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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.

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Summary of a Gas Transport Tracer Test in the Deep Cerros Del Rio Basalts, Mesita del Buey, Los Alamos NM.

Here we describe results from a tracer test in the Cerros del Rio basalt beneath Mesita del Buey, Technical Area 54 (TA-54) at Los Alamos National Laboratory (LANL or the Laboratory). This report follows from plans outlined in our previous Tracer Test Work Plan (LANL 2016). These activities were conducted by LANL to further characterize subsurface properties of the Cerros del Rio basalts at Material Disposal Area (MDA) L (Figure 1.1-1). The work presented follows from the “Interim Measures Work Plan for Soil-Vapor Extraction of Volatile Organic Compounds from Material Disposal Area L, Technical Area 54, Revision 1,” submitted to the New Mexico Environment Department (NMED) in September 2014 (LANL 2014). Remediation of the MDA L vapor plume by soil-vapor extraction (SVE) is recommended as part of the final remedy in the “Corrective Measures Evaluation Report for Material Disposal Area L, Solid Waste Management Unit 54-006, at Technical Area 54, Revision 2” to meet a remedial action objective of preventing groundwater from being impacted above a regulatory standard by the transport of volatile organic compounds (VOCs) to groundwater through soil vapor (LANL 2011).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Carbon Storage Assurance Facility Enterprise (CarbonSAFE): Integrated CCS Pre-Feasibility CarbonSAFE Illinois East Sub-Basin (Final Report)

Purpose of the project was to conduct a pre-feasibility assessment for commercial-scale CO 2 geological storage complexes in the East Sub-Basin of the Illinois Basin. This work will be used to advance the experience and knowledge about scaling up from demonstration to commercial-scale storage (CCS), and eventually readying sites for more than 50 million tonnes injection from one or more industrial sources. To accomplish this, the project formed a CCS coordination team with varied backgrounds to evaluate aspects of developing an integrated CCS project, including regulatory, legislative, technical, public policy, commercial and financial requirements. We developed a plan and strategy to address the technical and non-technical challenges to enable an economically feasible and publicly acceptable integrated CCS project. A significant output of this high-level technical evaluation of the East Sub-Basin was to identify suitable site(s) within the storage complex(es) that include detailed subsurface characterization and risk identification, along with evaluation of potential industrial CO 2 sources for sequestration.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Geo Thermal Cloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The project is motivated by the challenges, risks, and costs associated with geothermal exploration and production. Many processes and parameters impacting geothermal conditions are poorly understood. Diverse datasets are available to help characterize subsurface geothermal conditions (public and proprietary; satellite, airborne surveys, vegetation/water sampling, geological, geophysical, etc.). Yet, it is not clear how to properly leverage these datasets for geothermal exploration due to an incomplete understanding of how physical processes impacting subsurface geothermal conditions are represented in these observations. Recent advancements in machine learning (ML) provide great promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. Our goals and work under Phases 1 and 2 (as proposed) of this project address all these needs.

15 GEOTHERMAL ENERGY↗
DOE OSTI2014

Final Report on Reservoir Modeling (SECARB Phase III Work Product 4.2.4.c)

The SECARB Partnership Anthropogenic Test, located in the Southeast Unit of the Citronelle oilfield will inject CO 2 into the saline waters of the Cretaceous Paluxy sandstone formation for three years. In order to accurately model the injection, migration, and long-term fate of CO 2 in the subsurface, GEM, a Computer Modeling Group software program for transport and geochemical modeling, was selected for the simulation. GEM is a very robust compositional reservoir simulator, with all the necessary options available for modeling CO 2 trapping mechanisms (including solubility, residual gas and mineralization). This simulation tool is widely known and used in the petroleum and sequestration industries. A reservoir model was built, based on a previously developed comprehensive picture of the subsurface geology at the site (Work Product 4.2.1.d - Report on Subsurface Characterization for the Anthropogenic Test Project Site). This report describes in details the different inputs that were used to create the model as well as the results of the injection scenario in terms of achievable injection rates, plume extent and pressure response.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

HFTS-2 (Final Report)

This report delves into the research program, identified tasks, various studies, observations, results, and conclusions under the Hydraulic Fracturing Test Site (HFTS-2) project. This test site is located in Loving County close to the Texas, New Mexico border. The program targeted the Wolfcamp formation in Delaware Basin. The aim of the project was to improve our understanding of hydraulic fracturing processes to not only optimize unconventional resource development in US Basins but to minimize environmental footprint as well. Various new diagnostic technologies were demonstrated at this site, including distributed acoustic microseismic, high resolution strain using Rayleigh frequency band (DSS-RFS), and in-fill proppant logging tool. Significant conclusions from this study include: 1) lateral and vertical fracture growth mapping as well as proppant transport behavior, 2) Use of DSS-RFS as a diagnostic tool to understand cluster level behavior, 3) Validation of proppant log, both at core scale as well as in-fill well implementation, and 4) New modeling tools to map far-field strain response using distributed strain sensing data. Specifically, far-field fracture distribution is influenced by linear fracture corridors defined by the stress-state as well as clustering behavior influenced by spatio-temporal effects. Depletion impacts on new stimulation was demonstrated and technically quantified. Significant vertical fracture height growth was observed. However, our understanding is limited by available gauge data availability. Numerical strain modeling techniques have been developed and demonstrated to help understand observed strain response behavior. Finally, proppant log has been further validated as a useful tool to map propped zones within the SRV. It has also shown very strong correlation with observed drawdown behavior at this test site. This report will detail many of these studies as well as technical results and conclusions from said studies. These include general subsurface characterization work, completion designs as implemented, studies looking at fracture geometry, high resolution microseismic study including source mechanisms, advanced DSS-RFS studies including drainage characterization, core characterization results, proppant analysis (core and in-fill child well), geochemistry, etc.

02 PETROLEUM↗
DOE OSTI2023

HFTS-1 (Final Report)

Despite the long history of hydraulic fracturing, the optimal number of fracturing stages during multi-stage fracture stimulation in horizontal wells can be a complex subject. In addition to the increased expense of multistage fracturing in horizontal wells, increasing the number of fracturing stages does not always correlate with a commensurate increase in production. The problem is the application of a uniform fracture stimulation design to all stages with no consideration for geological variations along the wellbore. The result is an inefficient use and costly waste of energy and water. Optimization of the fracturing process requires an understanding of the cause-and-effect relationship between fracturing parameters and local geological properties at a given location along the wellbore. Realizing that the generalized rock mechanics theories and hypotheses are not truly applicable to fractured and laminated shales, quantifiable impacts of a shale’s geomechanical and depositional features are a prerequisite for design and implementation of optimized hydraulic fractures. The overarching goal of this project is to understand and define the relationships of shale geology and fracture dynamics using detailed field data that includes coring of the fracture domain. Finally, resource recovery from shale formations is estimated to be less than fifteen percent. Research proposed in this project will establish the foundations for investigating enhanced recovery techniques for increased resource recovery in existing fracture treated wells. Natural gas as an EOR fluid is in the initial stages of being used broadly in the oil and gas industry and as such, many aspects of the process need to be researched and addressed prior to widespread acceptance. In addition, re-fracturing techniques, particularly using cemented liners also need to be further investigated. This project explores several of these EOR techniques in greater detail, not just at the theoretical level, but through actual field test sites. This report delves into the research program, identified tasks, several studies, observations, results, and conclusions under the Hydraulic Fracturing Test Site (HFTS) project. This test project is subdivided into three distinct phases and each phase is discussed in detail under separate sections. The original test site for Phase 1 as well as the Phase 2 site are in Midland Basin in Texas and operated by Laredo Petroleum. In the first two phases, the program targeted the Wolfcamp formation in Midland Basin. Phase 3 of the study targeted the EagleFord play in South Texas with Devon Energy as the operator. Significant conclusions from this study include: 1) lateral and vertical fracture growth mapping as well as proppant transport behavior, 2) Use of field gas and its applicability for huff n puff EOR injection in the Midland Basin, 3) Development and validation of proppant log, both at core scale as well as in-fill well implementation by analyzing drilling mud return samples, 4) Applicability of liner refracturing including best practices for enhanced unit level productivity. This report will detail all these studies as well as technical results and conclusions from said studies. These include general subsurface characterization work, completion designs as implemented, studies looking at fracture geometry, high resolution microseismic study, advanced crosswell strain mapping studies including drainage characterization, core characterization results, proppant analysis (core and observation wells), geochemistry, etc. to name a few.

02 PETROLEUM↗
DOE OSTI2022

Capillary Pressure to Relative Permeability: Task 7.3.4 Relative Permeability Analysis Final Report

Surface tension affects all aspects of fluid flow in porous media. Through measurements of surface tension interaction under multiphase conditions, a relative permeability relationship can be determined. Relative permeability is a numerical description of the interplay between two or more fluids and the porous media they flow through. It is a critical parameter for various tools used to characterized subsurface multiphase flow systems, such as numerical simulation for oil and gas development, carbon sequestration, and groundwater contamination remediation. Therefore, it is critical to get a good statistic distribution of relative permeability in the porous media under study. Empirical relationships for determining relative permeability from capillary pressure are already well established but do not provide the needed flexibility required to match laboratory derive relative permeability relationships. By expanding the existing methods for calculating relative permeability from capillary pressure data, it is possible to create both two and three-phase relative permeability relationships. Existing laboratory-measured relative permeability data along with mercury intrusion capillary (MICP) data coupled with interfacial tension and contact angle measurements were used to determine the efficacy of this approach to relative permeability curve creation. The relative permeability relationships determined with this method were fit to existing laboratory data to elucidate common fitting parameters that were then used to create relative permeability relationships from MICP data that does not have an associated laboratory-measured relative permeability relationship.

58 GEOSCIENCES↗
DOE OSTI2024

New Matrix Framework to Determine Carbon Storage Technical Viability

Carbon storage is an integral component of reducing CO2 emissions. Research over the past two decades has developed workflows for volume assessments and economic project feasibility, providing necessary and useful tools to progress geologic carbon storage (GCS) projects. These workflows and assessments have focused primarily on determining the in-situ storage resource based on geologic and engineering parameters and do not integrate subsurface characterization with surface conditions, social factors, and environmental factors that may pose a benefit or impediment to the implementation of GCS. Furthermore, the data required to assess the technical viability of GCS are myriad and disparate. There is no current methodology that identifies technical viability criteria and systematically informs how to aggregate these factors for spatial assessments. To address this gap, the National Energy Technology Laboratory has developed a Carbon Storage Technical Viability Approach (CS TVA) Matrix that incorporates a wide variety of factors to inform and accelerate screening for GCS site selection in the United States.

Mulhern, Julia↗
DOE OSTI2024

Deep Learning for Full Waveform Inversion of Elastic Active-Source Seismic Data to Estimate P-Wave Velocity Models

Seismic imaging methods are critical for Global Security and Energy & Homeland Security missions and activities that rely on subsurface characterization, but traditional methods remain computationally expensive and require significant labor hours and expertise to execute. Within the past few years, machine learning (ML), namely deep learning (DL), has been used to develop data-driven end-to-end full waveform inversion (FWI) methods to estimate 2D P-wave velocity (Vp) models in a fraction of the time as conventional FWI. These methods, however, are trained on simplistic acoustic wave seismic data and Vp models that are not realistic nor representative of real-world observations, leaving a large gap between the state-of-the-art and deployable, feasible, and practical DL FWI methods. Here, we generate a synthetic active-source, 3D, elastic wave seismic data set and a variety of Vp models with realistic geologic structure for training DL FWI methods. We evaluate six different methods that have performed well for acoustic DL FWI or medical imaging tasks using our more realistic dataset. We find that these six trained models do not match the performance of published acoustic end-to-end DL FWI methods, indicating more training data may be needed, physics may need to be incorporated to achieve good accuracy at the sacrifice of the end-to-end advantage, and/or novel methods need to be developed to enable end-to-end DL FWI methods to perform well for real-world seismic data.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Application of Modified Meshgraphnets for Subsurface Prediction during CO2 Sequestration

In the face of the increasingly dire consequences of anthropogenic climate change, capturing and storing carbon dioxide is paramount. However, several impediments exist to the safe and effective subsurface storage of CO2, such as cost of transport, identification of suitable sites for subsurface storage, and assessment of long-term risk from storage in subsurface aquifers. Accurate subsurface modeling is necessary to ensure that CO2 storage is both safe and effective. Still, such modeling has traditionally required either substantial time and computational power (numerical simulation) or a substantial amount of pre-existing data for training (machine learning models). Additionally, these models lack flexibility in dealing with both changes in discretization of the input data and generalizability beyond the data on which they are trained. In order to address these issues, this research applies graph neural networks (GNNs) to predict subsurface saturation and pressure during CO₂ injection in a model of the Illinois Basin-Decatur Project (IBDP). GNNs provide a flexible, intuitive method for representing and manipulating complex unstructured data, which is often found in many practical domain problems such as fluid flow and subsurface characterization. These unstructured grids are easily represented in GNNs by representing spatially-localized features such as permeability, porosity, saturation, and pressure as nodes in a graph and relationships between these properties as edges connecting these nodes. This research applies a specific GNN model called MeshGraphNets (MGN) to model the change in CO2 saturation and pressure over a 50-month time period (36 months of injection, 14 months post-injection). The MGN model leverages a message passing process that allows the network to learn both the spatial and temporal dynamics of this system simultaneously. Additionally, training on a limited dataset (64 realizations, 20 time points each) resulted in a high degree of accuracy in saturation prediction both within the same timeframe as the training (20 months, 0.039 average RMSE) and when projecting out to the end of injection (36 months, 0.053 average RMSE). Temporal predictions such as those generated by MGNs and other similar models are prone to accumulated error over time; in order to address this, a multi-step rollout (MSR) training process was applied to calculate training loss. This method mimics the forward prediction during inference by “rolling out” multiple time points in a single training step using the previous prediction as input to the MGN model. By calculating the loss several time steps forward from the current prediction, the model is forced to find a more stable state over time. Application of MSR to the MGN model resulted in an average 15% reduction in inference error over time during forward prediction. This study showcases the immense potential of GNNs as a game-changing methodology for predicting pressure and saturation evolution in CCS projects, ultimately paving the way for more sustainable and effective carbon storage solutions. Presentation prepared for the 2024 AiChE Annual Meeting, October 27 to November 1 2024, San Diego, CA.

Holcomb, Paul↗
DOE OSTI2024

A Novel 'Smart Microchip Proppants' Technology for Precision Diagnostics of Hydraulic Fracture Networks (Edited Final Report)

This project introduces innovative technology to improve subsurface characterization, visualization, and diagnostics of unconventional reservoirs (fossil resources). Through a collaborative effort involving the University of Kansas, UCLA, MicroSilicon Inc., and EOG Resources, the project aims to deliver precision diagnostics for hydraulic fractures using novel high-resolution imaging technology based on smart microchip proppants. Additionally, it seeks to enhance the accuracy and predictability of integrated numerical, and machine-learning modeling techniques for hydraulic fracture characterization and simulation. This groundbreaking technology addresses significant gaps in understanding unconventional and tight reservoir behavior and optimizing well-completion strategies, enabling more cost-efficient recovery of unconventional resources.

02 PETROLEUM↗
DOE OSTI2025

Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties

This project developed machine learning (ML) methods, lab data sets, and field data to advance geothermal exploration and geothermal energy production. The work had three focus areas. One involved the development of ML methods to use microearthquakes (MEQs) for imaging geothermal reservoir properties and improving subsurface characterization – most importantly the evolution of permeability within the evolving reservoir. This part of the work included development of ML approaches for automated MEQ location, focal mechanism determination and identification of earthquake precursors. The second area focused on using MEQ signals generated by geothermal exploration and production to predict the relationship between fluid injection and seismicity. Here, we extended to reservoir scale our success in using ML to predict laboratory earthquakes and fault zone stress state. The third focus area was on lab experiments. Here, we developed new ML models for lab earthquake prediction and identification of precursors to failure to improve earthquake forecasting and early warning in geothermal settings. Major outcomes of our work include ML models that learn from MEQ signals during geothermal exploration and production to predict induced seismicity. MEQs occur naturally in connection with drilling and energy production. We developed ML methods to use the seismic waves from these events to characterize the elastic, hydraulic and poromechanical properties of reservoirs. Our work illuminated fracture geometry and the evolution of fracture permeability by incorporating seismic coda wave analysis and ML methods to relate fluid injection and seismicity. We significantly expanded laboratory earthquake prediction to include methods that use both passive measurements of microearthquakes within the lab fault zones and also active source acoustic measurements of fault zone elastic properties. These methods can now predict fault zone stress state, time to failure and the magnitude of lab earthquakes. Our work showed that repetitive stick- slip failure events during frictional sliding (the lab equivalent of earthquakes) are preceded by a cascade of micro-failure events that radiate energy in a manner that foretells unstable failure – manifest as laboratory MEQs. We documented a mapping between fracture properties and statistical attributes of elastic radiation. We extended existing works to geothermal reservoir scale and developed ML methods to determine reservoir permeability, fracture properties, and their evolution during geothermal energy production. An attractive feature of ML algorithms is their ability to handle big datasets and reveal patterns and correlations that may remain invisible to conventional analyses. Our work connected data from field, laboratory and intermediate scales to study permeability, stress, strength, fracture stiffness and geometry. At the field scale we used data from the Newberry Volcano field site, UtahFORGE, EGS Collab, and also the Bedretto underground research lab in Switzerland. These data sets are bridging the gap between the lab scale, theory, and reservoir scale. Our work produced plain language summaries to improve public understanding of DOE research. We also developed openly distributed ML and seismicity datasets for use by all researchers and we published connections between induced seismicity in geothermal areas and reservoir properties including permeability, fracture properties, and stress state. Our models are designed for the large data sets of induced seismicity typically associated with geothermal sites. We produced labeled event catalogs and used them on geothermal data to assess how ML can facilitate geothermal production and exploration. All datasets are available on the GDR Productivity: The project produced 32 publications in peer reviewed journals (two are in review). It supported the work of 6 PhD students, 40 conference presentations, 6 keynote talks at national meetings, and mentoring and professional development for 4 postdoctoral fellows.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Modeling Subsurface Performance of a Geothermal Reservoir Using Machine Learning

Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells—increasing or decreasing the fluid flow rates across the wells—and drilling new wells at appropriate locations. The latter is expensive, time-consuming, and subject to many engineering constraints, but the former is a viable mechanism for periodic adjustment of the available fluid allocations. In this study, we describe a new approach combining reservoir modeling and machine learning to produce models that enable such a strategy. Our computational approach allows us, first, to translate sets of potential flow rates for the active wells into reservoir-wide estimates of produced energy, and second, to find optimal flow allocations among the studied sets. In our computational experiments, we utilize collections of simulations for a specific reservoir (which capture subsurface characterization and realize history matching) along with machine learning models that predict temperature and pressure timeseries for production wells. We evaluate this approach using an “open-source” reservoir we have constructed that captures many of the characteristics of Brady Hot Springs, a commercially operational geothermal field in Nevada, USA. Selected results from a reservoir model of Brady Hot Springs itself are presented to show successful application to an existing system. In both cases, energy predictions prove to be highly accurate: all observed prediction errors do not exceed 3.68% for temperatures and 4.75% for pressures. In a cumulative energy estimation, we observe prediction errors that are less than 4.04%. A typical reservoir simulation for Brady Hot Springs completes in approximately 4 h, whereas our machine learning models yield accurate 20-year predictions for temperatures, pressures, and produced energy in 0.9 s. This paper aims to demonstrate how the models and techniques from our study can be applied to achieve rapid exploration of controlled parameters and optimization of other geothermal reservoirs.

15 GEOTHERMAL ENERGY↗
NASA NTRS2017

Radar Ambiguities and Signal Processing Design Tradeoffs in the REASON Radar Sounder

NASA’s planned Europa Clipper mission would carry a radar sounder which operates at HF and VHF frequencies that can penetrate the icy shell of Europa. The sounder would be used to characterize subsurface bodies of liquid water and to measure the thickness of the icy shell. The spacecraft would make observations during flyby to reduce radiation exposure. The varying geometry during the flyby is expected to cause varying radar ambiguity and surface clutter contamination. At the same time, data down-link restrictions would require on-board pulse summation to reduce data volume which would require on-board Doppler filtering to control clutter and ambiguity contamination levels. This paper will study some of the design trade-offs involved in specifying the on-board Doppler filtering.

Hawkins, D.↗
DOE OSTI2023

Preliminary report on applications of machine learning techniques to the Nevada play fairway analysis

We are applying machine learning (ML) techniques, including training set augmentation and artificial neural networks, to mitigate key challenges in the Nevada play fairway project. The study area includes ~85 active geothermal systems as potential training sites and >12 geologic, geophysical, and geochemical features. The main goal is to develop an algorithmic approach to identify new geothermal systems in the Great Basin region. Major objectives include: 1) integrate ML techniques into the geothermal community; 2) develop open community datasets, whereby all play fairway and ML datasets and algorithms are publicly released and available for modification by various user groups; 3) identify data acquisition targets with high value for future work; 4) identify new signatures to detect blind geothermal systems; and 5) foster new capabilities for characterizing subsurface temperature and permeability. Initially, ML techniques are being applied to the same play fairway datasets and workflow. ML will then be applied to both enhanced and additional datasets, with modification of the PFA workflow to incorporate the new datasets. Finally, ML will be applied to define new workflows using the enhanced and additional datasets. An algorithmic approach that empirically learns to estimate weights of influence for diverse parameters can potentially scale and perform better than the play fairway analysis. Initial work on this project has involved 1) evaluating potential positive and negative training sites, 2) transformation of datasets into formats suitable for ML, and 3) initial development and testing of ML techniques.

58 GEOSCIENCES↗
DOE OSTI2013

Subsurface Synthesis and Characterization of Ag Nanoparticles Embedded in MgO

Metal nanoparticles exhibit localized surface plasmon resonance (LSPR) which is very sensitive to the size and shape of the nanoparticle and the dielectric medium surrounding it. LSPR causes field enhancement near the surface of the nanoparticle making them interesting candidates for plasmonic applications. In particular, partially exposed metallic nanoparticles distributed in a dielectric matrix form hotspots which are prime locations for LSPR spectroscopy and sensing. This study involves synthesizing partially buried Ag nanoparticles in MgO and investigating the characteristics of this material system. Ag nanoparticles of different shapes and size distributions were synthesized below the surface of MgO by implanting 200 keV Ag+ ions followed by annealing at 10000C for 10 and 30 hours. A detailed optical and structural characterization was carried out to understand the evolution of Ag nanoparticle microstructure and size distribution inside the MgO matrix. Micro x-ray diffraction (MicroXRD) was employed to investigate the structural properties and estimate the crystallite size. The nanoparticles evolved from a spherical to faceted morphology with annealing time, assuming an octahedral shape truncated at the (001) planes as seen from aberration corrected transmission electron microscopy (TEM) images. The nanoparticles embedded in MgO were shown to be pure metallic Ag using atom probe tomography (APT). The nanoparticles were partially exposed to the surface employing plasma etch techniques to remove the overlaying MgO. Scanning electron microscopy (SEM) and atomic force microscopy (AFM) were employed to study the surface morphology and obtain a height distribution for the partially exposed nanoparticles.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2013

Development of Enabling Scientific Tools to Characterize the Geologic Subsurface at Hanford

This final report to the Department of Energy provides a summary of activities conducted under our exploratory grant, funded through U.S. DOE Subsurface Biogeochemical Research Program in the category of enabling scientific tools, which covers the period from July 15, 2010 to July 14, 2013. The main goal of this exploratory project is to determine the parameters necessary to translate existing borehole log data into reservoir properties following scientifically sound petrophysical relationships. For this study, we focused on samples and Ge-based spectral gamma logging system (SGLS) data collected from wells located in the Hanford 300 Area. The main activities consisted of 1) the analysis of available core samples for a variety of mineralogical, chemical and physical; 2) evaluation of selected spectral gamma logs, environmental corrections, and calibration; 3) development of algorithms and a proposed workflow that permits translation of log responses into useful reservoir properties such as lithology, matrix density, porosity, and permeability. These techniques have been successfully employed in the petroleum industry; however, the approach is relatively new when applied to subsurface remediation. This exploratory project has been successful in meeting its stated objectives. We have demonstrated that our approach can lead to an improved interpretation of existing well log data. The algorithms we developed can utilize available log data, in particular gamma, and spectral gamma logs, and continued optimization will improve their application to ERSP goals of understanding subsurface properties.

54 ENVIRONMENTAL SCIENCES↗