A Comparative Study of the Neural Network, Fuzzy Logic, and Nero-fuzzy Systems in Seismic Reservoir Characterization: An Example from Arab (Surmeh) Reservoir as an Iranian Gas Field, Persian Gulf Basin
Intelligent reservoir characterization using seismic attributes and hydraulic flow units has a vital role in the description of oil and gas traps. The predicted model allows an accurate understanding of the reservoir quality, especially at the un-cored well location. This study was conducted in two...
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| Format: | Article |
| Language: | English |
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Petroleum University of Technology
2017-10-01
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| Series: | Iranian Journal of Oil & Gas Science and Technology |
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| Online Access: | http://ijogst.put.ac.ir/article_53907_d8fc208afb1d6cf1cae18fb0c315675d.pdf |
| _version_ | 1818257026473525248 |
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| author | Reza Mohebian Mohammad Ali Riahi Ali Kadkhodaie-Ilkhchi |
| author_facet | Reza Mohebian Mohammad Ali Riahi Ali Kadkhodaie-Ilkhchi |
| author_sort | Reza Mohebian |
| collection | DOAJ |
| description | Intelligent reservoir characterization using seismic attributes and hydraulic flow units has a vital role in the description of oil and gas traps. The predicted model allows an accurate understanding of the reservoir quality, especially at the un-cored well location. This study was conducted in two major steps. In the first step, the survey compared different intelligent techniques to discover an optimum relationship between well logs and seismic data. For this purpose, three intelligent systems, including probabilistic neural network (PNN),fuzzy logic (FL), and adaptive neuro-fuzzy inference systems (ANFIS)were usedto predict flow zone index (FZI). Well derived FZI logs from three wells were employed to estimate intelligent models in the Arab (Surmeh) reservoir. The validation of the produced models was examined by another well. Optimal seismic attributes for the estimation of FZI include acoustic impedance, integrated absolute amplitude, and average frequency. The results revealed that the ANFIS method performed better than the other systems and showed a remarkable reduction in the measured errors. In the second part of the study, the FZI 3D model was created by using the ANFIS system.The integrated approach introduced in the current survey illustrated that the extracted flow units from intelligent models compromise well with well-logs. Based on the results obtained, the intelligent systems are powerful techniques to predict flow units from seismic data (seismic attributes) for distant well location. Finally, it was shown that ANFIS method was efficient in highlighting high and low-quality flow units in the Arab (Surmeh) reservoir, the Iranian offshore gas field. |
| first_indexed | 2024-12-12T17:37:06Z |
| format | Article |
| id | doaj.art-22378c0ce7a14d47981423274b3463b3 |
| institution | Directory Open Access Journal |
| issn | 2345-2412 2345-2420 |
| language | English |
| last_indexed | 2024-12-12T17:37:06Z |
| publishDate | 2017-10-01 |
| publisher | Petroleum University of Technology |
| record_format | Article |
| series | Iranian Journal of Oil & Gas Science and Technology |
| spelling | doaj.art-22378c0ce7a14d47981423274b3463b32022-12-22T00:17:11ZengPetroleum University of TechnologyIranian Journal of Oil & Gas Science and Technology2345-24122345-24202017-10-0164335510.22050/ijogst.2017.5390753907A Comparative Study of the Neural Network, Fuzzy Logic, and Nero-fuzzy Systems in Seismic Reservoir Characterization: An Example from Arab (Surmeh) Reservoir as an Iranian Gas Field, Persian Gulf BasinReza Mohebian0Mohammad Ali Riahi1Ali Kadkhodaie-Ilkhchi2Ph.D. Candidate, Institute of Geophysics, University of Tehran, Tehran, IranProfessor, Institute of Geophysics, University of Tehran, Tehran, IranAssociate Professor, Department of Petroleum Engineering, Curtin University of Technology, Perth, Western AustraliaIntelligent reservoir characterization using seismic attributes and hydraulic flow units has a vital role in the description of oil and gas traps. The predicted model allows an accurate understanding of the reservoir quality, especially at the un-cored well location. This study was conducted in two major steps. In the first step, the survey compared different intelligent techniques to discover an optimum relationship between well logs and seismic data. For this purpose, three intelligent systems, including probabilistic neural network (PNN),fuzzy logic (FL), and adaptive neuro-fuzzy inference systems (ANFIS)were usedto predict flow zone index (FZI). Well derived FZI logs from three wells were employed to estimate intelligent models in the Arab (Surmeh) reservoir. The validation of the produced models was examined by another well. Optimal seismic attributes for the estimation of FZI include acoustic impedance, integrated absolute amplitude, and average frequency. The results revealed that the ANFIS method performed better than the other systems and showed a remarkable reduction in the measured errors. In the second part of the study, the FZI 3D model was created by using the ANFIS system.The integrated approach introduced in the current survey illustrated that the extracted flow units from intelligent models compromise well with well-logs. Based on the results obtained, the intelligent systems are powerful techniques to predict flow units from seismic data (seismic attributes) for distant well location. Finally, it was shown that ANFIS method was efficient in highlighting high and low-quality flow units in the Arab (Surmeh) reservoir, the Iranian offshore gas field.http://ijogst.put.ac.ir/article_53907_d8fc208afb1d6cf1cae18fb0c315675d.pdfprobabilistic neural network (pnn)fuzzy logic (fl)adaptive neuro-fuzzy inference systems (anfis)flow zone index (fzi)arab (surmeh) reservoir |
| spellingShingle | Reza Mohebian Mohammad Ali Riahi Ali Kadkhodaie-Ilkhchi A Comparative Study of the Neural Network, Fuzzy Logic, and Nero-fuzzy Systems in Seismic Reservoir Characterization: An Example from Arab (Surmeh) Reservoir as an Iranian Gas Field, Persian Gulf Basin Iranian Journal of Oil & Gas Science and Technology probabilistic neural network (pnn) fuzzy logic (fl) adaptive neuro-fuzzy inference systems (anfis) flow zone index (fzi) arab (surmeh) reservoir |
| title | A Comparative Study of the Neural Network, Fuzzy Logic, and Nero-fuzzy Systems in Seismic Reservoir Characterization: An Example from Arab (Surmeh) Reservoir as an Iranian Gas Field, Persian Gulf Basin |
| title_full | A Comparative Study of the Neural Network, Fuzzy Logic, and Nero-fuzzy Systems in Seismic Reservoir Characterization: An Example from Arab (Surmeh) Reservoir as an Iranian Gas Field, Persian Gulf Basin |
| title_fullStr | A Comparative Study of the Neural Network, Fuzzy Logic, and Nero-fuzzy Systems in Seismic Reservoir Characterization: An Example from Arab (Surmeh) Reservoir as an Iranian Gas Field, Persian Gulf Basin |
| title_full_unstemmed | A Comparative Study of the Neural Network, Fuzzy Logic, and Nero-fuzzy Systems in Seismic Reservoir Characterization: An Example from Arab (Surmeh) Reservoir as an Iranian Gas Field, Persian Gulf Basin |
| title_short | A Comparative Study of the Neural Network, Fuzzy Logic, and Nero-fuzzy Systems in Seismic Reservoir Characterization: An Example from Arab (Surmeh) Reservoir as an Iranian Gas Field, Persian Gulf Basin |
| title_sort | comparative study of the neural network fuzzy logic and nero fuzzy systems in seismic reservoir characterization an example from arab surmeh reservoir as an iranian gas field persian gulf basin |
| topic | probabilistic neural network (pnn) fuzzy logic (fl) adaptive neuro-fuzzy inference systems (anfis) flow zone index (fzi) arab (surmeh) reservoir |
| url | http://ijogst.put.ac.ir/article_53907_d8fc208afb1d6cf1cae18fb0c315675d.pdf |
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