Optimization of Well Placement with a History Matching Approach

Abstract

Abstract Determining the optimum location of the wells is a critical and crucial decision to be made during a field development plan. The quality of the decision is strongly dependent upon the amount of the information available to the decision-maker at the time the decision is made. Knowing that the development phase of a reservoir is a dynamic period in which different categories of information are added to system from distinct sources, one should make the well placement decisions considering these time-dependent contributions of information. This study proposes an approach that addresses the value of time-dependent information to achieve better decisions in terms of reduced uncertainty and increased probable Net Present Value (NPV). A Hybrid Genetic Algorithm1 (HGA) was used as the optimization method to find the best locations of the wells. A utility framework, that enables the assessment of the uncertainty of the well-placement decisions, was used to find the optimum decisions for different risk attitudes. Through this new approach, production history data obtained from the wells, as they are drilled, are integrated into the well placement decisions. Unlike previous approaches, well placement optimization is coupled with recursive history matching steps. To test the results of the proposed approach, an example reservoir was investigated with multiple realizations, all of which match the history response of the reference. At each step of optimization, a reduction in the uncertainty of the multiple realizations was observed, as production history became available.