Use of Particle Swarm Optimization Algorithm to Reduce Drilling Costs by Finding Optimal Operational Parameters

Abstract

Oil and gas companies have played a major role in the energy sector, and constantly try to develop technology to maximize their overall revenue. One of the more substantial feats was the developed equipment that allowed for horizontal wells. These horizontal sections allow much more oil to reach the wellbore due to the extended length into reservoir supplies. However, as the wells continue to get drilled farther, the cost of drilling the wells continue to rise. Now more than ever, there is an increased need for better drilling optimization techniques, which could potentially reduce these drilling costs and increase the overall profit. Many individuals have researched optimizing constant operational parameters; however, these constant variables lead to wasted time and money for the operators. This is because formation variables constantly change throughout the drilling process; therefore, the concept of dynamic variables allow drillers to alter the drilling parameters to better adjust for changes in the formation. The research presented herein, incorporates a particle swarm optimization (PSO) algorithm to optimizeoperational parameters, weight on bit (WOB), revolutions per minute (RPM) of the bit,bit pull depth, and bit combination, with the goal to decrease the overall drilling cost perfoot. A rate of penetration (ROP) model was incorporated with the PSO algorithm inorder to calculate the drilling time and the associated costs from the given parameters.This research could be applied in numerous ways including as an artificial intelligenceoptimizer in an existing drilling simulator, or directly integrated by drilling engineersduring the planning stage. Long term use for this algorithm is to be the foundation for anautonomous driller including being the real time optimal solver.