Autonomous Inversion of In Situ Deformation Measurement Data for CO2 Storage Decision Support

TL;DR

This paper presents a flexible, autonomous inversion method for geomechanical monitoring data that improves the estimation of stress changes during CO2 storage, integrating physics-based and machine learning models.

Contribution

It introduces a gradient-based deterministic inversion approach that autonomously updates geomechanical models using real-time monitoring data, adaptable to nonlinear and data-driven constitutive models.

Findings

Enables real-time validation of geomechanical models.

Supports integration of machine learning models with physics-based simulations.

Improves accuracy of stress change estimates during CO2 injection.

Abstract

Current methods of estimating the change in stress caused by injecting fluid into subsurface formations require choosing the type of constitutive model and the model parameters based on core, log, and geophysical data during the characterization phase, with little feedback from operational observations to validate or refine these choices. It is shown that errors in the assumed constitutive response, even when informed by laboratory tests on core samples, are likely to be common, large, and underestimate the magnitude of stress change caused by injection. Recent advances in borehole-based strain instruments and borehole and surface-based tilt and displacement instruments have now enabled monitoring of the deformation of the storage system throughout its operational lifespan. This data can enable validation and refinement of the knowledge of the geomechanical properties and state of the system, but brings with it a challenge to transform the raw data into actionable knowledge. We demonstrate a method to perform a gradient-based deterministic inversion of geomechanical monitoring data. This approach allows autonomous integration of the instrument data without the need for time consuming manual interpretation and selection of updated model parameters. The approach presented is very flexible as to what type of geomechanical constitutive response can be used. The approach is easily adaptable to nonlinear physics-based constitutive models to account for common rock behaviors such as creep and plasticity. The approach also enables training of machine learning-based constitutive models by allowing back propagation of errors through the finite element calculations. This enables strongly enforcing known physics, such as conservation of momentum and continuity, while allowing data-driven models to learn the truly unknown physics such as the constitutive or petrophysical responses.