Proxy-based Bayesian inversion of strain tensor data measured during well tests

Roudini, Soheil; Murdoch, Lawrence C.; Shojaei, Mohammad; DeWolf, Scott

doi:10.1016/j.gete.2023.100506

Title: Proxy-based Bayesian inversion of strain tensor data measured during well tests

Journal Article · 22 September 2023 · Geomechanics for Energy and the Environment

DOI: https://doi.org/10.1016/j.gete.2023.100506 · OSTI ID:2371231

^[1];

^[2]; Shojaei, Mohammad ^[3]; DeWolf, Scott ^[2]

Clemson University, SC (United States); Clemson University
Clemson University, SC (United States)
Shahid Bahonar University of Kerman (Iran)

Recent instrument developments have made it possible to measure the strain tensor caused by injecting or pumping fluid from aquifers or reservoirs, but the full value of these data is limited because the long runtimes of poroelastic forward models makes it impractical to use many inversion schemes. This limits the interpretation of strain data for managing the recovery of resources or storage of wastes in the subsurface. This paper describes a method of inverting deformation data using a poroelastic numerical simulator so the results can be used to manage reservoirs or aquifers. We developed a workflow designed to reduce the number of simulations sufficiently to make it feasible to use DREAMzs, an advanced Bayesian inversion method that translates the uncertainties from different sources into unbiased posterior parameter distributions and uncertainty envelopes around the field data. Using a KNN proxy model for the poroelastic simulator is key to reducing the overall computations, and the workflow includes a strategy for ensuring the proxy model results converge on the results from the simulator. The workflow is tested using an idealized example that verifies the ability to correctly identify parameters and characterize noise used to perturb the data. Field data from an injection test at an oil reservoir near Tulsa, Oklahoma, are also used to evaluate the efficacy of the workflow with a real dataset. The workflow identified 265 history matching solutions out of 1240 total simulation runs (21% acceptance ratio), where the results were used to characterize posterior parameter distribution and evaluate the prediction uncertainty. Furthermore, this workflow is significant because it enables strain tensor, or other geomechanical measurements to be interpreted to guide decision-making during energy and environmental processes in the subsurface.

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Research Organization:: Clemson University, SC (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Office

Grant/Contract Number:: EE0007080; FE0028292

OSTI ID:: 2371231

Journal Information:: Geomechanics for Energy and the Environment, Journal Name: Geomechanics for Energy and the Environment Vol. 36; ISSN 2352-3808

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Title: Proxy-based Bayesian inversion of strain tensor data measured during well tests

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