provides a holistir approach, addressing all basic issues in geological relations as well as .... Data Optimization for Petroleum Geology--Chaves and Lewis ~ 73 ...
F r o m D a t a G a t h e r i n g t o R e s o u r c e s Assessment: A Holistic View of Petroleum
Geology
Hernani A. F. Chaves~ and M. Effie Lewis2
~Department of Geology/Geophysics, University of the State of Rio de Janeiro, Rio de Janeiro, Brazil, and zDepartment of Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
To integrate geological data to obtain an interpretation of the geology and natural resources of an area, we need a methodology that provides a holistir approach, addressing all basic issues in geological relations as well as uncertainties that arise w i t h the evolution of basic geological knowledge of an area. In spite of the major role played by geoinformaticsmthe applica. tion of mathematics, statistics, and computer science to solve geological p r o b l e m s ~ w e do not yet have a properly designed method for organizing geological data, including raw data, conceptual models, modeling results, and geological Integration. Such a rapid method should provide for updating of existing interpretations based on new data or new theories. A systematic view of data integration and Interpretation is im. portant in petroleum exploration and petroleum engineering. We point o u t the more striking tools already available but of restricted use and some of the possible solutions for known problems that still require research and development. Key words:. Petroleum geology Exploration Natural resources Multidisciplinary data integration Data storage Geocoded data bases
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9 1994Oxford UniversityPress0961-1444/94/$4.00
Introduction Assessing energy and mineral resources of an area or a country is challenging and requires ingenuity and creativity. Because of the paucity of methods for direct detection of hydrocarbons, evaluating sedimentary basins for energy resources is largely subjective. Assessing resources requires sound geological knowledge and at least above-average familiarity with mathematics, statistics, and economics. Resource assessments are normally accomplished by highly qualified professionals, resulting in separation between the assessor and the exploration geologist. We need a complete methodology that encompasses data storage, data reduction, data interpretation, data integration, extraction and understanding of geological information, and economic appraisal and assessment (fig. 1). This methodology should provide a well-documented holistic view of geological research and exploration with the assistance of geoinformatics--the application of mathematics, statistics, and computer science to solve geological problems--to provide responses with complete participation of all professionals. This article formulates a proposal for a systematic approach for a resource appraisal methodology, the geological relations and uncertainties involved in resources potential and exploration (Searl, 1975) according to the evolution and growth of basic and specific geological knowledge of an area, and taking the petroleum industry as a particular case. The main idea is to put together (possibly integrated in a computer system) well-known and discussed meth-
Q~OLOGY
odologies of data handling; automatic storage, retrieval and processing; and geological information extraction (automated, whenever possible). It should also include integration of geological data and resource assessment comprehensively treated through a generalization of characteristic analysis (Chaves, 1993), seen as a tool not only to improve the assessment but also for the digital storage of the final results of interpretation and favorability maps (fig. 2), consequently improving communication of results. New data processing tools, as knowledge-based data systems and other artificial intelligence tools, should also be considered as part of this system. The overall goal is to achieve a progressively growing, intelligent knowledge-based information system. This article is an offshoot of a paper presented at the "'ILP Research Conference on Advanced Data Integration in Mineral and Energy Resources Studies" (Spain, Nov.-Dec. 1988). The specific objectives of the conference were to review, anticipate, and identify opportunities related to data integration and information extraction techniques within Mineral and Energy Resources Studies (Gary Hill, written commun., August 30, 1988). Besides a review and clarification of the first part o f the original paper (Chaves, 1988), this article benefits from discussions and other papers presented at this conference. Energy Resource Assessments
Importance "How much oil is left in traditional oil producing areas, and how long will these areas stay in production?" "'What is the petroleum potential of developing nations? .... Are
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Data Optimization for Petroleum Geology--Chaves and Lewis ~ 73
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there possibilities for significant oil occurrence in frontier exploration areas?.... Can we hope for major oil fields in the continental rise areas other than the known occurrences in Brazilian deep waters (below 900 meters, 3,000 feet of bathymetry)?" These simple questions require complex answers. To answer these important questions, we must consider problems arising from the finite limits of nonrenewable resources, environmental constraints, and a geometric population growth (Grossling, 1976). There is an increasing need for updated estimates of oil and gas resources remaining to be discovered in traditional oil provinces and in frontier areas, and also for a refinement of the methodologies used in making these assessments. Important and recent results are presented in Rice (1986). This work includes many articles, one of which (Miller, 1986) discusses the resource appraisal methods used during the last three decades in the United States and Canada. Miller's main conclusions are summarized in the following paragraphs. Five basic categories of assessment methods are identified as follows: (1) areal and volumetric yield tech-
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niques, combined with geological analogy; (2) Delphi or subjective assessments; (3) historical performance of drilling and discoveries; (4) geochemical material balance techniques; and (5) combination methods using geological and statistical models (for example, exploratory p l a y analysis). The various methods applicable in a petroleum province, with increasing degree of geological assurance, depend on the different stages o f exploration--frontier, immature to semimature, and mature.
Pitfalls Reviewing and comparing assessments of oil and gas resources using different methodologies and applied to a wide range of geographic amplitude, from nationwide for Canada and the United States to regional, Miller (1986) pointed out numerous difficulties resulting from (1) lack o f documentation; (2) differences in various assumptions and constraints; and (3) evolution of purpose, motivation, and bias of the assessment methods. She concluded that a correlation pattern exists between the magnitude of resource estimates and the resource method chosen.
According to Miller (1986), the new methodology must address (l) the understanding of the fundamentals of geological occurrence of the resources; (2) the research of new concepts in geology and technologies; (3) new exploration tools and new recovery techniques; and (4) application of new statistical and computer techniques.
Proposal The five categories of assessment methods for oil and gas resources, mentioned by Miller (1986), utilize, in one way or another, the geological analogy to obtain an estimate, even or especially when using statistical methods. The development of a model is, by definition, using geological analogy to establish a conceptual analogy, even if applied to mature basins. Using the volume of discovered fields to estimate parameters of field size distribution, it is assumed that the exploration will follow a pattern of discoveries analogous to the past discovery, with no provision for "geological surprises." The most important and urgently needed new computer techniques, as previously mentioned, are those that provide special ways for properly recording and storing in digital media the uncertainties of geological interpretation, using graphical workstations. These techniques should provide quick updating of former assessments in view of new data or theories, through the recording and automation of data interpretation and integration. These goals are attainable by recording geological analogy (Chaves and Melo, 1989) through generalized characteristic analysis (Chaves, 1988, 1993) and managing geological inference through a knowledge-based system supported with Bayesian inference mechanisms. The record of the successive applications of geological analogy, as we will discuss subsequently, could be easily acquired by developing (1) the digital surface models (DSM), (2) the variance of estimation map, and (3) the favorability map for each variable considered in the analysis of the basin, including, as well, the automatic interpretation of the striking features of the various maps (Chaves, 1978). The goal is to use the same methodology for data integration in basin analysis and for assessment of energy resources, using all the available information in each of the various stages of exploration. In this way, it is possible to provide a complete record of all steps involved in the analysis, to permit revisions and updating according to the evolution of the exploration process. Last, but not least, this approach will lead to better communication of results, integrating exploration and production, in a dynamic, intelligent knowledge-based information system. All our discussions will focus on energy resources, but they can be generalized to include mineral resources as well.
P e t r o l e u m Exploration Scenario
The petroleum industry as a whole, and chiefly the exploration industry, is deeply affected by the way things are handled in United States petroleum industry, regarding its origin in the United States, its early development and activities abroad, and its leading position in the world. This is a well-known fact, but, to our view, not always clearly stated and understood. Related to the concerns of this study, the more relevant facts that affect the petroleum exploration scenario are the following: 1. Data arc stored in the files of hundreds of different (major and independent) companies, most of ~vhich never publicly release the data. In many cases, this leads to an incomplete picture of the whole basin. 2. Scouting, data integration, and basin analysis is expensive, affordable only by major companies, resulting in most cases in proprietary studies. 3. Tools to handle the huge amounts of existing data in the usual short time required by exploration activities are inadequate. This leads to published general studies conducted by governmental agencies, usually the U.S. Geological Survey or the State surveys. Despite being carded out by leading qualified scientists, these studies are often incomplete. This scenario further encourages the dichotomy between assessors and exploration geologists. A similar situation occurs when different countries share a sedimentary basin, as in Southwest Asia. The situation could be different in countries, such as Canada, where all data should be released to the government agencies, or in countries, such as Brazil, where all exploration is conducted by public companies. Even then, when dealing with active exploration basins, the huge amount of data leads, practically, to an incomplete picture of the whole basin. A sound, complete, and systematic picture of sedimentary basins is our motivation for formulating this proposal.
From Data to Assessment
Using mathematics, statistics, and computer science to solve geological problems has resulted in a new discipline, proposed to be named geoinformatics at the Fourth South American Symposium of COGEODATA (Commission on Storage, Automatic Processing and Retrieval of Geological Data of International Union of Geological Sciences) held in 1987 in Ouro Preto, Brazil. The major challenge of geoinformatics is to properly organize geological and related data to provide an integrated view of resource exploration, from data to assess-
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Figure 3, The role of geoinformatics in resourceexplorationman integration of data, models, and interpretation, through management and updating facilities. ment, thus making it easy to upclate the interpretations in the light of new data or new theories (fig. 3). Most ofthe tools required for a complete methodology are already available but not in widespread use. What follows is a suggestion of how to put these tools in order, pointing out some of the existing problems that can be improved or further researched. We will present existing technologies and methodologies in a step-by-step fashion, highlighting examples of their usage, their most important aspects, and certain research hints. Few commercially available systems incorporate recent concepts in computer science, like shells that integrate different seismic, well, and production data. However, some important features are still missing, such as appropriate tools for geological information retrieval, storage of interpretation results, and resource assessment (Table 1).
Data Gathering Technological developments mainly in the last two or three decades have resulted in new tools for data gathering, like remote sensing, and have improved the facilities in data acquisition radically, thus reducing dramatically the cost for each data item. This progress has greatly increased the amount of data available for geological interpretation. A single scene e r a remote sensor has about I00 Mb (10s bytes) of information, and a single seismic line may have two or three orders of magnitude more
71 | Nonren~wab|eResources
information. In seismic surveys, for instance, these improvements have led to a substantial reduction in the costs for the acquisition of data items, both in the sea and on land. The common depth point (CDP) technique and digital recording have increased from 1 to 5 Gigabytes (109 bytes) the number of sample points per kilometer of seismic line. On the other hand, when data acquisition requires personal interpretation, as in paleontology and petrography, we are still waiting for major breakthroughs and help from computers to improve data gathering. Paleontologists, for example, still rely on fossil identifications to provide age and paleoecological interpretations. In these cases, computers can be used to record readings at the microscope, improving not only the speed but also the quality of data gathering. We foresee the following major improvements in paleontology: (I) direct recording, with minimum effort and without coding, of fossil determinations by paleontologists at computer terminals; and (2) using visual and text document management (American Museum of Natural History, 1989), as well as knowledge-based systems, in computer-aided taxonomy.
Data Reduction Some types of data, like seismic, geophysical, geochemical, and remote sensing data, require specific treatment
Table 1, Appropriate tools for major steps in exploration Major steps in exploration
Some characteristics of the most important available tools
Some of the problems requiring R&D
Data gathering Petrography, paleontology, geochemistry, seismic surveys, remote sensing Data reduction
Cheaper data acquisition resulting in bigger Volumes of data as a result of technological developments
Estimation
Geostatistical estimation of digital surface model (DSM) statistical and mathematical tools for aerial correlations automatic construction of facies maps Graphical workstation for interactive interpretation
Some types of data still rely on "hand-crafted" work, for example, Paleontology and Petrography Possible tools: computer-aided taxonomy with knowledge-based systems Adequate statistical and mathematical tools (sampling theory still missing) Vector to raster transformations Ways to keep track of original sampling points Methods for estimation of directional data and from apparent dip
Data interpretation and regional maps Data storage
Data integration and resource estimation
Specific for the type of data
Raw data Data f(x, y, z, a)v. Relational data base Geographic Information Systems Integrated exploration and production systems (lIE&P-S) Single variable spatial model (favorability) Spatial covariation of variables Pattern recognition Extraction and understanding of geological information "Conceptual geological model of the area Characteristic analysis Resource appraisal and assessment
for data reduction before they can be used for geological interpretation. The improvements in data gathering have coincided with a parallel development of electronics and computer technology (including numerical methods and software engineering) that provide adequate means for data handling and data reduction. Supercomputers can process 36,6000 seismic traces per hour (Compagnie General de Geophysique, 1989). Ordering, which is a spatial relation among samples, is the most important characteristic of geological data. No.matter what type of data we have, data reduction methodology should preserve this relation. This necessitates certain theoretical and methodological improvements for better data management and processing. Such is the case, for instance, of adequate statistical and mathematical tools that take into account the spatial relations of geological observations. In statistics, for example, all formalized sampling theory relies on random independent sampling. Because of implicit spatial ordering and relations, however, geological observations are never truly independent. In most cases, geological observations are strongly auto-correlated, and very often they are not random but biased.
Knowledge-based systems able to record decisions under uncertainties and to update in view of new data Adequate GDBMS to handle points, lines, chains. polygons, triangles, grids Facilities for internal aerial consistency Quick updating
Geostatistical censored estimation Consistency using variance of estimation Applications of fuzzy set theory Knowledge-based systems to extract spatial relations in stratigraphic data files
Estimation
All observations of geological variables (v~)include geographic location (x, y), depth (d), and age (a), so really they are points in a four-dimensional space; their interpretation requires the identification and understanding of similarities and patterns of spatial distribution. For this reason, maps and sections have been the only adequate means for displaying and analyzing these relations. Mathematical modeling of the spatial behavior (in three-dimensional space) of a geological variable and its geological significance is achieved through the construction of a digital representation of the conceptual surface intended to be characteristic of the specific variable. This digital representation, the digital surface model (DSM), is obtained through statistical estimation of a regular grid that is fundamental for automatic interpretation and pattern recognition. This estimation must preserve the spatial relationships already detected in the sampled data or inferred by analogy with other known situations of equivalent relations and data. For variables expressed in interval or ratio scales and mapped in isopleth or isovalue maps, the most suitable method for constructing a DSM is geostatistical esti-
13.1t.1(3ntimiTatinnfor PetroleumGeoloovmChavesand Lewis | 77
II
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marion, on the basis of regionalized random variables that take into account and preserve the spatial structure (semivariogram) of the studied variable and the distribution of sampling points (Chaves, 1978), providing as wcU the variance of the estimation at each grid point. The DSM can also be obtained for variables expressed in nominal or ordinal scales (thematic maps). When dealing within closed systems (adding up to a constant), a DSM can be constructed automatically, including the decisions on the boundaries of contiguous patches (Almeida, 1985).
Data Interpretation and Regional Maps Data interpretation and regional mapping require special hardware and software to help geologists expand their capabilities. These tools will also provide means for better extraction of information, since all known facts will be at hand during the interpretation ofnew facts, resulting in more reliable outcomes. Graphics is an essential part of the geological idiom. Even if a geologist is not a good drawer, graphical interpretation is a basic requirement for obtaining results; the better the geologist can interpret graphics, the better the job will be accomplished. Geological interpretation begins with the correct understanding and interpretation of natural drawings and patterns recorded in the rocks, although this first step is not always an easy one. To understand the data and interpret spatial relations among the data, geologistsuse maps, sections,and graphics. The difficultyof creatinggraphics with computers was the main reason geologistsrefused to use graphics packagcs. All the pioneer work of the sixtiesat the Kansas Geological Survey with the Computer Contributions Scties edited by Dan Merriam, as well as the formation in 1968 of the InternationalAssociation for Mathematical Geology with its journals Mathematical Geology and Computers and Geoscience,was not sufficientto convince geologists to use computers in theirdaily work.
In the early eighties microcomputers were used more, even among geologists. However, low-cost graphical workstations best fit the needs o f geological interpretation. These workstations, which serve as electronic desks for geologists, are suitable for drawing (digitizing table), graphical analysis and interpretation (high resolution monitors), and graphical display (plotters and hard copies). Hardware alone is not enough to solve geologists' problems. Graphics generation and editing have only recently been used as an aid in geological interpretation. Yet, other interactive tools are necessary to explore adequately the possibilities of the existing hardware. A lot of geological data is interpreted under uncertainty, with cumbersome bookkeeping of the reasons for the final solution. New data collected in the same area may completely change any previous interpretation. Data interpretation and regional maps require special tools for recording the evidence that leads to the final interpretation; this is fundamental for quickly updating the maps with new data. In sedimentary basin analysis, a special data integration situation occurs that requires not only knowledge of the variable under consideration, but also its expected regional behavior and that of the related variable set. Figure 4 shows schematically a section of two surfaces, A and B, obtained by automatic processing. Since well III ended at total depth (TD) before reaching horizon B, its value will not be included among the sampled points used in the construction of the DMS for horizon B. Therefore, the estimation obtained for horizon B at the location of well III is above its TD, which is obviously wrong. Thus, a complete mapping package should include means for automatically taking into account restrictions like that of including well III for the estimation of horizon B, considering that the result at this point cannot be equal or above its TD (Dubrule and Kostov, 1986; Kostov and 9 Dubrule, 1986). Most of the existing map packages cannot automatically accommodate bounded information, leaving the geologist to introduce the limits manually. Quantification of data and map interpretations should also be considered. Storing grid values of a selected area in a graphical display requires good raster-to-vector and other topological transformations. The construction of favorability maps of a variable (Chaves, 1988, 1989, 1993) deserves special consideration. For some kinds of isopleth maps, this is achieved by expressing the favorability as a mathematical function, for example, in structural maps, using values greater than a certain value or closed features, or in geophysical or geochemical maps, by changes in regional gradients (McCammon and others, 1983). Some geological information, however, like reservoir attributes (Romeu, 1986) and descriptions of sed-
imentary facies, has an inherent uncertainty. For inStance, in the study of submarine fans with subsurface data, identification is certain when the samples come definitively from the proximal or the distal part of the fan. The question is how to pinpoint the different boundaries. A mathematical tool well-suited for describing this kind of natural phenomena is fuzzy set theory or the special ease of fuzzy numbers. Geological processes shape the framework affecting ecological, geochemical, and tectonic paleoenvironments recorded in the rocks. All data in a basin relate to each other, and 'they are consistently and coherently interpreted as a result of implicit geometrical relations. These relations are not easily searched for or displayed by the usual means available for data processing. Currently, the geologist must look to stratigraphic data, for instance, to discover unconformities, pinch-outs, and so on. This task is more easily accomplished on a computer than manually. In the early days, when people tried to persuade geologists to use computers, it was said that in the United States there was more oil hidden in company data files than would possibly be found by direct field exploration. We are not convinced that these times are over, and not only in the United States. We are sure that these hidden oil potentials can be modeled by rule-based knowledge systems.
Geological Data Storage Research should be undertaken to develop better statistical and mathematical tools for areal correlation and other types of relations. Geological information and geological relations must be handled by a geocoded data base management system (GDBMS), a tool that should provide support for the definition and storage of spatial data and relations: points, lines or chains, polygons, triangles, and grids. These kinds of relations are not in the scope of the relational data base model, the most commonly used model for data base management systems (DBMS). Only recently, commercially available data base management systems (DBMS) began providing some of these features (Stoneebraker and Rowe, Digital Equipment Co/poration, 1986). Geographic Information Systems (GIS) are a major improvement on classical relational data base but are still restricted to two dimensions. Lines or chains are spatial relations of points, polygons are spatial relations of lines, and both are relations in a plane (two-dimensional space); they require ways to be handled in a GDBMS. A natural extension of these relations would be solids and hypersolids, bodies in threeand four-dimensional spaces. Triangles and grids must be stored in a GDBMS because they are necessary for volumetric calculations, and for the storage of the various maps ofgeological variables
in a digital surface model (DSM). In this way, it is possible to keep track of the rationale behind the interpretation and enables quick updating of maps and interpretations whenever new data are available. To express and store data in DSM form or in favorability maps, especially in petroleum geology, it is fundamental to allow for data integration either through regular grid operations, for instance, in the calculation of regular and consistent isopachs (Rice, 1986), or through more elaborate techniques like characteristic analysis, which allows extraction and understanding of geological information (Chaves, 1988, 1993). Since ordering is an inherent characteristic of geological observations, a GDBMS should also provide for the internal areal consistency of data. Presently most data bases in petroleum geology are only repositories of information collected systematically through drilling activities. Only in revision or in regional studies are these data bases reinterpreted and areal inconsistencies detected. Some of these inconsistencies are simply due to recording errors; however, some indicate important geological facts not yet known, Spatial consistency (three dimensions or higher) of the data in the GDBMS requires tools for quick updating of the existing data base with new data and for direct detection of spatially unmatched relations. This will enable a kind of management by exception, obtaining prompt identifications of what might be a sample error or a new and unknown geological situation. At least some of these problems can be solved by (1) using mathematical techniques, such as geostatistical censored estimation, (2) providing bounded estimation, for example, for a new well to be drilled, or (3) using the variance of estimations to check the new variable value. We must improve the method currently used to update DSM's. Today, DSM's are updated by completely recalculating the model, resulting in the loss of refinement, chiefly on areas of low-density sampling. What is needed is a localized updating procedure that uses algorithms for fitting and adjusting potential surfaces. However, in this case, how far from the sample point should the DSM be adjusted? We must find a way for these algorithms to keep track of original sample points in relation to estimated ones, thus preserving the data samples already existing in the data base.
Interactive Integrated Exploration and Production Systems In the early stages of exploration of a sedimentary basin, and in the past even after these stages, the regular pattern of surveys would be, for example, surface geology data gathering, interpretation, and reporting, followed by a geophysical survey with the same sequence of activities.
Data Optimization for PetroleumGeology--Chavesand Lewis J 79
IN TANDEN INTERPRETATIONS
I d#a xeductlon Figure 5. Comparison of in tandem surveys with parallel surveys in resource assessment.
Even if the different surveys were somewhat synchronous, only with the release of the final report would their results be known to other people working in the same area, with the exception of personal contacts among specialists. This has led, in many cases, to different interpretations for the same areas in a basin. These hierarchical studies and reports, here designated "in tandem surveys," are superseded by what are called "parallel surveys," by analogy with computer architecture (fig. 5). With the use of computers, especially in the digital recording of seismic data, different kinds of surveys are assessed concurrently in an active exploration basin so as not to lose commercial opportunities. However, we still need reports to pass information from one department to another in the same company. Even if the various departments share the same computer installations, there is often a lack of integration of data bases; users must know the idiosyncrasies of each file or data base and apply interface programs between applications. With workstations and effective GDBMS, we can have not only parallel surveys but parallel reporting on strategic exploration and production decisions. For this, we need interactive integrated exploration and production systems (IIE&P-S). In this case, different data bases (exploration, geophysical, geochemical, production, and reservoir) and applications will share not only computer systems but also common geographical information, using a unique kernel of basic routines for processing and intelligent man-machine interfaces with all the peripherals (fig. 2). Since the data bank will contain not only original data but also various results stored as sections, DSM's, and favorability maps, users will have access to last-minute interpretations, allowing for better communication and increased knowledge of sedimentary basins. There are no systems like this one, complete and integrated, that provide for all the needs and possibilities from the beginning of exploration to enhanced recovery. A subset of IIE&P-S, not to be confused with the one
| Nonrenewable Resources
PARALLEL INTERPRETATIONS
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previously mentioned, includes systems suited for joint projects of geologists, geophysicists and petroleum engineers in restricted areas of a basin, for example, as in integrated studies ofreservoirs. Such systems are becoming more commonly available (Finder, 1988; Leonard, 1988; Integral, 1989; Discovery, 1990), even though they cannot deal with spatially defined objects and relations.
Data Integration and Resource Estimation The whole process culminates in an economic appraisal and assessment (fig. 1). The assessment provides a systematic view of the geological variables, their characteristics, and uncertainties regardless of how generalized or elaborate the assessment method used, from areal and volumetric-yield techniques to elaborate and sophisticated combination methods using geological and statistical models (for example, exploratory play analysis). Once in while, a team pursues basin analysis studies using in tandem surveys. For those studies, a snapshot of the data and interpretations available is taken to promol~ data integration and resource appraisal. The main dissatisfactions with these studies are (1) the previously, mentioned separation between the geologist in charge of field data collection and interpretation and the praodtioners of basin analysis and resource assessment; (2) initial interpretations based on different techniques, such as structural geology and seismic, do not mutually benefit each other; (3) major disagreements tb~t would require greater efforts to be removed; (4) in very active areas, the results may be out of date when completed; and (5) only when studies are completed will the results improve new exploration campaigns. By adopting the integration methodologies here proposed, with convenient storage facilities and proper use of graphical workstations, it is possible to have a systematic view of the whole process. Participants would know exactly the import.ance of their data and the relevance of new findings, especially the unexpected one that may
ultimately be responsible for changing exploration philosophy. The use of characteristic analysis (CHARAN) works in all the methodologies proposed. One important aspect is the adoption of the favorability maps for each relevant variable in the basin analysis. These maps reflect the most favorable places for each variable, according to the interpretation of the geologist in charge of surveying. Thus, we can have the favorability map prepared by the structuralist, paleontologist, geochemist, geophysicist, and so on. For the assessment team, the problem will be to apply CHARAN to (1) add these maps together, to find major mismatches, chiefly between groups of variables; (2) point out areas requiring or worthy of more detailed work; (3) decide on new plays or new prospects; and (4) evaluate not only the total amount of possible resources to be discovered. By applying quantified geological analogy, this evaluation could be used to estimate the size distribution of these possible occurrences (Chaves, 1989, 1993). The proposed approach allows the evaluation of uncertainties for each variable used and for each step used to interpret these variables. Actually, these uncertainties start, in oil exploration, with the variables chosen to describe the targets, since, as previously mentioned, there is currently no method for direct detection of hydrocarbons. In this respect, CHARAN can be considered a type of discriminant function, identifying the variables with higher discriminant power, thus allowing us to verify the model we are using for the accumulations or plays. CHARAN can be applied to any phenomena and not restricted to regional studies. It can be used to characterize reservoirs and to describe oil pools or any other discrete sample space (Chaves, 1988, 1993). Conclusions The modem methods available for data gathering in geology result in a plethora of information. This information must be integrated to construct a sound interpretation of the geology and natural resources of an area. .Despite the increased use of computers to process this huge volume of data, a complete methodology is lacking. Computer graphical workstations can act as an extension of geologists' minds, providing for a well-documented holistic approach for a total resource appraisal methodology that addresses all the basic issues relative to the geological relations and uncertainties involved in each step of resource appraisal and exploration. The techniques for resource assessment require data integration using subjective knowledge. Normally, subjective criteria are difficult to state clearly. Improvements in data storage management systems, allowing for spatial relations (three and four dimensions)
among graphically defined objects, and intelligent knowledge-based tools to explore the files of sedimentary basins for concealed statigraphic and structural relations can provide for a data base management by exception that can benefit the whole exploration cycle. Finally, general usage of characteristic analysis, a wellknown technique for mineral resource assessment and also proven to be useful for petroleum exploration, can be used as a tool for (1) data integration in sedimentary basin analysis; (2) quantification of geological analogy and petroleum assessment; and (3) indication of locations requiting or worthy of more detailed exploration efforts. References Almeida, H.P., 1985, Geracao automatica de mapas tematicos: apticacao a sistemas de dados ternados (Automatic Thematic Map Construction: Application to ternary data systems): Rio de Janeiro, Univ. Federal, COPPE, M. Sc. dissertation, 97 p. American Museum of Natural History, 1989, Micro Based Mark I--Systemrequirements: New York, MicropaleontologyPress, 79 p. Compagnie Generale de Geophysique, 1989, Benchmarks in seismic processing: Massy, France, 9 p. Chaves, H.A.F., 1978, Mapas batimetricos--Problemas de construcao e analise automatica [Bathymetric maps--Problems in automatic construction and analysis]: Rio de Janeiro, Pontificia Universidade Catolica, M.Sc. dissertation. -1988, Characteristic analysis applied to petroleum assessment of basins: ILP Research Conference on Advanced Data Integration in Mineral and Energy Resources Studies, Sotogrande, Spain, November 1988, 28 p. -1993, Characteristic analysis as an oil exploration tool, in Davis J.C. and Hertzfeld U.C., eds., Computers in geology: 25 years of progress: Oxford, Oxford University Press, pp. 99-I 12. Chaves, H.A.F., and Mr J.A., 1989, Characteristic analysis (CHAR.AN) applied to oil exploration: 28th International Geological Congress, Washington, D.C., Session M2: Quantitative Methods in Regional Resource Assessments [Abstracts]. Digital Equipment Corporation, 1986, The spatial/II database management system takes the petroleum industry into the future: Marlboro, Massachusetts. Discovery in: COGNSEIS DEVELOPMENT, Software Products for Exploration. Dubrule, O., and Kostov, C., 1986, An interpolation method taking into account inequalities constraints--l. Methodology: Mathematical Geology, v. 18, no. 1, p. 33-51. Finder, 1988, An executive summary: Massy, France, Exploration Systems Inc. Grossling, B., 1976, Window on oil--A survey of world petroleum sources: London, The Financial Times Ltd., 140 p.
Data Optimization for Petroleum Geolocjy~Chaves and Lewis I 81
Integral- Company Generale de Gcophysique, Massy, France, 1989. Kostov, C., and Dubrule, O., 1986, An interpolation method taking into account inequality constraints--II. Practical approach: Mathematical Geology, v. 18, no. l, p, 53-73. Leonard, J.A., 1988, Part I a: PC applications with well-filedata: Geobyte, November, p. 27-34. McCammon, R.B., Botbol, J.M., McCarthy, J.H., and Gott, G.B., 1983, Characteristic analysis applied to multiple geochemical anomalies over a concealed porphyry copper prospeel, Rowe Canyon, Nevada: American Institute of Mining, Metallurgical and Petroleum Engineers Transactions, v. 272, p. 1998-2002. Miller, B.M., 1986, Resource appraisal methods: Choice and outcome, in Rice, D.D., ed., Oil and gas assessment--Methods and applications: Tulsa, American Association of Petroleum Geologists, (AAPG Studies in Geology # 21), pp. 125. Rice, D.D., ed., 1986, Oil and gas assessment--Methods and applications: Tulsa, American Association of Petroleum Geologists, (AAPG Studies in Geology # 21), 267 p. Romeu, R.K., 1986. Projeto logico para um banco de dados de reservatorios de petroleo--uma aplicacao dos numeros difusos (Logical design of a data bank for petroleum reserv o i r s - a n application of fuzzy numbers): Ouro Preto, School of Mines, M. Sc. dissertation, 120 p. Searl, M.F., 1975, Resource assessment and supply curve development: Towards better methodologies, in Grenon, M., ed., First IIASA Conference on Energy Resources, Luxembourg, Austria, CP-76, p. 71-83.4. Stoneebraker, M., and Rowe, L.A., 1985, The design of POSTCRESS. Berkeley, University of California, College of Engineeriog, Electronic Research Laboratory, Memorandum no. UCB/ERL 85/95. Received February 28, 1992; revised August 9, 1993; accepted September 13, 1993. Many thanks to Charles Thoman, who read the draft. The ideas here exposed were discussed with colleagues Hilton P. de Almeida, Jorge Della Favera, Jos6 A. Melo; however, all mistakes are the authors' responsibility. The ideas presented here are a summary ofdiscussions, findings, proposed solutions, and frustrations faced by the Geoinformatic Laboratory of PETRCBRAS Research Center (CENPES) for more than a decade in its research on sedimentary basin assessment. This summary paper also benefited from discussions with the AI Croup at the ILP Conference in Spain and with Dr. Mouchmino and Dr. Prax at Massy, France. All figures were produced on the CAD system of the Geoinformatic Laboratory with the assistance of Nora de Castro Maia and Ronaldo Pereira de Oliveira. The help of AIdo Araujo da Costa was essential in revision and editing.
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