Critical evaluation of infill well placement and optimization of well spacing using the particle swarm algorithm

Abstract

Abstract Optimizing the placement of new wells and well spacing are vital issues in oilfield development. In recent years, the use of particle swarm algorithm (PSA) in many reservoir applications has gained wide acceptance. More importantly, the applications of PSA in determining optimal well placement and well spacing facilitate subsurface development in oil and gas fields. Due to the quest for hydrocarbons, there is the need to maximize oil recovery from petroleum reservoirs. Besides, drilling infill wells are one way to maximize oil recovery from reservoirs. However, the problem of infill well placement is very challenging. This is because many different well placement scenarios must be evaluated when undertaking the optimization program. Most often, the variables that affect the reservoir performance are nonlinearly correlated with some degree of uncertainty. Therefore, the use of computational algorithm has become increasingly common in handling well placement problems. In this paper, PSA has been efficiently used to determine optimal locations of infill wells and their spacings in a synthetic reservoir. The reservoir used in the optimization process is a two-dimensional implicit black-oil model. The objective function in this study is the net present value of the asset (reservoir). For optimal locations, 20-acre, 40-acre and 80-acre spacing were considered for maximization of the objective function. The spacing for optimal locations was varied between wells in the reservoir model. Multiple cases for infill well locations with six existing appraisal wells were considered. After various simulation runs, the optimum locations of infill wells, number of wells and the corresponding well spacings were determined. Consequently, 4 vertical infill wells located at 40-acre spacing predicted the optimum NPV of $3.973 × 109. Therefore, this infill design is recommended for field development. Pressure and saturation distribution maps were generated with the maximization of net present value as the objective function. The oil, water and gas productions from the reservoir after infill well drilling were also analyzed. The total oil production after implementation of infill drilling peaked at 44.0 MMSTB, representing 48.31% recovery. In addition, an uncertainty analysis was performed to evaluate the reservoir performance and its impact on economic parameters that directly affect the net present value. Probability estimates: P10, P50, and P90 were obtained from the uncertainty analysis to provide a basis to estimate the possible net present values and the options for evaluating the different reservoir development scenarios. The major contribution of this study is that a methodology for infill well design has been developed. This will be a useful tool to support petroleum engineers in deciding how to maximize the value of their asset—the petroleum reservoir.

Keywords

• Reservoir development
• Field optimization
• Particle swarm algorithm
• Infill well drilling
• Well placement
• Uncertainty analysis