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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Characterization of elastic mechanical properties of Tuscaloosa Marine Shale from well logs using the vertical transversely isotropic model

To avoid steep declines in the Tuscaloosa Marine Shale (TMS) production, wells are fracture-stimulated to release the hydrocarbons trapped in the matrix of the formation. An accurate estimation of Young’s modulus and Poisson’s ratio is essential for hydraulic fracture propagation. In addition, ignoring the highly heterogeneous and anisotropic character of TMS can lead to erroneous stress values, which subsequently affect hydraulic fracture width estimates and the overall hydraulic fracturing process. We have developed an empirical 1D geomechanical model that takes into account VTI anisotropy, and it is used to characterize the elastic mechanical properties of TMS in two wells. In the analyzed formation, the vertical Poisson’s ratio is less than the horizontal Poisson’s ratio, which suggests the necessity of an alternative to the ANNIE equations. The stiffness coefficients [Formula: see text] and [Formula: see text] were estimated using the relationships developed from the ultrasonic core data available for the two TMS. Further, correlations between the static and dynamic properties from laboratory tests were used to improve the minimum horizontal stress calculation. We compare VTI Young’s moduli, Poisson’s ratios, and minimum horizontal stress with the isotropic solution. VTI modeling improves the estimation of the elastic mechanical properties. The isotropic solution underestimates the minimum horizontal stress in the formation. Moreover, it was shown that the 20 ft shale interval below the TMS base is characterized by a low Young’s modulus (the vertical Young’s modulus is equal to 20 GPa, whereas the horizontal Young’s modulus is equal to 40 GPa) and may be a frac barrier.

Geochemistry & Geophysics↗
DOE OSTI2019

Enhancing trap and fault seal analyses by integrating observations from HR3D seismic data with well logs and conventional 3D seismic data, Texas inner shelf

An understanding of trap and fault seal quality is critical for assessing hydrocarbon prospectivity. To achieve this, modern analytical techniques leverage well data and conventional industry-standard 3D seismic data to evaluate the trap, and any faults displacing the reservoir and top seal intervals. Above all, geological interpretation provides the framework of trap and fault seal analyses, but can be hindered by the data resolution, quality and acquisition style of the conventional seismic data. Furthermore, limiting the analysis to only the petroleum system at depth may lead to erroneous perceptions because interpreting overburden features, such as shallow faults or gas chimneys, can provide valuable observations with respect to container performance, and can to help validate trap and fault seal predictions. Here, a supplement to conventional 3D data are high-resolution 3D seismic (HR3D) data, which provide detailed images of the overburden geology. This study utilizes an HR3D seismic volume in the San Luis Pass area of the Texas inner shelf, where shallow fault tips and a sizeable gas chimney are interpreted over an unsuccessful hydrocarbon prospect. Static post-drill fault seal and trap analyses suggest that the primary fault displacing the structural closure could have withheld columns of gas c. 100 m high, but disagree with our HR3D seismic interpretations and dry-well analyses. From our results, we hypothesize that tertiary gas migration through fault conduits reduced the hydrocarbon column in the prospective Early Miocene reservoir, and may have resulted from continued movement along the intersecting faults. Overall, this study reinforces the importance of understanding the overburden geology and geohistory of faulted prospects, and demonstrates the utility of pre-drill HR3D acquisition when conducting trap and fault seal analyses.

3D seismic↗
DOE OSTI2019

EGS Collab Experiment 2: Wireline Geophysical Well Logs

This is the full wireline geophysical datasets for characterization of the EGS Collab Experiment #2 testbed. A metadata fill is included within the dataset explaining the logs, fracture picks, etc. Eleven boreholes were drilled for this testbed and each one was logged with north seeking gyro, optical televiewer, acoustic televiewer, fluid temperature conductivity, resistivity and gamma, and full waveform sonic. In these folders are the processed results as text, csv and pdf files along with the raw data which will need to be read by WellCAD.

15 GEOTHERMAL ENERGY↗
NASA NTRS1996

A Model of the Chicxulub Impact Basin Based on Evaluation of Geophysical Data, Well Logs, and Drill Core Samples

Abundant evidence now shows that the buried Chicxulub structure in northern Yucatan, Mexico, is indeed the intensely sought-after source of the ejecta found world-wide at the Cretaceous-Tertiary (K/T) boundary. In addition to large-scale concentric patterns in gravity and magnetic data over the structure, recent analyses of drill-core samples reveal a lithological assemblage similar to that observed at other terrestrial craters. This assemblage comprises suevite breccias, ejecta deposit breccias (Bunte Breccia equivalents), fine-grained impact melt rocks, and melt-matrix breccias. All these impact-produced lithologies contain diagnostic evidence of shock metamorphism, including planar deformation features in quartz, feldspar, and zircons; diaplectic glasses of quartz and feldspar; and fused mineral melts and whole-rock melts. In addition, elevated concentrations of Ir, Re, and Os, in meteoritic relative proportions, have been detected in some melt-rock samples from the center of the structure. Isotopic analyses, magnetization of melt-rock samples, and local stratigraphic constraints identify this crater as the source of K/T boundary deposits.

Sharpton, Virgil L.↗
DOE OSTI2019

Sedimentary Geothermal Feasibility in Eastern Nevada and Millard County, Utah Well Databases

In order to gather data to assess sedimentary geothermal feasibility in Nevada and Utah, state oil and gas and geothermal databases were serached for well files and well logs. Well data from the Nevada Bureau of Mines and Geology oil and gas and geothermal databases was mined for the following areas in Nevada: Blackburn oil field, Marys River, North Willow Creek, Tomera Ranch, and Diamond Valley, all in the Elko Basin; Bacon Flat in Railroad Valley; and Steptoe Basin. For Pavant Butte in Utah, the State of Utah Division of Oil and Gas Well File and Well Log Searches were mined for well records in Millard County. Only wells close to Pavant Butte were mined for detailed information, but all Millard County wells are included in the database. Well files were searched for temperature, permeability, pressure information, and formation depth and thickness. Temperature values are uncorrected. Locations for some wells were converted from township and range locations to approximate latitude and longitude using Google Earth and Earth Point grids to locate well pads. If no well pad could be located for a well, the center of the smallest section available (usually partsect) was used as the approximate location.

15 GEOTHERMAL ENERGY↗
DOE OSTI2014

The Potosi Reservoir Model 2013

As a part of a larger project co-funded by the United States Department of Energy (US DOE) to evaluate the potential of formations within the Cambro-Ordovician strata above the Mt. Simon as potential targets for carbon sequestration in the Illinois and Michigan Basins, the Illinois Clean Coal Institute (ICCI) requested Schlumberger to evaluate the potential injectivity and carbon dioxide (CO2) plume size of the Cambrian Potosi Formation. The evaluation of this formation was accomplished using wireline data, core data, pressure data, and seismic data from the US DOE-funded Illinois Basin–Decatur Project (IBDP) being conducted by the Midwest Geological Sequestration Consortium in Macon County, Illinois. In 2010, technical performance evaluations on the Cambrian Potosi Formation were performed through reservoir modeling. The data included formation tops from mud logs, well logs from the VW1 and the CCS1 wells, structural and stratigraphic formation from three dimensional (3D) seismic data, and field data from several waste water injection wells for Potosi Formation. Intention was for two million tons per annum (MTPA) of CO2 to be injected for 20 years. In the preceding, the 2010 Potosi heterogeneous model (referred to as the "Potosi Dynamic Model 2010" in this topical report) was re-run using a new injection scenario; 3.2 MTPA for 30 years. The extent of the Potosi Dynamic Model 2010, however, appeared too small for the new injection target. It was not sufficiently large enough to accommodate the evolution of the plume. The new model, Potosi Dynamic Model 2013a, was built by extending the Potosi Dynamic Model 2010 grid to 30 miles x 30 miles (48.3km x48.3km), while preserving all property modeling workflows and layering. This model was retained as the base case of Potosi Dynamic Model 2013a. The Potosi reservoir model was updated to take into account the new data from the verification well VW2 which was drilled in 2012. The new porosity and permeability modeling was performed to take into account the log data from the new well. Revisions of the 2010 modeling assumptions were also done on relative permeability, capillary pressures, formation water salinity, and the maximum allowable well bottomhole pressure. Dynamic simulations were run using the injection target of 3.2 MTPA for 30 years. This new dynamic model was named Potosi Dynamic Model 2013b. Due to the major uncertainties on the vugs permeability, two models were built; the Pessimistic and Optimistic Cases. The Optimistic Case assumes vugs permeability of 9,000 mD, which is analog to the vugs permeability identified in the pressure fall off test of a waste water injector in the Tuscola site, approx. 40 miles (64.4km) away from the IBDP area. The Pessimistic Case assumes that the vugs permeability is equal to the log data, which does not take into account the permeability from secondary porosity. The probability of such case is deemed low and could be treated as the worst case scenario, since the contribution of secondary porosity to the permeability is neglected and the loss circulation events might correspond to a much higher permeability. It is considered important, however, to identify the range of possible reservoir performance since there are no rigorous data available for the vugs permeability. The Optimistic Case gives an average CO2 injection rate of 0.8 MTPA and cumulative injection of 26 MT in 30 years, which corresponds to 27% of the injection target. The injection rate is approx. 3.2 MTPA in the first year as the well is injecting into the surrounding vugs, and declines rapidly to 0.8 MTPA in year 4 once the surrounding vugs are full and the CO2 start to reach the matrix. This implies that according to this preliminary model, a minimum of four (4) wells could be required to achieve the injection target. This result is lower than the injectivity estimated in the Potosi Dynamic Model 2013a (43 MT in 30 years), since the permeability model applied in the Potosi Dynamic Model 2013b is more conservative. This revision was deemed necessary to treat the uncertainty in a more appropriate manner. As the CO2 follows the paths where vugs interconnection exists, a reasonably large and irregular plume extent was created. For the Optimistic Case, the plume extends 17 miles (27.4km) in E-W and 14 miles (22.5km) in N-S directions after 30 years. After injection is completed, the plume continues to migrate laterally, mainly driven by the remaining pressure gradient. After 100 years post injection, the plume extends 20 miles (32.2km) in E-W and 15.5 miles (24.9km) in N-S directions. Should the targeted cumulative injection of 96 MT be achieved; a much larger plume extent could be expected. For the Optimistic Case, the increase of reservoir pressure at the end of injection is approximately 1200 psia (8,274 kPa) around the injector and gradually decreases away from the well. The reservoir pressure increase is less than 30 psia (206.8 kPa) beyond 14 miles (22.5km) away from injector. Should the targeted cumulative injection of 96 MT be achieved; a much larger areal pressure increase could be expected. The initial reservoir pressure is nearly restored after approximately 100 years post injection. The presence of matrix slows down the pressure dissipations. The Pessimistic Case gives an average CO2 injection rate of 0.2 MTPA and cumulative injection of 7 MT in 30 years, which corresponds to 7% of the injection target. This implies that in the worst case scenario, a minimum of sixteen (16) wells could be required to achieve the injection target. The present evaluation is mainly associated with uncertainty on the vugs permeability, distribution, and interconnectivity. The different results indicated by the Optimistic and Pessimistic Cases signify the importance of vugs permeability characterization. Therefore, injection test and pressure interference test among the wells could be considered to evaluate the local vugs permeability, extent, and interconnectivity. Porosity mapping derived from the seismic inversion could also be used in the succeeding task to characterize the lateral porosity distribution within the reservoir. With or without seismic inversion porosity mapping, it is worth exploring whether increased lateral heterogeneity plays a significant role in Potosi injectivity. Investigations on vugular, dolomitic outcrops suggest that there may be significantly greater lateral heterogeneity than what has been modeled here. Facies modeling within the Potosi has yet to be thoroughly addressed. The carbonates during the time of deposition are believed to be regionally extensive. However, it may be worth delineating the reservoir with other regional wells or modern day analogues to understand the extent of the Potosi. More specifically, the model could incorporate lateral changes or trends if deemed necessary to represent facies transition. Data acquisitions to characterize the fracture pressure gradient, the formation water properties, the relative permeability, and the capillary pressure could also be considered in order to allow a more rigorous evaluation of the Potosi storage performance. A simulation using several injectors could also be considered to determine the required number of wells to achieve the injection target while taking into account the pressure interference.

Adushita, Yasmin↗
DOE OSTI2014

Restoration of the Potosi Dynamic Model 2010

In topical Report DOE/FE0002068-1 [2] technical performance evaluations on the Cambrian Potosi Formation were performed through reservoir modeling. The data included formation tops from mud logs, well logs from the VW1 and the CCS1 wells, structural and stratigraphic formation from three dimensional (3D) seismic data, and field data from several waste water injection wells for Potosi Formation. Intention was for two million tons per annum (MTPA) of CO2 to be injected for 20 years. In this Task the 2010 Potosi heterogeneous model (referred to as the "Potosi Dynamic Model 2010" in this report) was re-run using a new injection scenario; 3.2 MTPA for 30 years. The extent of the Potosi Dynamic Model 2010, however, appeared too small for the new injection target. It was not sufficiently large enough to accommodate the evolution of the plume. Also, it might have overestimated the injection capacity by enhancing too much the pressure relief due to the relatively close proximity between the injector and the infinite acting boundaries. The new model, Potosi Dynamic Model 2013a, was built by extending the Potosi Dynamic Model 2010 grid to 30 miles x 30 miles (48 km by 48 km), while preserving all property modeling workflows and layering. This model was retained as the base case. Potosi Dynamic Model 2013.a gives an average CO2 injection rate of 1.4 MTPA and cumulative injection of 43 Mt in 30 years, which corresponds to 45% of the injection target. This implies that according to this preliminary model, a minimum of three (3) wells could be required to achieve the injection target. The injectivity evaluation of the Potosi formation will be revisited in topical Report 15 during which more data will be integrated in the modeling exercise. A vertical flow performance evaluation could be considered for the succeeding task to determine the appropriate tubing size, the required injection tubing head pressure (THP) and to investigate whether the corresponding well injection rate falls within the tubing erosional velocity limit. After 30 years, the plume extends 15 miles (24 km) in E-W and 14 miles (22 km) in N-S directions. After injection is completed, the plume continues to migrate laterally, mainly driven by the remaining pressure gradient. After 100 years post-injection, the plume extends 17 miles (27 km) in E-W and 15 miles (24 km) in N-S directions. The increase of reservoir pressure at the end of injection is approximately 370 psia around the injector and gradually decreases away from the well. The reservoir pressure increase is less than 30 psia beyond 14 miles (22 km) away from injector. The initial reservoir pressure is restored after approximately 20 years post-injection. This result, however, is associated with uncertainties on the boundary conditions, and a sensitivity analysis could be considered for the succeeding tasks. It is important to remember that the respective plume extent and areal pressure increase corresponds to an injection of 43 Mt CO2. Should the targeted cumulative injection of 96 Mt be achieved; a much larger plume extent and areal pressure increase could be expected. Re-evaluating the permeability modeling, vugs and heterogeneity distributions, and relative permeability input could be considered for the succeeding Potosi formation evaluations. A simulation using several injectors could also be considered to determine the required number of wells to achieve the injection target while taking into account the pressure interference.

Adushita, Yasmin↗
DOE OSTI2013

Geographic Information System Tools for Management of US DOE Sites - 13489

The DOE Office of Legacy Management (LM) uses a variety of GIS tools to support long-term surveillance and maintenance (LTS and M) activities at DOE closure sites. These geo-spatial applications provide access to data both for external public viewing and for internal analysis and decision making. LM uses a custom geo-spatial application called geo-spatial Environmental Mapping System (GEMS) that draws validated information from a database of 4.6 million analytical results and 232,000 water level measurements for 58 LTS and M sites. These data were collected from transferred sites over a period of 40 years. The database is used to capture and store historical environmental information such as analytical chemistry data, groundwater depths and elevations, well logs, well construction data, geo-referenced boundaries, site physical features, and sampling locations from LTS and M sites. Stakeholders, regulators, and project personnel can use this Web-based application and data to display information in several forms, such as a tabular report, a graph, and a geo-spatial display, or the data can be labeled or highlighted in a map view. Institutional controls, with their LTS and M requirements and documentation, have recently been incorporated into a prototype GEMS Web page for the Weldon Spring, Missouri, Site. LM uses multiple internal GIS viewers to help ensure the future protection of human health and the environment. For example, at the Rocky Flats, Colorado, Site, LM uses a GIS application to display real property interests on authoritative maps. Another project is used to facilitate discussions at stakeholder meetings for the Rocky Flats site's Original Landfill. The Uranium Leasing Program uses multiple interactive maps that assist in ongoing monitoring and the oversight of lease-holders' activities. (authors)

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Fallon FORGE: Lithology Logs and Well 21-31 Drilling Data

This submission includes lithology logs for all Fallon FORGE area wells; determined from core, cuttings, and thin section. Wells included are 84-31, 21-31, 82-36, FOH-3D, 62-36, 18-5, 88-24, 86-25, FOH-2, 14-36, 17-16, 34-33, 35A-11, 51A-20, 62-15, 72-7, 86-15, Carson_Strat_1_36-32, and several others. Lithology logs last updated 3/13/2018 with confirmation well 21-31 data, and revisited existing wells. Also included is well logging data for Fallon FORGE 21-31. Well logging data includes daily reports, well logs (drill rate, lithology, fractures, mud losses, minerals, temperature, gases, and descriptions), mud reports, drilling parameter plots, daily mud loss summaries, survey reports, progress reports, plan view maps (easting, northing), and wireline logs (caliper [with GR], triple combo [GR, caliper, SP, resistivity, array induction, density, photoelectric factor, and neutron porosity], array induction with linear correlation [GR, SP, Array Induction, caliper, conductivity], and monopole compression dipole shear [GR, SP, Caliper, sonic porosity, delta-T compressional, and delta-T shear])

15 GEOTHERMAL ENERGY↗
DOE OSTI2019

Application of a convolutional neural network in permeability prediction: A case study in the Jacksonburg-Stringtown oil field, West Virginia, USA

Permeability is a critical parameter for understanding subsurface fluid flow behavior, managing reservoirs, enhancing hydrocarbon recovery, and sequestering carbon dioxide. In general, permeability is measured in the laboratory based on subsurface core samples, calculated from well logs or estimated from well tests. However, laboratory measurements and well tests are expensive, time-consuming, and usually limited to a few core samples or wells in a hydrocarbon field or carbon storage site. Machine-learning techniques are good options for generating a rapid, robust, and cost-effective permeability prediction model because of their strengths to recognize the potential interrelationships between input and output variables. Convolutional neural networks (CNN), as a good pattern recognition algorithm, are widely used in image processing, natural language processing, and speech recognition, but are rarely used with regression problems and even less often in reservoir characterization. We have developed a CNN regression model to estimate the permeability in the Jacksonburg-Stringtown oil field, West Virginia, which is a potential carbon storage site and enhanced oil recovery operations field. We also evaluate the concept of the geologic feature image, which is converted from geophysical well logs. Five variables, including two commonly available conventional well logs (the gamma rays [GRs] and bulk density) and three well-log-derived variables (the slopes of the GR and bulk density curves, and shale content), are used to generate a geologic feature image. The CNN treats the geologic feature image as the input and the permeability as the desired output. In addition, the permeability predicted using traditional backpropagation artificial neural networks, which are optimized by genetic algorithms and particle swarm optimization, is compared with the permeability estimated using our CNN. Our results indicate that the CNN regression model provides more accurate permeability predictions than the traditional neural network.

Geochemistry & Geophysics↗