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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Aspects of Solvers for Large-Scale Coupled Problems in Porous Media

This report summarizes solution strategies for discrete systems occurring in the simulation of processes in the subsurface. The focus is on scalable solvers for large and coupled systems. The goal of this work is to enable researchers to select suitable algorithms and parameter settings to efficiently solve their problems. The work provides an overview of existing methods, highlighting their features, potential, and also frequent pitfalls. Numerical examples are provided for single phase flow, density driven flow and poroelasticity.Aspects of multiphase flow are discussed briefly; a detailed discussion of reactive transport is beyond the scope of the article. Future trends are discussed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

The role of Northeast Pacific meltwater events in deglacial climate change

Columbia River megafloods occurred repeatedly during the last deglaciation, but the impacts of this fresh water on Pacific hydrography are largely unknown. To reconstruct changes in ocean circulation during this period, we used a numerical model to simulate the flow trajectory of Columbia River megafloods and compiled records of sea surface temperature, paleo-salinity, and deep-water radiocarbon from marine sediment cores in the Northeast Pacific. The North Pacific sea surface cooled and freshened during the early deglacial (19.0-16.5 ka) and Younger Dryas (12.9-11.7 ka) intervals, coincident with the appearance of subsurface water masses depleted in radiocarbon relative to the sea surface. We infer that Pacific meltwater fluxes contributed to net Northern Hemisphere cooling prior to North Atlantic Heinrich Events, and again during the Younger Dryas stadial. Abrupt warming in the Northeast Pacific similarly contributed to hemispheric warming during the Bølling and Holocene transitions. These findings underscore the importance of changes in North Pacific freshwater fluxes and circulation in deglacial climate events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Editorial to the special issue on the 2015 TOUGH Symposium

This Special Issue of Transport in Porous Media features selected papers from the 2015 TOUGH Symposium, which took place on September 28–30, 2015, in Berkeley, California, USA. Such symposia are held every 3 years and are a forum where users of the TOUGH family of numerical simulators meet for an open exchange on the code applications and recent enhancements. The TOUGH codes (TOUGH is an acronym for Transport of Unsaturated Groundwater and Heat) were originally developed in the early 1980s for geothermal reservoir simulation by a team of researchers at the Lawrence Berkeley National Laboratory (LBNL) headed by Karsten Pruess (see http://esd.lbl.gov/TOUGH). The TOUGH codes have a modular structure in which different fluid components can be incorporated into the code by means of a replaceable equation of state (EOS) package. Since its first release, a variety of important EOS modules have been developed, enabling the modeling of volatile organic compounds, decay chains of multiple radionuclides, gas hydrates, tight and shale gas behavior, enhanced oil recovery, and supercritical water and CO 2 . Derivatives and descendants of the basic TOUGH code include coupled multi-phase flow and reactive chemical transport; coupled thermo-hydrologic-mechanical processes; inverse modeling for parameter estimation, sensitivity analysis, and uncertainty quantification; and parallelization for multi-core PCs, workstation clusters, and supercomputers. The TOUGH codes have been applied to a large variety of problems, including geothermal and petroleum reservoir engineering, nuclear waste and CO 2 geologic sequestration, subsurface environmental remediation, methane gas hydrate production, compressed air energy storage, and many other energy and environmental applications. Finally, the international TOUGH user community currently numbers almost 400 organizations (research laboratories, government agencies, private companies, universities, etc.) in over 60 countries.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

Joint Body- and Surface-Wave Tomography of Yucca Flat, Nevada, Using a Novel Seismic Source

In preparation for the next phase of the Source Physics Experiments, we acquired an active–source seismic dataset along two transects totaling more than 30 km in length at Yucca Flat, Nevada, on the Nevada National Security Site. Yucca Flat is a sedimentary basin which has hosted more than 650 underground nuclear tests (UGTs). The survey source was a novel 13,000 kg modified industrial pile driver. This weight drop source proved to be broadband and repeatable, richer in low frequencies (1–3 Hz) than traditional vibrator sources and capable of producing peak particle velocities similar to those produced by a 50 kg explosive charge. In this study, we performed a joint inversion of P–wave refraction travel times and Rayleigh–wave phase–velocity dispersion curves for the P– and S–wave velocity structure of Yucca Flat. Phase–velocity surface–wave dispersion measurements were obtained via the refraction microtremor method on 1 km arrays, with 80% overlap. Our P–wave velocity models verify and expand the current understanding of Yucca Flat’s subsurface geometry and bulk properties such as depth to Paleozoic basement and shallow alluvium velocity. Areas of disagreement between this study and the current geologic model of Yucca Flat (derived from borehole studies) generally correlate with areas of widely spaced borehole control points. This provides an opportunity to update the existing model, which is used for modeling groundwater flow and radionuclide transport. Scattering caused by UGT–related high–contrast velocity anomalies substantially reduced the number and frequency bandwidth of usable dispersion picks. Here, the S–wave velocity models presented in this study agree with existing basin–wide studies of Yucca Flat, but are compromised by diminished surface–wave coherence as a product of this scattering. As nuclear nonproliferation monitoring moves from teleseismic to regional or even local distances, such high–frequency (>5 Hz) scattering could prove challenging when attempting to discriminate events in areas of previous testing.

58 GEOSCIENCES↗
DOE OSTI2022

Kimberlina 1.2 CCUS Geophysical Models and Synthetic Data Sets

This synthetic multi-scale and multi-physics data set was produced in collaboration with teams at the Lawrence Berkeley National Laboratory, National Energy Technology Laboratory, Los Alamos National Laboratory, and Colorado School of Mines through the Science-informed Machine Learning for Accelerating Real-Time Decisions in Subsurface Applications (SMART) Initiative. Data are associated with the following publication: Alumbaugh, D., Gasperikova, E., Crandall, D., Commer, M., Feng, S., Harbert, W., Li, Y., Lin, Y., and Samarasinghe, S., “The Kimberlina Synthetic Geophysical Model and Data Set for CO2 Monitoring Investigations”, The Geoscience Data Journal, 2023, DOI: 10.1002/gdj3.191. The dataset uses the Kimberlina 1.2 CO2 reservoir flow model simulations based on a hypothetical CO2 storage site in California (Birkholzer et al., 2011; Wainwright et al., 2013). Geophysical properties models (P- and S-wave seismic velocities, saturated density, and electrical resistivity) were produced with an approach similar to that of Yang et al. (2019) and Gasperikova et al. (2022) for 100 Kimberlina 1.2 reservoir models. Links to individual resources are provided below: [CO2 Saturation Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-co2-saturation-models); Resistivity Models – [part 1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-1), [part 2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-2), and [part 3](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-3); [Vp Velocity Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vp-velocity-models); [Vs Velocity Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vs-velocity-models); [Density Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-density-models). The 3D distributions of geophysical properties for the 33 time stamps of the SIM001 model were used to generate synthetic seismic, gravity, and electromagnetic (EM) responses for 33 times between zero and 200 years. Synthetic surface seismic data were generated using 2D and 3D finite-difference codes that simulate the acoustic wave equation (Moczo et al., 2007). 2D data were simulated for six point-pressure sources along a 2D line with 10 m receiver spacing and a time spacing of 0.0005 s. 3D simulations were completed for 25 surface pressure sources using a source separation of 1 km in both the x and y directions and a time spacing of 0.001 s. Links to individual resources are provided below: [2D velocity models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-2d-velocity-models) and [2D surface seismic data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-2d-surface-seismic-data). [3D velocity models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-velocity-models), and 3D seismic data [year0](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year0), [year1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year1), [year2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year2), [year5](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year5), [year10](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year10), [year15](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year15), [year20](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year20), [year25](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year25), [year30](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year30), [year35](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year35), [year40](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year40), [year45](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year45), [year49](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year49), [year50](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year50), [year51](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year51), [year52](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year52), [year55](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year55), [year60](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year60), [year65](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year65), [year70](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year70), [year75](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year75), [year80](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year80), [year85](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year85), [year90](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year90), [year95](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year95), [year100](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year100), [year110](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year110), [year120](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year120), [year130](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year130), [year140](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year140), [year150](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year150), [year175](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year175), [year200](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year200). The Python scripts to read these models and data are provided [here](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-python-scripts). EM simulations used a borehole-to-surface survey configuration, with the source located near the reservoir level and receivers on the surface using the code developed by Commer and Newman (2008). Pseudo-2D data for the source at [2500 m](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-pseudo-2d-csem-data-tz2500m) and [3025 m](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-pseudo-2d-csem-data-tz3025m), used a 2D inline receiver configuration to simulate a response over 3D resistivity models. The [3D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-csem-data) contain electric fields generated by borehole sources at monitoring well locations and measured over a surface receiver grid. Vector gravity data, both on the surface and in boreholes, were simulated using a modeling code developed by Rim and Li (2015). The simulation scenarios were parallel to those used for the EM: [pseudo-2D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-gravity-data) were calculated along the same lines and within the same boreholes, and [3D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-gravity-data) were simulated over 3D models on the surface and in three monitoring wells. A series of [synthetic well logs](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-well-logs) of CO2 saturation, acoustic velocity, density, and induction resistivity in the injection well and three monitoring wells are also provided at 0, 1, 2, 5, 10, 15, and 20 years after the initiation of injection. These were constructed by combining the low-frequency trend of the geophysical models with the high-frequency variations of actual well logs collected in the Kimberlina 1 well that was drilled at the proposed site. Measurements of permeability and pore connectivity were made on cores of Vedder Sandstone, which forms the primary reservoir unit: [CT micro scans](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-ct-micro-scans-of-vedder-formation) and [Industrial CT Images](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-industrial-ct-images-vedder-formation). These measurements provide the range of scales in the otherwise synthetic data set to be as close to a real-world situation as possible. References: Birkholzer, J.T., Zhou, Q., Cortis, A. and Finsterle, S., 2011. A sensitivity study on regional pressure buildup from large-scale CO2 storage projects. Energy Procedia, 4, 4371-4378. Commer, M., and Newman, G.A., 2008. New advances in three-dimensional controlled-source electromagnetic inversion, Geophysical Journal International, 172, 513-535. Gasperikova, E., Appriou, D., Bonneville, A., Feng, Z., Huang, L., Gao, K., Yang, X., Daley, T., 2022, Sensitivity of geophysical techniques for monitoring secondary CO2 storage plumes, Int. J. Greenh. Gas Control, Volume 114, 103585, ISSN 1750-5836, https://doi.org/10.1016/j.ijggc.2022.103585. Moczo, P., J.O. Robertsson and L. Eisner, 2007, The finite-difference time-domain method for modeling of seismic wave propagation: Advances in geophysics, 48, 421-516. Rim, H., and Y. Li, 2015, Advantages of borehole vector gravity in density imaging, Geophysics, 80, G1-G13. Wainwright, H. M.; Finsterle, S.; Zhou, Q.; Birkholzer, J. T., 2013. Modeling the Performance of Large-Scale CO2 Storage Systems: A Comparison of Different Sensitivity Analysis Methods. International Journal of Greenhouse Gas Control, 17, 189205. https://doi.org/10.1016/j.ijggc.2013.05.007, DOI: 10.18141/1603331. Yang, X., Buscheck, T.A., Mansoor, K., Wang, Z., Gao, K., Huang, L., Appriou, D., and Carroll, S.A., 2019. Assessment of geophysical monitoring methods for detection of brine and CO2 leakage in drinking water aquifers, International Journal of Greenhouse Gas Control, 90, 102803, https://doi.org/10.1016/j.ijggc.2019.102803.

CCUS↗
DOE OSTI2021

Predictive Data-driven Platform for Subsurface Energy Production

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.

58 GEOSCIENCES↗
DOE OSTI2023

Closed Loop Geothermal Working Group: GeoCLUSTER App, Subsurface Simulation Results, and Publications

To better understand the heat production, electricity generation performance, and economic viability of closed-loop geothermal systems in hot-dry rock, the Closed-Loop Geothermal Working Group -- a consortium of several national labs and academic institutions has tabulated time-dependent numerical solutions and levelized cost results of two popular closed-loop heat exchanger designs (u-tube and co-axial). The heat exchanger designs were evaluated for two working fluids (water and supercritical CO2) while varying seven continuous independent parameters of interest (mass flow rate, vertical depth, horizontal extent, borehole diameter, formation gradient, formation conductivity, and injection temperature). The corresponding numerical solutions (approximately 1.2 million per heat exchanger design) are stored as multi-dimensional HDF5 datasets and can be queried at off-grid points using multi-dimensional linear interpolation. A Python script was developed to query this database and estimate time-dependent electricity generation using an organic Rankine cycle (for water) or direct turbine expansion cycle (for CO2) and perform a cost assessment. This document aims to give an overview of the HDF5 database file and highlights how to read, visualize, and query quantities of interest (e.g., levelized cost of electricity, levelized cost of heat) using the accompanying Python scripts. Details regarding the capital, operation, and maintenance and levelized cost calculation using the techno-economic analysis script are provided. This data submission will contain results from the Closed Loop Geothermal Working Group study that are within the public domain, including publications, simulation results, databases, and computer codes. GeoCLUSTER is a Python-based web application created using Dash, an open-source framework built on top of Flask that streamlines the building of data dashboards. GeoCLUSTER provides users with a collection of interactive methods for streamlining the exploration and visualization of an HDF5 dataset. The GeoCluster app and database are contained in the compressed file geocluster_vx.zip, where the "x" refers to the version number. For example, geocluster_v1.zip is Version 1 of the app. This zip file also contains installation instructions. **To use the GeoCLUSTER app in the cloud, click the link to "GeoCLUSTER on AWS" in the Resources section below. To use the GeoCLUSTER app locally, download the geocluster_vx.zip to your computer and uncompress this file. When uncompressed this file comprises two directories and the geocluster_installation.pdf file. The geo-data app contains the HDF5 database in condensed format, and the GeoCLUSTER directory contains the GeoCLUSTER app in the subdirectory dash_app, as app.py. The geocluster_installation.pdf file provides instructions on installing Python, the needed Python modules, and then executing the app.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

Mineralogical and transport controls on the evolution of porous media texture using direct numerical simulation

The evolution of porous media due to mineral dissolution and precipitation can change the bulk properties of subsurface materials. The pore-scale structure of the media, including its physical and mineralogical heterogeneity, exerts controls on porous media evolution via transport limitations to reactive surfaces and mineral accessibility. Here in this paper we explore how these controls affect the evolution of the texture in porous media at the pore scale. For this purpose, a pore-scale flow and reactive transport model is developed that explicitly tracks mineral surfaces as they evolve using a direct numerical simulation approach. Simulations of dissolution in single-mineral domains provide insights into the transport controls at the pore scale, while the simulation of a fracture surface composed of bands of faster-dissolving calcite and slower-dissolving dolomite provides insights into the mineralogical controls on evolution. Transport-limited conditions at the grain-pack scale may result in unstable evolution, a situation in which dissolution is focused in a fast-flowing, fast-dissolving path. Due to increasing velocities, the evolution in these regions is like that observed under conditions closer to strict surface control at the pore scale. That is, grains evolve to have oblong shapes with their long dimensions aligning with the local flow directions. Another example of an evolving reactive transport regime that affects local rates is seen in the evolution of the fracture surface. As calcite dissolves, the diffusive length between the fracture flow path and the receding calcite surfaces increases. Thus, the calcite dissolution reaction becomes increasingly limited by diffusion.

36 MATERIALS SCIENCE↗
DOE OSTI2016

A 2.5D Reactive Transport Model for Fracture Alteration Simulation

Understanding fracture alteration resulting from geochemical reactions is critical in predicting fluid migration in the subsurface and is relevant to multiple environmental challenges. Here in this paper, we present a novel 2.5D continuum reactive transport model that captures and predicts the spatial pattern of fracture aperture change and the development of an altered layer in the near-fracture region. The model considers permeability heterogeneity in the fracture plane and updates fracture apertures and flow fields based on local reactions. It tracks the reaction front of each mineral phase and calculates the thickness of the altered layer. Given this treatment, the model is able to account for the diffusion limitation on reaction rates associated with the altered layer. The model results are in good agreement with an experimental study in which a CO 2 -acidified brine was injected into a fracture in the Duperow Dolomite, causing dissolution of calcite and dolomite that result in the formation of a preferential flow channel and an altered layer. Finally, with an effective diffusion coefficient consistent with the experimentally observed porosity of the altered layer, the model captures the progressive decrease in the dissolution rate of the fast-reacting mineral in the altered layer.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Tagging CO 2 to Enable Quantitative Inventories of Geological Carbon Storage

In the wake of concerns about the long term integrity and containment of sub-surface CO 2 sequestration reservoirs, many efforts have been made to improve the monitoring, verification, and accounting methods for geo-sequestered CO 2 . Our project aimed to demonstrate the feasibility of a system designed to tag CO 2 with carbon isotope 14 C immediately prior to sequestration to a level that is normal on the surface (one part per trillion). Because carbon found at depth is naturally free of 14 C, this tag would easily differentiate pre-existing carbon from anthropogenic injected carbon and provide an excellent handle for monitoring its whereabouts in the subsurface. It also creates an excellent handle for adding up anthropogenic carbon inventories. Future inventories in effect count 14 C atoms. Accordingly, we have developed a 14 C tagging system suitable for use at the part-per-trillion level. This system consists of a gas-exchange apparatus to make disposable cartridges ready for controlled injection into a fast flowing stream of pressurized CO 2 . We built a high-pressure injection and tagging system, and a 14 C detection system. The disposable cartridge and injection system have been successfully demonstrated in the lab with a high-pressure flow reactor, as well as in the field at the CarbFix CO 2 sequestration site in Iceland. The laser-based 14 C detection system originally conceived has been shown to possess inadequate sensitivity for ambient levels. Alternative methods for detecting 14 C, such as saturated cavity absorption ringdown spectroscopy and scintillation counting, may still be suitable.

anthropogenic carbon↗
DOE OSTI2018

Subsidence Risk Mitigation in LLW Final Covers: A Case History - 18286

Typical waste packaging and disposal practices at low-level radioactive waste (LLW) disposal facilities, while adequate for safe transportation and facility operations, introduce significant subsidence risk for the facilities' final cover systems. Subsidence is often caused by the collapse of voids in the waste packaging and surrounding cover soils that eventually propagate to the ground surface. This subsidence could result in the loss of final cover system integrity, stormwater ponding, concentrated flow of surface water and/or gas, increased leachate generation, transport of contaminants from the landfill, and potential adverse chemical reactions within the waste mass. For new LLW facilities, steps can be taken to minimize the subsidence potential of newly placed waste. However, there is a significant inventory of existing disposal facilities (trenches, pits, shafts, vaults, etc.), tank farms, and historic, subsurface burial grounds that require final covers - all of which present real subsidence risks. Final covers for these existing LLW disposal sites can be engineered to mitigate these risks. A case history of a 60-acre LLW final cover system design and construction project illustrates an innovative approach to this problem that offers superior risk mitigation compared to typical final cover design practice. This approach combines engineering simulations of the projected final cover mechanical performance, specific probabilistic performance targets, and targeted cover system improvements to manage the subsidence risk in the final design. The result is a successful final cover project that meets the needs of the community, state and federal regulators, and facility owner. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Grain-Size Based Additivity Models for Scaling Multi-rate Uranyl Surface Complexation in Subsurface Sediments

This study statistically analyzed a grain-size based additivity model that has been proposed to scale reaction rates and parameters from laboratory to field. The additivity model assumed that reaction properties in a sediment including surface area, reactive site concentration, reaction rate, and extent can be predicted from field-scale grain size distribution by linearly adding reaction properties for individual grain size fractions. This study focused on the statistical analysis of the additivity model with respect to reaction rate constants using multi-rate uranyl (U(VI)) surface complexation reactions in a contaminated sediment as an example. Experimental data of rate-limited U(VI) desorption in a stirred flow-cell reactor were used to estimate the statistical properties of multi-rate parameters for individual grain size fractions. The statistical properties of the rate constants for the individual grain size fractions were then used to analyze the statistical properties of the additivity model to predict rate-limited U(VI) desorption in the composite sediment, and to evaluate the relative importance of individual grain size fractions to the overall U(VI) desorption. The result indicated that the additivity model provided a good prediction of the U(VI) desorption in the composite sediment. However, the rate constants were not directly scalable using the additivity model, and U(VI) desorption in individual grain size fractions have to be simulated in order to apply the additivity model. An approximate additivity model for directly scaling rate constants was subsequently proposed and evaluated. The result found that the approximate model provided a good prediction of the experimental results within statistical uncertainty. This study also found that a gravel size fraction (2-8mm), which is often ignored in modeling U(VI) sorption and desorption, is statistically significant to the U(VI) desorption in the sediment.

Zhang, Xiaoying↗
DOE OSTI2017

A box model for representing estuarine physical processes in Earth system models

Appropriately treating riverine freshwater discharge into the oceans in Earth system models is a challenging problem. Commonly, the river runoff is discharged into the ocean models with zero salinity and arbitrarily distributed either horizontally or vertically over several grid cells. Those approaches entirely neglect estuarine physical processes that modify river inputs before they reach the open ocean. Here in order to realistically represent riverine freshwater inputs in Earth system models, a physically based Estuary Box Model (EBM) is developed to parameterize the mixing processes in estuaries. The EBM represents the estuary exchange circulation with a two-layer box structure. It takes as input the river volume flux from the land surface model and the subsurface salinity at the estuary mouth from the ocean model. It delivers the estuarine outflow salinity and net volume flux into and out of the estuary to the ocean model. An offline test of the EBM forced with observed conditions for the Columbia River system shows good agreement with observations of outflow salinity and high-resolution simulations of the exchange flow volume flux. To illustrate the practicality of use of the EBM in an Earth system model, the EBM is implemented for all coastal grid cells with river runoff in the Community Earth System Model (CESM). Compared to the standard version of CESM, which treats runoff as an augmentation to precipitation, the EBM increases sea surface salinity and reduces stratification near river mouths. The EBM also leads to significant regional and remote changes in CESM ocean surface salinities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Developing Methods to Assess Changes in Mechanical Properties of Shale Modified by Engineered Mineral Precipitation

Fractures in subsurface shale formations serve multiple purposes, for example, in the recovery of resources in hydraulic fracturing or as potential harmful leakage passages through caprocks that may contribute undesired fluids to the atmosphere or functional groundwater aquifers. A proposed method to seal or influence fracture properties is Ureolysis-Induced Calcium Carbonate Precipitation (UICP), a bio-mineralization technology driven by the enzymatic hydrolysis of urea, resulting in the formation of calcium carbonate. Sporosarcina pasteurii is a common microbe used as the source of the urease enzyme that catalyzes the chemical reaction. The resulting calcium carbonate can bridge the gaps in fractured shale and reduce fluid flow through fractures. However, there is little information on how this process affects the mechanical properties of the resulting biomineralized shale. This study represents the first step toward determining the influence of UICP treatment on shale material and its subsequent mechanical strength properties. This methods development study aims to determine the effect that temperature has on the tensile strength of intact, unfractured shale cores (2.54 cm (1 in) diameter, 5.08 cm (2 in) long). Tensile strength was determined indirectly using a modified Brazilian test where the splitting tensile strength is attained by applying a compressive load onto the core. Shale cores from Eagle Ford and Wolfcamp formations were tested at both room temperature and 60°C to determine if increased temperature influences the tensile strength of the rock. This data will help to assess the necessity of testing biomineralized cores at temperature. Though 60°C may not mimic subsurface temperatures of the shales used in this study, it was chosen due to limitations of the UICP process while still approaching temperatures of shale formations. This project aims to evaluate what effect temperature has on the mechanical properties of intact shale cores so that engineered or natural rock fractures that are sealed by biomineralization can be better understood.

Bedey, Kayla↗
DOE OSTI2021

Striving to translate shale physics across ten orders of magnitude: What have we learned?

Shales will play an important role in the successful transition of energy from fossil-based resources to renewables in the coming decades. Aside from being a significant source of low-carbon intensity fuels, like natural gas, they also serve as geologic seals of subsurface formations that may be used to isolate nuclear waste, sequester CO 2 , or store intermittent energy (e.g., solar hydrogen). Despite their importance, shales pose significant engineering and environmental challenges due to their nanoporous structure and extreme heterogeneity that spans at least ~10 orders of magnitude in spatial scale. Two challenges inhibit a system-level understanding: (1) the physics of fluid flow and phase behavior in shales are poorly understood due to the dominant molecular interactions between minerals and fluids under confinement, and (2) the apparent lack of scale separation that prevents a reliable (closed) description of the physics at any single scale of observation. In this review, we focus on the latter issue and discuss scale translation, which in its broadest sense is transforming data or simulations from one spatiotemporal scale to another. While effective scale translation is not exclusive to shales, but all geologic porous media, the need for it is especially acute in shales given their high degree of heterogeneity. Classical theories like homogenization, while indispensable, fail when scales are not separated. Other methods, like numerical upscaling, scale-translate in only one direction: small to large, but not the reverse, called downscaling. However, the confluence of advances in three areas are bringing challenging problems such as shales within reach: increased computational power and scalable algorithms; high-resolution imaging and multi-modal data acquisition; and machine learning to process massive amounts of data. While these advances equip geoscientists with a wide array of experimental and computational tools, no individual tool can probe the entire gamut of heterogeneity in shales. Their effective use, therefore, requires an ability to bridge between various data types obtained at different scales. The aim of this review is to present a coherent account of computational and experimental methods that may be used to achieve just that, i.e., to perform scale translation. We provide a broader definition of scale translation, one that transcends classical homogenization and upscaling methods, but is consistent with them and accommodates notions like downscaling and data translation. After a brief introduction to homogenization, we review hybrid methods, numerical upscaling and its recent extensions, multiscale computing, high-resolution imaging, and machine learning. We place particular emphasis on multiscale computing and propose an algorithmic framework to bridge between the pore (micro) and Darcy (macro) scales. Throughout the paper, we draw comparisons between the various methods and highlight their (often hidden) similarities, differences, benefits, and pitfalls. We finally conclude with two case studies on shales that exemplify some of the methods presented.

58 GEOSCIENCES↗
DOE OSTI2018

An experimental and numerical study of wellbore leakage mitigation using pH-triggered polymer gelant

The potential leakage of hydrocarbon fluids or carbon dioxide from subsurface formations is a primary concern in wellbore integrity, oil and gas production, and CO 2 storage. Leaky wells with fractured cement or debonded microannuli are common sources of subsurface fluid leakage. The hydrocarbon fluid or CO 2 can migrate through such pathways to shallower formations and ultimately to surface. Cement fractures may have apertures on the order of microns, which are difficult to seal with typical workover techniques. A material that provides low viscosity during the injection but much higher viscosity after injection, with a minimum pressure gradient to yield flow at the target zone, is a potentially effective approach to seal the leakage pathways through cement fractures. pH-triggered polymers are such a material: aqueous solutions with low viscosity at low pH, containing pH-sensitive microgels which viscosify upon neutralization to become highly swollen gels with substantial yield stress that can block fluid flow. For the wellbore leakage application, the large alkalinity of wellbore cement provides the required neutralization. Our coreflood and rheological experiments show that pH-triggered polymer sealants such as polyacrylic acid polymer provide a robust seal if the process is properly designed; however, its long-term applicability depends on the dynamic geochemical environment of the wellbore. The process comprises three stages: (1) injection of a chelating agent as the preflush to ensure a favorable environment for the polymer gel, (2) injection of polymer solution, and (3) shut-in for the polymer gelation. A systematic study was done to understand the conditions under which the polymer gel remains stable and effectively seals the leakage pathways. A numerical model, based on polymer rheological properties and governing mechanisms observed in the laboratory experiments, was developed to simulate the reactive flow and transport of pH-triggered polymers in narrow fractures. Comparison with experiments shows a generally good agreement, despite the relative simplicity of the model. The numerical model was used to investigate further the underlying mechanisms of the process. The results can be used to design effectively the remediation process for a known fracture aperture size of the target zone.

42 ENGINEERING↗
DOE OSTI2018

Dual in-aquifer and near surface processes drive arsenic mobilization in Cambodian groundwaters

Millions of people globally, and particularly in South and Southeast Asia, face chronic exposure to arsenic from reducing groundwaters in which. Arsenic release to is widely attributed largely to reductive dissolution of arsenic-bearing iron minerals, driven by metal reducing bacteria using bioavailable organic matter as an electron donor. However, the nature of the organic matter implicated in arsenic mobilization, and the location within the subsurface where these processes occur, remains debated. In a high resolution study of a largely pristine, shallow aquifer in Kandal Province, Cambodia, we have used a complementary suite of geochemical tracers (including 14 C, 3 H, 3 He, 4 He, Ne, δ 18 O, δD, CFCs and SF 6 ) to study the evolution in arsenic-prone shallow reducing groundwaters along dominant flow paths. The observation of widespread apparent 3 H- 3 He ages of <55years fundamentally challenges some previous models which concluded that groundwater residence times were on the order of hundreds of years. Surface-derived organic matter is transported to depths of >30m, and the relationships between age-related tracers and arsenic suggest that this surface-derived organic matter is likely to contribute to in-aquifer arsenic mobilization. A strong relationship between 3 H- 3 He age and depth suggests the dominance of a vertical hydrological control with an overall vertical flow velocity of ~0.4±0.1m·yr-1 across the field area. A calculated overall groundwater arsenic accumulation rate of ~0.08±0.03μM·yr -1 is broadly comparable to previous estimates from other researchers for similar reducing aquifers in Bangladesh. Although apparent arsenic groundwater accumulation rates varied significantly with site (e.g. between sand versus clay dominated sequences), rates are generally highest near the surface, perhaps reflecting the proximity to the redox cline and/or depth-dependent characteristics of the OM pool, and confounded by localized processes such as continued in-aquifer mobilization, sorption/desorption, and methanogenesis.

54 ENVIRONMENTAL SCIENCES↗