LAUSR

Abstract Drilling is an important means of obtaining resources. It is important to determine appropriate drilling states adjustment priority to guide operation of the drilling. However, the priority of drilling states adjustment is difficult to determine because of the influence of multiple parameters. In this paper, a priority comprehensive evaluation method is developed to solve this problem. Firstly, support vector regression (SVR) method and long short-term memory (LSTM) neural network are introduced to build rate of penetration (ROP) prediction model and mud pit volume (MPV) prediction model, respectively. Then, the comprehensive evaluation vector is obtained by fuzzy comprehensive evaluation method based on analysis of formation drillability, rock characteristic, pump pressure variation, ROP and MPV fluctuations. Finally, the drilling states adjustment priority is determined by the principle of maximum membership and comprehensive analysis method. The simulation based on actual drilling data indicates that the proposed method can determine the adjustment priority and guide the operation of the drilling process.