Evaluating Water-Flooding Incremental Oil Recovery Using Experimental Design, Middle Miocene to Paleocene Reservoirs, Deep-Water Gulf of Mexico*
Richard Dessenberger1, Kenneth McMillen2, and Joseph Lach1
Search and Discovery Article #40256 (2007)
Posted September 5, 2007
*Adapted from extended abstract prepared for presentation at AAPG Annual Convention, Long Beach, California, April 1-4, 2007
1Knowledge Reservoir, 1800 West Loop South, Suite 1000, Houston, TX 77027 ([email protected])
2Consultant and Knowledge Reservoir, Sonoma, CA 95476, and Knowledge Reservoir, 1800 West Loop South, Suite 1000, Houston, TX 77027
Many deep-water Gulf of Mexico discoveries and field development plans of the past five years involve middle Miocene to Paleocene reservoirs with lower porosity and permeability resulting from compaction and cementation. Middle Miocene fields and discoveries include Atlantis, Neptune, K-2, and Shenzi. Eocene-Paleocene fields and discoveries include Great White, St Malo, Jack, and Cascade. In this setting, rock compaction may be less important as a production drive mechanism, and aquifer support (possibly augmented by water flooding) assumes more significance. Porosity and permeability decrease is related to greater burial depth and compaction as well as temperature-related cementation. Structural styles of these fields include compressional anticlines, turtle structures, and sub-salt three-way dip closures. Some of these structures are highly compartmentalized by faulting.
We used an experimental design approach to analyze dynamic simulation of two static models loosely based on the stratigraphy and reservoir properties from a thick-bedded middle Miocene reservoir (e.g., Tahiti Field) and a thinner-bedded Paleocene (e.g., Great White Field). Modeled variables included geological parameters (structural dip, faulting, facies, and aquifer size), reservoir parameters (absolute permeability and heterogeneity), fluid properties and production variables.
The results of the dynamic simulation were evaluated using Experimental Design. The interpretation process involved five steps: identifying uncertainty parameters and ranges, running simulations for a wide variety of parameters, generating relationships of recovery factor as a function of uncertainty, identifying parameter importance, and determining incremental oil recovery due to water injection. For these experiments, the incremental recovery for aquifer-supported fields is small with a P50 value of 7%. Key water-flooding variables are depofacies, aquifer size, permeability, fault transmissibility, and oil saturation. The least important are bed dip, injection voidage-replacement, and PVT properties.
Figure and Table Captions
Introduction and Problem Statement
Many deep-water Gulf of Mexico (GoM) discoveries of the past five years are in water depths greater than 4000 feet and in older Tertiary reservoirs of middle Miocene to Paleocene age. Middle Miocene fields and discoveries include Atlantis, Tahiti, Neptune, K-2, Thunder Horse, and Shenzi. Eocene-Paleocene fields and discoveries include Great White, Trident, St Malo, Jack, and Cascade. Structural styles of these lower slope fields include compressional anticlines, turtle structures and sub-salt three-way dip closures against salt faces (Figures 1 and 2). Some of these reservoirs are highly compartmentalized by faulting. In this setting, rock compaction may be less important as a production drive mechanism, and aquifer support (possibly augmented by water flooding) assumes more significance. Porosity and permeability decrease is related to greater burial depth and compaction as well as temperature-related cementation.
Much of the production experience in the deep-water Gulf of Mexico is from upper Miocene through Pleistocene reservoirs. The characteristics observed in these reservoirs and fields are summarized as follows:
The above reservoir characteristics result in high primary recovery factors and only a few developments have included waterflooding ; e.g., Lobster and Petronius.
By contrast, older middle Miocene to Paleocene reservoirs are characterized by the following:
The requirement for water injection to supplement reservoir drive energy, to improve oil rate, and to maintain oil production rates is of primary consideration in development planning for the new, ultra-deep water discoveries. The objective of this study was to quantify the incremental oil recovery potential for a range of the reservoir properties observed in these new middle Miocene through Paleocene discoveries.
A parametric simulation study was performed using experimental design to calculate increment oil recovery due to water injection and to identify the influence of parameters on recovery factor. The experimental design workflow is summarized below:
A total of eleven uncertainty parameters were used in the parametric study. The parameters and range of uncertainty for each are detailed in Table1. Both static and dynamic parameters were considered.
The geologic uncertainty parameters incorporated into the static models include: structural dip, faulting, facies, aquifer size, and reservoir parameters (absolute permeability and heterogeneity). Dynamic uncertainty parameters include: fluid properties, water injection variables (timing and injection rates), and relative permeability variables (residual oil saturation and endpoints). Two static models were constructed based on the stratigraphy and reservoir properties from a thick-bedded middle Miocene reservoir (e.g., Tahiti Field) and a thinner-bedded Paleocene (e.g., Great White Field, Figure 3). Geocellular and dynamic simulation models were built with 200 x 200 ft cells having a thickness of 5 ft. Simple depofacies consisting of sheet, distal sheet, channel and shale were populated, and reservoir properties were distributed in these depofacies. Upscaled depofacies and properties are compared to the wireline logs in Figure 4, and a cross section showing injector and producer well locations in the dynamic model is shown in Figure 5. Permeability distributions were generated for three different Dykstra- Parson’s coefficients; 0.27, 0.6, and 0.8. Porosity-permeability cross-plots are shown in Figure 6. Three different fluids were considered with GOR (API) of 1,800 scf/stb (35° API), 1,100 scf/stb (30°API), and 500 scf/stb (27° API).
Experimental design matrices were generated for both primary and water flood scenarios, based on the eleven uncertainty parameters. Eighteen primary cases and twenty-seven water flood cases were run. Proxy equations for both primary and water flood oil recovery were generated from the simulation results.
Cumulative probability functions, “S-curves,” of oil recovery for both primary and water flood were calculated from the proxy equations using Monte-Carlo simulation (Figure 7). P50 oil recovery is 30% for primary and 37% for water flood, yielding incremental recovery of 7% of OOIP. As expected, incremental recovery for water flood is larger when primary recovery is low and lower when primary recovery is high. It is important to focus on incremental oil recovery rather than absolute recovery factor due to the modeling of a single producer-injector well pair.
The key parameters impacting water flood performance are depofacies and net-to-gross (representing thief zones or limited connectivity), and aquifer size (Figure 8). Secondary parameters impacting water flood performance are permeability, faulting, residual oil saturation, and relative permeability endpoints. The least important parameters are beddip, injection voidage-replacement-ratio, and PVT properties.