Selection of Alternatives for Oil Field Development; Nonlinear Optimization Approach

Abstract

Abstract In the petroleum Industry, the major task is to develop a strategy to maximize the hydrocarbon production within existing physical and economic constrains. The major entities involved in solving this problem are: the oil reservoir and the oil wells. Each of these entities has a number of variables and parameters. Searching for the best configuration of these parameters, for optimum return on investment, constitute an optimization problem. In other to apply the optimization approach, an objective function must be involved. In this study, Net Present Value (NPV) was selected as the profit indicator for return on investment (ROI). Optimization is complicated when more parameters are involved and needed to be optimized simultaneously. Major success will rely on the ability to incorporate effectively, factors (physical and economic) that influence the entire production process in an economic model called the Objective function with or without constraints. A suitable optimization method is also required. In this work, a robust NPV objective function was developed for a reservoir under production. Reservoir, production and economic uncertainties where fully captured. A multivariate nonlinear optimization approach was employed in determining the best configuration of the decision variable and their effect on NPV. Nonlinear optimization methods based on Newton's technique locate the extrema by approximating the objective function with a nonlinear quadratic model. The quadratic model is solved for the stationary point where its gradient goes to zero. If the quadratic model is a good approximation of the objective function, then the stationary point of the quadratic model should be near a stationary point of the objective function. The stationary point of the quadratic model is taken as the new estimate of the objective function's stationary point and the process is repeated until some form of convergence criteria is satisfied. Sensitivity plots where generated for diagnostic purposes as well as to strengthen decision processes. Field application of the NPV model was carried out and reliable results where obtain.