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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 Geophysicist Amoco Production Company 501 West Lake Park Boulevard Houston, TX 77253, USA
Sailes K. Sengupta Lawrence Livermore National Laboratory Livermore, CA 94511, USA; formerly professor at: 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 1 st edition 1992
All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer-Verlag New York, Inc., 175 Fifth Avenue, New York, NY 10010, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use of general descriptive names, trade names, trademarks, etc., in this publication, even if the former are not especially identified, is not to be taken as a sign that such names, as understood by the Trade Marks and Merchandise Marks Act, may accordingly be used freely by anyone.
Production coordinated by Chernow Editorial Services, Inc. and managed by Linda H. Hwang. Typeset by Publishers Service of Montana Inc., Bozeman, MT.
9 8 7 6 5 432 1
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 varying 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 applications 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 sessions 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 technologies. 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.
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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 Evaluation." 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 "Intelligent 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 knowledgebased 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 developed 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 exploration in his chapter, "Expert Systems for Seismic Interpretations and Validation 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 transition trees to implement parsing of the questions and the formation of answers in natural language. In his seismic data interpretation expert system, SIES, Huang illustrates how pattern matching, backward chaining, and augmented transition trees can be effectively used. SEIS is a prototype
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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 recognition methods based on the author's long experience in real and synthetic seismograph analysis. The study is geared to the detection and recognition 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 analysis, tree classification, and different variants or syntactic pattern recognition. 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 hydrocarbon 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 incorporation 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 multimodal characteristics or "non-Gaussianness" of the projected computation of the entropy index and its derivative, which, in turn, is done by extensive use of fast Fourier transform, making it computationally efficient. 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 literature. 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 Particle Shape" by Starkey and Rutherford is the culmination of several earlier studies on image analysis by these authors. Digitized images of thin sections under the petrographic microscope are first subjected to standard gray level thresholding to delineate the particle boundaries. Then a best fit
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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 Determining Petrophysical Properties" with a case study of the successful classification 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 Processing 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 waveform 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 animation helped provide a mental picture of the third coordinate. The authors' approach is different. They choose to display the waveform attribute by a
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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 KlJU-Yuan Huang
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Chapter 8 Pattern Recognition for Marine Seismic Exploration 0 000 0 0 000 0 0 0 155 Ferial El-Hawary
Chapter 9 Clustering of Attributes by Projection Pursuit for Reservoir Characterization 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 0 173 AoT. Walden
Chapter 10 Exploring the Fractal Mountains 0 0 000 0 0 0 0 0 0 0 000 0 000 0 0 0 0 0 0 000 201 Brian Klinkenberg and Keith C. Clarke
Chapter 11 Image Analysis of Particle Shape 0 000 0 0 0 0 0 0 0 000 0 0 0 0 0 000 0 0 000 0 213 John Starkey and Sandra Rutheiford
Chapter 12 Interactive Image Analysis of Borehole Televiewer Data 0 0 0 0 0 0 0 0 0 223 Colleen Ao Barton, Lawrence Go Tesler, and Mark Do Zoback
Chapter 13 Petrographic Image Analysis: An Alternate Method for Determining Petrophysical Properties 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 249 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 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 0 265 UU Chaojun
Chapter 15 Interactive Three-Dimensional Seismic Display by Volumetric Rendering 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 0 285 Robert Ho Wolfe, Jr. and C.No Liu
Index 00000000000000000000000000000000000000000000000000 293
Contributors
Olivier Allain, Petroleum Engineering Department, Stanford University, Stanford, CA 94305, USA
Colleen A. Barton, Geophysics Department, Stanford University, Stanford, CA 94305, USA
Hit Chaojun, Department of Applied Geophysics, China University of Geosciences, Wuhan, China
Keith C. Clarke, Department of Geology and Geography, Hunter College, City University of New York, New York, NY 10021, USA
Peter Crisi, Geophysicist, Mobil E&P Service Inc., 3000 Pegaus, Dallas, TX 75247, USA
Barrie K. Davis, 17 Lynmouth Road, Fortos Green, London N29 NR, United Kingdom
Ferial El-Hawary, Signal Analysis Laboratory, Technical University of Nova Scotia, Halifax, Nova Scotia B3J 2X4, Canada
R.E. Gerard, Core Laboratories, 10201 Westheimer, Houston, TX 77042, USA
Roland N. Horne, Petroleum Engineering Department, Stanford University, Stanford, CA 94305, USA
Kou-Yuan Huang, Institute and Department of Information Science, National Chiao Tung University, Hsinchu, Taiwan 30050, Republic of China
Brian Klinkenberg, Department of Geography, University of British Columbia, Vancouver, British Columbia V6T IW5, Canada
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Tsai-Bao Kuo, ARCO Oil and Gas Company, Plano, TX 75075, USA
CN. Liu, Computer Science Department, T.J. Watson Research Center, mM, P.O. Box 704, Yorktown Heights, NY 10598, USA
F.M. Manni, Core Laboratories, 10201 Westheimer, Houston, TX 77042, USA
D.M. Marschall, Core Laboratories, 10201 Westheimer, Houston, TX 77042, USA
Ibrahim Pa/az, Geophysicist, Amoco Production Company, Houston, TX 77253, USA
CA. Philipson, Core Laboratories, 10201 Westheimer, Houston, TX 77042, USA
Sandra Rutherford, Department of Geology, University of Western Ontario, London, Ontario N6A 5B7, Canada
Sailes K. Sengupta, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA; formerly professor at South Dakota School of Mines and Technology, Rapid City, SD 57701, USA
John Starkey, Department of Geology, University of Western Ontario, London, Ontario N6A 5B7, Canada
Richard A. Startzman, Petroleum Engineering Department, Texas A&M University, College Station, TX 77843-3116, USA
Lawrence G. Tesler, Geophysics Department, Stanford University, Stanford, CA 94305, USA
A.T. Walden, Department of Mathematics, Imperial College of Science, Technology, and Medicine, Huxley Building, 180 Queen's Gate, London SW7 2BZ, United Kingdom
I. Williamson, Department of Geology, Imperial College of Science, Technology, and Medicine, London SW7 2BP, United Kingdom
Robert H. KUlje, Jr., Computer Science Department, T.J. Watson Research Center, IBM, P.G. Box 704, Yorktown Heights, NY 10598, USA
StevenA. KUng, ARCO Oil and Gas Company, Plano, TX 75075, USA
Mark D. Zoback, Geophysics Department, Stanford University, Stanford, CA 94305, USA
Contributors
Automated Pattern Analysis in Petroleum Exploration
