Design and Application of Well Logging Based on Artificial Intelligence

Logging design is a prerequisite for the smooth progress of logging operations, and selecting appropriate, advanced, cost-effective, and safe well logging projects and instruments is crucial for swiftly addressing geological and engineering challenges in target wells. Collect historical logging data from 150 wells in China Seas to serve as training sample data. Extract various engineering attributes including lithology, formation temperature and pressure, well type, and reservoir porosity and permeability. Develop real-time decision models for well logging series and projects, leveraging machine learning techniques including decision trees, random forests, and vector inference machines. Collect sample data on the appliwireline conditions, operation records, and service prices of 116 types of wireline logging instruments and 38 types of logging-while-drilling instruments from COSL, Schlumberger, Baker Hughes, and Halliburton companies that have been serving in the Chinese Sea for a long time. Establish an optimization model that matches well logging instruments with geological characteristics using filtering algorithm. Simultaneously utilizing the Recurrent Neural Network (RNN) algorithm for job risk analysis using tension simulation. The algorithm has been applied in 24 wells in 4 oil fields in China Seas, including high-temperature and high-pressure wells, high inclination wells, pre-exploration wells. The effective application effect has enhanced the timeliness and scientificity of pre-logging design, providing a novel approach for intelligent logging operations.