A Committee Machine with Intelligent Systems for Estimation of Total Organic Carbon Content from Petrophysical Data: an Example from Kangan and Dalan Reservoirs in South Pars Gas Field, Iran
Total Organic Carbon (TOC) content present in reservoir rocks is one of the important parameters which could be used for evaluation of residual production potential and geochemical characterization of hydrocarbon bearing units. In general, organic rich rocks are characterized by higher porosity, hig...
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| Format: | Journal Article |
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Pergamon, Elsevier
2009
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| Online Access: | http://hdl.handle.net/20.500.11937/24000 |
| _version_ | 1848751307531223040 |
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| author | Kadkhodaie Ilkhchi, A. Rahimpour-Bonab, H. Rezaee, M. Reza |
| author_facet | Kadkhodaie Ilkhchi, A. Rahimpour-Bonab, H. Rezaee, M. Reza |
| author_sort | Kadkhodaie Ilkhchi, A. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Total Organic Carbon (TOC) content present in reservoir rocks is one of the important parameters which could be used for evaluation of residual production potential and geochemical characterization of hydrocarbon bearing units. In general, organic rich rocks are characterized by higher porosity, higher sonic transit time, lower density, higher gamma-ray, and higher resistivity than other rocks. Current study suggests an improved and optimal model for TOC estimation by integration of intelligent systems and the concept of committee machine with an example from Kangan and Dalan Formations, in South Pars Gas Field, Iran. This committee machine with intelligent systems (CMIS) combines the results of TOC predicted from intelligent systems including fuzzy logic (FL), neuro-fuzzy (NF), and neural network (NN), each of them has a weight factor showing its contribution in overall prediction. The optimal combination of weights is derived by a genetic algorithm (GA). This method is illustrated using a case study. One hundred twenty-four data points including petrophysical data and measured TOC from three wells of South Pars Gas Field were divided into eighty-seven training sets to build the CMIS model and thirty-seven testing sets to evaluate the reliability of the developed model. The results show that the CMIS performs better than any one of the individual intelligent systems acting alone for predicting TOC. |
| first_indexed | 2025-11-14T07:50:39Z |
| format | Journal Article |
| id | curtin-20.500.11937-24000 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:50:39Z |
| publishDate | 2009 |
| publisher | Pergamon, Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-240002017-09-13T15:58:58Z A Committee Machine with Intelligent Systems for Estimation of Total Organic Carbon Content from Petrophysical Data: an Example from Kangan and Dalan Reservoirs in South Pars Gas Field, Iran Kadkhodaie Ilkhchi, A. Rahimpour-Bonab, H. Rezaee, M. Reza fuzzy logic South Pars Gas Field neural network genetic algorithm Total organic carbon neuro-fuzzy committee machine petrophysical data Total Organic Carbon (TOC) content present in reservoir rocks is one of the important parameters which could be used for evaluation of residual production potential and geochemical characterization of hydrocarbon bearing units. In general, organic rich rocks are characterized by higher porosity, higher sonic transit time, lower density, higher gamma-ray, and higher resistivity than other rocks. Current study suggests an improved and optimal model for TOC estimation by integration of intelligent systems and the concept of committee machine with an example from Kangan and Dalan Formations, in South Pars Gas Field, Iran. This committee machine with intelligent systems (CMIS) combines the results of TOC predicted from intelligent systems including fuzzy logic (FL), neuro-fuzzy (NF), and neural network (NN), each of them has a weight factor showing its contribution in overall prediction. The optimal combination of weights is derived by a genetic algorithm (GA). This method is illustrated using a case study. One hundred twenty-four data points including petrophysical data and measured TOC from three wells of South Pars Gas Field were divided into eighty-seven training sets to build the CMIS model and thirty-seven testing sets to evaluate the reliability of the developed model. The results show that the CMIS performs better than any one of the individual intelligent systems acting alone for predicting TOC. 2009 Journal Article http://hdl.handle.net/20.500.11937/24000 10.1016/j.cageo.2007.12.007 Pergamon, Elsevier fulltext |
| spellingShingle | fuzzy logic South Pars Gas Field neural network genetic algorithm Total organic carbon neuro-fuzzy committee machine petrophysical data Kadkhodaie Ilkhchi, A. Rahimpour-Bonab, H. Rezaee, M. Reza A Committee Machine with Intelligent Systems for Estimation of Total Organic Carbon Content from Petrophysical Data: an Example from Kangan and Dalan Reservoirs in South Pars Gas Field, Iran |
| title | A Committee Machine with Intelligent Systems for Estimation of Total Organic Carbon Content from Petrophysical Data: an Example from Kangan and Dalan Reservoirs in South Pars Gas Field, Iran |
| title_full | A Committee Machine with Intelligent Systems for Estimation of Total Organic Carbon Content from Petrophysical Data: an Example from Kangan and Dalan Reservoirs in South Pars Gas Field, Iran |
| title_fullStr | A Committee Machine with Intelligent Systems for Estimation of Total Organic Carbon Content from Petrophysical Data: an Example from Kangan and Dalan Reservoirs in South Pars Gas Field, Iran |
| title_full_unstemmed | A Committee Machine with Intelligent Systems for Estimation of Total Organic Carbon Content from Petrophysical Data: an Example from Kangan and Dalan Reservoirs in South Pars Gas Field, Iran |
| title_short | A Committee Machine with Intelligent Systems for Estimation of Total Organic Carbon Content from Petrophysical Data: an Example from Kangan and Dalan Reservoirs in South Pars Gas Field, Iran |
| title_sort | committee machine with intelligent systems for estimation of total organic carbon content from petrophysical data: an example from kangan and dalan reservoirs in south pars gas field, iran |
| topic | fuzzy logic South Pars Gas Field neural network genetic algorithm Total organic carbon neuro-fuzzy committee machine petrophysical data |
| url | http://hdl.handle.net/20.500.11937/24000 |