Determination of oil well production performance using artificial neural network (ANN) linked to the particle swarm optimization (PSO) tool

Abstract

Greater complexity is involved in the transient pressure analysis of horizontal oil wells in contrast to vertical wells, as the horizontal wells are considered entirely horizontal and parallel with the top and underneath boundaries of the oil reserve. Therefore, there is an essential need to estimate productivity of horizontal wells accurately to examine the effectiveness of a horizontal well in terms of technical and economic prospects. In this work, novel and rigorous methods based on two different types of intelligent approaches including the artificial neural network (ANN) linked to the particle swarm optimization (PSO) tool are developed to precisely forecast the productivity of horizontal wells under pseudo-steady-state conditions. It was found that there is very good match between the modeling output and the real data taken from the literature, so that a very low average absolute error percentage is attained (e.g., <0.82%). The developed techniques can be also incorporated in the numerical reservoir simulation packages for the purpose of accuracy improvement as well as better parametric sensitivity analysis.

Keywords

• Well productivity
• Drainage area
• Skin factor
• Least square Support vector machine
• Hybrid connectionist model
• Particle swarm optimization