Prospective Interest of Deep Learning for Hydrological Inference

Abstract : Fractured bedrock reservoirs are of socio-economical importance, as they may be used for storage or retrieval of fluids and energy. In particular, the hydromechanical behavior of fractures needs to be understood as it has implications on flow and governs stability issues (e.g. microseismicity). Laboratory, numerical or field experiments have brought considerable insights to this topic. Nevertheless, in situ hydromechanical experiments are relatively uncommon, mainly because of technical and instrumental limitations. Here, we present the early stage development and validation of a novel approach aiming at capturing the integrated hydromechanical behavior of natural fractures. It combines the use of surface tiltmeters to monitor the deformation associated with the periodic pressurization of fractures at depth in crystalline rocks. Periodic injection and withdrawal advantageously avoids mobilizing or extracting significant amounts of fluid and it hinders any risk of reservoir failure. The oscillatory perturbation is intended to: 1) facilitate the recognition of its signature in tilt measurements; 2) vary the hydraulic penetration depth in order to sample different volumes of the fractured bedrock around the inlet, and thereby assess scale effects typical of fractured systems. By stacking tilt signals, we managed to recover small tilt amplitudes associated with pressure-derived fracture deformation. Therewith, we distinguish differences in mechanical properties between the three tested fractures but we show that tilt amplitudes are weakly dependent on pressure penetration depth. Using an elastic model, we obtain fracture stiffness estimates that are consistent with published data. Our results should encourage further improvement of the method.
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Groundwater, Wiley, 2017, 〈10.1111/gwat.12557〉
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Soumis le : lundi 21 août 2017 - 14:12:55
Dernière modification le : jeudi 11 janvier 2018 - 06:24:06

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Jean Marçais, Jean-Raynald De Dreuzy. Prospective Interest of Deep Learning for Hydrological Inference. Groundwater, Wiley, 2017, 〈10.1111/gwat.12557〉. 〈insu-01574652〉

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