These data are from tidal resource characterization measurements collected between April and July 2017 in Western Passage near Eastport, Maine, USA. The dataset contains the following four sub-datasets, each of which is described in more detail in the README.pdf. 1. A bottom-mounted Teledyne RDI Workhorse 600 kHz acoustic Doppler current profiler (ADCP) was deployed at 44.92015 N, 66.98915 W in ~50 m of water from 3 April to 18 July (106 days). Data were recorded in 6-minute increments in the ENU (East, magnetic North, Up) coordinate system with bin-mapping enabled. 2. A bottom-mounted Nortek Signature 500 kHz ADCP was deployed at 44.92192 N, 66.98913 W in ~50 m of water from 4 April to 18 July (105 days). Data were sampled and recorded at 2 Hz and recorded in the ENU (East, magnetic North, Up) coordinate system. 3. Between those stations along a cross-channel transect, a Stable Tidal Turbulence Mooring (STTM) positioned ~10 m above the seabed was deployed for one week during a spring tide. The STTM was outfitted with two Nortek Vector acoustic Doppler velocimeters equipped with inertial motion units (ADVs), a bottom-tracking downward-looking Teledyne RDI Workhorse 600 kHz ADCP to provide motion-corrected flow and turbulence characteristics at high temporal resolution, and an upward-looking Teledyne RDI Sentinel V20 ADCP. The STTM was deployed at 44.92098 N, 66.98922 W from 24-31 May. 4. A vessel-mounted Teledyne RDI Workhorse 300 kHz ADCP collected current data along three transects over two days, 4-5 April. The data processing used DOLfYN version 0.11.2. All hdf5 files (i.e., files ending in `.h5`) contained here can be opened using that version of DOLfYN (e.g., `dat = dolfyn.load('')`). All distances are in meters (e.g., depth, range, MLLW, hab, eta, z_), and all velocities in m/s. See the DOLfYN documentation https://lkilcher.github.io/dolfyn/), and/or the Nortek and Teledyne RDI documentation for additional details. Additional details on the dataset can be found in the README.pdf, including: - Format details of each data file. - How to regenerate the data-processing (using the files in the `wp2017_processing.zip` archive).

Analysis of Unit Mobility Ratio Well-to-Well Tracer Flow to Determine Reservoir Heterogeneity DOE/SF/11564-1

In numerical reservoir simulation naturally fractured reservoirs are commonly divided into matrix and fracture systems. The high permeability fractures are usually entirely responsible for flow between blocks and flow to the wells. The flow in these fractures is modeled using Darcy`s law and its extension to multiphase flow by means of relative permeabilities. The influence and measurement of fracture relative permeability for two-phase flow in fractured porous media have not been studied extensively, and the few works presented in the literature are contradictory. Experimental and numerical work on two-phase flow in fractured porous media has been initiated. An apparatus for monitoring this type of flow was designed and constructed. It consists of an artificially fractured core inside an epoxy core holder, detailed pressure and effluent monitoring, saturation measurements by means of a CT-scanner and a computerized data acquisition system. The complete apparatus was assembled and tested at conditions similar to the conditions expected for the two-phase flow experiments. Fine grid simulations of the experimental setup-were performed in order to establish experimental conditions and to study the effects of several key variables. These variables include fracture relative permeability and fracture capillary pressure. The numerical computations show that the flow is dominated by capillary imbibition, and that fracture relative permeabilities have only a minor influence. High oil recoveries without water production are achieved due to effective water imbibition from the fracture to the matrix. When imbibition is absent, fracture relative permeabilities affect the flow behavior at early production times.

This package includes data and footage from two rounds of downhole camera surveys performed at the Sanford Underground Research Facility (SURF) on the 4850 level. The exercise was performed once on 25 May 2018 and once on 21 December 2018. On May 25th, the first round was done during fluid injection at the 164-ft stimulation zone in the injection well (E1-I). On December 21st, the second round was carried out during fluid injection at the 142-ft stimulation zone. Prior to the injections, downhole instrumentation was removed from the production well (E1-P) to allow room for the downhole camera system. The water within E1-P was then lifted out by the application of air pressure and the downhole camera system was conveyed into the production well. Finally, the water was injected into E1-I and the camera was used to scan for jetting points, or fluid entry, in E1-P. There is a survey description in this package that further describes the procedure of the survey and the overall results. Additionally, there is a detailed analysis of the surveys in the form of a PowerPoint, which includes animations/visualizations from the camera footage, presents interpretations in detail, and provides some general conclusions. Three animations, along with the two video segments that show the jetting into E1-P, are also provided. The video footage was collected using a GeoVISION Dual-Scan Micro Video Camera, the specs of which are also included in this package as a resource.

These data and test descriptions comprise a chilled circulation test conducted at the 164' fracture in the EGS Collab Experiment 1 testbed on the 4850 ft level of the Sanford Underground Research Facility. Descriptions of the meta data, design drawings for the flow testing system, and evaluation of the thermistor data are provided here. The test ran from April 2019 through early March of 2020, when testing was concluded at the experiment 1 site. These data are are complementary to the stimulation data provided in another submission which is linked below (i.e. stimulation at the 164' notch). More information about the test itself as well as the rationale and process of data processing is available on the EGS Collab Experiment 1 Long Term Circulation Test wiki page which is also linked below.

Stimulation data from Experiment 1 of EGS Collab, which occurred on the 4850 ft level of the Sanford Underground Research Facility (SURF). A detailed description of the stimulation data is provided in the StimulationDataNotes.docx and is also available on the EGS Collab Wiki. A Meta Data Cheat Sheet, which describes all of the channels in the Raw CSV files, is available as well. Note that this cheat sheet is a comprehensive meta data descriptor and channels were added as the experiment evolved. This means that some columns may not be populated in early data. Additionally, we have included the chat logs from these experiments. The experiments were broadcast over teleconferencing software and real-time data displays were available to remote observers. The logs contain important observations from those personnel performing the experiment and the remote contributors. Finally, we have included summary and individual plots of all of the data for the user to compare to.

This submission contains the presentation slides and recordings from the first 98 EGS Collab Modeling and Simulation Working Group teleconferences. These teleconferences served three objectives for the project: 1) share simulation results, 2) communicate field activities and results to the simulation teams, and 3) hold open scientific discussions on EGS topics.

This package includes data and models that support hydraulic fracture stimulation and fluid circulation experiments in the Sanford Underground Research Facility (SURF). A paper by Schwering et al. (2020) describes the deterministic basis for developing a "common" discrete fracture network (CDFN) model of significant natural fractures in EGS Collab Testbed 1 on the 4850-Level of SURF. The ReadMe for this model shows drift, wells, scanlines, fracture data, interpreted fractures, and geophysical visualizations. There is also a summary of the data that was used in this experiment and includes results from reviewing core, televiewer (TV) logs, core-TV depth/feature registration, and from mapping weeps in the 4850-Level drift. The CDFN is intended to be a baseline model of the pre-stimulated testbed (though some observations from stimulation helped inform the model).

DOE/BC/10842-5

Flow in Porous Media, Phase and Ultralow Interfacial Tensions: Mechanisms of Enhanced Petroleum Recovery

Flow of Foam Through Porous Media DOE/SF/11564-6

This folder contains the GEOPHIRES codes and input files for running the base case scenarios for the six deep direct-use (DDU) projects. The six DDU projects took place during 2017-2020 and were funded by the U.S. Department of Energy Geothermal Technologies Office. They investigated the potential of geothermal deep direct-use at six locations across the country. The projects were conducted by Cornell University, West Virginia University (WVU), University of Illinois (U of IL), Sandia National Laboratory (SNL), Portland State University (PSU), and National Renewable Energy Laboratory (NREL). Four projects (Cornell, WVU, U of IL, SNL) investigated geothermal for direct heating of a local campus or community, the project by PSU considered seasonal subsurface storage of solar heating, and the NREL project investigated geothermal heating for turbine inlet cooling using absorption chillers. To allow comparison of techno-economic results across the six DDU projects, GEOPHIRES simulations were set up and conducted for each project. The GEOPHIRES code was modified for each project to simulate the local application and incorporate project-specific assumptions and results such as reservoir production temperature or financing conditions. The base case input file is included which simulates the base case conditions assumed by each project team. The levelized cost of heat (LCOH) is calculated and matches the base case LCOH reported by the project teams.

A virtual time machine that maps the location and temporal distribution of water surfaces at the global scale over the past 3.5 decades, and provides statistics on their extent and change to support better informed water-management decision-making. Data is provided on surface water occurrence, change in occurrence, surface water seasonality, surface water recurrence, transitions in surface water class (permanent or seasonal) and maximum extent over the time period of the data.

Graph theory is useful for estimating time-dependent model parameters via weighted least-squares using interferometric synthetic aperture radar (InSAR) data. Plotting acquisition dates (epochs) as vertices and pair-wise interferometric combinations as edges defines an incidence graph. The edge-vertex incidence matrix and the normalized edge Laplacian matrix are factors in the covariance matrix for the pair-wise data. Using empirical measures of residual scatter in the pair-wise observations, we estimate the variance at each epoch by inverting the covariance of the pair-wise data. We evaluate the rank deficiency of the corresponding least-squares problem via the edge-vertex incidence matrix. We implement our method in a MATLAB software package called GraphTreeTA available on GitHub (https://github.com/feigl/gipht). We apply temporal adjustment to the data set described in Lu et al. (2005) at Okmok volcano, Alaska, which erupted most recently in 1997 and 2008. The data set contains 44 differential volumetric changes and uncertainties estimated from interferograms between 1997 and 2004. Estimates show that approximately half of the magma volume lost during the 1997 eruption was recovered by the summer of 2003. Between June 2002 and September 2003, the estimated rate of volumetric increase is (6.2 +/- 0.6) x 10^6 m^3/yr. Our preferred model provides a reasonable fit that is compatible with viscoelastic relaxation in the five years following the 1997 eruption. Although we demonstrate the approach using volumetric rates of change, our formulation in terms of incidence graphs applies to any quantity derived from pair-wise differences, such as wrapped phase or wrapped residuals. Date of final oral examination: 05/19/2016 This thesis is approved by the following members of the Final Oral Committee: Kurt L. Feigl, Professor, Geoscience Michael Cardiff, Assistant Professor, Geoscience Clifford H. Thurber, Vilas Distinguished Professor, Geoscience

Groundwater flow model for East Maui. Data is from the following sources: Whittier, R. and A.I. El-Kadi. 2014. Human and Environmental Risk Ranking of Onsite Sewage Disposal Systems For the Hawaiian Islands of Kauai, Molokai, Maui, and Hawaii - Final. Prepared by the University of Hawaii, Dept. of Geology and Geophysics for the State of Hawaii Dept. of Health, Safe Drinking Water Branch. September 2014; and Whittier, R.B., K. Rotzoll, S. Dhal, A.I. El-Kadi, C. Ray, G. Chen, and D. Chang. 2004. Hawaii Source Water Assessment Program Report - Volume V - Island of Maui Source Water Assessment Program Report. Prepared for the Hawaii Department of Health, Safe Drinking Water Branch. University of Hawaii, Water Resources Research Center. Updated 2008.

Groundwater flow model for Hawaii Island. Data is from the following sources: Whittier, R.B., K. Rotzoll, S. Dhal, A.I. El-Kadi, C. Ray, G. Chen, and D. Chang. 2004. Hawaii Source Water Assessment Program Report - Volume II - Island of Hawaii Source Water Assessment Program Report. Prepared for the Hawaii Department of Health, Safe Drinking Water Branch. University of Hawaii, Water Resources Research Center. Updated 2008; and Whittier, R. and A.I. El-Kadi. 2014. Human and Environmental Risk Ranking of Onsite Sewage Disposal Systems For the Hawaiian Islands of Kauai, Molokai, Maui, and Hawaii - Final. Prepared by the University of Hawaii, Dept. of Geology and Geophysics for the State of Hawaii Dept. of Health, Safe Drinking Water Branch. September 2014.

Groundwater flow model for Kauai. Data is from the following sources: Whittier, R. and A.I. El-Kadi. 2014. Human and Environmental Risk Ranking of Onsite Sewage Disposal Systems For the Hawaiian Islands of Kauai, Molokai, Maui, and Hawaii - Final. Prepared by the University of Hawaii, Dept. of Geology and Geophysics for the State of Hawaii Dept. of Health, Safe Drinking Water Branch. September 2014. Whittier, R.B., K. Rotzoll, S. Dhal, A.I. El-Kadi, C. Ray, G. Chen, and D. Chang. 2004. Hawaii Source Water Assessment Program Report - Volume IV - Island of Kauai Source Water Assessment Program Report. Prepared for the Hawaii Department of Health, Safe Drinking Water Branch. University of Hawaii, Water Resources Research Center. Updated 2015.

Groundwater flow model for the island of Oahu. Data is from the following sources: Rotzoll, K., A.I. El-Kadi. 2007. Numerical Ground-Water Flow Simulation for Red Hill Fuel Storage Facilities, NAVFAC Pacific, Oahu, Hawaii - Prepared TEC, Inc. Water Resources Research Center, University of Hawaii, Honolulu.; Whittier, R.B., K. Rotzoll, S. Dhal, A.I. El-Kadi, C. Ray, G. Chen, and D. Chang. 2004. Hawaii Source Water Assessment Program Report - Volume VII - Island of Oahu Source Water Assessment Program Report. Prepared for the Hawaii Department of Health, Safe Drinking Water Branch. University of Hawaii, Water Resources Research Center. Updated 2008.; and Whittier, R. and A.I. El-Kadi. 2009. Human and Environmental Risk Ranking of Onsite Sewage Disposal Systems - Final. Prepared by the University of Hawaii, Dept. of Geology and Geophysics for the State of Hawaii Dept. of Health, Safe Drinking Water Branch. December 2009.

Groundwater flow model for West Maui. Data is from the following sources: Whittier, R. and A.I. El-Kadi. 2014. Human and Environmental Risk Ranking of Onsite Sewage Disposal Systems For the Hawaiian Islands of Kauai, Molokai, Maui, and Hawaii - Final. Prepared by the University of Hawaii, Dept. of Geology and Geophysics for the State of Hawaii Dept. of Health, Safe Drinking Water Branch. September 2014. Whittier, R.B., K. Rotzoll, S. Dhal, A.I. El-Kadi, C. Ray, G. Chen, and D. Chang. 2004. Hawaii Source Water Assessment Program Report - Volume V - Island of Maui Source Water Assessment Program Report. Prepared for the Hawaii Department of Health, Safe Drinking Water Branch. University of Hawaii, Water Resources Research Center. Updated 2008.

The dataset consist of acoustic Doppler current profiler (ADCP) velocity measurements in the wake of a 3-meter diameter vertical-axis hydrokinetic turbine deployed in Roza Canal, Yakima, WA, USA. A normalized hub-centerline wake velocity profile and two cross-section velocity contours, 10 meters and 20 meters downstream of the turbine, are presented. Mean velocities and turbulence data, measured using acoustic Doppler velocimeter (ADV) at 50 meters upstream of the turbine, are also presented. Canal dimensions and hydraulic properties, and turbine-related information are also included.

Identification of Cross-Formation Flow in Multireservoir Systems Using Isotopic Techniques (Phase One) NIPER-538

NIPER-511

The shapefile displays those basins within which development projects qualify for using the existing land cover condition as the stormwater flow control default target. This is a lower flow control default target than the target used for most of western Washington, which is based upon use of the historic land cover condition.

The shapefile displays those basins within which development projects potentially qualify for using the existing land cover condition as the stormwater flow control default target. This is a lower flow control default target than the target used for most of western Washington, which is based upon use of the historic land cover condition.

Excel files with data on CO2 injection including temperature and pressure data for Charlton 4-30.

A Register of Hydrometric Stations in Ireland.

The primary objective of this project is to develop a three-blade MHK rotor with low manufacturing and maintenance costs. The proposed program will design, fabricate and test a novel half-scale low cost, net shape fabricated single piece three-blade MHK rotor with integrated health management technology to demonstrate significant Capital Expenditures (CAPEX) and Operational Expenditures (OPEX) cost reductions due to the novel design and manufacturing process. The proposed project is divided into three major tasks: Task 1: Single Piece Three-blade Kinetic Hydropower System (KHPS) Rotor Full-Scale and Half-Scale Design; Task 2: Composite Manufacturing Trials and Half-Scale Prototype Rotor Fabrication; and Task 3: Material Characterization and Half-Scale Prototype Test and Evaluation. These three tasks include design and analysis of full-scale and half-scale three-blade rotor prototypes using computational fluid dynamics (CFD) and finite-element analysis (FEA), demonstration of a novel half-scale net shape fabrication process, determination of a fatigue threshold composite strain allowable, three-blade rotor mold design, manufacture of half-scale rotor clam shell mold, three-blade rotor test fixture design and fabrication, development of final manufacturing and test plans, manufacture of the half-scale net shape composite single blade and three-blade prototypes, and test and evaluation of the half-scale rotor.

This is an updated and simplified version of the New Mexico heat flow data already on the NGDS that was used for Play Fairway analysis.

Field measurements of mean flow and turbulence parameters at the Kvichak river prior to and after the deployment of ORPC's RivGen hydrokinetic turbine. Data description and turbine wake analysis are presented in the attached manuscript "Wake measurements from a hydrokinetic river turbine" by Guerra and Thomson (recently submitted to Renewable Energy). There are three data sets: NoTurbine (prior to deployment), Not_Operational_Turbine (turbine underwater, but not operational), and Operational_Turbine. The data has been quality controlled and organized into a three-dimensional grid using a local coordinate system described in the paper. All data sets are in Matlab format (.mat). Variables available in the data sets are: qx: X coordinate matrix (m) qy: Y coordinate matrix (m) z : z coordinate vector (m) lat : grid cell latitude (degrees) lon: grid cell longitude (degrees) U : velocity magnitude (m/s) Ux: x velocity (m/s) Vy: y velocity (m/s) W: vertical velocity (m/s) Pseudo_beam.b_i: pseudo-along beam velocities (i = 1 to 4) (m/s) (structure with raw data within each grid cell) beam5.b5: 5th-beam velocity (m/s) (structure with raw data within each grid cell) tke: turbulent kinetic energy (m2/s2) epsilon: TKE dissipation rate (m2/s3) Reynolds stresses: uu, vv, ww, uw, vw (m2/s2) Variables from the Not Operational Turbine data set are identified with _T Variables from the Operational Turbine data set are identified with _TO

A range of data for the Orange-Senqu basin, including narrative and numerical data covering rainfall, evaporation, radiation, soil type, groundwater recharge, yield, groundwater quality, dam infrastructure, surface water flows, surface water quality, flood, irrigation, urban water supply. The database can be searched by category or keywork, and will produce particular studies, with coverage of particular regions or the whole basin. Where data is available, it will be linked within the study pages, and provided either in pdf, xls, or GIS-compatable formats.

This submission includes a fault map of the Oregon Cascades and backarc, a probability map of heat flow, and a fault density probability layer. More extensive metadata can be found within each zip file. For information about "Oregon Faults," contact John David Trimble, Oregon State University. trimbljo@onid.oregonstate.edu

The maps in this submission include: heat flow, alkalinity, Cl, Mg, SiO2, Quaternary volcanic rocks, faults, and land ownership. All of the Oregon Cascade region. The work was done by John Trimble, in 2015, at Oregon State University.

Attached are the .cas and .dat files for the Reynolds Averaged Navier-Stokes (RANS) simulation of a single lab-scaled DOE RM1 turbine implemented in ANSYS FLUENT CFD-package. In this case study the flow field around and in the wake of the NREL Phase VI wind turbine, modeled is MHK turbine, is simulated using Actuator Disk Model (ADM) (a.k.a Porous Media) by solving RANS equations coupled with a turbulence closure model. It should be highlighted that in this simulation the actual geometry of the rotor blade is not modeled. The effect of turbine rotating blades are modeled using the Actuator Disk Theory (see the stated section of attached M.Sc. thesis for more details).

Attached are the .cas and .dat files for the Reynolds Averaged Navier-Stokes (RANS) simulation of a single full scale DOE RM1 turbine implemented in ANSYS FLUENT CFD-package. In this case study taking advantage of the symmetry of the DOE RM1 geometry, only half of the geometry is modeled using (Single) Rotating Reference Frame model [RRF]. In this model RANS equations, coupled with k-\omega turbulence closure model, are solved in the rotating reference frame. The actual geometry of the turbine blade is included and the turbulent boundary layer along the blade span is simulated using wall-function approach. The rotation of the blade is modeled by applying periodic boundary condition to sets of plane of symmetry. This case study simulates the performance and flow field in both the near and far wake of the device at the desired operating conditions. The results of these simulations showed good agreement to the only publicly available numerical simulation of the device done in the NREL. Please see the attached paper.

Rock Matrix and Fracture Analysis of Flow in Western Tight Gas Sands; December 1987

Rock Matrix and Fracture Analysis of Flow in Western Tight Gas Sands; February 1987

The San Juan-Chama Drinking Water Project was completed in 2008, ending Albuquerque’s sole reliance on an overtaxed aquifer by tapping into surface water transported from the Colorado River basin. The water, rights to which have been purchased in perpetuity, is part of New Mexico’s allotment of Colorado River water, which has been apportioned among Western states. It is not native Rio Grande water. Officials first proposed use of the water for drinking after scientific studies in the early 1990s showed that Albuquerque’s aquifer – once thought to be virtually limitless – was smaller than originally believed, and being pumped twice as fast as nature could replenish it. But switching to surface water would be no easy task. It took more than $400 million in new infrastructure to divert the water from the Rio Grande, treat the water to safe drinking water standards, and deliver it to customers. The project was financed with seven dedicated rate increases over several years. Among other things, those rate increases paid for: 38 miles of distribution pipeline (some of it underneath the Rio Grande itself). An adjustable diversion dam and intake structure on the Rio Grande. Funding of programs to preserve the endangered Rio Grande silvery minnow and its habitat, and inclusion of fish screens and passages at the diversion site to minimize Project impacts on fish populations. A Raw Water Pump Station on the Rio Grande, built to resemble a Spanish mission church the better to blend in with its surroundings. Eight miles of raw water pipeline to transport water from the Raw Water Pump Station. A $160 million Water Treatment Plant. The plant, with a capacity of about 80 million gallons per day, uses a series of chemical and mechanical processes to remove sediment and other contaminants from the water. It employs the same treatment process now in use by such communities as Fresno, California and Tampa, Florida. Ratepayer dollars also were used to fund an ongoing conservation program. Permit requirements for the San Juan Chama Drinking Water Project called for an eventual reduction in Albuquerque’s per capita water usage to 155 gallons per day. That goal has already been met and surpassed. Project construction, which began in 2004, was completed in 2008. Pipeline construction wrapped up in April of 2008, and the Water Treatment Plant was finished in November of 2008.

Low permeability geothermal reservoirs can be stimulated by hydraulic fracturing to create Enhanced (or Engineered) Geothermal Systems (EGS) with higher permeability and improved heat transfer to increase heat production. In this paper, we document our effort to develop a numerical simulator with explicit geomechanics-discrete flow network coupling by utilizing and further advancing the simulation capabilities of the Livermore Distinct Element Code (LDEC). The important modules of the simulator include an explicit finite element solid solver, a finite volume method flow solver, a joint model using the combined FEM-DEM capability of LDEC, and an adaptive remeshing module. The numerical implementation is verified against the classical KGD model. The interaction between two fractures with simple geometry and the stimulation of a relatively complex existing fracture network under different in-situ stress conditions are studied with the simulator.

The United States Geological Survey (USGS) Reservoir dataset is one of various hydrological datasets provided for the Southern Great Plains 1997 (SGP97) project. This dataset contains reservoir data from stations in SGP97 domain. The data collected at USGS gaging stations consist of records of stage and measurements of discharge of streams or canals, and stage, surface area, and contents of lakes or reservoirs. This dataset contains only the USGS reservoir data. For a lake or reservoir, capacity tables giving the contents for any stage are prepared from stage-area relation curves defined by surveys. The application of the stage to the capacity table gives the contents, from which the daily, monthly, or yearly change in contents is computed. If the stage-capacity curve is subject to changes because of deposition of sediment in the reservoir, periodic resurveys of the reservoir are necessary to define new stage-capacity curves. During the period between reservoir surveys, the computed contents may be increasingly in error due to the gradual accumulation of sediments. For some gaging stations there are periods when no gage-height record is obtained or the recorded gage height is so faulty that it cannot be used to compute daily discharge or contents. This happens when the recorder stops or otherwise fails to operate properly, intakes are plugged, the float is frozen in the well, or for various other reasons. For such periods, the daily contents may be estimated on the basis of operator's log, prior and subsequent records, inflow-outflow studies, and other information. The USGS reservoir data are provided in a single file and are provided "as is" in their original card image format. There are six different types of "cards images" which appear in the USGS reservoir dataset. Each card has a unique format, but the first character of a card image always indicates the card type. Depending upon the card type, the card image may contain metadata and/or data. No additional quality control was performed by the University Corporation for Atmospheric Research/Joint Office for Science Support (UCAR/JOSS).

The United States Geological Survey (USGS) stream flow dataset is one of various datasets provided for the Southern Great Plains 1997 (SGP97) project. This dataset contains stream flow data from 997 USGS stations in the SGP97 domain. The data collected at USGS gaging stations consist of records of stage and measurements of discharge of streams or canals, and stage, surface area, and contents of lakes or reservoirs. This dataset contains only the USGS stream flow data. For USGS stream-gaging stations, the daily mean discharge is computed from gage heights and rating tables. These rating tables are prepared from stage-discharge-relation curves and give the discharge for any stage. If the stage-discharge relation for a station is temporarily changed by the presence of aquatic growth or debris on the control, the daily mean discharge is computed by what is basically the shifting-control method. At some USGS gaging stations, acoustic velocity meter (AVM) systems are used to compute discharge. The AVM system measures the stream's velocity at one or more paths in the cross section. Coefficients are developed to relate this path velocity to the mean velocity in the cross section. Cross-sectional area curves are developed to relate stage to cross section area. Discharge is computed by multiplying path velocity by the appropriate stage related coefficient and area. Changing stage, backwater from reservoirs, tributary streams, or other sources, and ice in the winter affect the stage-discharge relation. Special methods, such as using comparable records of discharge for other stations, are then used to compute discharge. If no gage-height record can be obtained from a gaging station due to failed equipment, etc., daily discharge values are estimated using various means. The USGS stream flow dataset contains three metadata parameters and three data parameters. The metadata parameters identify the network, station, and time at which the data was collected. Each record contains one month's data. The three data parameters (stream flow, stage, and hour of observation) are repeated once for each UTC day (0000 to 2300). All records contain data for 31 days regardless of the actual number of days in a month. Months with less than 31 days are padded with missing values (e.g., -999.99). The stream flow values are reported in cubic meters per second and are 24 hour averages. There are no stage values in this dataset, so the stage values are shown as missing. The hour of observation is the beginning UTC hour for the 24 hour period for which the stream flow value is valid. No additional quality control was performed by the University Corporation for Atmospheric Research/Joint Office for Science Support (JOSS).

Study of Multiphase Flow Useful to Understanding Scaleup of Coal Liquefaction Reactors 1981-84 Final Report

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. Using history matching with tracers and available production data, the model should be tuned to represent the subsurface reservoir as accurately as possible 5. A large number of simulations is run using the history-matched reservoir model. Each simulation assumes a different wellbore flow rate allocation across the injection and production wells, where the individual selected flow rates do not violate the practical constraints for the corresponding wells. 6. ML models are trained using the simulation data. The code in our GitHub repository demonstrates how these models can be trained and evaluated. 7. The trained ML models can be used to evaluate a large set of candidate flow allocations with the goal of selecting the most optimal allocations, i.e., producing the largest amounts of thermal energy over the modeled period of time. The referenced paper provides more details about this optimization process

Mesh, properties, initial conditions, injection/withdrawal rates for modeling thermal, hydrological, and mechanical effects of fluid injection to and withdrawal from ground for Stockton University reservoir cooling system (aquifer storage cooling system), Galloway, New Jersey, on large scale grid, with some results. First simulation of J.T. Smith, E. Sonnenthal, P. Dobson, P. Nico, and M. Worthington, 2021. Thermal-hydrological-mechanical modeling of Stockton University reservoir cooling system, Proceedings of the 46th Workshop on Geothermal Reservoir Engineering, Stanford University, SGP-TR-218, from which Figures 1-5 pertain.

In this submission is the groundwater composite risk segment (CRS) used for play fairway analysis. Also included is a heat flow probability map, and a shaded relief map of the Tularosa Basin, NM.

The vast supply of geothermal energy stored throughout the Earth and the exceedingly long time required to dissipate that energy makes the world's geothermal energy supply nearly limitless. As such, this resource holds the potential to provide a large supply of the world's energy demands; however, like all natural resources, it must be utilized in an appropriate manner if it is to be sustainable. Understanding sustainable use of geothermal resources requires thorough characterization efforts aimed at better understanding subsurface properties. The goal of this work is to understand which critical subsurface properties exert the most influence on sustainable geothermal production as a means to provide targeted future resource characterization strategies. Borehole temperature and reservoir pressure data were analyzed to estimate reservoir thermal and hydraulic properties at an active geothermal site. These reservoir properties then served as inputs for an analytical model which simulated net power production over a 30-year period. The analytical model was used to conduct a sensitivity analysis to determine which parameters were most critical in constraining the sustainability of a geothermal reservoir. Modeling results reveal that the number of preferential flow pathways (i.e. fractures) used for heat transport provides the greatest impact on geothermal reservoir sustainability. These results suggest that early and pre-production geothermal reservoir exploration would achieve the greatest benefit from characterization strategies which seek to delineate the number of active flow pathways present in the system.

This submission contains a shapefile of heat flow contour lines around the FORGE site located in Milford, Utah. The model was interpolated from data points in the Milford_wells shapefile. This heat flow model was interpolated from 66 data points using the kriging method in Geostatistical Analyst tool of ArcGIS. The resulting model was smoothed 100%. The well dataset contains 59 wells from various sources, with lat/long coordinates, temperature, quality, basement depth, and heat flow. This data was used to make models of the specific characteristics.

This archive contains data from the tracer test performed during the Utah FORGE well 16A(78)-32 stimulation. This includes: 1. the raw data file from Pason in the csv format; 2. a one-page Word document from Pason that explains the headers and units used in their data file; 3. an Excel spreadsheet with concentration data and relevant Pason flow data; 4. a Word document with jpg versions of concentration charts (Figs. 1-3) and a tracer mass recovery chart (Fig. 4).

The U.S. Department of Energy's (U.S. DOE) Frontier Observatory for Research in Geothermal Energy (FORGE) is a field laboratory that provides a unique opportunity to develop and test new technologies for characterizing, creating and sustaining Enhanced Geothermal Systems (EGS) in a controlled environment. In 2018, the U.S. DOE selected a site in south-central Utah for the FORGE laboratory. Numerous geoscientific studies have been conducted in the region since the 1970s in support of geothermal development at Roosevelt Hot Springs. A vertical scientific well, 58-32, was drilled and tested to a depth of 2290 m (7515 ft) GL in 2017 on the FORGE site to provide additional characterization of the reservoir rocks. The well encountered a conductive thermal regime and a bottom hole temperature of 199degC (390degF). More than 2000 natural fractures were identified, but measured permeabilities are low, less than 30 micro-darcies. Induced fractures indicate that the maximum horizontal stress trends NNE-SSW, consistent with geologic and well observations from the surrounding area. Approximately 45 m (147 ft) at the base of the well was left uncased. A maximum wellhead pressure of 27.6 MPa (4000 psig) at an injection rate of ~1431 L/min (~9 bpm) was measured during stimulation testing in September 2017. Conventional diagnostic evaluations of the data suggest that hydraulic fracturing and shearing occurred. Estimates of the stress gradient for delta_h_min range from of 16.7 to 17.6 kPa/m (0.74 to 0.78 psi/ft). A gradient of 25.6 kPa/m (1.13psi/ft) was calculated for delta_V. In 2019, the 2017 open-hole stimulation in well 58-32 was repeated with injection rates up to 2385 L/min (15 bpm). Two additional stimulations were conducted in the cased portion of the well; one to stimulate critically stressed fractures and the second to test noncritically stressed fractures. Breakdown of the zone spanning critically-stressed fractures occurred at a surface pressure of approximately 29.0 MPa (4200 psig). Although stimulation of the noncritically stressed fractures was interrupted by failure of the bridge plug beneath the perforated interval, micro-seismic data suggests stimulation of the fractures may have been initiated at a surface pressure of 45.5 MPa (6600 psig). These stimulation results support the conclusion the Mineral Mountains granitoid is an appropriate host for EGS development. Micro-seismicity was monitored during the stimulations using surface and downhole instrumentation. Five seismometers and a nodal array of 150 seismic sensors were deployed on the surface. A Distributed Acoustic Sensing (DAS) cable and a string of 12 geophones were deployed in well 78-32, drilled to a depth of 998 m (3274 ft) GL. A broadband sensor and a high-temperature geophone were deployed in well 68-32, drilled to a depth of 303 m (994 ft) GL. More than 420 micro-seismic events were detected by the geophone string. Other instruments detected fewer events.

Pressure, temperature, and flow data from open-hole, upper perforation, and lower perforation well stimulations gathered from various tools collected at well 58-32 during phase 2C.

The Walnut Gulch Experimental Watershed (WGEW) sediment collection program, established in 1953, provides event-based data for semiarid rangeland erosion, sediment transport, and yield research. Sediment loads carried through the channel network on the WGEW are high, but are typical of semiarid rangelands, and are influenced by soils, geologic parent material, and geomorphology. Typical monsoon thunderstorm generated flows in dryland regions are characterized by high velocities, short durations, and heavy and coarse sediment loads. Sediment is measured in conjunction with discharge measurements [Stone et al., 2008] that are integral to converting sample values to runoff event-based values. Sampling initiated in the 1960s was done with point intake pump samplers. The single point sampler intake tubes were later replaced with tubes that rise in response to flow and are perforated to collect depth integrated samples. Sampling with each of these systems is limited to suspended sediment smaller than the 0.635 cm diameter of the intake slots. Pump samplers are in use at the outlet of small watersheds where overland flow is the dominant hydrologic driver of sediment transport, and particles are small. As watershed size increases on the WGEW, in general, the channel network can dominate sediment delivery processes as it evolves to carry an increasingly coarse, and vertically sorted, sediment load. A traversing slot sediment sampler was designed in response to limitations of alternative sampling methods such as the pump sampler. The data collection network was expanded in 2002 and pit traps were added below the overfall at flumes 63.103 and 63.104. Analysis of these data, and efforts to process and make available the historic data, are ongoing.