A joint inversion-segmentation approach to assisted seismic interpretation

Ravasi, Matteo; Birnie, Claire

doi:10.1093/gji/ggab388

Cited by 13 publications

(8 citation statements)

References 36 publications

(39 reference statements)

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“…Note that the right‐hand‐side of Equation can be interpreted as a series of post‐stack seismic inversions (one post‐stack inversion per petrophysical parameter), which are solved here using the PyLops computational framework (Ravasi & Vasconcelos, 2020). Moreover, whilst we have considered a single location for simplicity in this derivation, the final step of inversion is usually carried out for all spatial locations at the same time, such that spatial regularization in the form of Laplacian or Total Variation (e.g., Ravasi & Birnie, 2022) can be introduced.…”

Section: The Seis2rock Methodsmentioning

confidence: 99%

“…Finally, an inverse problem is solved to undo the effect of the wavelet W and time-derivative T operators: where R is the matrix containing the petrophysical reflectivities from the well log used in training of size n ⋅ N t × n m , and H p = V T p Λ 1 p is a matrix of size N t × p. Note that the right-hand-side of Equation 5 can be interpreted as a series of post-stack seismic inversions (one post-stack inversion per petrophysical parameter), which are solved here using the PyLops computational framework (Ravasi & Vasconcelos, 2020). Moreover, whilst we have considered a single location for simplicity in this derivation, the final step of inversion is usually carried out for all spatial locations at the same time, such that spatial regularization in the form of Laplacian or Total Variation (e.g., Ravasi & Birnie, 2022) can be introduced.…”

Section: Inference Stagementioning

confidence: 99%

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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: The Seis2rock Methodsmentioning

confidence: 99%

Section: Inference Stagementioning

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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“…In this paper, we propose the use of the JIS algorithm [9,7,10], which in addition to TV regularization terms, it incorporates a segmentation constraint over the estimated 4D difference. As such, 4D JIS not only retrieves highresolution baseline and monitor acoustic impedance models, but also segments the monitor-baseline acoustic impedance difference into multiple probability volumes based on user-defined time-lapse classes.…”

Section: Regularizationmentioning

confidence: 99%

“…Equation 4presents a non-convex optimization problem that becomes convex when fixing one of the optimization variables ( m, V ). Therefore its solution is found in an alternating fashion, where each variable is optimized independently at every iteration using the Primal-Dual algorithm [11]; for more implementation details, we refer the reader to [9].…”

Section: Regularizationmentioning

confidence: 99%

“…In this work, we leverage PyProximal 2 , a python library under early development that provides an extensive set of proximal operators and solvers and allows for a direct integration with PyLops' linear operators. More specifically, for our problem of interest, we use the Primal-Dual algorithm [11] to solve the TV-regularized 4D seismic inversion in equation 2 since this has been shown to outperform other proximal algorithms in seismic inversion [9,7].…”

Section: Proximal Operatorsmentioning

confidence: 99%

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Robust joint inversion and segmentation of 4D seismic data

Romero

Ravasi

Luiken

2022

Second International Meeting for Applied Geoscience &Amp; Energy

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Seismic inversion is the leading method to map and quantify changes from time-lapse (4D) seismic datasets, with applications ranging from monitoring of hydrocarbon-producing fields to carbon capture and sequestration. Time-lapse seismic inversion is however, a notoriously ill-posed inverse problem: the band-limited nature of seismic data, alongside inaccuracies in the repeatability of consecutive acquisition surveys make it challenging to obtain high-resolution, clean estimates of 4D effects. Adding prior information to the inversion process in the form of properly crafted regularization (or preconditioning) is therefore essential to successfully extract weak signals that are usually buried under strong noise. In this work, we leverage the fact that 4D seismic inversion can be described as a coupled inversion of its baseline and monitor 3D seismic datasets. In existing approaches, the coupling is introduced by penalizing the squared L 2 -norm of difference between the baseline and the monitor acoustic impedances, as this is usually assumed to be small. A major downside of such a regularization is that, whilst reducing the overall level of noise in the estimated acoustic impedance differences, the resulting 4D effects are usually oversmoothed and their strength is underestimated. We instead propose to adapt the joint inversion and segmentation algorithm introduced by Ravasi and Birnie (2021) to the problem of 4D seismic inversion. Our technique produces two acoustic impedance models by inverting the corresponding 3D seismic datasets, regularized by Total-Variation. Moreover, the objective function to optimize is augmented with a segmentation term that renders solutions consistent with the expected 4D effects (obtained, for example, as part of a 4D feasibility study by means of Gassmann fluid substitution). A numerical experiment is presented to validate the effectiveness of the proposed approach and its superiority over state-of-the-art 4D inversion methods.

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