Automated Pattern Analysis in Petroleum Exploration

314 Pages·1992·14.727 MB·English

by  Olivier Allain, Roland N. Horne (auth.), Ibrahim Palaz, Sailes K. Sengupta (eds.)

Automated Pattern Analysis
in Petroleum Exploration
Ibrahim Palaz Sailes K. Sengupta
Editors
Automated Pattern
Analysis in Petroleum
Exploration
With 213 Illustrations, 23 in Full Color
Springer-Verlag
New York Berlin Heidelberg London Paris
Tokyo Hong Kong Barcelona Budapest
Ibrahim Palaz Sailes K. Sengupta
Geophysicist Lawrence Livermore National Laboratory
Amoco Production Company Livermore, CA 94511, USA;
501 West Lake Park Boulevard formerly professor at:
Houston, TX 77253, USA South Dakota School of Mines and Technology
Rapid City, SD 57701, USA
Cover illustration: Random distributions of the views from a thin section, Figure 13.2b,
page 252.
Library of Congress Cataloging-in-Publication Data
Automated pattern analysis in petroleum exploration / Ibrahim Palaz,
Sailes Sengupta, editors.
p. cm.
Includes bibliographical references and index.
ISBN -13: 978-1-4612-8751-3
1. Petroleum-Prospecting-Data processing. 2. Expert systems
(Computer science) 3. Pattern recognition systems. 1. Palaz,
Ibrahim. II. Sengupta, Sailes, 1935-
TN271.P4A86 1991
622'. 1828-dc20 91-2814
Printed on acid-free paper.
© 1992 Springer-Verlag New York Inc.
Sof tcover reprint of the hardcover 1s t edition 1992
All rights reserved. This work may not be translated or copied in whole or in part
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tion with reviews or scholarly analysis. Use in connection with any form of infor
mation storage and retrieval, electronic adaptation, computer software, or by simi
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may accordingly be used freely by anyone.
Production coordinated by Chernow Editorial Services, Inc. and managed
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ISBN -13:978-1-4612-8751-3 e-ISBN-13 :978-1-4612-4388-5
DOl: 10.1007/978-1-4612-4388-5
Preface
Computers contributed greatly to the evolution of petroleum exploration.
Today the complexity or size of an exploration task is no longer a limiting
factor for most computers. From field geology to enhanced oil recovery,
every aspect of finding hydrocarbons involves the use of computers at vary
ing levels.
The impact of computers on technologies such as pattern recognition
(PR), image analysis (IA), and artificial intelligence (AI) has been even
greater than on petroleum exploration. These technologies did not have
meaningful applications until the arrival of faster and more sophisticated
computers. Since the 1960s there has been an increasing number of appli
cations of PR, lA, and AI in scientific and engineering disciplines as they
were proved to be very powerful tools. In the early 19808 there were few
applications of these technologies in petroleum exploration and they were
mostly in research laboratories. In the late 1980s there were special ses
sions dedicated to the application of these technologies at international
petroleum meetings. This was a clear reflection of the growing interest
among explorationists to utilize one or more of these technologies to solve
old problems.
This book is a collection of carefully selected papers. In each chapter PR,
lA, or AI is applied to some petroleum exploration task. This book is not
intended to be a discussion of the pros and cons of these technologies.
Readers who are interested in the theory of these techniques can refer to
publications listed in the reference section of each chapter.
The fields in which PR, lA, and AI are applied in this book are not
limited to geology, geophysics, and petroleum engineering. Chapters cover
topics from sand grain shape analysis to well test analysis. We had two
objectives in collecting such a wide range of applications. The first was to
illustrate that every aspect of exploration can potentially use these technol
ogies. The second was that petroleum exploration is an integrated effort of
the geologist, geophysicist, and engineer, and topics in this book reflect
this. Most of our problems are common, but tools are different, and there
is the distinct possibility of integratipg a number of tools to tackle common
problems. PR, lA, and AI can help us not only to solve problems but also
to make the integration of exploration efforts a reality.
v
VI Preface
The first chapter, by Allain and Horne, is titled "The Use of Artificial
Intelligence for Model Identification in Well Test Interpretation:' The
authors describe the well test interpretation as an inverse problem. Their
aim is to determine a system in which the input and the system response are
known. This chapter is an excellent example of full automation. By using
artificial intelligence, automation is accomplished in model identification.
Such a system has clear advantages not only in the interpretation of well
test data but also in actually monitoring the test. The authors illustrate an
example using real data.
The second chapter is titled '~rtificial Intelligence in Formation Evalua
tion." Kuo et al. share their wealth of experience in the use of artificial
intelligence for formation evaluation. The authors describe basic concepts
in AI and in formation evaluation. They review expert systems developed
in their field and present their approach to the problem. Similarly they
cover the topics of edge detection and pattern recognition and present their
approaches to both.
In the third chapter, I. Williamson addresses some basic issues in
developing an intelligent knowledge-based system, in his chapter "Intelli
gent Knowledge Based Systems and Seismic Interpretation." Issues such as
languages and structuring knowledge bases are discussed and Williamson
specifically talks about possible uses and benefits of utilizing a knowledge
based system in seismic sequence analysis. He presents a simple example of
a knowledge-based system.
The fourth chapter by Davis is an excellent illustration of the use of an
expert system to solve an important problem. The title of the chapter is '~n
Expert System for the Design of Array Parameters for Onshore Seismic
Surveys:' This expert system can be used in the office as well as in the field.
It is designed to determine parameters for proper acquisition of seismic
data. The author briefly introduces the array theory and then describes the
system in detail. His system is particularly interesting because it was devel
oped in Quickbasic rather than in LISP or PROLOG. He demonstrates on
an example run how his expert system works.
Crisi, in his chapter '~n Expert System to Assist in Processing Vertical
Seismic Profiles;' illustrates how he acquired seismic information and
coded it into a knowledge base. Starting with field tapes, his system can
advise what would be the most appropriate processing flow along with the
processing parameters which would help produce the best final section. His
system hints that it is possible to develop a much needed expert system for
seismic data processing.
Huang, who has long been involved with the application of AI and PR to
petroleum exploration, illustrates two expert systems for seismic explora
tion in his chapter, "Expert Systems for Seismic Interpretations and Valida
tion of Simulation Stacking Velocity Functions." The first is for velocity
analysis of seismic data processing. His VELXPERT utilizes an inference
engine with forward chaining to select the rules and uses of amended tran
sition trees to implement parsing of the questions and the formation of
answers in natural language. In his seismic data interpretation expert sys
tem, SIES, Huang illustrates how pattern matching, backward chaining,
and augmented transition trees can be effectively used. SEIS is a prototype
Preface vii
system that needs to be tested thoroughly; the author illustrates test runs of
both systems.
"Pattern Recognition to Seismic Exploration" by Huang is an odyssey
through a gamut of both classical and relatively modern pattern recogni
tion methods based on the author's long experience in real and synthetic
seismograph analysis. The study is geared to the detection and recogni
tion of structural seismic patterns, including the detection of physical
anomalies leading to the possible discovery of hydrocarbon deposits.
The theoretical techniques include linear and quadratic discriminant analy
sis, tree classification, and different variants or syntactic pattern recogni
tion. The application of these techniques to seismic data analysis is well
documented in this chapter.
In her chapter "Pattern Recognition for Marine Seismic Exploration;' F.
El-Hawary presents a scholarly exposition of how an expert system
approach can be employed for marine seismic identification of hydrocar
bon formation. The task involves image acquisition, processing, pattern
recognition, and, above all, a great deal of expert knowledge and judgment.
The author carefully examines each step in the process and its incorpora
tion in the overall framework of a proposed expert system.
Projection pursuit is a technique that has been in use in the exploratory
analysis of multivariate data since the pioneering work in 1974. Several
variants of this technique have been proposed since then, and A.T. Walden
in his chapter "Clustering of Attributes by Projection Pursuit for Reservoir
Characterization" outlines a version suitable for use as an aid to reservoir
characterization. In projection pursuit, clustering of data is facilitated by
projecting the multidimensional data along a direction that, at least locally,
maximizes a certain "entropy index;' which in turn is a measure of the mul
timodal characteristics or "non-Gaussianness" of the projected computa
tion of the entropy index and its derivative, which, in turn, is done by
extensive use of fast Fourier transform, making it computationally effi
cient. The author brings this powerful tool from multivariate analysis to
within the reach of quantitative geoscientists.
In their chapter "Exploring the Fractal Mountains;' Klinkenberg and
Clarke explore in a leisurely fashion a timely topic, bringing it within the
access of geoscientists. The importance of fractal geometry in the study of
scientific phenomena has been well documented in the scientific litera
ture. The authors present the topic from a geomorphological perspective.
They also point out the importance of a comparative study of the methods
for determining the fractal dimensions based on "truly fractal" data sets,
indicating some practical difficulties encountered in topographic studies.
Several caveats as well as some useful recommendations coupled with a
good body of references for fractal application in geosciences make this
work particularly useful.
Particle size and shape have been under study in various disciplines
associated with the mineral industries. The chapter "Image Analysis of Par
ticle Shape" by Starkey and Rutherford is the culmination of several earlier
studies on image analysis by these authors. Digitized images of thin sec
tions under the petrographic microscope are first subjected to standard
gray level thresholding to delineate the particle boundaries. Then a best fit
viii Preface
ellipse is used to approximate its shape and size in an automated manner to
provide reliable estimates for the corresponding statistics of the aggregate.
The emphasis is on automation with accuracy.
In the chapter "Interactive Image Analysis of Borehole Televiewer
Data;' Barton, Tesler, and Zoback provide a valuable tool with which
the practical geophysicist can analyze both large and fine scale features
in a televiewer image by permitting access to a graphics window. This is
implemented by a popular product, MacApp, written in an object-oriented
language Object Pascal, supported by C subroutines for the image analysis.
The software has been used very effectively in the analysis of data from
the Cajon Pass well in California and is, currently, being used for the KTB
(Germany) well site data. The software is flexible enough to allow easy
extension of the analytical tools to a wide variety of other types of
geophysical image data.
Standard image processing techniques coupled with some basic models
in spatial statistics can aid in handling problems in petrophysical analysis
of difficult pore complexes. This is demonstrated by Gerand et al. in their
chapter "Petrographic Image Analysis: An Alternate Method for Determin
ing Petrophysical Properties" with a case study of the successful classifica
tion of hydrocarbon reservoirs by a quantitative characterization of its pore
complexes. This, in turn, permits a ranking of such reservoirs as an aid to
decision making in exploration. The authors achieve their objective in two
steps. They first segment the cross-sectional image by rendering it as a
binary image representing pores and rock materials only. Next, they use a
"sizing" technique to derive the three-dimensional petrophysical properties
from two-dimensional fractal/geometric properties derived from the
resulting image. Other potential uses of their technique are indicated.
Some standard image processing algorithms such as smoothing, edge
enhancement, and histogram equalization can be employed in a variety of
instances for greater ease in scientific data interpretation. For the past two
decades they have been used in remote sensing, biomedical, and robotics
applications. The use of these techniques for magnetic data processing is
fairly recent and is illustrated by Wu Chaojun in his chapter "Image Pro
cessing of Magnetic Data and Application of Integrated Interpretation for
Mineral Resources Detection in Yieshan Area, East China." He presents his
techniques of potential transforms in an integrated fashion. The author has
found these techniques useful in the detection of mineral resources. He also
indicates their potential usefulness in the interpretation of gravity data. His
methods can clearly be applied to any gravity and magnetic data for
petroleum exploration.
In their chapter "Interactive Three-Dimensional Seismic Display by
Volumetric Rendering;' Wolfe and Liu provide us with an extremely useful
display technique for seismic data. For years geophysicists have had to
tackle the dual problem of displaying simultaneously the spatial and wave
form attributes of such data. The standard way had been to display the data
in three dimensions, the waveform in one dimension and two of the three
coordinates ofthe wave position in the other two dimensions. Then anima
tion helped provide a mental picture of the third coordinate. The authors'
approach is different. They choose to display the waveform attribute by a
Preface ix
thresholded color coding, while considering all three dimensions of the
wave position simultaneously in the display. By avoiding having to go
through a multitude of two-dimensional sections, the method enables one
to gain three-dimensional views of an underground structure in an effective
manner, even with modest computing resources. In addition, the flexibility
made available in the preprocessing stage makes it a valuable tool in the
hands of exploration geophysicists.
Ibrahim Palaz
Sailes K. Sengupta
Contents
Preface. . . . . . ... . . . . . . . .. . . . . . .. . ... . . ... . . .. . . . .. . . . . .. v
Contributors XIll
Chapter 1
The Use of Artificial Intelligence for Model Identification
in Well Test Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Olivier Allain and Roland N. Horne
Chapter 2
Artificial Intelligence in Formation Evaluation ................ 33
Tsai-Bao Kuo, Steven A. UVng, and Richard A. Startzman
Chapter 3
Intelligent Knowledge Based Systems and Seismic Interpretation. . 61
I. Williamson
Chapter 4
An Expert System for the Design of Array Parameters
for Onshore Seismic Surveys. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
Barrie K. Davis
Chapter 5
An Expert System to Assist in Processing
Vertical Seismic Profiles. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
Peter Crisi
Chapter 6
Expert Systems for Seismic Interpretations and Validation
of Simulated Stacking Velocity Functions . . . . . . . . . . . . . . . . . . . . . 99
KiJU-Yuan Huang
Chapter 7
Pattern Recognition to Seismic Exploration 121
KlJU-Yuan Huang
xi
xii Contents
Chapter 8
Pattern Recognition for Marine Seismic Exploration 155
0 000 0 0 000 0 0 0
Ferial El-Hawary
Chapter 9
Clustering of Attributes by Projection Pursuit
for Reservoir Characterization 173
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
AoT. Walden
Chapter 10
Exploring the Fractal Mountains 201
0 0 000 0 0 0 0 0 0 0 000 0 000 0 0 0 0 0 0 000
Brian Klinkenberg and Keith C. Clarke
Chapter 11
Image Analysis of Particle Shape 213
0 000 0 0 0 0 0 0 0 000 0 0 0 0 0 000 0 0 000 0
John Starkey and Sandra Rutheiford
Chapter 12
Interactive Image Analysis of Borehole Televiewer Data 223
0 0 0 0 0 0 0 0 0
Colleen Ao Barton, Lawrence Go Tesler, and Mark Do Zoback
Chapter 13
Petrographic Image Analysis: An Alternate Method
for Determining Petrophysical Properties 249
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
RoEo Gerard, C.Ao Philipson, F.Mo Manni,
and DoMo Marschall
Chapter 14
Image Processing of Magnetic Data and Application
of Integrated Interpretation for Mineral Resources Detection
in Yiesan Area, East China 265
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
UU Chaojun
Chapter 15
Interactive Three-Dimensional Seismic Display
by Volumetric Rendering 285
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Robert Ho Wolfe, Jr. and C.No Liu
Index 293
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