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10 results for “Petrophysics”
Videos of the processed microscope images and time series of the petrophysical parameters from image processing and geochemical simulation and of the measured induced polarisation [Video][Dataset]
<p>Supporting Information for the manuscript <em>Microfluidics and spectral induced polarization for direct observation and petrophysical modeling of calcite dissolution</em> published in Geophysical Research Letters</p> <ul> <li><strong>Data Set S1.</strong> Porosity, water saturation, and calcite sample perimeter from image<br>processing.</li> <li><strong>Data Set S2.</strong> Porosity, water conductivity, and pH from geochemical simulation.</li> <li><strong>Data Set S3.</strong> Real and imaginary components of the complex electrical conductivity at<br>2.5 Hz and CEC from petrophysical modeling.</li> <li><strong>Movie S1.</strong> Dissolution of the calcite sample with the detected contour superimposed in<br>white on the grayscale images. Time, length scale, and flow direction are indicated. In<br>case of problems launching the file, we recommend using VLC Media Player software.</li> <li><strong>Movie S2.</strong> Segmented images of the CO2 bubbles produced by the calcite dissolution.<br>Time, length scale, and flow direction are indicated. In case of problems launching the<br>file, we recommend using VLC Media Player software.</li> </ul>
EuroPeg_PetroDB: A petrophysical database of European pegmatite ores and wall rocks.
<p>A petrophysical database of European pegmatite ores and wall rocks. Samples of the first three versions cover LCT- and NYF-type pegmatites, their wall rocks, and country rocks from pegmatite locations in Austria, Ireland, Norway, Portugal, and Spain. The database contains petrophysical properties derived from rock samples and from geophysical borehole logging. The petrophysical sample analysis was carried out at the laboratory of the Geological Survey of Norway (NGU). Borehole logging was carried out by terratec Geophysical Services. A detailed description of the database, its content, the measuring instruments, and uncertainties is found in the <a href="https://aps.ngu.no/pls/oradb/rf.Visdok?c_dokid=0000067784">NGU Report 2022.017</a> and in <a href="https://www.mdpi.com/2075-163X/12/12/1498">Haase & Pohl (2022)</a>. Please consult the README provided below.</p> <p>The database was compiled as part of the GREENPEG project: New Exploration Tools for European Pegmatite Green-Tech Resources. The project was funded by European Commission’s Horizon 2020 innovation programme under grant agreement No 869274 and ended in October 2024. The database is planned to be updated in the future if relevant data is provided.</p> <p>For more information on the project, please visit the project website: <a href="https://www.greenpeg.eu/">https://www.greenpeg.eu/</a></p>
Valgarður: A Database of the Petrophysical, Mineralogical, and Chemical Properties of Icelandic Rocks
<p>PLEASE NOTE: There was an issue with some of the total porosity data in a previous version of the database (the decimal place was shifted). Please use this version of the database. Please contact samuelwarrenscott--at--gmail.com if there are any questions. </p> <p>The <em>Valgarður </em>database is a compilation of data describing the physical and geochemical properties of Icelandic rocks. The dataset comprises 1072 samples obtained from fossil and active geothermal systems, as well as relatively fresh volcanic rocks erupted in sub-aerial or sub-aqueous environments. The database includes petrophysical properties (effective and total porosity, grain density, permeability, electrical resistivity, acoustic velocities), as well as mineralogical and geochemical data obtained by point-counting, X-ray Fluorescence (XRF), quantitative X-ray Diffraction (XRD), and Cation Exchange Capacity (CEC) analyses. The motivation behind this database is threefold: (i) aid in the interpretation of geophysical data including uncertainty estimations, (ii) facilitate the parameterization of numerical reservoir models, and (iii) improve our understanding of the relationship between rock type, hydrothermal alteration and petrophysical properties.</p>
3-D geological and petrophysical models with synthetic geophysics based on data from the Hamersley region (Western Australia)
<p>3-D geological and petrophysical models with synthetic geophysics based on data from the Hamersley region (Western Australia)</p> <p>M. Jessell<sup>1,2</sup>, J. Giraud<sup>1,2</sup>, M. Lindsay<sup>1,2 </sup></p> <p><sup>1</sup>Centre for Exploration Targeting (School of Earth Sciences), University of Western Australia, 35 Stirling Highway, 6009 Crawley, Australia</p> <p><sup>2</sup>Mineral Exploration Cooperative Research Centre, School of Earth Sciences, University of Western Australia, 35 Stirling Highway, WA Crawley 6009, Australia</p> <p>Contact author: Jeremie Giraud (jeremie.giraud@uwa.edu.au)</p> <p>Companion dataset to the paper:</p> <p>Structural, petrophysical and geological constraints in potential field inversion using the Tomofast-x open-source code, J. Giraud, V. Ogarko, R. Martin, M. Lindsay, M. Jessell, Geoscientific Model Development Discussions.</p> <p>This dataset contains models and data shown in the paper, in both 2D and 3D:</p> <p>1. Geological model</p> <ul> <li>Reference lithology voxet:</li> </ul> <p>The reference geological model was obtained using public data from the Geological Survey of Western Australia and modified subsequently (stretched vertically and flattened at surface level) for the purpose of this study.</p> <ul> <li>Probability voxet<br> The lithology probability voxet was derived using Monte Carlo simulations for uncertainty estimation as mentioned in the paper.</li> </ul> <p>2. True and inverted models for density and magnetic susceptibility</p> <p>Derivation is detailed in the paper; it uses fictitious density and magnetic susceptibility values.</p> <p>3. Bouguer and total magnetic field anomaly</p> <p>Calculation is detailed in the paper.</p> <p>The authors are supported, in part, by Loop – Enabling Stochastic 3D Geological Modelling (LP170100985) and the Mineral Exploration Cooperative Research Centre (MinEx CRC) whose activities are funded by the Australian Government's Cooperative Research Centre Program. This is MinEx CRC Document 2021/3. Mark Lindsay acknowledges funding from the ARC and DECRA DE190100431.</p> <p>It is a companion dataset to: <br> Vitaliy Ogarko, Jeremie Giraud, & Roland. (2021, February 5). Tomofast-x v1.0 source code (Version 1.0). Zenodo. <a href="http://doi.org/10.5281/zenodo.4452620">http://doi.org/10.5281/zenodo.4452620</a></p>
English-language articles related to petrophysics retrieved from the SSCI sub-database of Web of Science core database (Time: 2000.01.01-2022.12.31)
<p>According to the research content of petrophysics, petrophysics can be divided into eight branches: rock electricity, rock acoustics, rock nuclear physics, rock mechanics, rock thermophysics, rock nuclear magnetic resonance spectroscopy(NMR), rock gravimetry (density), and rock magnetism. This dataset presents the scientific literature retrieved by selecting the "Science Citation Index Expanded(SCI-EXPANDED)-- 1982-present" sub-library from the core collection database of Web of Science. </p>
Petrophysical data for 29 samples from the Chicxulub impact crater.
<p>Note: ɸ-porosity, ρ<sub>b</sub>-bulk density, ρ<sub>g</sub>-grain density, k-permeability, F-formation factor, m-cementation exponent, τ<sup>2</sup>-tortuosity, C<sub>s</sub>-surface conductivity, Vp-acoustic velocity of compressional waves. Uncertainty for porosity, density, permeability, velocity and conductivity is 5%. Uncertainty for formation factor, cementation exponent and tortuosity is 8%). Lith <sup>1 </sup>and Unit <sup>1</sup> after Morgan et al. (2017), Unit <sup>2</sup> after de Graaf et al. (2021, UIM-upper impact melt rock unit, LIMB-lower impact melt rock-bearing unit)) and Kaskes et al. (2021).</p> <p> </p> <p>Morgan, J. V., Gulick, S. P. S., Bralower, T. J., Chenot, E., Christeson, G. L., Claeys, P., et al. (2016). The formation of peak rings in large impact craters. Science, 354(6314), 878–882. <a href="https://doi.org/10.1126/science.aah6561">https://doi.org/10.1126/science.aah6561</a></p> <p>de Graaff, S. J., Kaskes, P., Déhais, T., Goderis, S., Vinciane, D., Ross, C. H., et al. (2021). New insights into the formation and emplacement of impact melt rocks within the Chicxulub impact structure, following the 2016 IODP-ICDP Expedition 364. Geological Society of America Bulletin. <a href="https://doi.org/doi:">https://doi.org/doi:</a> <a href="https://doi.org/10.1130/B35795.1">https://doi.org/10.1130/B35795.1</a></p> <p>Kaskes, P., de Graaff, S. J., Feignon, J. G., Déhais, T., Goderis, S., Ferrière, L., et al. (2021). Formation of the crater suevite sequence from the Chicxulub peak ring: A petrographic, geochemical, and sedimentological characterization. Geological Society of America Bulletin. <a href="https://doi.org/https://doi.org/10.1130/B36020.1">https://doi.org/https://doi.org/10.1130/B36020.1</a></p>
CNN-based Network Application for Petrophysical Parameter Inversion: Sensitivity Analysis of Input-output Parameters and Network Architecture
<p>The uploaded file includes four groups of data. They have original elastic and reservoir parameters data, the k=5 and k=25 (k means the sampling interval in inline and crossline.), and testing dataset.</p>
Regression committee machine and petrophysical model jointly driven parameter reservoirs prediction from wireline logs for tight sandstone
<pre>This data comes from this study: "Regression committee machine and petrophysical model jointly driven parameter reservoirs prediction from wireline logs for tight sandstone". It is the intelligent prediction result of porosity, permeability and water saturation of two wells in the Ordos Basin, China</pre>
Petrophysical properties of limestone and sandstone used as structural material for Roman buildings on the island of Rab in Croatia
<p>The results of EDS analysis for Lopar sandstones and limestones.</p>
Data from: Petrophysical characterization of high-rank coal by nuclear magnetic resonance: a case study of the Baijiao coal reservoir, SW China
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