Pengwei Zhang , Jiadong Ren , Fengda Zhao , Xianshan Li , Haitao He , Yufeng Jia , Xiaoqing Shao
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MS-CGAN: Fusion of conditional generative adversarial networks and multi-scale spatio-temporal features for lithology identification
Lithology identification constitutes a crucial undertaking in formation evaluation and reservoir characterization. However, the need for improved precision arises in conventional lithology identification models due to the difficulties presented by unequal distributions of small-sample logging data. An effective combination of domain expertise and data-driven models to predict lithology is essential due to the intricate and nonlinear connection between logging parameters and lithology, combined with the distinct characteristics of the oilfield environments. In this paper, we proposed a multi-scale conditional generative adversarial network(MS-CGAN) method, which combines conditional generative adversarial networks with multi-scale spatio-temporal features to address data imbalance issues and enhance the accuracy of lithology classification. Our approach, tested on two small datasets from the Hugoton and Panoma fields, USA, and the Daqing production wells, China, stands out as the optimal choice compared to other models. Comprehensive evaluation results indicate promising practical applications and potential benefits of the new model in enhancing lithology identification using limited data.
期刊介绍:
The Journal of Applied Geophysics with its key objective of responding to pertinent and timely needs, places particular emphasis on methodological developments and innovative applications of geophysical techniques for addressing environmental, engineering, and hydrological problems. Related topical research in exploration geophysics and in soil and rock physics is also covered by the Journal of Applied Geophysics.