Well Placement Optimization with Geological Uncertainty Reduction Shahed Rahim, Zukui Li* *Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta, Canada T6G 2V4 (E-mail: zukui@ualberta.ca) Abstract: Well placement optimization aims to determine optimal well locations so that the economic benefit from oil production can be maximized. Geological uncertainty has a significant impact on the optimal well placement plan and therefore has to be considered in the well placement optimization problem. A geological realization reduction framework for well placement under geological uncertainty is proposed in this work. The objective is to optimally select a small subset of realizations and incorporate them into the well placement optimization problem, so as to reduce the computational efforts. A reservoir case study demonstrates that the selected smaller subset of realizations is a very good representation of a larger superset of realizations and can significantly decrease the computational time associated with the well placement optimization problem. Keywords: geological uncertainty, well placement, optimization, uncertainty reduction 1. INTRODUCTION For oil reservoir operations, the production amount of oil greatly depends on the well locations and the geological property of the reservoir. To achieve the maximum economic benefit, well placement optimization is necessary for determining the best locations for placing wells in a reservoir. Reservoir flow simulation is commonly used in well placement optimization problems. The well positions are determined by maximizing the output variable of interest such as the cumulative oil production (COP) or net present value (NPV) generated by a reservoir flow simulator. The objective function for the well placement optimization problem is evaluated by running the reservoir flow simulator with given well positions. As a result, the computational time for the flow simulator significantly increases with the size of the reservoir grid and the number of wells to be placed. In the literature, various methods have been used in well placement optimization to determine optimal well positions of a reservoir. In most cases, the objective function for the well placement optimization problem is to maximize the NPV or COP (Nasrabadi et al., 2012). Optimization methods used in well placement include: mixed integer programming (Rosenwald and Green, 1974), gradient-based optimization using finite difference method (Bangerth et al., 2006), genetic algorithms (Bittencourt and Horne, 1997), simulated annealing (Beckner and Song, 1995) and particle swarm optimization (Onwunalu and Durlofsky, 2010), etc. The complexity of the well placement optimization problem is further increased by incorporating uncertainty associated with geological properties of the reservoir. Geological uncertainty in well placement optimization is generally considered by incorporating multiple geological realizations of the reservoir in the optimization model. Hence, the calculation of COP or NPV is based on the flow simulation on multiple geological realizations. However, since flow simulation for a large number of realizations is a very computationally demanding task and impractical for larger realistic reservoirs with multiple wells, a smaller subset of realizations are generally selected and used in the well placement optimization model to account for geological uncertainty. Thus, reducing the number of geological realizations for flow simulation becomes an important step in well placement optimization. Yeten et al. (2003) used multiple equiprobable geological realizations in the determination of objective function of well placement optimization to account for the geological uncertainty associated in a reservoir. Wang et al. (2012) selected a smaller subset of realization to quantify geological uncertainty in well placement optimization using k-means clustering. K-means clustering uses cumulative field oil production which requires to be calculated for every possible locations of well and therefore is computationally intensive. Yasari et al. (2013) used robust well placement optimization under uncertainty using a risk weighted objective function for multiple realizations. They selected a subset of realization from a superset by calculating the NPV for all the realizations using base case well position and then used ranking to select the small subset of realization. Similarly, Yang et al. (2011) combined Steam Assisted Gravity Drainage (SAGD) well production and placement optimization under uncertainty by selecting a subset of realizations using traditional ranking method based on the NPV of all the realizations for a base case scenario. In this study, reservoir well placement optimization considering geological uncertainty is studied based on a novel method for geological uncertainty reduction. The well placement optimization problem is solved using derivative free optimization method. Geological uncertainty is considered by using a reduced subset of geological realization from a superset of realization in the well placement optimization model. An optimal realization reduction method using geological property of the reservoir and static measures is used in selecting the subset of Preprints of the 9th International Symposium on Advanced Control of Chemical Processes The International Federation of Automatic Control June 7-10, 2015, Whistler, British Columbia, Canada MoM2.1 Copyright © 2015 IFAC 57