Development of a Private Cloud Platform for Distributed Logging Big Data and Its Application to Geo-Engineering Evaluation of Geothermal Fields
Cheng Xi*, Fu Hai-cheng, He Jun
1. College of Earth Science and Engineering, Xi'an Shiyou University, Xi'an, Shaanxi, 710065, China
2. State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Southwest Petroleum University)
3. Academician Expert Workstation, Xi'an Shiyou University, Xi'an, Shaanxi, 710065, China
4. Research Institute of Petroleum Exploration & Development, Beijing 100083, China
5. PetroChina Natural Gas Marketing Shaanxi Company, Xi'an,Shaanxi,710000, China
Abstract：
The development of machine learning and deep learning algorithms as well as the improvement of hardware arithmetic power provide a rare opportunity for logging big data private cloud. With the deepening of exploration and development and the requirements of low-carbon development, the focus of exploration and development in the oil and gas industry is gradually shifting to the exploration and development of renewable energy sources such as deep sea, deep earth and geothermal energy. The traditional petrophysical evaluation and interpretation model has encountered great challenges in the face of new evaluation objects. To establish a distributed logging big data private cloud platform with a unified learning model as the key, which realizes the distributed storage and processing of logging big data, and enables the learning of brand-new knowledge patterns from multi-attribute data in the large function space in the unified logging learning model integrating the expert knowledge and the data model, so as to solve the problem of geoengineering evaluation 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. New knowledge of geothermal evaluation is found in a large function space and applied to Geo-engineering evaluation of geothermal fields. The examples show its good application in the selection of logging series in geothermal fi elds, quality control of logging data, identification of complex lithology in geothermal fi elds, evaluation of reservoir fluids, checking of associated helium, evaluation of cementing quality, evaluation of well-side fractures, and evaluation of geothermal water recharge under the remote logging module of the cloud platform. The first and second cementing surfaces of cemented wells in geothermal fields were evaluated, as well as the development of well-side distal fractures, fracture extension orientation. According to the well-side fracture communication to form a good fluid pathway and large flow rate and long flow diameter of the thermal storage fissure system, the design is conducive to the design of the recharge program of geothermal water.
作者简介: Cheng Xi, Associate Professor, Ph.D. in Engineering, is currently affiliated with Xi'an Shiyou University. He engages in research and teaching activities focused on big data mining, machine learning, artificial intelligence (AI) applications in oil and gas, and artificial intelligence logging (AIL).
. Development of a Private Cloud Platform for Distributed Logging Big Data and Its Application to Geo-Engineering Evaluation of Geothermal Fields[J]. APPLIED GEOPHYSICS, 2025, 22(4): 1205-1219.
2025, Vol. 22 
