The deployment of fiber-optic-based distributed temperature sensing (DTS) in hydraulically fractured wells has enabled us to observe the dynamic temperature profile along the wellbore during treatment, flow back and production not only as a postprocessing step but also in real-time monitoring applications of the hydraulic fracturing process. Fracture initiation points, vertical coverage and number of created fractures can be identified by DTS data. However, to evaluate the well performance, optimize future treatments and better understand fracture modeling, additional accurate quantitative information such as fracture conductivity and geometries need to be inferred from DTS data. In this study, we propose to set up a stochastic inverse problem to infer hydraulic fracture characteristics such as fracture conductivity and geometries by integrating real-time DTS monitoring data. We develop a synthetic non-isothermal simulation model containing a horizontal well with multi-stage transverse hydraulic fractures amenable for realist real-time DTS data. We also provide a comprehensive understanding of the effectiveness of different fracture and reservoir parameters in the monitored temperature data by means of sensitivity analysis. To estimate the hydraulic fracture characteristics, we employ the ensemble Kalman filter (EnKF), an ensemble based sequential model updating method, to assimilate DTS data. The EnKF enables us to perform quantitative fracture characterization and automatic history matching. The EnKF also offers several advantages for this application, including the ensemble formulation for uncertainty assessment, convenient gradient-free implementation, and the flexibility to incorporate additional monitoring data types.

Examples are presented to illustrate the suitability of the EnKF-based fracture characterization for the inversion of DTS data to infer fracture geometries and conductivity. We demonstrate that by means of the EnKF we can identify accurately fracture halflength and fracture permeability from temperature inversion.

You can access this article if you purchase or spend a download.