Logging data has the characteristics of diverse types, strong professionalism, and complex data operations. Its use runs through the entire life cycle of oil and gas exploration and development, and is a key basic data for understanding and evaluating oil and gas reservoirs. The large-scale application of cloud computing, big data, and artificial intelligence technology today has once again promoted the development of intelligent logging interpretation. Based on a multi-disciplinary data fusion data lake, intelligent interpretation modules are developed on the basis of professional software, introducing intelligent interpretation of logging into traditional interpretation processes to assist logging analysts in quickly mining hidden high-value information. At present, oil and gas field enterprises are facing dual pressures of low oil prices and inferior resources. In the middle and later stages of oilfield development, in order to further tap into the potential of oil reservoirs, complex re-evaluation of old wells may become the focus of work in the following period. Digitization and intelligence have become important means for enterprises to reduce costs, increase efficiency, and achieve high-quality development. This article delves into conventional logging information, using standard logging big data and regional core analysis and laboratory data to intelligently generate single well interpretation result maps, four property relationship maps, plan maps, and continuous well profile maps. It completes a series of intelligent logging evaluations from point to surface to body, forming an intelligent logging interpretation work mode in geological and oil reservoir backgrounds. Develop an intelligent logging evaluation method to promote the company’s intelligent logging evaluation process.