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Knowledge sharing-based multi-block federated learning for few-shot oil layer identification

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  • Chen, Bingyang
  • Zeng, Xingjie
  • Zhang, Weishan
  • Fan, Lulu
  • Cao, Shaohua
  • Zhou, Jiehan

Abstract

Oil Layer Identification (OLI) plays a significant role in petroleum development, but is unavoidably negatively affected by small samples, non-reservoirs interference, and layer classes imbalance. Existing transfer learning methods partially address the problem of insufficient samples in OLI. Nevertheless, they ignore the geological differences between blocks. This paper introduces Multi-Block Federated Learning (MBFL) to train a generalized pre-trained model, which consists of a Mask Attention Network (MAN), Class Balance Module (CBM), and Dynamic Weighted Fusion (DWF). MBFL uses MAN to dynamically avoid non-reservoir interference while learning the relationship between reservoirs and non-reservoirs; MBFL uses CBM to overcome layer classes imbalance and uses DWF to optimize traditional federated learning for addressing geological differences among blocks. Experimental results demonstrate that MBFL achieves an average accuracy of 89.32%, and an average F1 score of 81.74% with a real-world dataset. The results of two public datasets demonstrate the generalization of MBFL.

Suggested Citation

  • Chen, Bingyang & Zeng, Xingjie & Zhang, Weishan & Fan, Lulu & Cao, Shaohua & Zhou, Jiehan, 2023. "Knowledge sharing-based multi-block federated learning for few-shot oil layer identification," Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s0360544223018005
    DOI: 10.1016/j.energy.2023.128406
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