Predicting Drill Bit Footage Method Based on PSO-WNN

ABSTRACT: The drill bit serves as a critical downhole component that directly facilitates rock fragmentation during drilling operations. Premature bit failure and excessive wear can significantly compromise rock-breaking efficiency, adversely impacting drilling cycle duration and operational costs. Accurate prediction of bit footage proves essential for optimizing resource allocation, minimizing non-productive time, and enhancing well construction efficiency through informed tripping decisions. While conventional methodologies rely on empirical formulae and statistical analysis of historical data to establish predictive models, these approaches are inherently constrained by substantial subjectivity and limited predictive accuracy. This study proposes a novel hybrid prediction framework integrating Particle Swarm Optimization (PSO) with Wavelet Neural Networks (WNN) to address these limitations. The methodology initiates with rigorous data preprocessing of historical drilling datasets, incorporating outlier detection and noise reduction protocols to ensure data integrity. Subsequently, wavelet transformation is employed for multi-scale feature extraction, followed by the construction of a multi-layer WNN architecture to capture nonlinear relationships within the drilling parameters. The PSO is systematically implemented to optimize network topology and hyperparameters, thereby enhancing model convergence efficiency while reducing manual tuning requirements. Experimental validation demonstrates superior performance metrics, with the optimized model achieving a determination coefficient (R2) of 0.9619 and a mean absolute percent error (MAPE) of 11.01%. Comparative analysis with benchmark methods including random forest and fully connected neural networks confirms a 23.6% improvement in prediction accuracy. For practical implementation, the developed algorithm has been modularized into a software interface and successfully integrated into an intelligent drilling navigation system. Field deployment in Xinjiang Oilfield has yielded an 83.75% prediction accuracy across eight monitored wells, substantiating its operational applicability in real-world drilling scenarios. This research contributes to safer and more efficient well construction through data-driven drilling optimization.