Intelligent knowledge management for identifying excess water production in oil wells
In hydrocarbon production, certain amount of water production is inevitable and sometimes even necessary. Problems arise when water rate exceeds the WOR (water/oil ratio) economic level, producing no or little oil with it. A lot of resources are set aside for implementing strategies to effectively m...
| Main Authors: | , |
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| Format: | Conference Paper |
| Published: |
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/23812 |
| _version_ | 1848751254676701184 |
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| author | Rabiei, M. Gupta, Ritu |
| author_facet | Rabiei, M. Gupta, Ritu |
| author_sort | Rabiei, M. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In hydrocarbon production, certain amount of water production is inevitable and sometimes even necessary. Problems arise when water rate exceeds the WOR (water/oil ratio) economic level, producing no or little oil with it. A lot of resources are set aside for implementing strategies to effectively manage the production of the excessive water to minimize its environmental and economic impact. Water shutoff technologies are available to effectively manage excess water production; however, their use requires the knowledge of the underlying cause. The conventional diagnostic techniques are only capable of identifying the existence of excess water and cannot pinpoint the exact type and cause of the water production mechanism (WPM). A common industrial practice is to monitor the trend of changes in WOR against time to identify two types of WPMs, namely coning and channelling. However, it has been demonstrated that WOR plots are not general and there are deficiencies in the current usage of these plots. In this paper we present a new technique for diagnosing WPMs. We extracted predictive data points from plots of WOR against the oil recovery factor and collect information on a range of basic reservoir characteristics. This information is processed through tree-based ensemble classifiers. Next we construct a new dataset smeared from the original dataset, and generate a depictive tree for ensemble using a combination of the new and original datasets. To generate the depictive tree we used a new class of tree classifiers called logistic model tree (LMT). Our results show high prediction accuracy rates of at least 93% and easy to implement workflow. Adoption of this methodology would lead to accurate and timely management of water production saving oil and gas companies considerable time and money. |
| first_indexed | 2025-11-14T07:49:48Z |
| format | Conference Paper |
| id | curtin-20.500.11937-23812 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:49:48Z |
| publishDate | 2013 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-238122017-09-13T14:01:06Z Intelligent knowledge management for identifying excess water production in oil wells Rabiei, M. Gupta, Ritu In hydrocarbon production, certain amount of water production is inevitable and sometimes even necessary. Problems arise when water rate exceeds the WOR (water/oil ratio) economic level, producing no or little oil with it. A lot of resources are set aside for implementing strategies to effectively manage the production of the excessive water to minimize its environmental and economic impact. Water shutoff technologies are available to effectively manage excess water production; however, their use requires the knowledge of the underlying cause. The conventional diagnostic techniques are only capable of identifying the existence of excess water and cannot pinpoint the exact type and cause of the water production mechanism (WPM). A common industrial practice is to monitor the trend of changes in WOR against time to identify two types of WPMs, namely coning and channelling. However, it has been demonstrated that WOR plots are not general and there are deficiencies in the current usage of these plots. In this paper we present a new technique for diagnosing WPMs. We extracted predictive data points from plots of WOR against the oil recovery factor and collect information on a range of basic reservoir characteristics. This information is processed through tree-based ensemble classifiers. Next we construct a new dataset smeared from the original dataset, and generate a depictive tree for ensemble using a combination of the new and original datasets. To generate the depictive tree we used a new class of tree classifiers called logistic model tree (LMT). Our results show high prediction accuracy rates of at least 93% and easy to implement workflow. Adoption of this methodology would lead to accurate and timely management of water production saving oil and gas companies considerable time and money. 2013 Conference Paper http://hdl.handle.net/20.500.11937/23812 10.2495/PMR120161 restricted |
| spellingShingle | Rabiei, M. Gupta, Ritu Intelligent knowledge management for identifying excess water production in oil wells |
| title | Intelligent knowledge management for identifying excess water production in oil wells |
| title_full | Intelligent knowledge management for identifying excess water production in oil wells |
| title_fullStr | Intelligent knowledge management for identifying excess water production in oil wells |
| title_full_unstemmed | Intelligent knowledge management for identifying excess water production in oil wells |
| title_short | Intelligent knowledge management for identifying excess water production in oil wells |
| title_sort | intelligent knowledge management for identifying excess water production in oil wells |
| url | http://hdl.handle.net/20.500.11937/23812 |