Conversion of 3D seismic attributes to reservoir hydraulic flow units using a neural network approach: An example from the Kangan and Dalan carbonate reservoirs, the world's largest non-associated gas reservoirs, near the Persian Gulf
This study presents an intelligent model based on probabilistic neural networks (PNN) to produce a quantitative formulation between seismic attributes and hydraulic flow units (HFUs). Neural networks have been used for the last several years to estimate reservoir properties. However, their applicati...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Universidad Nacional de Colombia
2013-07-01
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| Series: | Earth Sciences Research Journal |
| Subjects: | |
| Online Access: | https://revistas.unal.edu.co/index.php/esrj/article/view/34000 |
| _version_ | 1828520644407459840 |
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| author | Mohammad Amin Dezfoolian Mohammad Ali Riahi Ali Kadkhodaie-Ilkhchi |
| author_facet | Mohammad Amin Dezfoolian Mohammad Ali Riahi Ali Kadkhodaie-Ilkhchi |
| author_sort | Mohammad Amin Dezfoolian |
| collection | DOAJ |
| description | This study presents an intelligent model based on probabilistic neural networks (PNN) to produce a quantitative formulation between seismic attributes and hydraulic flow units (HFUs). Neural networks have been used for the last several years to estimate reservoir properties. However, their application for hydraulic flow unit estimation on a cube of seismic data is an interesting topic for research. The methodology for this application is illustrated using 3D seismic attributes and petrophysical and core data from 6 wells from the Kangan and Dalan gas reservoirs in the Persian Gulf basin. The methodology introduced in this study estimates HFUs from a large volume of 3D seismic data. This may increase exploration success rates and reduce costs through the application of more reliable output results in hydrocarbon exploration programs. 4 seismic attributes, including acoustic impedance, dominant fre- quency, amplitude weighted phase and instantaneous phase, are considered as the optimal inputs for pre- dicting HFUs from seismic data. The proposed technique is successfully tested in a carbonate sequence of Permian-Triassic rocks from the studied area. The results of this study demonstrate that there is a good agreement between the core and PNN-derived flow units. The PNN used in this study is successful in modeling flow units from 3D seismic data for which no core data or well log data are available.
Resumen
Este estudio presenta un modelo inteligente basado en redes neuronales probabilísticas (PNN) para pro- ducir una formulación cuantitativa entre atributos sísmicos y unidades de flujo hidráulico (HFU). Las redes neuronales han sido utilizadas durante los últimos años para estimar las propiedades de reserva. Sin embargo, su aplicación para estimación de unidades de flujo hidráulico en un cubo de datos sísmicos es un tema importante de investigación. La metodología para esta aplicación está ilustrada a partir de datos tridimensionales y datos petrofísicos y de núcleo tomados en 6 pozos de las reservas de Kangan y Dalan, de la cuenca del Golfo Pérsico. La metodología introducida en este estudio estima las HFU de un gran volúmen de datos sísmicos tridimensionales. Esto podría incrementar los índices positivos de explora- ción y reducir los costos a través de una aplicación más confiable en resultados de producción para los programas de exploración en hidrocarbonos. Cuatro atributos sísmicos, obstrucción acústica, frecuencia dominante, fase de amplitud media y fase instantánea, son considerados en este trabajo como aportes claves para predecir los datos sísmicos de las HFU. La técnica propuesta ha sido evaluada exitosamente en una secuencia carbonada de rocas del Pérmicotriásico tomadas del área de estudio. Los resultados de este trabajo demuestran que hay concordancia entre la base de las PNN y las unidades derivadas de flujo. Las PNN utilizadas en este estudio son capaces de modelar unidades de flujo de datos sísmicos tridimen- sionales para los cuales no hay un centro de datos o una secuencia de datos disponible. |
| first_indexed | 2024-12-11T19:34:35Z |
| format | Article |
| id | doaj.art-056db71200264b27a8be3e3ed4bcf8eb |
| institution | Directory Open Access Journal |
| issn | 1794-6190 2339-3459 |
| language | English |
| last_indexed | 2024-12-11T19:34:35Z |
| publishDate | 2013-07-01 |
| publisher | Universidad Nacional de Colombia |
| record_format | Article |
| series | Earth Sciences Research Journal |
| spelling | doaj.art-056db71200264b27a8be3e3ed4bcf8eb2022-12-22T00:53:11ZengUniversidad Nacional de ColombiaEarth Sciences Research Journal1794-61902339-34592013-07-0117235886Conversion of 3D seismic attributes to reservoir hydraulic flow units using a neural network approach: An example from the Kangan and Dalan carbonate reservoirs, the world's largest non-associated gas reservoirs, near the Persian GulfMohammad Amin Dezfoolian0Mohammad Ali Riahi1Ali Kadkhodaie-Ilkhchi21Young Researchers Club, Science and Research Branch, Islamic Azad University, Tehran, IranDepartment of Mining Engineering, Science and Research Branch, Islamic Azad University, Tehran, IranDepartment of Mining Engineering, Science and Research Branch, Islamic Azad University, Tehran, IranThis study presents an intelligent model based on probabilistic neural networks (PNN) to produce a quantitative formulation between seismic attributes and hydraulic flow units (HFUs). Neural networks have been used for the last several years to estimate reservoir properties. However, their application for hydraulic flow unit estimation on a cube of seismic data is an interesting topic for research. The methodology for this application is illustrated using 3D seismic attributes and petrophysical and core data from 6 wells from the Kangan and Dalan gas reservoirs in the Persian Gulf basin. The methodology introduced in this study estimates HFUs from a large volume of 3D seismic data. This may increase exploration success rates and reduce costs through the application of more reliable output results in hydrocarbon exploration programs. 4 seismic attributes, including acoustic impedance, dominant fre- quency, amplitude weighted phase and instantaneous phase, are considered as the optimal inputs for pre- dicting HFUs from seismic data. The proposed technique is successfully tested in a carbonate sequence of Permian-Triassic rocks from the studied area. The results of this study demonstrate that there is a good agreement between the core and PNN-derived flow units. The PNN used in this study is successful in modeling flow units from 3D seismic data for which no core data or well log data are available. Resumen Este estudio presenta un modelo inteligente basado en redes neuronales probabilísticas (PNN) para pro- ducir una formulación cuantitativa entre atributos sísmicos y unidades de flujo hidráulico (HFU). Las redes neuronales han sido utilizadas durante los últimos años para estimar las propiedades de reserva. Sin embargo, su aplicación para estimación de unidades de flujo hidráulico en un cubo de datos sísmicos es un tema importante de investigación. La metodología para esta aplicación está ilustrada a partir de datos tridimensionales y datos petrofísicos y de núcleo tomados en 6 pozos de las reservas de Kangan y Dalan, de la cuenca del Golfo Pérsico. La metodología introducida en este estudio estima las HFU de un gran volúmen de datos sísmicos tridimensionales. Esto podría incrementar los índices positivos de explora- ción y reducir los costos a través de una aplicación más confiable en resultados de producción para los programas de exploración en hidrocarbonos. Cuatro atributos sísmicos, obstrucción acústica, frecuencia dominante, fase de amplitud media y fase instantánea, son considerados en este trabajo como aportes claves para predecir los datos sísmicos de las HFU. La técnica propuesta ha sido evaluada exitosamente en una secuencia carbonada de rocas del Pérmicotriásico tomadas del área de estudio. Los resultados de este trabajo demuestran que hay concordancia entre la base de las PNN y las unidades derivadas de flujo. Las PNN utilizadas en este estudio son capaces de modelar unidades de flujo de datos sísmicos tridimen- sionales para los cuales no hay un centro de datos o una secuencia de datos disponible.https://revistas.unal.edu.co/index.php/esrj/article/view/34000seismic attributesseismic inversionflow zone indicatorreservoir quality indexhydraulic flow unitprobabilistic neural networks. |
| spellingShingle | Mohammad Amin Dezfoolian Mohammad Ali Riahi Ali Kadkhodaie-Ilkhchi Conversion of 3D seismic attributes to reservoir hydraulic flow units using a neural network approach: An example from the Kangan and Dalan carbonate reservoirs, the world's largest non-associated gas reservoirs, near the Persian Gulf Earth Sciences Research Journal seismic attributes seismic inversion flow zone indicator reservoir quality index hydraulic flow unit probabilistic neural networks. |
| title | Conversion of 3D seismic attributes to reservoir hydraulic flow units using a neural network approach: An example from the Kangan and Dalan carbonate reservoirs, the world's largest non-associated gas reservoirs, near the Persian Gulf |
| title_full | Conversion of 3D seismic attributes to reservoir hydraulic flow units using a neural network approach: An example from the Kangan and Dalan carbonate reservoirs, the world's largest non-associated gas reservoirs, near the Persian Gulf |
| title_fullStr | Conversion of 3D seismic attributes to reservoir hydraulic flow units using a neural network approach: An example from the Kangan and Dalan carbonate reservoirs, the world's largest non-associated gas reservoirs, near the Persian Gulf |
| title_full_unstemmed | Conversion of 3D seismic attributes to reservoir hydraulic flow units using a neural network approach: An example from the Kangan and Dalan carbonate reservoirs, the world's largest non-associated gas reservoirs, near the Persian Gulf |
| title_short | Conversion of 3D seismic attributes to reservoir hydraulic flow units using a neural network approach: An example from the Kangan and Dalan carbonate reservoirs, the world's largest non-associated gas reservoirs, near the Persian Gulf |
| title_sort | conversion of 3d seismic attributes to reservoir hydraulic flow units using a neural network approach an example from the kangan and dalan carbonate reservoirs the world s largest non associated gas reservoirs near the persian gulf |
| topic | seismic attributes seismic inversion flow zone indicator reservoir quality index hydraulic flow unit probabilistic neural networks. |
| url | https://revistas.unal.edu.co/index.php/esrj/article/view/34000 |
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