Hybrid extreme learning machine for real-time rate of penetration prediction

This study presents a comparative analysis of hybrid Extreme Learning Machine (ELM) models optimized with metaheuristic algorithms Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Grey Wolf Optimizer (GWO) for real-time Rate of Penetration (ROP) prediction in drilling operations. The study aims to improve ROP prediction accuracy and computational efficiency while addressing the challenges of real-time implementation in dynamic drilling environments. The methodology involves a formation-specific modelling approach, where separate ELM models are trained for each formation using surface drilling parameters such as weight on bit (WOB), rotary speed (RPM), and flow rate. Metaheuristic algorithms optimize the ELM’s weights and biases to improve predictive performance. The models were trained and validated using a dataset of 13,262 data points from an Algerian field, with different statistical metrics used for evaluation. Sensitivity analysis using SHapley Additive exPlanations (SHAP) identified drilling torque and standpipe pressure as key ROP influencers. Results indicate that all hybrid models outperform standalone ELM, with ELM-GWO achieving the highest accuracy and fastest convergence. The real-time modelling framework, incorporating incremental learning and formation-based model recalibration, which ensures robust and adaptive ROP predictions and minimizing reliance on difficult-to-measure parameters such as unconfined compressive strength (UCS). This work contributes to the existing body of literature by introducing a formation-specific and real-time hybrid ELM modelling approach, demonstrating its potential for ROP optimization and cost reduction in petroleum drilling.