Application of Multivariate Statistical Methods and Artificial Neural Network for Facies Analysis from Well Logs Data: An Example of Miocene Deposits
The main purpose of the study is a detailed interpretation of the facies and relate these to the results of standard well logs interpretation. Different methods were used: firstly, multivariate statistical methods, like principal components analysis, cluster analysis and discriminant analysis; and s...
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MDPI AG
2020-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/7/1548 |
| _version_ | 1817991021097648128 |
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| author | Edyta Puskarczyk |
| author_facet | Edyta Puskarczyk |
| author_sort | Edyta Puskarczyk |
| collection | DOAJ |
| description | The main purpose of the study is a detailed interpretation of the facies and relate these to the results of standard well logs interpretation. Different methods were used: firstly, multivariate statistical methods, like principal components analysis, cluster analysis and discriminant analysis; and secondly, the artificial neural network, to identify and discriminate the facies from well log data. Determination of electrofacies was done in two ways: firstly, analysis was performed for two wells separately, secondly, the neural network learned and trained on data from the W-1 well was applied to the second well W-2 and a prediction of the facies distribution in this well was made. In both wells, located in the area of the Carpathian Foredeep, thin-layered sandstone-claystone formations were found and gas saturated depth intervals were identified. Based on statistical analyses, there were recognized presence of thin layers intersecting layers of much greater thickness (especially in W-2 well), e.g., section consisting mainly of claystone and sandstone formations with poor reservoir parameters (Group B) is divided with thin layers of sandstone and claystone with good reservoir parameters (Group C). The highest probability of occurrence of hydrocarbons exists in thin-layered intervals in facies C. |
| first_indexed | 2024-04-14T01:07:43Z |
| format | Article |
| id | doaj.art-eca5a4a97c4c42c6a84a7e6a7f0ca660 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-14T01:07:43Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-eca5a4a97c4c42c6a84a7e6a7f0ca6602022-12-22T02:21:11ZengMDPI AGEnergies1996-10732020-03-01137154810.3390/en13071548en13071548Application of Multivariate Statistical Methods and Artificial Neural Network for Facies Analysis from Well Logs Data: An Example of Miocene DepositsEdyta Puskarczyk0Department of Geophysics, Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, Krakow, Mickiewicza 30 Ave. 30-059 Kraków, PolandThe main purpose of the study is a detailed interpretation of the facies and relate these to the results of standard well logs interpretation. Different methods were used: firstly, multivariate statistical methods, like principal components analysis, cluster analysis and discriminant analysis; and secondly, the artificial neural network, to identify and discriminate the facies from well log data. Determination of electrofacies was done in two ways: firstly, analysis was performed for two wells separately, secondly, the neural network learned and trained on data from the W-1 well was applied to the second well W-2 and a prediction of the facies distribution in this well was made. In both wells, located in the area of the Carpathian Foredeep, thin-layered sandstone-claystone formations were found and gas saturated depth intervals were identified. Based on statistical analyses, there were recognized presence of thin layers intersecting layers of much greater thickness (especially in W-2 well), e.g., section consisting mainly of claystone and sandstone formations with poor reservoir parameters (Group B) is divided with thin layers of sandstone and claystone with good reservoir parameters (Group C). The highest probability of occurrence of hydrocarbons exists in thin-layered intervals in facies C.https://www.mdpi.com/1996-1073/13/7/1548well logfaciescluster analysisprincipal component analysisdiscriminant analysisartificial neural network |
| spellingShingle | Edyta Puskarczyk Application of Multivariate Statistical Methods and Artificial Neural Network for Facies Analysis from Well Logs Data: An Example of Miocene Deposits Energies well log facies cluster analysis principal component analysis discriminant analysis artificial neural network |
| title | Application of Multivariate Statistical Methods and Artificial Neural Network for Facies Analysis from Well Logs Data: An Example of Miocene Deposits |
| title_full | Application of Multivariate Statistical Methods and Artificial Neural Network for Facies Analysis from Well Logs Data: An Example of Miocene Deposits |
| title_fullStr | Application of Multivariate Statistical Methods and Artificial Neural Network for Facies Analysis from Well Logs Data: An Example of Miocene Deposits |
| title_full_unstemmed | Application of Multivariate Statistical Methods and Artificial Neural Network for Facies Analysis from Well Logs Data: An Example of Miocene Deposits |
| title_short | Application of Multivariate Statistical Methods and Artificial Neural Network for Facies Analysis from Well Logs Data: An Example of Miocene Deposits |
| title_sort | application of multivariate statistical methods and artificial neural network for facies analysis from well logs data an example of miocene deposits |
| topic | well log facies cluster analysis principal component analysis discriminant analysis artificial neural network |
| url | https://www.mdpi.com/1996-1073/13/7/1548 |
| work_keys_str_mv | AT edytapuskarczyk applicationofmultivariatestatisticalmethodsandartificialneuralnetworkforfaciesanalysisfromwelllogsdataanexampleofmiocenedeposits |