Real-Time Drilling Parameter Optimization Model Based on the Constrained Bayesian Method - Jinbo Song; Jianlong Wang; Bingqing Li; Linlin Gan; Feifei Zhang; Xueying Wang; Qiong Wu (Energies, 2022)
To solve the problems of the low energy efficiency and slow penetration rate of drilling, we took the geological data of adjacent wells, real-time logging data, and downhole engineering parameters as inputs; the mechanical specific energy and unit footage cost as multi-objective optimization functions; and the machine pump equipment limit as the constraint condition. A constrained Bayesian optimization algorithm model was established for the optimization solution, and drilling parameters such as weight-of-bit, revolutions per minute, and flowrate were optimized in real time. Through a comparison with NSGA-II, random search, and other optimization algorithms, and the application results of example wells, we show that the established Bayesian optimization algorithm has a good optimization effect while maintaining timeliness. It is suitable for real-time optimization of drilling parameters, can aid a driller in identifying the drilling rate and potential tapping area, and provides a decision-making basis for avoiding the low-efficiency rock-breaking working area and improving rock-breaking efficiency.