Enhancing Horizontal Drilling Efficiency with Particle Swarm Optimization Algorithm

Abstract The objective of this study is to optimize the trajectory of horizontal drilling in the Groningen field using Particle Swarm Optimization (PSO) and ultra-deep resistivity data. This approach aims to enhance hydrocarbon recovery by accurately navigating complex reservoir structures and maximizing hydrocarbon saturation along the wellbore path. The methodology integrates pre-drilling and real-time optimization stages. Initially, porosity and clay content data from a 3D reservoir model are combined with resistivity measurements for trajectory planning. During drilling, the trajectory is updated based on real-time measurements from Logging While Drilling (LWD) tools and PSO algorithms. Specifically, a variant of PSO with adaptive inertia and constriction coefficients is employed, allowing iterative adjustment of trajectory parameters such as azimuth and inclination in response to nearby resistivity data. These adjustments minimize the risk of intersecting water-bearing formations. Additionally, kriging and Monte Carlo methods were used to quantify uncertainty by generating multiple hydrocarbon saturation scenarios. Three probabilistic trajectories were generated and evaluated based on cumulative hydrocarbon (HC) saturation at different quantiles (P0, P50, P100). The high-quantile trajectory increased cumulative HC saturation by 46.4% compared to the baseline path, while maintaining optimal spatial alignment within the most hydrocarbon-saturated reservoir layers. The PSO algorithm exhibited high efficiency in processing real-time data, demonstrating rapid convergence and effective trajectory refinement based on resistivity signals. By leveraging ultra-deep resistivity tools, this approach successfully maximized contact with hydrocarbon zones while avoiding water-saturated layers, resulting in substantial improvements in well productivity and drilling accuracy. This work presents a novel application of PSO with real-time resistivity data to dynamically optimize well trajectory, improving HC saturation outcomes by 46.4% over initial trajectories. This adaptive approach introduces new insights into utilizing resistivity and reservoir models for real-time trajectory correction, contributing to enhanced field development strategies in complex geological formations.