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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.

15 GEOTHERMAL ENERGY↗
DOE OSTI2019

Figures of merit for production HPC

This report summarizes a set of figures of merit of interest in monitoring the hardware and hardware usage in a Sandia high performance computing (HPC) center. These figures are computable from high frequency monitoring data and other non-metric data and may aid administrators and customer support personnel in their decision processes. The figures are derived from interviews of the HPC center staff. The figures are in many cases simplistic data reductions, but they are our initial targets in creating dashboards that turn voluminous monitoring data into actionable information. Because simplistic reductions may obscure as well as reveal the situation under study, we also document the necessary 'drill-down' and %60exploration' views needed to make the data better understood quickly. These figures of merit may be compared to dashboarding tools documented by other HPC centers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Machine Learning Application to Assess Occurrence and Saturations of Methane Hydrate in Marine Deposits Offshore India

Artificial Neural Networks (ANN) were used to assess methane hydrate occurrence and saturation in marine sediments offshore India. The ANN analysis classifies the gas hydrate occurrence into three types: methane hydrate in pore space, methane hydrate in fractures, or no methane hydrate. Further, predicted saturation characterizes the volume of gas hydrate with respect to the available void volume. Log data collected at six wells, which were drilled during the India National Gas Hydrate Program Expedition 02 (NGHP-02), provided a combination of well log measurements that were used as input for machine learning (ML) models. Well log measurements included density, porosity, electrical resistivity, natural gamma radiation, and acoustic wave velocity. Combinations of well logs used in the ML models provide good overall balanced accuracy (0.79 to 0.86) for the prediction of the gas hydrate occurrence and good accuracy (0.68 to 0.92) for methane hydrate saturation prediction in the marine accumulations against reference data. The accuracy scores indicate that the ML models can successfully predict reservoir characteristics for marine methane hydrate deposits. In conclusion, the results indicate that the ML models can either augment physics-driven methods for assessing the occurrence and saturation of methane hydrate deposits or serve as an independent predictive tool for those characteristics.

58 GEOSCIENCES↗
DOE OSTI2022

Deep learning-accelerated 3D carbon storage reservoir pressure forecasting based on data assimilation using surface displacement from InSAR

Fast forecasting of the reservoir pressure distribution during geologic carbon storage (GCS) by assimilating monitoring data is a challenging problem. Due to high drilling cost, GCS projects usually have spatially sparse measurements from few wells, leading to high uncertainties in reservoir pressure prediction. To address this challenge, we use low-cost Interferometric Synthetic-Aperture Radar (InSAR) data as monitoring data to infer reservoir pressure build up. We develop a deep learning-accelerated workflow to assimilate surface displacement maps interpreted from InSAR and to forecast dynamic reservoir pressure. Employing an Ensemble Smoother Multiple Data Assimilation (ES-MDA) framework, the workflow updates three-dimensional (3D) geologic properties and predicts reservoir pressure with quantified uncertainties. We use a synthetic commercial-scale GCS model with bimodally distributed permeability and porosity to demonstrate the efficacy of the workflow. A two-step CNN-PCA approach is employed to parameterize the bimodal fields. The computational efficiency of the workflow is boosted by two residual U-Net based surrogate models for surface displacement and reservoir pressure predictions, respectively. The workflow can complete data assimilation and reservoir pressure forecasting in half an hour on a personal computer.

25 ENERGY STORAGE↗
NASA NTRS1977

Cratering motions and structural deformation in the rim of the Prairie Flat multiring explosion crater

Cratering motions and structural deformation are described for the rim of the Prairie Flat multiring crater, 85.5 m across and 5.3 m deep, which was formed by the detonation of a 500-ton TNT surface-tangent sphere. The terminal displacement and motion data are derived from marker cans and velocity gages emplaced in drill holes in a three-dimensional matrix radial to the crater. The integration of this data with a detailed geologic cross section, mapped from deep trench excavations through the rim, provides a composite view of the general sequence of motions that formed a transiently uplifted rim, overturned flap, inverted stratigraphy, downfolded rim, and deformed strata in the crater walls. Preliminary comparisons with laboratory experimental cratering and with numerical simulations indicate that explosion craters of the Prairie Flat-type generated by surface and near-surface energy sources tend to follow predictable motion sequences and produce comparable structural deformation. More specifically, central uplift and multiring impact craters with morphologies and structures comparable to Prairie Flat are inferred to have experienced similar deformational histories of the rim, such as uplift, overturning, terracing, and downfolding.

Roddy, D. J.↗
DOE OSTI2018

Impact of elliptical boreholes on in situ stress estimation from leak-off test data

We developed an inversion technique to determine in situ stresses for elliptical boreholes of arbitrary trajectory. In this approach, borehole geometry, drilling-induced fracture information, and other available leak-off test data were used to construct a mathematical model, which was in turn applied to finding the inverse of an overdetermined system of equations. The method has been demonstrated by a case study in the Appalachian Basin, USA. The calculated horizontal stresses are in reasonable agreement with the reported regional stress study of the area, although there are no field measurement data of the studied well for direct calibration. The results also indicate that 2% of axis difference in the elliptical borehole geometry can cause a 5% difference in minimum horizontal stress calculation and a 10% difference in maximum horizontal stress calculation.

42 ENGINEERING↗
DOE OSTI2015

Mock-Up activities for the extraction of samples from a salt-stone disposal unit - 15502

Mock-up testing with respect to the core-drilling of simulant salt-stone grout was conducted to support the future retrieval of field-emplaced salt-stone samples from salt-stone Disposal Unit (SDU) Cell 2A. The primary objectives of extracting samples from SDU Cell 2A are to enable measurement of field-emplaced samples with respect to properties that influence long-term performance, such as saturated hydraulic conductivity, and to compare the measured properties for field-emplaced and comparable samples prepared in the laboratory. Initial mock-up activities (Phases 1 and 2) were conducted in Fiscal Years (FY) 2013 and 2014 in order to demonstrate the viability of standard core-drilling techniques that are typically employed for sampling concrete structures. The primary requirement for the core-drilling technique was that it should minimize the potential of physical or chemical sample changes that would artificially degrade key grout properties. Mock-up activities were aimed at optimizing the techniques for core-drilling, and subsequent sample extraction, using a set-up that mimicked, to the extent practical, both the physical characteristics, and operational complexities, of core-drilling salt-stone grout from the roof of the SDU. Samples extracted from the grout monolith were subsequently characterized with respect to saturated hydraulic conductivity and compressive strength, and compared to data for cast samples prepared using the same simulant salt-stone material. The comparison of these properties for the two sample types was expected to highlight potential flaws (e.g., micro-cracking) introduced by the core-drilling process. While the data for saturated hydraulic conductivity were virtually identical (approximately 2 E-09 cm/sec), the compressive strength of the core-drilled samples (approximately 3.5 MPa (500 psi)) was on average half that measured for the cast samples. It was suspected that increased water saturation and surface scoring of the core-drilled samples might account for the disparate compressive Strength data. During Phases 1 and 2 it was determined that cores could be successfully drilled and extracted to a depth of approximately 0.9 meters (3 feet) below the grout surface. Phase 3 (FY 2105) has been initiated and is primarily directed at demonstrating the ability to core-drill through approximately 1.8 meters (6 feet) of overlying grout to retrieve the desired sample from the SDU. The original drill depth of 0.6 - 0.9 meters (2-3 feet) was based on an SDU fill height of 5.6 meters (18.5 feet) and sample location of approximately 4.9 meters (16 feet); recently the allowable fill height in the SDUs has been increased to 6.5 meters (21.25 feet); hence the increased depth of overlying grout above the samples to be retrieved. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Geothermal Deep Direct-Use for Low-Carbon Heating: A Case Study at Cornell University

Cornell University is developing a geothermal deep direct-use system to provide reliable, low carbon, and cost-competitive heating for its main campus in Ithaca, New York. This geothermal system, also referred to as Earth Source Heat, would provide the majority of the campus heating load and represents a key pillar for the university to become carbon neutral by 2035. Over the last 10 years, numerous desktop, seismic, and geophysical studies have been undertaken at Cornell to characterize the local subsurface, predict long-term reservoir performance, design the integration into the existing district heating network, and estimate capital and levelized costs. Recently, the university received funding for drilling an observatory well to obtain in-situ subsurface data (e.g., reservoir temperatures, pressures, fluid chemistry, stress field), significantly lowering subsurface uncertainty and de-risking the project. Drilling of the test well is planned for fall 2021. This paper provides an overview and status of the project, including a summary of past studies and a description of the proposed well and planned testing activities.

Cornell University↗
NASA NTRS1974

Regolith history from cosmic-ray-produced nuclides

A model is given for regolith development by which the meteoroid impact parameters (time of last major impact, soil escape rate, and soil turnover depths) are determined from the Gd isotope data for the Apollo 15 and 16 drill stems. The time of the last major impact, cosmic-ray-produced-nuclide-clock reset time, at the Apollo 15 site is 0.6-1.3 aeon. The reset time at the Apollo 16 site is between 1.54 plus or minus 0.14 and 3.3-4.0 aeon. The average soil escape rate from the moon corresponding to the 1.54 plus or minus 0.14 aeon reset time is between 70 and 110 cm/aeon and corresponding to the 3.3 and 4.0 aeon reset time is between 36 and 56 cm/aeon. The soil turnover depth for the 1.54 plus or minus 0.14 aeon reset time is 250 cm and for the 3.3-4.0 aeon reset time is 750 cm. The Gd data restrict the change with time of the meteoroid flux during the past 1.4 aeon.

Fireman, E. L.↗
DOE OSTI2021

EGS Collab Experiment 2: Testbed Well Orientation Data

This dataset contains the north seeking gyro data for each of the 11 boreholes drilled at the Experiment 2 testbed on the 4100 foot level of the SURF (Sanford Underground Research Facility). Each gyro file is in individual folders for each well. A single folder called 'E2 All Well Trajectories' contains the trajectories for all the wells but in the mine coordinate system. An image of the well layout is provided in a PowerPoint.

15 GEOTHERMAL ENERGY↗
DOE OSTI2013

Advancements in 3D Structural Analysis of Geothermal Systems

Robust geothermal activity in the Great Basin, USA is a product of both anomalously high regional heat flow and active fault-controlled extension. Elevated permeability associated with some fault systems provides pathways for circulation of geothermal fluids. Constraining the local-scale 3D geometry of these structures and their roles as fluid flow conduits is crucial in order to mitigate both the costs and risks of geothermal exploration and to identify blind (no surface expression) geothermal resources. Ongoing studies have indicated that much of the robust geothermal activity in the Great Basin is associated with high density faulting at structurally complex fault intersection/interaction areas, such as accommodation/transfer zones between discrete fault systems, step-overs or relay ramps in fault systems, intersection zones between faults with different strikes or different senses of slip, and horse-tailing fault terminations. These conceptualized models are crucial for locating and characterizing geothermal systems in a regional context. At the local scale, however, pinpointing drilling targets and characterizing resource potential within known or probable geothermal areas requires precise 3D characterization of the system. Employing a variety of surface and subsurface data sets, we have conducted detailed 3D geologic analyses of two Great Basin geothermal systems. Using EarthVision (Dynamic Graphics Inc., Alameda, CA) we constructed 3D geologic models of both the actively producing Brady’s geothermal system and a ‘greenfield’ geothermal prospect at Astor Pass, NV. These 3D models allow spatial comparison of disparate data sets in 3D and are the basis for quantitative structural analyses that can aid geothermal resource assessment and be used to pinpoint discrete drilling targets. The relatively abundant data set at Brady’s, ~80 km NE of Reno, NV, includes 24 wells with lithologies interpreted from careful analysis of cuttings and core, a 1:24,000 scale detailed geologic map and cross-sections, 2D seismic reflection profiles and other geophysical data, and downhole temperature data. The 3D geologic model based on these data consists of 61 fault planes, 25 distinct stratigraphic units, and 2 intrusive bodies. Geothermal fluids are produced from a left step-over/relay ramp within the Brady’s Fault Zone (BFZ). Under local stress conditions, fault segments that strike NNE-to-NE are most likely to slip and/or dilate, and therefore transmit geothermal fluids. The 3D model defines the locations of discrete fault intersections within the BFZ and indicates that the densest zones of structurally controlled fracture permeability are ~10-to-10s of meters in diameter and plunge ~55° NW-NNW beneath the heart of the BFZ step over. The locations of high intersection density, high fault slip and dilation tendency, high subsurface temperature, and lithologies known to support high fracture permeability are combined to produce 3D ‘fairway’ maps useful in both assessments of geothermal resource potential and for defining drilling targets. Astor Pass is located on the Pyramid Lake Paiute Reservation, ~80 km north of Reno, NV. It is a prospective ‘greenfield’ geothermal area, and thus subsurface data are relatively sparse. Available data include: two relatively deep wells (~1400 m) and one shallower well (~500 m) with lithologies interpreted from drill cuttings, several 2D seismic reflection profiles, a 1:24,000 scale geologic map and cross-section, a shallow temperature survey, and downhole temperature data. 3D modeling based on these data has defined 19 distinct fault planes and 16 stratigraphic units. Based on the stress field calculated from borehole breakouts, drilling induced tensile cracks and petal-centerline cracks in the two relatively deep wells, 3D slip and dilation tendency analysis indicates that northerly striking fault segments are most likely to slip and/or dilate, and therefore transmit geothermal fluids. Analysis of fault intersection density indicates that the highest density of structurally controlled permeability within the field lies in a narrow (10-to-10s of m) zone plunging moderately (~35°) to the NNW beneath Pleistocene tufa deposits. This zone of increased fracture density, which we interpret as the primary upflow zone, is controlled by the intersection of N-to-NNW striking normal faults and a WNW striking dextral fault zone and represents the most promising target for future drilling. Construction of a 3D geologic model involves integration of a variety of data into an internally consistent framework. A robust model allows for spatial comparison between the various types of data (structural, stratigraphic, geophysical, temperature, etc.) that are commonly used independently to site geothermal wells. Furthermore, highly detailed 3D geologic models provide the basis for additional quantitative analysis, including 3D fault slip and dilation tendency analysis and the precise location of structurally controlled permeability pathways. These analyses provide detailed information relating to the internal dynamics of geothermal systems and can mitigate the costs and risks of geothermal exploration and development by contributing to better well targeting and more accurate evaluations of resource potential.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

lllinois Storage Corridor CarbonSAFE Phase III: Pre-drilling Site Assessment: Prairie State Generating Company

The Illinois Storage Corridor project will drill a stratigraphic test well as part of the Illinois Storage Corridor CarbonSAFE Phase 3 project near the Prairie State Generating Company coal-fired power plant near Marissa, Illinois. The pre-drilling site evaluation has considered the primary target reservoirs, the Potosi Dolomite and St. Peter Sandstone, and primary seal, the Maquoketa Group. Data to be collected from the well include core, fluid samples, in situ well tests, geophysical logs intended to provide information on lithologic, geomechanical, and geophysical characteristics to determine the feasibility for the geologic sequestration of 50 million metric tons or more of injected carbon dioxide. The planned drilling site has been evaluated using available subsurface geologic data and analyses from the Illinois Basin. These data provide lithologic and structural information, shallow groundwater resource distribution, location of known nearby wellbores, and regional drilling characteristics. The data were used to generate geologic structure and isopach maps for the target reservoir and caprock strata and for prognosing the tops of major lithologic units to aid drilling and coring procedures. The regional analyses indicate that no known structural features are expected to negatively impact the target storage reservoir or caprock. No protected and sensitive areas, groundwater resources, or existing resource development are expected to be impacted by the proposed well drilling activities. The well is planned to be drilled to a total depth of approximately 5,600 feet (1,707 m) and terminate in the Precambrian. Cores (up to 5 intervals) will be collected from the Maquoketa Group, confining units above the St. Peter Sandstone, St. Peter Sandstone, confining units of the Potosi Dolomite and the Potosi Dolomite. Water samples will be attempted to be collected from the St. Peter Sandstone and Potosi Dolomite. Potential impact on drilling progress is a lost circulation zone in the Potosi Dolomite, which has been demonstrated to have intermittent cavernous porosity from karstification elsewhere in the Illinois Basin. This document also presents a preliminary coring and sampling program, proposed logging suite, and well testing program, all of which will be reviewed during drilling.

01 COAL, LIGNITE, AND PEAT↗
NASA NTRS2008

Estimating Hardness from the USDC Tool-Bit Temperature Rise

A method of real-time quantification of the hardness of a rock or similar material involves measurement of the temperature, as a function of time, of the tool bit of an ultrasonic/sonic drill corer (USDC) that is being used to drill into the material. The method is based on the idea that, other things being about equal, the rate of rise of temperature and the maximum temperature reached during drilling increase with the hardness of the drilled material. In this method, the temperature is measured by means of a thermocouple embedded in the USDC tool bit near the drilling tip. The hardness of the drilled material can then be determined through correlation of the temperature-rise-versus-time data with time-dependent temperature rises determined in finite-element simulations of, and/or experiments on, drilling at various known rates of advance or known power levels through materials of known hardness. The figure presents an example of empirical temperature-versus-time data for a particular 3.6-mm USDC bit, driven at an average power somewhat below 40 W, drilling through materials of various hardness levels. The temperature readings from within a USDC tool bit can also be used for purposes other than estimating the hardness of the drilled material. For example, they can be especially useful as feedback to control the driving power to prevent thermal damage to the drilled material, the drill bit, or both. In the case of drilling through ice, the temperature readings could be used as a guide to maintaining sufficient drive power to prevent jamming of the drill by preventing refreezing of melted ice in contact with the drill.

Bar-Cohen, Yoseph↗
DOE OSTI2012

Screening Assessment of Potential Human-Health Risk from Future Natural-Gas Drilling Near Project Rulison in Western Colorado

The Project Rulison underground nuclear test was conducted in 1969 at a depth of 8,400 ft in the Williams Fork Formation of the Piceance Basin, west-central Colorado (Figure 1). The U.S. Department of Energy Office of Legacy Management (LM) is the steward of the site. Their management is guided by data collected from past site investigations and current monitoring, and by the results of calculations of expected behavior of contaminants remaining in the deep subsurface. The purpose of this screening risk assessment is to evaluate possible health risks from current and future exposure to Rulison contaminants so the information can be factored into LM's stewardship decisions. For example, these risk assessment results can inform decisions regarding institutional controls at the site and appropriate monitoring of nearby natural-gas extraction activities. Specifically, the screening risk analysis can provide guidance for setting appropriate action levels for contaminant monitoring to ensure protection of human health.

(Figure↗
DOE OSTI2013

Preliminary Drill Sites

Preliminary locations for intermediate depth temperature gradient holes and/or resource confirmation wells based on compilation of geological, geophysical and geochemical data prior to carrying out the DOE-funded reflection seismic survey.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

Assessing the response of methane hydrates to environmental change at the Svalbard continental margin

This project reflects the results of cooperative research efforts between the US Department of Energy and scientists from Norway and Germany, who collaboratively designed the study, collected and analyzed samples from the water column and sediment and arrive at conclusions presented in joint publications. The main goal of the project was to assess the role of gas hydrate deposits in modulating methane discharge revealed through water column data, and assess the changes in environmental parameters, geochemical and microbiological conditions that drive the methane release in this high latitude region. Samples and data were collected from seven expeditions that encompassed extensive water column surveys, gravity coring, remotely-operated towed instrumentation and seafloor drilling campaigns. Geochemical and microbiological results were interpreted in the context of correlative geophysical data and numerical models, to generate a comprehensive and interdisciplinary view of the processes operating along the Svalbard margin. Incubation experiments complement the filed observations and provide additional data on the microbiological response to methane change. Collectively our results show that methane seepage in this margin is a component of a large methane plume, the Svalbard plume, which is not associated with gas hydrate processes. Consistently, numerical models of data from shallow (gravity cores offshore Storfjordrenna, bearing gas hydrate) and deep (MeBo drilling offshore Prins Karls Foreland) sediment samples show that methane and gas hydrate dynamics at sites that lie within the upper edge of gas hydrate stability are not responding to modern changes in bottom water temperature. Rather the data point to a long history of methane release, dominantly controlled by large scale Earth system changes (e.g., geology, oceanography, and glaciology) with gas hydrate as a temporary methane reservoir. Further results document the episodicity of the methane release, the role of microbes in sequestering methane carbon into authigenic carbonate, reaction networks involved in carbon recycling pathways at the sulfate-methane transition, and response of microbial communities to changes in methane flux allowing for a richer understanding of microbial ecology in response to change at methane seep sites.

58 GEOSCIENCES↗
DOE OSTI2015

Conductance Steamflow relationship

These histograms represent our calibration of conductance of a volcanic geothermal field (with a clay cap) and the observed steam flow rates. See the following paper for further description: Trainor-Guitton, Hoversten,Nordquist, Intani, Value of information analysis using geothermal field data: accounting for multiple interpretations & determining new drilling locations. SEG Abstracts 2015.

15 GEOTHERMAL ENERGY↗
DOE OSTI2014

Application of Crunch-Flow Routines to Constrain Present and Past Carbon Fluxes at Gas-Hydrate Bearing Sites

In November 2012, Oregon State University initiated the project entitled: Application of Crunch-Flow routines to constrain present and past carbon fluxes at gas-hydrate bearing sites. Within this project we developed Crunch-Flow based modeling modules that include important biogeochemical processes that need to be considered in gas hydrate environments. Our modules were applied to quantify carbon cycling in present and past systems, using data collected during several DOE-supported drilling expeditions, which include the Cascadia margin in US, Ulleung Basin in South Korea, and several sites drilled offshore India on the Bay of Bengal and Andaman Sea. Specifically, we completed modeling efforts that: 1) Reproduce the compositional and isotopic profiles observed at the eight drilled sites in the Ulleung Basin that constrain and contrast the carbon cycling pathways at chimney (high methane flux) and non-chimney sites (low methane, advective systems); 2) Simulate the Ba record in the sediments to quantify the past dynamics of methane flux in the southern Hydrate Ridge, Cascadia margin; and 3) Provide quantitative estimates of the thickness of individual mass transport deposits (MTDs), time elapsed after the MTD event, rate of sulfate reduction in the MTD, and time required to reach a new steady state at several sites drilled in the Krishna-Godavari (K-G) Basin off India. In addition we developed a hybrid model scheme by coupling a home-made MATLAB code with CrunchFlow to address the methane transport and chloride enrichment at the Ulleung Basins chimney sites, and contributed the modeling component to a study focusing on pore-scale controls on gas hydrate distribution in sediments from the Andaman Sea. These efforts resulted in two manuscripts currently under review, and contributed the modeling component of another pare, also under review. Lessons learned from these efforts are the basis of a mini-workshop to be held at Oregon State University (Feb 2014) to instruct graduate students (OSU and UW) as well as DOE staff from the NETL lab in Albany on the use of Crunch Flow for geochemical applications.

03 NATURAL GAS↗