| Autor: |
Chavan, Hrishikesh K., Sinharay, Rajib K., Kumar, Vijay, Patel, Darvin |
| Zdroj: |
Petroleum Science & Technology; 2024, Vol. 42 Issue 25, p4350-4368, 19p |
| Abstrakt: |
Screening of an appropriate enhanced oil recovery (EOR) technique is important for the maximum reservoir recovery and economics of an oil exploration and production project. The conventional approaches for selecting EOR are often time-consuming and may fail to produce satisfactory results for the complex reservoirs. This paper focuses on implementing of selected five supervised machine learning (ML) techniques for accurate screening of EOR methods for complex reservoirs. Two of them are never used before for EOR screening. A global database of 358 successful EOR projects is collected of which 176 are used in the present study to train Neural Network (NN), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Gaussian Naive Bayes, and Random Forest (RF) classifier and tested their efficiencies. The result shows that the RF classification technique predicts the most suitable EOR with an accuracy value of 0.91 which is the highest accuracy among all other techniques. The study also ranks the reservoir and fluid parameters depending on their influences on the EOR screening. Viscosity is found to be the most impacting factor in the selection of an EOR technique with a feature importance of 34.6%. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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