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.
