Factor Analysis of Well Logs for Total Organic Carbon Estimation in Unconventional Reservoirs
Several approaches have been applied for the evaluation of formation organic content. For further developments in the interpretation of organic richness, this research proposes a multivariate statistical method for exploring the interdependencies between the well logs and model parameters. A factor...
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ftmdpi:oai:mdpi.com:/1996-1073/14/18/5978/ 2023-08-20T04:08:38+02:00 Factor Analysis of Well Logs for Total Organic Carbon Estimation in Unconventional Reservoirs Norbert P. Szabó Rafael Valadez-Vergara Sabuhi Tapdigli Aja Ugochukwu István Szabó Mihály Dobróka 2021-09-20 application/pdf https://doi.org/10.3390/en14185978 EN eng Multidisciplinary Digital Publishing Institute H: Geo-Energy https://dx.doi.org/10.3390/en14185978 https://creativecommons.org/licenses/by/4.0/ Energies; Volume 14; Issue 18; Pages: 5978 total organic carbon (TOC) tight gas shale gas source rock factor analysis (FA) interval inversion artificial neural network (ANN) Hungary Alaska Norway Text 2021 ftmdpi https://doi.org/10.3390/en14185978 2023-08-01T02:45:04Z Several approaches have been applied for the evaluation of formation organic content. For further developments in the interpretation of organic richness, this research proposes a multivariate statistical method for exploring the interdependencies between the well logs and model parameters. A factor analysis-based approach is presented for the quantitative determination of total organic content of shale formations. Uncorrelated factors are extracted from well logging data using Jöreskog’s algorithm, and then the factor logs are correlated with estimated petrophysical properties. Whereas the first factor holds information on the amount of shaliness, the second is identified as an organic factor. The estimation method is applied both to synthetic and real datasets from different reservoir types and geologic basins, i.e., Derecske Trough in East Hungary (tight gas); Kingak formation in North Slope Alaska, United States of America (shale gas); and shale source rock formations in the Norwegian continental shelf. The estimated total organic content logs are verified by core data and/or results from other indirect estimation methods such as interval inversion, artificial neural networks and cluster analysis. The presented statistical method used for the interpretation of wireline logs offers an effective tool for the evaluation of organic matter content in unconventional reservoirs. Text north slope Alaska MDPI Open Access Publishing Norway Energies 14 18 5978 |
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Open Polar |
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MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
total organic carbon (TOC) tight gas shale gas source rock factor analysis (FA) interval inversion artificial neural network (ANN) Hungary Alaska Norway |
spellingShingle |
total organic carbon (TOC) tight gas shale gas source rock factor analysis (FA) interval inversion artificial neural network (ANN) Hungary Alaska Norway Norbert P. Szabó Rafael Valadez-Vergara Sabuhi Tapdigli Aja Ugochukwu István Szabó Mihály Dobróka Factor Analysis of Well Logs for Total Organic Carbon Estimation in Unconventional Reservoirs |
topic_facet |
total organic carbon (TOC) tight gas shale gas source rock factor analysis (FA) interval inversion artificial neural network (ANN) Hungary Alaska Norway |
description |
Several approaches have been applied for the evaluation of formation organic content. For further developments in the interpretation of organic richness, this research proposes a multivariate statistical method for exploring the interdependencies between the well logs and model parameters. A factor analysis-based approach is presented for the quantitative determination of total organic content of shale formations. Uncorrelated factors are extracted from well logging data using Jöreskog’s algorithm, and then the factor logs are correlated with estimated petrophysical properties. Whereas the first factor holds information on the amount of shaliness, the second is identified as an organic factor. The estimation method is applied both to synthetic and real datasets from different reservoir types and geologic basins, i.e., Derecske Trough in East Hungary (tight gas); Kingak formation in North Slope Alaska, United States of America (shale gas); and shale source rock formations in the Norwegian continental shelf. The estimated total organic content logs are verified by core data and/or results from other indirect estimation methods such as interval inversion, artificial neural networks and cluster analysis. The presented statistical method used for the interpretation of wireline logs offers an effective tool for the evaluation of organic matter content in unconventional reservoirs. |
format |
Text |
author |
Norbert P. Szabó Rafael Valadez-Vergara Sabuhi Tapdigli Aja Ugochukwu István Szabó Mihály Dobróka |
author_facet |
Norbert P. Szabó Rafael Valadez-Vergara Sabuhi Tapdigli Aja Ugochukwu István Szabó Mihály Dobróka |
author_sort |
Norbert P. Szabó |
title |
Factor Analysis of Well Logs for Total Organic Carbon Estimation in Unconventional Reservoirs |
title_short |
Factor Analysis of Well Logs for Total Organic Carbon Estimation in Unconventional Reservoirs |
title_full |
Factor Analysis of Well Logs for Total Organic Carbon Estimation in Unconventional Reservoirs |
title_fullStr |
Factor Analysis of Well Logs for Total Organic Carbon Estimation in Unconventional Reservoirs |
title_full_unstemmed |
Factor Analysis of Well Logs for Total Organic Carbon Estimation in Unconventional Reservoirs |
title_sort |
factor analysis of well logs for total organic carbon estimation in unconventional reservoirs |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2021 |
url |
https://doi.org/10.3390/en14185978 |
geographic |
Norway |
geographic_facet |
Norway |
genre |
north slope Alaska |
genre_facet |
north slope Alaska |
op_source |
Energies; Volume 14; Issue 18; Pages: 5978 |
op_relation |
H: Geo-Energy https://dx.doi.org/10.3390/en14185978 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/en14185978 |
container_title |
Energies |
container_volume |
14 |
container_issue |
18 |
container_start_page |
5978 |
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1774721031910457344 |