A New Modified Genetic Algorithm for Well Placement Optimization under Geological Uncertainties
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Determining optimum location of wells is a crucial step in field development. A realistic geological model is an important factor in finding an accurate optimum well location. Usually history matching is used to come up with a realistic geological model. However, there is no unique solution to the history matching problem. Often several geological models could match the history data. This introduces some risk associated with the results of well placement optimization algorithm. In this work, we present a new modified genetic algorithm (GA) for well-placement optimization under geological uncertainty. The inputs of the algorithm are possible geological models, and the level of risk that the user can accept. In the algorithm, the classic GA is modified so that: 1) in evaluating the fitness value (cumulative production or net present value) of each individual (well location) all the possible geological models provided by users are evaluated, 2) upon convergence of the algorithm, the output is one optimum well location as the fittest individual taking into account all the provided geological models, and 3) this optimum well location is selected based on the input user-defined level of risk. Most current available algorithms in the literature do not allow the user to input the risk factor desired and individual weights for each realization. The risk-constrained algorithm will provide different optimum well locations depending on the level of risk the user wants to take. All these features make the new algorithm much more efficient and applied than current well-placement algorithms in the literature for handling geological uncertainty. We present the application of the risk-constrained algorithm to a horizontal well-placement optimization in a gas condensate reservoir, Qatar's North Field, with multiple possible permeability fields and with different user-defined risk factors. Copyright 2011, Society of Petroleum Engineers.
author list (cited authors)
Morales, A. N., Nasrabadi, H., & Zhu, D.