Class 

----- Object 

Attributes 

PLAY ATTRIBUTES 

GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

Presence of: 

Known fields and production 

Seepages (oil or gas) 

Live shows in wells 

Tar sands 

Tight gas sands 

Oil shales 

Unknown 

No evidence 

;.__ _______ Probable source beds 

Quaternary-OligoceneUpper Cretaceous 

(Coniacian) 

f----- Geologic age of source beds 

Middle (Turonian)-

Lower Cretaceous 

(Neocomian) 

Upper Jurassic-Upper Permian 

Lower Permian-Pennsylvanian/ 

Middle Mississippian 

(Visean-Serpukhovian) 

Lower Mississippian 

(Tournaisian)-Devonian 

Silurian 

Ordovician-Cambrian 

Upper Proterozoic (Vendian) 

Shale 

Clastics 

Siltstone 

Coal or peat 

Carbonates, mixed 

1------ Lithology of source beds --___.~====- Limestone Dolomite (primary) 

Chalk 

Evaporites 

Sandstone 

Unknown 

No evidence 

~:~:~:and lacustrine 

1------ Environment of deposition Marine and nonmarine (mixed) 

of source beds Nonmarine and lacustrine 

f------ Structural forms (reflecting tectonic stages 

and deposition in basin development) for 

source beds (Klemme, 1980) 

Nonmarine or terrestrial 

~~~~~ol~:ag Linear sag 

Foredeep 

Rifts 

Delta 

Half sag 

~~~u:reately mature (mixed) 

<-------- Maturity level of source beds ~~:::u~~t immature 

Highly mature 

Severely altered 

Unknown 

Figure 4 (above and following pages). GEOPLAY's hierarchy using the Class-Object-Attribute-Value format to define the 

decision-tree structure. 

9 
10 GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

Probable source beds 

r----- Type of general kerogens (organic material) 

predominant in source beds 

Type I. Alginite (sapropelic or lipid-rich) 

Properties: 

Hydrogen rich, H/C >0.5-1.5 

Oxygen low, 0/C <0.1 

Vitrinite reflectance, >0.5-0. 7 

Low reflectance 

High fluorescence 

Marine and lacustrine 

Oil prone 

Type II. Exinite (phytoplankton, zooplankton, other microorganisms) 

Properties: 

Hydrogen rich, H/C 0.7-1.3 

Oxygen low, 0/C 0.05-0.2 

Vitrinite reflectance, 0.45-0.6 

Intermediate reflectance 

No fluorescence (usually) 

Mainly marine, some lacustrine 

Both oil and gas prone 

Types I and II (both) 

Type III. Vitrinite and huminite (terrestrial plant debris) 

Properties: 

Hydrogen poor, H/C <1.0 

Oxygen rich, 0/C 0.09-0.30 

Vitrinite reflectance, 0.75-1.10 

High reflectance 

No fluorescence 

Terrestrial, marginal marine, or marginal lacustrine 

Gas prone (methane) 

Type IV. Inertinite "dead carbon" 

Properties: 

Low H/C (can't generate hydrocarbons) 

High 0/C 

...----- Total organic carbon in source beds------

(numeric single value, in percent) 

Good >1.0 L-____ __,_:::.__ Fair 0.5 

Poor <0.5 

1----- Quality of source beds 

~Good 

----~===- Fair 

~~~nown 

1----- Areal extent of source beds ------

(numeric single value, in square miles) 

r------- Average depth to source beds------

(numeric single value, in feet) 

'------ Average total thickness of source beds ------

(numeric single value, in feet) 
GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

'--------- Potential reservoir beds 

1--------- Geologic age of ----<;::--

reservoir beds 

Environment of deposition 

of reservoir beds 

1--------- Structural forms (reflecting tectonic stages 

and deposition in basin development for 

reservoir facies) Klemme, 1980) 

Quaternary-Oligocene-Upper Cretaceous 

(Coniacian) 

Middle (Turonian)-Lower Cretaceous 

(Neocomian) 

Upper Jurassic-Upper Permian 

Lower Permian-Pennsylvanian/ 

Middle Mississippian 

(Visean-Serpukhovian) 

Lower Mississippian 

(Tournaisian)-Devonian 

Silurian 

Ordovician-Cambrian 

Upper Proterozoic (Vendian) 

Sandstone 

Carbonates, mixed 

Limestone 

Dolomite, mixed 

Siltstone 

Evaporites 

Shale 

Igneous 

Metamorphic 

Unknown 

No evidence 

Marine 

Marine and lacustrine 

Marine and nonmarine (mixed) 

Nonmarine and lacustrine 

Nonmarine or terrestrial 

Platforms 

Circular sag 

Linear sag 

L-------~~---- Foredeep 

Rifts 

Delta 

Half sag 

Quality of reservoir beds 

1--------- Average effective porosity of reservoir beds in play 

(numeric single value, in percent) 

L-------- Average depth to reservoir beds in play 

(numeric single value, in feet) 

1--------- Average total thickness of reservoir beds in play _______ _ 

(numeric single value. in feet) 

L-------- Probable areal extent of reservoir bed in play _______ _ 

(numeric single value, in acres) 

1-------- Marginal play probability (MP), calculated 

(S X T X M X R = MP) ------

L_ _______ Number of drillable prospects (a play characteristic) 

FRACTILE LEVELS 

Probability of equal to or greater than 

(numeric single value) 

100% 

95% 

75% 

50% 

25% 

5% 

0% 

11 
12 GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

PROSPECT ATTRIBUTES 

1-------- Trapping mechanism (TM) 

Structural relations 

:-------- Trap type Block (Extension: horst, tilted fault blocks, 

uplifts, fault traps, arches, combinations) 

:------ Structural 

Fold (Compression: symmetrical to asymetrical, overthrust folds, fold belts, thrust zones, combinations) 

Growth (Extension and vertical load: listric growth 

faulting, rollover anticlines, slump features, topographic subsidence-depression, combinations) 

Flow (Vertical load commonly related to compression: 

flowage of salt, evaporites, shale, and igneous in 

form of diapir, dome, swell) 

Reef (Carbonate buildup, depositional, combination 

stratigraphic and drape traps) 

Unknown 

No evidence 

Stratigraphic relations 

Channels, lenses, bars 

Facies changes, porosity, permeability variations 

Unconformity related 

Onlap, transgressive facies 

1---- Stratigraphic -4~'------- Offlap, regressive facies 

Paleorelief, truncations, compaction 

Impermeable seals 

Hydrodynamic conditions 

Diagenetic conditions 

Unknown 

No evidence 

/Yes 

.__ ___ Combination ----<E""""'-- No 

Unknown 

~------- Status of traps 

!------ Number tested (numeric single value) 

1---- Number untested (numeric single value) 

r---- Unknown 

'------No evidence 

1-------- Effective porosity (>3 percent) (P) (numeric single value) _______ _ 

1-------- Hydrocarbon accumulation (HCA) 

. . ---- Favorable :---- Conditions -- Unfavorable 

...._ _______ Conditional deposit probability (CP), calculated 

(TM X p X HCA = CP) ------
GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

HYDROCARBON VOLUME ATIRIBUTES 

FRACTILE LEVELS 

Probability of equal to or greater than 

(numeric single value) 

Mooofdo•ure ~ 100% 

95% 

75% 

50% 

(' J03 aore•) ~ 25% 

5% 

0% 

FRACTILE LEVELS 

Probability of equal to or greater than 

(numeric single value) 

Reservoir 

vertical closure 

thickness/~ (feet) 

100% 

95% 

75% 

50% 

25% 

5% 

0% 

FRACTILE LEVELS 

Probability of equal to or greater than 

(numeric single value) 

Effootivo 

(pment) 

pom•lty ~ ~ 

100% 

95% 

75% 

50% 

25% 

1------- Trap fill 

(percent) 

5% 

0% 

FRACTILE LEVELS 

Probability of equal to or greater than 

(numeric single value) 

100% 

95% 

75% 

50% 

25% 

5% 

0% 

FRACTILE LEVELS 

Probability of equal to or greater than 

(numeric single value) 

w"'" "'""tion~ 100% 

95% 

75% 

50% 

25% (pmont) ~ 

5% 

0% 

FRACTILE LEVELS 

Probability of equal to or greater than 

(numeric single value) 

Reservo}rdepth ( x 10 feet) ~ ~~ 25% 

5% 

0% 

.___ ____ Reservoir engineering equation for volume of hydrocarbons (calculated) = ___ _ 

Proved reserves 

Oil ( x 106 barrels) 

(numeric single value) 

Gas (trillion cubic feet) 

(numeric single value) 

13 
14 GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

These geologic-age categories for source beds are based on 

the studies of Klemme and Ulmishek (1991). The "ages" of 

Klemme and Ulmishek (1991) are not geochronologic ages 

(early, middle, late) but rather are chronostratigraphic 

assignments (lower, middle, upper). Furthermore, their use 

of "Middle Cretaceous" and "Middle Mississippian" does 

not follow conventional usage. Their "Middle Cretaceous 

through Lower Cretaceous" category apparently comprises 

the interval from the Upper Cretaceous Turonian Stage 

through the Lower Cretaceous Berriasian Stage. The base of 

their "Middle Mississippian" appears to fall within the 

Lower Mississippian Osagean of conventional usage. 

Finally, the formal geochronologic subdivisions of the 

Archean and Proterozoic in the United States have no formal 

chronostratigraphic counterparts. Thus, the latest formal 

subdivision is the Late Proterozoic Era, but there is no formal 

"Upper Proterozoic" Erathem (USGS, Geologic Names 

Unit, 1994). 

OBJECT AND ATTRIBUTE EDITORS 

The construction of the class and member objects 

quickly develops into a hierarchy or network of knowledge. 

MAHOGANY's Object Editor allows the expert to create 

this object hierarchy. Although MAHOGANY creates 

objects and attributes automatically as rules are being built, 

the user can also build objects and attributes directly giving 

additional flexibility to developing the knowledge base. I 

chose this method of developing the object hierarchy as a 

natural step after the design of the hierarchical structure 

illustrated in figure 4. 

The Attribute Editor gives the expert more specific control over the design of each attribute in the knowledge base. 

It provides the expert with the means to fine tune the inferences involving a particular attribute, to create new 

attributes, and to preset values for each attribute in the knowledge base. Values represent the actual working answers by 

the geologist on which the inference engine bases its conclusions. Values can be expressed as a constant or numeric 

answer, an expression (such as a selection from a list of 

responses such as the geologic age categories), or a reference 

to another attribute (fig. 5). Appendix 1A displays the ObjectAttribute-Value hierarchy created by the expert for the superclasses Play Attributes and Prospect Attributes from the 

GEOPLA Y knowledge-base file. The Object-AttributeValue hierarchy created for the superclass Hydrocarbon Volume Attributes from the GEOPLA Y knowledge base is listed 

in appendix lB. This knowledge-base file, providing the 

geologist's input to the probability distributions for the reservoir attributes, interfaces with the Monte Carlo simulation 

methods as used in the original play-analysis procedures. 

Input from the knowledge-base file, in the form of probability 

distributions, can also be used in the current probabilistic 

methodology being used by the USGS for estimation of 

petroleum resources by play analysis (Crovelli, 1992). 

RULE-BASED SYSTEM 

GEOPLA Y uses rules as the building blocks to store the 

expert's knowledge structures in the knowledge base. Each 

rule consists of a discrete category of expert knowledge, usually implying a relationship between two or more distinct 

objects. Rules represent the knowledge statements composed of interrelated facts and rules of thumb that are used to 

solve problems; in this case, the characterization of the geologic criteria essential to the favorability of the occurrence of 

prospective plays and prospects. A rule consists of two parts. 

The first part, consisting of one or more IF clauses, establishes antecedent conditions or the premise that must apply 

or be true if a second part, consisting of one or more THEN 

clauses with consequent propositions, is to be concluded or 

acted upon. The rules are used to support the deductive processes, and they form the basis of knowledge formation. 

All of the knowledge-base design for GEOPLA Y 

focuses on a consultation process that assists the geologist in 

assigning probabilities for the essential geologic attributes as 

being favorable or present. This consultation process is 

designed to address the three groups of rules: (1) play-specific, (2) prospect-specific, and (3) reservoir-specific or 

hydrocarbon volume accumulation, as shown in figure 1. 

The first group of rules focuses on play-specific characteristics and lists the relationships between hydrocarbon 

source, timing, migration, and potential reservoir facies. The 

second group of rules, focusing on prospect-specific characteristics, distinguishes the trapping mechanism, effective 

porosity, and hydrocarbon accumulation attributes. The third 

group of rules, directed to the hydrocarbon volume parameters or reservoir-specific attributes, provides a means for 

estimating the area of closure, reservoir thickness, effective 

reservoir porosity, trap fill, reservoir depth, and water saturation of reservoirs. The queries in this last group request 

estimates from the geologist at the percent fractile levels 

(100, 95, 75, 50, 25, 5, 0), providing input for probability distributions for each of the attributes as an interface to the 

Monte Carlo simulation model for estimating the size and 

number of drillable prospects. 

The acquired knowledge is structured into an objectattribute-value format as shown in figure 4. Twenty-four key 

geologic criteria characterize such information as geologic 

age, lithology, environments of deposition, basin history, 

organics, and volumes and depths of source and reservoir 

beds for prospective plays and prospects. Seven additional 

criteria characterize the trapping mechanism for the prospect 

attributes. Queries are associated with each of the 31 criteria 

and are presented to the geologist through a menu-based 

window interface when specific information is required. 

Responses to the queries appear as a menu of choices in the 
GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 15 

File Edit Rules Objects Words Goals Inference 

F================== Enter At tribute Value ====================! 

Op 

is 

is 

Value 

SHALE 

COAL/PEAT 

CARBONATES MIXED 

CLASTICS 

DOLOMITE PRIMARY 

LIMESTONE 

SANDSTONE 

SILTSTONE 

Certainty 

1.00 

Fl-Cancel F2-Why? F3-Info F4-Unknown FS-Use 

A 

File Edit Rules Objects Words Goals Inference 

F================== Enter Attribute Value================~ 

Op Value Certainty 

6000 0.85 

Fl-Cancel F3-Info F4-Unknown FS-Use 

B 

Figure 5. Enter Attribute Value window in GEOPLAY.A, Example of automatic-value selection provided for geologist to answer query 

regarding lithology of source beds. The geologist has selected SHALE and COAL/PEAT as the entry values with a 100 percent certainty. 

B, Example of query requesting direct entry from user in form of single numeric value to the question, what is the average depth to source 

beds (in feet)? The geologist has entered the value of 6,000 ft with an 85 percent certainty. 

window, and the geologist can either select the appropriate 

answers or enter numeric values, as requested. At this time 

the geologist can indicate. belief or certainty in the answers 

to record his or her level of confidence in the response to the 

query. The Rule Editor incorporates the certainty values with 

the rules and their respective certainties as created by the 
16 GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

geologic experts. Certainties and threshold values do not 

have to be entered by the geologist if the automatic defaults 

of 1.00 for certainty values and 0.20 for threshold values are 

acceptable. 

The expert geological knowledge was compiled and 

organized by me and later modified after running repeated 

test cases. Application of GEOPLA Y by the geologist is discussed in greater detail later in this report. 

STRUCTURE OF A RULE 

Each rule is composed of four major parts: an IF, a 

THEN, an ELSE, and an UNKNOWN part. The IF part of a 

rule is basically a test. IF clauses specify conditions that must 

be satisfied under the current circumstances for the THEN 

part to be activated. THEN clauses do the work. When the IF 

conditions are satisfied, the THEN clauses initiate a task or 

store the knowledge. 

When the IF clause conditions are not satisfied, the 

ELSE clause takes action and performs its function. ELSE is 

an optional clause. When the IF conditions cannot be found 

to be true or false due to a lack of information, the 

UNKNOWN clause is activated. UNKNOWN is also an 

optional clause. 

A rule takes on the basic form as shown below. 

Connector 

IF 

AND 

OR 

THEN 

AND 

ELSE 

UNKNOWN 

Object Attribute Operator Value 

Connectors within the IF part of the rule are AND or 

OR. Connectors are used to attach multiple clauses using 

Boolean logic. The object and attribute fields in each part of 

the rule designate which object and which attribute of that 

object is being used by that part of the rule. These are the 

same objects and attributes shown in the object-attribute 

hierarchy. Operators in IF clauses define the condition that 

must exist between the object-attribute and a specific value. 

This is the condition that will be tested when the rule is activated. These operators include Is, Is Not,> (greater than),;::: 

(greater than or equal to),< (less than),::; (less than or equal 

to), = (equal), and <> (not equal). Additional operators are 

available in THEN, ELSE, and UNKNOWN clauses such as 

TEXT, which displays a text file that contains only ASCII 

text, and SHOW, which displays a graphic image and must 

be in a. pcx graphic format along with its file name. 

The IF and THEN, IF and ELSE, and IF and 

UNKNOWN parts of the rule structure together can define 

three distinct relationships between two or more objects or 

conditions. This rule-structure format provides a powerful 

tool for creating the logic and deductive processes followed 

by a geologist in conducting geologic evaluation procedures. 

An example of a fairly simple rule in GEOPLA Y' s 

knowledge base is shown below as written in the Rule Editor. 

Rule 1 Priority 50 Highly Favorable Direct Evidence 

of Hydrocarbons 

IF ------------------------------------------

(1) the HYDROCARBON SOURCE DIRECT 

EVIDENCE OF SOURCE BEDS is "Known 

Fields/Production" [threshold 0.20] 

(2) OR the HYDROCARBON SOURCE DIRECT 

EVIDENCE OF SOURCE BEDS is "Live 

Shows in Wells" [threshold 0.20] 

(3) OR the HYDROCARBON SOURCE DIRECT 

EVIDENCE OF SOURCE BEDS is "Seepages" [threshold 0.20] 

(4) AND the MIGRATION (from source to reservoir 

beds) GEOLOGIC INDICATORS FOR 

MIGRATION is "Highly Favorable" 

[threshold 0.20] 

(5) AND the TIMING GEOLOGIC INDICATORS 

FOR TIMING is "Highly Favorable" 

[threshold 0.20] 

THEN ---------------------------------------

(1) the PLAY PROPERTY is "Highly Favorable with Good Direct Evidence of Hydrocarbons" 

Note: HYDROCARBON SOURCE, MIGRATION 

(from source to reservoir beds), TIMING, and PLAY are 

Objects; DIRECT EVIDENCE OF SOURCE BEDS, GEOLOGIC INDICATORS FOR MIGRATION, GEOLOGIC 

INDICATORS FOR TIMING, and PROPERTY are 

Attributes; and "Known Fields/Production," "Live Shows in 

Wells," "Seepages," and "Highly Favorable" are Values for 

the respective Attributes. 

Rule 1 is read in the following manner. IF a hydrocarbon source assumed to be present as expressed by the direct 

evidence of source beds such as the presence of known fields/ 

production, or by the presence of live shows in wells, or by 

the presence of oil and( or)gas seepages; and by migration 

from source to reservoir beds with geologic indicators for 

migration being highly favorable; and the timing for the origin and migration of petroleum expressed by geologic indicators for timing as being highly favorable; THEN the play 

property can be described as highly favorable with good 

direct evidence of hydrocarbons being present in the play. 

This rule is only the first in a series of rules evaluating the 

conditions of the hydrocarbon source beds relative to a 

potential play. 
GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 17 

RULE EDITOR 

The Rule Editor in MAHOGANY is simple to use and 

requires no specialized computer programming language 

skills. As soon as the rule is typed in and .sa. ved, that rule is a 

part of the knowledge base. The Rule Editor allows the 

expert to quickly create new rules and modify or delete old 

rules. Appendix 2 displays 18 rules and their respective IFTHEN rule structures showing the Object-Attribute-Value 

formats and their respective conclusions for the play-specific 

and prospect-specific evaluations. 

GOAL-BASED CONSULTATION 

The last phase in the building of GEOPLA Y using the 

MAHOGANY system is its basic goal structure. Goals 

define the specific categories of information that the geologist wants to retrieve from a knowledge base. Goals are 

unknowns, or questions that the geologist wants GEOPLA Y 

to address and to resolve. All goals are designed to identify 

an object and its respective attribute for GEOPLA Y and to 

attempt to find a value for it. This is done by querying the 

geologist for information and then by applying the rules of 

thumb built into the knowledge base. All of the certainty or 

belief values and priority rankings are automatically incorporated as basic information to evaluate the goals to be 

solved. All goals are designed as requests for information or 

to aid in decision-making procedures; each goal in the program attempts to find values for the attribute specified in the 

respective goal. 

The goals in GEOPLA Y are designed to aid in the consultation process, evaluating the essential geologic criteria 

that guide the geologist through a decision-making process 

and assigning probabilities for the play and prospect 

attributes as being favorable or present as displayed on the 

data form in figure 1. The outcome of GEOPLAY's search 

for the requested values in each goal is displayed in the Conclusions window. See appendix 4 for an example of the 

information shown in the Conclusions window. The content 

of the Conclusions window is controlled by the goals as 

designed and by their assigned priorities. In order to include 

the display of an attribute's value in the Conclusions window, a goal must be created for that attribute. Inference and 

conclusion procedures are discussed in more detail later in 

this report. 

GOAL EDITOR 

The Goal Editor is used to create, edit, and delete goals 

and contains three data entry fields: Object, Attribute, and 

Priority. The Object field identifies the object to be tested. 

The Attribute field identifies the object-attribute to be tested. 

The Priority field indicates which goal will be solved first if 

multiple goals exist. If no priority value is entered by the user 

the priority defaults to 50 (out of 100). Goals having higher 

priorities are solved first. Appendix 3 displays 15 goals and 

their objects, attributes, and assigned priorities as currently 

being used. Goals are easily designed, modified, or deleted 

using the Goal Editor, and can readily be changed depending 

on the information or conclusions the geologist is seeking 

from the knowledge base. 

INFERENCE STRATEGIES, CONSULTATION, 

AND CONCLUSIONS IN GEOPLAY 

Inference is the process by which new facts are derived 

from known facts. The part of the MAHOGANY system that 

finds these facts and makes decisions as displayed on the 

Conclusions screen is called the inference engine, as shown 

in figure 3. The inference process requires steps that vary 

depending on the method of inference selected by the user. 

Inference strategies in an expert system are usually 

based on either forward- or backward-chaining. In a forward-chaining system, data are retrieved from the knowledge base, and the inference engine reports on any 

conclusions it can make by using the IF-THEN rules. Thus, 

a forward-chaining system is said to be data driven. In a 

backward-chaining system, the inference engine works 

backward from the goals or the conclusions, trying to determine whether the subject of the rule is true for a particular 

goal. Backward-chaining systems are thus said to be goal 

driven. 

The forward- or backward-chaining system can be 

combined with either a depth-first or breadth-first search. 

Depth-first searches conduct a search through all levels of a 

decision-tree branch until a conclusion or a contradiction is 

reached. Breadth-first searches thoroughly search all 

branches at each level, then search the next level of positive 

responses only. This process is repeated until a conclusion is 

reached. Thus, the breadth-first search is a complete strategy, and, if the problem posed by the user has a solution 

within the knowledge base, this search will find it. 

GEOPLA Y follows a system for determining the value 

of an attribute as expressed in the respective goal. It searches 

the knowledge base for all target attributes in the order of 

their priorities, looking for an attribute value. Whenever a 

value cannot be resolved internally, it asks the geologist for 

a response. An inquiry can be designated for each attribute in 

the Attribute Editor. As soon as a value is found, GEOPLAY 

stops searching and uses that value. If GEOPLA Y examines 

all its sources without success, the attribute is considered to 

be unknown. The effect of an unknown attribute varies from 

knowledge base to knowledge base. 

The consultation phase begins when the GEOPLA Y 

system asks the geologist about relevant data and uses the 

data to deduce intermediate goals and conclusions. Additional information is collected and combined with existing 
18 GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

conclusions and with certainty and belief values. This process continues until all of the attribute-values are found or 

determined to be unknown and all of the goals satisfied to 

reach the designated conclusions. 

Whenever GEOPLA Y requires user input to a query the 

Enter Attribute Value window is presented on the screen. Figure 5 shows Enter Attribute Value windows with examples 

of an automatic-value selection provided for the geologist and 

a direct entry for a numeric answer requested of the geologist. 

USER STRATEGIES FOR RESULTS 

AND CONCLUSIONS 

To initiate the GEOPLAY decision-making procedure, 

the geologist selects from the Inference menu either the 

option Attempt All Goals or the option Attempt One Goal. 

Attempt All Goals is a general-purpose inference method 

used to evaluate all goals created within the Goal Editor. 

This procedure backchains on multiple goals, backchaining 

on each goal individually until all goals are resolved. The 

goal priorities determine the order in which the goals will be 

solved. The Attempt All Goals process can be made more 

exhaustive through the use of the Synergistic option on the 

control panel. When all backchaining is complete, the Synergistic option causes GEOPLA Y to forward chain on any 

updated values in the knowledge base. 

The option Attempt One Goal is similar to Attempt All 

Goals, except that the inference engine only backchains on 

one pre-set goal. When the geologist selects Attempt One 

Goal from the Inference menu, a Goal Select window 

appears so that the user can select a specific goal. In this 

option the back-chaining process asks the geologist to create 

a temporary goal for that particular inference pass. 

CONSULTATION PROCESS 

AND APPLICATIONS 

As a first approach to applying GEOPLA Y' s consultation process, which assists the geologist in evaluating the 

essential geologic criteria in the play- and prospect-analysis 

procedures, a set of values was defined for each of three 

favorability classes. These classes were designed specifically 

for evaluating the geologic characteristics for the direct evidence of hydrocarbons present, for probable source beds, and 

for potential reservoir beds. These favorability classes are: 

Class 1 Highly favorable play and prospect attributes with 

little known risk. Play favorability range recommended to user 80-100 percent 

Class 2 Favorable play and prospect attributes but with a 

higher degree of risk than in Class 1. Play favorability range recommended to user 30-79 percent 

Class 3 Unfavorable play and prospect attributes with a high 

degree of risk; or many of the geologic attributes 

are unknown at this time and more information is 

needed. Play favorability range recommended to 

user 1-29 percent 

Table 1 provides guidelines for the geologist in the 

choice of values for the source bed and reservoir bed 

attributes used in the rule-base. These values are used to 

define the three favorability classes that determine the probability of the play-specific attributes being favorable within 

a basin or province. The geologist would rarely expect all 

values to fall consistently within one class but rather would 

expect a mixture of favorable or unfavorable geologic conditions for the different attributes. Appendix 4 displays an 

example of the conclusions arrived at by GEOPLA Y using 

the attribute-values illustrated in table 1 as defined for a 

highly favorable play. 

The weighting factors used to aid in assigning the values for each of the geologic attributes for the favorability 

classes are based on the geologic experience of the experts 

and on results from published studies addressing many of the 

geologic criteria. For example, information for the distribution by geologic age of the world's petroleum resources as 

generated from the source beds and as trapped within the reservoir beds is taken from studies by Klemme and Ulmishek 

( 1991 ). These values for the geologic age of the source beds 

and the reservoir beds were assigned to the three favorability 

classes as follows: 

Geologic age 

Source bed Reservoir bed 

(percent distribution) 

Class 1 

Middle through Lower Cretaceous 31.6 

Upper Jurassic through Upper Permian 26.2 

Quaternary/Oligocene/Upper Cretaceous 15.3 

Class 2 

Silurian 9.0 

Lower Permian/Pennsylvanian/ 

Middle Mississippian 8.4 

Lower Mississippian through Devonian 8.3 

Ordovician through Cambrian 

Silurian 

Ordovician through Cambrian 

Upper Proterozoic (Vendian) 

Class 3 

1.0 

0.2 

28.1 

24.9 

26.9 

12.7 

2.1 

2.1 

0.2 

0.1 

See table 1 for all the attribute values assigned for each 

of the three favorability classes. The weighting factors and 

values assigned to these classes can easily be changed in the 

rule structures by using the Rule Editor. 
GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

Table 1. Guide to values for source-bed and reservoir-bed attributes for each of three favorability classes for GEOPLA Y. 

CONDITIONS HIGHLY FAVORABLE FOR A PLAY 

I. Probable source beds (rules 4 and 10) 

Geologic age of source beds 

1. Middle through Lower Cretaceous 

2. Upper Jurassic through Upper Permian 

3. Quaternary/Oligocene/Upper Cretaceous 

Environment of deposition of source beds 

*Marine 

*Marine and nonmarine (mixed) 

*Marine and lacustrine 

Lithology of source beds 

*Shale 

*Clastic 

*Siltstone 

Maturity level of source beds 

*Mature 

Proximity to reservoir beds 

*Highly favorable 

Quality of source beds 

*Good 

Structural forms/deposition in basin for source beds 

1. Linear sags 

2. Circular sags 

3. Platforms 

Types of general kerogens in source beds 

*Types I and II, both 

*Type II 

Total organic carbon in source beds 

·~1.00 percent 

Areal extent of source beds ~750 mi2 

Average depth to source beds ~5,000 ft AND 

Average depth to source beds ~ 15,000 ft 

Average total thickness of source beds ~00 ft 

II. Potential reservoir beds (rules 7 and 13) 

Geologic age of reservoir beds 

1. Middle through Lower Cretaceous 

2. Quaternary/Oligocene/Upper Cretaceous 

3. Upper Jurassic through Upper Permian 

Environment of deposition of reservoir beds 

*Marine 

*Marine and nonmarine (mixed) 

Lithology of reservoir beds 

*Sandstone 

*Carbonates, mixed 

*Limestone 

*Dolomite, mixed 

Quality of reservoir beds 

*Good 

31.6 percent generated of world's resources 

26.2 percent generated of world's resources 

15.3 percent generated of world's resources 

39.2 percent of world's resources 

19.9 percent of world's resources 

18.6 percent of world's resources 

28.1 percent of trapped world's resources 

26.9 percent of trapped world's resources 

24.2 percent of trapped world's resources 

19 
20 GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

Table 1. Guide to values for source-bed and reservoir-bed attributes for each of three favorability classes for GEOPLA YContinued. 

CONDITIONS HIGHLY FAVORABLE FOR A PLAY-Continued 

II. Potential reservoir beds (rules 7 and 13}--Continued 

Structural forms/deposition in basin for reservoir beds 

1. Linear sags 

2. Circular sags 

3. Platforms 

Probable areal extent of reservoir beds in play ~ 1 ,000 acres 

Average depth to reservoir beds in play ~5,000 ft AND 

Average depth to reservoir beds in play :5:15,000 ft 

Average total thickness of reservoir beds in play ~200 ft 

Average effective porosity of reservoir beds in play ~10 percent 

III. Direct evidence of hydrocarbon source (rules 1, 2, and 3) 

Hydrocarbon source 

Direct evidence of source beds 

*Known fields and production 

*Live shows in wells 

*Seepages 

Migration (from source beds to reservoir beds) 

Geologic indicators for migration 

*Highly favorable 

Timing 

Geologic indicators for timing 

*Highly favorable 

Source bed( s) 

Conditions 

*Highly favorable for HCs potential 

Potential 

*Highly favorable for a successful play 

Reservoir bed( s) 

Play 

Conditions 

*Highly favorable for potential reservoirs 

Potential 

*"Highly favorable for a successful play 

Property 

*Highly favorable with good direct evidence of HCs 

39.2 percent of world's resources 

1 9. 9 percent of world's resources 

18.6 percent of world's resources 

CONDITIONS FAVORABLE FOR A PLAY 

I. Probable source beds (rules 5 and 11) 

Geologic age of source beds 

4. Silurian 

5. Lower Permian/Pennsylvanian/Middle Mississippian 

6. Lower Mississippian through Devonian 

Environment of deposition of source beds 

*Marine and lacustrine 

*Nonmarine and lacustrine 

Lithology of source beds 

*Carbonates, mixed 

*Limestone 

*Coal or peat 

9.0 percent generated of world's resources 

8.4 percent generated of world's resources 

8.3 percent generated of world's resources 
GEOPLAY -A MODEL FOR EXPLORATION PLAY ANALYSIS 

Table 1. Guide to values for source-bed and reservoir-bed attributes for each of three favorability classes for GEOPLAYContinued. 

CONDITIONS FAVORABLE FOR A PLAY-Continued 

I. Probable source beds (rules 5 and 11 }-Continued 

Maturity level of source beds 

*Moderately mature, mixed 

*Somewhat inunature 

Proximity to reservoir beds 

*Favorable 

Quality of source beds 

*Fair 

Structural forms/deposition in basin for source beds 

4. Foredeep 

5. Rifts 

6. Deltas 

Types of general kerogens in source beds 

*Type I 

*Type ill 

Total organic carbon in source beds 

*~0.5 percent AND 

*S0.999 percent 

Areal extent of source beds ~300 me AND 

Areal extent of source beds S749 mi2 

Average depth to source beds ~.500ft AND 

Average depth to source beds S4,999 ft 

OR 

Average depth to source beds ~15,001 ft AND 

Average depth to source beds S24,999 ft 

Average total thickness of source beds ~50 ft AND 

Average total thickness of source beds S199 ft 

ll. Potential reservoir beds (rules 8 and 14) 

Geologic age of reservoir beds 

4. Lower Permian/Pennsylvanian/Middle Mississippian 

5. Lower Mississippian through Devonian 

6. Ordovician through Cambrian 

Environment of deposition of reservoir beds 

*Marine and lacustrine 

*Nonmarine and lacustrine 

Lithology of reservoir beds 

*Siltstone 

*Evaporites 

*Shale 

Quality of reservoir beds 

*Fair 

Structural forms/deposition in basin for reservoir beds 

4. Foredeep 

5. Rifts 

6. Deltas 

Probable areal extent of reservoir beds in play ~ 100 acres AND 

Probable areal extent of reservoir beds in play ::;;999 acres 

9.2 percent of world's resources 

5.7 percent of world's resources 

4.5 percent of world's resources 

12.7 percent of trapped world's resources 

5.4 percent of trapped world's resources 

2.1 percent of trapped world's resources 

9.2 percent of world's resources 

5. 7 percent of world's resources 

4.5 percent of world's resources 

21 
22 GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

Table 1. Guide to values for source-bed and reservoir-bed attributes for each ofthree favorability classes for GEOPLAYContinued. 

CONDITIONS FAVORABLE FOR A PLAY-Continued 

II. Potential reservoir beds (rules 8 and 14)-Continued 

Average depth to reservoir beds in play ~.500ft AND 

Average depth to reservoir beds in play S4,999 ft 

OR 

Average depth to reservoir beds in play ;c:15,001 ft AND 

Average depth to reservoir beds in play S25,000 ft 

Average total thickness of reservoir beds in play ;c:SO ft AND 

Average total thickness of reservoir beds in play S199 ft 

Average effective porosity of reservoir beds in play ;c:S.O percent AND 

Average effective porosity of reservoir beds in play S9.999 percent 

III. Direct evidence ofhydrocarbon source (rules 1, 2, and 3) 

Hydrocarbon source 

Direct evidence of source beds 

*Oil shale 

*Tar sands 

*Tight gas sands 

Migration (from source beds to reservoir beds) 

Geologic indicators for migration 

*Favorable 

Timing 

Geologic indicators for timing 

*Favorable 

Source bed( s) 

Conditions 

*Favorable for HCs potential 

Potential 

*Favorable for a potential play 

Reservoir bed( s) 

Play 

Conditions 

*Favorable for potential reservoirs 

Potential 

*Favorable for a successful play 

Property 

*Favorable with fair direct evidence ofHCs 

CONDITIONS UNFAVORABLE OR UNKNOWN FOR A PLAY 

I. Probable source beds (rules 6 and 12) 

Geologic age of source beds 

7. Ordovician through Cambrian 

8. Upper Proterozoic (Vendian) 

Environment of deposition of source beds 

*Nonmarine or terrestrial 

Lithology of source beds 

*Dolomite, primary 

*Chalk 

*Evaporites 

*Sandstone 

*No evidence 

*Unknown 

1.0 percent generated of world's resources 

0.2 percent generated of world's resources 
GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

Table 1. Guide to values for source-bed and reservoir -bed attributes for each of three fa vorability classes for GEOPLA YContinued. 

CONDITIONS UNFAVORABLE OR UNKNOWN FOR A PLAY-Continued 

I. Probable source beds (RULES 6 and 12}--Continued 

Maturity level of source beds 

*hnmature 

*Highly mature 

*Severely altered 

*Unknown 

Proximity to reservoir beds 

*Unfavorable 

*Unknown 

Quality of source beds 

*Poor 

*Unknown 

Structural forms/deposition in basin for source beds 

7. Halfsag 

Types of general kerogens in source beds 

*Type IV 

Total organic carbon in source beds 

*S0.4999 percent 

Areal extent of source beds S299 me 

Average depth to source beds S2,499 ft OR 

Average depth to source beds ~5,000 ft 

Average total thickness of source beds S49 ft 

ll. Potential reservoir beds (rules 9 and 15) 

Geologic age of reservoir beds 

7. Silurian 

8. Upper Proterozoic (Vendian) 

Environment of deposition of reservoir beds 

*Nonmarine or terrestrial 

Lithology of reservoir beds 

*Igneous 

*Metamorphic 

*No evidence 

*Unknown 

Quality of reservoir beds 

*Poor 

*Unknown 

Structural fonns/deposition in basin for reservoir beds 

7. Halfsag 

Probable areal extent of reservoir beds in play S99.999 acres 

Average depth to reservoir beds in play S2,499 ft OR 

Average depth to reservoir beds in play ~5,001 ft 

Average total thickness of reservoir beds in play S49.999 ft 

Average effective porosity of reservoir beds in play S4.999 percent 

2. 9 percent of world's resources 

0.2 percent of trapped world's resources 

0.1 percent of trapped world's resources 

2.9 percent of world's resources 

23 
24 GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

Table 1. Guide to values for source-bed and reservoir-bed attributes for each of three favorability classes for GEOPLAYContinued. 

CONDITIONS UNFAVORABLE OR UNKNOWN FOR A PLAY-Continued 

ill. Direct evidence of hydrocarbon source (rules 1, 2, and 3) 

Hydrocarbon source 

Direct evidence of source beds 

*No evidence 

*Unknown 

Migration (from source beds to reservoir beds) 

Geologic indicators for migration 

*Unfavorable 

*Unknown 

Timing 

Geologic indicators for timing 

*Unfavorable 

*Unknown 

Source bed( s) 

Conditions 

*Unfavorable for HCs potential or HCs potential is unknown 

Potential 

*Unfavorable for a potential play, or very risky, or source potential unknown 

Reservoir bed( s) 

Play 

Conditions 

*Unfavorable for potential reservoirs or reservoir conditions unknown 

Potential 

*Unfavorable for a successful play 

Property 

*Unfavorable or has no direct evidence of HCs 

PROSPECI' ATTRIBUTES 

Trapping mechanism (TM) 

Trap types 

Structural traps 

*Block (extension: horsts, tilted fault blocks, uplifts, fault traps, arches, combinations) 

*Fold (compression: fold belts, overthrust folds, thrust zones, combinations) 

*Growth (extension and vertical load: listric growth faults, rollover anticlines, 

slump features, topographic subsidence/depression, combinations) 

*Flow (vertical load conunonly related to compression: flowage of salt, evaporites, shale, and 

igneous in form of diapirs, domes, swells) 

*Reef (carbonate buildups, depositional, combination stratigraphic and drape traps) 

*Unknown 

*No evidence 

Stratigraphic traps 

*Channels, lenses, bars 

*Facies changes, porosity and permeability variations 

*Unconfonnity related 

*Onlap, transgressive facies 

*Offiap, regressive facies 

*Paleorelief, trunctions, compaction 

*hnpermeabile seals 

*Hydrodynamic conditions 

*Diagenetic conditions 

*Unknown 

*No evidence 

61 percent 

16 percent 

5 percent 

7 percent 

6.5 percent 

4.5 percent 
NEW DEVELOPMENTS IN EXPERT SYSTEMS 25 

Table 1. Guide to values for source-bed and reservoir -bed attributes for each of three favorability classes for GEOPLA YContinued. 

PROSPECf ATTRIBUTEs-continued 

Trapping mechanism (TM}-Continued 

Status of traps 

*Tested, drilled 

Favorable 

Unfavorable 

*Untested traps 

*Unknown 

*No evidence of traps 

Hydrocarbon accumulation (HCA) 

Conditions 

*Favorable 

*Unfavorable 

*Unknown 

Evidence 

*Known accumulations or direct evidence ofHCs 

*No direct evidence of HCs 

*Unknown 

A DYNAMIC SYSTEM 

One of the most important aspects of the GEOPLA Y 

expert system is the implementation of an appropriate and 

user-friendly interface. In an advisory role, communication 

between GEOPLA Y and the geologist must be extremely 

flexible so that the flow of information is efficient and credible. The flexibility and dynamics of this system, designed to 

assist the geologist as a diagnostic consultation tool, allows 

the knowledge base to be readily maintained, updated, and 

even expanded without changing the control structure. 

As the geologist gains experience in the use and applications of GEOPLA Y, the need for new solutions to new 

problems and new goals can be easily added to the rules and 

goal structures and old goals deleted from the knowledge 

base. The IF-THEN conditions for the rules can be modified 

arid new values or goals (answers) added to the rule base. The 

object-attribute-value decision tree (fig. 4) can easily be 

modified as information or experience from additional basins 

and plays are added to GEOPLA Y's knowledge base. 

MAHOGANY is a flexible expert-system shell and 

through the use of the easy to use editors built into the program 

for the Object -Attribute-Value formats, rule-based structures, 

and goals, GEOPLA Y can be quickly modified to meet 

changing knowledge-base needs in the form of additions and 

updates to the information, and to provide for new interpretative uses. 

NEW DEVELOPMENTS IN 

EXPERT SYSTEMS 

Although expert systems are not currently in wide use in 

the petroleum industry, they have been the object of a large 

effort in research and development activities. One reason for 

limited use of expert systems in petroleum exploration and 

resource assessment studies is that the rules governing the 

exploration processes and the applications of interpretative 

exploration geology and geophysics are for the most part 

subjective. 

Recent advances in some areas of research with expert 

systems, along with the availability of cost-effective and fast 

computer workstations, offer promising opportunities to 

overcome some of the obstacles of working with subjective 

judgments and inexact data. Specific areas of research in 

dealing with uncertainty in information systems include the 

use of concepts such as evidential reasoning, fuzzy logic, and 

neural networks in expert systems that may make the integration of multidisciplinary knowledge sources, the implementation of inexact and qualitative information, and selflearning more practical. 

Both evidential reasoning and fuzzy logic provide a 

means to incorporate more effectively uncertainty in expert 

systems. Evidential reasoning, based on the Dempster-Shafer 

theory (Dempster, 1967; Shafer, 1976), is an effective 

method to represent ignorance, incomplete information, and 

inexact rules in expert systems (Aminzadeh, 1991). The 

Dempster-Shafer theory also provides a mechanism to handle 

conflicting data and rules. Dempster-Shafer techniques are 

incorporated in the expert system designed by Cheong and 

others (1993). 

In contrast to Boolean true-or-false logic systems, fuzzy 

logic aims at formalizing modes of reasoning that are 

approximate rather than exact (Zadeh, 1992). The imprecise 

and uncertain data that frequently are a part of an objectoriented knowledge base are the primary reason for considering integration of fuzzy logic and expert systems (Miller, 

1993). The foundation for approximate reasoning is 
26 GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

grounded in fuzzy logic; that is, the logic underlying the 

mode of approximate or inexact reasoning. A fuzzy logic 

expert system for identification of minerals in thin section is 

described by Fang and others (1991). 

Another development in computer science that may 

have a significant impact on expert systems is neural network technology (Wasserman, 1989). Through the self-organizing network capability, an expert system equipped with a 

neural network can, theoretically, expand the knowledge 

base through self-learning as more and more problems or 

analogs are handled. The use of a neural network in a seismic 

expert system is described by Veezhinathan and others 

(1991). 

All of these alternative techniques when incorporated 

into near-future expert systems research and development, 

will pave the way for more practical and useful expert systems in the fields of the interpretative sciences. 

CONCLUSIONS 

GEOPLA Y is a knowledge-based expert system that 

performs as a diagnostic consultant to assist the geologist in 

organizing, documenting, and analyzing in a consistent manner geologic data in order to differentiate potential petroleum 

plays, prospects, and reservoirs as input to an exploration 

play-analysis model. This play-analysis model is used to 

conduct probabilistic assessments of the most essential geologic characteristics in a basin and its respective plays that 

relate to the occurrence and the assessment of hydrocarbon 

.resources. 

This report describes the development and implementation of GEOPLA Y as an expert-systems model for conducting play-analysis methods. In GEOPLA Y three basic sets of 

unique geologic conditions are identified to characterize the 

play-specific, prospect-specific, and reservoir-specific properties for the play-analysis model. These specific geologic 

conditions are evaluated for each of the identified plays 

within a basin and constitute the basic geologic data in the 

model. These data are defined in terms of essential geologic 

characteristics such as geologic age, lithology, environment 

of deposition, depositional systems, stratigraphy, structural 

style, organic sources, and effective porosity to evaluate 

source-bed and reservoir-bed conditions and trapping mechanisms. 

GEOPLA Y' s knowledge base is a hierarchical system 

based on a multilinked object-oriented structure or decision 

tree showing the relations between the various geological 

criteria defining plays, prospects, and reservoir conditions. 

The hierarchical system follows the order of geologic reasoning used by an experienced geologist to arrive at a logical 

set of conclusions relative to the geologic conditions being 

evaluated. The knowledge-based structures in GEOPLA Y 

are made up of classes, objects, attributes, values, IF-THEN 

rules, and goals. These elements together represent 

knowledge and the relationships linking the various geologic 

criteria within the knowledge-based system. This system 

results in a better knowledge representation of the relationships among basin and play characteristics than is currently 

available for play-analysis procedures and improves on the 

accommodation of uncertainties inherent in geological data 

analysis. 

Alternative techniques such as evidential reasoning, 

fuzzy logic, and neural networks are being investigated as 

advanced techniques to be incorporated into expert systems. 

These new techniques may improve on the integration of 

multidisciplinary knowledge sources, provide the means to 

incorporate uncertainties and inexact information into expert 

systems more effectively, and make self-learning knowledge-based expert systems more practical for geologic applications. 

GEOPLA Y is a flexible and dynamic system when used 

as a diagnostic consultation tool. It allows the knowledge 

base to be readily maintained, updated, and expanded with 

additions by the geologist of new rules, new problems, and 

new goals. GEOPLA Y needs further refinement and a 

greater expansion of its knowledge base; however, the current GEOPLA Y expert system has been demonstrated to be 

a useful diagnostic tool in defining the occurrence of hydrocarbon resources and in providing the evaluation of hydrocarbon plays, prospects, and reservoir characteristics as 

input to play-analysis methods for the assessment of petroleum resources. 

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Cheong, Dae-Kyo, Stobel, John, Biswas, Gautam, Lee, Gyesung, 

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Eckbo, P., Jacoby, H., and Smith, J., 1978, Oil supply forcasting-A disaggregated approach: Bell Journal of Economics, v. 

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Lahiri, Sharbari, and Stillman, M.J., 1992, Expert systems 

diagnosing the cause of problem AAS data: Analytical 

Chemistry, v. 64, no. 4, p. 283-291. 

McCammon, R.B., 1986, The muPROSPECTOR mineral consultant system: U.S. Geological Survey Bulletin 1697, 35 p. 

--1990, PROSPECTOR III-Towards a map-based expert 

system: Geological Survey of Canada Colloquium on Statistical Applications in the Earth Sciences: Canada Geological Survey Paper 89-9, p. 395-404. 

Miller, B.M., 1986, Building an expert system helps classify sedimentary basins and assess petroleum resources: GEOBYTE, v. 

1,no.2,p.44-50,83-84. 

---1987a, The muPETROL expert system for classifying 

world sedimentary basins: U.S. Geological Survey Bulletin 

1810, 87 p. 

---19871b, Sedimentary basin models documented on computer 

diskettes for USGS Bulletin 1810-The muPETROL expert 

Published in the Central Region, Denver Colorado 

Manuscript approved for publication November 18, 1994 

Edited by Judith Stoeser 

Photocomposition by Mari L. Kauffmann 

Graphics by Springfield & Springfield and the author 

system for classifying world sedimentary basins: U.S. Geological Survey Open-File Report 87-404, 5 p., 1 5.25-in. diskette. 

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National Petroleum Reserve in Alaska, in Geology and exploration of the National Petroleum Reserve in Alaska, 1974 to 

1982: U.S. Geological Survey Professional Paper 1399, p. 

117-128. 

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sedimentary basins, in Aminzadeh, Fred, and Simaan, Marwan, eds., Expert systems in exploration: Society of Exploration Geophysicists, Geophysical Development Series, v. 3, p. 

221-239. 

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Colorado: U.S. Geological Survey Bulletin 2025, 37 p. 

---1993, Object-oriented expert systems and their applications 

to sedimentary basin analysis: U.S. Geological Survey Bulletin 

2048,31 p. 

Sacerdoti, E.D., 1991, Managing expert-system development: AI 

Expert, v. 6, no. 5, p. 26-33. 

Shafer, Glen, 1976, A mathematical theory of evidence: Princeton, 

N.J., Princeton University Press, 297p. 

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Research Analysis, 1979, Final report of the 105(b) economic 

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exploration, development, production, transportation and distribution of the petroleum resources of the National Petroleum 

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of Exploration Geophysicists, Geophysical Development 

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VanNostrand Reinhold, 230 p. 

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v. 7,no.3,p.23-27. 

APPENDIXES 
30 GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

Appendix 1. Printouts of Object-Attribute-Value hierarchy taken directly from the GEOPLAY-MAHOGANY expert-system program. A. Superclasses Play Attributes and Prospect Attributes. 

* * * 0 B J E C T S * * * of C:\MAHOGANY\PLAYAN.KB 

* * * 

HYDROCARBON SOURCE (S) 

DIRECT EVIDENCE OF SOURCE BEDS 

[question] - WHAT IS (ARE) THE DIRECT EVIDENCE OF 

HYDROCARBONS IN THE BASIN OR PLAY? 

automatic values: 

is Known Fields/Production 

is Live Shows in Wells 

is Seepages 

is Oil Shales 

is Tar Sands 

is Tight Gas Sands 

is No Evidence 

is Unknown 

MIGRATION (FROM SOURCE BEDS TO RESERVOIR BEDS) (M) 

GEOLOGIC INDICATORS FOR MIGRATION 

[question] - GEOLOGIC INDICATORS FOR MIGRATION FRO 

M SOURCE BEDS TO RESERVOIR BED(S) ARE: ? 

PLAY 

automatic values: 

PROBABILITY 

Property 

is HIGHLY FAVORABLE 

is FAVORABLE 

is UNFAVORABLE 

is Unknown 

[Auto Values] 

POTENTIAL RESERVOIR BED(S) 

AVERAGE DEPTH TO RESERVOIR BED(S) IN PLAY 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE AVERAGE DEPTH TO RESERVOI 

R BED{S) IN PLAY? (in feet) 

AVERAGE EFFECTIVE POSOSITY OF RESERVOIR BED(S) IN PLAY 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE AVERAGE EFFECTIVE POROSIT 

Y OF RESERVOIR BED(S) IN PLAY? (in percent) 

AVERAGE TOTAL THICKNESS OF RESERVOIR BED(S) IN PLAY 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE AVERAGE TOTAL THICKNESS 0 

F RESERVOIR BED(S) IN PLAY? (in feet) 

ENVIRONMENT OF DEPOSITION 0¥ RESERVOIR BED(S) 
APPENDIXES 

Appendix tA-Continued. 

automatic values: 

is MARINE 

is MARINE & NONMARINE (MIXED) 

is MARINE & LACUSTRINE 

is NONMARINE & LACUSTRINE 

is NONMARINE/TERRESTRIAL 

GEOLOGIC AGE OF RESERVOIR BED{S) 

automatic values: 

is QUATERNARY/OLIGOCENE/UPPER CRETACEOUS 

is MIDDLE THRU LOWER CRETACEOUS 

is UPPER JURASSIC THRU UPPER PERMIAN 

31 

is LOWER PERMIAN/PENNSYLVANIAN/MIDDLE MISSISSIPPIA 

N 

is ORDOVICIAN THRU CAMBRIAN 

is LOWER MISSISSIPPIAN THRU DEVONIAN 

is SILURIAN 

is UPPER PROTEROZOIC {VENDIAN) 

PROBABLE AREAL EXTENT OF RESERVOIR BED(S) IN PLAY 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE PROBABLE AREAL EXTENT OF 

RESERVOIR BED{S) IN PLAY? (acres) 

QUALITY OF RESERVOIR BED(S) 

automatic values: 

is GOOD 

is FAIR 

is POOR 

is Unknown 

RESERVOIR LITHOLOGY(IES) 

automatic values: 

is SANDSTONE 

is CARBONATES MIXED 

is LIMESTONE 

is DOLOMITE MIXED 

is SILTSTONE 

is EVAPORITES 

is SHALE 

is IGNEOUS 

is METAMORPHIC 

is No Evidence 

is Unknown 

STRUCTURAL FORMS/DEPOSITION IN BASIN FOR RESERVOIR BED(S) 

automatic values: 

is LINEAR SAGS 

is CIRCULAR SAGS 

is PLATFORMS 

is FOREDEEP 

is RIFTS 

is DELTA 

is HALF SAG 
32 GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

Appendix 1A -Continued. 

PROBABLE SOURCE BEDS 

AREAL EXTENT OF SOURCE BEDS 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE AREAL EXTENT OF THE SOURC 

E BED(S)? (in sq. miles) 

AVERAGE DEPTH TO SOURCE BEDS 

[Numeric] [Single Value] [No Auto Values] 

(question] - WHAT IS THE AVERAGE DEPTH TO SOURCE B 

ED(S)? (in feet) 

AVERAGE TOTAL THICKNESS OF SOURCE BEDS 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE TOTAL THICKNESS OF THE SO 

URCE BED(S)? (in feet) 

ENVIRONMENT OF DEPOSITION OF SOURCE BEDS 

[question] - WHAT IS THE ENVIRONMENT(S) OF DEPOSIT 

ION FOR THE SOURCE BED(S)? 

automatic values: 

is MARINE 

is MARINE & NONMARINE (MIXED) 

is MARINE & LACUSTRINE 

is NONMARINE/TERRESTRIAL 

is NONMARINE & LACUSTRINE 

GEOLOGIC AGE OF SOURCE BEDS 

[No Protection] 

[question] - WHAT IS THE GEOLOGIC AGE(S) OF THE PR 

OBABLE SOURCE BEDS? 

N 

automatic values: 

is QUATERNARY/OLIGOCENE/UPPER CRETACEOUS 

is MIDDLE THRU LOWER CRETACEOUS 

is UPPER JURASSIC THRU UPPER PERMIAN 

is ORDOVICIAN THRU CAMBRIAN 

is UPPER PROTEROZOIC (VENDIAN) 

is LOWER PERMIAN/PENNSYLVANIAN/MIDDLE MISSISSIPPIA 

is LOWER MISSISSIPPIAN THRU DEVONIAN 

is SILURIAN 

LITHOLOGY OF SOURCE BEDS 

RCE BED(S)? 

[question] - WHAT IS THE LITHOLOGY(IES) OF THE SOU 

automatic values: 

is SHALE 

is CLASTICS 

is SILTSTONE 

is DOLOMITE PRIMARY 

is CHALK 

is EVAPORITES 

is SANDSTONE 
APPENDIXES 

Appendix !A-Continued. 

is No Evidence 

is Unknown 

is CARBONATES MIXED 

is LIMESTONE 

is COAL/PEAT 

MATURITY LEVEL OF SOURCE BEDS 

[question] - WHAT IS THE MATURITY LEVEL(S) OF THE 

PROBABLE SOURCE BED(S)? 

automatic values: 

is MATURE 

is HIGHLY MATURE 

is SEVERELY ALTERED 

is IMMATURE 

is Unknown 

is MODERATELY MATURE MIXED 

is SOMEWHAT IMMATURE 

PROXIMITY TO RESERVOIR BEDS 

(No Protection] 

(question] - WHAT IS THE PROXIMITY OF SOURCE BEDS 

TO RESERVOIR BEDS? 

automatic values: 

is HIGHLY FAVORABLE 

is UNFAVORABLE 

is Unknown 

is FAVORABLE 

QUALITY OF SOURCE BEDS 

33 

[question] - WHAT IS THE OVERALL QUALITY OF SOURCE 

BED(S)? 

automatic values: 

is GOOD 

is POOR 

is Unknown 

is FAIR 

STRUCTURAL FORMS/DEPOSITION IN BASIN FOR SOURCE BEDS 

[question] - WHAT IS (ARE) THE STRUCTURAL FORMS/DE 

POSITION IN THE BASIN FOR THE SOURCE BED(S)? 

automatic values: 

is LINEAR SAGS 

is CIRCULAR SAGS 

is PLATFORMS 

is HALF SAG 

is FOREDEEP 

is RIFTS 

is DELTAS 

TOTAL ORGANIC CARBON IN SOURCE BEDS 

[Numeric] [Single Valu~] [No Auto Values] 

[question] - WHAT IS THE TOTAL ORGANIC CARBON IN T 

HE SOURCE BEDS? (in percent) 
34 GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

Appendix 1A -Continued. 

TYPES OF GENERAL KEROGENS IN SOURCE BEDS 

[question] - WHAT IS (ARE) THE TYPE(S) OF KEROGENS 

IN THE SOURCE BED(S)? 

automatic values: 

is TYPES I AND II BOTH 

is TYPE II. 

is TYPE IV. 

is TYPE I. 

is TYPE III. 

RESERVOIR BED(S) 

CONDITIONS 

automatic values: 

is Unfavorable for Potential Reservoirs or Reservo 

ir Conditions Unknown 

is Highly Favorable for Potential Reservoirs 

is Favorable for Potential Reservoirs 

POTENTIAL 

SOURCE BED(S) 

automatic values: 

is Unfavorable for a successful play 

is Highly favorable for a successful play 

is Favorable for a Successful Play 

CONDITIONS 

is Unknown 

automatic values: 

is Highly Favorable for HCs Potential 

is Favorable for HCs Potential 

is Unfavorable for HCs Potential or HCs Potential 

POTENTIAL 

automatic values: 

is Unfavorable for a potential play, or very risky 

, or source potential unknown 

TIMING (T) 

is Highly favorable for a successful play 

is Favorable for a potential play 

GEOLOGIC INDICATORS FOR TIMING 

[question] - GEOLOGIC INDICATORS FOR TIMING RE SOU 

RCE BED(S)/RESERVOIR BED(S) ARE: ? 

TRAP TYPES 

automatic values: 

is HIGHLY FAVORABLE 

is FAVORABLE 

is UNFAVORABLE 

is Unknown 
APPENDIXES 

Appendix !A-Continued. 

COMBINATION 

[question] - DO YOU HAVE A COMBINATION TRAP? IF 'Y 

ES' THEN ALSO ANSWER RE STRUCTURAL AND STRATIGRAPHIC TYPES. 

STATUS OF TESTED TRAPS 

[Single Value] [No Auto Values] 

[question] - WHAT ARE THE NUMBER OF KNOWN TESTED T 

RAPS? 

STATUS OF UNTESTED TRAPS 

[Single Value] [No Auto Values] 

[question] - WHAT ARE THE NUMBER OF KNOWN UNTESTED 

TRAPS? 

STRATIGRAPHIC 

[Not Single] 

[question] - WHAT ARE THE STRATIGRAPHIC CONDITIONS 

FOUND FOR TRAPPING MECHANISMS? 

values: 

(1.00) 

STRUCTURAL 

SENT? 

values: 

tions) (1.00) 

is CHANNELS/LENSES/BARS (1.00) 

is FACIES CHANGES/POROSITY/PERMEABILITY VARIATIONS 

is UNCONFORMITY RELATED (1.00) 

is ONLAP-TRANSGRESSIVE FACIES (1.00) 

is OFFLAP-REGRESSIVE FACIES (1.00) 

is PALEO-RELIEF/TRUNCATIONS/COMPACTION (1.00) 

is IMPERMEABLE SEALS (1.00) 

is HYDRODYNAMIC CONDITIONS (1.00) 

is DIAGENETIC CONDITIONS (1.00) 

is Unknown (1.00) 

is No Evidence (1.00) 

[info ] -

[question] - WHAT TYPE OF STRUCTURAL TRAPS ARE PRE 

is BLOCK (Extension: fault traps, uplifts, combina 

is FOLD (Compression: fold belts, overthrust folds , thrust zones, combinations) (1.00) 

is GROWTH (Extension and vertical load: listric gr 

owth faulting, roll-over anticlines, slump features, topographic subsi 

dencejdepression, combinations) (1.00) 

is FLOW (Vertical load often related to compressio 

n: flowage of salt, evaporites, shale, and igneous in form of diapir, 

dome, swell) (1.00) 

is REEF (Carbonate build-up, depositional, combina 

tion stratigraphic and drap traps) (1.00) 

* * * * * * 

is Unknown (1.00) 

is No Evidence (1.00) 

0 B J E C T S * * * of C:\MAHOGANY\PLAYAN.KB 

35 
36 GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

Appendix 1. Printouts of Object-Attribute-Value hierarchy taken directly from the GEOPLAY-MAHOGANY expert-system program. 

B. Superclass Hydrocarbon Volume Attributes. 

* * * 0 B J E C T S * * * 

* * * 

AREA OF CLOSURE (x 1000 acres) (HVA) 

0 % FRACTILE LEVEL (Probability) 

of C:\MAHOGANY\RESERVOI.KB 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED AREA OF CLOSURE 

(x 1000 acres) WITH THE PROBABILITY OF EQUAL TO OR GREATER THAN, AT T 

HE 0% FRACTILE LEVEL? 

100 % FRACTILE LEVEL (Probability) 

[Numeric] (Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED AREA OF CLOSURE 

(x 1000 acres) WITH THE PROBABILITY OF EQUAL TO OR GREATER THAN, AT T 

HE 100% FRACTILE LEVEL? 

25 % FRACTILE LEVEL (Probability) 

[Numeric] (Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED AREA OF CLOSURE 

(x 1000 acres) WITH THE PROBABILITY OF EQUAL TO OR GREATER THAN, AT T 

HE 25% FRACTILE LEVEL? 

5 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED AREA OF CLOSURE 

(x 1000 acres) WITH THE PROBABILITY OF EQUAL TO OR GREATER THAN, AT T 

HE 5% FRACTILE LEVEL? 

50 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED AREA OF CLOSURE 

(x 1000 acres) WITH THE PROBABILITY OF EQUAL TO OR GREATER THAN, AT T 

HE 50% FRACTILE LEVEL? 

75 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED AREA OF CLOSURE 

(x 1000 acres) WITH THE PROBABILITY OF EQUAL TO OR GREATER THAN, AT T 

HE 75% FRACTILE LEVEL? 

95 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED AREA OF CLOSURE 

(x 1000 acres) WITH THE PROBABILITY OF EQUAL TO OR GREATER THAN, AT T 

HE 95% FRACTILE LEVEL? 

EFFECTIVE POROSITY (>3%) (HVA) 

0 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED PERCENT EFFECT! 

VE POROSITY WITH THE PROBABILITY OF EQUAL TO OR GREATER THAN, AT THE 0 

% FRACTILE LEVEL? 
APPENDIXES 37 

Appendix lB-Continued. 

100 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value) [No Auto Values] 

[question] - WHAT IS THE ESTIMATED PERCENT EFFECT! 

VE POROSITY WITH THE PROBABILITY OF EQUAL TO OR GREATER THAN, AT THE 1 

00% FRACTILE LEVEL? 

25 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED PERCENT EFFECT! 

VE POROSITY WITH THE PROBABILITY OF EQUAL TO OR GREATER THAN, AT THE 2 

5% FRACTILE LEVEL? 

5 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] (No Auto Values] 

[question] - WHAT IS THE ESTIMATED PERCENT EFFECT! 

VE POROSITY WITH THE PROBABILITY OF EQUAL TO OR GREATER THAN, AT THE 5 

% FRACTILE LEVEL? 

50 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] (No Auto Values] 

[question] - WHAT IS THE ESTIMATED PERCENT EFFECT! 

VE POROSITY WITH THE PROBABILITY OF EQUAL TO OR GREATER THAN, AT THE 5 

0% FRACTILE LEVEL? 

75 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED PERCENT EFFECT! 

VE POROSITY WITH THE PROBABILITY OF EQUAL TO OR GREATER THAN, AT THE 7 

5% FRACTILE LEVEL? 

95 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED PERCENT EFFECT! 

VE POROSITY WITH THE PROBABILITY OF EQUAL TO OR GREATER THAN, AT THE 9 

5% FRACTILE LEVEL? 

NUMBER OF DRILLABLE PROSPECTS (play characteristic) 

0 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] (No Auto Values] 

[question] - WHAT ARE THE ESTIMATED NUMBER OF DRIL 

LABLE PROSPECTS IN THE PLAY WITH PROBABILITY OF EQUAL TO OR GREATER TH 

AN, AT THE 0% FRACTILE LEVEL? 

100 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT ARE THE ESTIMATED NUMBER OF DRIL 

LABLE PROSPECTS IN THE PLAY WITH THE PROBABILITY OF EQUAL TO OR GREATE 

R THAN, AT THE 100% FRACTILE LEVEL? 

25 % FRACTILE LEVEL (Probability) 

(Numeric] (Single Value] [No Auto Values] 

[question] - WHAT ARE THE ESTIMATED NUMBER OF DRIL 
38 GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

Appendix IB-Continued. 

LABLE PROSPECTS IN THE PLAY WITH PROBABILITY OF EQUAL TO OR GREATER TH 

AN, AT THE 25% FRACTILE LEVEL? 

5 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT ARE THE ESTIMATED NUMBER OF DRIL 

LABLE PROSPECTS IN THE PLAY WITH PROBABILITY OF EQUAL TO OR GREATER TH 

AN, AT THE 5% FRACTILE LEVEL? 

50 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT ARE THE ESTIMATED NUMBER OF DRIL 

LABLE PROSPECTS IN THE PLAY WITH THE PROBABILITY OF EQUAL TO OR GREATE 

R THAN, AT THE 50% FRACTILE LEVEL? 

75 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT ARE THE ESTIMATED NUMBER OF DRIL 

LABLE PROSPECTS IN THE PLAY WITH THE PROBABILITY OF EQUAL TO OR GREATE 

R THAN, AT THE 75% FRACTILE LEVEL? 

95 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT ARE THE ESTIMATED NUMBER OF DRIL 

LABLE PROSPECTS IN THE PLAY WITH THE PROBABILITY OF EQUAL TO OR GREATE 

R THAN, AT THE 95% FRACTILE LEVEL? 

RESERVOIR DEPTH (x 1000 ft) (HVA) 

0 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] (No Auto Values] 

[question] - WHAT IS THE ESTIMATED RESERVOIR DEPTH 

(x 1000 ft) IN THE PLAY WITH THE PROBABILITY OF EQUAL TO OR GREATER T 

HAN, AT THE 0% FRACTILE LEVEL? 

100 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED RESERVOIR DEPTH 

(x 1000 ft) IN THE PLAY WITH THE PROBABILITY OF EQUAL TO OR GREATER T 

HAN, AT THE 100% FRACTILE LEVEL? 

25 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED RESERVOIR DEPTH 

(x 1000 ft) IN THE PLAY WITH THE PROBABILITY OF EQUAL TO OR GREATER T 

HAN, AT THE 25% FRACTILE LEVEL? 

5 % FRACTILE LEVEL (Probability) 

[Numeric] (Single Value] (No Auto Values] 

[question] - WHAT IS THE ESTIMATED RESERVOIR DEPTH 

(x 1000 ft) IN THE PLAY WITH THE PROBABILITY OF EQUAL TO OR GREATER T 

HAN, AT THE 5% FRACTILE LEVEL? 

50 % FRACTILE LEVEL (Probability) 
APPENDIXES 39 

Appendix lB--Continued. 

[Numeric} [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED RESERVOIR DEPTH 

(x 1000 ft) IN THE PLAY WITH THE PROBABILITY OF EQUAL TO OR GREATER T 

HAN, AT THE 50% FRACTILE LEVEL? 

75 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED RESERVOIR DEPTH 

(x 1000 ft) IN THE PLAY WITH THE PROBABILITY OF EQUAL TO OR GREATER T 

HAN, AT THE 75% FRACTILE LEVEL? 

95 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED RESERVOIR DEPTH 

(x 1000 ft) IN THE PLAY WITH THE PROBABILITY OF EQUAL TO OR GREATER T 

HAN, AT THE 95% FRACTILE LEVEL? 

RESERVOIR THICKNESS/VERTICAL CLOSURE (ft) (HVA) 

0 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED RESERVOIR THICK 

NESS OR VERTICAL CLOSURE (in ft) WITH THE PROBABILITY OF EQUAL TO OR G 

REATER THAN, AT THE 0% FRACTILE LEVEL? 

100 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED RESERVOIR THICK 

NESS OR VERTICAL CLOSURE (in ft) WITH THE PROBABILITY OF EQUAL TO OR G 

REATER THAN, AT THE 100% FRACTILE LEVEL? 

25 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [Auto Values] 

[question] - WHAT IS THE ESTIMATED RESERVOIR THICK 

NESS OR VERTICAL CLOSURE (in ft) WITH THE PROBABILITY OF EQUAL TO OR G 

REATER THAN, AT 25% FRACTILE LEVEL? 

5 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED RESERVOIR THICK 

NESS OR VERTICAL CLOSURE (in ft) WITH THE PROBABILITY OF EQUAL TO OR G 

REATER THAN, AT THE 5% FRACTILE LEVEL? 

50 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED RESERVOIR THICK 

NESS OR VERTICAL CLOSURE (in ft) WITH THE PROBABILITY OF EQUAL TO OR G 

REATER THAN, AT THE 50% FRACTILE LEVEL? 

75 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED RESERVOIR THICK 

NESS OR VERTICAL CLOSURE (in ft) WITH THE PROBABILITY OF EQUAL TO OR G 

REATER THAN, AT THE 75% FRACTILE LEVEL? 
40 GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

Appendix lB-Continued. 

95 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED RESERVOIR THICK 

NESS OR VERTICAL CLOSURE (in ft) WITH THE PROBABILITY OF EQUAL TO OR G 

REATER THAN, AT THE 95% FRACTILE LEVEL? 

TRAP FILL (%) (HVA) 

0 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED PERCENT TRAP FI 

LL WITH THE PROBABILITY OF EQUAL TO OR GREATER THAN, AT THE 0% FRACTIL 

E LEVEL? 

100 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED PERCENT TRAP FI 

LL WITH THE PROBABILITY OF EQUAL TO OR GREATER THAN, AT THE 100% FRACT 

ILE LEVEL? 

25 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED PERCENT TRAP FI 

LL WITH THE PROBABILITY OF EQUAL TO OR GREATER THAN, AT THE 25% FRACTI 

LE LEVEL? 

5 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED PERCENT TRAP FI 

LL WITH THE PROBABILITY OF EQUAL TO OR GREATER THAN, AT THE 5% FRACTIL 

E LEVEL? 

50 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED PERCENT TRAP FI 

LL WITH THE PROBABILITY OF EQUAL TO OR GREATER THAN, AT THE 50% FRACTI 

LE LEVEL? 

75 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED PERCENT TRAP FI 

LL WITH THE PROBABILITY OF EQUAL TO OR GREATER THAN, AT THE 75% FRACTI 

LE LEVEL? 

95 % FRACTILE LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED PERCENT TRAP FI 

LL WITH THE PROBABILITY OF EQUAL TO OR GREATER THAN, AT THE 95% FRACTI 

LE LEVEL? 

WATER SATURATION (%) (HVA) 

0 % FRACTILE LEVEL (Probability) 
APPENDIXES 41 

Appendix lB--Con\:inued. 

[Numeric) [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED PERCENT WATER S 

ATURATION WITH THE PROBABILITY OF EQUAL TO OR GREATER THAN, AT THE 0% 

FRACTILE LEVEL? 

100 % FRACTILE LEVEL {Probability) 

[Numeric) [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED PERCENT WATER S 

ATURATION WITH THE PROBABILITY OF EQUAL TO OR GREATER THAN, AT THE 100 

% FRACTILE LEVEL? 

25 % FRACTILE LEVEL {Probability) 

[Numeric] [Single Value) [No Auto Values] 

[question] - WHAT IS THE ESTIMATED PERCENT WATER S 

ATURATION WITH THE PROBABILITY OF EQUAL TO OR GREATER THAN, AT THE 25% 

FRACTILE LEVEL? 

5 % FRACTILE LEVEL (Probability) 

[Numeric) [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED PERCENT WATER S 

ATURATION WITH THE PROBABILITY OF EQUAL TO OR GREATER THAN, AT THE 5% 

FRACTILE LEVEL? 

50 % 

ATURATION 

FRACTILE 

75 % 

ATURATION 

FRACTILE 

95 % 

ATURATION 

FRACTILE 

* * * 

* * * 

FRACTILE 

WITH THE 

LEVEL? 

FRACTILE 

WITH THE 

LEVEL? 

FRACTILE 

WITH THE 

LEVEL? 

LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED PERCENT WATER S 

PROBABILITY OF EQUAL TO OR GREATER THAN, AT THE 50% 

LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question) - WHAT IS THE ESTIMATED PERCENT WATER S 

PROBABILITY OF EQUAL TO OR GREATER THAN, AT THE 75% 

LEVEL (Probability) 

[Numeric] [Single Value] [No Auto Values] 

[question] - WHAT IS THE ESTIMATED PERCENT WATER S 

PROBABILITY OF EQUAL TO OR GREATER THAN, AT THE 95% 

0 B J E C T S * * * of C:\MAHOGANY\RESERVOI.KB 
42 GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

Appendix 2. Printouts of IF-THEN rule-structures illustrating 18 rules in the Object-Attribute-Value format for play-specific and 

prospect-specific evaluations taken directly from the GEOPLAY-MAHOGANY expert system program. 

* * * R U L E S * * * of C:\MAHOGANY\PLAYAN.KB * * * 

RULE #1 priority 50 - Highly favorable direct evidence of HCs 

IF ------------------------------ (1) the HYDROCARBON SOURCE (S) DIRECT EVIDENCE OF SOURCE BED 

S is "Known Fields/Production" [threshold 0.20] 

(2) or the HYDROCARBON SOURCE (S) DIRECT EVIDENCE OF SOURCE BEDS 

is "Live Shows in Wells" [threshold 0.20] 

(3) or the HYDROCARBON SOURCE (S) DIRECT EVIDENCE OF SOURCE BEDS 

is Seepages [threshold 0.20] 

(4) and the MIGRATION (FROM SOURCE BEDS TO RESERVOIR BEDS) (M) G 

EOLOGIC INDICATORS FOR MIGRATION is "HIGHLY FAVORABLE" [threshold 0.20 

] 

(5) and the TIMING (T) GEOLOGIC INDICATORS FOR TIMING is "HIGHLY 

FAVORABLE" [threshold 0.20] 

THEN ---------------------------- (1) PLAY Property is "Highly Favorable with Good Direct Evid 

ence of HCs" [certainty 1.00] 

RULE #2 priority 50 - Favorable with Fair Direct Evidence of HCs 

IF ------------------------------ (1) the HYDROCARBON SOURCE (S) DIRECT EVIDENCE OF SOURCE BED 

S is "Oil Shales" [threshold 0.20] 

(2) or the HYDROCARBON SOURCE (S) DIRECT EVIDENCE OF SOURCE BEDS 

is "Tar Sands" [threshold 0.20] 

(3) or the HYDROCARBON SOURCE (S) DIRECT EVIDENCE OF SOURCE BEDS 

is "Tight Gas Sands" [threshold 0.20] 

(4) and the MIGRATION (FROM SOURCE BEDS TO RESERVOIR BEDS) (M) G 

EOLOGIC INDICATORS FOR MIGRATION is FAVORABLE [threshold 0.20] 

(5) and the TIMING (T) GEOLOGIC INDICATORS FOR TIMING is FAVORABL 

E [threshold 0.20] 

THEN ----------------------------

(1) PLAY Property is "Favorable with Fair Direct Evidence of 

HCs" [certainty 1.00] 

RULE #3 priority 50 - Unfavorable or no direct evidence of HCs 

IF ------------------------------

(1) the HYDROCARBON SOURCE (S) DIRECT EVIDENCE OF SOURCE BED 

S is "No Evidence" [threshold 0.20] 

(2) or the HYDROCARBON SOURCE (S) DIRECT EVIDENCE OF SOURCE BEDS 

is Unknown [threshold 0.20] 

(3) and the MIGRATION (FROM SOURCE BEDS TO RESERVOIR BEDS) (M) G 

EOLOGIC INDICATORS FOR MIGRATION is UNFAVORABLE [threshold 0.20] 

(4) or the MIGRATION (FROM SOURCE BEDS TO RESERVOIR BEDS) (M) G 

EOLOGIC INDICATORS FOR MIGRATION is Unknown [threshold 0.20] 

(5) and the TIMING (T) GEOLOGIC INDICATORS FOR TIMING is UNFAVORA 

BLE [threshold 0.20] 

(6) or the TIMING (T) GEOLOGIC INDICATORS FOR TIMING is Unknown 

[threshold 0.20] 

THEN ----------------------------

(1) PLAY Property is "Unfavorable or has no Direct Evidence 

of HCs" [certainty 1.00] 
APPENDIXES 43 

Appendix 2---Continued. 

RULE #4 priority 50 - Conditions highly favorable for HCs potential 

IF ------------------------------

(1) the PROBABLE SOURCE BEDS GEOLOGIC AGE OF SOURCE BEDS is 

"QUATERNARY/OLIGOCENE/UPPER CRETACEOUS" [threshold 0.20] 

(2) or the PROBABLE SOURCE BEDS GEOLOGIC AGE OF SOURCE BEDS is 11 

MIDDLE THRU LOWER CRETACEOUS" [threshold 0.20] 

(3) or the PROBABLE SOURCE BEDS GEOLOGIC AGE OF SOURCE BEDS is 11 

UPPER JURASSIC THRU UPPER PERMIAN" [threshold 0.20] 

(4) and the PROBABLE SOURCE BEDS ENVIRONMENT OF DEPOSITION OF SOU 

RCE BEDS is MARINE [threshold 0.20] 

(5) or the PROBABLE SOURCE BEDS ENVIRONMENT OF DEPOSITION OF SOU 

RCE BEDS is "MARINE & NONMARINE (MIXED)" [threshold 0.20] 

(6) or the PROBABLE SOURCE BEDS ENVIRONMENT OF DEPOSITION OF SOU 

RCE BEDS is "MARINE & LACUSTRINE" [threshold 0.20] 

(7) and the PROBABLE SOURCE BEDS LITHOLOGY OF SOURCE BEDS is SHAL 

E [threshold 0.20] 

(8) or the PROBABLE SOURCE BEDS LITHOLOGY OF SOURCE BEDS is CLAS 

TICS [threshold 0.20] 

(9) or the PROBABLE SOURCE BEDS LITHOLOGY OF SOURCE BEDS is SILT 

STONE [threshold 0.20] 

(10) and the PROBABLE SOURCE BEDS MATURITY LEVEL OF SOURCE BEDS i 

s MATURE [threshold 0.20] 

(11) and the PROBABLE SOURCE BEDS PROXIMITY TO RESERVOIR BEDS is 

"HIGHLY FAVORABLE" [threshold 0.20] 

(12) and the PROBABLE SOURCE BEDS QUALITY OF SOURCE BEDS is GOOD 

[threshold 0.20] 

(13) and the PROBABLE SOURCE BEDS STRUCTURAL FORMS/DEPOSITION IN 

BASIN FOR SOURCE BEDS is "LINEAR SAGS" [threshold 0.20] 

(14) or the PROBABLE SOURCE BEDS STRUCTURAL FORMS/DEPOSITION IN 

BASIN FOR SOURCE BEDS is "CIRCULAR SAGS" [threshold 0.20] 

(15) or the PROBABLE SOURCE BEDS STRUCTURAL FORMS/DEPOSITION IN 

BASIN FOR SOURCE BEDS is PLATFORMS [threshold 0.20] 

(16) and the PROBABLE SOURCE BEDS TYPES OF GENERAL KEROGENS IN SO 

URCE BEDS is "TYPES I AND II BOTH" [threshold 0.20] 

(17) or the PROBABLE SOURCE BEDS TYPES OF GENERAL KEROGENS IN SO 

URCE BEDS is "TYPE II." [threshold 0.20] 

(18) and the PROBABLE SOURCE BEDS TOTAL ORGANIC CARBON IN SOURCE 

BEDS >= 1.000 [threshold 0.20] 

THEN ---------------------------- (1) SOURCE BED(S) CONDITIONS is "Highly Favorable for HCs Po 

tential" [certainty 1.00] 

RULE #5 priority 50 - Conditions favorable for HCs potential 

IF ------------------------------

(1) the PROBABLE SOURCE BEDS GEOLOGIC AGE OF SOURCE BEDS is 

"LOWER PERMIAN/PENNSYLVANIAN/MIDDLE MISSISSIPPIAN" [threshold 0.20] 

(2) or the PROBABLE SOURCE BEDS GEOLOGIC AGE OF SOURCE BEDS is " 

LOWER MISSISSIPPIAN THRU DEVONIAN" [threshold 0.20] 

(3) or the PROBABLE SOURCE BEDS GEOLOGIC AGE OF SOURCE BEDS is S 

ILURIAN [threshold 0.20] 

(4) and the PROBABLE SOURCE BEDS ENVIRONMENT OF DEPOSITION OF SOU 

RCE BEDS is "MARINE & LACUSTRINE" [threshold 0.20] 

(5) or the PROBABLE SOURCE BEDS ENVIRONMENT OF DEPOSITION OF SOU 

RCE BEDS is "NONMARINE & LACUSTRINE" [threshold 0.20] 
44 GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

Appendix 2-Continued. 

(6) and the PROBABLE SOURCE BEDS LITHOLOGY OF SOURCE BEDS is "CAR 

BONATES MIXED" [threshold 0.20] 

(7) or the PROBABLE SOURCE BEDS LITHOLOGY OF SOURCE BEDS is LIME 

STONE [threshold 0.20] 

{8) or the PROBABLE SOURCE BEDS LITHOLOGY OF SOURCE BEDS is "COA 

L/PEAT" [threshold 0.20] 

{9) and the PROBABLE SOURCE BEDS MATURITY LEVEL OF SOURCE BEDS is 

"MODERATELY MATURE MIXED" [threshold 0.20] 

{10) or the PROBABLE SOURCE BEDS MATURITY LEVEL OF SOURCE BEDS i 

s "SOMEWHAT IMMATURE" [threshold 0.20] 

{11) and the PROBABLE SOURCE BEDS PROXIMITY TO RESERVOIR BEDS is 

FAVORABLE [threshold 0.20] 

{12) and the PROBABLE SOURCE BEDS QUALITY OF SOURCE BEDS is FAIR 

[threshold 0.20] 

{13) and the PROBABLE SOURCE BEDS STRUCTURAL FORMS/DEPOSITION IN 

BASIN FOR SOURCE BEDS is FOREDEEP [threshold 0.20] 

{14) or the PROBABLE SOURCE BEDS STRUCTURAL FORMS/DEPOSITION IN 

BASIN FOR SOURCE BEDS is RIFTS [threshold 0.20] 

{15) or the PROBABLE SOURCE BEDS STRUCTURAL FORMS/DEPOSITION IN 

BASIN FOR SOURCE BEDS is DELTAS [threshold 0.20] 

{16) and the PROBABLE SOURCE BEDS TYPES OF GENERAL KEROGENS IN SO 

URCE BEDS is "TYPE I." [threshold 0.20] 

(17) or the PROBABLE SOURCE BEDS TYPES OF GENERAL KEROGENS IN SO 

URCE BEDS is "TYPE III." [threshold 0.20] 

(18) and the PROBABLE SOURCE BEDS TOTAL ORGANIC CARBON IN SOURCE 

BEDS >= 0.500 [threshold 0.20] 

{19) and the PROBABLE SOURCE BEDS TOTAL ORGANIC CARBON IN SOURCE 

BEDS <= 0.999 [threshold 0.20] 

THEN ---------------------------- {1) SOURCE BED{S) CONDITIONS is "Favorable for HCs Potential 

" [certainty 1.00] 

RULE #6 priority 50 - Conditions unfavorable for HCs potential or unkn 

own HCs potential 

IF ------------------------------

{1) the PROBABLE SOURCE BEDS GEOLOGIC AGE OF SOURCE BEDS is 

"ORDOVICIAN THRU CAMBRIAN" [threshold 0.20] 

(2) or the PROBABLE SOURCE BEDS GEOLOGIC AGE OF SOURCE BEDS is " 

UPPER PROTEROZOIC (VENDIAN)" [threshold 0.20] 

{3) and the PROBABLE SOURCE BEDS ENVIRONMENT OF DEPOSITION OF SOU 

RCE BEDS is "NONMARINE/TERRESTRIAL" [threshold 0.20] 

{4) and the PROBABLE SOURCE BEDS LITHOLOGY OF SOURCE BEDS is "DOL 

OMITE PRIMARY" [threshold 0.20] 

(5) or the PROBABLE SOURCE BEDS LITHOLOGY OF SOURCE BEDS is CHAL 

K [threshold 0.20] 

{6) or the PROBABLE SOURCE BEDS LITHOLOGY OF SOURCE BEDS is EVAP 

CRITES [threshold 0.20] 

(7) or the PROBABLE SOURCE BEDS LITHOLOGY OF SOURCE BEDS is SAND 

STONE [threshold 0.20] 

{8) or the PROBABLE SOURCE BEDS LITHOLOGY OF SOURCE BEDS is "No 

Evidence" [threshold 0.20] 

{9) or the PROBABLE SOURCE BEDS LITHOLOGY OF SOURCE BEDS is Unkn 

own [threshold 0.20] 

(10) and the PROBABLE SOURCE BEDS MATURITY LEVEL OF SOURCE BEDS i 
APPENDIXES 45 

Appendix 2--Continued. 

s "HIGHLY MATURE" [threshold 0.20] 

(11) or the PROBABLE SOURCE BEDS MATURITY LEVEL OF SOURCE BEDS i 

s "SEVERELY ALTERED" [threshold 0.20] 

(12) or the PROBABLE SOURCE BEDS MATURITY LEVEL OF SOURCE BEDS i 

s IMMATURE (threshold 0.20] 

(13) or the PROBABLE SOURCE BEDS MATURITY LEVEL OF SOURCE BEDS i 

s Unknown [threshold 0.20] 

(14) and the PROBABLE SOURCE BEDS PROXIMITY TO RESERVOIR BEDS is 

UNFAVORABLE [threshold 0.20] 

(15) or the PROBABLE SOURCE BEDS PROXIMITY TO RESERVOIR BEDS is 

Unknown [threshold 0.20] 

(16) and the PROBABLE SOURCE BEDS QUALITY OF SOURCE BEDS is POOR 

[threshold 0.20] 

(17) or the PROBABLE SOURCE BEDS QUALITY OF SOURCE BEDS is Unkno 

wn [threshold 0.20] 

(18) and the PROBABLE SOURCE BEDS STRUCTURAL FORMS/DEPOSITION IN 

BASIN FOR SOURCE BEDS is "HALF SAG" (threshold 0.20] 

(19) and the PROBABLE SOURCE BEDS TYPES OF GENERAL KEROGENS IN SO 

URCE BEDS is "TYPE IV." [threshold 0.20] 

(20) and the PROBABLE SOURCE BEDS TOTAL ORGANIC CARBON IN SOURCE 

BEDS <= 0.499 [threshold 0.20] 

THEN ----------------------------

(1) SOURCE BED(S) CONDITIONS is "Unfavorable for HCs Potenti 

al or HCs Potential is Unknown" (certainty 1.00] 

RULE #7 priority 50 - Highly favorable conditions for potential reserv 

airs 

IF ------------------------------

(1) the POTENTIAL RESERVOIR BED(S) ENVIRONMENT OF DEPOSITION 

OF RESERVOIR BED(S) is MARINE [threshold 0.20] 

(2) or the POTENTIAL RESERVOIR BED(S) ENVIRONMENT OF DEPOSITION 

OF RESERVOIR BED(S) is "MARINE & NONMARINE (MIXED)" [threshold 0.20] 

(3) and the POTENTIAL RESERVOIR BED(S) GEOLOGIC AGE OF RESERVOIR 

BED(S) is "QUATERNARY/OLIGOCENE/UPPER CRETACEOUS" (threshold 0.20] 

(4) or the POTENTIAL RESERVOIR BED(S) GEOLOGIC AGE OF RESERVOIR 

BED(S) is "MIDDLE THRU LOWER CRETACEOUS" [threshold 0.20] 

(5) or the POTENTIAL RESERVOIR BED(S) GEOLOGIC AGE OF RESERVOIR 

BED(S) is "UPPER JURASSIC THRU UPPER PERMIAN" [threshold 0.20] 

(6) and the POTENTIAL RESERVOIR BED(S) QUALITY OF RESERVOIR BED(S 

is GOOD [threshold 0.20] 

(7) and the POTENTIAL RESERVOIR BED(S) RESERVOIR LITHOLOGY(IES) i 

s SANDSTONE [threshold 0.20] 

(8) or the POTENTIAL RESERVOIR BED(S) RESERVOIR LITHOLOGY(IES) i 

s "CARBONATES MIXED" [threshold 0.20] 

(9) or the POTENTIAL RESERVOIR BED(S) RESERVOIR LITHOLOGY(IES) i 

s LIMESTONE [threshold 0.20] 

(10) or the POTENTIAL RESERVOIR BED(S) RESERVOIR LITHOLOGY(IES) 

is "DOLOMITE MIXED" [threshold 0.20] 

(11) and the POTENTIAL RESERVOIR BED(S) STRUCTURAL FORMS/DEPOSIT! 

ON IN BASIN FOR RESERVOIR BED(S) is "LINEAR SAGS" [threshold 0.20] 

(12) or the POTENTIAL RESERVOIR BED(S) STRUCTURAL FORMS/DEPOSIT! 

ON IN BASIN FOR RESERVOIR BED(S) is "CIRCULAR SAGS" [threshold 0.20] 

(13) or the POTENTIAL RESERVOIR BED(S) STRUCTURAL FORMS/DEPOSIT! 

ON IN BASIN FOR RESERVOIR BED(S) is PLATFORMS [threshold 0.20] 
46 GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

Appendix 2-Continued. 

THEN ---------------------------- (1) RESERVOIR BED(S) CONDITIONS is "Highly Favorable for Pot 

entia! Reservoirs" [certainty 1.00] 

RULE #8 priority 50 - Favorable conditions for potential reservoirs 

IF ------------------------------ (1) the POTENTIAL RESERVOIR BED(S) ENVIRONMENT OF DEPOSITION 

OF RESERVOIR BED(S) is "MARINE & LACUSTRINE" [threshold 0.20] 

(2) or the POTENTIAL RESERVOIR BED(S) ENVIRONMENT OF DEPOSITION 

OF RESERVOIR BED(S) is "NONMARINE & LACUSTRINE" [threshold 0.20] 

(3) and the POTENTIAL RESERVOIR BED(S) GEOLOGIC AGE OF RESERVOIR 

BED(S) is "LOWER PERMIAN/PENNSYLVANIAN/MIDDLE MISSISSIPPIAN" [threshol 

d 0.20] 

(4) or the POTENTIAL RESERVOIR BED(S) GEOLOGIC AGE OF RESERVOIR 

BED(S) is "ORDOVICIAN THRU CAMBRIAN" [threshold 0.20] 

(5) or the POTENTIAL RESERVOIR BED(S) GEOLOGIC AGE OF RESERVOIR 

BED(S) is "LOWER MISSISSIPPIAN THRU DEVONIAN" [threshold 0.20] 

(6) and the POTENTIAL RESERVOIR BED(S) QUALITY OF RESERVOIR BED(S 

) is FAIR [threshold 0.20] 

(7) and the POTENTIAL RESERVOIR BED(S) RESERVOIR LITHOLOGY(IES) i 

s SILTSTONE [threshold 0.20] 

(8) or the POTENTIAL RESERVOIR BED(S) RESERVOIR LITHOLOGY(IES) i 

s EVAPORITES [threshold 0.20] 

(9) or the POTENTIAL RESERVOIR BED(S) RESERVOIR LITHOLOGY(IES) i 

s SHALE [threshold 0.20] 

(10) and the POTENTIAL RESERVOIR BED(S) STRUCTURAL FORMS/DEPOSIT! 

ON IN BASIN FOR RESERVOIR BED(S) is FOREDEEP [threshold 0.20] 

(11) or the POTENTIAL RESERVOIR BED(S) STRUCTURAL FORMS/DEPOSIT! 

ON IN BASIN FOR RESERVOIR BED(S) is RIFTS [threshold 0.20] 

(12) or the POTENTIAL RESERVOIR BED(S) STRUCTURAL FORMS/DEPOSIT! 

ON IN BASIN FOR RESERVOIR BED(S) is DELTA [threshold 0.20] 

THEN ---------------------------- (1) RESERVOIR BED(S) CONDITIONS is "Favorable for Potential 

Reservoirs" [certainty 1.00] 

RULE #9 priority 50 - Unfavorable conditions for potential reservoirs 

IF ------------------------------

(1) the POTENTIAL RESERVOIR BED(S) ENVIRONMENT OF DEPOSITION 

OF RESERVOIR BED(S) is "NONMARINE/TERRESTRIAL" [threshold 0.20] 

(2) and the POTENTIAL RESERVOIR BED(S) GEOLOGIC AGE OF RESERVOIR 

BED(S) is SILURIAN [threshold 0.20] 

(3) or the POTENTIAL RESERVOIR BED(S) GEOLOGIC AGE OF RESERVOIR 

BED(S) is "UPPER PROTEROZOIC (VENDIAN)" [threshold 0.20] 

(4) and the POTENTIAL RESERVOIR BED(S) QUALITY OF RESERVOIR BED(S 

) is POOR [threshold 0.20] 

(5) or the POTENTIAL RESERVOIR BED(S) QUALITY OF RESERVOIR BED(S 

) is Unknown [threshold 0.20] 

(6) and the POTENTIAL RESERVOIR BED(S) RESERVOIR LITHOLOGY(IES) i 

s IGNEOUS [threshold 0.20] 

(7) or the POTENTIAL RESERVOIR BED(S) RESERVOIR LITHOLOGY(IES) i 

s METAMORPHIC [threshold 0.20] 

(8) or the POTENTIAL RESERVOIR BED(S) RESERVOIR LITHOLOGY(IES) i 

s "No Evidence" [threshold 0.20] 

(9) or the POTENTIAL RESERVOIR BED(S) RESERVOIR LITHOLOGY(IES) i 
APPENDIXES 47 

Appendix 2-Continued. 

s Unknown [threshold 0.20] 

{10) and the POTENTIAL RESERVOIR BED(S) STRUCTURAL FORMS/DEPOSIT! 

ON IN BASIN FOR RESERVOIR BED(S) is "HALF SAG" [threshold 0.20] 

THEN ---------------------------- (1) RESERVOIR BED(S) CONDITIONS is "Unfavorable for Potentia 

1 Reservoirs or Reservoir Conditions Unknown" [certainty 1.00] 

RULE #10 priority 50 - Highly favorable for a successful play 

IF ------------------------------ (1) the PROBABLE SOURCE BEDS AREAL EXTENT OF SOURCE BEDS >= 

750 [threshold 0.20] 

(2) and the PROBABLE SOURCE BEDS AVERAGE DEPTH TO SOURCE BEDS >= 

5000 [threshold 0.20] 

(3) and the PROBABLE SOURCE BEDS AVERAGE DEPTH TO SOURCE BEDS <= 

15000 [threshold 0.20] 

(4) and the PROBABLE SOURCE BEDS AVERAGE TOTAL THICKNESS OF SOURC 

E BEDS >= 200 [threshold 0.20] 

(5) and the SOURCE BED(S) CONDITIONS is "Highly Favorable for HCs 

Potential" [threshold 0.20] 

THEN ---------------------------- (1) SOURCE BED(S) POTENTIAL is "Highly favorable for a succe 

ssful play" [certainty 1.00] 

RULE #11 priority 50 - Favorable for a potential play 

IF ------------------------------ (1) the PROBABLE SOURCE BEDS AREAL EXTENT OF SOURCE BEDS >= 

300 [threshold 0.20) 

(2) and the PROBABLE SOURCE BEDS AREAL EXTENT OF SOURCE BEDS <= 7 

49 [threshold 0.20] 

(3) and the PROBABLE SOURCE BEDS AVERAGE DEPTH TO SOURCE BEDS >= 

2500 [threshold 0.20] 

(4) and the PROBABLE SOURCE BEDS AVERAGE DEPTH TO SOURCE BEDS <= 

4999 [threshold 0.20] 

(5) or the PROBABLE SOURCE BEDS AVERAGE DEPTH TO SOURCE BEDS >= 

15001 [threshold 0.20] 

(6) and the PROBABLE SOURCE BEDS AVERAGE DEPTH TO SOURCE BEDS <= 

24999 [threshold 0.20] 

(7) and the PROBABLE SOURCE BEDS AVERAGE TOTAL THICKNESS OF SOURC 

E BEDS >= 50 [threshold 0.02] 

(8) and the PROBABLE SOURCE BEDS AVERAGE TOTAL THICKNESS OF SOURC 

E BEDS <= 199 [threshold 0.02] 

(9) and the SOURCE BED(S) CONDITIONS is "Favorable for HCs Potent 

ial" [threshold 0.20] 

THEN ---------------------------- (1) SOURCE BED(S) POTENTIAL is "Favorable for a potential pl 

ay" [certainty 1.00] 

RULE #12 priority 50 - Unfavorable for a potential or source potential 

unknown 

IF ------------------------------

(1) the PROBABLE SOURCE BEDS AREAL EXTENT OF SOURCE BEDS <= 

299 [threshold 0.20) 

(2) and the PROBABLE SOURCE BEDS AVERAGE DEPTH TO SOURCE BEDS <= 

2499 [threshold 0.20] 
48 GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

Appendix 2----Continued. 

(3) or the PROBABLE SOURCE BEDS AVERAGE DEPTH TO SOURCE BEDS >= 

25000 [threshold 0.20) 

(4) and the PROBABLE SOURCE BEDS AVERAGE TOTAL THICKNESS OF SOURC 

E BEDS <= 49 [threshold 0.20) 

(5) and the SOURCE BED(S) CONDITIONS is "Unfavorable for HCs Pote 

ntial or HCs Potential is Unknown" [threshold 0.20] 

THEN ---------------------------- (1) SOURCE BED(S) POTENTIAL is "Unfavorable for a potential 

play, or very risky, or source potential unknown" [certainty 1.00] 

RULE #13 priority 50 - Reservoir beds highly favorable for a potential 

play. 

IF ------------------------------ (1) the POTENTIAL RESERVOIR BED(S) AVERAGE DEPTH TO RESERVOI 

R BED(S) IN PLAY >= 5000 [threshold 0.20] 

(2) and the POTENTIAL RESERVOIR BED(S) AVERAGE DEPTH TO RESERVOIR 

BED(S) IN PLAY <= 15000 [threshold 0.20) 

(3) and the POTENTIAL RESERVOIR BED(S) AVERAGE EFFECTIVE POSOSITY 

OF RESERVOIR BED(S) IN PLAY >= 10 [threshold 0.20] 

(4) and the POTENTIAL RESERVOIR BED(S) AVERAGE TOTAL THICKNESS OF 

RESERVOIR BED(S) IN PLAY >= 200 (threshold 0.20] 

(5) and the POTENTIAL RESERVOIR BED(S) PROBABLE AREAL EXTENT OF R 

ESERVOIR BED(S) IN PLAY >= 1000 [threshold 0.20] 

THEN ---------------------------- (1) RESERVOIR BED(S) POTENTIAL is "Highly favorable for a su 

ccessful play" [certainty 1.00] 

RULE #14 priority 50 - Reservoir beds favorable for a potential play. 

IF ------------------------------

(1) the POTENTIAL RESERVOIR BED(S) AVERAGE DEPTH TO RESERVOI 

R BED(S) IN PLAY >= 2500 [threshold 0.20] 

(2) and the POTENTIAL RESERVOIR BED(S) AVERAGE DEPTH TO RESERVOIR 

BED(S) IN PLAY <= 4999 [threshold 0.20] 

(3) or the POTENTIAL RESERVOIR BED(S) AVERAGE DEPTH TO RESERVOIR 

BED(S) IN PLAY >= 15001 (threshold 0.20] 

(4) and the POTENTIAL RESERVOIR BED(S) AVERAGE DEPTH TO RESERVOIR 

BED(S) IN PLAY <= 25000 [threshold 0.20] 

(5) and the POTENTIAL RESERVOIR BED(S) AVERAGE EFFECTIVE POSOSITY 

OF RESERVOIR BED(S) IN PLAY >= 5 [threshold 0.20] 

(6) and the POTENTIAL RESERVOIR BED(S) AVERAGE EFFECTIVE POSOSITY 

OF RESERVOIR BED(S) IN PLAY <= 9.999 [threshold 0.20] 

(7) and the POTENTIAL RESERVOIR BED(S) AVERAGE TOTAL THICKNESS OF 

RESERVOIR BED(S) IN PLAY >= 50 [threshold 0.20] 

(8) and the POTENTIAL RESERVOIR BED(S) AVERAGE TOTAL THICKNESS OF 

RESERVOIR BED(S) IN PLAY <= 199 [threshold 0.20] 

(9) and the POTENTIAL RESERVOIR BED(S) PROBABLE AREAL EXTENT OF R 

ESERVOIR BED(S) IN PLAY >= 100 [threshold 0.20) 

(10) and the POTENTIAL RESERVOIR BED(S) PROBABLE AREAL EXTENT OF 

RESERVOIR BED(S) IN PLAY <= 999 [threshold 0.20) 

THEN ----------------------------

(1) RESERVOIR BED(S) POTENTIAL is "Favorable for a Successfu 

1 Play" [certainty 1.00] 

RULE #15 priority 50 - Reservoir beds unfavorable for a potential play 
APPENDIXES 

Appendix 2---Continued. 

IF ------------------------------

(1) the POTENTIAL RESERVOIR BED(S) AVERAGE DEPTH TO RESERVOI 

R BED(S) IN PLAY <= 2499 [threshold 0.20] 

(2) or the POTENTIAL RESERVOIR BED(S) AVERAGE DEPTH TO RESERVOIR 

BED(S) IN PLAY >= 25001 [threshold 0.20] 

(3) and the POTENTIAL RESERVOIR BED(S) AVERAGE EFFECTIVE POSOSITY 

OF RESERVOIR BED(S) IN PLAY <= 4.999 [threshold 0.20] 

(4) and the POTENTIAL RESERVOIR BED(S) AVERAGE TOTAL THICKNESS OF 

RESERVOIR BED(S) IN PLAY <= 49.999 [threshold 0.20] 

(5) and the POTENTIAL RESERVOIR BED(S) PROBABLE AREAL EXTENT OF R 

ESERVOIR BED(S) IN PLAY <= 99.999 [threshold 0.20] 

THEN ---------------------------- (1) RESERVOIR BED(S) POTENTIAL is "Unfavorable for a success 

ful play" [certainty 1.00] 

RULE #16 priority 50 - Highly favorable play conditions 

IF ------------------------------ (1) the SOURCE BED(S) CONDITIONS is "Highly Favorable for HC 

s Potential" [threshold 0.20] 

(2) and the SOURCE BED(S) POTENTIAL is "Highly favorable for a su 

ccessful play" [threshold 0.20] 

(3) and the RESERVOIR BED(S) CONDITIONS is "Highly Favorable for 

Potential Reservoirs" [threshold 0.20] 

(4) and the RESERVOIR BED(S) POTENTIAL is "Highly favorable for a 

successful play" [threshold 0.20] 

(5) and the TIMING (T) GEOLOGIC INDICATORS FOR TIMING is "HIGHLY 

FAVORABLE" [threshold 0.20] 

(6) and the MIGRATION (FROM SOURCE BEDS TO RESERVOIR BEDS) (M) G 

EOLOGIC INDICATORS FOR MIGRATION is "HIGHLY FAVORABLE" [threshold 0.20 

] 

THEN ----------------------------

(1) PLAY PROBABILITY is "Suggested to be between 80 and 100 

percent favorable or present" [certainty 1.00] 

RULE #17 priority 50 - 0 0 0 0 0 0 0 0 0 A A A A • •COMSPEC=C:\COMMAN 

D.COM 

IF ------------------------------

(1) the SOURCE BED(S) CONDITIONS is "Favorable for HCs Poten 

tial" [threshold 0.20] 

(2) and the SOURCE BED(S) POTENTIAL is "Favorable for a potential 

play" [threshold 0.20] 

(3) and the RESERVOIR BED(S) CONDITIONS is "Favorable for Potenti 

al Reservoirs" [threshold 0.20] 

(4) and the RESERVOIR BED{S) POTENTIAL is "Favorable for a Succes 

sful Play" [threshold 0.20] 

{5) and the TIMING (T) GEOLOGIC INDICATORS FOR TIMING is FAVORABL 

E [threshold 0.20] 

{6) and the MIGRATION (FROM SOURCE BEDS TO RESERVOIR BEDS) (M) G 

EOLOGIC INDICATORS FOR MIGRATION is FAVORABLE [threshold 0.20] 

THEN ----------------------------

{1) PLAY PROBABILITY is "Suggested to be between 30 and 79.9 

9 percent favorable or present" [certainty 1.00] 

49 
50 GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

Appendix 2--Continued. 

RULE #18 priority 50 - Unfavorable or unknown play conditions 

IF ------------------------------ (1) the SOURCE BED(S) CONDITIONS is "Unfavorable for HCs Pot 

ential or HCs Potential is Unknown" [threshold 0.20] 

(2) and the SOURCE BED(S) POTENTIAL is "Unfavorable for a potenti 

al play, or very risky, or source potential unknown" [threshold 0.20] 

(3) and the RESERVOIR BED(S) CONDITIONS is "Unfavorable for Paten 

tial Reservoirs or Reservoir Conditions Unknown" [threshold 0.20] 

(4) and the RESERVOIR BED(S) POTENTIAL is "Unfavorable for a succ 

essful play" [threshold 0.20] 

(5) and the TIMING (T) GEOLOGIC INDICATORS FOR TIMING is UNFAVORA 

BLE [threshold 0.20] 

(6) or the TIMING (T) GEOLOGIC INDICATORS FOR TIMING is Unknown 

[threshold 0.20] 

(7) and the MIGRATION (FROM SOURCE BEDS TO RESERVOIR BEDS) (M) G 

EOLOGIC INDICATORS FOR MIGRATION is UNFAVORABLE [threshold 0.20] 

(8) or the MIGRATION (FROM SOURCE BEDS TO RESERVOIR BEDS) (M) G 

EOLOGIC INDICATORS FOR MIGRATION is Unknown [threshold 0.20] 

THEN ----------------------------

(1) PLAY PROBABILITY is "Suggested to be between 1.00 and 29 

.99 percent favorable or present" [certainty 1.00] 

* * * R U L E S * * * of C:\MAHOGANY\PLAYAN.KB * * * 
APPENDIXES 51 

Appendix 3. Printout of goal-structures illustrating 15 goals in the Object-Attribute format for play-specific and prospect-specific evaluatins based on the rules shown in appendix 2, taken directly from the GEOPLAY-MAHOGANY expert-system program. 

* * * G 0 A L S * * * of C:\MAHOGANY\PLAYAN.KB * * * 

POTENTIAL RESERVOIR BED(S) AVERAGE TOTAL THICKNESS OF RESERVOIR BED(S) 

IN PLAY (priority 60) 

POTENTIAL RESERVOIR BED(S) PROBABLE AREAL EXTENT OF RESERVOIR BED(S) I 

N PLAY (priority 60) 

POTENTIAL RESERVOIR BED(S) AVERAGE EFFECTIVE POSOSITY OF RESERVOIR BED 

(S) IN PLAY (priority 60) 

POTENTIAL RESERVOIR BED(S) AVERAGE DEPTH TO RESERVOIR BED(S) IN PLAY 

(priority 60) 

PROBABLE SOURCE BEDS TOTAL ORGANIC CARBON IN SOURCE BEDS (priority 60 

) 

PROBABLE SOURCE BEDS AVERAGE TOTAL THICKNESS OF SOURCE BEDS (priority 

60) 

PROBABLE SOURCE BEDS AREAL EXTENT OF SOURCE BEDS (priority 60) 

PROBABLE SOURCE BEDS AVERAGE DEPTH TO SOURCE BEDS (priority GO) 

PLAY PROBABILITY (priority 50) 

RESERVOIR BED(S) POTENTIAL (priority 50) 

SOURCE BED(S) POTENTIAL (priority 50) 

PROBABLE SOURCE BEDS GEOLOGIC AGE OF SOURCE BEDS (priority 50) 

RESERVOIR BED(S) CONDITIONS (priority 50) 

SOURCE BED(S) CONDITIONS (priority 50) 

PLAY Property (priority 50) 

* * * G 0 A L S * * * of C:\MAHOGANY\PLAYAN.KB 

* 

* * 
52 GEOPLA Y -A MODEL FOR EXPLORATION PLAY ANALYSIS 

Appendix 4. Printout of an example of the Conclusions arrived at by GEOPLAY using the Attribute-Values illustrated in table 1 as 

defined for a highly favorable play, taken directly from the GEOPLAY-MAHOGANY expert-system program 

SOURCE BED(S) CONDITIONS is Highly Favorable for HCs Potential (ce 

rtainty 1.00] 

SOURCE BED(S) POTENTIAL is Highly favorable for a successful play 

[certainty 1.00] 

RESERVOIR BED(S) CONDITIONS is Highly Favorable for Potential Reser 

voirs [certainty 1.00] 

RESERVOIR BED(S) POTENTIAL is Highly favorable for a successful pla 

y [certainty 1.00] 

PLAY PROBABILITY is Suggested to be between 80 and 100 percent favo 

rable or present [certainty 1.00] 

PLAY Property is Highly Favorable with Good Direct Evidence of HCs 

[certainty 1.00] 

* * * * * * * * * * C 0 N C L U S I 0 N S * * * * * * * * * * 

the POTENTIAL RESERVOIR BED(S) AVERAGE TOTAL THICKNESS OF RESERVOIR BE 

D(S) IN PLAY 

is 400 [certainty 1.00] 

the POTENTIAL RESERVOIR BED(S) PROBABLE AREAL EXTENT OF RESERVOIR BED( 

S) IN PLAY 

is 2000 [certainty 1.00] 

the POTENTIAL RESERVOIR BED(S) AVERAGE EFFECTIVE POSOSITY OF RESERVOIR 

BED(S) IN PLAY 

is 11 [certainty 1.00] 

the POTENTIAL RESERVOIR BED(S) AVERAGE DEPTH TO RESERVOIR BED(S) IN PL 

AY 

is 6000 [certainty 1.00] 

the PROBABLE SOURCE BEDS TOTAL ORGANIC CARBON IN SOURCE BEDS 

(no values) 

the PROBABLE SOURCE BEDS AVERAGE TOTAL THICKNESS OF SOURCE BEDS 

is 400 [certainty 1.00] 

the PROBABLE SOURCE BEDS AREAL EXTENT OF SOURCE BEDS 

is 789 [certainty 1.00] 

the PROBABLE SOURCE BEDS AVERAGE DEPTH TO SOURCE BEDS 

is 9000 [certainty 1.00] 

the PLAY PROBABILITY 

is Suggested to be between 80 and 100 percent favorable or present 

[certainty 1.00] 

the RESERVOIR BED(S) POTENTIAL 

is Highly favorable for a successful play [certainty 1.00] 

the SOURCE BED(S) POTENTIAL 

is Highly favorable for a successful play [certainty 1.00] 

the PROBABLE SOURCE BEDS GEOLOGIC AGE OF SOURCE BEDS 
APPENDIXES 53 

Appendix 4-Continued. 

is MIDDLE THRU LOWER CRETACEOUS [certainty 1.00] 

the RESERVOIR BED(S) CONDITIONS 

is Highly Favorable for Potential Reservoirs [certainty 1.00] 

the SOURCE BED(S) CONDITIONS 

is Highly Favorable for HCs Potential [certainty 1.00] 

the PLAY Property 

is Highly Favorable with Good Direct Evidence of HCs [certainty 1. 

00] 

* * * * * * * * * * C 0 N C L U S I 0 N S * * * * * * * * * * 



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