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All sources NASA NTRS DOE OSTI

DOE OSTI2022

A robust deep learning workflow to predict multiphase flow behavior during geological C O 2 sequestration injection and Post-Injection periods

Simulation of multiphase flow in porous media is essential to manage the geologic CO 2 sequestration (GCS) process, and physics-based simulation approaches usually take prohibitively high computational cost due to the nonlinearity of the coupled physics. This paper contributes to the development and evaluation of a deep learning workflow that accurately and efficiently predicts the temporal-spatial evolution of pressure and CO 2 plumes during injection and post-injection periods of GCS operations. Based on a Fourier Neural Operator, the deep learning workflow takes input variables or features including rock properties, well operational controls and time steps, and predicts the state variables of pressure and CO 2 saturation. To further improve the predictive fidelity, separate deep learning models are trained for CO 2 injection and post-injection periods due to the difference in primary driving force of fluid flow and transport during these two phases. We also explore different combinations of features to predict the state variables. We use a realistic example of CO 2 injection and storage in a 3D heterogeneous saline aquifer, and apply the deep learning workflow that is trained from physics-based simulation data and emulate the physics process. Through this numerical experiment, we demonstrate that using two separate deep learning models to distinguish post-injection from injection period generates the most accurate prediction of pressure, and a single deep learning model of the whole GCS process including the cumulative injection volume of CO 2 as a deep learning feature, leads to the most accurate prediction of CO 2 saturation. For the post-injection period, it is key to use cumulative CO 2 injection volume to inform the deep learning models about the total carbon storage when predicting either pressure or saturation. The deep learning workflow not only provides high predictive fidelity across temporal and spatial scales, but also offers a speedup of 250 times compared to full physics reservoir simulation, and thus will be a significant predictive tool for engineers to manage the long-term process of GCS.

58 GEOSCIENCES↗

DOE OSTI2020

Revised Kimberlina Groundwater Simulations

Kimberlina Groundwater Qaulity Simulations (Version 3: 2015 October) by Lawrence Livermore National Labratory. Submitted to the EDX team at the National Energy Technology Laboratory for research use. This dataset contains 562 updated groundwater quality simulations based on the Kimberlina site. It is a six-layer model including the aquifers and aquitards inbetween the ground surface and the reservoir. The input files and associated leakage information for these simulations are included in the zip file "nrap_kim_well31_fluxes and inputs.tar.gz". These 562 simulations are the successful results out of 1000 monte carlo simulations, with time length greater than 5 years. Each simulation consists 6 output files, therefore, there are 3372 files (6*562) in total. One single example simulation of full time length (200 year) is also provided in the zip file "single_simulation_200yr_sim0012.tar.gz". These 6 groundwater quality parameters are: - pH (use absolute value) - TDS (mg/kg) - Pressure (Pa) - CO2 Liquid Saturation (fraction range 0.0-1.0) - CO2 Gas Concentration (mol/L) - CO2 Liquid Concentration (mol/kg) Their correponding file names, take the first simulation for example, are: ph0001.ntab (for pH) , tds0001.ntab (for TDS), usnt0001.p.ntab (for pressure), usnt0001.s_liquid.ntab (for liquid saturation), usnt0001.c_co2_gas.ntab (for CO2 gas concentration) and usnt0001.c_co2_liquid.ntab (for CO2 liquid concentration). Each output file contains at least 19 columns (variables). The total number of columns depends on the simulation time length. The column data include the information of numerical records, geologic location and sizes and the simulated parameter values over time. The first 13 variables are about numerical records and relative geologic information for a simulation grid: 1. index: simulation index 2. i: the ith grid of x-axis 3. j: the jth grid of y-axis 4. k: the kth grid of z-axis 5. element_ref: element type reference 6. nuft_ind: nuft index 7. x: grid location in the x axis direction 8. y: grid location in the y axis direction 9. z: grid location in the z axis direction 10. dx: grid length in the x axis direction 11. dy: grid length in the y axis direction 12. dz: grid length in the z axis direction 13. volume: volume of the simulation grid The rest (14, 15, 16...) variables are the simulated parameter values over time, take pH as an example, are: 14. ph000y: initial pH value. 15. ph001y: simulated pH value at the end of the 1st year. 16. ph002y: simulated pH value at the end of the 2nd year. ... (and so on)... The zip input files have the input fluxes of brine and CO2 (in kg/s) allocated to model layers 1,2,3 and 5 (layers 4 and 6 are assumed to be no-flow aquitards) through the following nodes: (src1 WELL 7 7 25 25 10 32) #Etchegoin (src2 WELL 7 7 25 25 33 72) #Macoma Chanac (src3 WELL 7 7 25 25 73 83) #Santa Margarita Mclure (src4 WELL 7 7 25 25 84 89) #Fruitvale Round Mountain (aquitard) (src5 WELL 7 7 25 25 90 96) #Olcese Sand (thief zone) (src6 WELL 7 7 25 25 97 99) #tremblor Freeman (aquitard) ex: brn1 is assigned to src 1 from i=(7,7), j=(25,25), k=(10,32) Edited by Ya-Mei Yang at National Energy Technology Laboratory

brine leakage↗

DOE OSTI2025

Model-based analysis of solute transport and potential carbon mineralization in the active layer of a hillslope underlain by permafrost with seasonal variability and climate change

Permafrost carbon, stored in frozen organic matter across vast Arctic and sub-Arctic regions, represents a substantial and increasingly vulnerable carbon reservoir. As global temperatures rise, the accelerated thawing of permafrost releases greenhouse gases, exacerbating climate change. However, freshly thawed permafrost carbon may also experience lateral transport by groundwater flow to surface water recipients such as rivers and lakes, increasing the terrestrial-to-aquatic transfer of permafrost carbon. Mobilization and subsurface transport mechanisms are poorly understood and not accounted for in global climate models, leading to high uncertainties in the predictions of the permafrost carbon feedback. Here, we focus on a hillslope in Endalen Valley, Svalbard, as a representative example of a high-Arctic hillslope underlain by continuous permafrost. We analyze solute transport in the form of a non-reactive tracer representing dissolved organic carbon (DOC) using a physics-based numerical model with the objective to study governing cryotic and hydrodynamic transport mechanisms relevant for warming permafrost regions. We first analyze transport times for DOC pools at different locations within the active layer under present-day climatic conditions and proceed to study susceptibility for deeper ancient carbon release in the upper permafrost due to thaw under different warming scenarios. Results suggest that DOC in the active layer near the permafrost table experiences rapid lateral transport upon thaw due to saturated conditions and lateral flow, while DOC close to the ground surface experiences slower transport due to flow in unsaturated soil. Deeper permafrost carbon release exhibits vastly different transport behaviors depending on warming and thaw rate. Gradual warming leads to small fractions of DOC being mobilized every year, while the majority moves vertically through percolation and cryosuction. Abrupt thaw resulting from a single very warm year leads to faster lateral transport times, similar to active layer DOC released in saturated conditions. Lastly, we analyze the potential susceptibility of DOC to mineralization to CO 2 prior to export due to soil moisture and temperature conditions. We find that high liquid saturation during transport coincides with very low mineralization rates and potentially inhibits mineralization into CO 2 before export. Overall, the results highlight the importance of subsurface hydrologic and thermal conditions for the retention and lateral export of permafrost carbon by subsurface flow.

Hamm, Alexandra [Stockholm Univ. (Sweden)] (ORCID:↗

DOE OSTI2025

Joint state-parameter estimation for the reduced fracture model via the united filter

Here, in this paper, we introduce an effective United Filter method for jointly estimating the solution state and physical parameters in flow and transport problems within fractured porous media. Fluid flow and transport in fractured porous media are critical in subsurface hydrology, geophysics, and reservoir geomechanics. Reduced fracture models, which represent fractures as lower-dimensional interfaces, enable efficient multi-scale simulations. However, reduced fracture models also face accuracy challenges due to modeling errors and uncertainties in physical parameters such as permeability and fracture geometry. To address these challenges, we propose a United Filter method, which integrates the Ensemble Score Filter (EnSF) for state estimation with the Direct Filter for parameter estimation. EnSF, based on a score-based diffusion model framework, produces ensemble representations of the state distribution without deep learning. Meanwhile, the Direct Filter, a recursive Bayesian inference method, estimates parameters directly from state observations. The United Filter combines these methods iteratively: EnSF estimates are used to refine parameter values, which are then fed back to improve state estimation. Numerical experiments demonstrate that the United Filter method surpasses the state-of-the-art Augmented Ensemble Kalman Filter, delivering more accurate state and parameter estimation for reduced fracture models. This framework also provides a robust and efficient solution for PDE-constrained inverse problems with uncertainties and sparse observations.

Bayesian inference↗

DOE OSTI2022

Summary: SEE4GEO

The seismoelectric effects technique (SEE) is a new and innovative approach for geothermal subsurface imaging and monitoring at reservoir scale. The objective of this project is to assess SEE in terms of data acquisition, cost and quality, and to determine its capability in comparison with classical imaging and monitoring techniques, particularly decoupled seismic and electromagnetic methods. This will be achieved by (1) development of a fast, true 3D numerical package, handling SEE imaging and subsurface properties characterization, including resistivity and permeability, (2) laboratory experiments performed in a controlled environment to define optimal deployment design, data quality, and inform field deployment, and (3) field surveys to ultimately test and draw lessons for practical use of SEE technology. There is a relatively extensive body of work in the literature on SEE, and members of this consortium have been involved in theoretical and numerical development of SEE modeling as well as laboratory experiments. Nevertheless, to our knowledge few, if any, documented efforts have been specifically targeting the use of SEE for geothermal subsurface imaging and monitoring. The strength and originality of our proposal rely on an integrated approach leveraging numerical, laboratory and field experiments, to properly document the practical use of SEE. Through this process, SEE in-hand technology for the geothermal industry will be able to progress from a TRL 1 to TRL 3.

58 GEOSCIENCES↗

DOE OSTI2022

Seismoelectric Effects for Geothermal Resources Assessment and Monitoring (SEE4GEO)

58 GEOSCIENCES↗

DOE OSTI2023

The Prospects for Pumped Storage Hydropower in Alaska

Key Takeaways: The resource mapping analysis confirmed that numerous locations in Alaska are suitable for the development of pumped storage hydropower (PSH) projects, both larger grid scale projects and smaller projects that could be suitable for remote communities; The resource assessment for larger, grid-scale projects showed the potential for more than 1,800 closed-loop systems in Alaska, with a total energy storage capacity of about 4 terawatt hours (TWh); Because of their small reservoir sizes and dam heights, many locations were identified as potentially suitable for small-scale PSH systems. Nearly 50% of the identified potentially suitable small-scale PSH sites are in Southeast Alaska; PSH candidate sites were part of the optimal capacity expansion solution in all scenarios analyzed for the Railbelt system. Depending on the scenario, the new PSH capacity that the model selected for the analysis period until 2046 ranged from 300 MW to 600 MW. The locations and timing of new PSH investments vary in different scenarios; Lithium-ion batteries were also selected a source of new generating capacity in all analyzed scenarios for the Railbelt system, indicating that the system will need a mix of short- and long-duration energy storage to support variable renewable energy sources and provide system reliability in the future; For rural communities, analysis results showed that PSH suitability is very site-specific; in addition to diesel fuel costs and PSH capital costs, suitability depends heavily on available renewable resources and existing infrastructure (e.g., reservoirs, transmission access and construction road access); The analysis for rural communities also showed that PSH projects with 10-hour energy storage are likely to be more economical for remote community applications in Alaska than those with larger reservoirs that could provide 10 days of energy storage; Lithium-ion batteries seem to be an economically more viable energy storage option for small, remote communities in Alaska.

13 HYDRO ENERGY↗

NASA NTRS1988

An illustration of the complexity of continent formation

It was pointed out that a consensus may be emerging in crustal growth models, considering the clustering of most growth curves and their uncertainties. Curves most distant from this clustering represent models involving extensive recycling of continental material back into the mantle, but the author wondered if geochemical signatures for this would be recognizable considering the lack of evidence from seismic tomography for discrete mantle reservoirs, and the likelihood of core-mantle interaction based on recent high pressure experiments. Unreactivated Archean rocks represent only 2 percent of present continental area, and the author was uncomfortable about basing inferences on what the early Earth was like on such a small amount of information. He feels that the hypothesis of continental assembly that needs testing is that of banging together of island arcs, such as in Indonesia today. As an example of how complex this process can be, the author described the geology of the Caribbean arc system, which shows evidence for reversals of subduction polarity, numerous collisional events, and substantial strike-slip movements. It seemed unlikely to the author that Archean examples would have been less complicated.

Burke, Kevin↗

DOE OSTI2025

Material Fracturing and Failure Simulation Datasets

Fracturing is a fundamental physics phenomena with broad relevance across multiple domains, ranging from infrastructure integrity, aerospace durability, reservoir production, and seismic events. We present a diverse dataset of simulated fracture evolution and material failure generated from two numerical solvers: the phase-field method and the combined finite-discrete element method (FDEM). These solvers differ in formulation, physical fidelity, and computational efficiency. The dataset includes five materials: PBX, anisotropic shale, tungsten, aluminum, and steel. For each, phase-field simulations span 400,000 cases: 200,000 under uniaxial tension and 200,000 under biaxial tension. The computationally expensive FDEM simulations include 90,000 split evenly among PBX, shale, and tungsten under uniaxial loading. All simulations begin with randomized initial fracture patterns. Each entry includes temporal data capturing fracture propagation dynamics. This comprehensive dataset is designed to support the development of foundational or surrogate machine learning approaches for predicting material failure. While no such models are introduced here, the dataset lays a robust foundation for advancing future research and innovation in these areas.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Multilevel Spectral Coarsening for Graph Laplacian Problems with Application to Reservoir Simulation

We extend previously developed two-level coarsening procedures for graph Laplacian problems written in a mixed saddle point form to the fully recursive multilevel case. The resulting hierarchy of discretizations gives rise to a hierarchy of upscaled models, in the sense that they provide approximation in the natural norms (in the mixed setting). This property enables us to utilize them in three applications: (i) as an accurate reduced model, (ii) as a tool in multilevel Monte Carlo simulations (in application to finite volume discretizations), and (iii) for providing a sequence of nonlinear operators in a full approximation scheme for solving nonlinear pressure equations discretized by the conservative two-point flux approximation. Finally, we illustrate the potential of the proposed multilevel technique in all three applications on a number of popular benchmark problems used in reservoir simulation.

multilevel Monte Carlo↗

NASA NTRS2013

The Martian Dust Cycle: Observations and Modeling

The dust cycle is critically important for Mars' current climate system. Suspended atmospheric dust affects the radiative balance of the atmosphere, and thus greatly influences the thermal and dynamical state of the atmosphere. Evidence for the presence of dust in the Martian atmosphere can be traced back to yellow clouds telescopically observed as early as the early 19th century. The Mariner 9 orbiter arrived at Mars in November of 1971 to find a planet completely enshrouded in airborne dust. Since that time, the exchange of dust between the planet's surface and atmosphere and the role of airborne dust on Mars' weather and climate has been studied using observations and numerical models. The goal of this talk is to give an overview of the observations and to discuss the successes and challenges associated with modeling the dust cycle. Dust raising events on Mars range in size from meters to hundreds of kilometers. During some years, regional storms merge to produce hemispheric or planet encircling dust clouds that obscure the surface and raise atmospheric temperatures by tens of kelvin. The interannual variability of planet encircling dust storms is poorly understood. Although the occurrence and season of large regional and global dust storms are highly variable from one year to the next, there are many features of the dust cycle that occur year after year. A low-level dust haze is maintained during northern spring and summer, while elevated levels of atmospheric dust occur during northern autumn and winter. During years without global-scale dust storms, two peaks in total dust loading are generally observed: one peak occurs before northern winter solstice and one peak occurs after northern winter solstice. Numerical modeling studies attempting to interactively simulate the Martian dust cycle with general circulation models (GCMs) include the lifting, transport, and sedimentation of radiatively active dust. Two dust lifting processes are commonly represented in these models: wind-stress lifting (i.e., saltation) and dust devil lifting. Although the predicted patterns of dust lifting and atmospheric dust loading from these simulations capture some aspects of the observed dust cycle, there are many notable differences between the simulated and observed dust cycles. For example, it is common for models to predict one peak in global dust loading near northern winter solstice due to excessive dust lifting in the Hellas basin at this season. Additionally, it is difficult for models to realistically capture the observed interannual variability in global dust storms. New avenues of dust cycle modeling research include exploring the effects of finite surface dust reservoirs and the effects of coupling the dust and water cycles on the predicted dust cycle.

Kahre, Melinda A.↗

DOE OSTI2024

Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration

Geologic carbon sequestration (GCS) is a safety-critical technology that aims to reduce the amount of carbon dioxide in the atmosphere, which also places high demands on reliability. Multiphase flow in porous media is essential to understand CO 2 migration and pressure fields in the subsurface associated with GCS. However, numerical simulation for such problems in 4D is computationally challenging and expensive, due to the multiphysics and multiscale nature of the highly nonlinear governing partial differential equations (PDEs). It prevents us from considering multiple subsurface scenarios and conducting real-time optimization. Here, we develop a Fourier-enhanced multiple-input neural operator (Fourier-MIONet) to learn the solution operator of the problem of multiphase flow in porous media. Fourier-MIONet utilizes the recently developed framework of the multiple-input deep neural operators (MIONet) and incorporates the Fourier neural operator (FNO) in the network architecture. Once Fourier-MIONet is trained, it can predict the evolution of saturation and pressure of the multiphase flow under various reservoir conditions, such as permeability and porosity heterogeneity, anisotropy, injection configurations, and multiphase flow properties. Compared to the enhanced FNO (U-FNO), the proposed Fourier-MIONet has 90% fewer unknown parameters, and it can be trained in significantly less time (about 3.5 times faster) with much lower CPU memory (<15%) and GPU memory (<35%) requirements, to achieve similar prediction accuracy. In addition to the lower computational cost, Fourier-MIONet can be trained with only 6 snapshots of time to predict the PDE solutions for 30 years. Furthermore, we observed that Fourier-MIONet can maintain good accuracy when predicting out-of-distribution (OOD) data. The excellent generalizability of Fourier-MIONet is enabled by its adherence to the physical principle that the solution to a PDE is continuous over time. Furthermore, the developed Fourier-MIONet makes it possible to solve the long-time evolution of geological carbon sequestration in a large-scale three-dimensional space accurately and efficiently.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Utah FORGE: Optimization of a Plug-and-Perf Stimulation (Fervo Energy)

Information around the plug-and-perf treatment design at Utah FORGE by Fervo Energy. Objective and Purpose: - Develop a multistage hydraulic stimulation approach designed specifically to target the top three factors that control the technical and commercial viability of an EGS system: i) Achieving sufficient injectivity to support high cross-well flow rates ii) Distributing flow evenly across the wellbore and reservoir to maximize heat mining efficiency, ensure sustained heat transfer, and mitigate thermal breakthrough iii) Overcoming the effects of stress heterogeneity, stress shadowing, and variations in natural fracture properties during the stimulation treatment, leading to a more predictable stimulated reservoir volume and offset well placement - The following activities will be performed: i) Design, plan, and execute a multistage plug-and-perf stimulation treatment at a Fervo site with data acquisition and well testing activities aimed at addressing key technical aspects of the issues above ii) Perform data processing and interpretation of field results to translate the results form the Fervo site to a site-specific design at the Utah FORGE site iii) Design, plan, and execute a multistage plug-and-perf stimulation treatment design at the Utah FORGE site Methods and Approach: - Design a detailed data acquisition plan to maximize learning around: i) DFIT testing ii) Petrophysical logging, image logging iii) Permanent DAS/DTS fiber optic monitoring iv) Deep borehole microseismic monitoring v) Shallow borehole induced seismicity monitoring vi) Injection/production testing (RTA analysis, tracer testing) vii) Integrated numerical modeling and production forecasting

15 GEOTHERMAL ENERGY↗

DOE OSTI2020

Simulating Fracture Sealing by Granular LCM Particles in Geothermal Drilling

Lost circulation occurs when the returned fluid is less than what is pumped into the well due to loss of fluid to pores or fractures. A lost-circulation event is a common occurrence in a geothermal well. Typical geothermal reservoirs are often under-pressured and have larger fracture apertures. A severe lost-circulation event is costly and may lead to stuck pipe, well instability, and well abandonment. One typical treatment is adding lost-circulation materials (LCMs) to seal fractures. Conventional LCMs fail to properly seal fractures because their mechanical limit is exceeded at elevated temperatures. In this paper, parametric studies in numerical simulations are conducted to better understand different thermal effects on the sealing mechanisms of LCMs. The computational fluid dynamics (CFDs) and the discrete element method (DEM) are coupled to accurately capture the true physics of sealing by granular materials. Due to computational limits, the traditional Eulerian–Eulerian approach treats solid particles as a group of continuum matter. With the advance of modern computational power, particle bridging is achievable with DEM to track individual particles by modeling their interactive forces between each other. Particle–fluid interactions can be modeled by coupling CFD algorithms. Fracture sealing capability is investigated by studying the effect of four individual properties including fluid viscosity, particle size, friction coefficient, and Young’s modulus. It is found that thermally degraded properties lead to inefficient fracture sealing.

15 GEOTHERMAL ENERGY↗

DOE OSTI2026

Analytic rate theory of polariton relaxation that explains long polariton lifetime

Hybridization of a molecular exciton with a quantized photon creates a polariton. Despite extensive experimental investigations, the apparent lifetime of the exciton–polariton is not well-understood. For this work, we examined the steady-state population dynamics for a Holstein–Tavis–Cumming Hamiltonian to illuminate the long-term polaritonic dynamics and lifetime of the exciton–polariton in an optical cavity. For a realistic description of polariton relaxation, cavity loss and various exciton decay channels are included in the model. We found that in the presence of weak but finite exciton loss, the apparent lifetime of the lower polariton coincides with the out-of-cavity exciton lifetime and is independent of cavity-matter detuning. This is a simple explanation for the experimentally observed lifetimes for exciton polaritons and theoretically justifies the dark state reservoir hypothesis. Furthermore, if the upper polariton is initially populated, the system reaches the steady state very quickly, leading to single-exponential polariton relaxation. Starting from the lower polariton leads to a longer pre-steady-state time period, leading to double-exponential relaxation. Finally, we considered the effect of site orientational disorders and the exciton frequency disorderers. Under the collective limit, the effects of this disorder can be included in Fermi’s golden rule population dynamics without explicit sampling. For the exciton energy disorders, numerical calculations are needed. Our theoretical framework is applicable to interpret exciton–polariton experiments, especially related to the measured apparent lifetime of polaritons.

Chemical dynamics↗

DOE OSTI2020

Analyzing the Validity of Brazilian Testing Using Digital Image Correlation and Numerical Simulation Techniques

Characterizing the mechanical behavior of rocks plays a crucial role to optimize the fracturing process in unconventional reservoirs. However, due to the intrinsic anisotropy and heterogeneity in unconventional resources, fracture process prediction remains the most significant challenge for sustainable and economic hydrocarbon production. During the deformation tracking under compression, deploying conventional methods (strain gauge, extensometer, etc.) is insufficient to measure the deformation since the physical attachment of the device is restricted to the size of the sample, monitoring limited point-wise deformation, producing difficulties in data retrieval, and a tendency to lose track in failure points, etc. Where conventional methods are limited, the application of digital image correlation (DIC) provides detailed and additional information of strain evolution and fracture patterns under loading. DIC is an image-based optical method that records an object with a camera and monitors the random contrast speckle pattern painted on the facing surface of the specimen. To overcome the existing limitations, this paper presents numerical modeling of Brazilian disc tests under quasi-static conditions to understand the full-field deformation behaviors and finally, it is validated by DIC. As the direct tensile test has limitations in sample preparation and test execution, the Brazilian testing principle is commonly used to evaluate indirectly the tensile strength of rocks. The two-dimensional numerical model was built to predict the stress distribution and full-field deformation on Brazilian disc under compression based on the assumptions of a homogenous, isotropic and linear elastic material. The uniaxial compression test was conducted using the DIC technique to determine the elastic properties of Spider Berea sandstone, which were used as inputs for the simulation model. The model was verified by the analytical solution and compared with the digital image correlation. The numerical simulation results showed that the solutions matched reasonably with the analytical solutions where the maximum deviation of stress distribution was obtained as 14.59%. The strain evolution (normal and shear strains) and displacements along the central horizontal and vertical planes were investigated in three distinguishable percentages of peak loads (20%, 40%, and 90%) to understand the deformation behaviors in rock. The simulation results demonstrated that the strain evolution contours consistently matched with DIC generated contours with a reasonable agreement. The changes in displacement along the central horizontal and vertical planes showed that numerical simulation and DIC generated experimental results were repeatable and matched closely. In terms of validation, Brazilian testing to measure the indirect tensile strength of rocks is still an issue of debate. The numerical model of fracture propagation supported by digital image correlation from this study can be used to explain the fracturing process in the homogeneous material and can be extended to non-homogeneous cases by incorporating heterogeneity, which is essential for rock mechanics field applications.

58 GEOSCIENCES↗

DOE OSTI2023

Natural Fracture Pattern Development Using 3D Coupled Numerical Model: Mechanics and Diagenesis

In the earth's crust, while tectonic processes can mechanically create natural fractures, cementation can simultaneously grow on the surfaces of the fractures. This crystallization can affect the growth of the fractures. To more accurately address the modeling of natural fracture development, both mechanical deformation and chemical diagenesis should be carried out in an integrated manner. Here, we achieve this with a three-dimensional (3D) simulator that couples mechanical and chemical processes for natural fracture pattern development in layer confined situations. Tectonic loading over geologic time is mimicked by displacement driven boundary conditions in a 3D displacement discontinuity method (DDM) scheme. Quartz diagenesis competes with mechanical fracture opening, resulting in three different cementation conditions - open, bridging or fully infilled – which define fracture element stiffness that is used as feedback within the 3D-DDM code during propagation. An important milestone of this work is that this coupled structural diagenetic model can reproduce power-law aperture population statistics which cannot be generated using mechanics alone. More accurately estimated mechanical fracture apertures and their cementation condition is a critical step toward better permeability estimation and fluid path analysis for subsurface reservoirs.

58 GEOSCIENCES↗

DOE OSTI2023

Convergence analysis of single rate and multirate fixed stress split iterative coupling schemes in heterogeneous poroelastic media

Recently, the accurate modeling of flow–structure interactions has gained more attention and importance for both petroleum and environmental engineering applications. Of particular interest is the coupling between subsurface flow and reservoir geomechanics. Different single rate and multirate iterative and explicit coupling schemes have been proposed and analyzed in the past. In addition, Banach fixed point contraction results were obtained for iterative coupling schemes, and conditionally stable results were obtained for explicit coupling schemes. In this work, we will consider the mathematical analysis of the single rate and multirate fixed stress split iterative coupling schemes for spatially heterogeneous poroelastic media. We will re–establish the contractivity for both schemes in the localized case, and we will show that heterogeneities come at the expense of imposing more restricted conditions on the number of fine flow time steps that can be taken within one coarse mechanics time step in the multirate case. Our mathematical analysis is supplemented by numerical simulations validating our derived upper bounds. Finally, to the best of our knowledge, this is the first rigorous mathematical analysis of the multirate fixed–stress split iterative coupling scheme in heterogeneous poroelastic media.

97 MATHEMATICS AND COMPUTING↗