Seismic Reservoir Modeling

Grana, Dario; Mukerji, Tapan; Doyen, P.M.

doi:10.1002/9781119086215

Cited by 52 publications

(21 citation statements)

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“…The prior probability of m can also be treated as a Gaussian distribution to provide general constraints on the inversion process [34,[48][49][50][51]. However, other distributions can be adopted by considering specific subsurface model properties [13].…”

Section: Discussionmentioning

confidence: 99%

Sequential Seismic Anisotropic Inversion for VTI Media with Simulated Annealing Algorithm Aided by Adaptive Setting of Optimization Parameters

Luo

Guo

2023

Remote Sensing

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As an important geophysical data processing technique, seismic inversion estimates subsurface rock properties with seismic observations. However, anisotropic inversion, intended for a vertical transverse isotropy (VTI) media that primarily describes shale gas/oil resources, suffers from high nonlinearity. Simulated annealing is a widely used global optimization algorithm for solving nonlinear seismic inverse problems, but it involves multiple optimization parameters (e.g., initial temperature, search limit, and perturbation range). The importance of such parameters has been proven whilst the relevant analysis is limited in seismic inversion studies. This work hereby proposes a sequential anisotropic inversion method for VTI media, wherein we combine Bayesian linear and simulated annealing nonlinear inversion schemes. The simulated annealing is featured by adaptive optimization parameters aided by the linear result. Rather than the conventional method, the adaptive setting can be implemented trace by trace for complex reservoirs, which endows the method with enhanced stability and extended applicability. Synthetic tests and practical application demonstrate the validity of the method, wherein the obtained stiffness parameters facilitate the characterization of potential shale reservoirs with an improved accuracy.

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Section: Discussionmentioning

confidence: 99%

Sequential Seismic Anisotropic Inversion for VTI Media with Simulated Annealing Algorithm Aided by Adaptive Setting of Optimization Parameters

Luo

Guo

2023

Remote Sensing

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“…Then, the elastic properties are further converted into synthetic seismic data using the forward seismic model. Numerous methods for numerical seismic modeling exist, including wave‐equation‐based modeling methods, for example, finite difference method, spectral element method and finite element method, (Igel, 2017), as well as simplified methods for layered earth models, for example, reflectivity method (Kennett, 1983) and convolution‐based method (Grana et al., 2021; Yilmaz, 2001). In seismic reservoir characterization, the data set generally consists of a set of partial angle stacks, and the convolution‐based seismic modeling method is commonly used due to its simplicity and computational efficiency.…”

Section: Methodsmentioning

confidence: 99%

“…of 22 as well as simplified methods for layered earth models, for example, reflectivity method(Kennett, 1983) and convolution-based method(Grana et al, 2021;Yilmaz, 2001). In seismic reservoir characterization, the data set generally consists of a set of partial angle stacks, and the convolution-based seismic modeling method is commonly used due to its simplicity and computational efficiency.…”

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confidence: 99%

Joint Inversion of Geophysical Data for Geologic Carbon Sequestration Monitoring: A Differentiable Physics‐Informed Neural Network Model

et al. 2023

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Geologic CO 2 sequestration (GCS) is one of the main mitigation strategies to prevent carbon dioxide (CO 2 ) from entering the atmosphere by capturing and injecting it into subsurface geological formations, such as depleted oil reservoirs and deep saline aquifers (Aminu et al., 2017;Metz et al., 2005). To ensure safe and long-term CO 2 storage, geophysical monitoring surveys are periodically acquired to surveil the behavior of the injected CO 2 and make informed decisions for storage management (Davis et al., 2019). Time-lapse (4-D) seismic acquisition is one of the monitoring techniques widely deployed in GCS projects, where a series of seismic surveys are sequentially acquired to quantify changes in reservoir properties (e.g., elastic properties, fluid pressure and saturation) during and after CO 2 injection (Caspari et al.

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“…Reservoir characterization aims to map the spatial variability of subsurface reservoir properties. This includes, but is not limited to, P‐ and S‐wave velocities, density, and a range of petrophysical properties such as porosity, permeability, and fluid saturation (Grana et al., 2021; Ravasi et al., 2023). This, in turn, allows a comprehensive understanding of the fluid flow behavior in the subsurface and facilitates optimal resource extraction and management (Azevedo & Soares, 2017; Haldorsen & Damsleth, 1993).…”

Section: Introductionmentioning

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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Seismic Reservoir Modeling

Cited by 52 publications

References 0 publications

Sequential Seismic Anisotropic Inversion for VTI Media with Simulated Annealing Algorithm Aided by Adaptive Setting of Optimization Parameters

Sequential Seismic Anisotropic Inversion for VTI Media with Simulated Annealing Algorithm Aided by Adaptive Setting of Optimization Parameters

Joint Inversion of Geophysical Data for Geologic Carbon Sequestration Monitoring: A Differentiable Physics‐Informed Neural Network Model

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

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