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Optimization of Vertical Well Placement for Oil Field Development Based on Basic Reservoir Rock Properties using Genetic Algorithm
Tutuka Ariadji1, Pudjo Sukarno1, Kuntjoro Adji Sidarto2, Edy Soewono2, Lala Septem Riza2 & Kenny David1
1Department of Petroleum Engineering, Institut Teknologi Bandung,
Bandung 40132, Indonesia
2Department of Mathematics, Institut Teknologi Bandung, Bandung 40132, Indonesia
Email: tutukaariadji@tm.itb.ac.id
Abstract. Comparing the quality of basic reservoir rock properties is a common practice to locate new infills or development wells for optimizing an oil field development using a reservoir simulation. The conventional technique employs a manual trial and error process to find new well locations, which proves to be time-consuming, especially, for a large field. Concerning this practical matter, an alternative in the form of a robust technique was introduced in order that time and efforts could be reduced in finding best new well locations capable of producing the highest oil recovery. The objective of the research was to apply Genetic Algorithm (GA) in determining wells locations using reservoir simulation to avoid the manual conventional trial and error method. GA involved the basic rock properties, i.e., porosity, permeability, and oil saturation, of each grid block obtained from a reservoir simulation model, which was applied into a newly generated fitness function formulated through translating the common engineering practice in the reservoir simulation into a mathematical equation and then into a computer program. The maximum of the fitness value indicated a final searching of the best grid location for a new well location. In order to evaluate the performance of the generated GA program, two fields that had different production profile characteristics, namely the X and Y fields, were applied to validate the proposed method. The proposed GA method proved to be a robust and accurate method to find the best new well locations for field development. The key success of this proposed GA method is in the formulation of the objective function.
Keywords: fitness function; field development; Genetic Algorithm; objective function; reservoir simulation; well locations; optimization; vertical well placement.
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