Study on Evaluation and Prediction for Shale Gas PDC Bit in Luzhou Block Sichuan Based on BP Neural Network and Bit Structure

Abstract

Deep and ultra-deep shale gas resources have great potential, but well drilling faces many challenges. The Polycrystalline Diamond Compact (PDC) bit has become the primary rock-breaking instrument for oil and gas drilling. Reasonable bit structure designs can promote rock-breaking efficiency and extend service life. In this study, reverse modeling technology is used to analyze the structural characteristics of PDC bits collected in the field, and the influence of the structural characteristics of the bit at a specific interval on the rate of penetration (ROP) and drill footage is investigated using the Spearman rank correlation coefficient method. The number of blades, cutting angle of the cutters, crown rotation radius, internal cone angle, and diameter of the cutters are discovered to be the main structural characteristics that affect the ROP and footage of the bits, and the degree of influence varies depending on the formation conditions. The number of blades, crown rotation radius, inner cone angle, and cutting angle of the cutters have a significant impact on the ROP, whereas blade thickness, gauge length, gauge width, nozzle equivalent diameter have a significant impact on the bit footage. In addition, a back propagation (BP) neural network is utilized to build a prediction model of bit footage and ROP over a certain interval based on the structural characteristics of the bit. The goodness of fit of the model is greater than 85%, and its accuracy is high. Based on the usage of the bit, the evaluation and prediction of the bit can provide a reference for the structural design and optimization of the bit in a specific interval, guide the bit selection work, rationally plan the drilling operation, and reduce the drilling cost.

Keywords

• bit structure
• Spearman rank correlation coefficient
• BP neural network
• ROP prediction