Characterization of induced microseismicity at a carbon dioxide (CO2) storage site is critical for preserving reservoir integrity and mitigating seismic hazards. We apply a multilevel machine learning (ML) approach that combines the nonnegative matrix factorization and hidden Markov model to extract spectral representations of microseismic events and cluster them to identify seismic patterns at the Illinois Basin-Decatur Project. Unlike traditional waveform correlation methods, this approach leverages spectral characteristics of first arrivals to improve event classification and detect previously undetected planes of weakness. By integrating ML-based clustering with focal mechanism analysis, we resolve small-scale fault structures that are below the detection limits of conventional seismic imaging. Our findings reveal temporal bursts of microseismicity associated with brittle failure, providing insights into the spatio-temporal evolution of fault reactivation during CO2 injection. This approach enhances seismic monitoring capabilities at CO2 injection sites by improving fault characterization beyond the resolution of standard geophysical surveys.
We numerically investigate the mechanisms that resulted in induced seismicity occurrence associated with CO2 injection at the Illinois Basin–Decatur Project (IBDP). We build a geologi-cally consistent model that honors key stratigraphic horizons and 3D fault surfaces inter-preted using surface seismic data and microseismicity locations. We populate our model with reservoir and geomechanical properties estimated using well-log and core data. We then performed coupled multiphase flow and geomechanics modeling to investigate the impact of CO2 injection on fault stability using the Coulomb failure criteria. We calibrate our flow model using measured reservoir pressure during the CO2 injection phase. Our model results show that pore-pressure diffusion along faults connecting the injection inter-val to the basement is essential to explain the destabilization of the regions where micro-seismicity occurred, and that poroelastic stresses alone would result in stabilization of those regions. Slip tendency analysis indicates that, due to their orientations with respect to the maximum horizontal stress direction, the faults where the microseismicity occurred were very close to failure prior to injection. These model results highlight the importance of accurate subsurface fault characterization for CO2 sequestration operations.
The transmission interference fringe (TIF) technique was developed to visualize the dynamics of evaporating droplets based on the Reflection Interference Fringe (RIF) technique for micro-sized droplets. The geometric formulation was conducted to determine the contact angle (CA) and height of macro-sized droplets without the need for the prism used in RIF. The TIF characteristics were analyzed through experiments and simulations to demonstrate a wider range of contact angles from 0 to 90°, in contrast to RIF's limited range of 0-30°. TIF was utilized to visualize the dynamic evaporation of droplets in the constant contact radius (CCR) mode, observing the droplet profile change from convex-only to convex-concave at the end of dry-out from the interference fringe formation. The TIF also observed the contact angle increase from the fringe radius increase. This observation is uniquely reported as the interference fringe (IF) technique can detect the formation of interference fringe between the reflection from the center convex profile and the reflection from the edge concave profile on the far-field screen. Unlike general microscopy techniques, TIF can detect far-field interference fringes as it focuses beyond the droplet-substrate interface. The formation of the convex-concave profile during CCR evaporation is believed to be influenced by the non-uniform evaporative flux along the droplet surface.
Estimating spatially distributed properties such as permeability from available sparse measurements is a great challenge in efficient subsurface CO2 storage operations. In this paper, a deep generative model that can accurately capture complex subsurface structure is tested with an ensemble-based inversion method for accurate and accelerated characterization of CO2 storage sites. We chose Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) for its realistic reservoir property representation and Ensemble Smoother with Multiple Data Assimilation (ES-MDA) for its robust data fitting and uncertainty quantification capability. WGAN-GP are trained to generate high-dimensional permeability fields from a low-dimensional latent space and ES-MDA then updates the latent variables by assimilating available measurements. Several subsurface site characterization examples including Gaussian, channelized, and fractured reservoirs are used to evaluate the accuracy and computational efficiency of the proposed method and the main features of the unknown permeability fields are characterized accurately with reliable uncertainty quantification. Furthermore, the estimation performance is compared with a widely-used variational, i.e., optimization-based, inversion approach, and the proposed approach outperforms the variational inversion method in several benchmark cases. We explain such superior performance by visualizing the objective function in the latent space: because of nonlinear and aggressive dimension reduction via generative modeling, the objective function surface becomes extremely complex while the ensemble approximation can smooth out the multi-modal surface during the minimization. This suggests that the ensemble-based approach works well over the variational approach when combined with deep generative models at the cost of forward model runs unless convergence-ensuring modifications are implemented in the variational inversion.
Morris, Joseph P.; Pyrak-Nolte, Laura J.; Yoon, Hongkyu; Bobet, Antonio; Jiang, Liyang
In this article, We present results from a recent exercise where participating organizations were asked to provide model-based blind predictions of damage evolution in 3D-printed geomaterial analogue test articles. Participants were provided with a range of data characterizing both the undamaged state (e.g., ultrasonic measurements) and damage evolution (e.g., 3-point bending, unconfined compression, and Brazilian testing) of the material. In this paper, we focus on comparisons between the participants’ predictions and the previously secret challenge problem experimental observations. We present valuable lessons learned for the application of numerical methods to deformation and failure in brittle-ductile materials. The exercise also enables us to identify which specific types of calibration data were of most utility to the participants in developing their predictions. Further, we identify additional data that would have been useful for participants to improve the confidence of their predictions. Consequently, this work improves our understanding of how to better characterize a material to enable more accurate prediction of damage and failure propagation in natural and engineered brittle-ductile materials.
Kim, Iltai I.; Lie, Yang; Park, Jaesung; Kim, Hyun J.; Kim, Hong C.; Yoon, Hongkyu
We show that the reflection interference fringe (RIF) is formed on a screen far away from the microdroplets placed on a prism-based substrate, which have low contact angles and thin droplet heights, caused by the dual convex-concave profile of the droplet, not a pure convex profile. The geometric formulation shows that the interference fringes are caused by the optical path difference when the reflected rays from the upper convex profile at the droplet-air interface interfere with reflection from the lower concave profile at oblique angles lower than the critical angle. Analytic solutions are obtained for the droplet height and the contact angle out of the fringe number and the fringe radius in RIF from the geometric formulation. Furthermore, the ray tracing simulation is conducted using the custom-designed code. The geometric formulation and the ray tracing show excellent agreement with the experimental observation in the relation between the droplet height and the fringe number and the relation between the contact angle and the fringe radius. This study is remarkable as the droplet's dual profile cannot be easily observed with the existing techniques. However, the RIF technique can effectively verify the existence of a dual profile of the microdroplets in a simple setup. In this work, the RIF technique is successfully developed as a new optical diagnostic technique to determine the microdroplet features, such as the dual profile, the height, the contact angle, the inflection point, and the precursor film thickness, by simply measuring the RIF patterns on the far-field screen.
This study introduces the Progressive Improved Neural Operator (p-INO) framework, aimed at advancing machine-learning-based reduced-order models within geomechanics for underground resource optimization and carbon sequestration applications.The p-INO method transcends traditional transfer learning limitations through progressive learning, enhancing the capability of transferring knowledge from many sources.Through numerical experiments, the performance of p-INO is benchmarked against standard Improved Neural Operators (INO) in scenarios varying by data availability (different number of training samples).The research utilizes simulation data reflecting scenarios like single-phase, two-phase, and two-phase flow with mechanics inspired by the Illinois Basin Decatur Project.Results reveal that p-INO significantly surpasses conventional INO models in accuracy, particularly in data-constrained environments.Besides, adding more priori information (more trained models used by p-INO) can further enhance the process.This experiment demonstrates p-INO's robustness in leveraging sparse datasets for precise predictions across complex subsurface physics scenarios.The findings underscore the potential of p-INO to revolutionize predictive modeling in geomechanics, presenting a substantial improvement in computational efficiency and accuracy for large-scale subsurface simulations.
Characterizing the subsurface properties such as permeability and thermal conductivity is important for stimulation planning and heat production in enhanced geothermal systems (EGS). Data assimilation methods, such as the Kalman-type methods, are widely used for characterization by assimilating observed dynamic data such as pressure and temperature. However, these approaches only perform well when the parameters follow a Gaussian distribution. The geothermal sites are usually highly heterogeneous with non-Gaussian distributed complex structures such as faults and fractures, which are difficult to characterize. Over the past few years, emerging deep generative models and their impressive applications in different tasks have provided a solution to produce images with complicated features. In this work, we use the Wasserstein Generative Adversarial Network (WGAN), a deep generative model, to generate fractured images from the low-dimensional and Gaussian distributed latent space. The ensemble method, a Kalman-type data assimilation method, is then applied to the latent variables to characterize the permeability fields of a fractured geothermal site using temperature data. A synthetic two-dimensional example is presented to show the performance of our approach.
Uncertainty quantification (UQ) plays a vital role in addressing the challenges and limitations encountered in full-waveform inversion (FWI). Most UQ methods require parameter sampling which requires many forward and adjoint solves. This often results in very high computational overhead compared to traditional FWI, which hinders the practicality of the UQ for FWI. In this work, we develop an efficient UQ-FWI framework based on unsupervised variational autoencoder (VAE) to assess the uncertainty of single and multi-parameter FWI. The inversion operator is modeled using an encoder-decoder network. The input to the network is seismic shot gathers and the output are samples (distribution) of model parameters. We then use these samples to estimate the mean and standard deviation of each parameter population, which provide insights on the uncertainty in the inversion process. To speed up the UQ process, we carried out the reconstruction in an unsupervised learning approach. Moreover, we physics-constrained the network by injecting the FWI gradients during the backpropagation process, leading to better reconstruction. The computational cost of the proposed approach is comparable to the traditional autoencoder full-waveform inversion (AE-FWI), which is encouraging to be used to get further insight on the quality of the inversion. We apply this idea for synthetic data to show its potential in assessing uncertainty in multi-parameter FWI.
Induced seismicity is an inherent risk associated with geologic carbon storage (GCS) in deep rock formations that could contain undetected faults prone to failure. Modeling-based risk assessment has been implemented to quantify the potential of injection-induced seismicity, but typically simplified multiscale geologic features or neglected multiphysics coupled mechanisms because of the uncertainty in field data and computational cost of field-scale simulations, which may limit the reliable prediction of seismic hazard caused by industrial-scale CO2 storage. The degree of lateral continuity of the stratigraphic interbedding below the reservoir and depth-dependent fault permeability can enhance or inhibit pore-pressure diffusion and corresponding poroelastic stressing along a basement fault. This study presents a rigorous modeling scheme with optimal geological and operational parameters needed to be considered in seismic monitoring and mitigation strategies for safe GCS.
Laboratory shear tests were conducted on pulverized Sierra White granite (SWG) to investigate slip mechanisms in naturally occurring faults. Synthetic fault geometries were constructed by sandwiching fine grained SWG powder in between steel forcing blocks. For dry experiments, ~3.5 g of SWG powder was poured onto the face of the lower steel forcing block and leveled. For saturated experiments, enough fluid was added to the ~3.5 g of Sierra White granite powder to form a slurry. This slurry was applied to the lower forcing block and leveled. Inclined forcing blocks with 25.4 mm diameter and 35° faces, which were machined from ground steel rods with fine teeth on the faces, help to hold the gouge in place and prevent delamination at the interface. The top forcing block had a 2.03 mm centered hole to allow pore fluid access to the gouge. A fine steel mesh prevented back flow of the gouge into pore fluid lines. Samples were isolated from the confining medium using three layers of heat shrink polyolefin, as shown in Figure 1. The outer layer was shrunk over the o-rings on the end caps to form an impermeable seal, which was reinforced with steel tie wires on both sides of the o-rings. Hardened steel spacers and copper shim stock was placed between the steel forcing blocks and the end caps to preserve the parallelism of the Hastelloy wetted parts. For dry samples, the end caps were plugged, while the end caps for the saturated samples were connected to pore lines.
Polymer concrete (PC) has been used to replace cement concrete when harsh service conditions exist. Polymers have a high carbon footprint when considering their life cycle analysis, and with increased climate change concerns and the need to reduce greenhouse gas emission, bio-based polymers could be used as a sustainable alternative binder to produce PC. This paper examines the development and characterization of a novel bio-polymer concrete (BPC) using bio-based polyurethane used as the binder in lieu of cement, modified with benzoic acid and carboxyl-functionalized multi-walled carbon nanotubes (MWCNTs). The mechanical performance, durability, microstructure, and chemical properties of BPC are investigated. Moreover, the effect of the addition of benzoic acid and MWCNTs on the properties of BPC is studied. The new BPC shows relatively low density, appreciable compressive strength between 20–30 MPa, good tensile strength of 4 MPa, and excellent durability resistance against aggressive environments. The new BPC has a low carbon footprint, 50% lower than ordinary Portland cement concrete, and can provide a sustainable concrete alternative in infrastructural applications.
This study presents a method for constructing machine learning-based reduced order models (ROMs) that accurately simulate nonlinear contact problems while quantifying epistemic uncertainty. These purely non-intrusive ROMs significantly lower computational costs compared to traditional full order models (FOMs). The technique utilizes adversarial training combined with an ensemble of Barlow twins reduced order models (BT-ROMs) to maximize the information content of the nonlinear reduced manifolds. These lower-dimensional manifolds are equipped with Gaussian error estimates, allowing for quantifying epistemic uncertainty in the ROM predictions. The effectiveness of these ROMs, referred to as UQ-BT-ROMs, is demonstrated in the context of contact between a rigid indenter and a hyperelastic substrate under finite deformations. The ensemble of BT-ROMs improves accuracy and computational efficiency compared to existing alternatives. The relative error between the UQ-BT-ROM and FOM solutions ranges from approximately 3% to 8% across all benchmarks. Remarkably, this high level of accuracy is achieved at a significantly reduced computational cost compared to FOMs. For instance, the online phase of the UQ-BT-ROM takes only 0.001 seconds, while a single FOM evaluation requires 63 seconds. Furthermore, the error estimate produced by the UQ-BT-ROMs reasonably captures the errors in the ROMs, with increasing accuracy as training data increases. The ensemble approach improves accuracy and computational efficiency compared to existing alternatives. The UQ-BT-ROMs provide a cost-effective solution with significantly reduced computational times while maintaining a high level of accuracy.
Bao, Jichao; Lee, Jonghyun; Yoon, Hongkyu; Pyrak-Nolte, Laura
Characterization of geologic heterogeneity at an enhanced geothermal system (EGS) is crucial for cost-effective stimulation planning and reliable heat production. With recent advances in computational power and sensor technology, large-scale fine-resolution simulations of coupled thermal-hydraulic-mechanical (THM) processes have been available. However, traditional large-scale inversion approaches have limited utility for sites with complex subsurface structures unless one can afford high, often computationally prohibitive, computations. Key computational burdens are predominantly associated with a number of large-scale coupled numerical simulations and large dense matrix multiplications derived from fine discretization of the field site domain and a large number of THM and chemical (THMC) measurements. In this work, we present deep-generative model-based Bayesian inversion methods for the computationally efficient and accurate characterization of EGS sites. Deep generative models are used to learn the approximate subsurface property (e.g., permeability, thermal conductivity, and elastic rock properties) distribution from multipoint geostatistics-derived training images or discrete fracture network models as a prior and accelerated stochastic inversion is performed on the low-dimensional latent space in a Bayesian framework. Numerical examples with synthetic permeability fields with fracture inclusions with THM data sets based on Utah FORGE geothermal site will be presented to test the accuracy, speed, and uncertainty quantification capability of our proposed joint data inversion method.
A natural clinoptilolite sample near the Nevada National Security Site was obtained to study adsorption and retardation on gas transport. Of interest is understanding the competition for adsorption sites that may reduce tracer gas adsorption relative to single-component measurements, which may be affected by the multi-scale pore structure of clinoptilolite. Clinoptilolite has three distinct domains of pore size distributions ranging from nanometers to micrometers: micropores with 0.4–0.7 nm diameters, measured on powders by CO2 adsorption at 273 K, representing the zeolite cages; mesopores with 4–200 nm diameters, observed using liquid nitrogen adsorption at 77 K; and macropores with 300–1000 nm diameters, measured by mercury injection on rock chips (~ 100 mesh), likely representing the microfractures. These pore size distributions are consistent with X-ray computed tomography (CT) and focused ion beam scanning electron microscope (FIB-SEM) images, which are used to construct the three-dimensional (3D) pore network to be used in future gas transport modeling. To quantify tracer gas adsorption in this multi-scale pore structure and multicomponent gas species environment, natural zeolite samples initially in equilibrium in air were exposed to a mixture of tracer gases. As the tracer gases diffuse and adsorb in the sample, the remaining tracer gases outside the sample fractionate. Using a quadrupole mass spectrometer to quantify this fractionation, the degree of adsorption of tracer gases in the multicomponent gas environment and multi-scale pore structure is assessed. The major finding is that Kr reaches equilibrium much faster than Xe in the presence of ambient air, which leads to more Kr uptake than Xe over limited exposure periods. When the clinoptilolite chips were exposed to humid air, the adsorption capability decreases significantly for both Xe and Kr with relative humidity (RH) as low as 3%. Both Xe and Kr reaches equilibrium faster at higher RH. The different, unexpected, adsorption behavior for Xe and Kr is due to their kinetic diameters similar to the micropores in clinoptilolite which makes it harder for Xe to access compared to Kr.
Migration of seismic events to deeper depths along basement faults over time has been observed in the wastewater injection sites, which can be correlated spatially and temporally to the propagation or retardation of pressure fronts and corresponding poroelastic response to given operation history. The seismicity rate model has been suggested as a physical indicator for the potential of earthquake nucleation along faults by quantifying poroelastic response to multiple well operations. Our field-scale model indicates that migrating patterns of 2015–2018 seismicity observed near Venus, TX are likely attributed to spatio-temporal evolution of Coulomb stressing rate constrained by the fault permeability. Even after reducing injection volumes since 2015, pore pressure continues to diffuse and steady transfer of elastic energy to the deep fault zone increases stressing rate consistently that can induce more frequent earthquakes at large distance scales. Sensitivity tests with variation in fault permeability show that (1) slow diffusion along a low-permeability fault limits earthquake nucleation near the injection interval or (2) rapid relaxation of pressure buildup within a high-permeability fault, caused by reducing injection volumes, may mitigate the seismic potential promptly.
Cement production for concrete has been responsible for ~7–8% of global greenhouse gas (GHG) emissions, and nearly equally contribution for steel production processes (EPA, 2020). In order to achieve carbon neutrality by 2050, a novel solution has to be investigated. This project aims to develop fundamental mechanistic understanding and experimental characterization to create a 3D printable biopolymer concrete using plant-based polyurethane as an innovative and sustainable alternative for Portland cement concrete, with significantly low carbon footprint. Future construction will utilize the advances in digital additive manufacturing (3D printing) to produce optimal geometries with a minimum waste of materials. Understanding the polymerization process, factors impacting the composite rheology, and the structural behavior of this biopolymer concrete will enable us to engineer the next generation of concrete structures with low carbon footprint. This project aims to improve the nation’s ability to control Greenhouse Gas emission neutrality for the set goal of 2050 via introducing a structurally viable bio-based polymer concrete.
Accurate estimation of greenhouse gases (GHGs) emissions is very important for developing mitigation strategies to climate change by controlling and reducing GHG emissions. This project aims to develop multiple deep learning approaches to estimate anthropogenic greenhouse gas emissions using multiple types of satellite data. NO2 concentration is chosen as an example of GHGs to evaluate the proposed approach. Two sentinel satellites (sentinel-2 and sentinel-5P) provide multiscale observations of GHGs from 10-60m resolution (sentinel-2) to ~kilometer scale resolution (sentinel-5P). Among multiple deep learning (DL) architectures evaluated, two best DL models demonstrate that key features of spatio-temporal satellite data and additional information (e.g., observation times and/or coordinates of ground stations) can be extracted using convolutional neural networks and feed forward neural networks, respectively. In particular, irregular time series data from different NO2 observation stations limit the flexibility of long short-term memory architecture, requiring zero-padding to fill in missing data. However, deep neural operator (DNO) architecture can stack time-series data as input, providing the flexibility of input structure without zero-padding. As a result, the DNO outperformed other deep learning architectures to account for time-varying features. Overall, temporal patterns with smooth seasonal variations were predicted very well, while frequent fluctuation patterns were not predicted well. In addition, uncertainty quantification using conformal inference method is performed to account for prediction ranges. Overall, this research will lead to a new groundwork for estimating greenhouse gas concentrations using multiple satellite data to enhance our capability of tracking the cause of climate change and developing mitigation strategies.
Prediction of flow, transport, and deformation in fractured and porous media is critical to improving our scientific understanding of coupled thermal-hydrological-mechanical processes related to subsurface energy storage and recovery, nonproliferation, and nuclear waste storage. Especially, earth rock response to changes in pressure and stress has remained a critically challenging task. In this work, we advance computational capabilities for coupled processes in fractured and porous media using Sandia Sierra Multiphysics software through verification and validation problems such as poro-elasticity, elasto-plasticity and thermo-poroelasticity. We apply Sierra software for geologic carbon storage, fluid injection/extraction, and enhanced geothermal systems. We also significantly improve machine learning approaches through latent space and self-supervised learning. Additionally, we develop new experimental technique for evaluating dynamics of compacted soils at an intermediate scale. Overall, this project will enable us to systematically measure and control the earth system response to changes in stress and pressure due to subsurface energy activities.
Accurate modeling of subsurface flow and transport processes is vital as the prevalence of subsurface activities such as carbon sequestration, geothermal recovery, and nuclear waste disposal increases. Computational modeling of these problems leverages poroelasticity theory, which describes coupled fluid flow and mechanical deformation. Although fully coupled monolithic schemes are accurate for coupled problems, they can demand significant computational resources for large problems. In this work, a fixed stress scheme is implemented into the Sandia Sierra Multiphysics toolkit. Two implementation methods, along with the fully coupled method, are verified with one-dimensional (1D) Terzaghi, 2D Mandel, and 3D Cryer sphere benchmark problems. The impact of a range of material parameters and convergence tolerances on numerical accuracy and efficiency was evaluated. Overall the fixed stress schemes achieved acceptable numerical accuracy and efficiency compared to the fully coupled scheme. However, the accuracy of the fixed stress scheme tends to decrease with low permeable cases, requiring the finer tolerance to achieve a desired numerical accuracy. For the fully coupled scheme, high numerical accuracy was observed in most of cases except a low permeability case where an order of magnitude finer tolerance was required for accurate results. Finally, a two-layer Terzaghi problem and an injection–production well system were used to demonstrate the applicability of findings from the benchmark problems for more realistic conditions over a range of permeability. Simulation results suggest that the fixed stress scheme provides accurate solutions for all cases considered with the proper adjustment of the tolerance. This work clearly demonstrates the robustness of the fixed stress scheme for coupled poroelastic problems, while a cautious selection of numerical tolerance may be required under certain conditions with low permeable materials.
Machine learning-based data-driven modeling can allow computationally efficient time-dependent solutions of PDEs, such as those that describe subsurface multiphysical problems. In this work, our previous approach (Kadeethum et al., 2021d) of conditional generative adversarial networks (cGAN) developed for the solution of steady-state problems involving highly heterogeneous material properties is extended to time-dependent problems by adopting the concept of continuous cGAN (CcGAN). The CcGAN that can condition continuous variables is developed to incorporate the time domain through either element-wise addition or conditional batch normalization. Moreover, this framework can handle training data that contain different timestamps and then predict timestamps that do not exist in the training data. As a numerical example, the transient response of the coupled poroelastic process is studied in two different permeability fields: Zinn & Harvey transformation and a bimodal transformation. The proposed CcGAN uses heterogeneous permeability fields as input parameters while pressure and displacement fields over time are model output. Our results show that the model provides sufficient accuracy with computational speed-up. This robust framework will enable us to perform real-time reservoir management and robust uncertainty quantification in poroelastic problems.
Large-scale CO2 sequestration into geological formations has been suggested to reduce CO2 emissions from industrial activities. However, much like enhanced geothermal stimulation and wastewater injection, CO2 sequestration has a potential to induce earthquake along weak faults, which can be considered a negative impact on safety and public opinion. This study shows the physical mechanisms of potential seismic hazards along basement faults driven by CO2 sequestration under variation in geological and operational constraints. Specifically we compare the poroelastic behaviors between multiphase flow and single-phase flow cases, highlighting specific needs of evaluating induced seismicity associated with CO2 sequestration. In contrast to single-phase injection scenario, slower migration of the CO2 plume than pressure pulse may delay accumulation of pressure and stress along basement faults that may not be mitigated immediately by shut-in of injection. The impact of multiphase flow system, therefore, needs to be considered for proper monitoring and mitigation strategies.
Natural convection in porous media is a highly nonlinear multiphysical problem relevant to many engineering applications (e.g., the process of CO2 sequestration). Here, we extend and present a non-intrusive reduced order model of natural convection in porous media employing deep convolutional autoencoders for the compression and reconstruction and either radial basis function (RBF) interpolation or artificial neural networks (ANNs) for mapping parameters of partial differential equations (PDEs) on the corresponding nonlinear manifolds. To benchmark our approach, we also describe linear compression and reconstruction processes relying on proper orthogonal decomposition (POD) and ANNs. Further, we present comprehensive comparisons among different models through three benchmark problems. The reduced order models, linear and nonlinear approaches, are much faster than the finite element model, obtaining a maximum speed-up of 7 × 106 because our framework is not bound by the Courant–Friedrichs–Lewy condition; hence, it could deliver quantities of interest at any given time contrary to the finite element model. Our model’s accuracy still lies within a relative error of 7% in the worst-case scenario. We illustrate that, in specific settings, the nonlinear approach outperforms its linear counterpart and vice versa. We hypothesize that a visual comparison between principal component analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) could indicate which method will perform better prior to employing any specific compression strategy.
Injecting CO2 into a deep geological formation (i.e., geological carbon storage, GCS) can induce earthquakes along preexisting faults in the earth's upper crust. Seismic survey and regional geo-structure analysis are typically employed to map the faults prone to earthquakes prior to injection. However, earthquakes induced by fluid injection from other subsurface energy storage and recovery activities show that systematic evaluation of the potential of induced seismicity associated with GCS is necessary. This study mechanistically investigates how multiphysical interaction among injected CO2, preexisting pore fluids and rock matrix alters stress states on faults and which physical mechanisms can nucleate earthquakes along the faults. Increased injection pressure is needed to overcome capillary entry pressure of the fault zone, driven by the contrast of fluids' wetting characteristics. Accumulated CO2 within the reservoir delays post shut-in reduction in pressure and stress fields along the fault that may enhance the potential for earthquake nucleation after terminating injection operations. Elastic energy generated by coupled processes transfers to low-permeability or hydraulically isolated basement faults, which can initiate slip of the faults. Our findings from generic studies suggest that geomechanical simulations integrated with multiphase flow system are essential to detect deformation-driven signals and mitigate potential seismic hazards associated with CO2 injection.
We propose the use of balanced iterative reducing and clustering using hierarchies (BIRCH) combined with linear regression to predict the reduced Young's modulus and hardness of highly heterogeneous materials from a set of nanoindentation experiments. We first use BIRCH to cluster the dataset according to its mineral compositions, which are derived from the spectral matching of energy-dispersive spectroscopy data through the modular automated processing system (MAPS) platform. We observe that grouping our dataset into five clusters yields the best accuracy as well as a reasonable representation of mineralogy in each cluster. Subsequently, we test four types of regression models, namely linear regression, support vector regression, Gaussian process regression, and extreme gradient boosting regression. The linear regression and Gaussian process regression provide the most accurate prediction, and the proposed framework yields R2 = 0.93 for the test set. Although the study is needed more comprehensively, our results shows that machine learning methods such as linear regression or Gaussian process regression can be used to accurately estimate mechanical properties with a proper number of grouping based on compositional data.
Kim, Sung E.; Seo, Yongwon; Hwang, Junshik; Yoon, Hongkyu; Lee, Jonghyun
Stochastic network modeling is often limited by high computational costs to generate a large number of networks enough for meaningful statistical evaluation. In this study, Deep Convolutional Generative Adversarial Networks (DCGANs) were applied to quickly reproduce drainage networks from the already generated network samples without repetitive long modeling of the stochastic network model, Gibb’s model. In particular, we developed a novel connectivity-informed method that converts the drainage network images to the directional information of flow on each node of the drainage network, and then transforms it into multiple binary layers where the connectivity constraints between nodes in the drainage network are stored. DCGANs trained with three different types of training samples were compared; (1) original drainage network images, (2) their corresponding directional information only, and (3) the connectivity-informed directional information. A comparison of generated images demonstrated that the novel connectivity-informed method outperformed the other two methods by training DCGANs more effectively and better reproducing accurate drainage networks due to its compact representation of the network complexity and connectivity. This work highlights that DCGANs can be applicable for high contrast images common in earth and material sciences where the network, fractures, and other high contrast features are important.
The earth texture with complex morphological geometry and compositions such as shale and carbonate rocks, is typically characterized with sparse field samples because of an expensive and time-consuming characterization process. Accordingly, generating arbitrary large size of the geological texture with similar topological structures at a low computation cost has become one of the key tasks for realistic geomaterial reconstruction and subsequent hydro-mechanical evaluation for science and engineering applications. Recently, generative adversarial neural networks (GANs) have demonstrated a potential of synthesizing input textural images and creating equiprobable geomaterial images for stochastic analysis of hydrogeological properties, for example, the feasibility of CO2 storage sites and exploration of unconventional resources. However, the texture synthesis with the GANs framework is often limited by the computational cost and scalability of the output texture size. In this study, we proposed a spatially assembled GANs (SAGANs) that can generate output images of an arbitrary large size regardless of the size of training images with computational efficiency. The performance of the SAGANs was evaluated with two and three dimensional (2D and 3D) rock image samples widely used in geostatistical reconstruction of the earth texture and Lattice-Boltzmann (LB) simulations were performed to compare pore-scale flow patterns and upscaled permeabilities of training and generated geomaterial images. We demonstrate SAGANs can generate the arbitrary large size of statistical realizations with connectivity and structural properties and flow characteristics similar to training images, and also can generate a variety of realizations even on a single training image. In addition, the computational time was significantly improved compared to standard GANs frameworks.
Subsurface energy activities such as unconventional resource recovery, enhanced geothermal energy systems, and geologic carbon storage require fast and reliable methods to account for complex, multiphysical processes in heterogeneous fractured and porous media. Although reservoir simulation is considered the industry standard for simulating these subsurface systems with injection and/or extraction operations, reservoir simulation requires spatio-temporal “Big Data” into the simulation model, which is typically a major challenge during model development and computational phase. In this work, we developed and applied various deep neural network-based approaches to (1) process multiscale image segmentation, (2) generate ensemble members of drainage networks, flow channels, and porous media using deep convolutional generative adversarial network, (3) construct multiple hybrid neural networks such as convolutional LSTM and convolutional neural network-LSTM to develop fast and accurate reduced order models for shale gas extraction, and (4) physics-informed neural network and deep Q-learning for flow and energy production. We hypothesized that physicsbased machine learning/deep learning can overcome the shortcomings of traditional machine learning methods where data-driven models have faltered beyond the data and physical conditions used for training and validation. We improved and developed novel approaches to demonstrate that physics-based ML can allow us to incorporate physical constraints (e.g., scientific domain knowledge) into ML framework. Outcomes of this project will be readily applicable for many energy and national security problems that are particularly defined by multiscale features and network systems.
Greater utilization of subsurface reservoirs perturbs in-situ chemical-mechanical conditions with wide ranging consequences from decreased performance to project failure. Understanding the chemical precursors to rock deformation is critical to reducing the risks of these activities. To address this need, we investigated the coupled flow-dissolution- precipitation-adsorption reactions involving calcite and environmentally-relevant solid phases. Experimentally, we quantified (1) stable isotope fractionation processes for strontium during calcite nucleation and growth, and during reactive fluid flow; (2) consolidation behavior of calcite assemblages in the common brines. Numerically, we quantified water weakening of calcite using molecular dynamics simulations; and quantified the impact of calcite dissolution rate on macroscopic fracturing using finite element models. With microfluidic experiments and modeling, we show the effect of local flow fields on the dissolution kinetics of calcite. Taken together across a wide range of scales and methods, our studies allow us to separate the effects of reaction, flow, and transport, on calcite fracturing and the evolution of strontium isotopic signatures in the reactive fluids.
Using a combination of geospatial machine learning prediction and sediment thermodynamic/physical modeling, we have developed a novel software workflow to create probabilistic maps of geoacoustic and geomechanical sediment properties of the global seabed. This new technique for producing reliable estimates of seafloor properties can better support Naval operations relying on sonar performance and seabed strength, can constrain models of shallow tomographic structure important for nuclear treaty compliance monitoring/detection, and can provide constraints on the distribution and inventory of shallow methane gas and gas hydrate accumulations on the continental shelves.
Park, Sung W.; Lee, Jonghyun; Yoon, Hongkyu; Shin, Sangwoo
High and low salinity water flooding are common oil recovery processes performed in the oil fields for extracting crude oil from the reservoir. These processes are often performed sequentially, naturally establishing non-uniform salinity in the porous subsurface. In this article, we investigate oil transport in porous media induced by salinity change upon flooding with high and low salinity water. As we observe a large number of impervious dead-ends from three-dimensional imaging of the actual reservoir, we identify that these areas play an important role in oil recovery where the oil transport is governed by the salinity change rather than hydrodynamics. The salinity gradients induced upon high salinity water flooding provide pathways to enhance the transport of oil drops trapped in the dead-end regions via non-equilibrium effects. However, above a critical salinity, we observe a rapid aggregation of drops that lead to the complete blockage of the pore space, thereby inhibiting oil recovery. We also find that, at an intermediate salinity where the drop aggregation is modest, the aggregation rather promotes the oil recovery. Our observations suggest that there exist optimal salinity conditions for maximizing oil recovery during chemical flooding.
55th U.S. Rock Mechanics / Geomechanics Symposium 2021
Jiang, Liyang; Yoon, Hongkyu; Bobet, Antonio; Pyrak-Nolte, Laura J.
Anisotropy in the mechanical properties of rock is often attributed to bedding and mineral texture. Here, we use 3D printed synthetic rock to show that, in addition to bedding layers, mineral fabric orientation governs sample strength, surface roughness and fracture path under mixed mode I and II three point bending tests (3PB). Arrester (horizontal layering) and short traverse (vertical layering) samples were printed with different notch locations to compare pure mode I induced fractures to mixed mode I and II fracturing. For a given sample type, the location of the notch affected the intensity of mode II loading, and thus affected the peak failure load and fracture path. When notches were printed at the same location, crack propagation, peak failure load and fracture surface roughness were found to depend on both the layer and mineral fabric orientations. The uniqueness of the induced fracture path and roughness is a potential method for the assessment of the orientation and relative bonding strengths of minerals in a rock. With this information, we will be able to predict isotropic or anisotropic flow rates through fractures which is vital to induced fracturing, geothermal energy production and CO2 sequestration.
Coupled poroelastic stressing and pore-pressure accumulation along pre-existing faults in deep basement contribute to recent occurrence of seismic events at subsurface energy exploration sites. Our coupled fluid-flow and geomechanical model describes the physical processes inducing seismicity corresponding to the sequential stimulation operations in Pohang, South Korea. Simulation results show that prolonged accumulation of poroelastic energy and pore pressure along a fault can nucleate seismic events larger than Mw3 even after terminating well operations. In particular the possibility of large seismic events can be increased by multiple-well operations with alternate injection and extraction that can enhance the degree of pore-pressure diffusion and subsequent stress transfer through a rigid and low-permeability rock to the fault. This study demonstrates that the proper mechanistic model and optimal well operations need to be accounted for to mitigate unexpected seismic hazards in the presence of the site-specific uncertainty such as hidden/undetected faults and stress regime.
Recent observations of seismic events at the subsurface energy exploration sites show that spatial and temporal correlations sometimes do not match the spatial order of the known or detected fault location from the injection well. This study investigates the coupled flow and geomechanical control on the patterns of induced seismicity along multiple basement faults that show an unusual spatiotemporal relation with induced seismicity occurring in the far field first, followed by the near field. Two possible geological scenarios considered are (1) the presence of conductive hydraulic pathway within the basement connected to the distant fault (hydraulic connectivity) and (2) no hydraulic pathway, but the coexistence of faults with mixed polarity (favorability to slip) as observed at Azle, TX. Based on the Coulomb stability analysis and seismicity rate estimates, simulation results show that direct pore pressure diffusion through a hydraulic pathway to the distant fault can generate a larger number of seismicity than along the fault close to the injection well. Prior to pore pressure diffusion, elastic stress transfer can initiate seismic activity along the favorably oriented fault, even at the longer distance to the well, which may explain the deep 2013–2014 Azle earthquake sequences. This study emphasizes that hydrological and geomechanical features of faults will locally control poroelastic coupling mechanisms, potentially influencing the spatiotemporal pattern of injection-induced seismicity, which can be used to infer subsurface architecture of fault/fracture networks.
This report summarizes the results obtained during the LDRD project entitled "Partitioning of Complex Fluids at Mineral Interfaces." This research addressed fundamental aspects of such interfaces, which are relevant to energy-water applications in the subsurface, including fossil energy extraction and carbon sequestration. This project directly addresses the problem of selectivity of complex fluid components at mineral-fluid interfaces, where complex fluids are defined as a mixture of hydrophobic and hydrophilic components: e.g., water, aqueous ions, polar/nonpolar organic compounds. Specifically, this project investigates how adsorption selectivity varies with surface properties and fluid composition. Both experimental and molecular modeling techniques were used to better understand trends in surface wettability on mineral surfaces. The experimental techniques spanned the macroscale (contact angle measurements) to the nanoscale (cryogenic electronic microscopy and vibrational spectroscopy). We focused on an anionic surfactant and a well-characterized mineral phase representative of clay phases present in oil- and gas-producing shale deposits. Collectively, the results consistently demonstrate that the presence of surfactant in the aqueous fluid significantly affects the mineral-fluid interfacial structure. Experimental and molecular modeling results reveal details of the surfactant structure at the interface, and how this structure varies with surfactant coverage and fluid composition.
Permeability prediction of porous media system is very important in many engineering and science domains including earth materials, bio-, solid-materials, and energy applications. In this work we evaluated how machine learning can be used to predict the permeability of porous media with physical properties. An emerging challenge for machine learning/deep learning in engineering and scientific research is the ability to incorporate physics into machine learning process. We used convolutional neural networks (CNNs) to train a set of image data of bead packing and additional physical properties such as porosity and surface area of porous media are used as training data either by feeding them to the fully connected network directly or through the multilayer perception network. Our results clearly show that the optimal neural network architecture and implementation of physics-informed constraints are important to properly improve the model prediction of permeability. A comprehensive analysis of hyperparameters with different CNN architectures and the data implementation scheme of the physical properties need to be performed to optimize our learning system for various porous media system.
In this work, we have characterized the calcium carbonate (CaCO3) precipitates over time caused by reaction-driven precipitation and dissolution in a micromodel. Reactive solutions were continuously injected through two separate inlets, resulting in transverse-mixing induced precipitation during the precipitation phase. Subsequently, a dissolution phase was conducted by injecting clean water (pH = 4). The evolution of precipitates was imaged in two and three dimensions (2-, 3-D) at selected times using optical and confocal microscopy. With estimated reactive surface area, effective precipitation and dissolution rates can be quantitatively compared to results in the previous works. Our comparison indicates that we can evaluate the spatial and temporal variations of effective reactive areas more mechanistically in the microfluidic system only with the knowledge of local hydrodynamics, polymorphs, and comprehensive image analysis. Our analysis clearly highlights the feedback mechanisms between reactions and hydrodynamics. Pore-scale modeling results during the dissolution phase were used to account for experimental observations of dissolved CaCO3 plumes with dissolution of the unstable phase of CaCO3. Mineral precipitation and dissolution induce complex dynamic pore structures, thereby impacting pore-scale fluid dynamics. Pore-scale analysis of the evolution of precipitates can reveal the significance of chemical and pore structural controls on reaction and fluid migration.
Quantifying in-situ subsurface stresses and predicting fracture development are critical to reducing risks of induced seismicity and improving modern energy activities in the subsurface. In this work, we developed a novel integration of controlled mechanical failure experiments coupled with microCT imaging, acoustic sensing, modeling of fracture initiation and propagation, and machine learning for event detections and waveform characterization. Through additive manufacturing (3D printing), we were able to produce bassanite-gypsum rock samples with repeatable physical, geochemical and structural properties. With these "geoarchitected" rock, we provided the role of mineral texture orientation on fracture surface roughness. The impact of poroelastic coupling on induced seismicity has been systematically investigated to improve mechanistic understanding of post shut-in surge of induced seismicity. This research will set the groundwork for characterizing seismic waveforms by using multiphysics and machine learning approaches and improve the detection of low-magnitude seismic events leading to the discovery of hidden fault/fracture systems.
Pore structure is an important parameter to quantify the reservoir rock adsorption capability and diffusivity, both of which are fundamental reservoir properties to evaluate the gas production and carbon sequestration potential for coalbed methane (CBM) and shale gas reservoirs. In this study, we applied small-angle neutron scattering (SANS) to characterize the total and accessible pore structures for two coal and two shale samples. We carried out in situ SANS measurements to probe the accessible pore structure differences under argon, deuterated methane (CD 4 ), and CO 2 penetrations. The results show that the total porosity ranges between 0.25 and 5.8% for the four samples. Less than 50% of the total pores are accessible to CD 4 for the two coals, while more than 75% of the pores were found to be accessible for the two shales. This result suggests that organic matter pores tend to be disconnected compared to mineral matter pores. Argon pressurization can induce pore contraction because of the mechanical compression of the solid skeleton in both the coal and shale samples. Hydrostatic compression has a higher effect on the nanopores of coal and shale with a higher accessible porosity. Both methane and CO 2 injection can reduce the accessible nanopore volume due to a combination of mechanical compression, sorption-induced matrix swelling, and adsorbed molecule occupation. CO 2 has higher effects on sorption-induced matrix swelling and pore filling compared to methane for both the coal and shale samples. Gas densification and pore filling could occur at higher pressures and smaller pore sizes. In addition, the compression and adsorption could create nanopores in the San Juan coal and Marcellus shale drilled core but could have an opposite effect in the other samples, namely, the processes could damage the nanopores in the Hazleton coal and Marcellus shale outcrop.
The fluid injection into the subsurface perturbs the states of pore pressure and stress on the pre-existing faults, potentially causing earthquakes. In the multiphase flow system, the contrast of fluid and rock properties between different structures produces the changes in pressure gradients and subsequently stress fields. Assuming two-phase fluid flow (gas-water system) and poroelasticity, we simulate the three-layered formation including a basement fault, in which injection-induced pressure encounters the fault directly given injection scenarios. The single-phase poroelasticity model with the same setting is also conducted to evaluate the multiphase flow effects on poroelastic response of the fault to gas injection. Sensitivity tests are performed by varying the fault permeability. The presence of gaseous phase reduces the pressure buildup within the highly gas-saturated region, causing less Coulomb stress changes, whereas capillarity increases the pore pressure within the gas-water mixed region. Even though the gaseous plume does not approach the fault, the poroelastic stressing can affect the fault stability, potentially the earthquake occurrence.
The fluid injection into the subsurface perturbs the states of pore pressure and stress on the pre-existing faults, potentially causing earthquakes. In the multiphase flow system, the contrast of fluid and rock properties between different structures produces the changes in pressure gradients and subsequently stress fields. Assuming two-phase fluid flow (gas-water system) and poroelasticity, we simulate the three-layered formation including a basement fault, in which injection-induced pressure encounters the fault directly given injection scenarios. The single-phase poroelasticity model with the same setting is also conducted to evaluate the multiphase flow effects on poroelastic response of the fault to gas injection. Sensitivity tests are performed by varying the fault permeability. The presence of gaseous phase reduces the pressure buildup within the highly gas-saturated region, causing less Coulomb stress changes, whereas capillarity increases the pore pressure within the gas-water mixed region. Even though the gaseous plume does not approach the fault, the poroelastic stressing can affect the fault stability, potentially the earthquake occurrence.
Jiang, Liyang; Yoon, Hongkyu; Bobet, Antonio; Pyrak-Nolte, Laura J.
Anisotropy in the mechanical properties of rock is often attributed to layering or mineral texture. Here, results from a study on mode I fracturing are presented that examine the effect of layering and mineral orientation fracture toughness and roughness. Additively manufactured gypsum rock was created through 3D printing with bassanite/gypsum. The 3D printing process enabled control of the orientation of the mineral texture within the printed layers. Three-point bending (3PB) experiments were performed on the 3D printed rock with a central notch. Unlike cast gypsum, the 3D-printed gypsum exhibited ductile post-peak behavior in all cases. The experiments also showed that the mode I fracture toughness and surface roughness of the induced fracture depended on both the orientation of the bedding relative to the load and the orientation of the mineral texture relative to the layering. This study found that mineral texture orientation, chemical bond strength and layer orientation play dominant roles in the formation of mode I fractures. The uniqueness of the induced fracture roughness is a potential method for the assessment of bonding strengths in rock.
Understanding of the potential to injection–induced seismicity along faults requires the response of fault zone system to spatiotemporal perturbations in pore pressure and stress. In this study, three–dimensional (3–D) model system consisting of the caprock, reservoir, and basement is intersected by vertical strike–slip faults. We examine the full poroelastic behavior of the formation and perform the mechanical analysis along each fault zone using the Coulomb stress change. The magnitude, rate, and location of potential earthquakes are predicted using the spatial distribution of stresses and pore pressure over time. Rapid diffusion of pore pressure into conductive faults initiates failure, but the majority of induced seismicity occurs at deep fault zones due to poroelastic stabilization near the injection interval. Less permeable faults can be destabilized by either delayed pore pressure diffusion or poroelastic stressing. A two–dimensional (2–D) horizontal model, representing the interface between the reservoir and the basement, limits diffusion of pore pressure and deformation of the formation in the vertical direction that may overestimate or underestimate the potential of earthquakes along the fault. Lastly, our numerical results suggest that the 3–D modeling of faulting system including poroelastic coupling can reduce the uncertainty in the seismic hazard prediction by considering the hydraulic and mechanical interaction between faults and bounding formations.
Injection of large amounts of fluid into the subsurface alters the states of pore pressure and stress in the formation, potentially inducing earthquakes. Increase in the seismicity rate after shut-in is often observed at fluid-injection operation sites, but mechanistic study of the rate surge has not been investigated thoroughly. Considering full poroelastic coupling of pore pressure and stress, the earthquake occurrence after shut-in can be driven by two mechanisms: (1) post shut-in diffusion of pore pressure into distant faults and (2) poroelastic stressing caused by fluid injection. Interactions of these mechanisms can depend on fault geometry, hydraulic and mechanical properties of the formation, and injection operation. In this work, a 2D aerial view of the target reservoir intersected by strike-slip basement faults is used to evaluate the impact of injection-induced pressure buildup on seismicity rate surge. A series of sensitivity tests are performed by considering the variation in (1) permeability of the fault zone, (2) locations and the number of faults with respect to the injector, and (3) well operations with time-dependent injection rates. Lower permeability faults have higher seismicity rates than more permeable faults after shut-in due to delayed diffusion and poroelastic stressing. Hydraulic barriers, depending on their relative location to injection, can either stabilize or weaken a conductive fault via poroelastic stresses. Gradual reduction of the injection rate minimizes the coulomb stress change and the least seismicity rates are predicted due to slower relaxation of coupling-induced compression as well as pore-pressure dissipation.
The fluid injection into deep geological formations altar the states of pore pressure and stress on the faults, potentially causing earthquakes. In the multiphase flow system, the interaction between fluid flow and mechanical deformation in porous media is critical to determine the spatio-temporal distribution of pore pressure and stress. The contrast of fluid and rock properties between different structures produces the changes in pressure gradients and subsequently stress fields. Assuming two-phase fluid flow (gas-water system), we simulate the two-dimensional reservoir including a basement fault, in which injection-induced pressure encounters the fault directly given injection scenarios. The single-phase flow model with the same setting is also conducted to evaluate the multiphase flow effects on mechanical response of the fault to gas injection. A series of sensitivity tests are performed by varying the fault permeability. The presence of gaseous phase reduces the pressure buildup within the gas-saturated region, causing less Coulomb stress change. The low-permeability fault prevent diffusion initially as observed in the single-phase flow system. Once gaseous phase approaches, the fault acts as a capillary barrier that causes increases in pressure within the fault zone, potentially inducing earthquakes even without direct diffusion.
The purpose of the project was to perform multiscale characterization of low permeability rocks to determine the effect of physical and chemical heterogeneity on the poromechanical and flow responses of shales and carbonate rocks with a broad range of physical and chemical heterogeneity . An integrated multiscale imaging of shale and carbonate rocks from nanometer to centimeter scales include s dual focused ion beam - scanning electron microscopy (FIB - SEM) , micro computed tomography (micro - CT) , optical and confocal microscopy, and 2D and 3D energy dispersive spectroscopy (EDS). In addition, mineralogical mapping and backscattered imaging with nanoindentation testing advanced the quantitative evaluat ion of the relationship between material heterogeneity and mechanical behavior. T he spatial distribution of compositional heterogeneity, anisotropic bedding patterns, and mechanical anisotropy were employed as inputs for brittle fracture simulations using a phase field model . Comparison of experimental and numerical simulations reveal ed that proper incorporation of additional material information, such as bedding layer thickness and other geometrical attributes of the microstructures, can yield improvements on the numerical prediction of the mesoscale fracture patterns and hence the macroscopic effective toughness. Overall, a comprehensive framework to evaluate the relationship between mechanical response and micro-lithofacial features can allow us to make more accurate prediction of reservoir performance by developing a multi - scale understanding of poromechanical response to coupled chemical and mechanical interactions for subsurface energy related activities.