This article is an expanded version of a paper presented at the 1998 meeting of the Gulf Coast Association of Geological Societies, Corpus Christi, Tex., and published in the Societies' Transactions, Vol. XLVIII, pp. 381-91.
In these times, all segments of the energy profession have available old, new, or a combination of geophysical and geochemical survey results of a geographical region or a geological setting for which a reliable and timely evaluation is desired for petroleum exploration. Consider, for example, 3D seismicity that is currently in worldwide vogue, or the regional surveys employing gravity, magnetics, radioactivity, and earth satellite data. The final results of such surveys represent the beginning, not the endpoint of geologic assessment.
What are the currently available choices for review and interpretation of these data? Conventional approaches are either totally subjective, in accord with the reviewer's personal experience or preferences, or are subjected to more formal methods of analyses such as Starmag, AVO, Associative Memory, Neural Networks, etc. Although useful, even the latest of these interpretative procedures are inherently limited because they are restricted to the initial bias of one or more analogs that have been chosen to optimize and create a "more favorable" outcome.
This article describes a totally new and unique technology for the rigorous evaluation of a project's information database. No initial predetermination is required. It is called Target Forecasting Explorer System (TFES).1
The following discussion is in three parts:
- An overview of the important elements of TFES for studying natural earth measures.
- A discussion of the TFES methodology without, however, the normal mathematical rigor.
- A presentation of a sample study that evaluates the utility of coarse-gridded data from the North America Geophysical Data Grid (NGDG)2 and that provides the means for a breakthrough assessment of South Texas petroleum geology.
All geological and geophysical exploration data require initial acquisition and processing before meaningful interpretation can be made and an exploration strategy developed.
The processing and interpretive methods to be described here comprise a computer-based mathematical system of an unbiased classification of digitized measurements capable of identifying stable attributes (mathematical structures) within a matrix of primary data. The TFES system possesses the unique capability to merge and interrelate dissimilar but basic primary data as obtained from the earth measures. The standard product is one or a set of final, routinely colored maps that delineate areas or trends of stable attributes. These stable attributes are related to the natural history of the geological environment. In the discussion that follows, they represent specific areas of high hydrocarbon potential.
The stable attributes are found within the processed data and are reflections of a natural geological character that have created the initial observed signals. They are associated with those portions of the lithosphere where the most representative totality of physiochemical processes are synergistically focused, such as lithogenesis, diagenesis, tectonism, fluid flow, fluid transformation, metasomatism, etc. In this sense, the geological character is related directly to the most favorable energetic setting for the referred processes to take place, including those that provide for the concentration of various natural resources in the upper layers of the earth. Based on the foregoing, it is highly probable that, in any petroleum-prone region, including the example cited in this article, stable attributes found in one or more representative data types are associated with high hydrocarbon potential.
TFES, the mathematical strategy employed in the analysis, is computer-driven and represents a new and unconventional approach to forecasting valuable earth resources based on digitized data matrices of geophysical (including Landsat) and geochemical systems.
The procedure represents an important departure from conventional methods in that detection of the foci of information-bearing mathematical structures is achieved first, ahead of any classification schemes, not the reverse. These structures are then used to achieve classification. The TFES methodology employs a unique selection of coherent algorithms that are used to process and transform all of the initial data sets into "information clusters" of a high order. Without initially employing an analog (target), the result of such transformations is an unbiased identification of objects of high exploratory interest in a digitized data format.
As mentioned, TFES has been created as an alternative to existing biased methods of forecasting. These biased methods tend to work well in relatively simple, transparent situations but fail in dealing with those of greater complexity. Their use is therefore limited and insufficient because of the following inherent traits:
- The number of characteristics or variables available for use in classifying objects of a digitized data matrix is usually small (N = n * 10, where n is seldom greater than 1). This leads to insufficient diversity of viewpoints on the environment under study and ultimately to inadequate solutions.
- The user subjectively chooses recognition algorithms and solution rules. They are based on his or her view of what the results should be and are influenced by personal knowledge, experience, intuition, and other subjective factors.
- Algorithms of these biased methods are oriented towards achieving a single solution. They lack constructive methods for the development of alternative solutions.
Alternatively, TFES is an unbiased approach and is therefore not constrained by such limitations. The operational procedure is characterized by the flow diagram of operational blocks shown in Fig. 1:
- Block 1. Contains the initial digitized data.
- Block 2. These data are organized into digitized input matrices.
- Block 3. This matrix undergoes a number of conventional functional transformations (Gaussian curvature, Laplace, mean curvature, etc.), up to 24, in an attempt to obtain sufficient diversity. Results are a like number of transformed matrices.
- Block 4. The transformed matrices are in turn converted into a dichotomy, i.e., two equipotent subsets of homogeneous objects designated by ones or zeros, and their inversion.
- Block 5. These dichotomies serve as the basis for developing a manifold of characteristics (chaos) on the order of N = n * 106 (n >=1). Thus, each characteristic is a primary and primitive cluster of information of a low order. The great number and diverse nature of these characteristics assure the necessary diversity of aspects. This is a fulfillment of the well-known Bohr's principle of complimentarity in quantum physics, according to which the more points of view that are incorporated in the process, the more adequate a classification is determined for a set of data.3
- Block 6. Unique coherent algorithms of selection are applied to the primitive clusters of low order derived from Block 5 so as to reorganize them into clusters of information of high order. "Cluster resonons" are formed in Block 6 by a selection of those specific primitive clusters that are most coherent with other such clusters from Block 5. Cluster resonons in quantum theory are distinguished by a large inherent resonance, that is, a large quantum confluence. In nature, clusters of this type are capable of accumulating both energy and information in large anomalous quantities.4
- Block 7. Selections are made from cluster resonons in such a way that their sets provide for the most compact filling of objects of a matrix (S), with a minimum loss of information (i.e., no overlap). These selections are defined as "Cluster opposites" (dj), thus:
These clusters comprise the output data (A) of the flow diagram (Fig. 1) and are defined as Targetless Forecasting (TLF) results. At this point, an investigation has been completed and can be submitted to the scrutiny of an expert for evaluation and action, independent of any predetermination or "second-guessing" about the nature and value of the original input data.
The following procedures are usually required for most geologic investigations; however, they are optional:
- Block 8. An important departure is now introduced whereby a specific cluster is named "target." It is formed by marking those objects of the matrix of Block 2 that spatially coincide with an a priori known distribution of a certain property or attribute, such as a geochemical halo, productive horizon, mineral deposit, etc., within the environment under study.
- Block 9. Primary clusters that are most closely associated with the Target cluster are selected from Block 5 and used to develop "cluster forecasts" that are, in turn, used to comprise the Target Forecasting (TF) output data (B) of Fig. 1.
- Block 10. Finally, a selection of the most-adequate forecasting solutions, "cluster indicators," is achieved. The cluster indicators associate the cluster opposites (TLF) obtained in Block 7 with the cluster forecasts (TF) obtained in Block 9. By employing both TLF and TF, cluster indicators comprise the final comprehensive result, TFES, as output data (C) of Fig. 1.
The three types of outputs just described, A, B, and C, are utilized in formulating two kinds of forecasting solutions: those solutions that can be explained from the standpoint of conventional geological modeling and those that cannot be so explained, either in whole or in part.
S. Texas application, setting
An area in South Texas bounded by latitude and longitude coordinates 28° -29° north and 97° 30'-99° 30' west, respectively, was chosen for a demonstration of the TF method (Fig. 2). It covers about 14,100 sq miles (22,700 sq km) and is centered on the convergence of the subsurface Cretaceous Stuart City and Sligo Shelf trends between Cotulla and Beeville, Tex. The map area has been subdivided by 15' lines of arc, both in latitude and longitude. The small areas thus enclosed by these lines are designated as panels. For convenience, they are referenced by a continuous number sequence from 1 to 32, beginning at the upper right hand panel.
Relationships were evaluated using the structural, stratigraphic, depositional, and reservoir types within the area as recorded in the Atlas of Major Texas Gas Reservoirs.5
Fig. 2 portrays the locations of large regional structures that are aligned northeast-southwest, such as the Pearsall arch, Charlotte fault zone, Karnes trough, Stuart City and Sligo Shelf margins, and the Wilcox fault zone. The site is in a mature stage of exploitation, and numerous oil and gas fields of both Mesozoic and Cenozoic age are referenced. Oil production predominates in the northwest, while to the southeast, gas predominates.
The thesis of this example application is to affirm that Target Forecasting6 is a sophisticated, unbiased mathematical methodology attuned to the needs of geologic areas in an early exploratory stage and equally to later exploration and development problems of mature regions, such as South Texas. The geological specialist applies his or her talents to the unbiased final results of TFES analysis and not as the popular current practice of providing initial input bias in advance of performing conventional analytical procedures. The present study has been conducted in both an unbiased (TLF) and an analog, target (TF) mode. In the latter case, a Cretaceous gas field, Pawnee Field, was selected as the analog. There is value in both approaches.
Primary sets of geoanalytical data comprising NGDG were selected for mathematical processing and interpretation of the subject area (Figs. 3A-3F). Sets were comprised of six basic responses: gravity (Bouguer and isostatic residual), aeromagnetics, and gamma spectrographic (uranium (Bi 214, thorium (Tl 208 and potassium (K 40). These data were specifically chosen for three reasons: 1) as a test of the value of merged solutions of multiple data types; 2) as a measure of the sensitivity of the technology to coarse gridded spacing (i.e., a low spatial density of 2 km); and 3) as a regional assessment of South Texas petroleum geology. The unbiased results are portrayed as mapped values that are normally color-coded in accord with their relative valuation.
All three radioactive surveys portray similar distribution (Figs. 3D-3F). Each correlates closely with the outcrop pattern of the Eocene Te4 Jackson Group and its shallow subcrop beneath the overlying exposed Miocene strata within the map area (Fig. 4). Jackson strata are known to be highly radioactive. Thus, it may appear that such surficial data are inappropriate for use in this examination. There are, however, some less-dramatic radioactive sources lying outside the major trend, and, in any event, one of the strengths of the TFES program is its ability to disregard extraneous information should the fact be so.
The targetless program mode, TLF, contains no analog or subjective geological information. This type of investigation results in solutions comprised of several unbiased choices of stable attributes that may be selected for final interpretation by the local geological specialist.
For the South Texas examples under discussion, six coarse-gridded geophysical and geochemical data types of a regional character, as obtained from the set of digitized NGDG data previously mentioned, were provided as input. As cited previously, the distribution and valuation of the separate input data are portrayed by Figs. 3A-3F.
TLF processing, i.e., without predeterminations, provides maps of three separate solutions, each as cluster opposites, A-1, A-2, and A-3 (Figs. 5-7), as obtained by following the procedures for Output (A), shown in Fig. 1. The maps employ a four-color pattern, each of which identifies the distribution of a particular root. Their development is shown in Fig. 1, Blocks 1-7. A target is not specified for TLF solutions, but a reference location is provided on the finished map for later comparison with TFES solutions that are to follow.
In Figs. 5 (at right) and 6 (below right), the Charlotte fault zone dominates the northwest quadrant. Map textural patterns 1 and 2 in Fig. 5 confirm the domal or elongated anticlinal character of the zone. This faulted structure is discordant to that occurring farther southeast, where a broad linear array, or band, of textural patterns 3 and 4, stretch across the map area from northeast to southwest. Both large structures illustrate their importance to oil and gas accumulation by virtue of the density and distribution of oil and gas well locations superimposed on the map.7
The broad band of textural patterns (cluster opposites 3 and 4) appears to characterize most of the two major Cretaceous shelf trends of the deep subsurface. The large Cretaceous reef gas field, Pawnee field, sits astride Panels 14 and 15, and the boundary between textural patterns 3 and 4. Transition boundaries such as this one between the blue and yellow cluster-opposites zones are frequently of great importance for defining areas of exploration potential. A similar relationship occurs near the top and western flank of panel 20, where the Cretaceous Tilden and Dilsworth fields, both carbonate reservoir producers, occur adjacent to the western boundary and into Panel 21. These subjects will be discussed again, when the target-selective analysis is reviewed. Smaller textural anomalies are proximal to Fashing field, Panel 3, and they continue into the top of Panel 14.
Finally, to the south and southeast regions of the map, cluster-opposites patterns 1 and 2 occur in an area that is beyond the Stuart City platform margin and where Mesozoic and older rocks are much deeper. The chronologically younger Tertiary, principally clastic strata, occupies the broad wedge dipping gulfward southeast of the Wilcox fault zone.
Fig. 6 displays a second, alternative solution (A-2) that results when analyzing these initial data in the TLF mode. The outcome differs from that of Fig. 5, as the lineation and displacement of the Charlotte fault zone is now more apparent, at the expense of the previously noted positive structural flexure of the trend. Also, the neutral (gray) pattern of open, linear separation (i.e., noncluster-bearing), which serves to define the northwest flank of the northeast-southwest structural trend that overlies the two Cretaceous shelf margins, is much more continuous and distinctive with this solution.
The third and last alternative solution, A-3, is provided by Fig. 7. It is essentially similar to that of the previous map A-2, except that it provides little measure of cluster opposite 2. Its significance is discussed in the following section on TFES.
Of what value are such analyses? These maps are equivalent to the initial interpretation of any area under primary geophysical and geochemical exploration -an area for which, theoretically, no surface studies have been undertaken and no subsurface drilling has taken place. As a primary exploration tool, TLF can serve as an important integrative exploration device that permits investigators to select the most prospective locales for more local and intensive data-gathering and analysis. Such data also lends itself to further interpretation by employing conventional TF as a separate but inclusive analysis.
Once again, the same area and input data are employed. An analog is established. One can choose a single analog or a composite of many analogs. It is up to the specialist. The selection of an analog is biased in accordance with one or many distinguishing characteristics. However, it is the spatial confinement based on such characteristics that determine the coordinate definition of the analog. The computer program is now driven to find any and all such similar analogs and to evaluate their distribution, geometry, and degree of correspondence to the initial target of choice.
For demonstration purposes, Pawnee field, Bee County, Tex., was chosen as the analog. It is introduced in Block 8 of Fig. 1. The field is comprised of 28 producers and 5 dry holes. Production is from the Cretaceous Edwards reefal limestone formation that was formed at the Cretaceous depositional shelf margin. Productive depths average around 14,000 ft. A few wells also produce from the deeper Sligo carbonates of an older Cretaceous carbonate shelf edge. Depths to the Sligo reach 16,000-17,000 ft. Individual well sites were indexed by latitude and longitude, then plotted and finally grouped so as to constitute a single entity. This is the target field plot located at the common margin of panels 14-15 on all of the map figures. Otherwise, the setting is exactly the same as for TLF analysis.
The explainable and the unexplainable results of TF analysis, from the viewpoint of contemporary subsurface geology, are apparent by inspection of Fig. 8. For convenience in interpretation, the map has been imprinted with the distribution of oil and gas wells taken from the US Geological Survey (Beeman, et al., 1996). This map portrays cluster indicators-an analysis of both cluster opposites and cluster forecasts-as Output (C), indicated by Blocks 8-10 (Fig. 1). Block 9 represents an intermediate step whereby primary clusters are used from Block 5 that most closely resemble the chosen target and is referred to as Output (B).
The advantage of TFES analysis is the capability to identify new targets similar to the chosen analog. Salient features of the Pawnee field analog are that:
- It originated as a biothermal buildup on the Cretaceous seafloor and is therefore constructional, comprised of in-situ biogenic material.
- Such constructions occur in shallow marine settings and usually upon or along structural highs or shelf margins of limited width and long lateral trend.
- It produces only gas from widely spaced wells. These features serve to provide a distinctive analog, dissimilar to most other choices that are related to geologically younger clastic reservoirs and to younger tectonic features that are not sourced in the deeper and more-consolidated pre-Tertiary rock interval.
The task for TFES, together with the chosen analog, is to examine the map area by employing the several data sets used previously. The purpose is to "discover" other fields, both existing and newly prospective, that have related diagnostic characteristics, to define their locations and to assess the degree of similarity to the analog. Existing fields are shown in Fig. 2 and are of two types: those of shelf-margin origin (reefal or nonreefal), such as Tilden S, Washburn, Dilworth SE, Cooke, Dilworth, and Stuart City; and those of interior carbonate platform origin, such as Panna Maria, Jourdanton, Fashing, and San Miguel Creek.
TFES analysis, shown in Fig. 8, easily identified the clustered setting of Tilden and Dilworth fields, and a very strong and broad feature centered in Panel 20. The latter should represent a new target area, because the Texas Gas Atlas did not record this field. In fact, however, the AWP Field exists at this locale and is producing from the Cretaceous Olmos sand.8 Also, and for the first time in this study, Fashing field, although not of shelf-margin origin, is nevertheless easily identified at the distal end of the Karnes structural trough in the southwest quarter of Panel 3. This is part of a major producing trend that also includes the southwest tip of Panna Maria Field located within the yellow cluster to the northeast near the top of Panel 2.
There are other clusters on the map whose status and locations are of interest shown in Fig. 8. At the southwestern limit of the broad central trend is a target-related cluster concentrated at the top of Panel 27. It lies between the traces of the two Cretaceous shelf margins and directly south of Washburn field. There are several possible origins for this feature, but it would be premature to speculate as to their validity. However, it should be recognized as a very strong affiliate of the primary analog and thus worthy of close scrutiny.
In a different manner, the three minor clusters just north of the city of Cotulla (Panel 23) are apparently natural extensions of the Charlotte fault trend that contain Jourdanton Field at the extreme northeastern limits of the structure. Although of uncertain affinity to the primary analog, they are bona fide clusters worthy of detailed investigation.
TFES analysis did not, however, locate other comparable fields of contemporary Cretaceous age that were previously known, such as Jourdanton, San Miguel Creek, Washburn, Cooke, or Stuart City. What are some of the features that these fields have in common? Could they be instrumental in counteracting other common factors that aid in identification with the given analog? Or do certain fundamental aspects of the descriptors that characterize the analog override some of the basic input properties of the investigative earth signals employed?
A common trait of this map is that these "invisible" productive fields of Cretaceous carbonates are all in the western and northwestern sections of the map area. What significance this holds is uncertain, however, when considered along with the fact that these Cretaceous fields produce from shallower depths and are oil, not gas, producers. This suggests that the physical state and the contents of the formation itself may be important parameters. At this point a review of the primary data is instructive. It has been observed that the areal distribution of radioactivity corresponds to the north-northeast/south-southwest outcrop pattern of the Te4 Eocene that is a known host to radioactive deposits and is therefore apparently unrelated to the areal distribution of the unrecognized field locations. If, however, the response is related to the gravity and magnetic input data, then there may be more-fundamental, deeper-lithospheric, influences. There are two, principal northwest-southeast breaks in the Cretaceous regional platform margin and structural trend that may reflect contemporary elements of a major dislocation.
These breaks are observable on all maps, but especially those of Figs. 7-8. The broad northeast-southwest belt of cluster opposites 3 and 4 are discontinuous in two major areas (gray color). Both discontinuities have northwest-southest linear extensions. The region lying between the breaks generally corresponds to the underlying southeastward extension of gravity maxima (Figs. 3A-3B). Numerous authors have referred to scenarios of tectonic movement in southern and western Texas that appear to involve the two major "breaks" in trend. The resulting gravity salient may therefore reflect multiple, parallel transcurrent faulting in basement rock, with the southwest margin representing an extension of the Val Verde lineament and the northeast margin representing an extension of the Pecos lineament (Fowler, 19569; Bolden, 1984, 198910; Adams, 198911), or an element of the Walker Lane lineament of Murray, 1989.12 Somewhat less likely is a simple extension of the gulf margin during separation of the continents (Salvador, 198713). An alternate possibility is thermal uplift and high-angle, tilted-block faulting (Carey, 1976,14 1983,15 198816; Elam, 1989,17 1990,18 199519; Elam and Chuber, 1995.20
Target Forecasting is not an image-recognition system. It is a mathematically sophisticated technology that examines any type of one or more arrays of digitized geological data without initial predetermination. Such data may be of any origin such as the gravity, magnetic, and radioactive sources employed in this paper. Although not presented, 2D and 3D seismic data are also ideal for the use of TFES analysis and interpretation.
In deference to other popular technologies of examination and interpretation, Target Forecasting provides alternative solutions that are not hampered by initial employment of preferences, analogs, models or other bias. That is, it is not necessary to know what type or types of objects are being sought. However, targets (analogs) are commonly employed as the natural extension of the technology. Neither does the procedure require knowledge of the nature of any or all data used as input, nor is it concerned with the geologic setting. These matters are left to the decision of the project initiator. The subsequent impartial study results are then provided to the project specialist for initial interpretation.
The region of study has been described and evaluated in both targetless and target modes. For the latter, Pawnee field was selected as an analog for a definitive analysis. Confirmations of some depositional trends, folds, and faults, as well as prospective sites for the future study of oil and gas accumulation were achieved by both methods. Neither method, however, succeeded in defining all of the geologic conditions, nor was this to be expected. Features and conditions insufficiently defined suggest that these several factors are not particularly distinctive. The apparent ability of TFES to relate regional structural setting to interregional tectonics, such as the Texas lineament, is considered particularly significant. Such fundamental tectonic features are early aids in compartmentalizing target areas for exploration and for use in prioritizing subsequent areas of development and secondary exploration drilling.
In this article, an examination of a part of South Texas has been presented using Target Forecasting methodology. In this example, TFES can be viewed as a new, effective tool for the study of both new and exhausted (old productive) regions on the basis of a new indicator of prospective zones. This indicator characterizes highly informative elements of the structural organization of the observed data.
An additional but related experiment was the use of input data of diverse geophysical origin but of low grid density. Obviously, this is an extreme handicap for an initial trial study. However, results have demonstrated the important value of such data to TFES methodology.
For those forecasting solutions that cannot be explained through conventional geologic modeling, corrections and modifications may be developed with the use of TFES methods. Such application may prove to be quite helpful in energy resource exploration efforts, particularly in so-called "exhausted regions" such as South Texas. So perhaps it is not the resources that have become exhausted, but our concepts about them.
The authors are indebted to Dr. Stewart Chuber and Daniel Ziegler, who provided guidance and material support for this study; to Sergey Roumyantsev and Lev Shvartzman for data processing; and to Naum Weinberg for managerial components.
- US Patent No. 5,606,499, Feb. 25, 1997.
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Perry O. Roehl is the executive consultant to Target Strike Inc., San Antonio. His responsibilities include program design, project innovation, professional presentation, and evaluation of E&P ventures. His early career was with Shell Oil Co. and Unocal Corp. and in professional consulting, followed by 13 years as Distinguished Professor of Geology at Trinity University, now emeritus. Together with P.W. Choquette, he co-authored and edited the book Carbonate Petroleum Reservoirs (Springer/Verlag, 1985). He is a certified professional geologist by AAPG and SIPES. Roehl holds BS, MS, and PhD degrees in geology from the universities of Ohio State, Stanford, and Wisconsin, respectively.
Emil Y. Ostrovsky is vice-president of Target Strike Inc. and the director of geophysics, computer modeling, and forecasting. He is the originator of the Target Forecasting methodology. He is an internationally recognized nuclear geophysicist. His responsibilities with the company are to design mathematical procedures and derive solutions for minerals discovery and production for clients. His methodology has successfully revealed valuable mineral deposits in many countries of the world. Ostrovsky is also responsible for coordinating the technical staff of the company and determines and establishes the operational methodology on a project-by-project basis. Ostrovsky holds a PhD degree in geophysics and geochemistry from Moscow State University, Russia.
Alexander Weinberg is president of Target Strike Inc. His duties with the company are organizational, including the development and execution of marketing strategies. He has studied and evaluated numerous advanced technologies for the purpose of developing prospective international ventures in exploration, development, urban pollution, oil production and refining, and biotechnology. Weinberg has worked closely with Dr. Ostrovsky on the Target Forecasting method, including patent and software development. He has conducted formal presentations to professional audiences and co-authored existing literature on Target Forecasting methodology. Weinberg holds BS and MS degrees from UTSA (University of Texas at San Antonio) and Trinity University, respectively.