Dan J. Hartmann, Edward A. Beaumont, and Edward Coalson
Search and Discovery Article #40005 (2000)
*Adaptation and revision for online presentation of part of Chapter 9, Predicting Reservoir System Quality and Performance, by Dan J. Hartmann and Edward A. Beaumont, in Exploring for Oil and Gas Traps, Edward A. Beaumont and Norman H. Foster, eds., Treatise of Petroleum Geology, Handbook of Petroleum Geology, 1999.
Predicting Sandstone Porosity and Permeability
Effect of Composition and Texture on Sandstone Diagenesis
Hydrology and Sandstone Diagenesis
Influence of Depositional Environment on Sandstone Diagenesis
Predicting Sandstone Reservoir Porosity
Predicting Sandstone Permeability from Texture
Estimating Sandstone Permeability from Cuttings
Figure 1. Porosity-depth plot for sandstones from two wells with different geothermal gradients. From Wilson, 1994a; courtesy SEPM.
Figure 2. Effects of sediment composition on mechanical stability and chemical stability. From Loucks et al., 1984; courtesy AAPG.
Figure 4. Eh-pH diagram, showing the approximate distribution of various types of subsurface fluids. From Shelley, 1985; courtesy W.H. Freeman and Co.
Figure 5. General trend of increasing dissolved solids in subsurface fluids with increasing depth From Shelley 1985; courtesy W.H. Freeman and Co.
Figure 6. Factors controlling sandstone diagenesis. From Stonecipher et al., 1984; courtesy AAPG.
Figure 8. Typical diagenetic pathways for warm and wet nonmarine sediments. From Burley et al., 1985; courtesy Blackwell Scientific.
Figure 9. Porosity-depth plot of various formations in U.S. Gulf Coast region. From Loucks et al., 1984; courtesy AAPG.
Figure 10. Provenance controls on porosity evolution. From Surdam et al., 1989; courtesy RMAG.
Figure 11. Effects of near-surface diagenesis on sandstone porosity. From Surdam et al., 1989; courtesy RMAG.
Figure 12. Effects of mechanical diagenesis on sandstone porosity. From Surdam et al., 1989; courtesy RMAG.
Figure 13. Diagenetic and burial history for Brent Group sandstones. From Wilson, 1994b; courtesy SEPM.
Figure 14. Use of burial history in predicting sandstone porosity. From Hayes, 1983; courtesy AAPG.
Figure 15. Effect of grain size on permeability and porosity. From Coalson et al., 1990.
Figure 16. Porosity-permeability relationships for kaolinite-, chlorite-, and illite-emented sandstones. From North, 1985; courtesy Allen & Unwin.
Figure 18. Types of detrital clay in sandstone. After Wilson and Pittman, 1977; courtesy Journal of Sedimentary Petrology.
Figure 19. SEM photographs of pore types IA, IB, IC, ID in sandstones. From Sneider and King, 1984; courtesy AAPG.
Figure 20. SEM photographs of pore types II and III in sandstones. From Sneider and King, 1984; courtesy AAPG.
Figure 21. Location map of Sorrento Field; structure on base of the Pennsylvanian. From Sonnenberg, 1985; courtesy RMAG.
Figure 22. Structure map with outline of valley fill. Modified from Sonnenberg, 1985; courtesy RMAG.
Figure 23. Ka/F cross plot for well 11 (Figure 22). From Hartmann and Coalson, 1990; courtesy RMAG.
Figure 24. Family of capillary-pressure curves. From Hartmann and Coalson, 1990; courtesy RMAG.
Figure 25. Pickett plot for data from well 11 (Figure 22). From Hartmann and Coalson, 1990; courtesy RMAG.
Figure 26. Petrophysical data for well 11 (Figure 22). From Hartmann and Coalson, 1990; courtesy RMAG.
Figure 27. Bulk-volume-water (Buckles) plot for well 11 (Figure 22). From Hartmann and Coalson, 1990; courtesy RMAG.
Figure 28. Petrophysical characteristics of well 4 (Figure 22). From Hartmann and Coalson, 1990; courtesy RMAG.
Figure 29. Petrophysical characteristics of well 8 (Figure 22). From Hartmann and Coalson, 1990; courtesy RMAG.
Figure 30. Petrophysical characteristics of well 1 (Figure 22). From Hartmann and Coalson, 1990; courtesy RMAG.
Figure 31. Sw-elevation plot for wells 4, 8, 11 (Figure 22). From Hartmann and Coalson, 1990; courtesy RMAG.
Table 2. Major diagenetic processes and their impact on porosity. From Surdam et al. (1989).
Table 5. Range in values of parameters Scherer (1987) used in his analysis of sandstone reservoirs.
The economic success of any prospect ultimately depends on reservoir system performance. The reservoir system controls two critical economic elements of a prospect: (1) the rate and (2) the amount of hydrocarbons recovered. In geologic terms, pore type and pore-fluid interaction are the most important elements determining reservoir system performance. Understanding how reservoir systems behave on a petrophysical basis helps us predict reservoir system behavior in wildcat situations.
The interrelationship of reservoir porosity, permeability, thickness, and lateral distribution determines reservoir system quality. Although quality prediction is most effective with large amounts of superior data, useful predictions can still be made from very limited data. This article discusses methods for predicting the quality of sandstone reservoir systems.
Sandstones and carbonates are the dominant reservoir rocks. Although quite similar, they are different. Table 1 (after Choquette and Pray, 1970) compares variables affecting reservoir system quality for sandstones vs. carbonates.
An effective method of predicting sandstone reservoir system porosity and permeability is (1) to predict sandstone porosity and permeability at deposition and then (2) to predict the probable changes to porosity and permeability as the sandstone was buried. Since other texts (Barwis et al., 1989; Galloway and Hobday, 1983) cover the impact of depositional environment on porosity and permeability, this subsection concentrates on predicting porosity and permeability by considering the effects of diagenesis.
This section contains the following topics:
Diagenesis alters the original pore type and geometry of a sandstone and therefore controls its ultimate porosity and permeability. Early diagenetic patterns correlate with environment of deposition and sediment composition. Later diagenetic patterns cross facies boundaries and depend on regional fluid migration patterns (Stonecipher and May, 1992). Effectively predicting sandstone quality depends on predicting diagenetic history as a product of depositional environments, sediment composition, and fluid migration patterns.
Cementation destroys pore space; grain leaching creates it. Compaction decreases porosity through grain rearrangement, plastic deformation, pressure solution, and fracturing.
Surdam et al. (1989) define diagenetic zones by subsurface temperatures. Depending on geothermal gradient, depths to these zones can vary. Table 2 summarizes major diagenetic processes and their impact on pore geometry.
Effect of Temperature
Depending on geothermal gradient, the effect of temperature on diagenesis can be significant. Many diagenetic reaction rates double with each 10oC increase (1000 times greater with each 100oC) (Wilson, 1994a). Increasing temperatures increase the solubility of many different minerals, so pore waters become saturated with more ionic species. Either (1) porosity-depth plots of sandstones of the target sandstone that are near the prospect area or (2) computer models that incorporate geothermal gradient are probably best for porosity predictions.
Figure 1 is a porosity-depth plot for sandstones from two wells with different geothermal gradients. The well with the greater geothermal gradient has correspondingly lower porosities than the well with lower geothermal gradient. At a depth of 7000 ft, there is a 10% porosity difference in the trend lines.
Effect of Pressure
The main effect of pressure is compaction. The process of porosity loss with depth of burial is slowed by overpressures. Basing his findings mainly on North Sea sandstones, Scherer (1987) notes sandstones retain approximately 2% porosity for every 1000 psi of overpressure during compaction. He cautions this figure must be used carefully because the influence of pressure on porosity depends on the stage of compaction at which the overpressure developed.
Effect of Age
In general, sandstones lose porosity with age. In other words, porosity loss in sandstone is a function of time. According to Scherer (1987), a Tertiary sandstone with a Trask sorting coefficient of 1.5, a quartz content of 75%, and a burial depth of 3000 m probably has an average porosity of approximately 26%. A Paleozoic sandstone with the same sorting, quartz content, and burial depth probably has an average porosity of approximately 13%.
Composition and Diagenesis
The higher the quartz content, the greater the mechanical stability (less compaction occurs).
The higher the variety of minerals, the lower the chemical stability (more cementation or dissolution occurs).
Sandstones with abundant lithics, feldspars, or chert have less occlusion of porosity by quartz overgrowths and more secondary porosity through dissolution of less stable grains. The ratio of quartz to ductile grains is key to compaction porosity loss.
Sediment Composition and Provenance
Provenance determines sand grain mineralogy and sediment maturity. Mechanical and chemical weathering affects sand grains during transportation. The final product reflects the origin, amount of reworking, and transport distance.
For example, sandstones derived from subduction trench margins are generally mineralogically immature. They often contain terrigenous detritus with abundant volcaniclastics and pelagic material. Sandstones derived from the margin of a cratonic basin tend to be mineralogically and texturally mature and contain reworked sedimentary detritus.
Figure 2 summarizes the effects of sediment composition on mechanical stability and chemical stability.
Influence of Grain Size on Porosity and Diagenesis
Sorting and grain size are textural parameters that intuitively might seem to have the same effects on the porosity of a reservoir system sandstone. Studies show, however, that porosity is largely independent of grain size for unconsolidated sand of the same sorting (Beard and Weyl, 1973). Size does affect permeability; the finer the sand, the lower the permeability. Permeability indirectly affects porosity through diagenesis. Stonecipher et al. (1984) suggest that slow fluid fluxes, resulting from low permeability, promote cementation; rapid fluxes promote leaching. In rapid fluxes, solutes do not remain in pore spaces long enough to build local concentration that promotes precipitation of cement. In slow fluxes, they do. Also, size affects the surface area available for diagenetic reactions: the finer the grain size, the greater the grain surface area for a volume of sediment or rock.
Influence of Sorting on Porosity
Sorting and porosity strongly correlate in unconsolidated sandstones (Beard and Weyl, 1973). The better the sorting, the higher the porosity. The initial porosities of wet, unconsolidated sands show a range of 44-28% porosity for well-sorted vs. poorly sorted grains. Well-sorted sands tend to have a higher percentage of quartz than do poorly sorted sands, and they tend to maintain higher porosities during burial than poorly sorted sands. Poorly sorted sands have more clay matrix and nonquartz grains.
Type of Water Flushes
Much diagenesis occurs in open chemical systems whose initial chemistry is set at deposition. After that, the chemistry of the system changes as flowing water moves chemical components through pores and causes either leaching or cementation of grains. Diffusion also moves chemicals in and out of rocks, although at significantly lower rates. During deep burial, chemical systems close and diagenesis is primarily by pressure solution and quartz overgrowths (Wilson and Stanton, 1994).
Galloway (1984) lists three types of flow of water in a basin:
Meteoric flow--water infiltrates shallow portions of a basin from precipitation or surface waters. Deeper infiltration occurs from (a) eustatic sea level changes and/or (b) tectonic elevation of basin margins.
Thermobaric flow--water moves in response to pressure gradients caused by generation of hydrocarbons, release of mineral-bound water, and/or increased heat flow.
Figure 3 shows the water movement processes mentioned above.
Depositional environment and climate control initial pore-water chemistry of a sandstone. When the rock is buried below the level of meteoric groundwater influence, pore-water chemistry changes as a result of two things:
Increasing mineral solubility due to increasing temperatures.
Figure 4 is an Eh-pH diagram, showing the approximate distribution of various types of subsurface fluids.
Pore-water Chemistry and Cements
Table 3 lists common sandstone cements and the water chemistry associated with precipitation.
Subsurface Dissolved Solids
Figure 5 shows the general trend of increasing dissolved solids in subsurface fluids with increasing depth.
Depositional environment influences many aspects of sandstone diagenesis. The flow chart (Figure 6) shows the interrelationship of depositional environment with the many factors controlling sandstone diagenesis.
Sediment Texture and Composition
Depositional environment affects sediment composition by determining the amount of reworking and sorting by size or hydraulic equivalence. Sediments that have a higher degree of reworking are more mechanically and chemically stable. The energy level of depositional environments affects sorting by size or hydraulic equivalence and consequently produces different detrital mineral suites (Stonecipher and May, 1992).
For example, different facies of the Wilcox Group along the Gulf Coast of Texas have different compositions that are independent of their source area (Stonecipher and May, 1992). Wilcox basal fluvial point bar sands are the coarsest and contain the highest proportion of nondisaggregated lithic fragments. Prodelta sands, deposited in a more distal setting, contain fine quartz, micas, and detrital clays that are products of disaggregation. Reworked sands, such as shoreline or tidal sands, are more quartzose.
Depositional Pore-Water Chemistry
Depositional pore-water chemistry of a sandstone is a function of depositional environment. Marine sediments typically have alkaline pore water. Nonmarine sediments have pore water with a variety of chemistries. In nonmarine sediments deposited in conditions that were warm and wet, the pore water is initially either acidic or anoxic and has a high concentration of dissolved mineral species (Burley et al., 1985).
Marine Pore-Water Chemistry
Marine water is slightly alkaline. Little potential for chemical reaction exists between normal marine pore water and the common detrital minerals of sediments deposited in a marine environment. Therefore, diagenesis of marine sandstones results from a change in pore-water chemistry during burial or the reaction of less stable sediment with amorphous material (Burley et al., 1985).
The precipitation of cements in quartzarenites and subarkoses deposited in a marine environment tends to follow a predictable pattern beginning with clay authigenesis associated with quartz and feldspar overgrowths, followed by carbonate precipitation. Clay minerals form first because they precipitate more easily than quartz and feldspar overgrowths, which require more ordered crystal growth. Carbonate cement stops the further diagenesis of aluminosilicate minerals.
Figure 7 summarizes typical diagenetic pathways for marine sediments.
Nonmarine Pore-Water Chemistry and Cements
Nonmarine pore-water chemistry falls into two climatic categories: (1) warm and wet or (2) hot and dry. The chemistry of pore waters formed in warm and wet conditions is usually acidic or anoxic with large concentrations of dissolved mineral species. The interaction of organic material with pore water is a critical factor with these waters. The depositional pore water of sediments deposited in hot and dry conditions is typically slightly alkaline and dilute.
Figure 8 shows typical diagenetic pathways for warm and wet nonmarine sediments.
Table 4, compiled from data by Thomas (1983), shows the cements that generally characterize specific depositional environments.
Diagenesis and Depositional Pore Waters
In the Wilcox of the Texas Gulf Coast, certain minerals precipitate as a result of the influence of depositional pore-water chemistry (Stonecipher and May, 1990):
Mica-derived kaolinite characterizes fluvial/distributary-channel sands flushed by fresh water.
Abundant siderite characterizes splay sands and lake sediments deposited in fresh, anoxic water.
Chlorite rims characterize marine sands flushed by saline pore water.
Glauconite or pyrite characterizes marine sediments deposited in reducing or mildly reducing conditions.
Illite characterizes shoreline sands deposited in the mixing zone where brackish water forms.
Chamosite characterizes distributary-mouth-bar sands rapidly deposited in the freshwater-marine water mixing zone.
We might have the impression that abundant data and powerful computer models are necessary for porosity prediction. They help. But even with sparse data, by using a little imagination we can predict ranges of porosity. This section presents different methods of predicting sandstone porosity. Choose the method(s) most appropriate to your situation.
A pitfall of using porosity-depth plots for porosity prediction is that regression relationship averages out anomalies and complicates predictions of unusually porous sandstones. Use porosity-depth plots for porosity prediction with caution. If enough porosity data are available to make a meaningful plot, keep the "data cloud" on the plot in order to view the ranges of porosity at different depths. In a frontier exploration setting, the usefulness of porosity-depth plots may be limited if global data sets must be used.
Figure 9 presents an example of regression porosity-depth plots for different formations in U.S. Gulf Coast region. Unfortunately it does not include the raw data, so we cannot see porosity variations within each formation. Formations on the left side of the plot, like the Vicksburg, tend to be quartz cemented. Formations on the right side, like the Frio (areas 4-6), tend to be clay cemented.
Equation for Porosity Prediction
Percentage of quartz grains
Depth of burial
Using regression analysis, he developed the following equation:
Porosity = 18.60 + (4.73 x in quartz) + (17.37/sorting) - (3.8 x depth x 10-3) - (4.65 x in age)
Porosity = percent of bulk volume
In quartz = percent of solid-rock volume
Sorting = Trask sorting coefficient
Depth = meters
In age = millions of years
The equation can be used with a high degree of confidence in uncemented to partly cemented sandstones. But if the reduction of porosity by cement exceeds 2.1% bulk volume, then corrections need to be made based on local sandstone quality characteristics. Numbers for percent solid volume quartz and sorting may be difficult to obtain. Use 75% for percent solid volume quartz and 1.5 for sorting when these values are not known.
Table 5 shows numbers that Scherer (1987) developed by his analysis of reservoir sandstones.
Predicting Effects of Diagenesis on Porosity
Step / Action
Estimate the original composition of the sandstone from provenance (use Figure 10) and depositional environment.
Estimate the effects of near-surface diagenetic processes (see Figure 11).
Estimate the effects of mechanical diagenetic processes (see Figure 12).
Estimate the effects of intermediate and deep burial diagenesis, especially with respect to the creation of secondary porosity.
Using information collected in steps 1 through 4, predict the final porosity ranges using burial history (next procedure).
Predicting Effect of Provenance on Diagenesis
Use Figure 10 to predict the effect of original sediment composition on subsequent diagenesis.
Estimating Effect of Near-Surface Diagenesis
Use Figure 11 to estimate the effects of near-surface diagenesis (depth to point where temperature reaches 80oC).
Predicting Effect of Mechanical Diagenesis
Use Figure 12 to predict the effects of mechanical diagenesis on sandstone porosity.
Using Burial History to Predict Porosity
Reconstructing burial history aids sandstone porosity prediction. A burial history diagram integrates tectonic and hydrologic history with diagenetic evolution to predict sandstone porosity. The steps, with recommended action, given below for predicting porosity from burial history and are illustrated in Figure 13.
Step / Action
Construct a burial history diagram for the formation of interest in the prospect area.
Plot the tectonic history of the basin in the prospect area along the lower x-axis.
Plot the hydrologic history of the prospect area along the lower x-axis. Use the tectonic history to infer the hydrologic history of the prospect.
Plot the porosity curve by combining concepts of diagenetic processes with burial and hydrologic histories of the prospect.
Example of Using Burial History
Figure 13 is an example of a diagram showing diagenetic and burial history for the Brent Group sandstones, North Sea. Line thicknesses indicate relative abundance of diagenetic components.
Figure 14 is an example of sandstone porosity prediction using burial history.
Analog porosity values for different depositional environments can help us predict the porosity of reservoir system rocks when the target formation is unsampled within the basin. Analog values, however, may have wide ranges within facies and subfacies of depositional environments. Therefore, we should use care when applying analog data.
Pore type, pore geometry, and fluid properties are critical factors affecting permeability. Sandstone texture directly affects pore type and geometry. Knowing what textures and fluids to expect, as well as what authigenic clays might be present, can help us predict permeability.
Effects of Pore Type and Geometry
Pore type, defined by pore throat size (i.e., macroporosity), directly controls rock permeability. Pore throat size limits flow capacity. Pore geometry also affects permeability, but not as much. The rougher the surface of the pore, the more difficult for fluid to flow through the pore and the lower the permeability.
Effects of Texture
Figure 15 shows how grain size affects permeability and porosity.
Rules of Thumb for Gas vs. Oil
At >10 md, the reservoir can produce oil without stimulation.
At >1 md, the reservoir can produce gas without stimulation.
At 1-10 md, the reservoir probably requires stimulation for oil production.
Effect of Authigenic Clays
Pore-bridging clays, like illite, decrease porosity slightly but can destroy sandstone permeability. Discrete particle clay, like kaolinite, lowers porosity and permeability only slightly. Figure 16 compares porosity-permeability relationships for kaolinite-, chlorite-, and illite-cemented sandstones. Note there is no significant change in porosities, but permeabilities range over four orders of magnitude.
Pore Geometry and Clay Minerals
Figure 17 shows pore lining and discrete particle clays that decrease porosity and permeability only slightly in contrast to pore-bridging clays, which decrease porosity slightly but substantially lower permeability.
Detrital clays can be part of sandstone matrix or grains. As matrix, detrital clays can obliterate permeability. Detrital grains of clay are often ductile and can be compacted into pore spaces during burial. The percentage of detrital clay in a rock determines permeability. Figure 18 shows different types of detrital clays in a sandstone.
Effect of Quartz Overgrowths
In general, as quartz cement precipitates, the pore-pore throat size ratio approaches 1 (Hartmann et al., 1985). Throats are reduced less than pore space; therefore, permeability is affected less than porosity. During cementation, the size of the pore spaces between the pore-filling crystals decreases until it approaches the size of the pore throats. Throats become more tabular or sheet-like. Sandstone porosity may be quite low (<5%) and still have some permeability (<10 md) where cemented with quartz.
Effect of Fractures
Fractures enhance the permeability of any sandstone reservoir. Fractures are especially important for improving the permeability of sandstone reservoirs with abundant microporosity or disconnected dissolution porosity.
Predicting sandstone reservoir permeability is possible as long as we realize that potential errors may be large. Any process that decreases pore throat size decreases permeability, so predict accordingly. Use steps, with recommended action below, to help predict sandstone reservoir permeability.
Step / Action
Estimate grain size, sorting, and porosity using the depositional environment. For example, if a reservoir is a beach sand, it should be fine- to medium-grained and well sorted with well-rounded quartz grains.
Apply information from Step 1 to the porosity-permeability-grain size plot (Figure 15). Use porosity and grain size from sandstone to estimate the permeability on the chart.
Sneider and King (1984) developed a cuttings-based method of permeability estimation. Where cuttings are available, permeability estimates can be made by examining the surfaces of cuttings for petrophysical permeability indicators. Estimates of the permeability for a particular formation can be extended into areas without data in order to predict permeability.
Sneider and others at Shell Oil Company developed a methodology for estimating permeability from cuttings by calibrating permeability measured from cores with rock-pore parameters described in cuttings. Cores of known permeability were ground up until chips from the core were the size of cuttings. By using comparators made from core chips, they estimated formation permeability from cuttings with surprising accuracy. Although Sneider and King (1984) describe the method for estimating sandstone permeability from cuttings (presented below), procedures could just as easily be developed to predict permeability of carbonates from cuttings.
Grain size and sorting
Degree of rock consolidation
Volume percent of clays
Pore sizes and pore interconnections
Size and distribution of pore throats
Sneider’s Pore Classification for Clastics
Sneider and King (1984) developed a simple method of classifying pore types from cuttings. The classification of clastic rock pore types from cuttings is made by comparing pore types with production tests and log analysis. The pore types are as follows:
Type / Description
Rocks with pores capable of producing gas without natural or artificial fracturing.
Rocks with pores capable of producing gas with natural or artificial fracturing and/or interbedded with type I rocks.
Rocks too tight to produce at commercial rates even with natural or artificial fracturing.
Table 6 lists the characteristics of pore types I, II, and III.
Examples of Pore Type I
The SEM photographs in Figure 19 are examples of rocks with types IA, IB, IC, and ID. Note the amount and connectivity of pore space of each subclass.
Pore Types II and III
The SEM photographs in Figure 20 are examples of rocks with types II and III. Note the amount and connectivity of pore space of each subclass.
Step / Action
Estimate grain size and sorting using standard size-sorting comparators, thin section and SEM photomicrographs, and rock photographs.
Estimate volume percentages using Terry-Chillingar charts made for volume estimates.
Estimate consolidation using the scheme described in the preceding table.
Describe the visible and pinpoint porosity and interconnectedness.
This section shows how saturation profiles can be used to understand the distribution of water saturations within a field or prospect.
The case study presented here is a summary of a larger study of the Sorrento field, southeast Colorado, by Hartmann and Coalson (1990). This study of cores and logs from four field wells shows how multiple oil-water contacts and apparent anomalies in saturation profiles in the Sorrento field were due to multiple flow units from two separate reservoirs. The study helps us understand shows and water saturations in wells outside Sorrento and therefore is useful for finding other traps in the same formation.
This section contains the following topics:
Setting and Structure of the Sorrento Field
Morrow Lithofacies and Pore Types
Sorrento Water Saturation Calculations
Petrophysical Analysis of Sorrento Field Wells
Water Saturation Profile for Sorrento Field
The Sorrento field is in southeastern Colorado on the north flank of the Las Animas Arch. The map (Figure 21) shows the location of the Sorrento field. Structure is contoured on the base of the Pennsylvanian.
Morrow Structure Map
The Sorrento field reservoir is Pennsylvanian Morrow valley-fill sandstones. As shown in Figure 22, structure contours on a marker bed above the Morrow Sandstone reflect the irregular thickness of the sandstone body and a small structural nose and closure. The oil column is 70 ft (20 m) and exceeds structural closure. This is a combination structural-stratigraphic trap. Fluvial sandstones lap onto marine shale at the margins of the valley, forming lateral seals.
In Figure 22, circled wells represent Marmaton wells; triangles, Mississippian wells; and large X’s, study wells. The rest of the oil wells produce from the Morrow. Each unit in the grid is 1 sq mi.
By studying core and log data from one well (well 11, see Figure 22), we see a picture of a clastic reservoir with wide heterogeneity in total porosities, pore-throat sizes, and capillary pressures. In addition, the depositional environment of these sandstones (fluvial valley fill and sandstone) indicates they probably have limited lateral continuity within the valley-fill complex.
Reservoir Lithologic Description
Morrow sandstones in the Sorrento field are slightly shaly, range in grain size from very coarse to fine, and are poorly sorted. As a consequence, pores and pore throats also have wide ranges in size. Hand-sample petrography indicates the dominant porosity is intergranular micro- to megaporosity. Clay crystals create minor intercrystalline microporosity in larger pores. Moldic (cement solution?) porosity also may be present but is minor.
Morrow sandstones in Sorrento field have a wide range in porosity and permeability. Maximum observed porosity (F) is 20-22%, but more typical values are 10-15%. Air permeabilities (Ka) are as great as 1-2 darcies but more commonly are 200-500 md.
In a Ka/F crossplot for well 11 (Figure 23), dots and polygons represent measured Ka/F values. Curves are the graphical solution of Winland’s r35 equation (Pittman, 1992) and represent equal r35 values (port size).
Extrapolated Capillary Pressure Curves and Pore Types
No capillary pressure measurements were available for this study. They were estimated y plotting r35 values on a semilog crossplot of fluid saturation vs. capillary pressure. A capillary pressure curve for each sample passes through its correlative r35 value. Calculations of r35 for well 11 indicate a large variety of capillary pressures and pore types. Pore types for the Morrow samples from this well are mega, macro, and micro.
The numbers on the curves in Figure 24 correspond to the numbers on the Ka/F crossplot on Figure 22. Minimum water saturations (“immobile” water) estimated from log calculations let us extrapolate the Pc curves into low Sw ranges.
Density logs were the primary source of porosity values. Matrix density appears to be about 2.68 g/cc, based on core-measured grain densities (consistent with the presumed mineralogy of the sandstones). Crossplot porosities were not used to avoid introducing a systematic error in these variably shaly sandstones (Patchett and Coalson, 1982).
Formation-water resistivities and water saturations were estimated from Pickett plots. The inferred cementation exponent (m) is 1.8 because of the presence of clays, well-connected solution pores (e.g., James, 1989; Muller and Coalson, 1989), or pyrite (Krystinik, L., personal communication). Formation factors measured on core samples from well 1 support this interpretation.
Saturation Exponents, n
Saturation exponents (n) measured on samples from well 1 showed variations that relate to pore geometry. Microporous siltstones displayed n greater than 2, indicating either very tortuous pore systems or incomplete saturation by brine during testing. Saturation exponents were less than 2 in the best porosity type. This implies the reservoir is somewhat shaly. However, n was assumed equal to 2 for log calculations because the lab data were not far from that value and because lab measurements of saturation exponents are notoriously difficult.
Well 11 Flow Units
Flow units were determined in well 11 by plotting and grouping routine core data. The top and bottom of the Morrow (flow units A and 5) are microporous, low-permeability sandstones that are wet but too tight to produce. Between these are 30 ft (8.5 m) of meso- to macroporous sandstone (flow units 1-4).
All pertinent petrophysical data for well 11 are summarized on Figure 26. Sandstone descriptors found on porosity logs are as follows:
VF = very fine grained
C = coarse grained
SLTY = silty
F = fine grained
VC = very coarse grained
SLT = siltstone
M = medium grained
SH = shale
Subsea elevation of -1,030 ft (-314 m) is marked in the depth track.
Well 11 Water Saturations
Flow unit 4 is macroporous but wet (Sw = 100%); this indicates an oil-water contact. Flow unit 3 is macroporous and has intermediate water saturation (Sw = 70%). This looks like a transition zone. Flow units 2 and 1 are mesoporous and are at immobile water saturation (Sw = 45%). This is verified by the well testing about 100 bo/d and 300 Mcfg/d (16 m3 oil and 8,500 m3 gas per day) with no water from perforations in these flow units and by a bulk-volume-water plot following. This lack of water production is remarkable, considering that the well lies only about 25 ft (7 m) above the free water level.
Figure 27 is the bulk-volume-water (Buckles) plot for well 11.
Well 4 hit the Morrow near the top of the oil column. It had the lowest saturations and best flow rates of all the wells studied, even though it had the thinnest reservoir. This is because it contained rock with large pore throats (r35 up to 50m) that was fully saturated with oil (Sw = 25-30%). The well tested 230 bo/d and 387 Mcfg/d (37 m3 oil and 11,000 m3 gas per day). Initial production was 51 bo/d and 411 Mcfg/d (8 m3 oil and 12,000 m3 gas per day). The difference could be due to a loss of reservoir thickness near the well bore, judging from the thinness of the reservoir.
Figure 28 summarizes the petrophysical characteristics of well 4.
Wells 8 and 1 both are interpreted as encountering transition zones, based on porosity types and log-calculated saturations. Well 8 encountered the Morrow just above the water level. Pore throats are meso- to macroporous. The two upper flow units probably are close to immobile water saturation. However, the two basal zones (3 and 4) have high saturations of mobile water. This explains why the well cut water on initial potential testing. This water production should increase with time as the water leg rises.
Figure 29 summarizes the petrophysical characteristics of well 8.
Well 1 (Figure 30) is similar to well 8, except that flow unit 2 of well 1 shows an anomalous low resistivity. The interval tested 32 bo/d and 15 Mcfg/d (5 m3 oil and 425 m3 gas per day) with no water. Therefore, the zone by definition is at immobile water saturation (Swi = 40%). The discrepancy suggests that the log resistivity was too low due to bed resolution problems. If true resistivity is 9 ohm-m2/m (used for the calculation), then the true water saturation is less than 40%.
While these petrophysical methods of analyzing wells are reliable and widely applicable in water-wet reservoirs, there is at least one source of potential error: the assumption that there are no lithologic changes that affect log-calculation parameters without affecting permeability-porosity relationships. Examples include vuggy or fracture porosity and variable shale effects. If such changes occur, then we must modify the relationships between calculated saturations and producibility.
Morrow sandstone reservoirs reportedly display multiple oil-water contacts in several fields in the area (Sonnenberg, personal communication). Reliably recognizing separate reservoirs in a field requires considering capillary pressures, heights above free water, and observed water saturations. One convenient way to do this is to plot water saturation against structural elevation while differentiating pore throat sizes.
An Sw-elevation plot (Figure 31) for study wells 4, 8, and 11 defines a trend of decreasing water saturation with increasing height. Well 1 is not on the same trend. Differences in water saturation attributable to differences in capillary pressures are apparent but are not great enough to explain the discrepancy. Ignoring possible hydrodynamic effects, the difference in trends probably represents two separate oil columns and therefore two reservoirs.
Barwis, J.H., J.G. McPherson, and J.R.J. Studlick, 1989, Sandstone Petroleum Reservoirs: New York, Springer-Verlag, 583 p. Contains case histories of fields with reservoirs that represent each of the major depositional environments.
Burley, S.D., J.D. Kantorowicz, and B. Waugh, 1985, Clastic diagenesis, in P.J. Brenchley and B.P.J. Williams, eds., Sedimentology: Recent Developments and Applied Aspects: London, Blackwell Scientific Publications, p. 189-228.
Choquette, P.W., and L.C. Pray, 1970, Geologic nomenclature and classification of porosity in sedimentary carbonates: AAPG Bulletin, vol. 54, no. 2, p. 207-250. Classic reference for basic concepts regarding carbonate porosity.
Galloway, W.E., and D.K. Hobday, 1983, Terrigenous Clastic Depositional Systems: Applications to Petroleum, Coal, and Uranium Exploration: New York, Springer-Verlag, 438 p. Summarizes reservoir characteristics of major sandstone depositional environments, especially with respect to sand body geometries.
Harrison, W.J., and R.N. Tempel, 1993, Diagenetic pathways in sedimentary basins, in A.D. Horbury and A.G. Robinson, eds., Diagenesis and Basin
Hartmann, D.J., and E.B. Coalson, 1990, Evaluation of the Morrow sandstone in Sorrento field, Cheyenne County, Colorado, in S.A. Sonnenberg, L.T. Shannon, K. Rader, W.F. von Drehle, and G.W. Martin, eds., Morrow Sandstones of Southeast Colorado and Adjacent Areas: RMAG Symposium, p. 91-100.
James, S.W., 1989, Diagenetic history and reservoir characteristics of a deep Minnelusa reservoir, Hawk Point field, Powder River basin, Wyoming, in E.B. Coalson, S.S. Kaplan, C.W. Keighin, C.A. Oglesby, and J.W. Robinson, eds., Petrogenesis and Petrophysics of Selected Sandstone Reservoirs of the Rocky Mountain Region: RMAG Symposium, p. 81-96.
_____, M.M. Dodge, and W.E. Galloway, 1984, Regional controls on diagenesis and reservoir quality in Lower Tertiary sandstones along the Texas Gulf Coast, in
Muller, M.M., and E.B. Coalson, 1989, Diagenetic and petrophysical variations of the Dakota sandstone, Henry field, Green River basin, Wyoming, in E.B. Coalson, S.S. Kaplan, C.W. Keighin, C.A. Oglesby, and J.W. Robinson, eds., Petrogenesis and Petrophysics of Selected Sandstone Reservoirs of the Rocky Mountain Region: RMAG Symposium, p. 149-158.
Neasham, J.W., 1977, The morphology of dispersed clay in sandstone reservoirs and its effect on sandstone shaliness, pore space, and fluid flow properties: Proceedings of the SPE Annual Meeting, October 9-12, paper SPE-6858.
North, F.K., 1985, Petroleum Geology: London, Allen & Unwin, 607 p.
Patchett, J.G., and E.B. Coalson, 1982, The determination of porosity in sandstone and shaly sandstone, part 2: effects of complex mineralogy and hydrocarbons: 23rd Annual SPWLA Logging Symposium, July 6-9, paper T, 50 p.
Shelley, R.C., 1985, Elements of Petroleum Geology: San Francisco, W.H. Freeman, 449 p.
Sneider, R.M., and H.R. King, 1984, Integrated rock-log calibration in the Elmworth field, Alberta, Canada: part I: reservoir rock detection and characterization, in J.A. Masters, ed., Elmworth--Case Study of a Deep Basin Gas Field: AAPG Memoir 38, p. 205-214.
Stonecipher, S.A., and J.A. May, 1990, Facies controls on early diagenesis: Wilcox Group, Texas Gulf Coast, in D. Meshri and P.J. Ortoleva, eds., Prediction of Reservoir Quality Through Chemical Modeling, I: AAPG Memoir 49, p. 25-44.
Stonecipher, S.A., R.D. Winn, Jr., and M.G. Bishop, 1984, Diagenesis of the Frontier Formation, Moxa Arch: a function of sandstone geometry, texture and composition, and fluid flux, in D.A. McDonald and R.C. Surdam, eds., Clastic Diagenesis: AAPG Memoir 37, p. 289-316.
Surdam, R.C., T.L. Dunn, D.B. MacGowan, and H.P. Heasler, 1989, Conceptual models for the prediction of porosity evolution with an example from the Frontier Sandstone, Big-horn basin, Wyoming, in E.B. Coalson, S.S. Kaplan, C.W. Keighin, L.A. Oglesby, and J.W. Robinson, eds., Sandstone Reservoirs: Rocky Mountain Association of Geologists, p. 7-21.
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Wilson, M.D., 1994a, Non-compositional controls on diagenetic processes, in M.D. Wilson, ed., Reservoir Quality Assessment and Prediction in Clastic Rocks: SEPM Short Course 30, p. 183-208. Discusses the effect that variables such as temperature and pressure have on diagenesis of sandstones. A good reference for predicting sandstone reservoir system quality.
_____, 1994b, Assessing the relative importance of diagenetic processes and controls, in M.D. Wilson, ed., Reservoir Quality Assessment and Prediction in Clastic Rocks: SEPM Short Course 30, p. 259-276.
_____ and E.D. Pittman, 1977, Authigenic clays in sandstones: recognition and influence on reservoir properties and paleoenvironmental analysis: Journal of Sedimentary Petrology, vol. 47, no. 1, p. 3-31.