Geochemical and Mechanical Evolution of Bluff and Entrada Sandstone Due to CO2-Fluid-Rock Interaction
Core scale analysis of outcrop cores from the formations being examined for geologic carbon storage in the San Juan Basin.
Simmons, Jason↗
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Dates and rates of loess deposition and soil formation on the Snake River Plain, Idaho, USA
Loess-paleosol sequences in eolian deposits on the Snake River Plain, Idaho, western United States, preserve records of late Pleistocene to Holocene glacial-interglacial cycles. Here, we examine climate-driven changes in the timing and rate of loess accretion, soil formation, and pedogenic carbonate accumulation in the eastern and central Snake River Plain. High rates of loess deposition often correspond with dry, cold, and windy conditions and/or high sediment supply, while soil formation indicates landscape stability. Optically stimulated luminescence dating of four loess-soil sequences provides a record of the timing of loess deposition, and radiocarbon dating ( 14 C) of soil inorganic carbon provides ages for soil formation and pedogenic carbonate precipitation. On the central Snake River Plain, marine isotope stage (MIS) 3 is marked by loess deposition between ca. 50 ka and 33 ka, and pedogenic carbonate accumulation between ca. 42 ka calibrated years before present (cal. B.P.) and 31 ka cal. B.P. During MIS 2, loess deposition between ca. 21 ka and 15 ka is contemporaneous with pedogenic carbonate precipitation. MIS 2 is characterized by diffuse pedogenic carbonate formation and rapid loess accumulation. Both the eastern and central Snake River Plain study sites experienced high rates of loess deposition during MIS 2; however, the eastern Snake River Plain had almost three times faster rates of loess accumulation (0.56−0.63 m/k.y. from ca. 25 ka to 17 ka) as compared to the central Snake River Plain (0.18−0.26 m/k.y. from ca. 21 ka to 15 ka). During MIS 1, loess deposition on the central Snake River Plain ca. 11 ka was coincident with pedogenic carbonate formation ca. 10 ka cal. B.P. MIS 3 and MIS 1 are characterized by A horizons and well-developed Bk horizons, indicating soil stability and lower rates of loess accumulation during these intervals. Finally, this study highlights the role of late Quaternary−scale climatic variability on rates and amounts of soil inorganic carbon storage.
Geosciences↗
Characterizing the Evolution of Trapped scCO 2 Curvature in Bentheimer and Nugget Sandstone Pore Geometry
During a Geologic Carbon Storage process, supercritical CO 2 (scCO 2 ) is subjected to a series of dynamic and static conditions where the relationship between pore geometry and the trapped scCO 2 curvature remains to be established. To mimic the dynamic process, two sandstones, Bentheimer and Nugget, were subjected to two successive drainage and imbibition (D-I) cycles and X-ray computed tomography scanned at each residual state to capture the wettability evolution at static conditions in the same pore geometry. Both sandstones contain similar grain size distributions, pore size distributions, and pore interconnectivity but differ in that the Nugget formation contains approximately half the porosity of the Bentheimer sandstone, and the pore network contains dead-end pores. scCO 2 size distributions, strain calculations, and geometric contact angle measurements were used to characterize the curvature of scCO 2 in different pore types between cycles. An increase in geometric contact angle was the greatest when advancement along the pore network of the same ganglion occurred between cycles while strain increased the most with pore-filling trapping. Moreover, Nugget sandstone results in a greater aggregated residual saturation and shows a clear increase in scCO 2 sizes with an additional D-I cycle while scCO 2 in the Bentheimer core shows a more complex response with some ganglion increasing and some decreasing in size with an additional D-I cycle. From this work, we suspect the pore geometry is playing a role in scCO 2 size distributions and use this information to suggest using water pulses to enhance trapping capacity in lower porosity sandstones.
58 GEOSCIENCES↗
Permanent Source - Distributed Acoustic Sensing (PESO-DAS) v1
The SOV/DAS software program focuses on seismic monitoring and data processing of permanent seismic sources Surface Orbital Vibrators (SOVs) and fiber-optics sensing Distributed Acoustic Sensing (DAS). Its key features include automated data processing of the continuous seismic monitoring data acquired with DAS, and output of processed shot gathers and QC plots. The software supports timelapse seismic for long-term reservoir monitoring, and is used for monitoring of geological carbon storage sites, geothermal reservoirs, and oil and gas, and in general subsurface resource management.
dos Santos Maia Correa, Julia [Lawrence Berkeley N↗
Inferring microbial interactions with their environment from genomic and metagenomic data
Microbial communities assemble through a complex set of interactions between microbes and their environment, and the resulting metabolic impact on the host ecosystem can be profound. Microbial activity is known to impact human health, plant growth, water quality, and soil carbon storage which has lead to the development of many approaches and products meant to manipulate the microbiome. In order to understand, predict, and improve microbial community engineering, genome-scale modeling techniques have been developed to translate genomic data into inferred microbial dynamics. However, these techniques rely heavily on simulation to draw conclusions which may vary with unknown parameters or initial conditions, rather than more robust qualitative analysis. To better understand microbial community dynamics using genome-scale modeling, we provide a tool to investigate the network of interactions between microbes and environmental metabolites over time. Using our previously developed algorithm for simulating microbial communities from genome-scale metabolic models (GSMs), we infer the set of microbe-metabolite interactions within a microbial community in a particular environment. Because these interactions depend on the available environmental metabolites, we refer to the networks that we infer as metabolically contextualized , and so name our tool MetConSIN: Met abolically Con textualized S pecies I nteraction N etworks.
59 BASIC BIOLOGICAL SCIENCES↗
Files and scripts to support manuscript Shuman et al 2023 FATES-SPITFIRE ecosystem assembly across tropics
The dataset includes the parameter and domain files, relevant output files, and scripts to generate simulations and perform analysis with Jupyter notebooks that support the manuscript Shuman, JK et al 2023 “Dynamic ecosystem assembly and escaping the “fire-trap” in the tropics: Insights from FATES_15.0.0”. We have adapted the fire-behavior and effects module, SPITFIRE, for use with the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a size-structured vegetation demographic model. We test how climate, fire regime and fire-tolerance plant traits interact to determine the biogeography of tropical forests and grasslands. We assign different fire-tolerance strategies based on crown, leaf and bark characteristics, which are key observed fire-tolerance traits across woody plants. For these simulations, three types of vegetation compete for resources: a fire-vulnerable tree with thin bark, a vulnerable deep crown and fire-intolerant foliage; a fire-tolerant tree with thick bark, a thin crown and fire-tolerant foliage; and a fire-promoting C4 grass. We explore the model sensitivity to a critical parameter governing fuel moisture, and show that drier fuels promote increased burning, an expansion of area for grass and fire-tolerant trees and a reduction of area for fire-vulnerable trees. This conversion to lower biomass or grass areas with increased fuel drying results in increased fire burned area and its effects, which could fee back to local climate variables. Simulated size-based fire mortality for trees less than 20 cm in diameter and those with fire-vulnerable traits is higher than that for larger and/or fire-tolerant trees, in agreement with observations. Fire-disturbed forests demonstrate reasonable productivity and capture observed patterns of aboveground biomass in areas dominated by natural vegetation for the recent historical period, but have a large bias in less disturbed areas. Though the model predicts a greater extent of burned fraction than observed in areas with grass dominance, the resulting biogeography of fire-tolerant, thick-bark trees and fire-vulnerable, thin-bark trees corresponds to observations across the tropics. In areas with more than 2500 mm of precipitation, simulated fire frequency and burned area are low, with fire intensities below 150 kW m-1, consistent with observed understory fire behavior across the Amazon. Areas drier than this demonstrate fire intensities consistent with those measured in savannas and grasslands, with high values up to 4000 kW m-1. The results support a positive grass-fire feedback across the region, and suggest that forests which have existed without frequent burning may be vulnerable at higher fire intensities, which is of greater concern under intensifying climate and land use pressures. The ability of FATES to capture the connection between fire disturbance and plant fire-tolerance strategies in determining biogeography provides a useful tool for assessing the vulnerability and resilience of these critical carbon storage areas under changing conditions across the tropics.
54 ENVIRONMENTAL SCIENCES↗
Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagation
We introduce a conditional pseudo-reversible normalizing flow (PR-NF) that directly learns conditional probability distributions from noisy physical models to efficiently quantify both forward and inverse uncertainty propagation. Traditional surrogate modeling approaches approximate only the deterministic component of physical models, requiring separate noise characterization and computationally expensive sampling methods for inverse problems. Here, in this work, we develop the conditional PR-NF model to directly learn and efficiently generate samples from the conditional probability density functions (PDFs). The training process utilizes dataset consisting of input-output pairs without requiring prior knowledge about the noise and the function. Once trained, our model efficiently generates samples from conditional PDFs for any input within the training domain. Moreover, the pseudo-reversibility feature allows for the use of fully connected neural network architectures, which simplifies the implementation and enables theoretical analysis. We provide a rigorous convergence analysis of the conditional PR-NF model, showing its ability to converge to the target conditional PDF using the Kullback−Leibler divergence. To demonstrate the effectiveness of our method, we apply it to several benchmark tests and a real-world geologic carbon storage problem.
97 MATHEMATICS AND COMPUTING↗
Cyclic Injection Leads to Larger and More Frequent Induced Earthquakes under Volume-Controlled Conditions
As carbon storage technologies advance globally, methods to understand and mitigate induced earthquakes become increasingly important. Although the physical processes that relate increased subsurface pore pressure changes to induced earthquakes have long been known, reliable methods to forecast and control induced seismic sequences remain elusive. Suggested reservoir engineering scenarios for mitigating induced earthquakes typically involve modulation of the injection rate. Some operators have implemented periodic shutdowns (i.e., effective cycling of injection rates) to allow reservoir pressures to equilibrate (e.g., Paradox Valley) or shut-in wells after the occurrence of an event of concern (e.g., Basel, Switzerland). Other proposed scenarios include altering injection rates, actively managing pressures through coproduction of fluids, and preinjection brine extraction. Here, in this work, we use 3D physics-based earthquake simulations to understand the effects of different injection scenarios on induced earthquake rates, maximum event magnitudes, and postinjection seismicity. For comparability, the modeled injection considers the same cumulative volume over the project’s operational life but varies the schedule and rates of fluid injected. Simulation results show that cyclic injection leads to more frequent and larger events than constant injection. Furthermore, with intermittent injection scenario, a significant number of events are shown to occur during pauses in injection, and the seismicity rate remains elevated for longer into the postinjection phase compared to the constant injection scenario.
58 GEOSCIENCES↗
Computed Tomography Scanning and Geophysical Measurements of the T.R. McMillen 2 Core
Carbon Storage TRS.
TRS↗
Sleepy Hollow Reagan Unit 86A Well Data
Computed tomography data described in the technical report series "Computed Tomography Scanning and Geophysical Measurements of the Integrated Mid-Continent Stacked Carbon Storage Hub Sleepy Hollow Reagan Unit 86A Well" by Thomas Paronish; Mathias Pohl; Alexis Parker; Rhiannon Schmitt; Johnathan Moore; Richard Spaulding; Igor Haljasmaa; Dustin Crandall; Valarie Smith; Andrew Duguid; and R. M. Joeckel
AS↗
Quantitative Biostratigraphic Analysis and Age Estimates of Middle Cretaceous Sequences in The Baltimore Canyon Trough, Offshore Mid-Atlantic U.S. Margin
ABSTRACT We applied quantitative methods to previously published biostratigraphic data from the Baltimore Canyon Trough (offshore of the Mid-Atlantic U.S.A.) to provide an improved chronostratigraphic framework for Cretaceous sequences. Here, we successfully used graphic correlation of 228 planktonic foraminifera, nannofossil, and palynological events spanning 22 wells to define assemblage and interval zones as well as major paleoenvironmental changes in the Dawson Canyon, Logan Canyon (three sequences), and Missisauga Formations (two sequences, undifferentiated here). Ranking and scaling techniques were not successful because of the of the limited number of usable biostratigraphic markers. The ages of the sequences previously identified using well logs and seismic profiles were temporally constrained based on chronostratigraphically significant biostratigraphic markers that we identified: the late Cenomanian to Turonian DCx sequence (Rotalipora cushmani and Thalmanninella greenhornensis); the early Cenomanian LC1 sequence; the middle and late Albian LC2 sequence (Braarudosphaera africana, Planomalina buxtorfi, and Spinidinium vestitum); the late Aptian LC3 sequence (Cyclonephelium tabulatum); and the early Aptian to Barremian Missisauga sequences (Aptea anaphrissa, Pseudoceratium pelliferum, and Muderongia simplex). These five biostratigraphic associations are correlated with six prominent seismic reflectors and sequence boundaries that can be traced across the basin. Duration of hiatuses associated with these sequence boundaries are uncertain, though our Monte Carlo analysis allows extraction of age estimates from broad and sometimes contradictory ranges and suggests correlation of hiatuses with global sea-level falls. Together, these seismic and biostratigraphic interpretations can be applied (1) to evaluate reservoir continuity and the viability of offshore carbon storage reservoirs in the Baltimore Canyon Trough, (2) to better define the tectonostratigraphic evolution of the basin, and (3) to contribute to the understanding of regional and global variations in Cretaceous sea level.
Paleontology↗
Optimization of Water-Alternating-CO2 Injection Field Operations Using a Machine-Learning-Assisted Workflow
Summary This paper will present a robust workflow to address multiobjective optimization (MOO) of carbon dioxide (CO2)-enhanced oil recovery (EOR)-sequestration projects with a large number of operational control parameters. Farnsworth unit (FWU) field, a mature oil reservoir undergoing CO2 alternating water injection (CO2-WAG) EOR, will be used as a field case to validate the proposed optimization protocol. The expected outcome of this work would be a repository of Pareto-optimal solutions of multiple objective functions, including oil recovery, carbon storage volume, and project economics. FWU’s numerical model is used to demonstrate the proposed optimization workflow. Because using MOO requires computationally intensive procedures, machine-learning-based proxies are introduced to substitute for the high-fidelity model, thus reducing the total computation overhead. The vector machine regression combined with the Gaussian kernel (Gaussian-SVR) is used to construct proxies. An iterative self-adjusting process prepares the training knowledge base to develop robust proxies and minimizes computational time. The proxies’ hyperparameters will be optimally designed using Bayesian optimization to achieve better generalization performance. Trained proxies will be coupled with multiobjective particle swarm Optimization (MOPSO) protocol to construct the Pareto-front solution repository. The outcomes of this workflow will be a repository containing Pareto-optimal solutions of multiple objectives considered in the CO2-WAG project. The proposed optimization workflow will be compared with another established methodology using a multilayer neural network (MLNN) to validate its feasibility in handling MOO with a large number of parameters to control. Optimization parameters used include operational variables that might be used to control the CO2-WAG process, such as the duration of the water/gas injection period, producer bottomhole pressure (BHP) control, and water injection rate of each well included in the numerical model. It is proved that the workflow coupling Gaussian-SVR proxies and the iterative self-adjusting protocol is more computationally efficient. The MOO process is made more rapid by squeezing the size of the required training knowledge base while maintaining the high accuracy of the optimized results. The outcomes of the optimization study show promising results in successfully establishing the solution repository considering multiple objective functions. Results are also verified by validating the Pareto fronts with simulation results using obtained optimized control parameters. The outcome from this work could provide field operators an opportunity to design a CO2-WAG project using as many inputs as possible from the reservoir models. The proposed work introduces a novel concept that couples Gaussian-SVR proxies with a self-adjusting protocol to increase the computational efficiency of the proposed workflow and to guarantee the high accuracy of the obtained optimized results. More importantly, the workflow can optimize a large number of control parameters used in a complex CO2-WAG process, which greatly extends its utility in solving large-scale MOO problems in various projects with similar desired outcomes.
Energy & Fuels↗
Deep learning techniques to disentangle water use efficiency, climate change, and carbon sequestration across ecosystem scales
Focal Areas: We plan to use machine learning (ML) to disentangle the impact of climate change on plant water use efficiency (WUE) across the leaf level, structured canopy, and community ecosystem scales. Due to the complex behavior of modeling plant WUE’s connection to carbon storage (and hidden co-varying interactions) we suggest that physics-based deep learning must be utilized. The second topic we propose is, based on a better understanding of WUE response to climate change, there is a need to predict how WUE will limit or enhance the recycling of water from transpiration by vegetation back to the land surface worldwide, and thus impact water storage capacity (i.e., natural and reservoirs), using machine learning optimization techniques such as genetic algorithms.
54 ENVIRONMENTAL SCIENCES↗
Evaluating Offshore Infrastructure Integrity
Drilling in the offshore environment involves a complex network of infrastructure including pipelines, platforms, rigs, subsea installations, ports, and terminals. Government and industry partners have developed this network over many decades and it remains a critical part of the United States (U.S.) energy portfolio. Many of the major components of this system have been designed with a 20- to 30-year lifespan, yet consistent and growing energy demands support the need to extend the design life of existing infrastructure or repurpose it for secondary needs (i.e. enhanced oil recovery, carbon storage, and new wells). As a result, a growing portion of the offshore infrastructure in the U.S. is approaching or has exceeded its original design life. A critical step in ensuring the continued safe and effective operation of offshore infrastructure is developing a comprehensive understanding of the state of offshore infrastructure and the factors that effect it. The purpose of this project is to assess the current state of existing infrastructure and identify the factors involved in infrastructure degradation through the development and application of big data analytics, machine learning, and advanced spatio-temporal analysis. The project leverages existing data at NETL and combines it with new information on offshore oil and gas structures and the ambient offshore environment in an effort to identify patterns associated with infrastructure integrity. Building on the identified trends and patterns, this project incorporates exploratory analytics and spatial analysis tools in conjunction with machine learning and statistical models to characterize the condition of existing platforms in the offshore environment and predict their risk of failure.
02 PETROLEUM↗
Computed Tomography Scanning and Geophysical Measurements of the Wabash No.1 Core
The computed tomography (CT) facilities and the Multi-Sensor Core Logger (MSCL) at the National Energy Technology Laboratory (NETL) in Morgantown, West Virginia were used to characterize Mt. Simon core from the Wabash CarbonSAFE project, Wabash No. 1 well. The primary impetus of the work in this report is a collaboration between NETL Research and Innovation Center and the Illinois State Geological Survey at the Prairie Research Institute, University of Illinois Urbana-Champaign in Champaign, Illinois. The resultant datasets are presented in this report and can be accessed from NETL's Energy Data eXchange (EDX) online system using the following link: https://edx.netl.doe.gov/dataset/wabash-no-1-well. The Wabash No. 1 well was drilled as part of a U.S. Department of Energy (DOE) funded Carbon Storage Assurance Facility Enterprise field project to assess the feasibility of developing a commercial-scale geological storage complex at the Wabash Valley Resources Integrated Gasification Combined Cycle plant near Terre Haute, Indiana. All equipment and techniques used were non-destructive, enabling future examinations and analyses to be performed on these cores. None of the equipment used was suitable for direct visualization of the pore space in fine-grained structures; fractures, discontinuities, and millimeter scale features were readily detectable with the methods tested. Imaging with the NETL medical CT scanner was performed on the entire core. Qualitative analysis of the medical CT images, coupled with X-ray fluorescence (XRF), P-wave, and magnetic susceptibility measurements from the MSCL were useful in identifying zones of interest for more detailed analysis. The ability to quickly identify key areas for more detailed study with higher resolution will save time and resources in future studies. The combination of methods used provides a multi-scale analysis of the core; the resulting macro and micro descriptions are relevant to many subsurface energy-related examinations traditionally performed at NETL.
54 ENVIRONMENTAL SCIENCES↗
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↗
Exploring Whether Subsurface Fluid Production Can Minimize Triggered Seismicity in Geothermal Fields
Fluid injection and production related to energy recovery and other industrial operations alter the pressure and stress state of subsurface reservoirs which can lead to induced seismicity. Here, the primary goal is to investigate the hypothesis that the modulation of stress state in subsurface reservoirs through fluid injection/production operations can reduce the likelihood of inducing seismicity. Validation of this mitigation strategy will provide an active operational method to control induced seismicity in subsurface reservoir exploitations such as geothermal energy recovery and carbon storage. In this project, we analyze the relationship between extensive fluid production and the paucity of aftershock activity at the Coso Geothermal plant (CGP) following the 2019 Ridgecrest earthquake sequence, where high rates of aftershock triggering were expected. We developed a high-fidelity multiphase coupled thermo-hydro-mechanical (THM) model to simulate fluid injection/production activities at the CGP between 1986 and 2020. THM results of surface subsidence due to high rates of fluid production agree well with field observations from global positioning system (GPS) and interferometric synthetic aperture radar (InSAR). Subsequently, the variations of pore pressure, temperature and stress state were used to drive numerical earthquake simulations of the aftershock response to the 2019 Ridgecrest earthquakes. The earthquake simulations show that seismic quiescence may occur following the Ridgecrest event, depending on the initial stress state at the time of the mainshock and at the initiation of geothermal energy production. Seismic quiescence occurs in 20% of the cases we explored, where rates are decreased by 50% of the background rate in the two years following the mainshock. In circumstances where aftershock rates increase near the CGP following Ridgecrest, the average rate change is a factor of two larger than background rates from simulations with no operations. These findings indicate that unlike many other operations that bring faults closer to failure, operations at the CGP are acting to stabilize faults such that triggering in the current stress state is minimal.
58 GEOSCIENCES↗
Computed Tomography Scanning and Petrophysical Measurements of the Wellington KGS 2-32 Core
This report describes the use of the computed tomography facilities and the Multi-Sensor Core Logger at the National Energy Technology Laboratory in Morgantown, West Virginia to characterize core from the Wellington KGS 2-32 well (API 15-191-22770). Core from the well was obtained as part of the Small-Scale Field Test Demonstrating Carbon Dioxide Sequestration in Arbuckle Saline Aquifer and by CO 2 -Enhanced Oil Recovery at Wellington Field, Sumner County, Kansas (DE-FE0006821). The primary impetus of this work was to capture a detailed a digital representation of the core from the Wellington KGS 2-32 well (Sumner County, Kansas). The collaboration between the U.S. Department of Energy’s (DOE) NETL and the Kansas Geological Survey (KGS) at the University of Kansas enables other research entities to access information about this potential carbon storage location and formations. The resultant datasets are presented in this report and can be accessed from NETL's Energy Data eXchange (EDX) online system using the following link: https://edx.netl.doe.gov/dataset/wellington2-32-core.
47 OTHER INSTRUMENTATION↗