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At least 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Microbial colonization and persistence in deep fractured shales is guided by metabolic exchanges and viral predation

Background: Microbial colonization of subsurface shales following hydraulic fracturing offers the opportunity to study coupled biotic and abiotic factors that impact microbial persistence in engineered deep subsurface ecosystems. Shale formations underly much of the continental USA and display geographically distinct gradients in temperature and salinity. Complementing studies performed in eastern USA shales that contain brine-like fluids, here we coupled metagenomic and metabolomic approaches to develop the first genome-level insights into ecosystem colonization and microbial community interactions in a lower-salinity, but high-temperature western USA shale formation. Results: We collected materials used during the hydraulic fracturing process (i.e., chemicals, drill muds) paired with temporal sampling of water produced from three different hydraulically fractured wells in the STACK (Sooner Trend Anadarko Basin, Canadian and Kingfisher) shale play in OK, USA. Relative to other shale formations, our metagenomic and metabolomic analyses revealed an expanded taxonomic and metabolic diversity of microorganisms that colonize and persist in fractured shales. Importantly, temporal sampling across all three hydraulic fracturing wells traced the degradation of complex polymers from the hydraulic fracturing process to the production and consumption of organic acids that support sulfate- and thiosulfate-reducing bacteria. Furthermore, we identified 5587 viral genomes and linked many of these to the dominant, colonizing microorganisms, demonstrating the key role that viral predation plays in community dynamics within this closed, engineered system. Lastly, top-side audit sampling of different source materials enabled genome-resolved source tracking, revealing the likely sources of many key colonizing and persisting taxa in these ecosystems. Conclusions: These findings highlight the importance of resource utilization and resistance to viral predation as key traits that enable specific microbial taxa to persist across fractured shale ecosystems. We also demonstrate the importance of materials used in the hydraulic fracturing process as both a source of persisting shale microorganisms and organic substrates that likely aid in sustaining the microbial community. Moreover, we showed that different physicochemical conditions (i.e., salinity, temperature) can influence the composition and functional potential of persisting microbial communities in shale ecosystems. Together, these results expand our knowledge of microbial life in deep subsurface shales and have important ramifications for management and treatment of microbial biomass in hydraulically fractured wells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Validation of Play Fairway Analysis of the geothermal potential of Camas Prairie, south-central Idaho, by an exploration well

Abstract Play Fairway Analysis (PFA) methodology was adapted for geothermal exploration at Camas Prairie, Idaho. Geophysical data, structural and geologic mapping, volcanic rock ages and vent locations, and the distribution of thermal springs and wells all indicated a relatively high geothermal potential along the southern margin of the Prairie. An exploration well (USU Camas-1) was drilled to a depth of 618.3 m to validate the PFA. A permeable zone was encountered at ~ 357.5 m with a maximum measured temperature of ~ 80 °C, which was suppressed following the injection of cold water. A moderate transmissivity of ~ 0.25–1 cm 2 /s estimated from an injection test as well a seasonal artesian flow at ~ 0.7 L/s corroborate the presence of a permeable zone. The existence of a lacustrine clay seal was confirmed near the bottom of the basin-fill sediment occupying the upper 314 m of the well. Geothermometers suggest the USU Camas-1 well water equilibrated at a reservoir temperature of ~ 120 °C. Based on the locations of both thermal and cold wells, geothermal fluids appear to be flowing upward along one or both of two fault systems. The presence of young basalts and elevated helium isotope ratios suggest that the heat source of Camas Prairie is magmatic. However, the faults may be acting as a conduit for geothermal fluids to rise from great depth without a shallow magmatic source being present. Camas Prairie is a promising area for geothermal development, but the relatively low reservoir temperatures indicate this resource may not be suitable for electric generation. Perhaps the best use would be for heating.

Lachmar, Thomas E. (ORCID:0000000249640119)↗
DOE OSTI2024

Detecting fractures and monitoring hydraulic fracturing processes at the first EGS Collab testbed using borehole DAS ambient noise

Enhanced geothermal systems (EGS) require cost-effective monitoring of fracture networks. We validate the capability of using borehole distributed acoustic sensing (DAS) ambient noise for fracture monitoring using core photos and core logs. The EGS Collab project has conducted 10 m scale field experiments of hydraulic fracture stimulation using 50–60 m deep experimental wells at the Sanford Underground Research Facility (SURF) in Lead, South Dakota. The first EGS Collab testbed is located at 1616.67 m (4850 ft) depth at SURF and consists of one injection well, one production well, and six monitoring wells. All wells are drilled subhorizontally from an access tunnel called a drift. The project uses a single continuous fiber-optic cable installed sequentially in the six monitoring wells to record DAS data for monitoring hydraulic fracturing during stimulation. We analyze 60 s time records of the borehole DAS ambient noise data and compute the noise root-mean-square (rms) amplitude on each channel (points along the fiber cable) to obtain DAS ambient noise rms amplitude depth profiles along the monitoring wellbore. Our noise rms amplitude profiles indicate amplitude peaks at distinct depths. We compare the DAS noise rms amplitude profiles with borehole core photos and core logs and find that the DAS noise rms amplitude peaks correspond to the locations of fractures or lithologic changes indicated in the core photos or core logs. We then compute the hourly DAS noise rms amplitude profiles in two monitoring wells during three stimulation cycles in 72 h and find that the DAS noise rms amplitude profiles vary with time, indicating the fracture opening/growth or closing during the hydraulic stimulation. Our results demonstrate that borehole DAS passive ambient noise can be used to detect fractures and monitor fracturing processes in EGS reservoirs.

58 GEOSCIENCES↗
DOE OSTI2020

Gas hydrate petroleum systems: What constitutes the “seal”?

The gas hydrate petroleum system (GHPS) approach, which has been used to characterize gas hydrates in nature, uses three distinct components: a methane source, a methane migration pathway, and a reservoir that not only contains gas hydrate, but also acts as a seal to prevent methane loss. Unlike GHPS, a traditional petroleum system (PS) approach further distinguishes between the reservoir, a unit with generally coarser sediment grains, and a separate overlying seal unit with generally finer sediment grains. Adopting this traditional PS distinction in the GHPS approach facilitates assessments of reservoir growth and production potential. The significance of the seal for the formation of a gas hydrate reservoir as well as for efficiency in methane extraction from the reservoir as an energy resource is evident in findings from recent offshore field expeditions, such as India’s second National Gas Hydrate Program expedition (NGHP-02). In regard to gas hydrate-bearing reservoir formations, the NGHP-02 gas chemistry data indicate a primarily microbial methane source. Fine-grained seal sediment in contact with coarser grained reservoir sediment can facilitate that microbial methane production. Logging-while-drilling and sediment core data also indicate that the overlying fine-grained seal sediment is less permeable than the underlying, highly gas hydrate-saturated reservoir sediment. The overlying seal’s capacity to act as a low-permeability boundary is important not only for preventing methane migration out of the reservoir over time, but also for preventing water invasion into the reservoir during methane extraction from the reservoir. Ultimately, the presence of an overlying, fine-grained, low-permeability “seal” influences how gas hydrate initially forms in a coarse-grained reservoir and dictates how efficiently methane can be extracted as an energy resource from the gas hydrate reservoir via depressurization.

Geochemistry & Geophysics↗
DOE OSTI2020

EXPERIMENTAL EVALUATION OF ULTRASONIC VELOCITIES AND ANISOTROPY IN THE TUSCALOOSA MARINE SHALE FORMATION

Tuscaloosa Marine Shale (TMS) formation is a clay- and organic-rich emerging shale play with a considerable amount of hydrocarbon resources. Despite the substantial potential, there have been only a few wells drilled and produced in the formation over the recent years. The analyzed TMS samples contain an average of 50 wt% total clay, 27 wt% quartz and 14 wt% calcite and the mineralogy varies considerably over the small intervals. The high amount of clay leads to pronounced anisotropy and the frequent changes in mineralogy result in the heterogeneity of the formation. We studied the compressional (V P ) and shear-wave (V S ) velocities to evaluate the degree of anisotropy and heterogeneity, which impact hydraulic fracture growth, borehole instabilities, and subsurface imaging. The ultrasonic measurements of P- and S-wave velocities from five TMS wells are the best fit to the linear relationship with R 2 = 0.84 in the least-squares criteria. We observed that TMS S-wave velocities are relatively lower when compared to the established velocity relationships. Most of the velocity data in bedding-normal direction lie outside constant V P /V S lines of 1.6–1.8, a region typical of most organic-rich shale plays. For all of the studied TMS samples, the S-wave velocity anisotropy exhibits higher values than P-wave velocity anisotropy. In the samples in which the composition is dominated by either calcite or quartz minerals, mineralogy controls the velocities and V P /V S ratios to a great extent. Additionally, the organic content and maturity account for the velocity behavior in the samples in which the mineralogical composition fails to do so. The results provide further insights into TMS Formation evaluation and contribute to a better understanding of the heterogeneity and anisotropy of the play.

Geochemistry & Geophysics↗
DOE OSTI2021

Evaluating proxies for the drivers of natural gas productivity using machine-learning models

We report the extensive development of unconventional reservoirs using horizontal drilling and multistage hydraulic fracturing has generated large volumes of reservoir characterization and production data. The analysis of this abundant data using statistical methods and advanced machine-learning (ML) techniques can provide data-driven insights into well performance. Most predictive modeling studies have focused on the impact that different well completion and stimulation strategies have on well production but have not fully exploited the available in situ rock property data to determine its role in reservoir productivity. We have used machine-learning techniques to rank rock mechanical properties, microseismic attributes, and stimulation parameters in the order of their significance for predicting natural gas production from an unconventional reservoir. The data for this study came from a hydraulically fractured well in the Marcellus Shale in Monongalia County, West Virginia. The data classes included measurements aggregated by well completion stage that included (1) gas production, (2) well-log-derived measurements including bulk density, elastic moduli, shear impedance, compressional impedance, brittleness, and gamma measurements, (3) microseismic attributes, (4) long-period long-duration (LPLD) event counts, (5) fracture counts, and (6) stimulation parameters that included the fluid injection volume and average pumping pressure. To identify observable proxies for the drivers of gas production, we evaluated five commonly used ML approaches including multivariate adaptive regression spline, Gaussian mixture model, random forest, gradient boosting, and neural network. We selected five variables including LPLD event count, seismogenic b-value, hydraulic diffusivity, cumulative moment, and fluid volume as the features most likely to impact gas productivity at the stage level in the study area. The data-driven selection of these parameters for their importance in determining gas production can help reservoir engineers design more effective hydraulic-fracture treatments in the Marcellus Shale and other similar unconventional reservoirs. Plain language summary: We use machine-learning methods and data-driven selection of reservoir parameters to rank and better understand their importance in determining gas production, which can help reservoir engineers design more effective hydraulic-fracture treatments in the Marcellus Shale and other similar unconventional reservoirs.

58 GEOSCIENCES↗
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 OSTI2023

Laser ultrasonic imaging of subsurface defects with the linear sampling method

Laser ultrasonics is a remote nondestructive evaluation technique suitable for real-time monitoring of fabrication processes in semiconductor metrology, advanced manufacturing, and other applications where non-contact, high fidelity measurements are required. Here we investigate laser ultrasonic data processing approaches to reconstruct images of subsurface side drilled holes in aluminum alloy specimens. We demonstrate through simulation that the model-based linear sampling method (LSM) can perform accurate shape reconstruction of single and multiple holes and produce images with well-defined boundaries. We experimentally confirm that LSM produces images that represent the internal geometric features of an object, some of which may be missed by conventional imaging.

42 ENGINEERING↗
DOE OSTI2019

EGS Collab Testbed 1: Second Set Tracer Test Results

The EGS Collab project is developing ~10-20 m-scale field sites where fracture stimulation and flow models can be validated against controlled, small-scale, in-situ experiments. The first multi-well experimental site was established at the 4850 level in the Homestake Mine in Lead, South Dakota, where hydraulic fractures were created at an injection well drilled sub-horizontal from the drift. This file contains the second set of tracer data (also include the data uploaded previously, https://gdr.openei.org/submissions/1128) for the EGS Collab testbed. The tracer tests were conducted during October 2018 - November 2019. Injected tracers include DNA, C-dots (fluorescein nano particles), fluorescein, rhodamine-b, sodium chloride, lithium bromide and cesium iodine. The tracers have been detected in three flowing wells located about 7.5 to 9 meters away from the injection interval. The tracer breakthrough curves from these locations have been adjusted to account for the residence time in the injection and production tubing. The details about the tracer test can be found in Background and Methods of Tracer Tests (Mattson et al. (2019,a,b)) (also included in this package). References Mattson, E.D., Neupane, G., Plummer, M.A., Hawkins, A., Zhang, Y. and the EGS Collab Team 2019a. Preliminary Collab fracture characterization results from flow and tracer testing efforts. In Proceedings 44th Workshop on Geothermal Reservoir Engineering, edited, Stanford University, Stanford, California. Mattson, E.D., Neupane, G., Hawkins, A., Burghardt, J., Ingraham, M., Plummer, M., and the EGS Collab Team, 2019b. Fracture tracer injection response to pressure perturbations at an injection well. GRC Transactions, Vol. 43, 2019. Neupane, Ghanashyam, Earl Mattson, Adam Hawkins, Mitchell Plummer, and Yuran Zhang. EGS Collab Testbed 1: Tracer data sets. No. 1128. DOE Geothermal Data Repository; Idaho National Laboratory, 2019.

15 GEOTHERMAL ENERGY↗
DOE OSTI2019

EGS Collab Testbed 2 Preliminary Test Wells Locations and Orientations

The EGS Collab project is evaluating a site for Experiment 2 (hydraulic fracturing/shearing) at a depth of 1.25 km in the Sanford Underground Research Facility (SURF) on the 4100 Level. Two early test holes were drilled in an alcove (formerly known as Battery Charging Station) near Yates Shaft. One of the test hole is about 50 m where as the other hole is about 10 m. This submission package includes well head locations, total lengths of boreholes, their orientations (gyro data). The reported boreholes orientation data were primarily obtained with REFLEX GYRO (TM) survey right after the completion of each hole. Well head locations were ascertained by Laser/LiDAR survey of the drift around the Testbed 2. Note: The coordinate system used is local Homestake Mine Coordinate (HMC) system from an old gold mine that was in operation for over 100 years.

15 GEOTHERMAL ENERGY↗
DOE OSTI2019

EGS Collab Testbed 2: Laser Scanned 4100 L Drift Map

The EGS Collab project is evaluating a site for Experiment 2 (hydraulic fracturing/shearing) at a depth of 1.25 km in the Sanford Underground Research Facility (SURF) on the 4100 Level. Two early test holes were drilled in an alcove (formerly known as Battery Charging Station) near Yates Shaft. Recently, we conducted a laser survey around the Testbed 2 to capture the details of the drift. This submission package includes the laser scanned drift map around the Testbed 2. The drift map data are presented in AutoCAD (*dxf), Leapfrog mesh (*.msh), and point cloud (*.csv) file formats. Note: The coordinate system used is local Homestake Mine Coordinate (HMC) system from an old gold mine that was in operation for over 100 years.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Utah FORGE: Well 16A(78)-32 Planned Trajectory

This submission includes an Excel spreadsheet containing the Utah FORGE deep well 16A(78)-32 planned trajectory including UTM coordinates, elevations, and depths from the well head to the toe. This proposed new injection well is expected to descend vertically to a depth of approximately 1810 m and then extend laterally at an angle to a MD of approximately 3290 m and a maximum vertical depth of approximately 2584 m. There is approximately 1500 m of basin fill at the well head, and less than that above the simulation region with the targeted reservoir region fully enclosed in granitoid basement. The last 60 m of the well will be open hole. Drilling is planned for October 2020. Please direct questions to the Utah FORGE team: utahforge@utah.edu and check the Utah FORGE website: https://utahforge.com after October for more information.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Utah FORGE: Phase 3 Ground Water Level Monitoring Data

Groundwater data around the Utah FORGE site has been collected to determine the piezometric levels and compositional variability. Field measurements in 2020 are designed to obtain and survey new groundwater wells that have been drilled on the distal periphery of the Utah FORGE project area. The spreadsheet included in this submission contains ground water level data from the Utah FORGE site as monitored from wells WOW-2 and WOW-3. The well locations are also provided as UTM, Zone 12, NAD83 coordinates.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Utah FORGE: Phase 3 Microgravity Data

A 4D microgravity survey began in early 2019 in order to benchmark surface and subsurface elevations prior to Phase 3 drilling and stimulation campaigns. Microgravity stations are collocated with GPS deformation benchmarks and use the same location survey data. Surveys are undertaken on a quarterly basis. The focus of current activities is comparative analysis of GPS survey results and InSAR analysis (see GDR Submission 1251) to check seasonal variance and to evaluate possible changes in the groundwater elevation. The attached zip file contains a text file and an Excel spreadsheet containing Utah FORGE Phase 3 processed "final" microgravity data through time. The data are identical in both files. The spreadsheet is only supplied for convenience. There is also a ReadMe.txt file that describes the field headings.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Utah FORGE Well 16A(78)-32 X-ray Diffraction Data

This submission contains x-ray diffraction data taken from 3,600 to 10,987 feet deep in well 16A(78)-32 in the Roosevelt Hot Springs Geothermal Area as part of the Utah FORGE project. 78 rock samples were taken. Samples are taken in a range of ten feet at 100 foot increments. Mineral abundances are given in weight percent of the sample, with results rounded to the nearest whole number. Fields marked with tr (trace) indicate that mineral is present, but that its abundance calculated from the Rietveld refinement was less than one weight percent; that it was observed in the clay-sized fraction, but not the bulk; and/or that it was observed in low abundance during petrographic analyses. The samples were taken between 10/29/2020 and 12/24/2020 during the drilling of well 16A(78)-32.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results

Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells - increasing or decreasing the fluid flow rates across the wells - and drilling new wells at appropriate locations. The latter is expensive, time-consuming, and subject to many engineering constraints, but the former is a viable mechanism for periodic adjustment of the available fluid allocations. Data and supporting literature from a study describing a new approach combining reservoir modeling and machine learning to produce models that enable strategies for the mitigation of decreased heat and power production rates over time for geothermal power plants. The computational approach used enables translation of sets of potential flow rates for the active wells into reservoir-wide estimates of produced energy and discovery of optimal flow allocations among the studied sets. In our computational experiments, we utilize collections of simulations for a specific reservoir (which capture subsurface characterization and realize history matching) along with machine learning models that predict temperature and pressure timeseries for production wells. We evaluate this approach using an "open-source" reservoir we have constructed that captures many of the characteristics of Brady Hot Springs, a commercially operational geothermal field in Nevada, USA. Selected results from a reservoir model of Brady Hot Springs itself are presented to show successful application to an existing system. In both cases, energy predictions prove to be highly accurate: all observed prediction errors do not exceed 3.68% for temperatures and 4.75% for pressures. In a cumulative energy estimation, we observe prediction errors that are less than 4.04%. A typical reservoir simulation for Brady Hot Springs completes in approximately 4 hours, whereas our machine learning models yield accurate 20-year predictions for temperatures, pressures, and produced energy in 0.9 seconds. This paper aims to demonstrate how the models and techniques from our study can be applied to achieve rapid exploration of controlled parameters and optimization of other geothermal reservoirs. Includes a synthetic, yet realistic, model of a geothermal reservoir, referred to as open-source reservoir (OSR). OSR is a 10-well (4 injection wells and 6 production wells) system that resembles Brady Hot Springs (a commercially operational geothermal field in Nevada, USA) at a high level but has a number of sufficiently modified characteristics (which renders any possible similarity between specific characteristics like temperatures and pressures as purely random). We study OSR through CMG simulations with a wide range of flow allocation scenarios. Includes a dataset with 101 simulated scenarios that cover the period of time between 2020 and 2040 and a link to the published paper about this project, where we focus on the Machine Learning work for predicting OSR's energy production based on the simulation data, as well as a link to the GitHub repository where we have published the code we have developed (please refer to the repository's readme file to see instructions on how to run the code). Additional links are included to associated work led by the USGS to identify geologic factors associated with well productivity in geothermal fields. Below are the high-level steps for applying the same modeling + ML process to other geothermal reservoirs: 1. Develop a geologic model of the geothermal field. The location of faults, upflow zones, aquifers, etc. need to be accounted for as accurately as possible 2. The geologic model needs to be converted to a reservoir model that can be used in a reservoir simulator, such as, for instance, CMG STARS, TETRAD, or FALCON 3. Using native state modeling, the initial temperature and pressure distributions are evaluated, and they become the initial conditions for dynamic reservoir simulations 4....

15 GEOTHERMAL ENERGY↗
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 OSTI2023

Steptoe Valley NV Data Compilation: Understanding a Stratigraphic Hydrothermal Resource through Geophysical Imaging

Sandia National Laboratories partnered with a multi-disciplinary group of subject matter experts to evaluate a stratigraphic geothermal resource in Steptoe Valley, Nevada using both established and novel geophysical imaging techniques. Provided here are a compilation of newly acquired data over the area and select modeling efforts. This encompasses a 3D geological model (inclusive of full Leapfrog files, Leapfrog viewer files, and XYZ data for faults and stratigraphy) with embedded geophysical modeling, controlled-source electromagnetic (CSEM) and magnetotelluric (MT) data packages, aqueous spring geochemistry data, seismic reflection interpretations, and a gravity data package. The stratigraphic reservoir in Steptoe Valley was previously discovered during oil and gas exploration. Subsequent studies, such as the Nevada Play Fairway Analysis, added data which further highlighted potential resource targets in the basin. Geophysical surveys, complimented with refined geologic mapping and geochemical sampling, were deployed to further characterize the resource. The resulting 3D geologic interpretation, conceptual model refinements, and reservoir simulations suggest that a power-capable reservoir is economically accessible in the Paleozoic carbonates of the deep/central basin. Additional geophysical characterization and exploration drilling efforts are recommended to calibrate interpretation and determine where/how to potentially develop the Steptoe resource. The geophysical tools, interpretations, lessons learned, and publicly available data generated by this study establish an exploration methodology to inform decisions for successful development of stratigraphic reservoirs.

15 GEOTHERMAL ENERGY↗