Despite the advances in water shutoff technologies, the lack of an efficient diagnostic technique to identify excess water production mechanisms in oil wells is preventing these technologies being applied to deliver the desired results, which costs oil companies a lot of time and money.
This paper presents a novel integrated approach for diagnosing water production mechanisms by extracting hidden predictive information from water-oil ratio (WOR) graphs and integrating it with static reservoir parameters. Two common types of excess water production mechanism (coning and channelling) were simulated where a wide range of cases were generated by varying a number of reservoir parameters. Plots of WOR against oil recovery factor were used to extract the key features of the WOR data. Tree-based ensemble classifiers were then applied to integrate these features with the reservoir parameters and build classification models for predicting the water production mechanism.
Our results show high rates of prediction accuracy for the range of WOR variables and reservoir parameters explored, which demonstrate the efficiency of the proposed ensemble classifiers. Proactive water control procedures based on proper diagnosis obtained by the proposed technique would greatly optimise oil productivity and reduce the environmental impacts of the unwanted water.
A novel approach in extracting predictive information from water-oil ratio for enhanced water production mechanism diagnosis
