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77 records · Page 5

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↗

Geophysical Characterization and Monitoring of the 200 Area Treated Effluent Disposal Facility to Support Permit Renewal; TEDF Geophysical Monitoring

The Treated Effluent Disposal Facility (TEDF), located in the 200 East Area of the Hanford Site, is a site where non-hazardous and non-radioactive liquid wastes are disposed into two state-permitted infiltration basins. In 2016, the Washington State Department of Ecology denied a permit renewal request for TEDF due to the inability to adequately assess the impact of TEDF discharge water on the underlying groundwater quality. TEDF overlies the relatively impermeable Ringold Lower Mud (RLM) unit, whose upper contact lies in the vadose zone approximately 30 m below ground surface and approximately 10 m above the water table boundary. The RLM is assumed to isolate TEDF discharge water from the natural groundwater aquifer, which is monitored using wellbores screened below the RLM. Therefore, samples collected from monitoring wells near TEDF are not considered representative of TEDF discharge water. Rather, TEDF discharge water is assumed to mound on top of the RLM to form a perched aquifer. To support permitting of the TEDF, a new monitoring well is required that can be used to sample water from the presumed perched water aquifer above the RLM. Ideally, the screened section of the well would be located at the peak of the perched water mound(s), which presumably occurs at the point of maximum vertical flux from TEDF to the RLM, or equivalently where the dominant infiltration flow paths reach the RLM. This report describes how time-lapse 3D electrical resistivity tomography (ERT) was used in conjunction with nominal TEDF discharge operations to image the dominant flow paths from each pond to the RLM. Results are summarized in Figure ES.1. Figure ES.1A shows a satellite image of the TEDF overlain by an array of surface ERT electrodes. The solid and dashed black circles denote the zones of maximum vertical flux at the RLM within the south and north ponds, respectively, and presumably the regions where perched water peaks during discharge. Figure ES.1B shows time-lapse difference imaging results approximately 15 days after switching discharge from the south pond to the north pond. Blue iso-surfaces beneath the north pond denote regions of increasing bulk electrical conductivity caused by increasing saturation due to infiltrating water. Red iso-surfaces beneath the south pond denote regions that were previously saturated during the south pond discharge and are now de-saturating, causing a decrease in bulk electrical conductivity. In both cases, the zones of maximum change mark the dominant flow paths to the RLM. Figure ES.1C shows time-lapse imaging results approximately 15 days after switching discharge from the north pond to the south pond. In this case, blue iso-surfaces mark the dominant flow paths to the RLM from the south pond. The red iso-surfaces mark the dominant flow paths that existed during discharge to the north pond. The regions of maximum change in bulk conductivity (due to saturation or desaturation) that mark the primary flow paths are equivalent in both cases and denoted by the dashed solid and black circles. If a perched water zone forms on the RLM, it is likely to mound within or near the dashed circle during discharge to the north pond, and in the solid circle during discharge to the south pond. In other words, if perched water mounding occurs, the circles mark the optimum locations suggested by the ERT imaging for monitoring boreholes to be placed, enabling samples to be collected that are representative of TEDF discharge impacts on groundwater quality.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Real-Time Seismic System for Monitoring, Imaging, and Characterization (RT-SEISMIC)

The goal of this Laboratory Directed Research and Development (LDRD) project was to develop a borehole seismic source and sensor array to enable real-time seismic imaging at scales and conditions relevant to the energy industry including both fossil-energy and geothermal. In FY21 and FY22, we designed, built, and tested both a prototype impulse source module for generating seismic energy and a sensing module for recording ground motions generated by the source module array. A pneumatically driven vibratory source was also designed. The source modules were fabricated with all high temperature components and the team has worked to incorporate the current RT-SEISMIC electronics design into a commercially available, high temperature silicon-on-insulator chip integrated circuit. Several issues were identified during fabrication and lab testing that led to redesign of several system components and subsequent retesting. The final round of testing showed that while metal/graphite-based seals worked quite well for static seals, they were unable to provide an adequate gas seal for dynamic, reciprocating part movements which necessitated a final redesign using Kalrez. This change will result in a continuous temperature rating of approximately 275 degrees C for the system. While a field test of the RT-SEISMIC system was targeted in FY22, due to the extended lab testing and redesign efforts, field testing was not achieved. As a result of this LDRD investment, several sponsors have expressed interest in RT-SEISMIC and we expect to continue towards a field demonstration of the full system in the future.

58 GEOSCIENCES↗

Geothermal Play-Fairway Analysis of Washington State Prospects: Final Report

The Washington State Geothermal Play-Fairway Analysis overcomes the exploration challenges posed by dense vegetation, glacial deposits, and extreme precipitation. The geothermal play-fairways we target are locations where heat, permeability, and saturated porosity are present in sufficient volume to provide adequate heat exchange at depths accessible by modern drilling technology. The three study areas lie along the Cascade Range magmatic arc and are near Mount Baker, Mount St. Helens, and the Wind River Valley. The seven-year project is divided into three phases. In Phase 1 we build on a previous statewide assessment of geothermal resources and develop an initial modeling approach. The results are a series of favorability, uncertainty, and risk maps for three targeted study areas. Based on these initial results, we collect new geologic and geophysical data to further refine our modeling and reduce exploration uncertainty in Phase 2. We improve the modeling method to handle the new data and update the favorability, uncertainty, and risk maps. We also update the conceptual geothermal resource models. In Phase 3 we validate our modeling approach by drilling two temperature-gradient holes and collecting and analyzing core, image logs, and new geochemistry. Our modeling approach improves on an earlier statewide method through a more-rigorous and detailed assessment of heat and permeability. Permeability potential is assessed through geomechanical modeling of the deformation that can generate and maintain reservoir porosity and permeability. Metrics to inform heat potential include temperature-gradient wells, which are sparse in Washington; proximity of Quaternary volcanic vents and young intrusive rock; spring temperature; and reservoir temperature inferred from geothermometry. We weight the individual components using an expert-guided approach known as the Analytical Hierarchy Process. During Phase 2 we also develop a fluid-filled fracture model, and an infrastructure model that helps to delineate areas which are more favorable for geothermal development based on proximity to transmission lines, elevation, land ownership and use restrictions, and availability of process water. New geologic and geophysical data is collected during Phase 2 in each of our three main study areas. At Mount Baker and north of Mount St. Helens we conduct 1:24,000-scale geologic mapping and lidar analysis to better constrain the location and character of surface faults; detailed mapping in the Wind River Valley was completed just prior to the start of this project. Ages of intrusive rocks are determined with 40 Ar/ 39 Ar geochronology, though all of our samples are Miocene or older. We collect ground based gravity observations (a total of 1,580 new stations) in all of our study areas and ground-based magnetic lines (a total of 93 km) at Mount Baker. These data are combined with existing gravity and aeromagnetic data and used to constrain fault locations and geometry. Two to three cross sections are constructed at each study area using the mapped surface geology and forward-modeling of the gravity and magnetic data; these cross sections form the basis for our updated conceptual models. We collect magnetotelluric surveys at Mount Baker and Mount St. Helens and these data are inverted to form a resistivity model from the surface to about 10 km depth; each model shows conductive zones that can be interpreted as upwelling geothermal fluids. At Mount St. Helens we deploy a passive seismic array and use the newly detected events to refine the location of the Saint Helens seismic zone. We also employ ambient-noise tomography to develop a detailed seismic-velocity model for the study area and use this model to help constrain our cross sections and conceptual model. Based on the new data collected during Phase 2—and our updated models—we develop a campaign of temperature-gradient holes and core analysis to validate our modeling in Phase 3. Drill hole MB76-31 is located near Little Park Creek, 11 km west-southwest of the summit of Mount Baker, and is 1,471 ft deep. About 410 ft of core from the lower portion of the hole—and image logs from ~175 ft below ground surface to the bottom—are collected and analyzed. Water samples are collected and processed for geothermometry. Drill hole MSH17-24 is located along upper Schultz Creek, 16 km north-northeast of Mount St. Helens and has core from 470 ft to the bottom at 1,053 ft. We did not collect image logs due to borehole stability concerns, but water samples are collected and analyzed for geothermometry. Repeat temperature-gradient measurements are made at both sites and thermal conductivity is measured from core samples. At MB76-31, the equilibrated temperature gradient of 64°C/km and calculated heat flow of 141–159 mW/m 2 is more than twice the regional average. Detailed mapping and analysis of the core, coupled with correlation to the image logs, indicates a history of permeability generation consistent with our predictions of high permeability. Because the site has high favorability in the Phase 2 model, we consider the results a positive validation of the modeling. At site MSH17-24, the equilibrated temperature gradient of ~15°C/km and calculated heat flow of 41–43 mW/m 2 are similar to regional. Geochemical analysis of the water samples indicates a meteoric source without any geothermal component. Detailed outcrop-based mapping of fault exposures near the drill site and analysis of image logs from nearby boreholes indicates a history of permeability generation consistent with our predictions. Because the site has low favorability in the Phase 2 model, we consider the results a positive validation of the modeling. Together, the two sites provide a reasonably positive validation of the Phase 2 modeling and should encourage future use of this modeling approach.

15 GEOTHERMAL ENERGY↗

Improving multiwell petrophysical interpretation from well logs via machine learning and statistical models

Well-log interpretation estimates in situ rock properties along well trajectory, such as porosity, water saturation, and permeability, to support reserve-volume estimation, production forecasts, and decision making in reservoir development. However, due to measurement errors, variability of well logs caused by multiple measurement vendors, different borehole tools, and nonuniform drilling/borehole conditions, estimations of rock properties with original well logs without proper preprocessing may not be accurate, especially in the context of multiwell estimation. Well-log normalization techniques such as two-point scaling and mean-variance normalization are commonly used to improve the robustness of multiwell rock-property estimation. However, these techniques do not consider the correlation between well logs and require subjective knowledge for their effective implementation. To reduce uncertainties and processing time associated with multiwell rock-property estimation from well logs, we develop discriminative adversarial (DA) and linear constraint models for well-log normalization and rock-property estimation. The DA neural network model developed for well-log normalization and interpretation can perform linear and nonlinear well-log normalization while considering the joint distribution of each well log and rock properties. However, the linear constraint model uses an ensemble of predictions from linear models to constrain well-log normalization and rock-property estimation. We also develop a divergence-based type well identification method to select type (training) wells for a test well based on the statistical similarity of associated well-log distributions instead of the interwell distance. We apply the DA model to perform well-log normalization and prediction of permeability for the Seminole San Andres Unit carbonate reservoir. Compared with the permeability predicted with the classical machine learning model without well-log normalization and models with two-point scaling normalization, the DA model yields the most accurate permeability prediction by decreasing the mean-squared error of permeability prediction by 20%–50%.

Geochemistry & Geophysics↗