History Matching of Heavy Oil Production for Comparing New Approaches to Generating Reservoir Property Distributions, West Coalinga Field, California S.E. Brame, J.W. Castle, SPE, O.K. Fawumi*, SPE and R.W. Falta, Clemson University * Now with Mobil Producing Nigeria Unltd. SPE 93469

This research was funded by the U.S. Department of Energy, Fossil Energy Oil Technology Program, through the National Petroleum Technology Office under contract number DE-AC26- 98BC15119.

Objectives Assess the suitability of different permeability distributions generated from geological and fractal modeling. Examine the feasibility of creating realistic permeability distributions from fractal theory for reservoir simulation. Use steam flood simulations of a portion of the West Coalinga oil field in California for model assessment.

Location of the West Coalinga Field

Heavy Oil Sands of Coalinga The West Coalinga oil field in California produces from heavy oil sands of the Miocene Temblor Formation. The oil in this field has low API gravity (12 o to 15 o API) and is highly viscous (900 cp) at the 40 o C initial reservoir temperature. This makes the field an ideal candidate for enhanced oil recovery through steam injection. Steam injection well at Coalinga

Methods Characterize geology in outcrop and core Identify lithofacies Group lithofacies into Facies Groups Identify Facies Tracts Characterize Fractal Facies Construct geologic models Assign permeability distributions Simulate steam flooding of different models and assess results

Geologic Characterization Coalinga offers a unique opportunity to observe and characterize the reservoir rocks on the surface, immediately adjacent to the oil production area. Thus it was possible to relate cores of the producing formation to nearby outcrops. Clemson Students examining outcrop of Temblor Fm. in hills north of Coalinga Field

Lithofacies Characterization A total of fifteen lithofacies were identified from outcrops and cores based on observed lithological differences. Statistical methods were used to consolidate the 15 different lithofacies groups into five facies groups. Facies GroupMajor LithologyMean Permeability 1Clean sand3180 mD 2Interlaminated sand and clay500 mD 3Burrowed clayey sand255 mD 4Bioturbated Sand525 mD 5Fossiliferous Sand225 mD

Facies Tract Characterization Five facies tracts were interpreted based on detailed sedimentological analysis: Facies TractLithologyMean Permeability Incised ValleyBasal conglomerate, cross-bedded sand, silt, and clay 562 mD Estuarine Interlaminated sand, silt, and clay, burrowed clay intervals, sandy clay intervals 316 mD Tide-to Wave- dominated shoreline Crossbedded sand with burrowed sand and clay; fossiliferous sand316 mD DiatomiteClay, silt, and fine sand22 mD SubtidalMassive burrowed sand with intervals of silt and clay 224 mD

Facies Correlations Between Wells Correlation of well 239 with Type Well 118A to determine the location of bounding surfaces, facies tracts, and facies groups. Well 118A BS = bounding surface (depths are in feet)

Model Area This map shows the 3 adjacent five-spot configurations that were modeled and the wells that were used Easting B 8-2B 228W A W W 238A 128B W 127B 236W W 229 Northing ProductionWell InjectionWell Section 36D

Geologic Model Construction 3-D geologic models were constructed using GOCAD. Inputs included the bounding surface horizons, geophysical logs, and facies group and facies tract data. The grid consisted of 9600 cells: - 32 vertical layers, - 10 cells in the x direction - 30 cells in the y direction

Geologic Models in GOCAD Facies Group 1 Facies Group 2 Facies Group 3 Facies Group 4 Facies Group ft Facies Tract Facies Group

Fractal Group Model Development The cores of five West Coalinga wells were analyzed foot by foot. Analysis of the individual facies data revealed that a unique Gaussian fractal structure was present in each one. These results and others led to the development of a new model for representing natural heterogeneity called the fractal/facies concept.

Reservoir Simulation The geologic model grids were used as the framework for the flow simulation mesh. Petrophysical properties were assigned to all cells of the mesh. Permeability distributions of the facies tract, facies group, and fractal group models were assigned. Numerical simulations of steam injection were used to assess the different models.

T2VOC Flow Simulator Uses a general finite difference formulation. Can solve multi-phase, multi-component mass and energy balance equations. Has been used to simulate a variety of subsurface processes such as:  nonaqueous phase liquid (NAPL) migration,  soil vapor extraction,  air-sparging,  steam injection, and  direct pumping of water and NAPL.

Permeability Distribution of the Facies Tract Model

Permeability Distribution of the Facies Group Model

Fractal Permeability Distribution Fractal permeabilities were stochastically generated for the 5 different facies groups. The values were assigned to the flow grid but using a finer grid (~3,000,000 cells). The fractal permeabilities were upscaled to the standard flow grid cell size (9600 cells). An arithmetic mean was used for the horizontal permeability. A harmonic mean was used for the vertical permeability.

Permeability Distribution of the Fractal Model

Simulation Period A five year period (Oct to Oct. 2000) was used. The injected steam volume changed monthly. Some injection wells were not online until All changes in production and injection were honored. Volume of Steam (bbls of water) Time (years)

Parameters Adjusted for Better Fit The oil-water relative permeability curves provided for the field were based on a data fit from a core. The initial oil saturation was interpolated from well logs. Problem: these values resulted in simulations where the water to oil ratio was off by a factor of 10 or more compared to field values!

Solution: The water relative permeability endpoint was reduced from.56 to.15, and the oil saturations were increased everywhere by 20% (upper limit of 70%). Kro Krw ■ Kro Field Data ● Krw Field Data Kro used in Model Krw used in Model Normalized Water-oil Relative Permeabilities Water Saturation (%)

Reservoir Temperatures at 5 Years Facies Tract Fractal Group Facies Group

Oil Saturations at 5 Years Fractal Group Facies Tract Facies Group

Simulated versus Field Production of Oil (Combined Production)

Simulated versus Field Production of Water (Combined Production)

Summary Three models were assessed using simulations of steam injection and heavy oil production. Lowering the water relative permeability curve endpoint decreased water production. Increasing the initial oil saturation resulted in a better prediction of oil production. The fractal realization matched oil production up to Year 3. Additional realizations could be generated to improve the history match.

Conclusions Permeability distributions from the geologic models provided reasonable matches of oil production. The fractal/facies approach is a viable method of generating geologically realistic permeability distributions. Steamflood simulations demonstrate that the fractal/facies approach is feasible for modeling heavy oil reservoirs.

Acknowledgments Chevron contributed core data, production data, and geophysical logs. Many thanks to Venton Shoemaker, George Anderson, Louis Klonsky, Paul Henshaw, and Mike Clark of Chevron. Fred Molz and Silong Lu of Clemson University performed the fractal analysis Ray Christopher of Clemson University assisted with the statistical treatment of the lithofacies.