Automated Well Placement and Well Controls Optimization for Improved Hydrocarbon Production and Field Development Planning: Deep-Water Reservoir Case Study

Abstract

Selecting an optimal injector well location is a challenging and time-consuming process. However, an optimal injector well location leads to an improved reservoir production performance whilst significantly reducing the drilling cost, especially in the challenging environment such as deepwater or permafrost. In this study, we show the automated approach to find the optimal injector well location to maximize oil production and minimize water production of the field. We perform multiple reservoir simulations for different well location scenarios, by moving the well across the grid using the -i and -j values of the grid as variables. The optimal well location is selected from the wide range of the simulated models and results in the high oil and the low water production profile. The present study compares the performance of two population-based algorithms - Particle Swarm Optimisation and Differential Evolution. The computational efficiency is achieved using the hyper-threading implementation, that improves parallelization of computations and results in fast reservoir simulation performance. Fast reservoir simulation performance allows considering a broad range of scenarios to increase the confidence of the decision making process during field development planning. The suggested optimization workflow was applied to the real deepwater turbidite reservoir located in the North Sea to find an optimal injector well location that leads to an improved reservoir production performance. The main results show that the proposed approach is able to improve and facilitate the process of finding the optimal injector well location and result in high oil production with low water production. We believe that the workflow could be extended to optimize multiple well locations, both injection and production wells.

AAPG Datapages/Search and Discovery Article #90332 © 2018 AAPG International Conference and Exhibition, Cape Town, South Africa, November 4-11, 2018