Unified SimCCS Platform for Decision-Making in Carbon Capture, Transport, and Storage Infrastructure
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Earth Sciences↗
Engineering topics
Mehana, Mohamed Zakaria Saad
Publications and source records attributed to Mehana, Mohamed Zakaria Saad.
Hydrogen Diffusion in Slit Pores: Role of Temperature, Pressure, Confinement, and Roughness
Diffusion of hydrogen (H 2 ) is important to understand the leakage risk and transport behavior for H 2 geologic storage. We applied molecular dynamics simulations to investigate the influencing factors of H2 diffusion in the slit pores of calcite, hematite, and quartz, owing to their abundance. It is revealed that the H2 self-diffusion coefficient increases with the temperature, regardless of the type of pore minerals. The diffusion of H 2 in the 20 nm slit pores falls into the bulk diffusion regime when the pressure is 10 MPa. The self-diffusion of H 2 decreases with pressure in all three types of slit pores, following a power law model with the exponents ranging from -0.825 to -0.964. Furthermore, the impact of confinement on H 2 diffusion is more pronounced for the slit pores with stronger interactions with H 2 -like calcite. The role of surface roughness in H 2 diffusion depends on the slit aperture. The rough surface enhances H 2 diffusion in the larger slit pores due to the enlarged effective pore space, whereas it weakens H 2 diffusion in the small slit pores due to stronger adsorption. These findings will fill the knowledge gap on the coupling effect of different factors influencing H 2 diffusion.
08 HYDROGEN↗
Enhancing predictive understanding and accuracy in geological carbon dioxide storage monitoring: Simulation and history matching of tracer transport dynamics
Co-injection of conservative tracers with carbon dioxide (CO 2 ) is a viable tool for monitoring subsurface processes during geological CO 2 storage (GCS). This research investigates the simulation and history-matching of a gas tracer (sulfur hexafluoride, SF 6 ) during CO 2 flooding, employing a core flooding result in Berea sandstone. Four extensively used saturation functions are assessed for their efficacy in history matching of CO 2 /SF 6 injection at the core scale. The history-matching process incorporates particle swarm optimization (PSO) to fine-tune constitutive relationships parameters. Next, employing transport models at the aquifer scale, we interrogate the impact on tracer transport and mixing of saturation function uncertainties, arising from the non-uniqueness of constitutive relationships parameters and saturation function types. To assess the effects of geological heterogeneity on behavior of tracer breakthrough curves (BTCs), we employ two normalized parameters assessing the degree of mixing and SF 6 breakthrough time. The aquifer-scale investigation encompasses both homogeneous and heterogeneous systems with and without capillary heterogeneity effects. Our findings underscore the critical importance of addressing saturation function uncertainties, emphasizing the significance of auxiliary experiments and innovative methodologies to enhance predictive accuracy. The findings highlight significant disparities in arrival times, BTC peaks, tails, and mixing levels, even under optimal conditions. Heterogeneity, with or without capillary heterogeneity, plays a crucial role in shaping BTC variations, resulting in accelerated SF 6 breakthrough times and reduced BTC peaks. Evaluation of monitoring points distant from the injector reveals a dampening effect on the SF6 BTC peak, particularly in heterogeneous systems with capillary heterogeneity, where the peak is halved. These insights underscore the challenges associated with tracer monitoring and the necessity for enhanced methodologies to improve predictive accuracy in subsurface processes during GCS.
58 GEOSCIENCES↗
Learning a General Model of Single Phase Flow in Complex 3D Porous Media
Modeling effective transport properties of 3D porous media, such as permeability, at multiple scales is challenging as a result of the combined complexity of the pore structures and fluid physics—in particular, confinement effects which vary across the nanoscale to the microscale. While numerical simulation is possible, the computational cost is prohibitive for realistic domains, which are large and complex. Although machine learning (ML) models have been proposed to circumvent simulation, none so far has simultaneously accounted for heterogeneous 3D structures, fluid confinement effects, and multiple simulation resolutions. By utilizing numerous computer science techniques to improve the scalability of training, we have for the first time developed a general flow model that accounts for the pore-structure and corresponding physical phenomena at scales from Angstrom to the micrometer. Using synthetic computational domains for training, our ML model exhibits strong performance (R 2 = 0.9) when tested on extremely diverse real domains at multiple scales.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Probing fluid behavior in rock nanopores under dynamic pressure conditions using small-angle neutron scattering [Slides]
Subsurface formations provide vast resource for energy generation, energy storage, and carbon sequestration. Sustainable energy generation from shale oil/gas and integrity of longterm storage necessitate understanding fluid behavior in nanoporous geomedia (e.g., shale caprock) under dynamic pressure conditions.
58 GEOSCIENCES↗
SimCCS 3.0 User GUIDE
Carbon Capture and Storage (CCS) is an important technology aimed at reducing CO 2 emissions and contributing to the mitigation strategies of climate change. The process involves three main steps: CO 2 capture, transport, and storage. Specifically, CO 2 is captured from industrial facilities, such as coal-fired power plants and natural gas processing plants. It is then transported through infrastructure networks, such as pipelines, rails, and ships, and injected into underground geological formations, such as depleted oil and gas reservoirs or deep saline aquifers, for permanent storage. The entire CCS value chain encompasses multiple components, including CO 2 sources, transport networks, and storage sites. The proper design of CCS transport infrastructure is an essential step for the deployment of CCS technology. However, the design is not a trivial process, as it presents many challenges and requires consideration of numerous economic, social, and environmental factors.
42 ENGINEERING↗
Capacity Assessment and Cost Analysis of Geologic Storage of Hydrogen: A Case Study in Intermountain-West Region USA
Hydrogen is an integral component of the current energy transition roadmap to decarbonize the economy and create an environmentally-sustainable future. However, surface storage options (e.g., tanks) do not provide the required capacity or durability to deploy a regional or nationwide hydrogen economy. In this study, we have analyzed the techno-economic feasibility of the geologic storage of hydrogen in depleted gas reservoirs, salt caverns, and saline aquifers in the Intermountain-West (I-WEST) region. We have identified the most favorable candidate sites for hydrogen storage and estimated the volumetric storage capacity. Our results show that the geologic storage of hydrogen can provide at least 72% of total energy consumption of the I-WEST region in 2020. We also calculated the capital and levelized costs of each storage option. We found that a depleted gas reservoir is the most cost-effective candidate among the three geologic storage options. Interestingly, the cushion gas type plays a significant role in the storage cost when we consider hydrogen storage in saline aquifers. The levelized costs of hydrogen storage in depleted gas reservoirs, salt caverns, and saline aquifers with large-scale storage capacity are approximately $\$$1.15, $\$$2.50, and $\$$3.27 per kg of H 2 , respectively. Here, this work provides essential guidance for the geologic hydrogen storage in the I-WEST region.
08 HYDROGEN↗
Probing fluid behavior in shale nanopores using small-angle neutron scattering [Slides]
SANS is a powerful tool for probing porosity and pore size distribution changes in situ. First-of-their-kind SANS measurements have led to new insights into hydrocarbon behavior under confinement in shale nanopores.
04 OIL SHALES AND TAR SANDS↗
Hydrological Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science
Hydrologic sciences depend on data monitoring, analyses, and simulations of hydrologic processes to ensure safe, sufficient, and equal water distribution. These hydrologic data come from but are not limited to primary (lab, plot, and field experiments) and secondary sources (remote sensing, UAVs, hydrologic models) that typically follow FAIR Principles (Findable, Accessible, Interoperable, and Reusable: (go-fair.org)). Easy availability of FAIR data has become possible because the hydrology-oriented organizations have pushed the community to increase coordination of the protocols for generating data and sharing model platforms. In addition, networking at all levels has emerged with an invigorated effort to activate community science efforts that complement conventional data collection methods. However, it has become difficult to decipher various complex hydrologic processes with increasing data. Machine learning, a branch of artificial intelligence, provide more accurate and faster alternatives to better understand different hydrological processes. The Integrated, Coordinated, Open, Networked (ICON) framework provides a pathway for water users to include and respect diversity, equity, and inclusivity. In addition, ICONs support the integration of peoples with historically marginalized identities into this professional discipline of water sciences. This article comprises three independent commentaries about the state of ICON principles in hydrology and discusses the opportunities and challenges of adopting them.
(ICON) principles to address↗
A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media
This paper presents a new deep learning data-driven model for predicting structure dependent pore-fluid velocity fields in rock. The model is based on a Convolutional Auto-Encoder (CAE) artificial neural network capable of learning from image data generated by direct numerical simulations of fluid flow through pore-structures, such as by Lattice Boltzmann or molecular dynamics methods. The main novelty of the model in comparison to previous CAE-based data-driven approaches consists of three parts. The first is a methodology for decomposing the full-domain of the porous media into sub-regions, or “sub-domains”, in order to reduce the overall size of the CAE, batch process the sub-domains in parallel, and enable the CAE to learn local and generalizable nonlinear mappings of pore-fluid velocities. The second consists of embedding the finite difference solutions of the incompressible Navier-Stokes and continuity equations into convolutional layers prior to the CAE in order to provide the CAE with knowledge of fluid dynamics physics (PhyFlow). The third main novelty is that the training of the CAE is regularized with a hierarchical loss function that encourages the learning of fluid flow patterns (in a way similar to ranked modes in principal component analysis), ranking from most to least important. This is shown to increase the stability in learning, reduce over-fitting, and promote interpretability of the CAE neural network layers (HierCAE). The comprehensive new data-driven model, which we call the PhyFlow-HierCAE model, is shown to exhibit improved accuracy and generalizability of flow field predictions over conventional CAE models, attributable to the embedded physical knowledge and the hierarchical regularization, as well as realize orders of magnitude speed-ups in computation times as a surrogate for the direct numerical simulations. Examples of training and forward predictions on unseen pore-structures are provided and evaluated for data from Lattice Boltzmann and molecular dynamics simulations of pore-fluid flow. The model is shown to be a fast and accurate emulator (or “surrogate”) for predicting effective permeability of unseen pore-structures based on learning from relatively small direct numerical simulation datasets.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Machine-learning predictions of the shale wells’ performance
The ultra-low permeability nature of shale reservoirs leads to an extended linear flow and necessitates horizontal wells with multi-stage engineered fractures to efficiently extract hydrocarbons resources. These artificially-generated and naturally-occurring fractures form complex networks that create complex flow regimes which control oil production. These fractures are neither identical nor equally-spaced, which leads to a production profile with a masked onset of the boundary-dominated flow. The combination of the extended linear flow with the indeterminate onset of the boundary-dominated flow challenges the current deterministic analytic approaches to forecast the estimated ultimate recovery (EUR). In this work, we propose a novel machine-learning approach which overcomes these challenges and provides reliable EUR estimates based on field-wide analyses. We implement a novel unsupervised machine learning (ML) methodology, which allows for automatic identification of the optimal number of features (signals) present in the data based on non-negative matrix/tensor factorization coupled with k-means clustering incorporating regularization and physics constraints. In the presented analyses, the input data to the ML algorithm is the available (public) production history from the field collected at existing unconventional reservoirs. We validate our approach through hindcasting of the production data, where we achieved an excellent agreement. In addition, our approach is able to identify the poorly-performing wells, which could benefit from early refracing. Our approach provides fast and accurate estimations of the well performance without presumptions about the state of the well or the flow regime.
03 NATURAL GAS↗
Reduced methane recovery at high pressure due to methane trapping in shale nanopores
By 2050, shale gas production is expected to exceed three-quarters of total US natural gas production. However, current unconventional hydrocarbon gas recovery rates are only around 20%. Maximizing production of this natural resource thus necessitates improved understanding of the fundamental mechanisms underlying hydrocarbon retention within the nanoporous shale matrix. In this study, we integrated molecular simulation with high-pressure small-angle neutron scattering (SANS), an experimental technique uniquely capable of characterizing methane behavior in situ within shale nanopores at elevated pressures. Samples were created using Marcellus shale, a gas-generative formation comprising the largest natural gas field in the United States. Our results demonstrate that, contrary to the conventional wisdom that elevated drawdown pressure increases methane recovery, a higher peak pressure led to the trapping of dense, liquid-like methane in sub-2 nm radius nanopores, which comprise more than 90% of the measured nanopore volume, due to irreversible deformation of the kerogen matrix. These findings have critical implications for pressure management strategies to maximize hydrocarbon recovery, as well as broad implications for fluid behavior under confinement.
58 GEOSCIENCES↗
Modeling Nanoconfinement Effects Using Active Learning
Predicting the spatial configuration of gas in nanopores of is relevant in applications such as fluid flow forecasting and hydrocarbon reserves estimation. For example, shale reservoirs have suffered from computationally intractable multiscale problems, since fluid properties such as viscosity, density, and adsorption must be calculated by using expensive molecular dynamics (MD) simulations within each nanopore, whereas flow through these connected nanopores must be simulated at the micrometer scale. We utilize machine learning techniques to quickly and accurately model nanoscale confinement effects as an important step toward bridging the nano and micro scales. Our workflow is based on building and training physics-based deep-neural-networks models by learning from a database of MD calculations. The model accounts for the adsorption phenomenon by predicting the statistical distribution of gas inside nanopores. Because large databases of MD calculations are expensive to create, we investigate active learning (AL) as a data set construction strategy. In this workflow, new data are selected based on the model uncertainty via the query-by-committee approach. We show that our workflow obtains accurate models that generalize to real scanning electron microscopy geometries with 1/10th of the number of MD calculations required vs random data set generation. Our method enables the possibility of modeling nanoconfinement effects at the mesoscale, where complex connected sets of nanopores affect flow.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Modeling CO 2 plume migration using an invasion-percolation approach that includes dissolution
Geological carbon dioxide (CO 2 ) sequestration has been proposed as a viable technique to decrease effective emissions of CO 2 into the atmosphere. However, the security of this sequestration is tied to our understanding of the long-term migration of CO 2 in subsurface. The dissolution of CO 2 in the reservoir brine is one of the main long-term trapping mechanisms. However, the assumptions used in large-scale reservoir simulations usually lead to an overestimation of the dissolution volume. We propose a modified approach based on the macroscopic invasion-percolation (MIP) theory that allows the dissolution of CO 2 into brine. We used a high-resolution geological model to compare the Darcy-, modified MIP-, and classic MIP-based simulation results. We observed a significant shrinkage in the nonaqueous plume volume when dissolution is considered during the MIP simulation. In the case of Darcy-based simulation, the plume was completely trapped inside the reservoir with limited migration even after a thousand-year simulation. On the other hand, the majority of the plume migrated out of the simulated reservoir in the case of MIP. Our approach provides more realistic estimation of the dissolution volume and nonaqueous plume extent while leveraging the computational efficiency enjoyed by MIP.
54 ENVIRONMENTAL SCIENCES↗