Data-Analytics Method Helps Engineers Optimize Well Placement Under Uncertainty

Abstract

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 188265, “Use of Data Analytics To Improve Well-Placement Optimization Under Uncertainty,” by Daniel Busby, SPE, Frédérik Pivot, and Amine Tadjer, Total, prepared for the 2017 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, 13–16 November. The paper has not been peer reviewed. Well-placement optimization is one of the more challenging problems in the oil and gas industry. Although several optimization methods have been proposed, the most-used approach remains that of manual optimization by reservoir engineers. The work flow proposed here uses a machine-learning algorithm trained on simulated data to evaluate the performance of possible well locations and configurations. Introduction Extensive literature has been published about well-placement optimization; authors have proposed many different approaches (e.g., advanced optimization techniques, adjoint methods, experimental design, and streamline solutions). Real field experience, however, has shown that these techniques are rarely used. In practice, expert engineers usually identify the main factors affecting a future well’s production. They consider these factors in obtaining the best well configurations. These are then evaluated with a full-field reservoir-simulation model. Uncertainty is handled in the process typically in a second phase to test the robustness of the proposed development plan by running a few alternative models corresponding to some low and high cases and, more rarely, on a full Monte Carlo set of models generated by the full prior-model uncertainty. The reasons the different optimization methods proposed in the literature are not used in operations can vary: difficulty in handling all the operational constraints during optimization, a prohibitive number of simulation runs, relatively poor performance of the optimization methods when considering many complex production and injection wells, and the complexity of the method and its configuration. The approach proposed here is not a proper well-placement optimization method; it is more a set of data-analytics tools aimed at helping reservoir engineers to explore the extremely large domain of possible solutions and, it is hoped, to find better solutions. This paper’s objective is to provide a methodology and some guidelines that can be adapted to each specific field case. The idea is to follow the typical work flow used by reservoir engineers while adding data-analytics techniques to improve the decision-making process. Methodology This work presents a methodology to address the problem of field development planning mainly for greenfields, although a similar work flow can be applied to mature fields. The first step in field development planning is to perform a study to incorporate geological, seismic, and fluid data into a reservoir-simulation model.