The electronic storage of geological and geophysical data collected during the entire period of operation of the gas storage facilities ensures the efficiency of their detailed analysis and use in solving production problems. At present, an urgent task is to develop software and/or software solutions to improve the efficiency of geological and geophysical information analysis. Therefore, appropriate information and software has been developed for the accumulation, verification, correction, and analysis of geological and geophysical information. It is intended for the automated solution of various geological and technological tasks using personal computers by means of prompt processing, systematization, accumulation of geophysical data, graphical and documented display of this information.
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Dell'Aversana, P. (2024). Reservoir geophysical monitoring supported by artificial general intelligence and Q-Learning for oil production optimization. AIMS Geosciences, vol. 10 (3), pp. 641-661. DOI: https://doi:10.3934/geosci.2024033
Guo, Zh., Wu, X., Liang, L., Sheng, H., Chen, N., & Bi, Zh. (2024). Cross-Domain Foundation Model Adaptation: Pioneering Computer Vision Models for Geophysical Data Analysis. ArXiv: 2408.12396v1. https://arxiv.org/html/2408.12396v1
Lai, J., Su, Y., Xiao, L., Zhao, F. et al. (2024). Application of geophysical well logs in solving geologic issues: Past, present and future prospect. Geoscience Frontiers, vol. 15 (no. 3). DOI: https://doi.org/10.1016/j.gsf.2024.101779
Lv, A., Cheng, L., Aghighi, M. A., Masoumi, H., & Roshan, H. (2021). A novel workflow based on physics-informed machine learning to determine the permeability profile of fractured coal seams using downhole geophysical logs. Marine and Petroleum Geology, vol. 131 (105171). DOI: https://doi.org/10.1016/j.marpetgeo.2021.105171
Mishra, A., Sharma, A., & Patidar, A. K. (2022). Evaluation and Development of a Predictive Model for Geophysical Well Log Data Analysis and Reservoir Characterization: Machine Learning Applications to Lithology Prediction. Natural Resources Research, vol. 31, pp. 3195-3222. DOI: https://doi.org/10.1007/s11053-022-10121-z
Zhang, H., Wu, W., & Wu, H. (2023). TOC prediction using a gradient boosting decision tree method: A case study of shale reservoirs in Qinshui Basin. Geoenergy Science and Engineering, 221. DOI: https://doi.org/10.1016/j.petrol.2022.111271