Hamiltonian Monte Carlo Probabilistic Joint Inversion of 2D (2.75D) Gravity and Magnetic Data

Zunino, Andrea; Ghirotto, Alessandro; Armadillo, Egidio; Fichtner, Andreas

doi:10.1029/2022gl099789

Cited by 9 publications

(1 citation statement)

References 55 publications

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“…Krahenbuhl & Li, 2006; Li & Oldenburg, 1998; Maag & Li, 2018; Mosher & Farquharson, 2013). The gravity inversion is ill‐posed, requiring constraints based on prior information and strong regularization to be implemented (Bijani et al., 2017; Zunino et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

Three‐axis borehole gravity monitoring for CO₂ storage using machine learning coupled to fluid flow simulator

Alyousuf

Krahenbuhl

et al. 2023

Geophysical Prospecting

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The field of geophysics faces the daunting task of monitoring complex reservoir dynamics and imaging carbon dioxide storage up to several decades into the future. This presents numerous challenges, including sensitivity to parameter changes, resolution of obtained results, and the cost of long‐term deployment. To effectively store CO2 subsurface, it is necessary to monitor and account for the injected CO2. The gravity method provides several advantages for CO2 monitoring, as changes in fluid saturation correspond directly and uniquely to observed density changes. Three‐axis borehole gravity has demonstrated significant promise as a next‐generation tool for reliably monitoring reservoir dynamics across a range of depths and sizes. However, the gravity inverse problem is highly ill‐posed, necessitating regularization that incorporates prior knowledge. To address this issue, we propose using a feed‐forward neural network, a machine learning (ML) method, to invert time‐lapse three‐axis borehole gravity data and monitor CO2 movement within a reservoir. By training the neural network on models that analyze changes in density and corresponding gravity responses resulting from perturbations made to the reservoir model, we can create scenarios that train the algorithm to identify unexpected CO2 migration in addition to the normal movement of CO2. Our method is demonstrated using reservoir models for the Johansen formation in offshore Norway. We convert reservoir saturation models into density changes and generate their corresponding three‐axis gravity data in a set of boreholes. Our results show that the developed ML inversion algorithm has high reliability and resolution for imaging density change associated with CO2 plumes, as demonstrated in the Johansen reservoir models utilized by the simulator. We also investigate ML inversion using regularization parameters and show that it is robust, with a strong tolerance for higher levels of noise. Our study demonstrates that the developed ML algorithm is a powerful tool for inverting three‐axis borehole gravity data and monitoring the migration and long‐term storage of injected CO2.This article is protected by copyright. All rights reserved

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Section: Introductionmentioning

confidence: 99%

Three‐axis borehole gravity monitoring for CO₂ storage using machine learning coupled to fluid flow simulator

Alyousuf

Krahenbuhl

et al. 2023

Geophysical Prospecting

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HMCLab: a framework for solving diverse geophysical inverse problems using the Hamiltonian Monte Carlo method

Zunino,

Gebraad,

Ghirotto

et al. 2023

Geophysical Journal International

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Summary The use of the probabilistic approach to solve inverse problems is becoming more popular in the geophysical community, thanks to its ability to address nonlinear forward problems and to provide uncertainty quantification. However, such strategy is often tailored to specific applications and therefore there is a need for common platforms to solve different geophysical inverse problems and showing potential and pitfalls of the methodology. In this work, we demonstrate a common framework within which it is possible to solve such inverse problems ranging from, e.g, earthquake source location to potential field data inversion and seismic tomography. This allows us to fully address nonlinear problems and to derive useful information about the subsurface, including uncertainty estimation. This approach, in fact, can provide probabilities related to certain properties or structure of the subsurface, such as histograms of the value of some physical property, the expected volume of buried geological bodies or the probability of having boundaries defining different layers. Thanks to its ability to address high-dimensional problems, the Hamiltonian Monte Carlo (HMC) algorithm has emerged as the state-of-the-art tool for solving geophysical inverse problems within the probabilistic framework. HMC requires the computation of gradients, which can be obtained by adjoint methods. This unique combination of HMC and adjoint methods is what makes the solution of tomographic problems ultimately feasible. These results can be obtained with “HMCLab”, a numerical laboratory for solving a range of different geophysical inverse problems using sampling methods, focusing in particular on the HMC algorithm. HMCLab consists of a set of samplers (HMC and others) and a set of geophysical forward problems. For each problem its misfit function and gradient computation are provided and, in addition, a set of prior models can be combined to inject additional information into the inverse problem. This allows users to experiment with probabilistic inverse problems and also address real-world studies. We show how to solve a selected set of problems within this framework using variants of the HMC algorithm and analyze the results. HMCLab is provided as an open source package written both in Python and Julia, welcoming contributions from the community.

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Cooperative geophysical inversion integrated with 3-D geological modelling in the Boulia region, QLD

Rashidifard,

Giraud,

Lindsay

et al. 2024

Geophysical Journal International

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Summary Reconciling rock unit boundary geometry is crucial for geological and geophysical studies aiming to achieve a comprehensive 3D subsurface model. To create a unified 3D parametrization suitable for both geological modeling and geophysical inversion, an integrated approach utilizing implicit modeling is essential. However, a key challenge lies in encapsulating all pertinent information within the 3D model, ensuring compatibility with the utilized datasets and existing constraints. In this study, we present a workflow that enables the generation of an integrated 3D subsurface model primarily using gravity and reflection seismic datasets. Our approach involves a cooperative geophysical inversion workflow, which incorporates the inverted model from the reflection seismic data while leveraging sparse petrophysical information. Despite advances in integrated modelling, the incorporation of implicit modelling approaches in cooperative inversion workflows remains unexplored. In our gravity inversion process, we employ a generalized level set method to refine the boundaries of rock units in the prior model. We integrate the inverted model, derived from seismic and other sparse petrophysical datasets, to create a comprehensive 3D prior model. To enhance the integration of reflection seismic datasets in the level set inversion, we introduce a weighting uncertainty matrix containing constraint terms. This step refines the model's accuracy and ensures greater consistency. Finally, we search for any missing rock units within inverted model through nucleation investigations. The introduced methodology has undergone successful testing in the Boulia region (Southern Mount Isa, Queensland), utilizing two 2D reflection seismic profiles and regional gravity datasets. This study primarily aims to reconstruct the geometry of major structures within the basement units and the basin at a regional scale. By combining seismic profiles and gravity datasets with constraining information, we are able to create a 3D model of the area that accurately represents distinct rock units and their boundary geometries. Additionally, relevant legacy datasets and prior modeling results from the region have been incorporated and refined, ensuring that the final model aligns with all available knowledge about the area.

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Hamiltonian Monte Carlo Probabilistic Joint Inversion of 2D (2.75D) Gravity and Magnetic Data

Abstract: Potential field data in the form of gravity and magnetic anomalies have been used for long to characterize the structure of the subsurface, ranging from applications to the small scale as in the context of exploration geophysics (e.g.,

Cited by 9 publications

References 55 publications

Three‐axis borehole gravity monitoring for CO₂ storage using machine learning coupled to fluid flow simulator

Three‐axis borehole gravity monitoring for CO₂ storage using machine learning coupled to fluid flow simulator

HMCLab: a framework for solving diverse geophysical inverse problems using the Hamiltonian Monte Carlo method

Cooperative geophysical inversion integrated with 3-D geological modelling in the Boulia region, QLD

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Hamiltonian Monte Carlo Probabilistic Joint Inversion of 2D (2.75D) Gravity and Magnetic Data

Abstract: Potential field data in the form of gravity and magnetic anomalies have been used for long to characterize the structure of the subsurface, ranging from applications to the small scale as in the context of exploration geophysics (e.g.,

Cited by 9 publications

References 55 publications

Three‐axis borehole gravity monitoring for CO2 storage using machine learning coupled to fluid flow simulator

Three‐axis borehole gravity monitoring for CO2 storage using machine learning coupled to fluid flow simulator

HMCLab: a framework for solving diverse geophysical inverse problems using the Hamiltonian Monte Carlo method

Cooperative geophysical inversion integrated with 3-D geological modelling in the Boulia region, QLD

Contact Info

Product

Resources

About

Three‐axis borehole gravity monitoring for CO₂ storage using machine learning coupled to fluid flow simulator

Three‐axis borehole gravity monitoring for CO₂ storage using machine learning coupled to fluid flow simulator