Efficient Design of Reservoir Simulation Studies for Development and Optimization

Abstract

Summary Development studies examine geologic, engineering, and economic factors to formulate and optimize production plans. If there are many factors, these studies are prohibitively expensive unless simulation runs are chosen efficiently. Experimental design and response models improve study efficiency and have been widely applied in reservoir engineering. To approximate nonlinear oil and gas reservoir responses, designs must consider factors at more than two levels—not just high and low values. However, multilevel designs require many simulations, especially if many factors are being considered. Partial factorial and mixed designs are more efficient than full factorials, but multilevel partial factorial designs are difficult to formulate. Alternatively, orthogonal arrays (OAs) and nearly-orthogonal arrays (NOAs) provide the required design properties and can handle many factors. These designs span the factor space with fewer runs, can be manipulated easily, and are appropriate for computer experiments. The proposed methods were used to model a gas well with water coning. Eleven geologic factors were varied while optimizing three engineering factors. An NOA was specified with three levels for eight factors and four levels for the remaining six factors. The proposed design required 36 simulations compared to 26,873,856 runs for a full factorial design. Kriged response surfaces are compared to polynomial regression surfaces. Polynomial-response models are used to optimize completion length, tubinghead pressure, and tubing diameter for a partially penetrating well in a gas reservoir with uncertain properties. OAs, Hammersley sequences (HSs), and response models offer a flexible, efficient framework for reservoir simulation studies. Complexity of Reservoir Studies Reservoir studies require integration of geologic properties, drilling and production strategies, and economic parameters. Integration is complex because parameters such as permeability, gas price, and fluid saturations are uncertain. In exploration and production decisions, alternatives such as well placement, artificial lift, and capital investment must be evaluated. Development studies examine these alternatives, as well as geologic, engineering, and economic factors to formulate and optimize production plans (Narayanan et al. 2003). Reservoir studies may require many simulations to evaluate the many factor effects on reservoir performance measures, such as net present value (NPV) and breakthrough time. Despite the exponential growth of computer memory and speed, computing accurate sensitivities and optimizing production performance is still expensive, to the point that it may not be feasible to consider all alternative models. Thus, simulation runs should be chosen as efficiently as possible. Experimental design addresses this problem statistically, and along with response models, it has been applied in engineering science (White et al. 2001; Peng and Gupta 2004; Peake et al. 2005; Sacks et al. 1989a) toMinimize computational costs by choosing a small but statistically representative set of simulation runs for predicting responses (e.g., recovery)Decrease expected error compared with nonoptimal simulation designs (i.e., sets of sample points)Evaluate sensitivity of responses to varying factorsTranslate uncertainty in input factors to uncertainty in predicted performance (i.e., uncertainty analysis)Estimate value of information to focus resources on reducing uncertainty in factors that have the most significant effect on response uncertainty to help optimize engineering factors.

Geological and production uncertainty assessments of the cyclic $$\text{CO}_2$$ CO 2 -assisted gravity drainage EOR process: a case study from South Rumaila oil field

“…Box-Behnken design creates the experiments by selecting combinations at the midpoints and centers of the process space edges. The number of factors and levels should be more than two to be treated in the Box-Behnken design (Kalla and White 2007). Figure 7 depicts a Box-Behnken design for three factors example.…”

“…This surface could be a polynomial function or established using ordinary kriging and the surface is subsequently used for uncertainty quantification. Comparative applications of polynomial and kriged response surface models have been conducted considering orthogonal arrays sampling, Latin Hypercube Sampling, and Hammersley Sequence design as efficient approaches for uncertainty quantification (Kalla and White 2007).…”

A New Model for Evaluation of Horizontal Well in Bottom Water Reservoirs

“…C.S.Kabir [4] et al developed a recovery factor correlation that can be used as a quick evaluation and screening tool for thin oil column exploitation. S Kalla [5] established a polynomial response model on the recovery of a gas well with water coning. Olugbenga Olamigoke [6] established a recovery model for oil reservoir and used the principle of response surface method to assess the impact on oil and gas recovery for a range of subsurface uncertainty.…”

Application of Design of Experiments for Well Pattern Optimization in Umiat Oil Field: A Natural Petroleum Reserve of Alaska Case Study

“…Ghaderi et al(2012)attempted optimization of WAG process for CO2EOR-storage in tight formation. The parameters included well spacing, well completion strategy, hydraulic fracture geometry, WAG ratio and CO2 slug size and the timing of the switch from primary or waterflood to WAG scheme and were used to assess CO2 EOR process Kalla and White (2007). used OAs and NOAs instead of factorial, or partial factorial designs to decrease the number of runs while optimizing large number of parameters such as completion length, tubing head pressure, and tubing diameter in a gas reservoir.…”