Infill location determination and assessment of corresponding uncertainty

Abstract

Accurate prediction of infill well production is crucial since the expected amount of incremental production is used in the decision-making process to choose the best infill locations. Making a good decision requires taking into account all possible outcomes and so it is necessary to quantify the uncertainty in forecasts. Many researchers have addressed the infill well location selection problem previously. Some of them used optimization algorithms, others presented empirical methods and some of them tried to solve this problem with statistical approaches. In this study, a reservoir simulation based approach was used to select infill well locations. I used multiple reservoir realizations to take different possible outcomes into consideration, generated probabilistic distributions of incremental field production and, finally, used descriptive statistical analysis to evaluate results. I quantified the uncertainty associated with infill location selection in terms of incremental field production and validated the approach on a synthetic reservoir model. Results of this work gave us the possible infill locations, which have a mean higher than the minimum economic limit, with a range of expected incremental production.