Stochastic Optimization of Oil Well Placement and Their Type and Trajectory for Maximizing the Net Present Value of Entire Project

Abstract

The optimal determination of the number of injection and production wells as well as their trajectories in an oil field depends on a series of factors which in most cases are of a stochastic nature. Among the uncertain factors we can mention the geological complexity, combinations of petrophysical, economic parameters, flow regimes, oil type, reservoir rock, sandstone or carbonate. The main reason is to develop optimal algorithms that provide the solution to the problem in a relatively short time or with a relatively small number of iterations. Exactly the latter, time and number of iterations has been a major handicap in the past where it took days to find a solution to the problem. So, the problem is that we should not give greater priority to the speed of computers than the way algorithms are developed in a smart way. The advantages in this regard have been quite good, suffice it to mention the genetic algorithm, particle swarm. So, it is imperative to develop intelligent algorithms that through reservoir simulations give us in a relatively short time, say some hours, the optimal solution to the optimization problem: the location of existing injection and production wells and the location of the trajectory of the candidate wells that will be drilled for both injection and exploitation to achieve the final goal which is the maximization of the Net Present Value. The problem we will pose for solution is of a dual stochastic nature: both in terms of geological uncertainty and economic uncertainty.