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At least 127 records · Page 7

Advancing subsurface analysis: Integrating computer vision and deep learning for the near real-time interpretation of borehole image logs in the Illinois Basin-Decatur Project

The accurate quantification and mapping of subsurface natural fracture systems using borehole imaging logs are critical for the success of CO 2 sequestration in geologic formations, optimization of engineered geothermal systems, and hydrocarbon production enhancement. However, traditional interpretation processes suffer from time-consuming procedures and human bias. To address these challenges and expedite fracture analysis, we investigated the application of integrated computer vision and DL workflows to automate image log analysis. Specifically, the design of our workflow was crafted to swiftly detect fractures and baffles by using actual electrical resistivity of borehole wall from microresistivity imaging device alongside their binary representation. This novel approach significantly reduces computational time while providing invaluable insights. By incorporating conventional logging and microseismic data, we present a regional subsurface natural fracture mapping technique. Through the minimization of human bias in image log analysis, our automated workflow achieves reduced fracture interpretation time and costs while ensuring robust and reproducible results. We demonstrated the efficacy of our approach by applying the workflow to the Illinois Basin-Decatur Project site. The automated workflow successfully identified major fractured zones, multiple baffles, and an interbedded layer with a high resolution of 0.01 ft or 0.12 in. (0.3 cm) and can be upscaled to any desired resolution. Validation through microseismic and image log interpretations allows for accurate and near-real-time mapping of fractures and baffles, significantly enhancing CO 2 pressure forecasting and postinjection site care. Our approach stands out due to its robustness, consistency, and reduced computational cost compared with alternative feature extraction technologies. It presents exciting possibilities for advancing CO 2 sequestration and engineered geothermal efforts by offering comprehensive and efficient fracture mapping solutions. This technology can contribute significantly to the optimization of CO 2 sequestration projects, facilitating sustainable environmental practices, and combating climate change.

Geochemistry & Geophysics↗

COMPASS-FME Synoptic Sites Level 1 Sensor Data v1-2

This is the version 1-2 Level 1 (L1) data release for COMPASS-FME environmental sensors located at our synoptic field sites. COMPASS-FME is studying sites in two distinct regions, the Chesapeake Bay and the Western Lake Erie Basin. We established the network at seven "synoptic" (observational) sites along the Chesapeake Bay and Lake Erie coastlines, collectively generating over three million observations per month, to track and comprehend environmental changes where land and water intersect. Additionally, the two regions provide an interesting contrast of saltwater and freshwater coasts that allow us to differentiate the impacts of inundation and coastal water chemistries in two nationally important coastal systems.L1 data are close to raw, but are units-transformed and have out-of-instrument-bounds and out-of-service flags added. Duplicates and missing data are removed but otherwise these data are not filtered, and have not been subject to any additional algorithmic or human QA/QC. Any scientific analyses of L1 data should be performed with care. **This dataset will be updated quarterly with new data for the duration of the project**This dataset includes:- An overall dataset README file that describes the current version, gives citation and contact information, etc.- Site- and year-specific folders, each holding up to 12 CSV (comma separated value) data files for each site and plot in that year.- Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, as well as a general description of the site.- Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are normally logged every 15 minutes. Please see v1-2 Synoptic L1 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning.

54 ENVIRONMENTAL SCIENCES↗

COMPASS-FME Synoptic Sites Level 1 Sensor Data v2-1

This is the version 2-1 Level 1 (L1) data release for COMPASS-FME environmental sensors located at our synoptic field sites. COMPASS-FME is studying sites in two distinct regions, the Chesapeake Bay and the Western Lake Erie Basin. We established the network at seven "synoptic" (observational) sites along the Chesapeake Bay and Lake Erie coastlines, collectively generating over three million observations per month, to track and comprehend environmental changes where land and water intersect. Additionally, the two regions provide an interesting contrast of saltwater and freshwater coasts that allow us to differentiate the impacts of inundation and coastal water chemistries in two nationally important coastal systems. L1 data are close to raw, but are units-transformed and have out-of-instrument-bounds, out-of-service, and outlier flags added. Duplicates and missing data are removed but otherwise these data are not filtered, and have not been subject to any additional algorithmic or human QA/QC. Any scientific analyses of L1 data should be performed with care. **This dataset will be updated quarterly with new data for the duration of the project** This dataset includes: - An overall dataset README file that describes the current version, gives citation and contact information, etc. - Site- and year-specific folders, each holding up to 12 CSV (comma separated value) data files for each site and plot in that year. - Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, as well as a general description of the site. - Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are normally logged every 15 minutes. Please see v2-0 Synoptic L1 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning. This dataset was updated 2026-03-12: (i) data now go through 2025-12-31 (previous end was 2025-06-30) and (ii) dataset and file names updated to “…v2-1” (previously was “v2-0”).

54 ENVIRONMENTAL SCIENCES↗

COMPASS-FME Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) Experiment Level 1 Sensor Data v2-1

This is the version 2-1 Level 1 (L1) data release for COMPASS-FME environmental sensors located at our Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experimental site. This manipulative, ecosystem-scale TEMPEST experiment addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in MD, USA. The experiment uses a large-unit (2000 m2), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments. L1 data are close to raw, but are units-transformed and have out-of-instrument-bounds, out-of-service, and outlier flags added. Duplicates and missing data are removed but otherwise these data are not filtered, and have not been subject to any additional algorithmic or human QA/QC. Any scientific analyses of L1 data should be performed with care. **This dataset will be updated quarterly with new data for the duration of the project** This dataset includes: - An overall dataset README file that describes the current version, gives citation and contact information, etc. - Site- and year-specific folders, each holding variable-specific CSV (comma separated value) data files for each site and plot in that year. - Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, detailed flood times, as well as a general description of the site. - Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are normally logged every 15 minutes. Please see v2-1 TEMPEST L1 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning. The TEMPEST flood events occurred on the following dates. They lasted for ~10 hours each day and delivered ~80,000 gallons to each plot; many data streams are available at 1 or 5 minute frequency during these periods. * Tests: Aug 25 (fresh plot) and Sep 9 (salt plot), 2021 * TEMPEST 1: June 22, 2022 * TEMPEST 2: June 6-7, 2023 * TEMPEST 3: June 11-13, 2024 This dataset was updated 2026-03-12: (i) data now go through 2025-12-31 (previous end was 2025-06-30) and (ii) dataset and file names updated to “…v2-1” (previously was “v2-0”).

54 ENVIRONMENTAL SCIENCES↗

Stepwise Dynamic Calibration of a Hydromechanical Simulation Using Time-Lapse Vertical Seismic Profile

This study aims to develop a methodology for calibrating subsurface stress changes through time-lapse Vertical Seismic Profiling (VSP) integration. The selected study site is the 13-10A injector well within the ongoing CO2-EOR operation of the Farnsworth Field Unit. The Time-lapse VSP dataset carries the combined effects of fluid substitution and mean effective stress changes, thereby providing a dataset amenable for the calibration of production and injection-induced stress changes. The concept is similar to calibrating a reservoir simulation model in that the process honor real field data to set up an inverse problem. The solution optimizes the independent and impactful geomechanical parameters that replicate the observed time-lapse seismic velocity changes. This stress calibration is enabled by 4D geomechanical modeling and the VSP Integration workflow. This calibration benefits from extensive geological, geophysical and geomechanical characterization through 3D seismic data, geophysical well logs, and core assessed as part of the 1D MEM conducted on the 13-10A subject well. These data are used to develop a site-specific rock physics model. The Biot Gassmann workflow combines rock physics and reservoir simulation outputs to determine the fluid substitution contribution to seismic velocity change. Additionally, modeled seismic velocity attributed to mean effective stress are determined from the geomechanical simulation outputs, and the stress-velocity relationship developed from the ultrasonic seismic velocity measurements on the extracted Morrow B core. A penalty function is then formed between the modeled seismic velocities and the observed time-lapse VSP dataset. Four independent and impactful geomechanical parameters have been determined. These are the bulk modulus and shear modulus for zero porosity and the shear and compressional seismic velocity to mean effective stress derivatives. The dataset of numerous coupled hydromechanical- geomechanical simulation realizations is built by combining variations of the four stated geomechanical parameters. A machine learning-assisted workflow comprised of an artificial neural network and a particle swarm optimizer are used to converge on the optimal geomechanical parameters. The successful execution of this workflow has affirmed the suitability of acoustic time-lapse measurements for 4D-VSP geomechanical stress calibration pending measurable stress sensitivities within the anticipated effective stress changes and the availability of suitable and reliable datasets for petroelastic modeling.

02 PETROLEUM↗

Raw Lidar and Camera Data Synchronized with Precipitation and Present Weather Data

As part of the sensor characterization task of the SMART 2.0 project, this dataset includes raw data from three spinning lidars ([Ouster OS2-128](https://ouster.com/products/scanning-lidar/os2-sensor/), [Velodyne Puck (VLP-16)](https://velodynelidar.com/products/puck/), and [Velodyne Ultra Puck (VLP-32)](https://velodynelidar.com/products/ultra-puck/)), one camera ([Mako G-319](https://www.alliedvision.com/en/camera-selector/detail/mako/g-319/)), and one present weather sensor ([Vaisala FD-70](https://www.vaisala.com/en/products/weather-environmental-sensors/forward-scatter-fd70)). All data were synchronized, with the log start time indicated in the file name (HHMMSS). The data can be filtered by date, log time (HHMMSS), sensor, frame ID, and weather classification. These data were gathered statically at the Argonne Testbed for Multiscale Observational Science (ATMOS). Two target stop signs were placed in view of the sensors to contribute a target for comparing sensor data under different conditions. The weather data for each day are stored in netCDF “.nc” files. The lidar data contain the X, Y, Z, intensity, reflectivity, and ring from Ouster OS2-128 rev6, Velodyne VLP-16, and Velodyne VLP-32 lidars. ![raw lidar image](LiDAR_pointcloud_ATMOS.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

LANL HPC Data Center Monitoring (Summer 2022 Talk) [Slides]

HPC Monitoring is tasked with the understanding of everything that happens in the Data Center. They get to that understanding by: collecting logs and metrics, processing data, analyzing data, displaying information, and receiving alerts on events. Their ultimate (and probably unreachable) goal is a holistic view of the Data Center and an understanding of how everything is correlated.

97 MATHEMATICS AND COMPUTING↗

Mesozoic Deserts and CO2 Storage: The Glen Canyon Group, Utah, USA

For much of the Mesozoic an extensive desert environment extended across the Western Interior of the USA. These desert systems deposited vast dunefields, including one of the largest ergs preserved in the rock record. The Glen Canyon Group in Utah is a Triassic-Jurassic aged aeolian succession, consisting of thick, laterally extensive aeolian dune deposits, interdune lakes and oases, and dryland fluvial systems. These aeolian deposits have been identified as potential CO2 sequestration reservoirs. The Jurassic-aged Navajo Sandstone has been the subject of research into its potential as a CO2 reservoir, as well as naturally occurring CO2 seeps in the Green River area. It has excellent reservoir properties, consisting of thick sandstones with high porosity and permeability, and has industry data including well logs and core. It occurs in both outcrop and subcrop, allowing for comparison and sense checking of interpretations across small and large scales. The Triassic Wingate Sandstone has received much less attention, however it too is composed of thick aeolian dune deposits. Like the Navajo Sandstone, the Wingate Sandstone has industry data, and occurs in both outcrop and subcrop. Using extensive industry data which exists across Utah is an effective way to pivot from a hydrocarbon focus to CO2 injection, and so contribute to green energy and carbon neutral emission goals. Here we use historic industry data in conjunction with field work, to study the Navajo and Wingate Sandstones and their potential as CO2 reservoirs, in addition to increasing our understanding of lithologic and stratigraphic complexity in one of the most significant aeolian systems in the world.

Mahon, Elizabeth↗

Steel Creek, Pen Branch, and D-Area Watershed Stream Gauging Stations

A network of stream gauging systems were installed in the Steel Creek, Pen Branch, D-Area Discharge Canal, and the D006 Stream in support of the groundwater modeling efforts for the P-Area Groundwater Operable Unit (OU); Chemical, Metals, and Pesticides (CMP) Pits OU; and the D-Area Watershed, respectively. Each location is monitored by a MACE Floseries3 FloPro data logger and a MACE doppler ultrasonic area/velocity sensor. Each stream gauging system is powered by an internal 12-volt battery supplied by a solar panel with a trickle charger. Information collected by each data logger is logged internally and telecommunicated via a cellular network to an online server for real time analysis and monitoring. The MACE doppler ultrasonic area/velocity sensor can measure stream depth and velocities to give output values of flow rates, total flow, net flow, and volumes. The water depth is measured by a ceramic pressure transducer located on the top of the sensor. The velocity is measured by a continuous wave doppler sensor to give an average velocity across the whole stream profile. This report discusses the equipment and methods used to install continuous stream gauging stations and provides a summary of data collected through FY2025.

54 ENVIRONMENTAL SCIENCES↗

Autonomous Inversion of In Situ Deformation Measurement Data for Injection-Induced Stress Change

Geologic carbon storage (GCS) is likely to play a key part of the global effort to dramatically reduce CO2 emissions and perhaps even reduce atmospheric CO2 concentrations through carbon negative operations. A critical part of effort to commercialize and widely deploy this technology is developing the capability to rapidly assimilate real-time monitoring data into a form that will enable site operators to make decisions to manage the safe and efficient operations. Two of the risks associate with GCS are the risk of inducing fractures in the sealing formations that can create leakage pathways and the risk of inducing earthquakes of sufficient magnitude to cause public concern, property damage, or safety risks. To properly manage these risks the site operator needs to know the initial state of stress, the change in stress induced by injection, and the relationship between operational parameters such as injection rate and pressure and the change in stress. Current methods of estimating the change in stress require choosing the type of constitutive model and the model parameters based on core, log, and geophysical data during the characterization phase, with little feedback from operational observations to validate or refine these choices. These characterization methods interrogate the geologic formations using length scales, loading rates or magnitudes that are quite different from those encountered by the actual storage system. It is shown that errors in the assumed constitutive response, even when informed by laboratory tests on core samples, are likely to be common, large, and underestimate the magnitude of stress change caused by injection. Recent advances in borehole-based strain instruments and borehole and surface-based tilt and displacement instruments have now enabled monitoring of the deformation of the storage system throughout its operational lifespan. This data can enable validation and refinement of the knowledge of the geomechanical properties and state of the system, but brings with it a challenge to transform the raw data into actionable knowledge. We demonstrate a method that uses automatic differentiation and a finite-element based geomechanical model perform a gradient-based deterministic inversion of geomechanical monitoring data. This approach allows autonomous integration of the instrument data without the need for time consuming manual interpretation and selection of updated model parameters. Furthermore, only isotropic linear elasticity is considered in this paper, the approach presented is very flexible as to what type of geomechanical constitutive response can be used. The approach is easily adaptable to nonlinear physics-based constitutive models to account for common rock behaviors such as creep and plasticity. The approach also enables training of machine learning-based constitutive models by allowing back propagation of errors through the finite element calculations. This enables strongly enforcing known physics, such as conservation of momentum and continuity, while allowing data-driven models to learn the truly unknown physics such as the constitutive or petrophysical responses.

Burghardt, Jeffrey A.↗

Field Results from New Tensor Borehole Optical Fiber Strainmeter Installations in Oklahoma and Utah

The time evolving strain field contains a wealth of information that can be used to interpret subsurface behavior. For example, injecting or removing fluids from reservoirs or aquifers causes deformation that can be used as a diagnostic signal in some cases, while it can interfere with geodetic interpretations in other cases. We've previously demonstrated the feasibility of measuring the strain tensor at a depth of 30m caused by injection into a reservoir at 530m. The observed strain signals were interpreted using four independent analytic and numerical methods that resulted in estimates of the poroelastic properties and geometry of the reservoir that was consistent with data from well logs. However, studies like these are only possible if these deformations can be reliably measured. Years of lab and field work has culminated in the development of a novel borehole strainmeter capable of resolving multiple components of strain using embedded optical fibers configured as Michelson interferometers. It features four horizontal gauges separated by 45° to resolve the horizontal strain tensor as well as a vertical strain gauge and a sixth null component for state-of-health monitoring. The downhole sensing package also includes an open pipe through its center for grout circulation during single-trip deployments and a fully welded stainless steel exterior for robustness and longevity. These instruments have a resolution of 2x10-13 strain that can easily measure the solid earth tides. Preliminary data are available from four strainmeters in shale at our Oklahoma site and four in compacted sand and gravel in Utah. These are deployed from 40-60m, except one of the strainmeters in Oklahoma is deployed at 500m. The data include strains from the initial grout curing, comparisons to predicted earth tide models and in-situ calibration results, barometric pressure admittances and spectral analyses as well as signals from underground injections and surface waves from teleseismic events. Preliminary analyses indicate behavior consistent with other strainmeter deployments, and comparison to data from a Gladwin strainmeter at the Oklahoma site validate the performance of the new design. Analyses from a suite of six well tests at the Oklahoma site show for the first time how the strain tensor field varies with location during well testing.

DeWolf, Scott↗

MASK4 Test Campaign for Sandia WaveBot Device

This data and report details the findings from a wave tank test focused on production of useful work of a wave energy converter (WEC) device. The experimental system and test were specifically designed to validate models for power transmission throughout the WEC system. Additionally, the validity of co-design informed changes to the power take-off (PTO) were assessed and shown to provide the expected improvements in system performance. These data describe the "MASK4" wave tank test of the Sandia WaveBot device. The WaveBot device has been tested a number of times in different permutations at the US Navy's Maneuvering and Sea Keeping (MASK) basin. Each test in this series is referred to as MASK1, MASK2, etc. The WaveBot device was first tested in one degree of freedom (heave) in 2016. This MASK1 test focused primarily on system identification and modeling. After MASK1, major modifications were performed to improve the overall real-time control and measurement system, improve the heave drive train, and add surge and pitch degrees of freedom. The second set of testing, which was broken up in to two stages: MASK2A and MASK2B, focused on bench testing and closed-loop control performance as well as nonlinear modeling. MASK3 then focused on multi-input, multi-output modeling and control for maximization of electrical power. The attached report presents the results from MASK4, which focuses on detailed modeling of the power conversion chain and validation co-design principles by way of the introduction of a magnetic spring. The test log, report, and data from the MASK4 test of the WaveBot augmented with a tunable magnetic spring. Processing codes can be found at the Github link below.

16 TIDAL AND WAVE POWER↗

Economic analysis of CCUS: Accelerated development for CO 2 EOR and storage in residual oil zones under the context of 45Q tax credit

Residual oil zones (ROZ) undergoing CO 2 Enhanced Oil Recovery (CO 2 -EOR) may benefit from specific strategies to maximize their value. We evaluated several strategies for producing from a Permian Basin, West Texas, USA field’s ROZ. This ROZ lies below the main pay zone (MPZ) of the field. Such brownfield ROZs occur in the Permian Basin and elsewhere. Since brownfield ROZs are hydraulically connected to the MPZs, development sequences and schemes influence oil production, CO 2 storage, and net present value (NPV). We conducted economic assessments of various CO 2 injection/production schemes in the stacked ROZ-MPZ reservoir based on flow simulations of a high-resolution geocellular model built from wireline logs and core data and calibrated through production history matching. Flow simulations of water alternating gas (WAG) injection, such as switching injection from the MPZ to the ROZ and commingled production, were studied. Simulation results showed that simultaneous CO 2 injection into the MPZ and ROZ lead to the largest oil production and, generally, the largest NPV. If instead, CO 2 was simultaneously injected into the MPZ and ROZ, then into the ROZ alone, this maximized CO 2 storage. CO 2 storage can be used as a tax credit under the Internal Revenue Code, Section 45Q. Storage performance depends on the development approach and WAG ratio. Developing the ROZ increased storage compared to only producing from the MPZ. The WAG ratio to maximize oil production did not always yield the largest NPV. These findings are potentially applied to other Brownfield ROZs, which are common below San Andres reservoirs in the Permian Basin and other basins. ROZ development can increase oilfields’ NPV and carbon storage potential. Our study can serve as an analog for similar reservoirs. Here this work provides valuable insights into the further optimization of brownfield ROZ development and information for operators to plan to develop stacked ROZ-MPZ reservoirs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Utah FORGE: Documentation on Discrete Fracture Network and Fracture Propagation Modelling

This dataset includes reports and a slide presentation on discrete fracture network (DFN) generation and hydraulic fracture modeling at the Utah FORGE site. It details the characterization of natural fractures using well log and core data, as well as stochastic modeling techniques. The reports describe simulations of hydraulic fracture propagation, fluid-mechanical interactions, and induced microseismicity. The dataset also includes history-matching of net pressure and analyses of fracture growth in naturally fractured geothermal reservoirs. The slides summarize key findings and future research directions.

15 GEOTHERMAL ENERGY↗

Municipality of Anchorage Household Travel Survey

The 2002 Household Travel Survey for the municipality of Anchorage, Alaska, collected demographic, socioeconomic, and travel information about households and persons (age five and older) on an assigned, 24-hour travel period. The main objective of the study, conducted by NuStats, was to improve the transportation system. 2,035 households were recruited to participate in the study. Of these, 1,293 completed travel logs depicting detailed data on driving habits, such as purpose of the trips, time of the data, and mode of transportation—spanning from April 1, 2002, to May 17, 2002. This dataset is part of the Metropolitan Travel Survey Archive, which includes travel surveys from numerous public agencies across the United States and is archived by the Transportation Secure Data Center to ensure their continued public availability.

1Hz data↗

Additively Manufactured Tamper Evident Container

Los Alamos National Laboratory has developed a revolutionary and reliable new tamper evident container (TEC) to monitor and protect the health, security, and distribution of high-value assets, technological advancements, products, materials and more that are critical to the operations of businesses and government organizations. Unlike traditional tamper evident seals, the entire TEC serves as a seal, with encrypted on-board electronics able to continuously monitor and log structural health data including but not limited to force, temperature, pressure, humidity, orientation, radiation, acceleration, or vibration as needed. Analog and encrypted digital boards safely stored inside the TEC permanently record the complete integrity history of the protected items and have the ability to alert the TEC owners or take immediate defensive actions should a disruptive or tamper event take place.

36 MATERIALS SCIENCE↗

TCCSP Site Characterization and Feasibility Report

The Tulare County Carbon Storage Project (TCCSP) focused on establishing the geological and commercial viability of a 50 million metric ton (MMT) carbon storage complex located in Tulare County, California. The geologic scope of work included collecting new and existing subsurface data, such as two-dimensional (2D) seismic data, advanced geophysical logs, core and fluid samples, and specialized core-analysis datasets, to support the integration and interpretation of findings from earlier tasks. These efforts contributed to the comprehensive feasibility study for commercial carbon capture and storage (CCS) development at the TCCSP site and in the surrounding region. The TCCSP project team evaluated the feasibility of the Lower Monterey Group (Santa Margarita Sand Member or its equivalent), Temblor Formation sand members (Olcese, Jewett, Vedder), and Lodo-Martinez Zone (Domengine Sandstone, Walker Formation, Lodo Formation, and the Martinez Sand) to serve as commercial CCS reservoirs along with potential for the Upper Monterey Group (Reef Ridge, Antelope Members) and the Kreyenhagen Formation to vertically seal underlying reservoirs from shallow above-zones.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Optimizing Deep Geothermal Drilling for Energy Sustainability in the Appalachian Basin

This study investigates the geological and geomechanical characteristics of the MIP 1S geothermal well in the Appalachian Basin to optimize drilling and address the wellbore stability issues encountered. Data from well logs, sidewall core analysis, and injection tests were used to derive elastic and rock strength properties, as well as stress and pore pressure profiles. A robust 1D-geomechanical model was developed and validated, correlating strongly with wellbore instability observations. This revealed significant wellbore breakout, widening the diameter from 12 ¼ inches to over 16 inches. Advanced technologies like Cerebro Force™ In-Bit Sensing were used to monitor drilling performance with high accuracy. This technology tracks critical metrics such as bit acceleration, vibration in the x, y, and z directions, Gyro RPM, stick-slip indicators, and bending on the bit. Cerebro Force™ readings identified hole drag caused by poor hole conditions, including friction between the drill string and wellbore walls and the presence of cuttings or debris. This led to higher torque and weight on bit (WOB) readings at the surface compared to downhole measurements, affecting drilling efficiency and wellbore stability. Optimal drilling parameters for future deep geothermal wells were determined based on these findings.

Environmental Sciences & Ecology↗