Partially Separated Metamodels With Evolution Strategies for Well-Placement Optimization

Abstract

Finding the optimal location of non-conventional wells increases significantly the project's Net Present Value (NPV). This problem is nowadays one of the most challenging problems in oil and gas fields development. When dealing with complex reservoir geology and high reservoir heterogeneities, stochastic optimization methods are the most suitable approaches for optimal well placement. However, these methods require in general a considerable computational effort (in terms of number of reservoir simulations, which are CPU time demanding). This paper presents the use of the CMA-ES (Covariance Matrix Adaptation -Evolution Strategy) optimizer, which is recognized as one of the most powerful derivative free optimizers, to optimize well locations and trajectories. A local regression based meta-model is incorporated into the optimization process in order to reduce the computational cost. The objective function (e.g., the NPV) can usually be split into local components referring to each of the wells: it depends in general on a smaller number of principal parameters, and thus can be modeled as a partially separable function. In this paper, we propose to exploit the partial separability of the objective function into CMA-ES coupled with meta-models, by building partially separated meta-models. Thus, different meta-models are built for each well or set of wells, which results in a more accurate modeling.An example is presented. Results show that taking advantage of the partial separability of the objective function leads to a significant decrease in the number of reservoir simulations needed to find the "optimal" well configuration, given a restricted budget of reservoir simulations. This approach is practical and promising when dealing with a large number of wells to be located. IntroductionNowadays, the environment and the conditions in which oil and gas fields are discovered are more and more complex and challenging. Both existing fields and new discoveries need an optimal production scheme to be economically viable. One of the most important issues that must be addressed by reservoir engineers to maximize a given project's asset value is to optimally decide where to drill wells. Hence, the well placement optimization problem is nowadays a major focus in the petroleum industry. Many studies already investigated the well placement using different optimization methods: stochastic methods such as genetic algorithms (e.g., Bukhamsin et al., 2010, Emerick et al., 2009, Guyaguler and Horne, 2000, Montes et al., 2001, Morales et al., 2010, Yeten et al., 2003) and deterministic methods in particular adjoint methods (e.g., Sarma & Chen, 2008, Forouzanfar et al., 2010, Zandvliet et al., 2008, Vlemmix et al., 2009).Stochastic optimization methods were shown to be an appropriate approach for the well placement problem given the multiple local optima and the non-smoothness of the objective function. A promising method is the Covariance Matrix AdaptationEvolution Strategy (CMA-ES), a state of the art stochastic optimizer first...

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