Enhancing Real-Time Drilling Efficiency: Mechanism of ROP Prediction Models and Novel Optimization Strategies in Chinese Oilfields

Abstract Drilling parameter optimization constitutes a pivotal technique for expediting the intricate processes involved in oil and gas drilling operations. Presently, optimization strategies predominantly hinge on statistical data analysis or data-driven models and optimization algorithms. However, these methods often overlook the real-time changes occurring in the downhole rock-breaking environment. Additionally, achieving high precision with data-driven rate of penetration (ROP) models proves challenging, as they may struggle to accurately reflect the intricate mechanisms involved in rock-breaking. Moreover, the inherent randomness and uncertainty associated with optimization algorithms pose difficulties in ensuring the consistent acceleration of the drilling process. This study introduces areal-time targets-based intelligent optimization and decision-making system tailored specifically for field drilling operations.The system workflow comprises data preprocessing, predictive optimization, and interpretive decision-making stages. In the crucial modeling and prediction phase, we developed a highly accurate ROP prediction model using advanced machine learning techniques and mechanistic empirical constraints. By employing three model updating mechanisms alongside a 3D heat map visualization technique, the most robust current ROP model can be selected, thereby enhancing the adaptability of the model to dynamic downhole environment. This process establishes a solid foundation for achieving specific ROP targets. In the optimization decision stage, this work proposes the optimal path decision process with specific target parameter optimization and small parameter fluctuation. The process begins by determining the ROP enhancement target for the next stage based on the average ROP of the current formation. Subsequently, the eligible combinations of drilling parameters undergo a preliminary screening. Afterwards, the optimal path decision-making process is informed by the consideration of parameter fluctuation variance and mechanical specific energy as a combined evaluation indicator. This approach ensures that drilling parameters are adjusted with utmost efficiency, all while meeting the predetermined ROP enhancement target. Moreover, it facilitates stable speed enhancement, minimizes drilling parameter fluctuations, and effectively manages complex working conditions. The application of the system in the Huizhouoilfield in China demonstrates the stability and effectiveness of the method. The optimized drilling parameters exhibit low fluctuations, with a maximum reductionin parameter variance of 63.8%compared to the original tuned parameters, ensuring robust real-time operability. Notably, the method achieves asmooth speed-up of 15% to 25%, while minimizing mechanical specific energy by a maximum of 19% and reducing bit energy loss. This study contributes novel insights into real-time drilling parameter optimization, with a dynamic adaptation mechanism poised to incorporate emerging drilling data streams.