Digital Rock Approach to Model the Permeability in an Artificially Heated and Fractured Granodiorite from the Liquiñe Geothermal System (39°S)

Molina, Eduardo; Arancibia, Gloria; Sepúlveda, Josefa; Roquer, Tomás; Mery, Domingo; Morata, Diego

doi:10.1007/s00603-019-01967-6

Cited by 10 publications

(11 citation statements)

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“…Published estimates of the P‐T emplacement conditions for the Jurassic plutons are close to those of the Cretaceous plutons, that is, 2.5–2.9 kbar and 680°C–694°C (Seifert et al., 2005). We calculate a geobarometric gradient of 0.27 kbar/km using a rock density of 2,700 kg/m 3 for the upper crust (e.g., J. Sepúlveda et al., 2020; Molina et al., 2020). Emplacement depths based on this gradient are ∼10 km for Jurassic plutons, ∼10–12 km for the Cretaceous monzogranite, and ∼7–9 km for the late Miocene granodiorite.…”

Section: Discussionmentioning

confidence: 99%

“…For each geothermal gradient, we ran one model that did not include faults and one model that did include faults. An 8% porosity was assigned to each fault (e.g., J. Sepúlveda et al., 2020; Molina et al., 2020). Including faults in the models produces clear disturbances in the local distribution of low‐temperature ages (Figure 11c).…”

Section: Discussionmentioning

confidence: 99%

“…(2005), and L. Lara and Moreno (2004). Names of faults in blue text represent different branches of the Liquiñe‐Ofqui Faut System and the Andean Transverse Faults (Astudillo‐Sotomayor et al., 2021; Cembrano et al., 2000; Lavenu & Cembrano, 1999; J. Sepúlveda et al., 2020; Molina et al., 2020; Peña et al., 2021; Potent, 2003; Roquer et al., 2017, 2020; Rosenau et al., 2006). Published isotopic ages from Glodny et al.…”

Section: Introductionmentioning

confidence: 99%

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Fault‐Driven Differential Exhumation in a Transpressional Tectonic Setting: A Combined Microstructural and Thermochronologic Approach From the Liquiñe‐Ofqui Fault System, Southern Andes (39°S)

et al. 2023

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Crustal deformation in transpressive tectonic settings is partitioned across fault‐bounded tectonic blocks whose borders may represent ideal loci for enhanced rock exhumation. Field and petrographic analysis, geothermobarometry, zircon U‐Pb geochronology, and zircon and apatite (U‐Th)/He thermochronology were applied to intrusive and metamorphic rocks to investigate exhumation patterns of fault blocks delimited by the Liquiñe‐Ofqui Fault System (LOFS), Southern Andes (39°S). Our integrated analyses document the relative influences of magmatism, fault‐driven differential exhumation, and fault‐controlled geothermal flow along the LOFS. Magmatism was concentrated in the Early to Late Jurassic (∼182–151 Ma), Early Cretaceous (∼116–104 Ma), and Miocene (∼17–6 Ma). Dextral mylonitic deformation was most likely coeval with the Miocene pulse of magmatism. Tectonic exhumation occurred across a positive flower structure during the Late Miocene to Early Pleistocene (∼6–2 Ma), and affected kilometric‐scale tectonic blocks bound by N‐striking, steeply dipping faults of the LOFS. Fault‐controlled geothermal flow occurred from the Early Pleistocene to the present‐day (∼1.5 Ma‐present). Our results suggest that individual faults not only facilitate exhumation of tectonic blocks but also act as pathways for long‐term hydrothermal fluid flow.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fault‐Driven Differential Exhumation in a Transpressional Tectonic Setting: A Combined Microstructural and Thermochronologic Approach From the Liquiñe‐Ofqui Fault System, Southern Andes (39°S)

et al. 2023

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“…The simplest and most important way to summarize the microstructural effects on flow is with the permeability, which is a volume-average property derived from the fluid velocity which describes how well a fluid can advance through its connected voidspace. Knowing the permeability is of interest for not only for petroleum engineering (Sun et al 2017), but for carbon capture/sequestration (Bond et al 2017), and aquifer exploitation (Cunningham and Sukop 2011), but also in geothermal engineering (Molina et al 2020), membrane design, and fuel cell applications (Holley and Faghri 2006).…”

Section: Introductionmentioning

confidence: 99%

Computationally Efficient Multiscale Neural Networks Applied to Fluid Flow in Complex 3D Porous Media

Santos

Yin

et al. 2021

Transp Porous Med

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The permeability of complex porous materials is of interest to many engineering disciplines. This quantity can be obtained via direct flow simulation, which provides the most accurate results, but is very computationally expensive. In particular, the simulation convergence time scales poorly as the simulation domains become less porous or more heterogeneous. Semi-analytical models that rely on averaged structural properties (i.e., porosity and tortuosity) have been proposed, but these features only partly summarize the domain, resulting in limited applicability. On the other hand, data-driven machine learning approaches have shown great promise for building more general models by virtue of accounting for the spatial arrangement of the domains’ solid boundaries. However, prior approaches building on the convolutional neural network (ConvNet) literature concerning 2D image recognition problems do not scale well to the large 3D domains required to obtain a representative elementary volume (REV). As such, most prior work focused on homogeneous samples, where a small REV entails that the global nature of fluid flow could be mostly neglected, and accordingly, the memory bottleneck of addressing 3D domains with ConvNets was side-stepped. Therefore, important geometries such as fractures and vuggy domains could not be modeled properly. In this work, we address this limitation with a general multiscale deep learning model that is able to learn from porous media simulation data. By using a coupled set of neural networks that view the domain on different scales, we enable the evaluation of large ($$>512^3$$ > 512 3 ) images in approximately one second on a single graphics processing unit. This model architecture opens up the possibility of modeling domain sizes that would not be feasible using traditional direct simulation tools on a desktop computer. We validate our method with a laminar fluid flow case using vuggy samples and fractures. As a result of viewing the entire domain at once, our model is able to perform accurate prediction on domains exhibiting a large degree of heterogeneity. We expect the methodology to be applicable to many other transport problems where complex geometries play a central role.

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“…The simplest and most important way to summarize the microstructural effects on flow is with a permeability, which is a volume-average property derived from the fluid velocity and describes how well a fluid can advance through its connected void-space. Knowing the permeability is of interest for not only for petroleum engineering [9], carbon capture and sequestration [10] or, aquifer exploitation [11], but also in geothermal engineering [12], membrane design, and fuel cell applications [13].…”

Section: Introductionmentioning

confidence: 99%

Computationally Efficient Multiscale Neural Networks Applied To Fluid Flow In Complex 3D Porous Media

Santos¹,

Yin²,

Jo³

et al. 2021

Preprint

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The permeability of complex porous materials is of interest to many engineering disciplines. This quantity can be obtained via direct flow simulation, which provides the most accurate results, but is very computationally expensive. In particular, the simulation convergence time scales poorly as simulation domains become tighter or more heterogeneous. Semi-analytical models that rely on averaged structural properties (i.e. porosity and tortuosity) have been proposed, but these features only summarize the domain, resulting in limited applicability.On the other hand, data-driven machine learning approaches have shown great promise for building more general models by virtue of accounting for the spatial arrangement of the domains' solid boundaries. However, prior approaches building on the Convolutional Neural Network (ConvNet) literature concerning 2D image recognition problems do not scale well to the large 3D domains required to obtain a Representative Elementary Volume (REV). As such, most prior work focused on homogeneous samples, where a small REV entails that that the global nature of fluid flow could be mostly neglected, and accordingly, the memory bottleneck of addressing 3D domains with ConvNets was side-stepped. Therefore, important geometries such as fractures and vuggy domains could not be well-modeled.In this work, we address this limitation with a general multiscale deep learning model that is able to learn from porous media simulation data. By using a coupled set of neural networks that view the domain on different scales, we enable the evaluation of large (> 512 3 ) images in approximately one second on a single Graphics Processing Unit. This model architecture opens up the possibility of modeling domain sizes that would not be feasible using traditional direct simulation tools on a desktop computer. We validate our method with a laminar fluid flow case using vuggy samples and fractures. As a result of viewing the entire domain at once, it is able to perform accurate prediction on domains exhibiting a large degree of heterogeneity. We expect the methodology to be applicable to many other transport problems where complex geometries play a central role.

show abstract

Digital Rock Approach to Model the Permeability in an Artificially Heated and Fractured Granodiorite from the Liquiñe Geothermal System (39°S)

Cited by 10 publications

References 122 publications

Fault‐Driven Differential Exhumation in a Transpressional Tectonic Setting: A Combined Microstructural and Thermochronologic Approach From the Liquiñe‐Ofqui Fault System, Southern Andes (39°S)

Fault‐Driven Differential Exhumation in a Transpressional Tectonic Setting: A Combined Microstructural and Thermochronologic Approach From the Liquiñe‐Ofqui Fault System, Southern Andes (39°S)

Computationally Efficient Multiscale Neural Networks Applied to Fluid Flow in Complex 3D Porous Media

Computationally Efficient Multiscale Neural Networks Applied To Fluid Flow In Complex 3D Porous Media

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