integrated reservoir characterization study of a carbonate ramp reservoir: seminole san andres unit,...

7/23/2019 Integrated Reservoir Characterization Study of a Carbonate Ramp Reservoir: Seminole San Andres Unit, Gaines C

1/9

Integrated Reservoir CharacterizationStudy of a Carbonate Ramp Reservoir:

Seminole San Andres Unit, GainesCounty, Texas

F.P. Wang,* SPE,F. Jerry Lucia, SPE, and Charles Kerans, SPE, Bureau of Economic Geology,

The U. of Texas at Austin

Summary

One of the important issues in constructing geologic and reservoirmodels is to define geologic frameworks. A geologic framework isfundamental to defining flow units, to interpolating well data, andthereby to modeling fluid flow. For the Seminole San Andres Unit(SSAU), the high-frequency cycles (HFCs) and rock-fabric faciesidentified on outcrop analogs and cores were used to correlatewireline logs. Reservoir and simulation models of the outcrop anda two-section area of SSAU were constructed with rock-fabric unitswithin the HFCs as a geologic framework. Simulations wereperformed using these models to investigate critical factors affect-ing recovery.

HFCs and rock-fabric units are the two critical scales formodeling shallow-water carbonate ramp reservoirs. Descriptions ofrock-fabric facies stacked within HFCs provide the most accurateframework for constructing geologic and reservoir models, becausediscrete petrophysical functions can be fit to rock fabrics and fluidflow can be approximated by the k hratios among rock-fabric flowunits. Permeability is calculated using rock-fabric-specific trans-forms between interparticle porosity and permeability. Core anal-ysis data showed that separate-vug porosity has a very strong effecton relative permeability and capillary pressure measurements.

The effect of stratigraphic constraints on stochastic simulationwas studied. Geologic models generated by a conventional linearinterpolation, a stochastic simulation with stratigraphic constraints,and a stochastic simulation without stratigraphic constraints were

compared. The stratigraphic features of carbonates can be observedin stochastic realizations only when they are constrained by rock-fabric flow units. Simulation results from these realizations aresimilar in recovery but different in production and injection rates.

Scale-up of permeability in the vertical direction was investi-gated in terms of the ratio of vertical permeability to horizontalpermeability (kvh). This ratio decreases exponentially with thevertical gridblock size up to the average cycle size of 20 ft (6.1 m)and remains at a value of 0.06 for a gridblock size of more than 20ft (6.1 m), which is the average thickness of HFCs. Simulationresults showed that the critical factors affecting recovery efficiencyare stacking patterns of rock-fabric flow units, kvh ratio, and densemudstone distribution.

Introduction

The SSAU lies on the northeastern margin of Central Basin Plat-form (Fig. 1) immediately south of the San Simon Channel.1 Itcovers approximately23 sq miles andcontains more than 600 wells.The field, discovered in 1936, is a solution-gas-drive reservoir witha small initial gas cap, and it has an estimated original oil in place(OOIP) of 1,100 MMSTB.2 Production comes from the Upper SanAndres Formation and the upper part of the Lower San AndresFormation. The crude is 35API andhas an initialformation volumefactor (FVF) of 1.39 and a solution/gas ratio of 684 scf/STB.

The field was developed during the 1940s and produced 120MMSTB (about 11% of OOIP) during the primary recovery from1936 to 1969, in which time the reservoir pressure dropped from2,020 to about 1,100 psig. Waterflooding was initiated in late 1969using alternating rows of 160-acre inverted nine-spot patterns. Infilldrilling occurred in 1976, converting the pattern to a mixed 80- and160-acre inverted nine spot. Waterflooding increased oil recoveryto 388 MMSTB. The characteristics of waterflooding are a shortfill-up time, a sharp increase in pressure, and a sharp decrease ingas/oil ratio. A second infill drilling program that converted thepattern to an 80-acre inverted nine spot occurred during 1984through 1985. Fieldwide CO2 flooding began in 1985. The CO2flooding further increased oil production, and the cumulative oilproduction was about 539 MMSTB in 1994.

The SSAU has an excellent suite of cores and a large amount ofcore, wireline log, and production data. A two-section area, Tract2328, which has 33 wells with complete porosity log suites and11 cored wells covering nearly the entire reservoir interval, wasselected for detailed geologic, petrophysical, and engineering char-acterization. This paper summarizes the results of this integratedoutcrop and subsurface characterization. More complete studieswere reported by Senger et al.,3 Keranset al.,1 Wanget al.,4 andLuciaet al.5 The objectives of this study were (1) to define criticalscales for constructing reservoir and simulation models for car-bonate ramp reservoirs, (2) to study the effects of rock fabrics onpetrophysical properties, (3) to determine important geostatistical

parameters from outcrop and subsurface data, (4) to investigate theeffect of stratigraphic constraints on stochastic simulation andrecovery efficiency, and (5) to study factors affecting recoveryefficiency, such as the stacking patterns of rock-fabric units andkvhratio using outcrop and subsurface models.

Rock-Fabric and Petrophysical-PropertyRelationships

Petrophysical properties of porosity, permeability, and saturationare a function of pore-size distribution, which is related directly torock fabrics.6 The Seminole San Andres reservoir produces fromanhydritic vuggy and nonvuggy dolomites and contains threerock-fabric/petrophysical classes7: (1) dolograinstone; (2) grain-dominated packstone and medium-crystalline mud-dominated do-lostone; and (3) fine-crystalline mud-dominated dolostone (Table1). These fabrics have unique stratigraphic locations and petro-physical characteristics.

Core Porosity and Core Permeability. The effect of rock fabricson porosity-permeability transforms was shown by Lucia6 andsubsequently was modified by Lucia et al.5 Data from the SSAU2505 well and Lawyer Canyon outcrop were used to developporosity-permeability transforms for the SSAU. Rock fabrics andpore types were described by point counting thin sections. Fig. 2shows core porosity and permeability from the SSAU 2505 well.For a given porosity, permeability varies with rock fabric by oneto two orders of magnitudes. Within a specific rock fabric, thepermeability increases with interparticle porosity.

Alternatively, these porosity-permeability transforms can be

used to estimate separate-vug porosity,

4

because separate-vug po-rosity contributes little to permeability. Normally, separate-vug

* Now with PGS Reservoir.

Copyright 1998 Society of Petroleum Engineers

Original SPE manuscript received for review 8 October 1996. Revised manuscriptreceived 10 November 1997. Paper peer approved 19 December 1998. Paper (SPE

36515) first presented at the 1996 SPE Annual Technical Conference and Exhibition,Denver, 69 October.

105SPE Reservoir Evaluation & Engineering, April 1998


2/9

porosity can be point counted from thin sections or estimated fromcore slab surfaces. Because thin sections are often too small to

represent whole cores or sometimes are unavailable (which iscommonly the case with relative permeability data), separate-vugporosity can be estimated by subtracting interparticle porosity fromtotal porosity where total porosity is the core porosity and inter-particle porosity is obtained from the porosity-permeability trans-form for a specific rock fabric at a given permeability value.

Effect of Separate-Vug Porosity on Relative Permeability. Theeffect of separate-vug porosity on capillary pressure and relativepermeability has not been studied extensively. Wang et al.,4 usinglaboratory data measured on Lawyer Canyon outcrop and SSAUcore samples, showed that separate-vug porosity has a strong effecton waterflood recovery and residual oil saturation. With additionalSSAU data, the correlation between waterflood recovery and sep-arate-vug porosity is improved.

Although special core analyses were performed in 1981 on coreplugs from two wellsSSAU 2310 and 4902these core plugswere not available for this study in 1992. Whole cores weretherefore sampled immediately adjacent to locations of the originalcore plugs used for special core analyses, and thin sections weremade and rock fabrics were determined.

Three sets of relative permeability curves (Fig. 3a) measuredfrom SSAU 2310 cores show that the relative permeability in-creases with core permeability. Without examining the cores, thelow relative permeabilities of oil for samples 4WC and 9WC wouldprobably be explained as mixed and strongly oil wet. However, thecore porosity of 19.5% and a permeability of 5.6 md for sample9WC indicate a high separate-vug porosity; the separate-vug po-rosity estimated from rock fabric, permeability, and porosity data

is 9%. The photomicrograph from thin sections adjacent to coreplug 9WC (Fig. 3b) clearly shows many separate intrafusulinid

Fig. 2Porosity, permeability, and rock-fabric relationships for

grainstone, dolopackstone, and dolowackestone having crystal

sizes of 20 m or less. Data from SSAU 2505 well.

Fig. 3(a) Three relative permeability curves from SSAU 2310,

and (b) photomicrograph of thin section at 5,229 ft adjacent to

core plug 9WC, showing both intrafusulinid (separate-vug) andinterparticle pores.

Fig. 1Location map of Seminole San Andres Unit (SSAU) in the

Permian Basin, West Texas, and two section area (bold outline).

TABLE 1ROCK-FABRIC/PETROPHYSICAL CLASSESCOMMON IN THE SEMINOLE SAN ANDRES STUDY

Class Rock Fabric Particle Size (PS)

I Dolograinstone 100 m

II Grain-dominated dolopackstoneand medium-crystallinedolowackestone

20 PS 100 m

III Mud-dominated dolopackstoneand fine-crystallinedolowackestone

20 m

106 SPE Reservoir Evaluation & Engineering, April 1998


3/9

vugs and interparticle pores. The core data and the photomicro-graph thus suggest that the separate-vug porosity is another factorother than wettability that is controlling the relative permeabilities.

All the relative permeability data from SSAU 2310 and 4902were summarized in terms of recovery and residual oil saturation(Fig. 4a). Recovery by waterflooding decreases with an increase inthe ratio of separate-vug porosity to total porosity (vug porosityratio, or Rvp), whereas residual oil saturation increases with R vp.Residual oil saturations determined from steady-state experimentsare much lower than those determined from unsteady-state exper-iments (Fig. 4b), because the real residual oil saturations were notreached in the unsteady-state method.

Capillary Pressures. Capillary pressure data from the SSAU 2310well are separated into grain-dominated dolopackstone and medi-um-crystalline mud-dominated dolostone (Fig. 5). In both rockfabrics the capillary pressure decreases with an increase of porositybut not of permeability. For example, vuggy carbonates have lowpermeability and high porosity, but relatively low capillary pressure.

Reservoir Modeling

One of the important issues in constructing geologic and reservoirmodels is to define geologic frameworks. A geologic framework isfundamental to defining flow units, interpolating well data intointerwell regions, and thereby modeling fluid flow. Scales ofreservoir and flow simulation models vary considerably. However,

simulations using too coarsely scaled models are not representative,whereas simulations using fine-scaled models are costly. Criticalscales are the scales at which depositional facies can be properlycorrelated and petrophysical properties and fluid flow can beproperly modeled. For carbonates, two critical scales are HFCs androck-fabric units.1,8,9 The stacking of rock-fabric units in an HFCdefines the framework.

The reason for using rock-fabric/petrophysical classes to defineflow units is that many petrophysical properties and correlations,such as porosity-permeability transforms, capillary pressure, rela-tive permeability, and residual oil saturation, can be better groupedaccording to rock-fabric/petrophysical classes than to strict depo-sitional facies groupings.7 For example, fusulinid packstone, ooid-

peloid packstone, and dasycladacean-mollusk wackestone may allbehave petrophysically as mud-dominated rock-fabric facies andtherefore can be grouped into a single rock-fabric/petrophysical class.

Geologic Framework. A detailed reservoir characterization studywas carried out on a two-section area, Tract 2328 of the SSAU.The depositional model used is of a carbonate ramp, a simple 0.2to 2 seaward-sloping depositional interface that extends from justabove sea level to a depth of several hundred feet. Discreteenvironmental belts on this ramp from landward to seaward includeinner ramp, ramp crest, and outer ramp. The ramp crest is thecritical belt where the fair-weather wave base intersects the dep-ositional profile, creating a 1- to 2-mile-wide belt of higher energy,grain-dominated rock-fabric facies. This belt, which occupies awater-depth range of 0 to 30 ft, separates lower energy 0- to 10-ft

water-depth inner-ramp deposits landward from outer-ramp depos-its seaward. The outer-ramp environment extends from 30 to 200ft of water depth and is characterized by heterolithic mud-domi-nated rock-fabric facies.

Eleven cores covering the reservoir were described in detail.Twelve HFCs and flow units as shown in Fig. 6 of the AmeradaHess SSAU 2505 well were identified in cores and uncored wells.The upper 9 HFCs (Cycles 1 to 9) record progradation of theramp-crest facies tract over the outer ramp during lower San Andrescomposite sequence progradation. These HFCs are typical up-ward-shallowing cycles having basal mudstones and wackestonesgrading upward into grain-dominated packstones and grainstones.Rock-fabric variability includes thin intercalation of Classes I, II,and III rock fabrics.7 The lower producing interval (Cycles 10 to12) is composed of outer-ramp facies. The HFCs are composed ofdolowackestone and grain-dominated packstone fabrics. However,pore size in these wackestone facies has been significantly en-hanced by dolomitization, and thus rock fabrics fall largely withinpetrophysical Class II.7

Petrophysics and Reservoir Model. To construct the reservoirmodel, core data were calibrated with log data using neutron,density, acoustic, and resistivity logs. Total porosity was calculatedusing the neutron, density, and acoustic logs. Separatevug porositywas calculated using a calibration of separate-vug porosity to totalporosity and acoustic transit time9 (Fig. 7). A Z-plot of totalporosity, water saturation, and rock fabric was used to definerock-fabric fields. Petrophysical properties of total porosity, sep-arate-vug porosity, water saturation, permeability, and rock fabrics

were calculated for 33 wells, and the results of the petrophysicalevaluation of the SSAU 2309 well are shown in Fig. 8. TheFig. 4(a) Waterflooding recovery and (b) residual oil saturationas a function of vuggy porosity ratio (Rvp).

Fig. 5Capillary pressure, water saturation, and rock-fabric

relationships for medium-crystalline mud-dominated do-

lowackestone. Data from SSAU 2310 well.



4/9

calculated permeability matches the core permeability better inCycles 6 and 8a than in Cycle 5 because of the differences in rockfabric calculated from logs and from core data.

A three-dimensional (3D) reservoir model of the two-sectionstudy area was constructed using a 3D geocellular modeling soft-ware. In modeling, the geologic framework was first built bymapping the tops of 12 HFCs. Porosity, permeability, and watersaturation values calculated from petrophysical analyses of eachwell location were interpolated among wells. At each location, thevertical block sizes are the same within each cycle but are differentamong cycles. The permeability distribution in a west-to-east cross

section shows that the permeability is generally more uniform andhigher in Cycles 9 to 12 than in Cycles 1 to 8 ( Fig. 9). The 3Dporosity distributions in Cycles 8 and 9 (Fig. 10) show the upward-shallowing patterns and significant lateral variability within arock-fabric flow unit.

Because most reservoir simulation programs do not allow fordiscontinuous layers, all flow-unit boundaries must be continuouswithin the model. This results in rock-fabric flow layers containingmore than one rock-fabric facies. No sharp boundaries are placedbetween the facies because no sharp boundaries have been foundin analog outcrops, and the average petrophysical values are in-terpolated between wells to fill the HFC framework.

Stochastic Simulation

One method commonly used in stochastic simulation is the gen-eration of stochastically distributed data over the entire reservoir in

one simulation. This method is easy and fast, but the realizationsare too random to accurately depict realistic stratigraphic distribu-tions. One of the recent trends in stochastic simulation is to generate

geologically realistic models using statistical techniques and engi-neering data. In several examples we found that deterministicstratigraphic constraints are the most applicable.

Two stratigraphic constraints used are the rock-fabric flow unitsand HFCs. Stochastic simulations were performed separately foreach rock-fabric unit using rock-fabric-specific geostatistical pa-rameters. The realization for the entire reservoir is accomplished bycombining all realizations of individual rock-fabric units. Fig. 11compares permeability distributions in Cycles 9 to 11 along a crosssection on the SSAU 2309 well, Tract 23-28. This comparisonincludes examples generated by a conventional linear interpolationand by stochastic simulations with and without stratigraphic con-straints. The linearly interpolated permeability patterns are smoothand continuous (Fig. 11a); the stochastically generated permeabilitydata without stratigraphic constraints (Fig. 11b) are toorandom, and

Fig. 7Wireline log/separate-vug porosity and rock-fabric re-

lationships. Relationship between acoustic transit time and sep-

arate-vug porosity form thin-section point counts.

Fig. 8 Comparison between core data and calculated porosity,

water saturation, permeability, and rock-fabric values frompetrophysical analysis of SSAU 2309.

Fig. 6Twelve high-frequency cycles and rock-fabric facies in

Amerada Hess SSAU 2505 well.



5/9

geologic features such as upward-shoaling sequences cannot befound; and the stochastically generated permeability data withstratigraphic constraints largely preserves the features of upward-shoaling sequences (Fig. 11c).

Scale-Up For Flow Model

One of the objectives of the 3D reservoir modeling is to generateflow models at various scales for reservoir simulation. To generateflow models, it is necessary to scale-up small-scale data into largersimulation blocks in both horizontal and vertical directions.Scale-up in the horizontal direction is discussed by Journel andHuijbregts10 and Perez and Kelkar11 using horizontal well data, and

by Wanget al.4 using Lawyer Canyon outcrop data. Scale-up in thevertical direction is related to topics of vertical permeability and theratio of vertical permeability to horizontal permeability. Coreanalyses commonly show a ratio of vertical to horizontal perme-abilityranging from 0.1 to 1. This general trend holds in SSAU coredata, as illustrated inFig. 12a, by data from the SSAU 2710 well.Open circles are data from Cycle 9, squares are data from Cycle 10,crosses are data from Cycle 11, and solid circles are data from Cycle12. Statistically speaking, the average kvh ratio is about 0.3.

Many formulas have been proposed for scale-up of thekvh ratiofrom core data to simulation block sizes.12,13 Wanget al.4 applieda simple analytical equation to illustrate interesting features inscale-up of the vertical permeability of carbonates (Fig. 12b). Thekvh (k

h/k

v) ratio decreases with the vertical gridblock size up to 20

ft (6.1 m) to a value of 0.06 and remains at a constant value at avertical gridblock size greater than 20 ft (6.1 m). This limiting

value of 20 ft (6.1 m) is close to the average thickness of HFCsand suggests that data variance increases significantly within acycle but only slightly among cycles.

Reservoir Simulation

Reservoir simulations were performed using outcrop and subsur-face models to study critical factors affecting recovery efficiency.Factors studied are geometry and distribution of rock-fabric units,direction of water injection, the kvh ratio, dense mudstone distri-bution, initial gas cap, and stochastic realizations.

Lawyer Canyon Outcrop. The flow model for the Lawyer Can-yon window (Fig. 13a) was constructed by overlaying the rock-fabric units on the stratigraphic framework and by assigning eachunit an average porosity and permeability (Table 2).3 The result isa geologically constrained description of the spatial distribution ofpetrophysical properties. Grainstone flow units in Cycles 1 and 2are continuous, and permeability is high throughout the entiremodel, whereas in Cycle 9 the grainstone flow unit appears only inthe south-central part. A number of two-dimensional waterfloodexperiments were conducted using this model to show the largeimpact of the geometry and distribution of rock-fabric facies3,8 and,particularly, the impact of low-permeability mudstone layers onperformance predictions.

Geometry and Distribution of Rock-Fabric Units. Senger et al.3

demonstrated the importance of the correct spatial permeability

distribution by comparing simulation results using the outcropmodel (Fig. 13a) with results using a simulated subsurface model

Fig. 11Permeability distribution in an east-west cross sections

of (a) linearly interpolated model, (b) stochastic realization with-

out stratigraphic constraints, and (c) stochastic realization with

stratigraphic with stratigraphic constraints.

Fig. 9 Eastwest cross section of permeability distribution.

Fig. 10 Three-dimensional image of porosity distribution of

Cycles 8 and 9 in Seminole San Andres Unit, Tract 23-28.



6/9

constructed by the linear interpolation of cycles and petrophysicaldata between the ends of the model (Fig. 13b). The difference in thepredicted recovery is about 13%of the OOIP, or 48% recovery fromthe interpolated model and 35% from the outcrop model (Fig. 13c)because the high-permeability grainstone in the middle of Cycle 9does not extend to either end and was missing in the linearlyinterpolated model.

The second experiment illustrated how the position of wellsrelative to the spatial distribution of permeability affects recovery.3

In Fig. 14a and b, water saturation and recovery of two runs,

Fig. 12(a) Vertical permeability vs. horizontal permeability

sorted by high-frequency cycles, and (b) kvh as a function of

vertical block size.

Fig. 13Lawyer canyon flow models. (a) The rock-fabric per-

meability model based on continuous outcrop data. (b) A linearinterpolation of permeability data taken from two pseudo-wells

on either end of the Lawyer Canyon window. (c) Comparison of

waterflooding performance between two models. The rock-

fabric permeability model gives lower recovery than the linearly

interpolated model because the permeable grainstone unit is

missing in the linearly interpolated model.

TABLE 2PROPERTIES OF ROCK-FABRIC FLOW UNITS FOR LAWYER CANYON OUTCROP MODEL(FROM SENGER ET AL.3)

FlowUnit Rock Fabric Porosity Permeability(md) Initial WaterSaturation Residual OilSaturation

1 Mudstone 0.040 0.01 0.900 0.01

2 Wackestone 0.105 0.30 0.405 0.40

3 Grain-dominated packstone I 0.085 4.50 0.214 0.35

4 Grain-dominated packstone II 0.129 1.80 0.400 0.35

5 Grain-dominated packstone III 0.118 5.30 0.243 0.35

6 Moldic grainstone I 0.145 0.70 0.091 0.40

7 Moldic grainstone II 0.159 2.20 0.077 0.40

8 Highly moldic grainstone 0.230 2.50 0.041 0.40

9 Grainstone I 0.095 9.50 0.189 0.35

10 Grainstone II 0.110 21.3 0.147 0.25

11 Grainstone III 0.135 44.0 0.103 0.25



7/9

flooding from left to right (a) and right to left (b), are compared.In both cases, water channels through the highly permeable grain-stones in Cycles 1, 2, and 9. Channeling is more severe in Cycles1 and 2 than in Cycle 9. Water channels through the grainstone inCycle 9 until it is laterally terminated, where it then flows downacross the basal mudstone of Cycle 9 into underlying Cycles 8 and7 and leaves oil in the middle of Cycle 7 unswept. Twelve percentmore oil is trapped when water is injected right to left than whenwater is injected from left to right (Fig. 14c) because the upstreambarrier of Cycle 9 is shorter and water is channeling faster in case(b) than in case (a).

Effects of kvh Ratio and Dense Mudstones. Many simulationstudies have shown that the kvh ratio is one of the most dominantparameters affecting recovery efficiency. Determining thekvh ratioof flow models is one of the major issues in reservoir analysis.14

The effect ofkvh ratio on recovery was tested in Lawyer Canyonoutcrop models with and without dense mudstone layers. Themodel without dense mudstone layers can be considered as ananalog of the coarse-scale simulation model where dense mudstone

layers are averaged in during scale-up. In each case, simulationswere run with a kvh ratio of 0.001, 0.01, 0.1, and 1.

This kvh effect on waterflooding is illustrated in Fig. 15, whichshows water saturation distributions after 20-year waterflooding forthree cases: kvh 0, 0.1, and 1. When kvh 0, the noncom-municating case, severe channeling occurs through high-perme-ability flow units. Whenkvh 0.1, crossflow increases and Cycles3 to 7 are better swept than in the noncommunicating case. Whenkvh 1, increased crossflow in Cycles 3 to 7 improves the sweepand recovery efficiencies.

Simulation results of these cases are summarized in terms ofrecovery efficiency with respect to pore volume of water injected(Figs. 16a and b). For allkvh ratios, recovery is higher in the modelwithout dense mudstone layers. In Fig. 16c, recovery efficiencies

at a 0.3 pore volume of water injected in both models are plottedwith respect to thekvh ratio. The upscaledkvh ratios for the modelwithout dense mudstone layers, corresponding to the same recoveryin the model with dense mudstone layers at kvh ratios of 0.3 and 1,are, respectively, 0.02 and 0.04. The kvh ratio range of 0.02 to 0.04agrees well with thekvh ratio reported in most large-scale reservoirsimulations,4,14 where coarse-scale simulation grids are used anddense mudstone layers are averaged in. Therefore, adding densemudstones to the flow model reduces the crossflow resulting fromthe artifact of scaling up from fine-scale reservoir models tocoarse-scale simulation models.

All the outcrop simulations were conducted using a dead oilwithout solution gas. However, the SSAU crude has a high initialsolution gas ratio and the field had a small initial gas cap. It is

Fig. 14Water saturation distribution after 24 years of water

injection in (a) a left-to-right injection experiment and (b) a

right-to-left injection experiment, showing crossflow points at

the downflow termination of high permeability in Cycle 9 and oil

left in the middle of Cycle 7. (c) Comparison of waterflooding

performances between (a) and (b).

Fig. 15Effect of kvh value on water-saturation distributions

after 20 years of water injection. Thekvhvalues varying from (a)0, (b) 0.1, and (c) 1.0.



8/9

therefore important to understand how the kvh ratio affects recoveryefficiency when SSAU crude is used and an initial gas cap ispresent. This kvh effect was studied using a SSAU cross-sectionmodel through SSAU 2316, 2602, 2506, 2502, 2505, and 2704wells. Three simulation runs were performed using kvh 0, 0.1,and 1. Recovery curves (Fig. 17) show a trend of decreasingrecovery with an increase ofkvh ratio, which is opposite to the trendobserved in the outcrop models (Fig. 16). The opposite trend stemsfrom an initial gas cap being presented in the subsurface model. Thehigh-mobility gas cap serves as a big conduit for water channeling,and increasing the kvh ratio increases the crossflow of water throughan unperforated gas cap. The kvh ratiocan be estimated by matchingsimulation results with production history. Wang et al.4 used akvhratioof 0.04 in an 80-acre 3D model to match the SSAU production.This suggests that the barrier effect in SSAU is strong.

Effect of Stochastic Realization. Because uncertainties in flowmodels generated from well data are high, the chances of matchingfield production using a linearly interpolated model are low, andhistory matching may be better achieved by using stochastic mod-

els. Flow simulations were performed on stochastic realizationsconditioned on SSAU well data andconstrained by rock-fabric flow

units to study their effect on recovery and production and injectionrates. Results on recovery (Fig. 18a) indicate that no significantdifferences occurred from the three realizations using a correlationlength of 1,000 ft (300 m). This was partly because these realiza-tions were constrained by rock-fabric units. Nevertheless, produc-tion and injection rates are different in these runs (Figs. 18b and18c). These differences stem from the change in permeabilitydistribution and effective permeability with realization.

Conclusions

Rock fabrics are defined on the basis of grain and crystal size andsorting, interparticle porosity, separatevug porosity, and the pres-ence or absence of touching vugs. Petrophysical properties ofporosity, permeability, relative permeability, and capillary pressurecan be grouped according to rock fabrics. Permeability profiles canbe calculated using rock-fabric-specific transforms between inter-particle porosity and permeability. Special core analysis data in-dicate that waterflood recovery decreases and residual oil saturationincreases with increasing separate-vug porosity. Residual oil sat-

Fig. 16 Effect of dense mudstones on the kvhvalue. (a) Lawyer

Canyon outcrop model with dense mudstones, (b) Lawyer Can-

yon outcrop model without dense mudstones, and (c) the tech-

nique to determine an effective kvhvalue for the model without

dense mudstones based on the kvh of core data.

Fig. 17Effect of kvhratio on waterflooding recovery using a 2D

cross section model with an initial gas cap in SSAU, Tract 23-28.

Fig. 18 Effect of stochastic realization on (a) waterfloodingrecovery, (b) production rate, and (c) injectivity.



9/9

urations determined by the steady-state method are significantlylower than those determined by the unsteady-state method, becausethe real residual oil saturation is not reached in the fast unsteady-state method.

HFCs and rock-fabric units are the two critical scales formodeling shallow-water carbonate ramp reservoirs. Descriptions ofrock-fabric facies stacked within HFCs provide the most accurateframework for constructing geologic and reservoir models becausediscrete petrophysical functions can be fit to rock-fabric units andfluid flow can be approximated scale-up within rock-fabric flow units.

Stochastic simulations without stratigraphic control can be toorandom for generating geologically realistic models. The upward-

shallowing sequences of carbonates can be observed in stochasticmodels only when they are constrained by rock-fabric flow units.

Scale-up of permeability in the vertical direction was investi-gated in terms of the kvh ratio. Because of the cyclic nature ofcarbonate reservoirs, thekvh ratio decreases exponentially with thevertical gridblock size up to the average cycle size of 20 ft (6.1 m)and remains at a value of 0.06 for a gridblock size of more than 20ft (6.1 m).

Simulation results showed that critical factors affecting recoveryefficiency are the stacking patterns of rock-fabric units, the kvhratio, and the dense mudstone distribution. Simulations usingoutcrop models demonstrated that the geometry and distribution ofrock-fabric units can significantly affect recovery efficiency and thedirection of water injection. The kvh ratio and dense mudstone

layers are the primary controlling parameters governing the recov-ery efficiency. Waterflood recovery increases with the kvh ratiowhen dead oil is used and decreases with the kvh ratio when aninitial gas cap is present. When stochastic models are constrainedby the rock-fabric framework, simulation results are similar inrecovery but different in production and injection rates.

Nomenclature

h thickness, m

k permeability, md

koi permeability at initial oil saturation, md

kro relative oil permeability, fraction

krw relative water permeability, fraction

kh horizontal permeability, md

kh arithmetic mean of horizontal permeability, md

kv vertical permeability, md

kv harmonic mean of horizontal permeability, md

kvh ratio of vertical permeability to horizontal permeability,fraction

Rvp ratio of separate-vug porosity to total porosity, fractions

Acknowledgments

This research was done at the Reservoir Characterization ResearchLaboratory of the Bureau of Economic Geology and was funded byindustrial sponsors and by DOE Contract no. AC22-89BC1440.Publication was authorized by the Director, Bureau of EconomicGeology, The U. of Texas at Austin. We thank Susan Lloyd forword processing and layout, and Tari Weaver and David M.Stephens, under the direction of Joel L. Lardon and Richard L.

Dillon, for preparation of illustrations.

References

1. Kerans, C.et al.:Characterization of Facies and Permeability Patterns

in Carbonate Reservoir Based on Outcrop Analogs, The U. of Texas

at Austin, Bureau of Economic Geology, final report, Contract No.

DE-AC2289BC14470 for the Assistant Secretary for Fossil Energy,

U.S. DOE, Washington, DC (1993) 160.

2. Galloway, W.E. et al.: Atlas of Major Texas Oil Reservoirs, The U. of

Texas at Austin, Bureau of Economic Geology (1983) 139.

3. Senger, R.K. et al.:Dominant Control on Reservoir-Flow Behavior in

Carbonate Reservoirs as Determined from Outcrop Studies,Reservoir

Characterization III,B. Linville et al. (eds.),Proc.,Third International

ReservoirCharacterization Technical Conference, Tulsa (November1993).

4. Wang,F.P. et al.:Critical Scales, Upscaling, and Modeling of Shallow-Water Carbonate Reservoirs, paper SPE 27715 presented at the 1994

SPE Permian Basin Oil and Gas Recovery Conference, Midland, Texas,

1618 March.

5. Lucia, F.J. et al.:Fluid-flow Characterization of Dolomitized Carbon-

ate-Ramp Reservoirs: San Andres Formation (Permian) of Seminole

Field and Algerita Escarpment, Permian Basin, Texas and New Mex-

ico, Hydrocarbon Reservoir Characterization: Geologic Framework

and Flow Unit Modeling, E.L. Stoudt and P.M. Harris (eds.), SEPM

Short Course No. 34 (1995) 129153.

6. Lucia, F.J.: Petrophysical Parameters Estimated from Visual Descrip-

tions of Carbonate Rocks: A Field Classification of Carbonate Pore

Space, JPT (March 1983) 629; Trans., AIME, 275.

7. Lucia, F.J.: Rock-Fabric/Petrophysical Classification of Carbonate

Pore Space for Reservoir Characterization, American Assn. of Petro-

leum Geologists Bull. (1995) 79, No. 9, 1275.

8. Kerans, et al.: Integrated Characterization of Carbonate Ramp Res-

ervoirs Using Permian San Andres Formation Outcrop Analogs, Amer-

ican Assn. of Petroleum Geologists Bull. (1994) 78, No. 2, 181.

9. Lucia, F.J. and Conti, R.D.: Rock Fabric, Permeability, and Log

Relationships in an Upward-Shoaling, Vuggy Carbonate Sequence, The

U. of Texas at Austin, Bureau of Economic Geology, Geological

Circular 87-5 (1987) 22.

10. Journel, A.G. and Huijbregts, Ch.J.: Mining Geostatistics, second edi-

tion, Academic Press, San Diego (1978) 599.

11. Perez, G., and Kelkar, M.: Assessing Distributions of Reservoir Prop-

erties Using Horizontal Well Data, Reservoir Characterization III, B.

Linville et al. (eds.), Proc., Third International Reservoir Character-

ization Technical Conference, Tulsa (November 1991).

12. Lishman, J.R.: Core Permeability Anisotropy, J. Cdn. Pet. Tech.

(AprilJune 1970) 79.

13. Haldorsen, H.H. and Lake, L.W.: A New Approach to Shale Man-

agement in Field-Scale Models, SPEJ(1984) 447;Trans.,AIME,277.

14. Harpole, K.J.: Improved Reservoir CharacterizationA Key to Future

Reservoir Management for the West Seminole San Andres Unit, JPT

(November 1980) 2009; Trans., AIME, 269.

SI Metric Conversion Factors

acre 4.046 873 E03 m2

ft 3.048* E01 mpsi 6.894 757 E00 kPa

sq mile 2.589 988 E00 km2

*Conversion factor is exact. SPEREE

Fred P. Wang is a senior reservoirengineer at PGSReservoir, Inc.,in Houston and previously worked at the Bureau of EconomicGeology, The U. of Texas at Austin. He holds an MS degree fromThe U. of Texas at Austin and a PhD degree from Stanford U.,both in petroleum engineering. F. Jerry Lucia is a Senior Re-search Fellow with the Bureau of Economic Geology develop-ing new techniques and methods for characterizing carbonatereservoirs to improve recovery from existing oil fields through theintegration of geological, petrophysical, engineering, and pro-duction data. Previously Lucia was a Consulting GeologicalEngineer forShellOil Co.assignedto theHead Officestaff when

he retired in 1985 with 31 years experience as a geologicalengineer in research and operations. Currently, he holds a BSdegree in engineering and an MS degree in geology from theU. of Minnesota. Charles Kerans is a seniorresearch scientist in theBureau of Economic Geology. He holds a BS degree from St.Lawrence U. anda PhD degreefromCarletonU., both in geology.

Wang Lucia Kerans


integrated reservoir characterization study of a carbonate ramp reservoir: seminole san andres unit,...

Documents