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Understanding Reservoir Compartmentalization Using Shale Gouge Ratio
C H A P T E R
17 Understanding Reservoir Compartmentalization Using Shale Gouge Ratio Christopher David Walker, Jonathan C. Evenick BP, Houston, TX, United States
O U T L I N E Discussion and Conclusions
230
References
230
Faults provide a number of challenges to the development of subsurface resources. For example, they can create weak zones that are difficult to drill through; juxtapose zones with different pressure regimes; reactivate under changing stress conditions and shear wellbores; and they can cut out reservoir intervals along the planned well path. All of these situations may require costly and inefficient sidetracks or cause the wellbore to be abandoned entirely. One of the major uncertainties in planning a hydrocarbon field development is the extent of reservoir compartmentalization (e.g., Harris et al., 2002). Faults in the subsurface are known to exhibit a range of transmissibility behaviors to fluid flow, ranging from completely open, to completely sealed (Manzocchi et al., 1999). Sealing faults will limit the drainage area for each individual wellbore and will control the communication between injector and producer wells. Therefore correctly modeling this behavior is necessary for optimizing the development of a hydrocarbon field. Incorrect assessment of the transmissibility behavior of faults can lead to accelerated water breakthrough, poor pressure support, inefficient reservoir sweep, or overinvestment in well stock, any of which will negatively impact project economics. Faults that place permeable reservoir intervals, such as sandstone, against impermeable nonreservoir intervals, such as shale, are referred to as juxtaposition seals. Faults with sand-on-sand juxtaposition across the fault are called membrane seals. In this case, the nature of the fault material itself must be analyzed to determine the impact on fault transmissibility. Multiple methods have been suggested to analyze the behavior of faults in clastic sedimentary reservoirs, such as clay smear potential, shale smear factor, and shale gouge ratio (SGR; e.g., Fisher and Knipe, 1998; Sperrevik et al., 2002; Pei et al., 2015; see Fossen, 2016 for an overview). Of these methods, SGR is the fault seal algorithm used most widely by petroleum geologists. This is a simple formula (Fig. 1) that calculates the composition of the material in the core of a fault by assuming that it is composed of a mixture of the various lithologies that have moved past any point on the fault plane. ΣðShale thicknessÞ 100 (1) SGR ¼ Fault throw Therefore a faulted sandy section will tend to have a lot of sand in the fault core and a correspondingly low SGR, whereas a shaley section will have a shale-rich fault core and a high SGR. With a large enough fault, the SGR will tend toward the net-to-gross of the overall system. A low SGR, sandier fault zone, will be more transmissible and less able to hold back pressure differences across the fault. A shalier fault zone, with a higher SGR and a lower permeability, will be more able to hold back pressure differences over time, and therefore be more likely to trap a fluid column.
Developments in Structural Geology and Tectonics, Volume 5 https://doi.org/10.1016/B978-0-12-814048-2.00017-X
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FIG. 1 Global calibration cross plot of shale gouge ratio against across-fault pressure difference by depth. Modified from Bretan, P., Yielding, G., Jones, H., 2003. Using calibrated shale gouge ratio to estimate hydrocarbon column heights. AAPG Bull. 87, 397–413.
The main input to the model is a Vshale, or Vclay curve derived from a gamma ray or spectral gamma ray log measured in a wellbore. Vshale and Vclay represent the estimated volume of shale (or clay) in an interval or given depth. The uncertainties on this parameter are constrained by the resolution of the gamma ray tool run in the well (usually a vertical resolution of around 30 cm), and the petrophysical model used to convert gamma ray to shale volume or clay volume. The second input into the equation is the fault throw, which can be measured in a well or estimated from a seismic interpretation. Uncertainty on this parameter can be large, as even high-frequency seismic reflectivity data may contain a 10% error on fault throw estimation at typical reservoir depths. To estimate the impact of the fault on transmissibility, various authors have collected data on the across-fault pressure difference maintained by faults with various SGRs at a range of depths (Fig. 1; e.g., Yielding, 2002; Yielding et al., 1997). These studies have shown that a SGR value of 20%–30% is needed to hold back a significant column of oil and gas over a geologic timescale (Bretan et al., 2003). The range quoted here results from the uncertainty on the input parameters previously outlined, the changing economic criteria for what makes for a significant column around the world, and the specific geohistory of each petroleum system relative to maximum burial depth and later uplift. Generally, a more precise value can be derived with local calibration to a specific hydrocarbon basin, if enough data is available. The results of a SGR calculation can be presented as a curve or mapped onto a 3D fault surface by many software packages to illustrate how it varies with the changing throw along a fault plane. One of the most powerful ways of displaying the SGR calculation is a triangle diagram (Fig. 2). This shows the impact of offsetting the Vshale curve from a wellbore against itself to model the impact of a fault with varying displacement. This quickly allows for the determination of reservoir connectivity across faults of various sizes and facilitates the communication of risk and uncertainty for the sealing potential of faults with particular throw distributions. In this case (Fig. 2), we can see that the vertical axis of the triangle diagram is a well log that consists of three main sand bodies, A, B, and C, shown by the low Vshale values in the left curve. Sand B is “clean,” in that it maintains a low Vshale profile through the entire sand body. Sands A and C are more “dirty,” in that the Vshale curve does not get as close to pure sand as in sand B, and frequently deviates to higher Vshale values. This probably represents a more interbedded sand/shale system, with thinner beds below the ability of the tool to fully resolve them. Similarly, the shale between sand A and sand B is not recorded as a 100% shale on the Vshale curve. The spikes in the curve again most likely represent thin sander beds deposited during the interval of shale sedimentation. A whole core through this interval or detailed mapping of any outcrop exposures would be required to fully describe the sedimentology and lithostratigraphy of this interval. The horizontal axis of the triangle diagram is fault throw, and the main area of the diagram shows the results of Eq. (1) calculated at each point, representing specific throw values on the denominator and the resulting sum of shale thickness that corresponds to that amount of fault throw as the numerator. The results of these calculations are then colored, using a range of colors to show the sealing potential. In this case, we have chosen green colors to indicate areas where the fault zone will leak, red colors to indicate where the fault will seal, and yellow to orange colors around the 20%–30% cut-off to indicate the uncertainty on the calibration. For example, yellow indicates we are in the sealing range, but at the low end, so within
V. GEOPHYSICAL AND STRUCTURAL TECHNIQUES IN PETROLEUM GEOSCIENCE AND BOREHOLE PROJECTS
17. UNDERSTANDING RESERVOIR COMPARTMENTALIZATION USING SHALE GOUGE RATIO
227
FIG. 2 Triangle diagram demonstrating the shale gouge ratio of material created in a fault plane by a fault of various sizes offsetting a Vshale log taken in well P1.
the uncertainty we should not be confident that this point of the fault will seal. Similarly orange indicates we are most likely above the sealing cut-off, but with enough uncertainty that the fault may not definitely seal. Experience has shown it is best to communicate the results in this way to capture the inherent uncertainty, rather than planning for a single outcome value. Grey colors represent where shale (defined by a Vshale cutoff of 0.5) is juxtaposed against shale, and therefore across fault transmissibility is assumed to be nonexistent. This example from the fictional Grove field illustrates an application of the SGR workflow to distinguish between competing interpretations of ambiguous data. This is a deepwater oil field that has experienced near constant subsidence and burial over the last 20 million years on a passive margin. Deformation over the last 5 million years has created a series of large anticlines and synclines with folds that have an amplitude of 3 km and a wavelength of 10 km. The primary reservoirs are 3 km below the surface, at the maximum burial depth they have experienced. The field has been on production for many years, with continued development drilling taking place at the margins of the field as seismic imaging improvements allow the subsurface team to identify new drilling targets in untested reservoir segments. The field produces from three high porosity (>20%) sandstone reservoirs that are in pressure communication over geological time but deplete as independent reservoirs during production. The oil-water contact for the field was previously penetrated by an injector well (WI1). A newly drilled development producer (P1) unexpectedly encountered an oil-water contact much shallower than expected. The subsurface team came up with two alternate scenarios to explain the well results (Fig. 3). In the first scenario, a fault with 100 ft (30 m) of throw close to the well traps a compartment of perched water on the downthrown side where the basal reservoir sand is juxtaposed against a shale layer on the upthrown side. In the second scenario, a small fault with <50 ft of throw further away from the well supports a larger perched water segment across a reservoir-on-reservoir fault juxtaposition of sand B against sand C, and sand C against sand C. Deciding between these scenarios (Fig. 4) will drive a decision to either produce the well as-is or abandon the wellbore and sidetrack to a different location at a cost of many tens of millions of dollars, and a potential loss of millions of barrels of reserves. The density of the aquifer water and oil column is well known for the field, as is the depth of the oil-water contact. The formation of the structure is interpreted to predate the timing of the hydrocarbon charge entering the reservoir, and the column is modeled to fill downward from the crest of the field segment. Using this
V. GEOPHYSICAL AND STRUCTURAL TECHNIQUES IN PETROLEUM GEOSCIENCE AND BOREHOLE PROJECTS
FIG. 3 Two alternative scenarios to explain the data collected in the well. (A) Fault with 100 ft (30 m) of throw near wellbore trapping a small amount of perched water. (B) Small fault with around 50 ft (15 m) of throw at a distance from the wellbore trapping a large volume of perched water.
FIG. 4 Map on base reservoir C illustrating the base case scenario before well P1 was drilled (left). Alternate scenarios to explain the shallow oil-water contact logged in Well P1 (right). Scenario 1 traps a small perch of water with a previously unmapped fault close to the well. Scenario 2 extends a previously mapped fault further to the north to trap a larger volume of perched water. All depths are true vertical depth subsea; contour interval is 100 ft (30 m); thin black north-south line indicates position of the geoseismic cross-sections in Fig. 3; green color indicates the expected fluid in the reservoir is oil; blue color indicates the expected reservoir fluid is water. Well WI1 previously tagged the regional oil-water contact at 9500 ft (2895 m) TVDSS.
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FIG. 5 Top-down filling history of structure setting up 20 psi across-fault pressure difference. Pressure profile on oil gradient outside perch indicated by thin gray lines.
information, a charge history for the field has been constructed (Fig. 5) This plot compares the pressure history of a location inside the perch with a location outside the perch. As the oil column fills down into the area, the pressure in the fluid column begins to rise above the background aquifer pressure. The volume of water that cannot escape is also overpressured above the regional aquifer pressure. As the water is denser than the oil on the other side of the fault, the pressure of the water inside the perch grows more quickly with depth than the pressure in the water outside the perch. This results in an across-fault pressure difference being generated, supported by the SGR of the material in the fault zone. QUESTIONS: (1) Using the data provided, which subsurface scenario do you think is more reasonable and why? (2) What other data could be collected to discriminate between the scenarios? Solution: (1) The more reasonable scenario is that the perch is small, contained by a 100 ft (30 m) fault close to the wellbore. This model requires only a sand-on-shale lithological juxtaposition to trap a pressure difference of 20 psi in the water leg. (Fig. 3A) If the fault was further from the wellbore, the downdip seal would rely on a C sand on C sand juxtaposition holding back an across-fault pressure difference of >20 psi (Fig. 3B). The triangle diagram (Fig. 2), shows that, where the top of the C sand is juxtaposed against the middle of the C sand, there is enough sand in the system to ensure that the SGR remains below 20%. From looking at the global calibration curve for a fault with a SGR of <20% at a depth of 3 km, we can see that the maximum across-fault pressure difference it could hold is less than 20 psi. (2) As the main differentiating factor between the two scenarios is the distance away from the well, then collecting data to determine the position of the fault would be useful. This could be done by running a VSP (Vertical Seismic Profile) in the well to get a clearer seismic image of the near wellbore area. Alternatively, a flow test on the well should be able to discern how
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17. UNDERSTANDING RESERVOIR COMPARTMENTALIZATION USING SHALE GOUGE RATIO
far away the closest barriers to the well are. Similarly, if the well is thought to be in communication with nearby producers or injectors, then monitoring the pressure in the reservoir while shutting in production/injection on a nearby well would create an interference test that may allow the details of the reservoir connectivity to be modeled. Each of these data collection efforts will cost money, and the value of collecting this information would need to be compared against the value of proceeding to complete the well as-is or side-tracking the well to a different location immediately as well as the probability of each of these scenarios representing the actual subsurface conditions.
DISCUSSION AND CONCLUSIONS Subsurface data is frequently imprecise and open to multiple interpretations. Data must be collected over broad areas through remote sampling techniques, and then processed using input models that cannot hope to incorporate the local heterogeneity. Data collected in a wellbore is typically more precise and higher resolution but may only sample the local condition of the rocks within a few feet of the borehole wall, and therefore any broad observations risk being swamped by the local heterogeneities. The challenge for subsurface characterization lies in reconciling these different data sets to create models that explain the subsurface behavior we are trying to understand. All interpretations are nonunique, and therefore best practice necessitates the construction of multiple models that can explain the observed data. Further data can then be collected and tested against the models to see which can best explain the observations. In this way, some models can be refined and carried forward as more likely to represent the actual conditions in the subsurface, whereas other models need to be significantly modified, dropped, or viewed as less probably representations of the “true” answer (Smalley et al., 2018). In this case, the subsurface team created multiple models that explained all the relevant data to various degrees. They then tested the models against the observations to find the two that best explained the observations at hand. Deciding which of these models was more probable then required the integration of various strands of data to test the assumptions in the model. This required input from the geologist interpreting the formation tops in the wells, the petrophysicist processing the logs and the pressure data, the petroleum systems analyst building the basin model for regional pressures, the geophysicist interpreting the seismic data, and the structural geologist completing the fault seal analysis. The key observation here is thinking through the implications for the fault sealing predictions of each model relative to what would be required for different basin filling histories. In this way, the team landed on a model they considered most likely, however, they were able to keep the alternative subsurface realizations in their decision scenario as less likely outcomes, with the view subject to change as more data is collected in the future.
References Bretan, P., Yielding, G., Jones, H., 2003. Using calibrated shale gouge ratio to estimate hydrocarbon column heights. AAPG Bull. 87, 397–413. Fisher, Q.J., Knipe, R.J., 1998. Fault sealing processes in siliciclastic sediments. Geol. Soc. Lond. Spec. Publ. 147, 117–134. Fossen, H., 2016. Structural Geology. Cambridge University Press, Cambridge. 510 pp. Harris, D., Yielding, G., Levine, P., Maxwell, G., Rose, P.T., Nell, P., 2002. Using shale gouge ratio (SGR) to model fault as transmissibility barriers in reservoirs: an example from the Strathspey field, North Sea. Pet. Geosci. 8, 167–176. Manzocchi, T., Walsh, J.J., Nell, P., Yielding, G., 1999. Fault transmissibility multipliers for flow simulation models. Pet. Geosci. 5, 53–63. Pei, Y., Paton, D., Knipe, R., Wu, K., 2015. A review of fault sealing behaviour and its evaluation in siliciclastic rocks. Earth Sci. Rev. 150, 121–138. Smalley, P.C., Walker, C.D., Belvedere, P.G., 2018. A practical approach for applying Bayesian logic to determine the probabilities of subsurface scenarios: example from an offshore oilfield. AAPG Bull. 102 (3), 429–445. https://doi.org/10.1306/06051717018. Sperrevik, S., Gillespie, P.A., Fisher, Q.J., Halvorsen, T., Knipe, R.J., 2002. Empirical estimation of fault rock properties. In: Koestler, A.G., Hunsdale, R. (Eds.), Hydrocarbon Seal Quantification. NPF Spec. Pub., vol. 11. Elsevier Science B. V., Amsterdam, pp. 109–125. Yielding, G., 2002. Shale gouge ratio—calibration by geohistory. In: Koestler, A.G., Hunsdale, R. (Eds.), Hydrocarbon Seal Quantification. NPF Special Publication, vol. 11. Elsevier Science B. V., Amsterdam, pp. 1–15. Yielding, G., Freeman, B., Needham, D.T., 1997. Quantitative fault seal analysis. AAPG Bull. 81 (6), 897–917.
V. GEOPHYSICAL AND STRUCTURAL TECHNIQUES IN PETROLEUM GEOSCIENCE AND BOREHOLE PROJECTS
17 Understanding Reservoir Compartmentalization Using Shale Gouge Ratio Christopher David Walker, Jonathan C. Evenick BP, Houston, TX, United States
O U T L I N E Discussion and Conclusions
230
References
230
Faults provide a number of challenges to the development of subsurface resources. For example, they can create weak zones that are difficult to drill through; juxtapose zones with different pressure regimes; reactivate under changing stress conditions and shear wellbores; and they can cut out reservoir intervals along the planned well path. All of these situations may require costly and inefficient sidetracks or cause the wellbore to be abandoned entirely. One of the major uncertainties in planning a hydrocarbon field development is the extent of reservoir compartmentalization (e.g., Harris et al., 2002). Faults in the subsurface are known to exhibit a range of transmissibility behaviors to fluid flow, ranging from completely open, to completely sealed (Manzocchi et al., 1999). Sealing faults will limit the drainage area for each individual wellbore and will control the communication between injector and producer wells. Therefore correctly modeling this behavior is necessary for optimizing the development of a hydrocarbon field. Incorrect assessment of the transmissibility behavior of faults can lead to accelerated water breakthrough, poor pressure support, inefficient reservoir sweep, or overinvestment in well stock, any of which will negatively impact project economics. Faults that place permeable reservoir intervals, such as sandstone, against impermeable nonreservoir intervals, such as shale, are referred to as juxtaposition seals. Faults with sand-on-sand juxtaposition across the fault are called membrane seals. In this case, the nature of the fault material itself must be analyzed to determine the impact on fault transmissibility. Multiple methods have been suggested to analyze the behavior of faults in clastic sedimentary reservoirs, such as clay smear potential, shale smear factor, and shale gouge ratio (SGR; e.g., Fisher and Knipe, 1998; Sperrevik et al., 2002; Pei et al., 2015; see Fossen, 2016 for an overview). Of these methods, SGR is the fault seal algorithm used most widely by petroleum geologists. This is a simple formula (Fig. 1) that calculates the composition of the material in the core of a fault by assuming that it is composed of a mixture of the various lithologies that have moved past any point on the fault plane. ΣðShale thicknessÞ 100 (1) SGR ¼ Fault throw Therefore a faulted sandy section will tend to have a lot of sand in the fault core and a correspondingly low SGR, whereas a shaley section will have a shale-rich fault core and a high SGR. With a large enough fault, the SGR will tend toward the net-to-gross of the overall system. A low SGR, sandier fault zone, will be more transmissible and less able to hold back pressure differences across the fault. A shalier fault zone, with a higher SGR and a lower permeability, will be more able to hold back pressure differences over time, and therefore be more likely to trap a fluid column.
Developments in Structural Geology and Tectonics, Volume 5 https://doi.org/10.1016/B978-0-12-814048-2.00017-X
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FIG. 1 Global calibration cross plot of shale gouge ratio against across-fault pressure difference by depth. Modified from Bretan, P., Yielding, G., Jones, H., 2003. Using calibrated shale gouge ratio to estimate hydrocarbon column heights. AAPG Bull. 87, 397–413.
The main input to the model is a Vshale, or Vclay curve derived from a gamma ray or spectral gamma ray log measured in a wellbore. Vshale and Vclay represent the estimated volume of shale (or clay) in an interval or given depth. The uncertainties on this parameter are constrained by the resolution of the gamma ray tool run in the well (usually a vertical resolution of around 30 cm), and the petrophysical model used to convert gamma ray to shale volume or clay volume. The second input into the equation is the fault throw, which can be measured in a well or estimated from a seismic interpretation. Uncertainty on this parameter can be large, as even high-frequency seismic reflectivity data may contain a 10% error on fault throw estimation at typical reservoir depths. To estimate the impact of the fault on transmissibility, various authors have collected data on the across-fault pressure difference maintained by faults with various SGRs at a range of depths (Fig. 1; e.g., Yielding, 2002; Yielding et al., 1997). These studies have shown that a SGR value of 20%–30% is needed to hold back a significant column of oil and gas over a geologic timescale (Bretan et al., 2003). The range quoted here results from the uncertainty on the input parameters previously outlined, the changing economic criteria for what makes for a significant column around the world, and the specific geohistory of each petroleum system relative to maximum burial depth and later uplift. Generally, a more precise value can be derived with local calibration to a specific hydrocarbon basin, if enough data is available. The results of a SGR calculation can be presented as a curve or mapped onto a 3D fault surface by many software packages to illustrate how it varies with the changing throw along a fault plane. One of the most powerful ways of displaying the SGR calculation is a triangle diagram (Fig. 2). This shows the impact of offsetting the Vshale curve from a wellbore against itself to model the impact of a fault with varying displacement. This quickly allows for the determination of reservoir connectivity across faults of various sizes and facilitates the communication of risk and uncertainty for the sealing potential of faults with particular throw distributions. In this case (Fig. 2), we can see that the vertical axis of the triangle diagram is a well log that consists of three main sand bodies, A, B, and C, shown by the low Vshale values in the left curve. Sand B is “clean,” in that it maintains a low Vshale profile through the entire sand body. Sands A and C are more “dirty,” in that the Vshale curve does not get as close to pure sand as in sand B, and frequently deviates to higher Vshale values. This probably represents a more interbedded sand/shale system, with thinner beds below the ability of the tool to fully resolve them. Similarly, the shale between sand A and sand B is not recorded as a 100% shale on the Vshale curve. The spikes in the curve again most likely represent thin sander beds deposited during the interval of shale sedimentation. A whole core through this interval or detailed mapping of any outcrop exposures would be required to fully describe the sedimentology and lithostratigraphy of this interval. The horizontal axis of the triangle diagram is fault throw, and the main area of the diagram shows the results of Eq. (1) calculated at each point, representing specific throw values on the denominator and the resulting sum of shale thickness that corresponds to that amount of fault throw as the numerator. The results of these calculations are then colored, using a range of colors to show the sealing potential. In this case, we have chosen green colors to indicate areas where the fault zone will leak, red colors to indicate where the fault will seal, and yellow to orange colors around the 20%–30% cut-off to indicate the uncertainty on the calibration. For example, yellow indicates we are in the sealing range, but at the low end, so within
V. GEOPHYSICAL AND STRUCTURAL TECHNIQUES IN PETROLEUM GEOSCIENCE AND BOREHOLE PROJECTS
17. UNDERSTANDING RESERVOIR COMPARTMENTALIZATION USING SHALE GOUGE RATIO
227
FIG. 2 Triangle diagram demonstrating the shale gouge ratio of material created in a fault plane by a fault of various sizes offsetting a Vshale log taken in well P1.
the uncertainty we should not be confident that this point of the fault will seal. Similarly orange indicates we are most likely above the sealing cut-off, but with enough uncertainty that the fault may not definitely seal. Experience has shown it is best to communicate the results in this way to capture the inherent uncertainty, rather than planning for a single outcome value. Grey colors represent where shale (defined by a Vshale cutoff of 0.5) is juxtaposed against shale, and therefore across fault transmissibility is assumed to be nonexistent. This example from the fictional Grove field illustrates an application of the SGR workflow to distinguish between competing interpretations of ambiguous data. This is a deepwater oil field that has experienced near constant subsidence and burial over the last 20 million years on a passive margin. Deformation over the last 5 million years has created a series of large anticlines and synclines with folds that have an amplitude of 3 km and a wavelength of 10 km. The primary reservoirs are 3 km below the surface, at the maximum burial depth they have experienced. The field has been on production for many years, with continued development drilling taking place at the margins of the field as seismic imaging improvements allow the subsurface team to identify new drilling targets in untested reservoir segments. The field produces from three high porosity (>20%) sandstone reservoirs that are in pressure communication over geological time but deplete as independent reservoirs during production. The oil-water contact for the field was previously penetrated by an injector well (WI1). A newly drilled development producer (P1) unexpectedly encountered an oil-water contact much shallower than expected. The subsurface team came up with two alternate scenarios to explain the well results (Fig. 3). In the first scenario, a fault with 100 ft (30 m) of throw close to the well traps a compartment of perched water on the downthrown side where the basal reservoir sand is juxtaposed against a shale layer on the upthrown side. In the second scenario, a small fault with <50 ft of throw further away from the well supports a larger perched water segment across a reservoir-on-reservoir fault juxtaposition of sand B against sand C, and sand C against sand C. Deciding between these scenarios (Fig. 4) will drive a decision to either produce the well as-is or abandon the wellbore and sidetrack to a different location at a cost of many tens of millions of dollars, and a potential loss of millions of barrels of reserves. The density of the aquifer water and oil column is well known for the field, as is the depth of the oil-water contact. The formation of the structure is interpreted to predate the timing of the hydrocarbon charge entering the reservoir, and the column is modeled to fill downward from the crest of the field segment. Using this
V. GEOPHYSICAL AND STRUCTURAL TECHNIQUES IN PETROLEUM GEOSCIENCE AND BOREHOLE PROJECTS
FIG. 3 Two alternative scenarios to explain the data collected in the well. (A) Fault with 100 ft (30 m) of throw near wellbore trapping a small amount of perched water. (B) Small fault with around 50 ft (15 m) of throw at a distance from the wellbore trapping a large volume of perched water.
FIG. 4 Map on base reservoir C illustrating the base case scenario before well P1 was drilled (left). Alternate scenarios to explain the shallow oil-water contact logged in Well P1 (right). Scenario 1 traps a small perch of water with a previously unmapped fault close to the well. Scenario 2 extends a previously mapped fault further to the north to trap a larger volume of perched water. All depths are true vertical depth subsea; contour interval is 100 ft (30 m); thin black north-south line indicates position of the geoseismic cross-sections in Fig. 3; green color indicates the expected fluid in the reservoir is oil; blue color indicates the expected reservoir fluid is water. Well WI1 previously tagged the regional oil-water contact at 9500 ft (2895 m) TVDSS.
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229
FIG. 5 Top-down filling history of structure setting up 20 psi across-fault pressure difference. Pressure profile on oil gradient outside perch indicated by thin gray lines.
information, a charge history for the field has been constructed (Fig. 5) This plot compares the pressure history of a location inside the perch with a location outside the perch. As the oil column fills down into the area, the pressure in the fluid column begins to rise above the background aquifer pressure. The volume of water that cannot escape is also overpressured above the regional aquifer pressure. As the water is denser than the oil on the other side of the fault, the pressure of the water inside the perch grows more quickly with depth than the pressure in the water outside the perch. This results in an across-fault pressure difference being generated, supported by the SGR of the material in the fault zone. QUESTIONS: (1) Using the data provided, which subsurface scenario do you think is more reasonable and why? (2) What other data could be collected to discriminate between the scenarios? Solution: (1) The more reasonable scenario is that the perch is small, contained by a 100 ft (30 m) fault close to the wellbore. This model requires only a sand-on-shale lithological juxtaposition to trap a pressure difference of 20 psi in the water leg. (Fig. 3A) If the fault was further from the wellbore, the downdip seal would rely on a C sand on C sand juxtaposition holding back an across-fault pressure difference of >20 psi (Fig. 3B). The triangle diagram (Fig. 2), shows that, where the top of the C sand is juxtaposed against the middle of the C sand, there is enough sand in the system to ensure that the SGR remains below 20%. From looking at the global calibration curve for a fault with a SGR of <20% at a depth of 3 km, we can see that the maximum across-fault pressure difference it could hold is less than 20 psi. (2) As the main differentiating factor between the two scenarios is the distance away from the well, then collecting data to determine the position of the fault would be useful. This could be done by running a VSP (Vertical Seismic Profile) in the well to get a clearer seismic image of the near wellbore area. Alternatively, a flow test on the well should be able to discern how
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17. UNDERSTANDING RESERVOIR COMPARTMENTALIZATION USING SHALE GOUGE RATIO
far away the closest barriers to the well are. Similarly, if the well is thought to be in communication with nearby producers or injectors, then monitoring the pressure in the reservoir while shutting in production/injection on a nearby well would create an interference test that may allow the details of the reservoir connectivity to be modeled. Each of these data collection efforts will cost money, and the value of collecting this information would need to be compared against the value of proceeding to complete the well as-is or side-tracking the well to a different location immediately as well as the probability of each of these scenarios representing the actual subsurface conditions.
DISCUSSION AND CONCLUSIONS Subsurface data is frequently imprecise and open to multiple interpretations. Data must be collected over broad areas through remote sampling techniques, and then processed using input models that cannot hope to incorporate the local heterogeneity. Data collected in a wellbore is typically more precise and higher resolution but may only sample the local condition of the rocks within a few feet of the borehole wall, and therefore any broad observations risk being swamped by the local heterogeneities. The challenge for subsurface characterization lies in reconciling these different data sets to create models that explain the subsurface behavior we are trying to understand. All interpretations are nonunique, and therefore best practice necessitates the construction of multiple models that can explain the observed data. Further data can then be collected and tested against the models to see which can best explain the observations. In this way, some models can be refined and carried forward as more likely to represent the actual conditions in the subsurface, whereas other models need to be significantly modified, dropped, or viewed as less probably representations of the “true” answer (Smalley et al., 2018). In this case, the subsurface team created multiple models that explained all the relevant data to various degrees. They then tested the models against the observations to find the two that best explained the observations at hand. Deciding which of these models was more probable then required the integration of various strands of data to test the assumptions in the model. This required input from the geologist interpreting the formation tops in the wells, the petrophysicist processing the logs and the pressure data, the petroleum systems analyst building the basin model for regional pressures, the geophysicist interpreting the seismic data, and the structural geologist completing the fault seal analysis. The key observation here is thinking through the implications for the fault sealing predictions of each model relative to what would be required for different basin filling histories. In this way, the team landed on a model they considered most likely, however, they were able to keep the alternative subsurface realizations in their decision scenario as less likely outcomes, with the view subject to change as more data is collected in the future.
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V. GEOPHYSICAL AND STRUCTURAL TECHNIQUES IN PETROLEUM GEOSCIENCE AND BOREHOLE PROJECTS