Bayesian RockAVO: Direct petrophysical inversion with hierarchical conditional GANs

Corrales, M.; Izzatullah, Muhammad; Ravasi, Matteo; Hoteit, Hussein

doi:10.1190/image2022-3745255.1

Cited by 5 publications

(1 citation statement)

References 11 publications

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“…In petrophysical inversion context, segmentation may be particularly beneficial as the link between facies and petrophysical properties is more direct than the link between facies and acoustic/elastic parameters as considered in Ravasi and Birnie (2022). In addition, our framework also permits the integration of new promising deep-based algorithms like the Plug-and-Play method with CNN- Based Denoisers (Romero et al, 2022) and its probabilistic extension (Corrales, Izzatullah, et al, 2022;Izzatullah et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Seis2Rock: A Data‐Driven Approach to Direct Petrophysical Inversion of Pre‐Stack Seismic Data

Corrales,

Hoteit,

Ravasi

2024

Earth and Space Science

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The inversion of petrophysical parameters from seismic data represents a fundamental step in the process of characterizing the subsurface, with applications ranging from subsurface resource exploration to geothermal, carbon capture and storage, and hydrogen storage. We propose a novel, data‐driven approach, named Seis2Rock, that utilizes optimal basis functions learned from well‐log information to create a direct link between pre‐stack seismic data and so‐called band‐limited petrophysical reflectivities. Seis2Rock is composed of two stages: training and inference. During training, a set of optimal basis functions are identified by performing singular value decomposition on one or more synthetic amplitude variation with offset gathers created from measured or rock‐physics synthesized elastic well logs. In inference, seismic pre‐stack data are first projected into a set of band‐limited petrophysical reflectivities using the previously computed basis functions; this is followed by regularized post‐stack seismic inversion of the individual properties. In this work, we apply the Seis2Rock methodology to a synthetic data set based on the Smeaheia reservoir model and to the open Volve field data set. Our numerical results reveal the ability of the proposed method in recovering accurate porosity, shale content, and water saturation models. Finally, the proposed methodology is applied in the context of reservoir monitoring to invert time‐lapse, pre‐stack seismic data for water saturation changes.

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Section: Discussionmentioning

confidence: 99%

Seis2Rock: A Data‐Driven Approach to Direct Petrophysical Inversion of Pre‐Stack Seismic Data

Corrales,

Hoteit,

Ravasi

2024

Earth and Space Science

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Posterior sampling with convolutional neural network-based plug-and-play regularization with applications to poststack seismic inversion

Izzatullah,

Alkhalifah,

Romero

et al. 2024

GEOPHYSICS

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Uncertainty quantification is a crucial component in any geophysical inverse problem, as it provides decision-makers with valuable information about the inversion results. Seismic inversion is a notoriously ill-posed inverse problem, due to the band-limited and noisy nature of seismic data; as such, quantifying the uncertainties associated with the ill-posed nature of this inversion process is essential for qualifying the subsequent interpretation and decision-making processes. Selecting appropriate prior information is a crucialyet, non-trivialstep in probabilistic inversion since it influences the ability of sampling-based inference algorithms to provide geologically-plausible posterior samples. However, the necessity to encapsulate prior knowledge into a probability distribution can greatly limit our ability to define expressive priors. To address this limitation, and following in the footsteps of the Plug-and-Play methodology for deterministic inversion, we present a regularized variational inference framework that performs posterior inference by implicitly regularizing the Kullback-Leibler divergence lossa measure of the distance between the approximated and target probabilistic distributionswith a CNN-based denoiser. We call this new algorithm Plug-and-Play Stein Variational Gradient Descent (PnP-SVGD) and demonstrate its ability to produce high-resolution, trustworthy samples that realistically represent subsurface structures. The proposed method is validated on both synthetic and field post-stack seismic data.

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GeosPIn: A MATLAB library for geostatistical petrophysical inversion

Grana,

Liu,

de Figueiredo

2024

GEOPHYSICS

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Petrophysical inversion of seismic data is one of the key components of seismic reservoir characterization. The goal of petrophysical inversion is to estimate petrophysical properties of reservoir rocks, such as porosity, volumes of minerals or lithologies, and water and hydrocarbon saturations from seismic data. This process can be performed by combining seismic AVO modeling and rock physics relations with deterministic or probabilistic inverse theory methods, either in a two-step approach based on seismic AVO inversion and rock physics inverse mapping or in a single loop step that includes both. We present a comprehensive MATLAB library named Geostatistical Petrophysical Inversion (GeosPIn). The novelty of the library lies in a single-loop geostatistical inversion method based on the Probability Field Simulation (PFS) and the Ensemble Smoother Multiple Data Assimilation (ES-MDA), and its implementation for applications to 3D datasets. The library also includes a Bayesian petrophysical inversion based on the two-step approach for comparison. We illustrate the main algorithms, explain the structure of the source codes, and demonstrate the library applications through 1D and 3D examples.

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Bayesian RockAVO: Direct petrophysical inversion with hierarchical conditional GANs

Cited by 5 publications

References 11 publications

Seis2Rock: A Data‐Driven Approach to Direct Petrophysical Inversion of Pre‐Stack Seismic Data

Seis2Rock: A Data‐Driven Approach to Direct Petrophysical Inversion of Pre‐Stack Seismic Data

Posterior sampling with convolutional neural network-based plug-and-play regularization with applications to poststack seismic inversion

GeosPIn: A MATLAB library for geostatistical petrophysical inversion

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