Inference of permeability heterogeneity from joint inversion of transient flow and temperature data

Characterization of the rock permeability distribution in compartmentalized deep aquifers, enhanced geothermal systems, and hydrocarbon reservoirs is important for predicting the flow and transport behavior in these formations. Reliable prediction of the fluid flow and transport processes can, in turn, lead to effective development of the subsurface energy and environmental resources. In deep formations where thermal gradients are significant, the transient temperature data can provide valuable information about the permeability distribution with depth and about the vertical fluid displacement. This paper examines the importance of temperature data in resolving the distribution of permeability with depth by jointly, and individually, integrating the transient temperature and flow data. We demonstrate that when estimating permeability distributions in deep geothermal reservoirs, incorporating temperature data can increase the resolution of the permeability distribution profile with depth. To illustrate the importance of temperature measurements, we adopt a coupled transient heat and fluid flow as a forward model to predict the heat and fluid transport in a geothermal reservoir and develop an adjoint model for efficient computation of the gradient information for model calibration. We perform a series of numerical experiments for integration of flow and pressure data alone, temperature data alone, and flow and pressure jointly with temperature data. In each case, we apply the maximum A-posteriori (MAP) method and the randomized maximum likelihood (RML) method for inversion and uncertainty quantification. Analysis of the sensitivity of temperature and production data to heterogeneous permeability distributions reveals that the temperature of fluid, even when measured at the surface, is sensitive to the permeability distribution in the vertical extent of the reservoir. Hence, temperature measurements can be augmented with flow-related data to enhance the resolution of the estimated permeability field with depth.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000340430400009&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Environmental SciencesLimnologyWater ResourcesSCI(E)EI4ARTICLEbehnam.jafarpour@usc.edu64710-47255