Design of a Private Cloud Platform for Distributed Logging Big Data Based on a Unified Learning Model of Physics and Data
Cheng X. Fu H. Mahabbat T.
June 2025Chinese Geophysical Society
Applied Geophysics
2025#22Issue 2499 - 510 pp.
Well logging technology has accumulated a large amount of historical data through four generations of technological development, which forms the basis of well logging big data and digital assets. However, the value of these data has not been well stored, managed and mined. With the development of cloud computing technology, it provides a rare development opportunity for logging big data private cloud. The traditional petrophysical evaluation and interpretation model has encountered great challenges in the face of new evaluation objects. The solution research of logging big data distributed storage, processing and learning functions integrated in logging big data private cloud has not been carried out yet. To establish a distributed logging big - data private cloud platform centered on a unified learning model, which achieves the distributed storage and processing of logging big data and facilitates the learning of novel knowledge patterns via the unified logging learning model integrating physical simulation and data models in a large - scale functional space, thus resolving the geo - engineering evaluation problem of geothermal fields. Based on the research idea of “logging big data cloud platform—unified logging learning model—large function space—knowledge learning & discovery—application”, the theoretical foundation of unified learning model, cloud platform architecture, data storage and learning algorithm, arithmetic power allocation and platform monitoring, platform stability, data security, etc. have been carried on analysis. The designed logging big data cloud platform realizes parallel distributed storage and processing of data and learning algorithms. The feasibility of constructing a well logging big data cloud platform based on a unified learning model of physics and data is analyzed in terms of the structure, ecology, management and security of the cloud platform. The case study shows that the logging big data cloud platform has obvious technical advantages over traditional logging evaluation methods in terms of knowledge discovery method, data software and results sharing, accuracy, speed and complexity.
logging big data private cloud , machine learning , Unified logging learning model
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College of Earth Science and Engineering, Xi’an Shiyou University, Shaanxi, Xi’an, 710065, China
State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Sichuan, Chengdu, 610500, China
Academician Expert Workstation, Xi’an Shiyou University, Shaanxi, Xi’an, 710065, China
Petrochina Research Institute of Petroleum Exploration and Development, Beijing, 100083, China
Geology and Oil-gas Business Institute named after K. Turyssov of Satbayev University, Almaty, 050013, Kazakhstan
College of Earth Science and Engineering
State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation
Academician Expert Workstation
Petrochina Research Institute of Petroleum Exploration and Development
Geology and Oil-gas Business Institute named after K. Turyssov of Satbayev University
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