Fault-seal analysis using astochastic multifault approach William R. James, Lee H. Fairchild, Gretchen P. Nakayama, Susan J. Hippler, and Peter J. Vrolijk ABSTRACT We have developed a stochastic multifault method for analysis of the impact of stratigraphic uncertainty on cross-fault leakage at sand-sand juxtapositions. This method assumes that all sand-sand juxtapositions leak across the fault. Stratigraphic uncertainty is modeled by stochastic variation of stratigraphic stacking. Structural uncertainty is addressed through variation of the input. Our objectives were to quantitatively predict the impact of uncertainties in stratigraphic and structural input and to simulate the complex system of structural spills and juxtaposition leak points that control hydrocarbon contact levels in traps with stacked reservoir systems and many faults. Three examples demonstrate how this stochastic multifault method has helped us evaluate uncertainty and understand complex leak fill-and-spill controls. The Ling Gu prospect demonstrates that widespread cross-fault leakage on two crestal faults with throw changes that exceed seal thickness causes only a single hydrocarbon column to accumulate in multiple-stacked reservoirs. This column is controlled by a juxtaposition leak point on a third, deeper fault. We have learned from examples like Ling Gu that the relative size of throw change and seal thickness is a fundamental control on the probability of cross-fault juxtapositions. An example at prospect A demonstrates the sensitivity of hydrocarbon entrapment to small faults in a sand-prone interval with thin seals. The prospect A analysis shows that if seals are thin, faults or channel incisions below seismic resolution can leak hydrocarbons out of stacked reservoirs that are interpreted as unfaulted on seismic data. This introduced significant predrill uncertainty and risk. Guntong field demonstrates that a thin sand in a juxtaposed seal interval can introduce large uncertainty in the prediction of hydrocarbon columns. These examples and many other analyses using the method demonstrate how small changes in stratigraphic and structural input to a fault-seal analysis can introduce significant uncertainty in the predicted range of hydrocarbon volumes. Such uncertainties need to be directly and systematically accounted for in a fault-seal analysis. Copyright #2004. The American Association of Petroleum Geologists. All rights reserved. Manuscript received June 12, 2003; provisional acceptance August 27, 2003; revised manuscript received October 23, 2003; final acceptance February 18, 2004. DOI:10.1306/02180403059 AAPG Bulletin, v. 88, no. 7 (July 2004), pp. 885 – 904 885 AUTHORS William R. James ExxonMobil Upstream Research Co., P.O. Box 2189, Houston, Texas Bill James earned his B.S. degree in geology from Earlham College and a Ph.D. from Northwestern University in 1968. He moved on to careers at the Corps of Engineers and the U.S. Geological Survey before starting at Exxon Production Research (now ExxonMobil Upstream Research) in 1979. He worked there, specializing in statistical applications in geology, assessment, and seal analysis, until his recent retirement. Lee H. Fairchild 4614 Baldwin Creek Dr., Mt. Hood, Oregon;
[email protected] Lee Fairchild has a B.A. degree in geology from the University of California, Berkeley, and an M.S. degree and a Ph.D. from the University of Washington. He joined Exxon Production Research (now ExxonMobil Upstream Research) in 1985, working on structural geology and fault-seal analysis. In 1999, he moved to Starpath Exploration as a geophysicist, prospecting in south Texas. In 2001, he began an independent consulting business. Gretchen P. Nakayama deceased Gretchen Nakayama earned her B.S. and M.S. degrees from the State University of New York, Rochester, and her Ph.D. in geology from the University of California, Davis, in 1990. She started her career at Exxon Production Research (now ExxonMobil Upstream Research) immediately, specializing in fault-seal analysis. We are saddened by our recent loss of Gretchen to cancer. Susan J. Hippler ExxonMobil Exploration Company, 233 Benmar, Houston, Texas;
[email protected] Susan Hippler has a B.A. degree in geology from Augustana College and a Ph.D. from the University of Leeds (1989). She then joined Exxon Production Research (now ExxonMobil Upstream Research) as an expert in fault-zone characterization and fault-zone migration. She transferred to ExxonMobil Exploration Co. in 1996, specializing in applications of integrated trap analysis to exploration, development, and production problems. Peter J. Vrolijk ExxonMobil Upstream Research Co., P.O. Box 2189, Houston, Texas;
[email protected] Peter Vrolijk earned his B.S. and M.S. degrees from the Massachusetts Institute of Technology and his Ph.D. in geology from the University of California, Santa Cruz, in 1982. In 1989, he joined Exxon Production Research (now ExxonMobil Upstream Research), doing research on a wide range of topics, including most recently fault-seal analysis and fault transmissibility. ACKNOWLEDGEMENTS The authors thank ExxonMobil, its Malaysian affiliate ExxonMobil Exploration and Production Malaysia Inc., Petronas, Sable Offshore Energy Inc. and its partners Shell, Imperial Oil Ltd., and ExxonMobil Canada for permission to publish this paper. We thank Eric Schmidtke, Mohd. Tahir Ismail, and Stan Malkiewicz for obtaining permission. David Reynolds and David Phelps provided material for the paper. Brooks Clark, Steve Davis, and Rod Meyers helped formulate the goals and subject matter of the manuscript. Yao Chang and Brooks Clark were instrumental in software development. Reviews by Eric Schmidtke, Emery Goodman, Tom Hauge, Dave Reynolds, Tom Bultman, George Ramsayer and AAPG reviewers Laurel Alexander, Terrilyn Olson, Graham Yielding, and John Lorenz improved this manuscript; their time and dedication are greatly appreciated. The authors are indebted to our many colleagues at ExxonMobil who have greatly improved stochastic multifault analysis through their discussions and application of the technique. Please direct inquiries regarding reprints or further information to Peter Vrolijk. 886 INTRODUCTION Our application of traditional fault-seal analysis repeatedly encountered problems because of uncertainty in our stratigraphic and structural input. Furthermore, we recognized that in systems with numerous faults and stacked reservoirs, our traditional analysis methods were unable to simulate the extremely complex system of structural spills and juxtaposition leak points that control hydrocarbon contact levels. This paper describes a method we developed to evaluate the impact of stratigraphic and structural uncertainty on our fault-seal analysis and the significant lessons that we learned as a result of applying this approach. Our traditional fault-seal analysis approach in a clastic section has been to deterministically identify sand-sand juxtapositions at faults, evaluate which will leak or seal, and consequently predict the hydrocarbon fill of the reservoirs. This traditional analysis typically starts with the construction of fault-plane sections, which are cross sections that depict the reservoirs on both sides of each fault (also called Allan diagrams; Allan, 1989). Because beds are offset across a fault, reservoirs can come in contact at the fault and potentially communicate with each other across the fault. This relationship is recognized on a fault-plane section by the intersection, or juxtaposition, of the sands. We describe these fault-plane sections as deterministic because they depict a single stratigraphy that is assumed to correctly depict the actual stacking of reservoirs and seals. The stratigraphy is typically derived from a nearby well or from seismic stratigraphic models or other sources if wells are not available. Juxtapositions are important because leakage at juxtapositions may limit the accumulation of hydrocarbons in simple fault-block traps. For this reason, juxtapositions are commonly referred to as leak points. If hydrocarbons fill a reservoir down to a juxtaposition leak point, leak across the fault into the sand on the opposite side, and then migrate away from the trap, then hydrocarbons can fill the reservoir no further, and the leak point fixes the hydrocarbon contact. Furthermore, juxtapositions on internal faults in a structural closure facilitate communication between reservoirs in different fault blocks. The second step in our traditional approach has been to evaluate the seal potential of fault-zone materials. Outcrop observations have shown that faults in clastic sequences typically have a clay-prone gouge. Capillary seal by the gouge may impede the flow of hydrocarbons across faults between juxtaposed sands. Traditional fault-seal analyses typically attempt to model this potential using some algorithm to predict the sealing potential of gouge (Downey, 1984; Bouvier et al., 1989; Jev et al., 1993; Gibson, 1994; Yielding et al., 1997; Alexander and Handschy, 1998; Bretan et al., 2003; Davies and Handschy, 2003; Davies et al., 2003; Gibson and Bentham, 2003). In some cases, the analysis may include a prediction of the potential for enhanced seal from other processes such as cementation. Fault-zone materials can be important when they seal a juxtaposition that otherwise would allow hydrocarbons to migrate Fault-Seal Analysis Using a Stochastic Multifault Approach even in simple structures. if reservoirs A and B in block 1 are the drilling target. Figure 1a shows a simple. Stratigraphic uncertainty may arise from a change in reservoir stacking patterns between the nearest well control and the prospect of interest. Yielding. Reservoir A communicates with itself between blocks 1 and 2 at a leak point with an elevation of 1150 m. STOCHASTIC MULTIFAULT ANALYSIS PROCEDURE Although it is important to generally understand the procedure we follow to conduct an analysis. The process of hydrocarbon fill and spill can be quite complicated. because we felt that we needed to establish whether it was useful to incorporate uncertainty into a juxtaposition analysis before undertaking this more difficult problem. which causes uncertainty in the definition of juxtapositions across faults. Consequently. Structural uncertainty is caused primarily by a degradation in seismic quality near faults. Analyses of enhanced seal from fault-zone materials are combined with juxtaposition analysis to predict the level of fill of reservoirs in the trap. We developed the stochastic multifault analysis approach to address two key issues that we encountered with this traditional approach. For example. we can generally anticipate the cases in which it is likely to be important and account for the process with a separate analysis. so that seismic facies or stratigraphic models provide the only stratigraphic constraint at a prospect. This issue will be addressed in detail in the discussion section. these fill-and-spill systems become so complex in multifault traps with many stacked reservoirs that it is impractical (because of time constraints) or impossible to identify them and predict the resulting hydrocarbon contact using traditional fault-plane sections. A computerized method is needed to track fill and spill at juxtaposition leak points and to determine resulting contact levels. Finally. Stochastic multifault analysis addresses the effect of stratigraphic and structural uncertainty on cross-fault leak where reservoirs are juxtaposed on faults. assume that hydrocarbons migrate into the trap from the left into reservoir A. Figure 1b). the details of the software that we developed are less important than the issues (above) that the software seeks to redress or the lessons that we learned from its application. We incorporated into the software the ability to seal any chosen set of leak points. then it connects to reservoir C at the 1200-m leak point (dashed line. We have observed that there is commonly enough stratigraphic or structural uncertainty that it invalidates the use of deterministic fault-plane sections because these sections incorrectly depict juxtapositions. which provided a procedure to evaluate the potential effects of sealing gouge. 2003. it first leaks to itself at the 1150-m leak point (Figure 1b). We have been surprised by the success that we have had using only juxtaposition analysis that incorporates uncertainty. Dip leak appears to be prevalent primarily in areas where effective stresses are conducive to tensile or shear failure in the fault zone (G. our approach is merely one of many that could be employed. or because there are no nearby wells.away from the trap because this allows hydrocarbons to fill to a deeper level. The second issue is the need to simulate the extremely complex system of structural spills and juxtaposition leak points that control hydrocarbon contact levels in multifault traps with stacked reservoir systems. Consequently. Communication across three leak points on two faults creates this common hydrocarbon system. it connects to reservoir B in block 1 at the 1230-m leak point (Figure 1c). should be addressed systematically and consistently. to such a degree that it has caused us to undertake a reassessment of our analysis of gouge seal. although there can be many other sources of uncertainty. we chose not to address the more complex problem of seal by fault-zone materials. It then communicates with reservoir C at a leak point on the second fault (1200 m) and with reservoir B at a deeper leak point on the first fault (1230 m). It does not address dip leak along fault zones (instead of across fault zones) or seal enhancement by fault gouge. 887 . a reduction in computer-contouring accuracy near faults. Ultimately. In our experience. Initially. Thus. these leak points allow a common hydrocarbon column to accumulate in these reservoirs that is controlled by a structural spill at 1240 m in reservoir C in block 3 (Figure 1d). James et al. personal communication). The first issue is that stratigraphic and structural uncertainty. we concluded that it is prudent to first test the utility of uncertainty analysis for the simpler juxtaposition problem before attacking the much more complex problem of uncertainty in seal by faultzone materials. faulted anticline with three reservoirs and three fault blocks that are separated by two faults. we would expect them to have a common contact that is controlled by a structural spill in a separate sand at the opposite end of the structure. or generally poor seismic resolution. As reservoir A fills. Although we recognize that this is a shortcoming of the approach. For simplicity. Dashed line shows fill to just below the second leak point at 1200. faulted anticline with three reservoirs (A. All depths are in meters. (d) Maximum fill. green arrows are structural spillpoints out of the trap. It then communicates with reservoir C in block 3 at a leak point at 1200 and with reservoir B in block 1 at a 1230 leak point. Reservoir A in block 1 communicates with itself at a leak point at 1150. so that there is one column with a common contact shared by reservoirs A. B. block 3 at 1240. .888 Fault-Seal Analysis Using a Stochastic Multifault Approach Figure 1. (b) Fill down to just below the first leak point at 1150 (green). (c) Fill to just below the third leak point at 1230. The entire accumulation is controlled by a spillpoint in reservoir C. and C. C). B. Two-way white arrows denote leak points. OWC = oil-water contact. (a) A simple. 40. predictions can vary significantly with a small change in the cutoff. Results are then combined for a full prospect summary. that faults are vertical over that interval. then the stratigraphy is divided into multiple. Clearly. and the process used to create stratigraphic models differs depending on which analysis is conducted. The limitations of this approach are that complex structural and stratigraphic relationships such as significant downdip fault throw gradients or channelized stratigraphic bodies are simulated by indirect constructs. an upper structure map will be applied to an upper stratigraphic interval and a lower map to a lower interval that has different structural relationships. then leak and seal beds are distinguished on the basis of log analysis of a clay fraction curve such as a V-shale curve. Models can be generated rapidly (typically in minutes to 1 hr) and modified quickly for sensitivity analysis. If a deterministic analysis is done. We found that predictions were generally accurate for cutoff values between 0. Intelligent application of the 1-D method can commonly minimize the impact of these simplifications. based on the cutoff. This introduces a significant additional uncertainty that must be addressed. We favor the 1-D approach for most exploration problems where detailed facies variability is commonly poorly known and deadlines are short. Assumptions for One-Dimensional Analysis Two assumptions were made to employ 1-D models. If a well is James et al. During the calibration. potential cutoff values between 0. which allows us to greatly simplify the input of both stratigraphic and structural data. Based on this result. the benefit of speed generally outweighs the limitations of the 1-D model. Leak beds have a clay fraction below a specified cutoff value of 0. The software blocks the log by computing the tops and bases of leak and seal beds based on the calibrated cutoff value. We use the term ‘‘leak’’ instead of ‘‘sand’’ to emphasize the fact that this cutoff may include sands that have poorer quality than reservoir sands. both use a single model of stacked seal and leak beds to represent the stratigraphic stacking. then commonly. Seals have a clay fraction above the same cutoff and typically represent silty shales to high-quality clays that will not allow cross-fault leakage when juxtaposed. broad stratigraphic packages from which the program can create a unique stacking of leak and seal beds for each trial. The benefit of these simplifications is speed.50 were used to predict hydrocarbon accumulations in each field. we will focus on the 1-D approach.40. Analysis Procedure Step 1: Stratigraphic Model Stochastic multifault analysis represents stratigraphy as a stack of leak and seal beds (Figure 2). we use a standardized cutoff of 0. They are beds that. this assumption may be incorrect in traps with narrow channel sands. Vertical structural variation is accommodated by subdividing the trap vertically. Stochastic multifault analysis allows either a deterministic or stochastic analysis.40.35 and 0. The effect that this assumption has on our results will be addressed later in the discussion section. Our specific requirement is that each sand represented in a model is sufficiently continuous to reach important leak points and structural spills in the trap. and it is blocked into a sequence of leak and seal beds to provide the stratigraphic model. an analog well is selected. For example. If a suitable analog well is available. more continuous sands than the prospect we are modeling. are sufficiently rich in sand that they leak when juxtaposed across faults. but that cutoff values outside of this range led to a significant degradation in prediction accuracy. and consequently. If a stochastic analysis is conducted.30 and 0. This is done for as many intervals as the structural variation requires. In basins with a high percentage of sediments with V shale values very close to 0. our models may have fewer. but we routinely vary the cutoff to test sensitivity to this value. In these cases. One-dimensional models attempt to describe complex 3-D relationships in a simple 1-D manner by having laterally uniform stratigraphy and vertically uniform structure.We chose two approaches to stochastic multifault analysis: one that is one-dimensional (1-D) and another that is fully three-dimensional (3-D). The 3-D approach is commonly preferable for production problems where greater precision is required and more information and time are generally available. A deterministic analysis is identical to a single trial in a stochastic analysis. instead of geometrically correct definitions. The cutoff value was determined by calibration to approximately 30 fields where the hydrocarbon accumulations were known well. 889 . The first is that sands and shales are laterally uniform and continuous. In this paper. A fully 3-D approach employs a faulted geologic model with nonuniform stratigraphy that requires more knowledge and requires considerably longer to build or modify for sensitivity analysis. The second assumption is that structure remains uniform vertically over each interval analyzed.45. the interpreter examines the stratigraphic stacking in the well and subdivides the stratigraphy into packages (typically tens to hundreds of meters thick). or other methods. the interpretation team works 890 Fault-Seal Analysis Using a Stochastic Multifault Approach together to generate a model stratigraphy. available. Each of these three intervals is treated as a separate package. average leak thickness. but these packages are defined based on the team’s interpretation of alternative input data. percentage seal. regional interpretation. the stratigraphy has a reservoir section bounded by two seals. In rank exploration settings. and average seal thickness for each package. In Figure 2. mathematical distributions of leak and seal thickness are computed from the average leak thickness and leak percentage provided as input. with no wells nearby. A Monte Carlo . each with relatively uniform leak percent and bed thickness.Figure 2. Step 1A To create a trial stratigraphy from this stochastic model. each of which is defined by these basic parameters. which may be based on seismic facies. The complete stratigraphic model is a stack of these packages. Stratigraphic packages are defined. The well log is blocked to determine the percentage leak. along with the anticipated percent leak and seal and average bed thickness of each package. seismic inversion. Chart showing the procedure followed by stochastic and deterministic multifault analysis. This information is sufficient to calculate elevations of leak points throughout the trap and to define the reservoirs and compartments that are juxtaposed at each leak point. Systematic shifting of the well location can also reveal a key elevation where substantial leak point controls are concentrated if a small downflank shift in the well location yields dramatically different results. the results for the first trial stratigraphic model are recorded. In the case of a deterministic analysis where only one input stratigraphy is used. We can then reevaluate our confidence in these elements or reinterpret them if appropriate. For each compartment. elevations of the crest. This is done for each package. but with a different stacking pattern of leaks and seals. The well location constrains how hydrocarbon contacts are counted: only hydrocarbon columns that fill below the level of the well are counted because only these would be encountered by the well. The crest and spill are used to define block closure. The geologist also provides the name of each fault and identifies which compartments are located on the upthrown and downthrown sides. After the final trial. The trap is subdivided into compartments that are typically the equivalent of fault blocks. Delta Throw vs. and then the results for the packages are stacked to create a complete trial stratigraphy.process is then used to randomly select from these distributions to create a model with average leak thickness and leak percentage properties that are similar to the original input package. including the ability to query any predicted contact to determine which leak point or spill controls that contact. and then determines hydrocarbon contact levels for each leak bed in each fault block. LESSONS LEARNED During the application of stochastic and deterministic multifault analysis. To evaluate the causes of this variability. For instance. average pay thickness. Step 3 The analysis proceeds by convolving the stratigraphic model with the structural model. the fault on which it occurs. This has commonly helped us focus on certain stratigraphic or structural characteristics that have the greatest impact on the potential of the prospect. and a summary of all hydrocarbon contact and column data are provided for each sand and each fault block. and total pay thickness for the success cases For these latter parameters. we included the capability to examine the results of any of the trials as a single. Step 2: Structural Model The structural model is very simple in the 1-D approach. A schematic James et al. average column height. Seal Thickness Delta throw is defined as the magnitude of throw change along a specified fault segment. we have recognized new concepts that have improved our understanding of the effects of fault juxtapositions on hydrocarbon accumulations. which has a different stacking than the previous models. The analyst can then process these results using a variety of tools. For each stratigraphic package in each fault block. Fault-plane sections are defined by offset depth pairs along the fault. the analyst may decide to look at trials that are similar to the average result or look at the trials with the most or least trapped hydrocarbons. structural spill. the analysis is complete at this point. they can determine what geologic changes are responsible for the variation in the prediction. and the spill is also a possible control for hydrocarbon contacts. its elevation. builds an array of all leak and structural spillpoints. the following information is provided: Chance of success (fraction of trials having one or more columns at a specified well location) Average number of columns. deterministic model. the software records the statistical summary of results. The software returns to the input stochastic stratigraphic model and randomly creates a new trial stratigraphic model. and different well locations can be tested. With this capability. 891 . In a stochastic analysis. and which reservoirs are juxtaposed. A wide range of outcomes has been observed in many of our analyses. This information is invaluable in determining exactly where key leak points or spill controls occur. both the average of all runs and the full probability distribution from P99 to P01 are provided. and a well location are specified. By comparing trials with different results. and then the process is repeated for a specified number of trials (commonly 500). This new model is then convolved with the structure model to generate and record results for that trial. These concepts will be illustrated by examples in the following section. It creates fault-plane sections. predictions can be made at a specified well location. then the same delta throw will create fewer juxtapositions. The ratio of delta throw to seal thickness is the fundamental control on juxtaposition probability. Schematic fault-plane section showing the relationship of the ratio delta throw/seal thickness to the likelihood of fault juxtaposition leak points. This fault offsets several evenly spaced reservoirs. throw is very large. and because delta throw . If the average seal thickness can be estimated. Throw remains small across the right-hand third of the profile. but again. the probability that a bed will encounter a juxtaposition leak point is higher in segments along the fault with high delta throw. The importance of this ratio can be understood by following the downthrown blue bed across the segment with high delta throw. As the ratio of delta throw to seal thickness increases.Figure 3. which shows that the fault has a zero-throw tip at the right end of the profile. fault-plane section illustrates the relationship of delta throw to the probability of juxtaposition (Figure 3). which is defined by the relative magnitudes of delta throw and seal thickness. High delta throw may arise from a map or interpretation error. Juxtaposition must occur every time the blue bed crosses another bed. For this reason. Note that delta throw is used instead of the throw gradient because the length of the fault segment is irrelevant. and this occurs every time the throw increases by approximately the thickness of the seal bed. which is highlighted in dark blue on both sides of the fault. the delta throw is small. Fault segments with high delta throw can be identified by simply annotating a map with fault throws and identifying segments with large changes in throw. these will be the areas with the highest probability for juxtapositions. the probability of juxtaposition leak points increases. Highside beds are yellow. what matters is how many bed intersections there are. delta throw is typically compared to the average seal thickness. This concept can be exploited as a quick-look tool to identify areas in a prospect with high leak probability or potential map errors. because most stratigraphic intervals have variable seal thickness. If the delta throw is five times the seal thickness. The profile shows clearly that virtually all juxtapositions occur in the area of high delta throw. lowside beds are light blue. any fault segment where delta throw significantly exceeds seal thickness can then be highlighted. On the left-hand side. In addition. high delta throw can occur if an interpreter fails to recognize an intersecting fault. then approximately five juxtaposi892 Fault-Seal Analysis Using a Stochastic Multifault Approach tions will occur. The throw relationship is emphasized by the dark blue bed. except for reservoir A. If seals are thicker. For instance. The number of juxtapositions is the same in the large delta throw segment whether it is 100 or 1000 m (330 or 3300 ft) wide. so delta throw is small there. Delta throw is large where the amount of throw increases dramatically across the middle third of the profile. It also shows that there is no correlation between the magnitude of throw and juxtaposition likelihood in an interval with evenly spaced reservoirs. An example is shown schematically in Figure 4. The fault-plane section extends across the anticline and includes the unfaulted north flank. In contrast. we routinely focus more careful interpretation on the segments with high delta throw. Dashed lines indicate beds on the downthrown side of the fault. 893 . allowing leakage out of the upper sands. Conversely.Figure 4. and small faults are of little concern. Importance of Small Faults If the average seal thickness is small. (B) A slightly thicker seal causes this sand to fill to spill by eliminating the shallow leak point. a small change in stratigraphy or fault throw yields an extremely large change in the pre- Ling Gu is a simple. This is commonly because delta throw on the faults is either much larger or much smaller than seal thickness. Assume that beds continue upward. other traps (see the Ling Gu example later) are very insensitive to stratigraphic or structural uncertainty. stochastic analysis typically predicts a large range of outcomes. and we attempt to rectify any errors or interpretation uncertainty. (A) Case with a thinner intermediate seal. faulted anticline (Figure 5) with two reservoir intervals. the sand then fills to spill. faults with as little as 20 m (66 ft) delta throw (or with as little as 20 m [66 ft] of maximum throw if one or both fault tips are in the trap) can introduce significant fault leak potential (see the prospect A example below). Schematic fault-plane section for a fault that dies out near the crest of an anticline. In this case. thick seals require much larger delta throw to generate high juxtaposition probability. In these situations. which causes a leak point near the crest in the underlying sand (see arrow). Because there are no other leak points. EXAMPLES Highly Sensitive Traps Ling Gu-1 Well Postdrill Evaluation In some traps. then a small delta throw can create significant probability of juxtaposition. where a slight thickening of an intermediate seal eliminates a leak point near the structural crest of a trap (case B). The map at the base shows the fault on the south flank of the anticline (defined by one offset contour). A small change in either variable induces little response in predicted outcomes. which are designated the A and James et al. creating a much larger accumulation that includes multiple sands. dicted size of hydrocarbon columns (see the Guntong example later). average seal thickness is commonly less than 20 m (66 ft). is such an important control on juxtaposition. and solid lines indicate the upthrown side. In high net/gross settings. Stochastic multifault analysis was performed using these structure and stratigraphic models as input. where it migrates to the next structure in the trend. There are four faults that could affect the accumulation. the Ling Gu-1 well found only 1 gas column in 6 sands in the A interval and 1 gas column in 13 sands of the B interval. When Ling Gu was drilled. creating large delta throw near the crest of the trap. The results correctly indicate that Ling Gu-1 should not have encountered many gas columns (Figure 7). but closure heights are sufficiently different that the upper sand map was used for predictions in the upper sands. which was derived from Ling Gu-1. Multifault analysis was performed to test whether the analysis would correctly replicate the poor result and to understand why Ling Gu-1 had failed to meet expectations. The stratigraphic model. moderately continuous to channelized sands bounded by seal intervals (Figure 6). expectations were high because most anticlines in the area trap large hydrocarbon columns in many stacked reservoirs. Gas charge comes from sources interbedded with the reservoirs and at deeper stratigraphic levels. The sand packages have relatively high percentages of thin leak beds. or by leaking across the western fault and migrating west. The Ling Gu-1 well is located approximately 70 m (230 ft) below the crest of the trap. but sparse thief sands are interbedded with thick shales. Gas can exit the trap either at a saddle on the east. The . B sands. The quality of the seals in this area is excellent. whereas the lower sand map was applied to the lower sands. each with maximum throw between 40 and 60 m (130 and 200 ft). which are packages of coastal plain to deltaic. The map of the lower sands is very similar to this map. Depth structure map of Ling Gu trap on the top of the A sands. The solid red line labeled GWC is the gas-water contact as observed in the uppermost (mapped) sand. comprises the A and B sands. Charge is clearly adequate in the area 894 Fault-Seal Analysis Using a Stochastic Multifault Approach because similar nearby anticlines are filled with large volumes of gas that migrated from the same source areas. Arrows show important juxtaposition leak point controls. However. so that the average seal thickness in these intervals is quite small. as shown by the statistics for the model stochastic packages. The map on the lower sands is very similar to the map on the upper sands.Figure 5. Throw on two of the faults (C and D) dies near the anticlinal culmination. except the closure amplitude is larger on the lower sands. and closure east of fault B has more steeply dipping flanks. The dashed lines approximate the average gas outline in deeper sands that were limited by shallow juxtaposition leak points. Figure 7.Figure 6. COS = chance of success. All realizations for the A sands predicted one column. and the vertical black line shows the predicted P95-P05 range. The yellow bars in the middle panel denote the leak % in each stratigraphic package used in the stochastic model. so there is no vertical bar shown. or the percentage of trials that encountered hydrocarbons in the well. James et al. The horizontal black line shows the average stochastic multifault prediction. Stochastic multifault analysis results for columns in the Ling Gu-1 well. The gray vertical bar shows the observed values in the Ling Gu-1 well. 895 . All column heights are measured from the crest of the structure. Stochastic stratigraphic model for Ling Gu-1. Statistical data for the packages are tabulated on the right. The V shale well trace for the Ling Gu-1 well is shown on the left. which is the value that the postdrill analysis is attempting to replicate. we will focus on the B sands. However. 6 in the A interval and 13 in the B interval.Figure 8. only two of the sands actually fill past the tip of fault D. The effect of these juxtapositions is magnified by the crestal location of fault D. Deeper juxtapositions (arrows in Figure 8) drain the lower reservoirs and limit accumulations to extremely small sizes. most sands in this block have only small accumulations (dashed red line in Figure 5) that were too small to be observed in Ling-Gu-1. the area of gas in most sands is quite small (dashed red line in Figure 5). Fault-plane section on the D fault showing one realization of the lower B sands. resulting in much more total gas. Red arrows show leakage that limits accumulations in deeper sands. Consequently. where the key relationships are best demonstrated. A well that is drilled far . although the number of sands varies within a small range. each sand could have had a column as large as that in the uppermost sand. Without these juxtapositions. The primary problem at Ling Gu is that there is a high probability of juxtaposition on fault D because seals are thin in the reservoir intervals (5 m [16 ft] in the A sand interval and 16 m [52 ft] in the B sand interval) compared to delta throw of 45 m (150 ft) on fault D. Juxtaposition leak points occur where the beds cross. considering that the predicted (and observed) number of columns is so small compared to the large number of potential hydrocarbonbearing sands. where juxtapositions play the same role. Yellow lines are highside beds. and orange are lowside. the problem is that despite this 896 Fault-Seal Analysis Using a Stochastic Multifault Approach common system. The juxtapositions on fault D are responsible for this result. The vertical arrow shows a hypothetical downflank well in a position similar to that of Ling Gu-1. Black circles indicate juxtapositions that facilitate communication between sands in the common hydrocarbon system. predicted number of columns and column heights replicated the outcome extremely well. Figure 8 is a fault-plane section on fault D for one realization in the B sands. Red indicates the accumulations of hydrocarbons predicted for that realization. On first inspection. This result is especially encouraging. To demonstrate the effect of these juxtapositions on hydrocarbon contacts. Because juxtapositons on fault D cause such small accumulations. Juxtapositions shown by black circles in Figure 8 allow communication between the upper sands. Most trials were similar to this. which establishes the common system between these. Trials were examined. most sands are water wet on the downthrown side of fault C. leading to significant hydrocarbon accumulation only in the uppermost sands in the fault block tested by Ling Gu-1. the accumulations of hydrocarbons look quite promising because of a large common contact beneath the top seal. As in the crestal fault block. although the chance of success for the A sands is slightly pessimistic. This in turn creates the identical problem at fault C. and the well location was varied to determine the factors that cause this trap to hold an uneconomic accumulation. The seismic data in this area do not have sufficient resolution to identify faults or channel incisions of this size. hypothetical fault at the crest of the structure with a maximum of 10 m (33 ft) of throw (Figure 9). the sand-prone nature of the sediments at prospect A introduces a significant potential risk (Table 1). Furthermore. Specifically. If the fault or stacked channel complex is present. The Res-E reservoir interval. the anticipated average shale thickness is only approximately 10 m (33 ft). Both intervals have very thin bedding. However. In summary. every bed is filled to spill with the number of beds. Prospect A Prospect A is a small gas prospect located immediately north of a significant gas discovery. with shales this thin. One concern was that the most likely model with 41% leak beds might underestimate the sand likely to be present at prospect A. Chance of success (the fraction of trials with gas columns) and column height did not vary significantly between scenarios. it can fill to deeper leak points. This risk was evaluated by conducting an analysis with the unfaulted structure and an analysis that included a small. particularly if the higher net/gross model proved to be correct (Figure 10). 897 . the number of columns was predicted to be between 20 and 28. it can be commercial only if a large number of stacked sands fill to structural spill. the range of leak point elevations is extremely small. is uppermost of many objective intervals in a very sand-prone. three leak points at almost the same elevation (leaks 2. no gas columns were found in the Res-E interval and the interval had a leak percent near 50%. The uppermost sands in each sand interval fill with gas because they are juxtaposed against their respective top seals. Alternatively. As a consequence. When the well was drilled. the analysis replicated the observations at Ling Gu-1 well and provided an explanation for the limited volume of gas. as was observed in Ling Gu-1. arrow in Figure 8) would encounter gas only at the top of the interval. which is the primary focus of this discussion. The overburden was given the same properties as the reservoir interval to reflect the uncommonly sand-prone section above the reservoir. These leak points establish one column at the top of the interval that is deep enough to be encountered by the well. The stratigraphic model (Table 1) was derived using seismic inversion to extrapolate from wells in the nearby gas discovery. which is reasonable given the large fault just west of the trap and the fact that sources are interbedded with reservoir. particularly for the B sands. depending the number of reservoirs in the interval. there appears to be no faultseal issue at the Res-E level because the trap has 30 m (100 ft) of fault-independent closure (Figure 9). One possible explanation for even fewer columns than predicted in the faulted scenarios is that a fault is present that has more than 10 m (33 ft) James et al. there could be as few as one column if the interval proved to have the higher net /gross sand. vertically connected channel incisions. it was assumed that each bed had access to charge. In the unfaulted model. where the probability of being juxtaposed against a thief sand is relatively small. In the unfaulted anticline case. this modeled fault could be envisioned to represent an area with numerous. At the B sand level.downflank in either fault block (for example. 3. so a model was also run with 60% leak beds. The results indicate that a subseismic fault or widespread channel incisions could significantly reduce the number and size of potential accumulations at prospect A. Because of the small area of this trap. At the A sand level. The chances of success in our model indicate that this seal is good in a large majority of the trials. marginal marine to coastal-plain interval with both continuous and channelized sands. channel incisions could connect sands vertically. delta throw is so much larger than seal thickness on the crestal faults that reasonable variation in stratigraphy yields little change in juxtaposition risk. Prospect A has the unusual situation that the overburden was expected to contain as much sand as the objective interval. In trials where the uppermost sand is sealed. Any fault with only 10 m (33 ft) of delta throw can create juxtapositions that have the potential to drain sands and significantly reduce the number of columns. Thus. slight changes in the trap geometry cause leak 3 to dominate. and 4) control the contact in different trials. which accounts for the very narrow range of predicted column heights. Ling Gu is an example of a trap where stratigraphic uncertainty introduces very little uncertainty in the prediction of fault-sealed hydrocarbons because the fault throw relationships are such that there is very little sensitivity to this uncertainty. The overburden was not considered a viable target more because of a likely lack of top seal adequacy than because of the lack of sand expected in a top seal. On first inspection. the danger exists that unresolved faults or channels could significantly reduce the number of stacked gas columns in prospect A and render the trap uneconomic. In both cases. The hypothetical crestal fault used in the sensitivity model is shown.0 6.52 Fault-Seal Analysis Using a Stochastic Multifault Approach . Alternatively.Figure 9. Summary of Stratigraphic Input Used for Prospect A Interval Overburden Res-E 898 Interval Thickness (m) Sand (%) Average Sand Thickness (m) Average Seal Thickness (m) 100 320 41.62 9. of offset but still is too small to be resolved by the relatively poor seismic data.52 9. Guntong. On a plot of column height against reservoir. oil columns are distributed in a distinctive sawtooth pattern Table 1. the analyst correctly highlighted the irreducible risk in this interval. The contour interval is 10 m (33 ft).0 41. By focusing attention on the potential problem introduced by an undetected fault. Reservoirs are relatively channelized coastal-plain sands. Malay Basin Guntong is a producing oil and gas field on a large faulted anticline in the Malay Basin (Figure 11).62 6. Depth structure map on the Res-E horizon at prospect A. unresolved channel incisions may have had a similar effect or may have combined with a small fault to drain the trap. James et al. Depth structure map on the I-40 (upper group I) at Guntong field. a sharp increase in column height in the lower group I. The largest columns occur immediately below significant regional seals that are thicker than the other intrareservoir seals. and another sharp increase in the J group. which is located closest to an important fault system (Figure 13). a single stratigraphic model is provided. The analysis also highlights the possible effect of trap sensitivity on our results. Arrows denote segments of the faults where key leak points are located. (Figure 12). The contour interval is 20 m (66 ft). The pattern is characterized by a large column in the top sand followed by a steady decline in column height in the upper group I. The objective of this analysis was to explain the origin of this column height pattern using deterministic multifault analysis. Because fault Figure 11. again followed by a decline. In a deterministic analysis. 899 .Figure 10. Stochastic multifault analysis predictions of the number of columns in the Res-E interval at prospect A. The model was generated by blocking the Guntong-4 well. The arrow points to the critical thin thief sand in the I-68 seal.Figure 12. based on the Guntong-4 well. Figure 13. These packages are based in part on an understanding of the regional stratigraphic packages. 900 Fault-Seal Analysis Using a Stochastic Multifault Approach . The vertical bar shows the height of column in each sand. Observed column height distribution in the group I and group J sands at Guntong. The right-hand panel shows the stochastic packages that were defined. Deterministic and stochastic stratigraphic models for Guntong. The log has been blocked into leak (yellow) and seal (red) intervals to define a deterministic model of the stratigraphy. depending either on the efficacy of fault gouge seal or the pinchout of the sand. but predicted only small columns in the lower I (Figure 14A). in turn. a separate structure model was used for each group of declining columns. A stochastic model was made in which the I-68 seal was represented by a package that James et al. leaked into the upper I at a leak point on the same fault segment. This prevented fill past this leak point and limited the I-68A column to 70 m (230 ft). The structure model was based on a map from a reservoir in each column group (the map for the upper interval is provided in Figure 11). leakage occurs near arrow A. Recognition of this sensitivity led to a concern about the influence that it would have on a stochastic prediction. The prediction now simulates the observed pattern well. so that the sand beneath each of these seals does not leak at this location. The deeper sands in the lower I interval were also leaking at juxtapositions on this segment of the fault. The thickness of the regional seals exceeds the delta throw on this fault segment. 901 . large columns form beneath the much thicker I-68 seal (Figure 14B). The effect that this single thief sand in the I-68 seal had on our predicted column heights is an excellent example of a highly sensitive trap.Figure 14. (A) The initial prediction with the thief sand present in the I-68 seal. Investigation of other wells revealed that this thief sand was present only in the Guntong-4 well. The thief. The results from the three models were then combined to derive the predicted contact pattern. These leak points control the larger oil columns. An analysis of controls on these columns shows that where intrareservoir seals are thin. Column height distributions predicted by deterministic multifault analysis. The initial analysis reproduced the upper I and J columns successfully. throws vary vertically. (B) Prediction without the thief sand. we removed the sand from the deterministic model to simulate either the absence of the sand at the fault leak point or seal of the leak point by fault gouge. The interplay of the delta throw/seal thickness ratio with this combination of leak points accounts for the very distinctive pattern of oil columns. These sands continue to fill down. until they encounter leak points on a second fault segment at a significantly deeper level where delta throw exceeds these thicker seals (arrow B in Figure 11). With the thief sand removed. Therefore. Investigation of the small column in the I-68A sand revealed that this sand was leaking to a small thief sand in the overlying I-68 seal at leak point near the arrow labeled A in Figure 11 (the thief is highlighted by an arrow in Figure 13). To some unknown degree. The more significant result is the very large range in possible outcomes. Reservoirs commonly are more discontinuous laterally than our simple 1-D approach assumes. when present. Stochastic multifault analysis prediction compared to the deterministic predictions. and the vertical bar shows the P95 –P05 range for 500 trials. An examination of trials indicates that this large uncertainty arises both from the effect of the thief sand in the I-68 seal and. DISCUSSION: RECONSIDERING THE ASSUMPTIONS AND SIMPLIFICATIONS OF STOCHASTIC MULTIFAULT ANALYSIS After gaining experience with stochastic multifault analysis. the deterministic model predicts the hydrocarbon accumulation very poorly because the thief sand is present in the top seal.Figure 15. could occur anywhere in the seal package. The predicted uncertainty range (P95 to P05) does not include the deterministic outcome because the deterministic thief sand is located in a very unlikely. Because . and this effectively communicates the large uncertainty introduced by the sensitiv902 Fault-Seal Analysis Using a Stochastic Multifault Approach ity. The results for the lower I (the interval below the I-68 seal) are displayed in Figure 15. The average column height in the stochastic prediction is 160 m (525 ft). A possible effect of assuming lateral sand continuity is that our models will be more leak prone than the actual prospect because continuous sands are more likely than a narrow sand to intersect a fault. particularly in fluvial or deep-water channel systems. In this case. from the stacking of sands in the lower I interval. to a lesser degree. For the stochastic prediction. yet very critical place in the seal interval. the effect of greater continuity is counterbalanced by the fact that our models will have fewer sand bodies than a model with discontinuous sands and an equivalent net/gross. The vertical bars show the average column height of all columns in the lower group I interval. from 65 to 260 m (210 to 850 ft). The average column height observed in the lower I is 207 m (680 ft). Deterministic predictions are shown by a horizontal line because only one average column height is predicted in a deterministic analysis. which underestimates the observed average somewhat. In a stochastic analysis. the horizontal line shows the predicted average column height. this sand will be absent in some trials and. but the analyst may not anticipate this issue in a predrill situation and thus may rely on the 55-m (180-ft) prediction. Sensitivity to a stratigraphic or structural input creates a wide range of outcomes in a stochastic prediction. The first line is for the model with the thief sand present in the I-68 seal. included the thief sand. we reconsidered the potential importance of the key simplifying assumptions we made to facilitate the stochastic treatment of uncertainty. The deterministic prediction using the Guntong-4 well is 55 m (180 ft). but the predicted range of outcomes captures the observed column height. The prediction improves considerably to 170 m (560 ft) if the thief sand is removed from Guntong-4. One assumption of the 1-D approach is that beds are laterally uniform across the trap. the second line is for the model in which the thief sand was removed. then analysis of fault-zone materials should focus on the probability that gouge discontinuities are present over each juxtaposition. the effect of the uniform bed assumption is difficult to quantify.of these competing factors. we believe that a systematic treatment of uncertainty in the analysis of seal by fault-zone materials. it was much more difficult to prove individual examples of seal by fault gouge in our data set than we previously believed. A shift from leak points on one fault to much deeper leak points on another fault created the large change in hydrocarbon column height at Guntong. we have considerably fewer proven examples in our data set than we had previously. we found that stratigraphic and structural uncertainty affected gouge analysis in two ways. When we reexamined the calibration data set for the ExxonMobil equivalent of shale gouge ratio (SGR). We have identified two possible reasons why this juxtaposition-based analysis that neglects seal by fault-zone materials could effectively predict hydrocarbon column heights. tested predictions and postdrill calibrations match observations relatively well. is beneficial. because both juxtaposition risk and leak by fault-zone materials tend to be covariant to some degree (both increase with increasing sand in an interval). there is commonly great uncertainty about whether two sands with different contacts or pressures are actually juxtaposed. even in traps with channelized coastal-plain sands where the majority of the calibration was conducted. The ability to simulate the extremely complex system of structural spills and juxtaposition leak points has helped us understand the controls on hydrocarbon contact levels in multifault traps with stacked reservoir systems. Furthermore. In general. but because of uncertainty. leakage at juxtapositions on the crestal fault at Ling Gu dictated the behavior of a second fault lower on the structure. Our second observation is based on outcrop studies where we commonly have observed that fault-zone materials have discontinuities and would not be continuous enough to hold a hydrocarbon column over geologic time. We have also been concerned that stochastic multifault analysis does not address seal by fault-zone materials. Second. In our experience. SUMMARY We have developed a stochastic multifault method for analysis of the impact of stratigraphic uncertainty on cross-fault leak at sand-sand juxtapositions. This method simulates the complex system of structural spills and juxtaposition leak points that control hydrocarbon contact levels and quantitatively predicts the impact of uncertainties in stratigraphic and structural input. and on the uncertainty associated with predicting this probability. For example. seal by fault-zone materials may be a secondary factor compared to cross-leak at juxtapositions. First. First. building on an analysis of juxtaposition uncertainty. There clearly are examples of seal by fault-zone materials. the predicted range of outcomes predicted by stochastic multifault analysis has generally captured the ultimate outcome. At this point. Even a small number of discontinuities would allow the fault to leak given enough time (Doughty. there is uncertainty in the calculated SGR value. we have made two observations that we consider significant. First. 2003) and could lessen the role that seal by fault gouge plays. We continue to explore these issues and encourage further evaluation by others. as we might expect if we were not accounting for significant seal by faultzone materials. but evaluation of this issue has raised significant issues that deserve consideration. 903 . it is possible that our analysis could be successful even where gouge seal is important. however. In particular. We have not consistently predicted much smaller hydrocarbon accumulations than are observed. A second explanation is that in many cases. This suggests that the effect may be small enough to neglect for most exploration applications. we continue to assess the relative importance of juxtaposition risk and seal by fault-zone materials. Recognizing this uncertainty has made us less confident about our conclusions regarding the importance of seal by fault-zone materials. However. A concern about this is that one would not expect general success if a method does not account directly and accurately for the primary control on leak or seal. If this is true. we have found many cases where small faults have exerted great influence on the size of hydrocarbon columns. uncertainty greatly affects the analysis of seal by fault-zone materials. particularly one that addresses discontinuities in fault gouge. which controlled the contacts in the drilled fault block. it is likely that we are incidentally accounting for the effect of seal by fault-zone materials to some unknown degree in our calibration of the V shale cutoff value. Prior to our ability to simultaneously evaluate James et al. In pursuing this hypothesis. We found that because of uncertainty. Cornette. An. C. 1994. Wilkie. Bouvier. L. irresolvable uncertainty. Stochastic multifault analysis offers one of many possible approaches to addressing uncertainty. Because this typically was not known at the time of initial interpretation. v. T. the analysis may identify some stratigraphic characteristic that is particularly important. W. p. A. v. T. Bretan. Kluesner.. v. A reconsideration of the stratigraphic model may lead to additional effort that could refine the model or provide greater confidence in the model. Bentham. 1389 – 1404. For example. v. the Guntong example shows the wide range of possible hydrocarbon column heights that depend on whether a single thief sand is present or sealed by fault-zone materials. 465 – 478. G. Jev. offshore Louisiana: AAPG Bulletin. Kaars-Sijpesteijn.. Watts. Introduction to AAPG Bulletin thematic issue on fault seals: AAPG Bulletin.. offshore Trinidad: AAPG Bulletin. D. L. p.. p. clay smearing. 397 – 413. 87. W. Nigeria: AAPG Bulletin. Handschy. p. 2003. 87. 427 – 444. L. 803 – 811. Nun River field. 68. K. Needham. M. 377 – 380. T. G. Nigeria: Use of integrated 3-D seismic. and RFT pressure data on fault trapping and dynamic leakage: AAPG Bulletin. S. Onyejekwe. R. Understanding the key controls on hydrocarbon contact levels commonly focuses our stratigraphic and structural interpretations. fault slicing. Clay smear seals and fault sealing potential of an exhumed growth fault. P. and P. p. p. C. This improved focus also has facilitated more effective postdrill analyses. U.. Quantitative fault seal prediction: AAPG Bulletin.. Downey. Yielding. G. 2003. Allan. 1989. v. 77. 1998.. such as the small thief sand in the Guntong-4 well. van der Pal. P. Using calibrated shale gouge ratio to estimate hydrocarbon column heights: AAPG Bulletin. and R. offshore Trinidad: AAPG Bulletin. Three-dimensional seismic interpretation and fault sealing investigations. C. Yielding. 87. Fluid flow in a faulted reservoir system: Fault trap analysis for the Block 330 field in Eugene Island. v. 1989. 73. Columbus Basin. 897 – 917. B. 73. C. and communicate this uncertainty in a fault-seal analysis. Akaso field. Handschy. 81. v. p. and H. It is beneficial to consider the effect of fault geometries on the probability of leak in an interval or the relationship between shale thickness and the probability of juxtapositions being present. v. and J. p. P. Doughty. Freeman.. . A. K. M. v. B. A reexamination can lead to either a better understanding of this critical control or a refinement of the interpretation in that area. 87.. 1372 – 1385. p. 2003. and C. v.leak points on all faults and calculate resulting hydrocarbon columns. 2003. H. the interpreter commonly has not taken any additional care interpreting this crucial area. N. analyze. Fault-seal analysis South Marsh Island 36 field. J. p. 78. p. Our applications of stochastic multifault analysis have demonstrated that uncertainty commonly has a significant impact on fault-seal predictions. such as the crestal faults at Ling Gu. C. we may have neglected these faults to make the visual interpretation of deterministic faultplane sections tractable.. p. R. H. For instance.. 2003. 1752 – 1763. 387 – 411. Model for hydrocarbon migration and entrapment within faulted structures: AAPG Bulletin. Kaars-Sijpesteijn. Rio Grande rift. It is therefore critical to recognize. Jones. Jones. A. REFERENCES CITED Alexander. The important conclusion is that uncer- 904 Fault-Seal Analysis Using a Stochastic Multifault Approach tainty is sufficiently important that it should be systematically addressed in an analysis of leak at fault juxtapositions and in all aspects of fault-seal analysis. 87. P. 1397 – 1414. M. Fault-zone seals in siliciclastic strata of the Columbus basin. 82. Davies. The possibility of a small fault at prospect A introduced large. critical leak points commonly are concentrated on a particular fault segment. R. L. G. Peters. R. 1997. v. Gulf of Mexico: AAPG Bulletin. Gibson.. 1993. Mathis. 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