Stochastic Representation and Uncertainty Assessment of a Deep Geothermal Reservoir Using Cross-Borehole ERT: A 3D Synthetic Case

Designing and monitoring of geothermal systems is a complex task which requires a multidisciplinary approach. Deep geothermal reservoir models are prone to greater uncertainty, with a lack of direct data and lower resolution of surface geophysical methods. However, recent technical advances have enabled the potential use of permanent downhole vertical resistivity arrays for monitoring fluid injection. As electrical resistivity is sensitive to temperature changes, such data could provide valuable information for deep geothermal reservoir characterization.

The objective of this study is to assess the potential of time-lapse cross-borehole ERT to constrain 3D realizations of geothermal reservoir properties. The synthetic case of a permeable geothermal reservoir in a sedimentary basin was set up, as a confined deep and saline sandstone aquifer with intermediate reservoir temperatures (150ºC), depth (1 km) and 30m thickness. The reservoir permeability distribution is heterogeneous, as the result of a fluvial depositional environment. The ERT monitoring system design is a triangular arrangement of 3 wells at 150 m spacing, including 1 injection and 1 extraction well. The optimal number and spacing of electrodes of the ERT array design is site-specific and has been assessed through a sensibility study. Dipole-dipole and pole-pole electrode configurations were used.

The study workflow was the following: 1) Generation of a reference reservoir model and 100 stochastic realizations of permeability; 2) Simulation of saturated single-phase flow and heat transport of reinjection of cooled formation fluid (50ºC) with TOUGH2 software; 3) Time-lapse forward ERT modeling on the reference model and all realizations (observed and simulated apparent resistivity change); 4) heuristic optimization on ERT computed and calculated data. Preliminary results show significant reduction of parameter uncertainty, hence realization space, with assimilation of cross-borehole ERT data. Loss in sensitivity of ERT between boreholes is compensated here by the stochastic modeling approach, rather than using a deterministic inversion scheme. Our results suggest stochastic reservoir simulations, together with assimilation of cross-borehole ERT data, could be useful tools for design and monitoring of deep geothermal systems.