Petrophysical seismic inversion based on lithofacies classification to enhance reservoir properties estimation: a machine learning approach
Abstract For estimation of petrophysical properties in industry, we are looking for a methodology which results in more accurate outcome and also can be validated by means of some quality control steps. To achieve that, an application of petrophysical seismic inversion for reservoir properties estim...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2020-10-01
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| Series: | Journal of Petroleum Exploration and Production Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s13202-020-01013-0 |
| _version_ | 1811199561976250368 |
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| author | Amir Abbas Babasafari Shiba Rezaei Ahmed Mohamed Ahmed Salim Sayed Hesammoddin Kazemeini Deva Prasad Ghosh |
| author_facet | Amir Abbas Babasafari Shiba Rezaei Ahmed Mohamed Ahmed Salim Sayed Hesammoddin Kazemeini Deva Prasad Ghosh |
| author_sort | Amir Abbas Babasafari |
| collection | DOAJ |
| description | Abstract For estimation of petrophysical properties in industry, we are looking for a methodology which results in more accurate outcome and also can be validated by means of some quality control steps. To achieve that, an application of petrophysical seismic inversion for reservoir properties estimation is proposed. The main objective of this approach is to reduce uncertainty in reservoir characterization by incorporating well log and seismic data in an optimal manner. We use nonlinear optimization algorithms in the inversion workflow to estimate reservoir properties away from the wells. The method is applied at well location by fitting nonlinear experimental relations on the petroelastic cross-plot, e.g., porosity versus acoustic impedance for each lithofacies class separately. Once a significant match between the measured and the predicted reservoir property is attained in the inversion workflow, the petrophysical seismic inversion based on lithofacies classification is applied to the inverted elastic property, i.e., acoustic impedance or V p/V s ratio derived from seismic elastic inversion to predict the reservoir properties between the wells. Comparison with the neural network method demonstrated this application of petrophysical seismic inversion to be competitive and reliable. |
| first_indexed | 2024-04-12T01:50:44Z |
| format | Article |
| id | doaj.art-afafc81e334a40d3b7e54b433bf104d6 |
| institution | Directory Open Access Journal |
| issn | 2190-0558 2190-0566 |
| language | English |
| last_indexed | 2024-04-12T01:50:44Z |
| publishDate | 2020-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Petroleum Exploration and Production Technology |
| spelling | doaj.art-afafc81e334a40d3b7e54b433bf104d62022-12-22T03:52:57ZengSpringerOpenJournal of Petroleum Exploration and Production Technology2190-05582190-05662020-10-0111267368410.1007/s13202-020-01013-0Petrophysical seismic inversion based on lithofacies classification to enhance reservoir properties estimation: a machine learning approachAmir Abbas Babasafari0Shiba Rezaei1Ahmed Mohamed Ahmed Salim2Sayed Hesammoddin Kazemeini3Deva Prasad Ghosh4Geoscience Department, Center of Seismic Imaging and Hydrocarbon Prediction, University Teknologi PETRONASGeoscience Department, Center of Seismic Imaging and Hydrocarbon Prediction, University Teknologi PETRONASGeoscience Department, Center of Seismic Imaging and Hydrocarbon Prediction, University Teknologi PETRONASAlphaReservoir PlusGeoscience Department, Center of Seismic Imaging and Hydrocarbon Prediction, University Teknologi PETRONASAbstract For estimation of petrophysical properties in industry, we are looking for a methodology which results in more accurate outcome and also can be validated by means of some quality control steps. To achieve that, an application of petrophysical seismic inversion for reservoir properties estimation is proposed. The main objective of this approach is to reduce uncertainty in reservoir characterization by incorporating well log and seismic data in an optimal manner. We use nonlinear optimization algorithms in the inversion workflow to estimate reservoir properties away from the wells. The method is applied at well location by fitting nonlinear experimental relations on the petroelastic cross-plot, e.g., porosity versus acoustic impedance for each lithofacies class separately. Once a significant match between the measured and the predicted reservoir property is attained in the inversion workflow, the petrophysical seismic inversion based on lithofacies classification is applied to the inverted elastic property, i.e., acoustic impedance or V p/V s ratio derived from seismic elastic inversion to predict the reservoir properties between the wells. Comparison with the neural network method demonstrated this application of petrophysical seismic inversion to be competitive and reliable.https://doi.org/10.1007/s13202-020-01013-0Petrophysical inversionNonlinear optimizationReducing uncertaintyLithofacies class |
| spellingShingle | Amir Abbas Babasafari Shiba Rezaei Ahmed Mohamed Ahmed Salim Sayed Hesammoddin Kazemeini Deva Prasad Ghosh Petrophysical seismic inversion based on lithofacies classification to enhance reservoir properties estimation: a machine learning approach Journal of Petroleum Exploration and Production Technology Petrophysical inversion Nonlinear optimization Reducing uncertainty Lithofacies class |
| title | Petrophysical seismic inversion based on lithofacies classification to enhance reservoir properties estimation: a machine learning approach |
| title_full | Petrophysical seismic inversion based on lithofacies classification to enhance reservoir properties estimation: a machine learning approach |
| title_fullStr | Petrophysical seismic inversion based on lithofacies classification to enhance reservoir properties estimation: a machine learning approach |
| title_full_unstemmed | Petrophysical seismic inversion based on lithofacies classification to enhance reservoir properties estimation: a machine learning approach |
| title_short | Petrophysical seismic inversion based on lithofacies classification to enhance reservoir properties estimation: a machine learning approach |
| title_sort | petrophysical seismic inversion based on lithofacies classification to enhance reservoir properties estimation a machine learning approach |
| topic | Petrophysical inversion Nonlinear optimization Reducing uncertainty Lithofacies class |
| url | https://doi.org/10.1007/s13202-020-01013-0 |
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