Search NASA⌕ Search

SEARCH · Search NASA

Results for “Well Log Correlation”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Machine Learning for Well Log Analysis in Uranium Mining

This project explores the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques to automate well log analysis for uranium mining. Geophysical log data—spontaneous potential, resistivity, and gamma ray—were used to classify lithology, correlate well logs and identify roll front zonation patterns, which are critical for locating uranium ore bodies. Supervised ML algorithms such as eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Random Forest were trained to classify lithology with high accuracy. Gradient Boosting Machines (GBM), XGBoost, Random Forest, and Neural Networks were also used for role front zone identification. Moreover, a Fast Dynamic Time Warping (FastDTW) algorithm was employed for well log correlation. Additionally, sample lag was addressed using dynamic programming. Results demonstrate the potential of AI and ML to streamline well log analysis and enhance uranium exploration workflows.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Facies Analysis of the Prairie Du Chien Group in the Illinois Basin and Analogous Rocks in Missouri and Kentucky

Funded in 2023 by the U.S. Department of Energy’s Phase II Carbon Storage Assurance Facility Enterprise (CarbonSAFE) initiative, a Heidelberg Materials cement plant in Mitchell, Indiana, is currently being evaluated as a potential Carbon Capture and Storage (CCS) subsurface injection site. The Heidelberg CCS project targets the middle to upper Prairie du Chien Group (Early Ordovician) in southwestern Indiana. Assessment of reservoir feasibility requires collection of field data, seismic surveys, well-log correlation, geologic modeling, characterization well drilling, well testing, and reservoir simulation. However, the proposed Heidelberg CCS site is in a data-limited region, lacking both outcrop analogs and deep wells penetrating the target interval, which makes geologic modelling difficult prior to drilling a characterization well. To directly address this problem, the present study was undertaken to understand the sedimentologic composition and stratigraphic architecture of the Prairie du Chien Group from analogous outcrops and cores in the Illinois Basin and adjacent regions.

Ali, Shah Bilawal [Univ. of Illinois at Urbana-Cha↗
DOE OSTI2025

Evaluation of Drilling Performance at The Geysers with Machine Learning Methods Using Geologic Data

A recent well, GDC-36, was drilled in The Geysers Geothermal Field served in a Department of Energy-industry to demonstrate improved drilling performance with polycrystalline diamond compact (PDC) bits. Both PDC and roller cone drill bits were used to drill this well. Key challenges encountered during drilling included lost circulation in the mud-drilled section, and bit damage interfacial severity in the deeper, air-drilled section. The objective of this study is to evaluate the drilling performance in relation to the local geological characteristics using machine learning methods. By applying K-clustering to the sonic log data, we were able to identify areas correlated with measured lost circulation. Also, the boundaries defined by clustering of the mineralogical and lithological data from the mud logs correlate well with interfacial severity during drilling. A random forest model was employed to build correlation between drilling data and rock strength. The confined compressive strength (CCS) of the rock in the training of the machine learning model was inferred from the dipole sonic log. The R-squared of the testing data is 0.78, and the RMSE (Root Mean Squared Error) is 0.06. The trained model was used to forecast rock strength for the section where sonic log data are not available. CCS could also be inferred from mud logs provided the relationship between mineralogy and rock strength is established through core testing data.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Supplemental Material: Cretaceous sequence stratigraphy of the northern Baltimore Canyon Trough: Implications for basin evolution and carbon storage

We focus on potential reservoirs in the Logan Canyon Formation. Based on lithostratigraphy, Libby-French originally divided the Logan Canyon sandstones into lower and upper Logan Canyon sandstone units separated by the Sable Shale equivalent. Miller et al. reinterpreted the well-log correlations with biostratigraphy using a sequence stratigraphic approach and found three distinct Logan Canyon highstand sandstones associated with three Aptian to lower Cenomanian stratigraphic sequences, the LC3, LC2, and LC1, respectively. In addition, Miller et al. found that the Sable Shale Formation identified by Libby-French was placed above and below the LC1 basal sequence boundary in some wells, highlighting issues with correlating units based only on lithology. The Upper Cretaceous strata in the BCT are termed the Dawson Canyon Equivalent Formation, correlate to the DCx composite sequence, and cap the Logan Canyon sequences below made up of a very thick, calcareous Cretaceous sequence stratigraphy mudstone, divided by a single, thick, laterally continuous Coniacian to Campanian sandstone called the Middle Sandstone.

58 GEOSCIENCES↗
DOE OSTI2025

Structural Evolution of the Hogback Monocline and Its Tectonic Significance in the San Juan Basin

The San Juan Basin is recognized as a Laramide foreland basin. It is located within the Colorado Plateau, a broad tectonic province characterized by a thick sedimentary sequence that was segmented into smaller sub basins during the Late Cretaceous to Paleogene Laramide orogeny. The Hogback Monocline lies along the northwestern margin of the San Juan Basin and is considered a Laramide-age structure formed in response to compressional stress. In this study, we interpret surface and subsurface datasets to construct a structural geological model and evaluate its tectonic significance. Through seismic data, we identify key fault and fold geometries at depth. The seismic dataset used in this study was reprocessed in depth and constrained with well log velocity data to enhance seismic imaging quality. Additionally, we performed well log correlations to identify formation tops and assess variations in basin infill and thickness geometry. A series of structural cross-sections, constructed using seismic data and a high density of boreholes, are presented to evaluate geometric variations along the structure and its evolution during basin development. Furthermore, kinematic restoration and forward modeling analyses were conducted to validate our structural interpretation. This work suggests that the Hogback Monocline formed through fault-propagation folding and flexural slip affecting the pre-Laramide sedimentary sequence under compressional stresses associated with the Laramide orogeny. This structure is interpreted as a high-angle reverse fault that influenced the geometry of the late basin infill. Additionally, monocline bending along the structure may have been controlled by fault relay systems and, in some cases, influenced by strike-slip faulting.

Reyes, Martin [New Mexico Bureau o fGeology and Mi↗
DOE OSTI2023

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↗
DOE OSTI2023

Aperture size distribution, length, and preferential location of bed-parallel veins in shale

Bed-parallel, calcite-filled veins (BPVs) are common in shale formations, and although they have been widely described in other studies, little is known about their population aperture size distribution. To address this knowledge gap, we analyzed BPV sizes in outcrops and cores from the Vaca Muerta Formation, Neuquén Basin, Argentina; in two cores from the Marcellus Formation, Appalachian Basin, northeast Pennsylvania; and in one core from the Wolfcamp Shale, Delaware Basin, West Texas. Nine out of ten aperture size populations follow a negative exponential distribution, with one following a weak power law. Bed-parallel vein size distribution and intensity vary among formations and within the same shale. We define three groups of distributions: (1) Vaca Muerta outcrops, with the highest BPV intensity and the largest BPVs (cumulative frequency of 4.9 BPVs per meter [BPVs/m] for apertures 0.265 mm to 8.7 cm); (2) Vaca Muerta cores with a similar BPV intensity overall but with no apertures wider than 1.2 cm; and (3) Vaca Muerta, Wolfcamp, and Marcellus cores with the fewest BPVs (cumulative frequency up to 0.63 BPVs/m) and very few wider than 1 cm. Aperture and length in two outcrop data sets are weakly positively correlated and follow power laws with exponents of 0.44 and 0.49. Mechanical interfaces at boundaries between different lithologies exert a strong control on BPV location, with 65–75% of observed interfaces having BPVs along them. Only 25–30% of the BPVs occur at observed material interfaces, however, and unless subtle, unobserved mechanical layering is present, other factors must also control location. BPV intensity and organic richness (TOC) from Vaca Muerta well logs are correlated in some instances but not in others, indicating TOC is not always a good proxy for BPV location or intensity. Furthermore, these findings provide useful information for modeling of hydraulic fracture treatments where BPVs may influence development of the stimulated fracture network, for example by limiting height growth.

58 GEOSCIENCES↗
DOE OSTI2023

Multi-Scale Seismic Measurements for Site Characterization and CO2 Monitoring in an Enhanced Oil Recovery/Carbon Capture, Utilization, and Sequestration Project, Farnsworth Field, Texas

To address the challenges of climate change, significantly more geologic carbon sequestration projects are beginning. The characterization of the subsurface and the migration of the plume of supercritical carbon dioxide are two elements of carbon sequestration that can be addressed through the use of the available seismic methods in the oil and gas industry. In an enhanced oil recovery site in Farnsworth, TX, we employed three separate seismic techniques. The three-dimensional (3D) surface seismic survey required significant planning, design, and processing, but produces both a better understanding of the subsurface structure and a three-dimensional velocity model, which is essential for the second technique, a timelapse vertical seismic profile, and the third technique, cross-well seismic tomography. The timelapse 3D Vertical Seismic Profile (3D VSP) revealed both significant changes in the reservoir between the second and third surveys and geo-bodies that may represent the extent of the underground carbon dioxide. The asymmetry of the primary geo-body may indicate the preferential migration of the carbon dioxide. The third technique, cross-well seismic tomography, suggested a strong correlation between the well logs and the tomographic velocities, but did not observe changes in the injection interval.

Energy & Fuels↗
DOE OSTI2024

Well-Log Derived Geomechanical Analysis of Microseismicity in the Mt. Simon Saline Aquifers (Illinois Basin - Decatur Project)

The Illinois Basin Decatur Project (IBDP) successfully demonstrated the safe geologic storage of carbon dioxide at a commercial scale. Within the IBDP project three deep wells (injection (CCS1), monitoring (VW1), geophysical (GM1)) were competed and geophysical logs were recorded. During injection and post-injection periods microseismic monitoring was conducted to create a miscoseismic catalog. The correlations between microseimic attributes and geomechanical well logs define major geomechanical drivers of microseismic expression to understand a reservoir response to CO2 injection in geological context. Utilizing standard sonic and density well logs, the dynamic elastic moduli were calculated and employed to correlate with microseismic pseudo-logs. A multi-dimensional Mu-rho and Lambda-rho (MRLR) hyperdimensional plots display of meaningful data and uncovered subtle relationships between elastic properties of sandstones and the seismological attributes of recorded microseismicity.

Myshakin, Evgeniy↗
DOE OSTI2023

Assessing the potential of composite confining systems for secure and long-term CO 2 retention in geosequestration

A potential geologic target for CO 2 storage should ensure secure containment of injected CO 2 . Traditionally, this objective has been achieved by targeting reservoirs with overlying seals-regionally extensive, low permeability units that have been proven capable of retaining buoyant fluid accumulations over geologic time. However, considering that the amount of CO 2 is limited by a decadal injection period, vertical migration of CO 2 can be effectively halted by a composite system of discontinuous shale/silt/mudstone barriers in bedded sedimentary rocks. Here, we studied the impact of depositional architectures in a composite confining system on CO 2 migration and confinement at reservoir scale. We stochastically generated lithologically heterogeneous reservoir models containing discontinuous barriers consistent with statistical distributions of net-sand-to-gross-shale ratio (NTG) and horizontal correlation lengths derived from well log data and observations of producing hydrocarbon fields in Southern Louisiana. We then performed an extensive suite of reservoir simulations of CO 2 injection and post-injection to evaluate the sensitivity of CO 2 plume migration and pressure response of the composite system to a series of geologic and fluid parameters including the lateral continuity of barriers, NTG, permeability anisotropy within the sand body, and capillary pressure contrast between the sand and shale facies. The results indicate that lateral continuity of barriers and NTG are the dominant controls on CO 2 plume geometry and pressure build-up in the reservoir, while the impact of NTG is particularly pronounced. The significance of intraformational barriers becomes apparent as they facilitate the local capillary trapping of CO 2 . Those barriers improve the pore space occupancy by promoting a more dispersed shape of the plume and ultimately retard the buoyancy-driven upward migration of the plume post injection.

58 GEOSCIENCES↗
DOE OSTI2022

Feasibility of source-free DAS logging for next-generation borehole imaging

Characterizing and monitoring geologic formations around a borehole are crucial for energy and environmental applications. However, conventional wireline sonic logging usually cannot be used in high-temperature environments nor is the tool feasible for long-term monitoring. We introduce and evaluate the feasibility of a source-free distributed-acoustic-sensing (DAS) logging method based on borehole DAS ambient noise. Our new logging method provides a next-generation borehole imaging tool. The tool is source free because it uses ever-present ambient noises as sources and does not need a borehole sonic source that cannot be easily re-inserted into a borehole after well completion for time-lapse monitoring. The receivers of our source-free DAS logging tool are fiber optic cables cemented behind casing, enabling logging in harsh, high-temperature environments, and eliminating the receiver repeatability issue of conventional wireline sonic logging for time-lapse monitoring. We analyze a borehole DAS ambient noise dataset to obtain root-mean-squares (RMS) amplitudes and use these amplitudes to infer subsurface elastic properties. We find that the ambient noise RMS amplitudes correlate well with anomalies in conventional logging data. The source-free DAS logging tool can advance our ability to characterize and monitor subsurface geologic formations in an efficient and cost-effective manner, particularly in high-temperature environments such as geothermal reservoirs. Further validation of the source-free DAS logging method using other borehole DAS ambient noise data would enable the new logging tool for wider applications.

58 GEOSCIENCES↗
DOE OSTI2023

Lattice quantum chromodynamics at large isospin density

We present an algorithm to compute correlation functions for systems with the quantum numbers of many identical mesons from lattice quantum chromodynamics (QCD). The algorithm is numerically stable and allows for the computation of n-pion correlation functions for n ϵ {1, … , N} using a single N × N matrix decomposition, improving on previous algorithms. We apply the algorithm to calculations of correlation functions with up to 6144 charged pions using two ensembles of gauge field configurations generated with quark masses corresponding to a pion mass m π = 170 MeV and spacetime volumes of (4.4 3 × 8.8) fm 4 and (5.8 3 × 11.6) fm 4 . We also discuss statistical techniques for the analysis of such systems, in which the correlation functions vary over many orders of magnitude. In particular, we observe that the many-pion correlation functions are well-approximated by log-normal distributions, allowing the extraction of the energies of these systems. Using these energies, the large-isospin-density, zero-baryon-density region of the QCD phase diagram is explored. A peak is observed in the energy density at an isospin chemical potential μ I ~ 1.5m π , signaling the transition into a Bose-Einstein condensed phase. The isentropic speed of sound, c s , in the medium is seen to exceed the ideal-gas (conformal) limit ($c^{2}_{s} ≤ 1/3)$ over a wide range of chemical potential before falling towards the asymptotic expectation at μ I ~ 15m π . These, and other thermodynamic observables, indicate that the isospin chemical potential must be large for the system to be well described by an ideal gas or perturbative QCD.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

A Systematic Interpretation of Subsurface Proppant Concentration from Drilling Mud Returns: Case Study from Hydraulic Fracturing Test Site (HFTS-2) in Delaware Basin

The aim of this study is generation and validation of a proppant log using analysis of drilling mud returns for child wells. Proppant log provides qualitative as well as quantitative insights into spatial distribution of proppant sand particles from prior stimulation of parent wells. While the basic methodology was developed and formalized during analysis of material collected from through fracture cores at Hydraulic Fracturing Test Site in Midland Basin (HFTS – 1), the test wells at HFTS – 2 in the neighboring Delaware Basin allowed the opportunity to validate the workflow on actual mud return samples from subsurface. As a child well is being drilled, periodic mud return samples are collected at the rig site and preserved for analysis. The workflow involves systematic cleaning of the samples including various steps such as washing, drying and segregation of samples into relevant size fractions of interest (< Mesh 20) based on specifications of pumped sand during stimulation of the parent well. Clean samples are imaged using high resolution transparency scanning. Scan images are then systematically analyzed for particles of interest using computer vision techniques. Sample counts are further validated using elemental analysis of smaller sub-samples at various depths of interest. This step is necessary to isolate proppant versus other naturally occurring minerals such as sulphates and carbonates which show similar optical properties. We successfully correlated proppant distribution against the existing parent well and validated propped versus relatively un-propped zones for a child well at the test site. The advantage of testing the proppant log concept at the HFTS – 2 site is the plethora of additional diagnostic data that is available to validate our primary observations. We can correlate spatial proppant distribution against variability in stimulation response based on independent observations such as image logs, microseismic attributes as well as DAS response, all of which tend to corroborate one another. One of our significant successes was being able to describe varying degrees of impact of the parent well along the lateral length of a stimulated child well. Our workflow represents a systematic and one-of-a-kind interpretation of spatial proppant distribution while drilling child wells. This provides unique opportunities to better understand the current state of the Downloaded from http://onepetro.org/URTECONF/proceedings-pdf/21URTC/2-21URTC/D021S031R003/2477415/urtec-2021-5189-ms.pdf/1 by Carol Worster on 28 February 2022 URTeC 5189 2 reservoir being targeted including zones which are likely more drained relative to others and how the planned completion of the child well can be improved. Lastly, this log can be useful is validating optimal well spacing in relatively new fields under development.

58 GEOSCIENCES↗
DOE OSTI2022

Cretaceous sequence stratigraphy of the northern Baltimore Canyon Trough: Implications for basin evolution and carbon storage

We evaluate the Cretaceous stratigraphy and carbon sequestration potential of the northern Baltimore Canyon Trough (NBCT) using >10,000 km of multi-channel seismic profiles integrated with geophysical logs, biostratigraphy, and lithology from 29 offshore wells. We identify and map six sequences resolved primarily at the stage level. Accommodation was dominated by thermal and non-thermal subsidence, though sequence boundaries correlate with regional and global sea-level changes, and the record is modified by igneous intrusion, active faulting, and changes in sediment supply and sources. Our stratigraphic maps illustrate a primary southern (central Appalachian) Early Cretaceous source that migrated northward during the Aptian and Albian. During the Cenomanian, sedimentation rates in the NBCT increased and depocenters shifted northward and landward. We show that deposition occurred in three phases: (1) earlier Cretaceous paleoenvironments were primarily terrestrial indicated by variable amplitude, chaotic seismic facies, serrated gamma logs, and heterolithic sandstones and mudstones with terrestrial microfossils; (2) the Albian to Cenomanian was dominated by deltaic paleoenvironments indicated by blocky, funnel-shaped, gamma-ray logs and clinoforms characterized by continuous high-amplitude seismic reflections with well-defined terminations; and (3) the Cenomanian and younger was marine shelf, inferred from mudstoneprone lithologies, peak gamma-ray values in well logs, and foraminiferal evidence. Long-term transgression and maximum water depths at the Cenomanian/Turonian boundary correlative with Ocean Anoxic Event 2 were followed by a regression and relative sea-level fall. We show that porous and permeable sandstones of three Aptian to Cenomanian highstand systems tracts are high-volume reservoirs for supercritical CO 2 storage that are confined by overlying deep water mudstones.

58 GEOSCIENCES↗
DOE OSTI2021

Utah FORGE: Well 56-32 Drilling Data and Logs

This dataset consists of drilling data (Pason data spreadsheets, daily reports, days v. depth, mud logs), Schlumberger logs (FMI, shear anisotropy analysis, memory, sonic, array induction/spectral density/dual spaced neutron/gamma ray/caliper, spectral GR/temperature, and Gardner density correlation), and an end of well report (EOWR) for Utah FORGE well 56-32. This is a vertical well that will be used for seismic monitoring. It was drilled between February 7th and February 21st 2021 to a depth of 9,145 feet. More information about this well can be found at: https://utahforge.com/2021/02/09/drilling-progress-of-well-56-32/ (linked below)

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Leveraging Natural Language Processing and Generative Models in Molecular Chemistry: Property Prediction and Novel Compound Generation

The accurate prediction of molecular properties is important for the rational design and the advancement of green chemistry and sustainable materials research. However, the predictive power of traditional computational chemistry methods is limited due to computational restrictions. Here, in this study, we examine an alternative approach to the accurate prediction of properties of organic compounds: natural language processing (NLP)-based molecular embedding. Using viscosity, partition coefficient (log P), and enthalpy of vaporization as test properties through a survey of comprehensive datasets comprising 5695 data points for viscosity, 25 870 data points for log P, and 2296 data points for enthalpy of vaporization. These are important properties for the design of greener, safer, and sustainable chemical processes. Models were trained using NLP methods such as Mol2vec and fine-tuned ChemBERTa, and results were compared with traditional input featurization techniques such as Morgan fingerprints and quantum chemistry derived sigma profiles and DFT features. Among the various machine learning models, Mol2vec demonstrated superior predictive capabilities, achieving the highest correlation coefficient (R 2 = 0.945) and lowest RMSE (0.106 mPa s) for viscosity, as well as high accuracy for log P and enthalpy of vaporization predictions. These findings establish the Mol2vec featurization technique, graph-convolutional neural networks (GCNN), and fine-tuned ChemBERTa model as powerful tools for predictive modeling of organic compounds properties, offering a significant improvement over previously used featurization techniques and opening up strategies for very-high-throughput computational screening. Finally, we integrated ML models with hybrid language-model-based generative adversarial networks (LM-GAN) to generate novel molecular sequences with desirable properties for different research applications. The ability to computationally design solvents with lower viscosity, lower log P, and lower enthalpy of vaporization offers a data-driven route to accelerating the discovery of sustainable alternatives to traditionally toxic solvents.

ChemBERTa↗
DOE OSTI2022

Tuscaloosa Marine Shale Laboratory

The Tuscaloosa Marine Shale (TMS) in Louisiana and Mississippi is an Upper Cretaceous source rock formation sandwiched between the sands of the upper and lower Tuscaloosa sections. The TMS is believed to be the source rock for underlying prolific Tuscaloosa sand formation. The TMS has an unproven estimate of 7,000,000,000 bbls of recoverable oil while its current total average production is about 3,000 bbls of oil per day in 2017. In 2013 and 2014, more than 80 wells were drilled horizontally into the TMS that were fractured using multi-stage fracturing technology. The results from this have been mixed, but recent production for several wells show an appealing initial oil production rate of more than 1000 bbl/day. The preliminary core analysis by industry partners and a few literature studies shows that the TMS is one of the most clay-rich and sensitive shales to water. Due to these and other technical problems, there is high risk for the economic development of TMS compared to other shale plays. The experiences of major industrial players in the TMS show the necessity of open and collaborative efforts to better understand the critical gaps in the development of this challenging and potentially highly economic shale play to enable more cost-efficient and environmentally-sound recovery from this unconventional liquid-rich shale play. The overall objective of this project is to form a consortium of science and industry partners to address the following six major objectives using scientific and technical approaches: 1. To improve wellbore integrity by better understanding the sources of the wellbore instability issues, proposing innovative mud and cement design for the TMS. 2. To improve formation evaluation using laboratory techniques for the evaluation of mineralogical composition, organic content, and produced-water chemistry as well as well log and geophysical analysis. 3. To determine the role of geologic discontinuities on fracture growth and shale creep behavior using digital image correlation technique. 4. To investigate the application of stable CO 2 foam and super-hydrophobic proppants for improved reservoir stimulation. 5. To better understand the nature of water/hydrocarbon/CO 2 flow in clay and organic-rich formation and the role of water/fluid interaction on recovery. 6. To prepare better socio-economic environment for TMS development by community engagement. Subsequently, the TMS virtual laboratory conducted testing and analysis of various properties of rock and formation fluids from the TMS, including but not limited to the following: Analyzing reports and logs to better understand the source of wellbore instability in TMS wells; Experiments to design a customized cement based on TMS requirements; Experiments to obtain the mineralogical and geochemical composition of TMS samples; Seismic analysis of TMS geophysical data to better predict total organic carbon (TOC) content and brittleness in TMS; Well log analysis to better estimate the TOC and geo-mechanical properties of TMS; Experiments on formation water to understand the chemistry of produced water; Experiments to determine the role of lamination and natural fractures on fracture propagation or rock deformation using digital image correlation technique in in-direct tensile tests, semi-circular bend test and creep tests Experiments to determine the stability and rheological properties of nanoparticle-stabilized CO 2 foam in TMS rock samples; Experiments to determine fluid dynamics in un-propped TMS fractures and the role of nano-coating of proppants on fluid dynamics in fractures with proppants; Micro-fluidics experiments to enhance the understanding of fluid dynamics in tight liquidrich pores with high clay content; Socio-economic studies to better engage communities in TMS development.

58 GEOSCIENCES↗
DOE OSTI2020

Visualization of Multifractal Superconductivity in a Two-Dimensional Transition Metal Dichalcogenide in the Weak-Disorder Regime

Eigenstate multifractality is a distinctive feature of noninteracting disordered metals close to a metal–insulator transition, whose properties are expected to extend to superconductivity. While multifractality in three dimensions (3D) only develops near the critical point for specific strong-disorder strengths, multifractality in 2D systems is expected to be observable even for weak disorder. Here we provide evidence for multifractal features in the superconducting state of an intrinsic, weakly disordered single-layer NbSe 2 by means of low-temperature scanning tunneling microscopy/spectroscopy. The superconducting gap, characterized by its width, depth, and coherence peaks’ amplitude, shows a characteristic spatial modulation coincident with the periodicity of the quasiparticle interference pattern. The strong spatial inhomogeneity of the superconducting gap width, proportional to the local order parameter in the weak-disorder regime, follows a log-normal statistical distribution as well as a power-law decay of the two-point correlation function, in agreement with our theoretical model. Furthermore, the experimental singularity spectrum f(α) shows anomalous scaling behavior typical from 2D weakly disordered systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗