Accelerated Inversion Workflow Coupling Deep Learning and ES-MDA for DTS Data: A Case Study of Horizontal Well in Middle East Carbonate Reservoir
Authors: Deng Rui , Zhang Liang , Wang Lian , Zhao Xing , Kang Bo , Wang Hehua , Xu Bing , Jiang Liping , Deng Qi 
Published: October 2024

Distributed temperature sensing (DTS) has been widely adopted in the real-time well monitoring of horizontal wells in Middle East carbonate reservoirs, efficiently inversing DTS data plays an essential role in identifying key water injection channels, capitalizing on residual oil reserves, and accurately forecasting production metrics. Meanwhile, in the interpretation of temperature profiles from DTS data, the flow-thermal coupled numerical models serve as the forward model while the inversion algorithms are utilized to obtain reservoir and production parameters. Unfortunately, the inversion algorithms developed so far lack robustness and efficiency when facing an extensive set of parameters and computationally expensive forward models. To address this issue, an accelerated interpretation workflow coupling deep learning (DL) and ensemble smoother with multiple data assimilation (ES-MDA) called DL-ESMDA is proposed for efficient inversion of the temperature profiles from DTS to interpret parameter sets along the wellbore direction including reservoir permeability, water saturation, and grid well indices. The proposed DL-ESMDA workflow couples deep learning (DL) with ES-MDA to efficiently solve the inverse problem of DTS temperature profiles, even when faced with extensive parameters and a computationally expensive forward model. Specifically, the Long Short-Term Memory auto-encoder (LSTMAE) technique is adopted to adeptly reduce the dimensionality of the parameter space by converting interpretation parameter sets into considerably more compact latent vector representations. On this account, the assimilation process is carried out within the latent variable space of the neural network rather than in the traditional parameter space of the forward model which significantly improves its efficiency and robustness. Subsequently, the ES-MDA strategy is applied to renew latent variables through assimilating DTS temperature profiles into the forward model. Furthermore, a case study is executed on a horizontal well in the Middle East carbonate reservoir to validate the excellent performance of the DL-ESMDA workflow. The results indicate that compared to commercial software, the relative error of the DL-ESMDA workflow in interpreting production profiles is only 3.46% which validates its effectiveness and reliability. Additionally, a series of comparison analyses among the DL-ESMDA method against the conventional ES-MDA method, the LSTMAE-PSO (Particle Swarm Optimization) method and the LSTMAE-SA (Simulated Annealing) are conducted to demonstrate the remarkable enhancements in the inversion efficiency of the DL-ESMDA workflow. Under the same computational resources, the temperature profile matching accuracy of the DL-ESMDA method was about 11.3%, and 5.8% higher than the other two methods, respectively. The DL-ESMDA workflow achieves the goal of robustness and efficient interpreting parameter sets along the wellbore direction including reservoir permeability, water saturation, and grid well indices through the inversion of DTS data. Moreover, the accurate interpretation results obtained by the DL-ESMAD method could significantly contribute to a more efficacious and precise evaluation and monitoring of horizontal well performance that bears considerable significance for the optimized exploitation of water-flooding carbonate reservoirs with horizontal wells.