Reducing Drilling Cost by finding Optimal Operational Parameters using Particle Swarm Algorithm

Abstract

Over the years, oil companies have continually looked for unique ways to advance technology in the drilling industry, with one of the significant achievements being horizontal drilling techniques. Horizontal wells allow for a more extended reach into the reservoir, resulting in more oil being extracted per well, however, these horizontal sections increase drilling distance and ultimately cost. These higher drilling costs increase the need for better optimization methods. Many researchers have analyzed theoretical ROP equations for optimization, though most have only incorporated constant drilling parameters in these equations. Since formation variables change with depth, so should the optimum drilling variables. Therefore, constant parameters waste both time and money, which could be immensely improved with the use of dynamic drilling parameters. The method presented herein, incorporates a Particle Swarm Optimization (PSO) algorithm to a rate of penetration (ROP) model in order to minimize the overall cost per foot of the well. This is achieved by allowing the PSO to find the best combination of drilling parameters, downhole weight on bit (DWOB) and revolutions per minute (RPM) of the drill bit, along with optimized pull depth and bit combinations. The application of this algorithm could be applied in a variety of ways including for operating oil and gas companies to plan for new wells or as an artificial intelligence component of a drilling simulator. A long term use could be as an autonomous driller constantly getting information updates and solving real time optimal solutions.

Drilling Parameter Optimization of Shale Gas Wells Based on Saw-Tooth Genetic Algorithm to Reduce Drilling Costs

“…(1) The parameter data used to support the findings of this study are included within the article. (2) The actual drilling parameter data used to support the findings of this study are available from the corresponding author upon request (Some of the actual drilling parameter data should be kept confidential at the request of the drilling company).…”

“…Particle swarm optimization (PSO) and genetic algorithm (GA) have problems of poor stability and local convergence, which limit their search efficiency and accuracy [1]. Self et al [2] put forward a method of combining particle swarm optimization algorithm with a permeability model and conducted in-depth research on the optimization of dynamic drilling parameters to minimize the total cost of each well. Abbas et al [3] for the first time, a genetic algorithm was used as an ROP optimizer.…”

Permeability extraction from multiple well logs using particle swarm optimization based factor analysis

“…For instance, Nguyen et al (2020) utilized it for feature selection in data mining. Self et al (2016) suggested PSO for selecting optimal drilling parameters to minimize the overall cost. Shaw and Srivastava (2007) evaluated the applicability of PSO to inversion of direct current, induced polarization and magnetotelluric data and concluded that the results are consistent with ridge regression and genetic algorithm based inversions.…”

Real-Time Drilling Parameter Optimization Model Based on the Constrained Bayesian Method

“…Solving such optimization problems using traditional optimization methods is difficult. With the continuous development of optimization methods, some methods for solving multi-parameter and multi-objective optimization problems have emerged, such as the particle swarm optimization (PSO) algorithm [18] and genetic algorithm (GA) [19]. However, these algorithms are unstable and experience some problems, such as limited search efficiency and local optimality.…”