Moreno, Jose A (2014-08). Implementation of the Ensemble Kalman Filter in the Characterization of Hydraulic Fractures in Shale Gas Reservoirs by Integrating Downhole Temperature Sensing Technology. Master's Thesis.
Thesis
Multi-stage hydraulic fracturing in horizontal wells has demonstrated successful results for developing unconventional low-permeability oil and gas reservoirs. Despite being vastly implemented by different operators across North America, hydraulic fracture treatments are still not fully comprehended and have proved to have a more complex behavior than initially thought. To fill this knowledge gap and monitor the performance of hydraulic fracture treatments, fracture diagnostic tools are deployed in order to obtain information that can give a better insight of the reservoir and hydraulic fracture conditions. A technique that has demonstrated great potential in the monitoring of hydraulic fracture treatments is distributed temperature sensing technology. In situations where pressure and/or flow rate data is not reliable or in conflict with the known physics of the reservoir, continuous temperature data can be used as an alternative source of information since it effectively responds to pressure or flow rate changes when looked at a finer scale. Qualitative information such as fracture initiation points, vertical coverage and number of created fractures can be identified via distributed temperature sensors however; more quantitative results are needed in order to accurately characterize hydraulic fractures in shale gas reservoirs. In this study, a stochastic inverse problem is set up with the objective of inferring hydraulic fracture characteristics, such as fracture half-length and permeability, by assimilating data from downhole temperature sensors. The ensemble Kalman filter is implemented to assimilate DTS data and estimate fracture parameters. This inverse method is suitable for applications to non-linear assimilation problems and is, by nature, an appropriate approach for monitoring. In this way, the ensemble Kalman filter enables a quantitative fracture characterization and automatic history matching. Furthermore, the EnKF 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. The validity of the method is examined using synthetic models, and finally, field data from a horizontal gas well in the Marcellus shale.
