An Inteligent Rate of Penetration Prediction Method Based on Lithology and LWD data

Rate of penetration (ROP) is an essential parameter in the drilling process, which directly affects the efficiency of drilling and the cost of drilling. With so much competition in the oil and gas industry today, ROP forecasting is becoming a major concern for the oil and gas industry's future. ROP has many influencing factors, but most studies focus on the correlation between engineering parameters and ROP while ignoring the influence of formation lithology on ROP. To solve this problem, this paper investigates the correlation between geological information and ROP from lithology and well logging data, and achieves a more accurate ROP prediction by combining it with engineering parameters. In terms of the prediction model, this paper compares the traditional statistics-based method with the intelligent approach based on neural networks. Through quantitative evaluation and analysis, the intelligent method shows more accuracy and reliability than the conventional method in predicting ROP.