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| Title: | Predicting injection profiles using ANFIS |
| Author (s): | Wei, Mingzhen Bai, Baojun Sung, Andrew H. Liu, Qingzhong Wang, Jiachun Cather, Martha E. |
| Department/Lab Affiliations: | Energy Research and Development Center Geological Sciences & Engineering |
| Keywords: | ANFIS data quality problems grid partition petroleum industry subtractive clustering |
| Issue Date: | 2007-10-15 |
| Publisher: | Elsevier |
| Citation: | Wei, Mingzhen, Baojun Bai, Andrew H. Sung, Qingzhong Liu, Jiachun Wang, and Martha E. Cather. "Predicting Injection Profiles Using ANFIS." Information Sciences, vol. 177, no. 20, 2007. |
| Abstract: | Decision making pertaining to injection profiles during oilfield development is one of the most important factors that affect the oilfield's performance. Since injection profiles are affected by multiple geological and development factors, it is difficult to model their complicated, non-linear relationships using conventional approaches. In this paper, two adaptive-network-based fuzzy inference systems (ANFIS) based neuro-fuzzy systems are presented. The two neuro-fuzzy systems are: (1) grid partition based fuzzy inference system (FIS), named ANFIS-GRID, and (2) subtractive clustering based FIS, named ANFIS-SUB. We compare the performance of resultant FIS and study the effect of parameters. A real-world injection profile data set from the Daqing Oilfield, China is used. FIS are generated and tested using training and testing data from that data set. The impact of data quality on the performance of FIS is also studied. Experiments demonstrate that although soft computing methods are somewhat of tolerant of inaccurate inputs, cleaned data results in more robust models for practical problems. ANFIS-GRID outperforms ANFIS-SUB due to its simplicity in parameter selection and its fitness in the target problem. |
| Type: | Article - Journal text |
| In Title: | Information Sciences |
| Copyright Notice: | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. Pre-print: author can archive with restrictions;Restriction: This does not include Cell Press; Post-print: author can archive; FULL COPYRIGHT INFORMATION: |
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| title | Predicting injection profiles using ANFIS |
| contributor.author | Wei, Mingzhen |
| contributor.author | Bai, Baojun |
| contributor.author | Sung, Andrew H. |
| contributor.author | Liu, Qingzhong |
| contributor.author | Wang, Jiachun |
| contributor.author | Cather, Martha E. |
| contributor.deptlab | Energy Research and Development Center |
| contributor.deptlab | Geological Sciences & Engineering |
| contributor.sponsor | New Mexico Petroleum Recovery Research Center |
| subject | ANFIS |
| subject | data quality problems |
| subject | grid partition |
| subject | petroleum industry |
| subject | subtractive clustering |
| date.issued | 2007-10-15 |
| publisher | Elsevier |
| identifier.citation | Wei, Mingzhen, Baojun Bai, Andrew H. Sung, Qingzhong Liu, Jiachun Wang, and Martha E. Cather. "Predicting Injection Profiles Using ANFIS." Information Sciences, vol. 177, no. 20, 2007. |
| identifier.pub.URI | |
| description.abstract | Decision making pertaining to injection profiles during oilfield development is one of the most important factors that affect the oilfield's performance. Since injection profiles are affected by multiple geological and development factors, it is difficult to model their complicated, non-linear relationships using conventional approaches. In this paper, two adaptive-network-based fuzzy inference systems (ANFIS) based neuro-fuzzy systems are presented. The two neuro-fuzzy systems are: (1) grid partition based fuzzy inference system (FIS), named ANFIS-GRID, and (2) subtractive clustering based FIS, named ANFIS-SUB. We compare the performance of resultant FIS and study the effect of parameters. A real-world injection profile data set from the Daqing Oilfield, China is used. FIS are generated and tested using training and testing data from that data set. The impact of data quality on the performance of FIS is also studied. Experiments demonstrate that although soft computing methods are somewhat of tolerant of inaccurate inputs, cleaned data results in more robust models for practical problems. ANFIS-GRID outperforms ANFIS-SUB due to its simplicity in parameter selection and its fitness in the target problem. |
| type | Article - Journal |
| type.DCMIType | text |
| rights | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. |
| rights | Pre-print: author can archive with restrictions;Restriction: This does not include Cell Press; Post-print: author can archive; |
| rights.URI | |
| relation.isPartOf | Information Sciences |
| date.accessioned | 2008-09-19T20:56:56Z |
| date.available | 2008-07-29T14:19:47Z |
| identifier.persist.URI |