인공신경망을 이용한 석유 회수증진 기법 선정 연구
Artificial Neural Network Approach to Selection of Enhanced Oil Recovery Method
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Enhanced Oil Recovery (EOR) has gained more attention as the results of increasing oil prices and higher oil demands. A large number of EOR methods have been developed, but the selection of EOR method is still a challenging task to the petroleum engineer. One of the main tools used to predict the EOR efficiency is a reservoir simulation which requires detailed reservoir description that may not be available or unreliable at the initial evaluation stage, and also needs extensive time. The other method is to use an expert opinion, but it tends to be biased by the expert's operational experience. This paper proposes an Artificial Neural Network (ANN) approach to enable the petroleum engineer to select the appropriate EOR method based on the reservoir characteristics. The structure of the ANN is optimized to be consisting of four layers by the repeated trial and error during the training. After being trained successfully with the successful EOR field data, the ANN is tested against the new data which are not used for the training, and contain the certain level of error, respectively. The results showed that the ANN model developed in this study can be used to select the most appropriate EOR process in a time and cost effective way.
Abstract
Enhanced Oil Recovery (EOR) has gained more attention as the results of increasing oil prices and higher oil demands. A large number of EOR methods have been developed, but the selection of EOR method is still a challenging task to the petroleum engineer. One of the main tools used to predict the EOR efficiency is a reservoir simulation which requires detailed reservoir description that may not be available or unreliable at the initial evaluation stage, and also needs extensive time. The other method is to use an expert opinion, but it tends to be biased by the expert's operational experience. This paper proposes an Artificial Neural Network (ANN) approach to enable the petroleum engineer to select the appropriate EOR method based on the reservoir characteristics. The structure of the ANN is optimized to be consisting of four layers by the repeated trial and error during the training. After being trained successfully with the successful EOR field data, the ANN is tested against the new data which are not used for the training, and contain the certain level of error, respectively. The results showed that the ANN model developed in this study can be used to select the most appropriate EOR process in a time and cost effective way.
참고문헌 (11)
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