Research on lithology identification method based on mechanical specific energy principle and machine learning theory

• Published in: Expert systems with applications 2022-03, Vol.189, p.116142, Article 116142
• Main Authors: Liang, Haibo, Chen, Haifeng, Guo, Jinhong, Bai, Jing, Jiang, Yingjun
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Coring; Drilling machines (tools); Identification methods; Learning theory; Lithology; Lithology recognition; Machine learning; Mechanical specific energy; Model accuracy; Optimization; Recognition; Response time; Simulated annealing; Simulation; Specific energy; Support vector machines; Technical services
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2021.116142

•Applying MSE to the field of lithology discrimination.•Designing a lithology recognition model based on SA optimized SVM algorithm.•Forming a set of lithology recognition methods based on machine learning.•Establishing a lithology intelligent recognition model. Lithology identification is an important part of petroleum drilling engineering. Accurate identification of lithology is the foundation to ensure the smooth operation of drilling engineering. Conventional lithology recognition mainly relies on human experience. The recognition accuracy depends on the level of technical personnel and the recognition response time is lagging. It is difficult to meet the demand. How to achieve rapid and intelligent recognition of lithology is one of the core technical problems faced by oil drilling. In order to solve this problem, this paper uses the mechanical specific energy and other ground parameters to establish a rapid and intelligent recognition method of lithology based on the simulated annealing optimization support vector machine model. In order to improve the accuracy of the model recognition, a large number of methods have been developed from two classes and multiple classes, simulation analysis results show that the lithology recognition model based on the principle of mechanical specific energy and the simulated annealing optimization support vector machine algorithm can predict a priori unknown data with a prediction accuracy of over 90%. Compared with the support vector machine model and the K-means model, simulated annealing optimization support vector machine is used for comparative analysis, the algorithm to establish a lithology recognition model has better performance and higher accuracy. The intelligent identification model of lithology based on the principle of mechanical specific energy and simulated annealing optimization support vector machine algorithm established in this paper can quickly and accurately identify lithology, and provide new technical support for oil drilling formation analysis.