Spatiotemporal prediction of two-dimensional surface deformation in gas field incorporating MSBAS-InSAR and deep learning

Abstract

Continuous extraction-induced deformation from oil and gas fields can trigger destructive geological hazards, significantly threatening underground infrastructure safety. Therefore, reliable surface deformation prediction is crucial for early warning of potential risks in oil and gas fields. However, no existing studies focus on spatiotemporal prediction in oil and gas fields. Moreover, previous spatiotemporal prediction studies do not consider the complementary information among multi-directional deformations and their joint prediction. They also ignore the constraint of empirical physical model during data normalization. To address the above issues, this study presents a novel approach for spatiotemporal prediction of two-dimensional (EW and vertical) deformation in oil and gas fields by incorporating the Multi-dimensional Small Baseline Subset InSAR (MSBAS-InSAR) method and deep learning. Specifically, we propose a dual-branch coupled encoder-decoder spatiotemporal prediction model enhanced with a Multi-Scale Channel-Spatial Attention (MSCA) mechanism, which jointly learns from both deformation components. Additionally, we introduce a function-fitting normalization strategy based on a time decay model to better conform with geomechanical laws and improve prediction stability. In this paper, we tested the proposed model separately on three gas reservoirs within the Sebei gas field. Experimental results demonstrate that the proposed model achieves smaller errors and better predictive performance across the three gas reservoirs compared to several baseline models, indicating its generalization capability in different reservoir deformation scenarios. Ablation studies further confirm the soundness of the model design. This study offers a generalizable framework for predicting diverse reservoir deformation scenarios, helping to ensure safe production and early geohazard prevention.