An adaptive ensemble learning by opposite multiverse optimizer and its application in fluid identification for unconventional oil reservoirs - Zhang, Yongan; Zhang, Xingyu; Sun, Youzhuang; Gong, An; Li, Mengyan (Frontiers in Earth Science, 2023)
Unconventional reservoirs are rich in petroleum resources. For these reservoirs, reservoir fluid property identification is an essential process in unconventional oil reservoir evaluation methods, significant for enhancing reservoir recovery ratio and economic efficiency.However, due to the mutual interference of several factors, such as reservoir lithology and pore structure, identifying the properties of oil and water using traditional reservoir fluid identification methods or a single predictive model for unconventional oil reservoirs is inadequate in terms of accuracy. In this paper, we propose a new ensemble learning model that combines 12 base learners using Multi-Verse Optimizer to improve the accuracy of reservoir fluid identification for unconventional reservoirs. The experimental results show that the overall classification accuracy of AIL-OMO is 0.85. Compare with six conventional reservoir fluid identification models, AIL-OMO with accuracy of on dry layer, oil-water layer and oil layer reached 94.33%, 90.46% and 90.66%, shows highest ACC and better generalization on fluid identification of unconventional oil reservoirs.