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At least 127 records · Page 7

Geomechanical in situ testing of fault reactivation in argillite repositories

Abstract. Pressurization of natural faults as a result of repository-induced effects can lead to their reactivation and permeability generation in case such features are present near disposal tunnels. Potential driving forces for such pressurization are the temperature increase caused by heat-producing high-level radioactive waste and the generation of hydrogen and other gases due to corrosion of engineered materials. We are primarily concerned about pressurization and fault activation in host rocks and faults that have very low natural permeability for pore pressure increases to dissipate, such as the argillite rocks currently investigated in Switzerland, France and other countries. This presentation discusses a series of in situ experiments of fault activation by fluid injection conducted in the argillite rock (Opalinus clay) at the Mont Terri underground research laboratory in Switzerland. A multi-model monitoring test bed was installed at Mont Terri that includes distributed fiber optics for strain, temperature, and acoustics; local fault pore pressure and three-dimensional displacement sensors; active seismic imaging; and passive seismic monitoring. The fault experiments (and their subsequent analysis via hydromechanical modeling) provide a new fundamental understanding of the coupling between pore pressure, fault deformation and permeability generation as a function of time and allow exploring how seismic and aseismic events may impact the integrity of faulted argillite host rock. Our experimental observations demonstrate that significant flow (and transport) can occur along the initially impermeable argillite fault when rupture is activated. However, the fault permeability decreases to almost its pre-activation value when fluid injection ceases and fluid pressure drops. Dilatant slip on the fault plane alone does not explain the observed increase in fault permeability; pressure-induced fault opening also plays a role, favored by the softness of the shale along with the fact that the structure of the fault zone prevents fluids from diffusing into the adjacent damage zone. Rupture initiation and permeability generation is initially aseismic, associated only with an increase in noise level and emerging tremors. Micro-earthquakes are initiated later in the experiments and typically occur away from the fluid-pressurized area. Hydromechanical models show that stress transferred from the initial aseismic deformation can build up to stress criticality and later induce seismic rupture. After presenting the experimental results, we will close the presentation with an outlook to future experimental campaigns using the fault test bed at Mont Terri. We are currently planning a controlled thermal stimulation of the fault, where instead of fluid injection as a trigger mechanism we will heat up the nearby rock volume and measure potential effects on fault stability. Lessons learned from our past and future experiments will help inform the safety assessment of geologic disposal in argillite host rock.

Birkholzer, Jens T.↗

Porous Colloidal Nanoparticles as Injectable Multimodal Contrast Agents for Enhanced Geophysical Sensing

Injecting fluids into underground geologic structures is crucial for the development of long-term strategies for managing captured carbon and facilitating sustainable energy extraction operations. Here, we have previously reported that the injection of metal–organic frameworks (MOFs) into the subsurface can enhance seismic monitoring tools to track fluids and map complex structures, reduce risk, and verify containment in carbon storage reservoirs because of their absorption capacity of low-frequency seismic waves. Here, we demonstrate that water-based Cr/Zn/Zr MOF colloidal suspensions (nanofluids) are multimodal geophysical contrast agents that enhance near-wellbore logging tools. Based on experimental fluid-only measurements, MIL-101(Cr), ZIF-8, and UiO-66 nanofluids have distinct complex conductivity and/or low-field nuclear magnetic resonance (NMR) signatures that are relevant to field-deployed technologies, implying the potential to enhance near-wellbore monitoring of CO 2 injection and associated processes with downhole logging tools. Small- and wide-angle X-ray scattering characterization of ~0.5 wt % MIL-101(Cr) suspensions confirmed phase stability and provided insight into the fractal nature of colloidal nanoparticles. Finally, low-field (2 MHz) NMR measurements of MIL-101(Cr) nanofluid injection into a prototypical Berea sandstone demonstrate how paramagnetic high-surface area MOFs may dominate the relaxation times of hydrogen-bearing fluids in porous geologic matrices, enhancing the mapping of near-surface and near-wellbore transport pathways and advancing sustainable subsurface energy technologies.

NMR↗

Analysis and Optimization of Seismo-Acoustic Monitoring Networks with Bayesian Optimal Experimental Design

The Bayesian optimal experimental design (OED) problem seeks to identify data, sensor configurations, or experiments which can optimally reduce uncertainty. The goal of OED is to find an experiment that maximizes the expected information gain (EIG) about quantities of interest given prior knowledge about expected data. Therefore, within the context of seismic monitoring, we can use Bayesian OED to configure sensor networks by choosing sensor locations, types, and fidelity in order to improve our ability to identify and locate seismic sources. In this work, we develop the framework necessary to use Bayesian OED to optimize the ability to locate seismic events from arrival time data of detected seismic phases. In order to do utilize Bayesian OED we must develop four elements:1. A likelihood function that describes the uncertainty of detection and travel times; 2. A Bayesian solver that takes a prior and likelihood to identify the posterior; 3. An algorithm to compute EIG; and, 4. An optimizer that finds a sensor network which maximizes EIG. Once we have developed this framework, we can explore many relevant questions to monitoring such as: how and what multiphenomenology data can be used to optimally reduce uncertainty, how to trade off sensor fidelity and earth model uncertainty, and how sensor types, number, and locations influence uncertainty

47 OTHER INSTRUMENTATION↗

Quantitative interpretation of time-lapse seismic data at Farnsworth field unit: Rock physics modeling, and calibration of simulated time-lapse velocity responses

Here, this study investigates the contribution of fluid saturation variation to the time-lapse velocity response by performing fluid substitution modeling. The methodology is exemplified by the time-lapse seismic monitoring of carbon dioxide at Farnsworth field unit (FWU). In order to evaluate the fluid distribution in a matured oil reservoir, the Southwest Regional Partnership (SWP) acquired multiple vertical seismic profile (VSP) surveys at different times during the CO 2 –water alternatinggas (WAG) injection period. In this work, we present a thorough methodology for computing the elastic response of the saturated rock for different fluid saturations using a site-specific petro-elastic model (PEM). The output from the PEM was combined with results from a fluid compositional model to compute the seismic velocities at times corresponding to each VSP survey. To produce a calibrated simulated response, the measured time-lapse seismic velocities were integrated into the numerical simulation model. The mismatches between the predicted and measured time-lapse velocities were minimized through an iterative calibration process using a trained artificial neural network proxy (ANN) coupled with a particle swarm optimizer (PSO). Our study indicates that the hybrid optimization workflow can effectively perform the history matching. With an accurate prediction of the hydrodynamic properties, the migration of CO 2 within the subsurface was modeled by predicting the spatial velocity distribution for a radius of 305 m around the injection well. The technology demonstrated and the expertise gained from this study can guide similar CO 2 -WAG projects.

58 GEOSCIENCES↗

Creation of a Mixed-Mode Fracture Network at Mesoscale Through Hydraulic Fracturing and Shear Stimulation

Enhanced Geothermal Systems could provide a substantial contribution to the global energy demand if their implementation could overcome inherent challenges. Examples are insufficient created permeability, early thermal breakthrough, and unacceptable induced seismicity. In this study we report on the seismic response of a mesoscale hydraulic fracturing experiment performed at 1.5-km depth at the Sanford Underground Research Facility. We have measured the seismic activity by utilizing a 100-kHz, continuous seismic monitoring system deployed in six 60-m length monitoring boreholes surrounding the experimental domain in 3-D. The achieved location uncertainty was on the order of 1 m and limited by the signal-to-noise ratio of detected events. These uncertainties were corroborated by detections of fracture intersections at the monitoring boreholes. Three intervals of the dedicated injection borehole were hydraulically stimulated by water injection at pressures up to 33 MPa and flow rates up to 5 L/min. We located 1,933 seismic events during several injection periods. The recorded seismicity delineates a complex fracture network comprised of multistrand hydraulic fractures and shear-reactivated, preexisting planes of weakness that grew unilaterally from the point of initiation. We find that heterogeneity of stress dictates the seismic outcome of hydraulic stimulations, even when relying on theoretically well-behaved hydraulic fractures. Once hydraulic fractures intersected boreholes, the boreholes acted as a pressure relief and fracture propagation ceased. In order to create an efficient subsurface heat exchanger, production boreholes should not be drilled before the end of hydraulic stimulations.

58 GEOSCIENCES↗

Physics-based Induced Earthquake Forecasting: Process Understanding, and Hazards Mitigation

Disposal of saltwater co-produced with oil and gas is linked to elevated seismicity in the Central and Midwest US. There is a concern that these events may lead to widespread damage and an overall increase in seismicity. Thus an improved understanding of the spatially and temporally variable deformation and stress field associated with fluid injection operation is critically necessary for evaluating time-varying seismic hazards. Despite the improvements in seismic monitoring capacity and the resulting decrease in the magnitude detection threshold, estimates of induced earthquake probability remain elusive due to insufficient models incapable of accounting for the complex physics governing the process of induced seismicity. The proposed research effort will comprehensively analyze, integrate, and interpret geodetic, injection, and seismic data in the vicinity of the injection sites in Oklahoma to resolve the 4-dimensional distribution of pore pressure and stress in the shallow crust. This project, in particular, is focused on exploring the statistical relation between injection operation and increased earthquake hazard. The amplitude of and the extent to which pore pressure changes are determined by some factors, in particular, the hydrogeological properties of the rocks, such as diffusivity. Thus the available deformation data will be used to constrain hydrogeological properties of the medium, to accurately resolve the evolution of crustal stresses due to fluid injection. Having the time-varying models of stress changes, a statistical framework will be implemented to estimate the time-dependent probability of large earthquakes on the nearby fault systems. These data and models help to improve seismic hazard estimates and aid in constructing operational-induced earthquake forecast models. This information can also be integrated into the updated U.S. National Seismic Hazard Map, which local communities and authorities use in their earthquake risk estimates and mitigation efforts.

58 GEOSCIENCES↗

Locating Seismic Events with Local-Distance Data

As the seismic monitoring community advances toward detecting, identifying, and locating ever-smaller natural and anthropogenic events, the need is constantly increasing for higher resolution, higher fidelity data, models, and methods for accurately characterizing events. Local-distance seismic data provide robust constraints on event locations, but also introduce complexity due to the significant geologic heterogeneity of the Earth’s crust and upper mantle, and the relative sparsity of data that often occurs with small events recorded on regional seismic networks. Identifying the critical characteristics for improving local-scale event locations and the factors that impact location accuracy and reliability is an ongoing challenge for the seismic community. Using Utah as a test case, we examine three data sets of varying duration, finesse, and magnitude to investigate the effects of local earth structure and modeling parameters on local-distance event location precision and accuracy. We observe that the most critical elements controlling relocation precision are azimuthal coverage and local-scale velocity structure, with tradeoffs based on event depth, type, location, and range.

42 ENGINEERING↗

Pycheron: A Python-Based Seismic Waveform Data Quality Control Software Package

Supplementing an existing high-quality seismic monitoring network with openly available station data could improve coverage and decrease magnitudes of completeness; however, this can present challenges when varying levels of data quality exist. Without discerning the quality of openly available data, using it poses significant data management, analysis, and interpretation issues. Incorporating additional stations without properly identifying and mitigating data quality problems can degrade overall monitoring capability. If openly available stations are to be used routinely, a robust, automated data quality assessment for a wide range of quality control (QC) issues is essential. To meet this need, we developed Pycheron, a Python-based library for QC of seismic waveform data. Pycheron was initially based on the Incorporated Research Institutions for Seismology’s Modular Utility for STAtistical kNowledge Gathering but has been expanded to include more functionality. Pycheron can be implemented at the beginning of a data processing pipeline or can process stand-alone data sets. Its objectives are to (1) identify specific QC issues; (2) automatically assess data quality and instrumentation health; (3) serve as a basic service that all data processing builds on by alerting downstream processing algorithms to any quality degradation; and (4) improve our ability to process orders of magnitudes more data through performance optimizations. This article provides an overview of Pycheron, its features, basic workflow, and an example application using a synthetic QC data set.

58 GEOSCIENCES↗

Utah FORGE: GES Well 16A(78)-32 and Well 16B(78)-32 Stimulation Seismic Event Catalogs

This dataset contains seismic event catalogs from the hydraulic stimulation of wells 16A(78)-32 and 16B(78)-32 at the Utah FORGE site in April 2024. The data was collected by Geo Energy Suisse (GES) using a variety of seismic monitoring technologies, including 3-component (3C) geophones and distributed acoustic sensing (DAS) systems. These technologies were deployed across several locations, including wells 16A, 16B, and Delano-1, with sensor arrays at multiple depths to capture microseismic activity during the stimulations. The catalogs provide both real-time and manually checked seismic event locations, with detailed parameters such as trigger conditions, velocity models, and data acquisition settings. The dataset includes information on the stimulation stages, event rates, and hydraulic injection conditions for each well, with a report detailing the data acquisition configuration and seismic event location methodologies. Users will need to reference the included report for a complete understanding of the sensor network, data processing techniques, and accuracy considerations.

15 GEOTHERMAL ENERGY↗

Utah FORGE 6-3629: Application of Machine Learning, Geomechanics, and Seismology for Real-Time Decision Making Tools During Stimulation - 2024 Annual Workshop Presentation

This is a presentation on the Cutting Edge Application of Machine Learning, Geomechanics, and Seismology for Real-Time Decision Making Tools During Stimulation by the University of Utah, presented by No'am Zach Dvory. This video slide presentation, by the University of Utah, discussed the technical objectives of developing a real-time decision-making platform to enhance seismic monitoring and risk management during stimulation activities. This presentation was featured in the Utah FORGE R&D Annual Workshop on August 15, 2024.

15 GEOTHERMAL ENERGY↗

Compilation of a Comprehensive Earthquake Catalog and Relocations in the Caucasus Region

Instrumental seismic monitoring has a long history in the Caucasus and started in 1899 when the first seismograph was installed in Tbilisi, Georgia. Much of the analog paper records from this time period are preserved in the Tbilisi archives because Georgia served as the regional data center. In the 1990s, due to the collapse of the Soviet Union and the political turmoil in the region, the analog networks and the communication between the newly formed national networks deteriorated. In Georgia, for the next 13 yr, the seismic network coverage was poor until the 2002 Tbilisi earthquake. Following this earthquake, the first permanent digital seismic station in Georgia was established in Tbilisi in 2003. The digital era progressively improved the ability to collect and archive data and today more than a hundred broadband seismic stations (including temporary arrays) are operating in the southern Caucasus. Until recently, the region lacked a coordinated effort to catalog all analog and digital era data collected by different countries into a single repository. As a result of collaboration between Lawrence Livermore National Laboratory, the Ilia State University, and the Republican Seismic Survey Center of Azerbaijan, a comprehensive earthquake catalog was compiled for the Caucasus and neighboring areas as part of a broader probabilistic seismic hazard assessment project. Here this project digitized Soviet-era paper bulletins, compiled a unified earthquake catalog from regional bulletins, developed 1D reference velocity model, and used it to relocate the events. The final catalog contains 16,963 events with magnitudes 3.7 and above, bringing together all the available data sets in the Caucasus region from 1900 to 2015, significantly improving locations, and generating the most complete earthquake catalog in the region, temporally and geographically.

58 GEOSCIENCES↗

Terrestrial geophysics in the SeaRISE project

Some areas of research in the SeaRISE project are briefly discussed. They are as follows: (1) Radar Sounding serves multiple purposes. The most general and obvious is mapping ice thickness and the surface and bedrock topography of the ice sheet. (2) The purpose of Seismic Shooting, in addition to water depth measurements on floating ice, is to provide information about the internal physical characteristics of the ice sheet, the rock beneath it, and the interface between the two. (3) Passive Seismic monitoring of microearthquakes can be used to study brittle fracture within the ice or the rock beneath it. Common parameters available from these studies are fault location, orientation, and displacement, as well as the size of the rupture area, stress drop, and energy released. (4) There is a large contrast in Electrical Resistivity between ice or permafrost on the one hand and liquid water or wet rock on the other hand. Thus, electrical resistivity profiles have the ability of revealing the depth to the melting point, whether it is found at the base of the ice or in the subglacial rock. (5) Gravity anomalies, especially combined with seismic measurements, are an effective tool for determining deeper crustal structure. Anomalies averaged over extensive areas are useful also for their potential to reveal isostatic imbalance, which is a measure of average glacial change over the last several hundred years.

Bentley, C. R.↗

Utah FORGE: Microseismic Surface Network Catalogs for Wells 16A(78)-32 and 16B(78)-32 Stimulation 2022 and Circulation 2023

This dataset includes microseismic surface network catalogs for Utah FORGE. Data were recorded during the stimulation of well 16A(78)-32 in 2022 and the circulation tests between wells 16A(78)-32 and 16B(78)-32 in 2023. Near-surface seismic monitoring during circulation experiments in wells 16A(78)-32 and 16B(78)-32 revealed fracture growth after shut-in. The catalog provided here offers a comprehensive list of microseismic events, including details on their spatial and temporal distribution, magnitude, and clustering behavior.

15 GEOTHERMAL ENERGY↗

WHOLESCALE: Seismic Survey Data from San Emidio Nevada 2021

This dataset includes raw and processed seismic data from the 2021 seismic survey at the San Emidio geothermal field in Nevada. In April and May 2021, 37 tri-axial short period seismographs were deployed in a 1.8km diameter cluster centered on 40.367278 N, 119.409019 W. The first data record started at 2021-04-06T07:09:10Z UTC and the last record ended at 2021-05-11T02:58:52Z UTC. The 37 stations included 29 SmartSolo IGU-16HR 3C all-in-one 5 Hz seismographs and 8 DataCube seismographs with 4.5 Hz HGS HG-6(B coil) tri-axial geophones. The raw format (level 0) data includes 353 GB of 500 sps SmartSolo data in native DLD format, 113 GB of 400 sps DataCube data in native DataCube format, and 3.4 GB of GPS data collected during the RTK GPS survey. The SAC data (level 1) files include 564 GB of hourly SAC files. The experiment was designed to monitor seismic activity before, during, and after the planned three-day plant maintenance shutdown April 19-21, 2021. The pumping stopped at 2021/04/19 12:51:45 UTC and resumed about 2021/04/21 21:00:00 UTC. The dataset is hosted in an AWS data lake, with an associated GDR data set providing the metadata. Links to both of these resources are included below. Additionally, this collection features data and metadata from a 2016 seismic survey at the same site for comparative analysis.

15 GEOTHERMAL ENERGY↗

WHOLESCALE: Seismic Survey Metadata from San Emidio Nevada 2021

This is a collection of metadata from the 2021 seismic survey at the San Emidio geothermal field in Nevada. In April and May 2021, 37 tri-axial short period seismographs were deployed in a 1.8km diameter cluster centered on 40.367278 deg N, 119.409019 deg W. The first data record started at 2021-04-06T07:09:10Z UTC and the last record ended 2021-05-11T02:58:52Z UTC. The 37 stations included 29 SmartSolo IGU-16HR 3C all-in-one 5 Hz seismographs and 8 DataCube seismographs with 4.5 Hz HGS HG-6(B coil) tri-axial geophones. The raw format (level 0) data includes 353 GB of 500 sps SmartSolo data in native DLD format, 113 GB of 400 sps DataCube data in native DataCube format, and 3.4 GB of GPS data collected during the RTK GPS survey. The SAC data (level 1) files include 564 GB of hourly SAC files. The experiment was designed to monitor seismic activity before, during, and after the planned three-day plant maintenance shutdown April 19-21, 2021. The pumping stopped at 2021/04/19 12:51:45 UTC and resumed about 2021/04/21 21:00:00 UTC. The raw and processed data are in an associated GDR submission, linked below. The metadata here includes files containing experiment details, station locations, seismic data logger specifications, instrumentation specifications, and descriptions of data. Also included are data and metadata from a 2016 seismic survey at the same site.

15 GEOTHERMAL ENERGY↗

Signal Decomposition for Intrusion Detection in Reliability Assessment in Cyber Resilience (Summary Report)

The complexity of assuring cyber resilience for physical process interactions in connected systems such as energy grids increases dramatically as the coupling between processes becomes more direct and responsive. An example of this growing complexity is provided by Integrated Energy Systems (IES), in which various processes such as nuclear heat generation and commodity production are being directly coupled for increased responsiveness to highly variable signals such as market pricing or electricity demand. As such, the potential attack surface of the coupled processes is larger than the two processes independently. Securing these complex systems requires two-fold monitoring: cybersecure monitoring for potential malicious incursion, and physics monitoring for system tampering. Physics monitoring includes analyzing the behavior of the signals within the system for anomalous behavior. This analysis has been shown to be insufficient if approached by only data-driven machine learning and artificial intelligence (MLAI) techniques or only low-level model comparison. Previous efforts at Purdue University suggested combining high-fidelity models with MLAI algorithms as a basis for a software tool for detecting anomalies in physical processes. This work built on that suggestion, developing an advanced library for signal decomposition and analysis using both MLAI and high-fidelity physics algorithms for greatly improved anomaly detection, especially false data injection. This software can be used as part of a secure imbedded intelligence (SEI) system designed under Consequence-driven Cyber-informed Engineering (CCE) for complex coupled systems. This library established a foundation for online and posteriori analysis of digital signals for the purpose of detecting potential malicious tampering in digital signals representing physical processes. Demonstrations carried out throughout the development highlight the effective use of characterization algorithms to detect signal perturbations, particularly triangle attack-style perturbations, in three wide-ranging applications: seismic monitoring, nuclear thermal hydraulics system simulation, and custom manufacturing.

97 MATHEMATICS AND COMPUTING↗

Using Natural Gas Liquids to Recover Unconventional Oil and Gas Resources (Final Report)

This document presents final technical findings for the project Using Natural Gas Liquids to Recover Unconventional Oil and Gas Resources (FE0031782). The project is part of the U.S. Department of Energy Oil and Gas Program to develop and advance technologies that can significantly improve the recovery efficiencies of unconventional oil and gas resources. The overall objective of this project is to improve the ultimate recovery from unconventional oil and gas (UOG) resources in the United States by developing a method for using unrefined natural gas liquids (NGLs) as treatment fluids to improve hydrocarbon production. Horizontal extended-lateral drilling coupled with high volume hydraulic fracturing has significantly increased production from UOG resources in the U.S. However, the recovery efficiency is low compared to the estimated oil and gas in place. Recent data indicate that less than 10% of the oil in the liquid-rich UOG reservoirs is produced. Alternative completion methods using NGLs could increase production (Battelle, 2016; Wan et al., 2013; Wan, 2013; Downey et al, 2021); however, field validation tests are needed to develop an approach that is economical, efficient, and compatible in the UOG setting to advance towards commercial deployment. This project aims to develop and field test a method to improve recovery of oil resources in UOG shale plays by using Y-Grade NGLs, or a similar combination of NGLs, as treatment fluids. Refined NGLs have been used as a hydraulic treatment fluid in UOG plays for decades and are shown to be particularly effective because their miscibility with oil allows oil to flow more freely; however, the use of Y-Grade (unrefined) NGLs has not been studied. The use of Y-Grade NGLs would be advantageous over refined NGLs because Y-Grade NGLs do not require infrastructure or investment in refining and are already being produced from many UOG reservoirs. The concept was tested and monitored in the field at a commercial well site owned by project partner Hopco, Ltd. The project team, which consists of multiple oil and gas operators, Linde Gas North America LLC (Linde) and the Ohio Division of Geological Survey (ODGS), has extensive experience with oil and gas production in the Appalachian basin and the ability to work together quickly to solve technical issues and research needs. A key part of the proposed work was the use of existing wells for field testing and monitoring. A total of four wells (three vertical and one horizontal) were available for this project. One of the vertical wells was utilized as the test well for the NGL treatment test. A nearby vertical well was used for microseismic monitoring. The remaining vertical well and the horizontal well provided a baseline for typical UOG production in the oil window of the Utica/Point Pleasant (UPP). Major technical tasks of the project include characterization of the geotechnical properties of the UPP with an emphasis on the field site; design and planning for the NGLs testing; field testing and monitoring; analysis and integration of field data; and economic and resource/reserve assessment. The Shoman monitoring well and Doughty NGL treatment well were successfully plugged back during September-October 2020 in preparation for treatment and monitoring. A nitrogen diagnostic fracture injection test (DFIT) was completed on the Doughty well on July 22, 2021, consisting of 133,000 scf (91 Bbl.) of nitrogen. A nitrogen foam frac was completed in the Utica-Point Pleasant interval on August 17, 2021 with funding from outside sources. A microseismic monitoring array was installed in the Shoman well and monitored microseismic activity during the Doughty well frac job. Y-Grade NGL injection commenced on August 26, 2021. A total of 215 Bbl. was injected but the job was shut down due to a small leak on the suction hose on the pump truck. The Y-Grade treatment resumed on August 27, 2021 and an additional 726 Bbl. of Y-Grade NGL was injected at a well head pressure of 3850 psi. Total volume of injected Y-Grade over the two days of injection was 941 Bbl. The well was shut-in for 17 days following injection to allow the Y-Grade NGLs to soak on the formation. Y-Grade treatment flow back commenced on 9/13/2021 on a weekly basis. Production data, including surface pressures, oil, nitrogen, natural gas, and flow times was measured and recorded. Periodic gas samples were collected and analyzed to determine composition of flowback gas. As of July 2022, the treatment well had produced 726 Bbl of oil and 2,888 mcf gas. In August 2022, tubing and packer in the well were removed and a pump was installed to enhance oil recovery. Currently, the operator is producing the well about 2 days a week for a few hours. The performance of the NGL treatment test was evaluated based on reservoir simulations of the treatment process, processing of well testing data, analysis of micro seismic monitoring data, and production data analysis. This analysis suggested that oil production in the small test would continue through 2025. Upscaling the treatment to a horizontal Utica Point-Pleasant well would allow more oil recovery, but the process would involve more investment, services, and operational support. An economic analysis was conducted for scenarios aimed at upscaling the NGL treatment process for more typical horizontal UPP wells in the Appalachian Basin.

02 PETROLEUM↗

Unsupervised Clustering of Microseismic Events and Focal Mechanism Analysis at the CO 2 Injection Site in Decatur, Illinois

Characterization of induced microseismicity at a carbon dioxide (CO 2 ) storage site is critical for preserving reservoir integrity and mitigating seismic hazards. We apply a multilevel machine learning (ML) approach that combines the nonnegative matrix factorization and hidden Markov model to extract spectral representations of microseismic events and cluster them to identify seismic patterns at the Illinois Basin-Decatur Project. Unlike traditional waveform correlation methods, this approach leverages spectral characteristics of first arrivals to improve event classification and detect previously undetected planes of weakness. By integrating ML-based clustering with focal mechanism analysis, we resolve small-scale fault structures that are below the detection limits of conventional seismic imaging. Our findings reveal temporal bursts of microseismicity associated with brittle failure, providing insights into the spatio-temporal evolution of fault reactivation during CO 2 injection. This approach enhances seismic monitoring capabilities at CO 2 injection sites by improving fault characterization beyond the resolution of standard geophysical surveys.

Willis, Rachel Marie [Sandia National Laboratories↗