Evaluation of in-fill well placement and optimization using experimental design and genetic algorithm

Thesis Overview

<p> </p><p><strong>INTRODUCTION AND STATEMENT OF THE PROBLEM</strong></p><p><strong>1.1</strong>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<strong>Introduction</strong></p><p>There is a growing demand to develop petroleum reservoirs through the drilling of in-fill wells to exploit the hydrocarbon reserves not properly drained by existing producing wells. Well placement can be referred to as all activities associated with drilling a wellbore to intercept one or more specified locations. The term is usually used in reference to vertical, directional or horizontal wells that are oriented to maximize contact with the most productive parts of reservoirs. As well spacing is decreased, the shifting well patterns alter the formation-fluid flow paths and increase sweep to areas where greater hydrocarbon saturations exist. A wide well spacing will leave some oil and gas bearing sands in areas not penetrated, while a close spacing will cause some oil and gas bearing sands to be penetrated by two wells or more, causing interference and lowering the reserves drained by the wells and economic profit. This study is done to determine the optimal locations for well placement to support field development plans.</p><p>One of the most challenging and influential problems associated with drilling in-fill wells is finding the optimum number of wells and their placement in the reservoir. In this problem, there are many variables to consider like geological, well configurations, production variables and economic variables. All these variables, together with reservoir geological uncertainty, make the determination of a suitable development plan for a given field difficult, since the design has to evaluate hundreds or thousands of potential infill alternatives.</p><p>The task of optimization of infill well placement is challenging, because the evaluation of the production capacity of many wells may be required, with each evaluation requiring the performance of a simulation run; and for large or complicated reservoir models, the simulation run time can be excessive. The number of simulations required depends on the number of optimization variables, the size of the search space, and on the type of optimization algorithm employed.</p><p>Different optimization methods can be used to determine the optimum well locations in a reservoir. This optimization problem is nonlinear and generally contains multiple local minima. Gradient-free optimization algorithms are commonly used for well placement problems because of their computational efficiency. Genetic Algorithm (GA) is one of the Gradient-free optimization methods used in the industry. GA will be used as the main optimization engine in this work because of its success application by several authors in solving complex optimization problems with high dimensionality and nonlinearity. The main focus of this work is to employ an efficient optimization technique for in-fill well placement and optimization; i.e., to determine the best possible locations of infill wells for optimal development of a field. We intend to identify the significant parameters that affect well placement in the reservoir and use some screening parameters to identify potential locations for well placement. The use of Experimental Design (ED) and Response Surface Methodology (RSM) has been shown to be effective tools for uncertainty analysis. They were utilized in this work to consider a range of values for the controlling parameters and to build a proxy model that can be used to predict the objective function. Experimental design methodology offers not only an efficient way of assessing uncertainties by providing inference with minimum number of simulations, but also can identify the key parameters governing uncertainty in production forecast.</p> <br><p></p>