Optimizing petrophysical property prediction in fluvial-deltaic reservoirs: a multi-seismic attribute transformation and probabilistic neural network approach
Abstract Conventional techniques, which depend on geostatistical modeling, frequently fail to capture reservoir variability, especially when well data are sparse. To overcome this limitation, we develop a combined approach that integrates Multi-Seismic Attribute Transformation (MSAT) and Probabilist...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-02-01
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| Series: | Journal of Petroleum Exploration and Production Technology |
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| Online Access: | https://doi.org/10.1007/s13202-024-01912-6 |
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| author | Muhammad Khan Andy Anderson Bery Yasir Bashir Sya’rawi Muhammad Husni Sharoni Joseph Gnapragasan Qazi Sohail Imran |
| author_facet | Muhammad Khan Andy Anderson Bery Yasir Bashir Sya’rawi Muhammad Husni Sharoni Joseph Gnapragasan Qazi Sohail Imran |
| author_sort | Muhammad Khan |
| collection | DOAJ |
| description | Abstract Conventional techniques, which depend on geostatistical modeling, frequently fail to capture reservoir variability, especially when well data are sparse. To overcome this limitation, we develop a combined approach that integrates Multi-Seismic Attribute Transformation (MSAT) and Probabilistic Neural Network (PNN) techniques. By leveraging data from eight wells and post-stack seismic data from the Poseidon 3D area, gas-saturated deltaic-fluvial settings, we analyzed twelve seismic attributes, among which acoustic impedance low-frequency attribute, relative geological time, amplitude envelope, and amplitude-weighted frequency emerge as the most significant. Using the MSAT approach, we established strong connections between these attributes and porosity. Six attributes provide a correlation coefficient of 0.65 with the target log. To enhance precision, PNN and fine-tuning the sigma factor using well drop-out cross-validation analysis was utilized. This leads to a significant enhancement in model accuracy, reaching 76%. In addition, when the model training is concentrated within a 10-millisecond range around the Plover reservoir zone, the accuracy increases significantly to 89%. Our approach has proven to be highly effective, with a success rate of 73% as evidenced by validation through well drop-out analysis. This demonstrates that our method surpasses traditional methods. This innovative integration of seismic attribute-driven methodologies has indicated a major leap forward in identifying and characterizing reservoir heterogeneity. The results demonstrate that it enables more efficient future well-planning strategies and deepens our comprehension of deltaic-fluvial reservoir dynamics. |
| format | Article |
| id | doaj-art-dfbd6a15075c4df8868dd6db061b0f81 |
| institution | Kabale University |
| issn | 2190-0558 2190-0566 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Petroleum Exploration and Production Technology |
| spelling | doaj-art-dfbd6a15075c4df8868dd6db061b0f812025-02-09T12:13:35ZengSpringerOpenJournal of Petroleum Exploration and Production Technology2190-05582190-05662025-02-0115212010.1007/s13202-024-01912-6Optimizing petrophysical property prediction in fluvial-deltaic reservoirs: a multi-seismic attribute transformation and probabilistic neural network approachMuhammad Khan0Andy Anderson Bery1Yasir Bashir2Sya’rawi Muhammad Husni Sharoni3Joseph Gnapragasan4Qazi Sohail Imran5School of Physics, Universiti Sains MalaysiaSchool of Physics, Universiti Sains MalaysiaDepartment of Geophysical Engineering, Faculty of Mines, İstanbul Technical UniversitySchool of Physics, Universiti Sains MalaysiaSchool of Physics, Universiti Sains MalaysiaCentre for Subsurface Imaging, Department of Geosciences, Universiti Teknologi PETRONASAbstract Conventional techniques, which depend on geostatistical modeling, frequently fail to capture reservoir variability, especially when well data are sparse. To overcome this limitation, we develop a combined approach that integrates Multi-Seismic Attribute Transformation (MSAT) and Probabilistic Neural Network (PNN) techniques. By leveraging data from eight wells and post-stack seismic data from the Poseidon 3D area, gas-saturated deltaic-fluvial settings, we analyzed twelve seismic attributes, among which acoustic impedance low-frequency attribute, relative geological time, amplitude envelope, and amplitude-weighted frequency emerge as the most significant. Using the MSAT approach, we established strong connections between these attributes and porosity. Six attributes provide a correlation coefficient of 0.65 with the target log. To enhance precision, PNN and fine-tuning the sigma factor using well drop-out cross-validation analysis was utilized. This leads to a significant enhancement in model accuracy, reaching 76%. In addition, when the model training is concentrated within a 10-millisecond range around the Plover reservoir zone, the accuracy increases significantly to 89%. Our approach has proven to be highly effective, with a success rate of 73% as evidenced by validation through well drop-out analysis. This demonstrates that our method surpasses traditional methods. This innovative integration of seismic attribute-driven methodologies has indicated a major leap forward in identifying and characterizing reservoir heterogeneity. The results demonstrate that it enables more efficient future well-planning strategies and deepens our comprehension of deltaic-fluvial reservoir dynamics.https://doi.org/10.1007/s13202-024-01912-6Probabilistic neural networkMulti-seismic attribute transformationDip-steer median filterSeismic attributesPorosity predictionSeismic inversion |
| spellingShingle | Muhammad Khan Andy Anderson Bery Yasir Bashir Sya’rawi Muhammad Husni Sharoni Joseph Gnapragasan Qazi Sohail Imran Optimizing petrophysical property prediction in fluvial-deltaic reservoirs: a multi-seismic attribute transformation and probabilistic neural network approach Journal of Petroleum Exploration and Production Technology Probabilistic neural network Multi-seismic attribute transformation Dip-steer median filter Seismic attributes Porosity prediction Seismic inversion |
| title | Optimizing petrophysical property prediction in fluvial-deltaic reservoirs: a multi-seismic attribute transformation and probabilistic neural network approach |
| title_full | Optimizing petrophysical property prediction in fluvial-deltaic reservoirs: a multi-seismic attribute transformation and probabilistic neural network approach |
| title_fullStr | Optimizing petrophysical property prediction in fluvial-deltaic reservoirs: a multi-seismic attribute transformation and probabilistic neural network approach |
| title_full_unstemmed | Optimizing petrophysical property prediction in fluvial-deltaic reservoirs: a multi-seismic attribute transformation and probabilistic neural network approach |
| title_short | Optimizing petrophysical property prediction in fluvial-deltaic reservoirs: a multi-seismic attribute transformation and probabilistic neural network approach |
| title_sort | optimizing petrophysical property prediction in fluvial deltaic reservoirs a multi seismic attribute transformation and probabilistic neural network approach |
| topic | Probabilistic neural network Multi-seismic attribute transformation Dip-steer median filter Seismic attributes Porosity prediction Seismic inversion |
| url | https://doi.org/10.1007/s13202-024-01912-6 |
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