Using Chemical Composition of Crude Oil and Artificial Intelligence Techniques to Predict the Reservoir Fluid Properties

Baarimah, Salem O.; Al-Aidroos, Naziha M.; Ba-Jaalah, Khaled Saeed

doi:10.1109/icoice48418.2019.9035135

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…With the rapid development of artificial intelligence techniques in recent years, regression algorithm (Bestagini et al, 2017), support vector machine techniques (Al-Anazi and Gates, 2010;Tohidi-Hosseini et al, 2016), clustering algorithm (Baarimah et al, 2019), genetic algorithm (Guerreiro et al, 1998), artificial neural network (Onwuchekwa, 2018), decision tree algorithm (He et al, 2020), random forest (Wang et al, 2020), and thermodynamicsinformed neural network (Zhang and Sun, 2021) are used in oil reservoir evaluation.…”

Section: Introductionmentioning

confidence: 99%

An adaptive ensemble learning by opposite multiverse optimizer and its application in fluid identification for unconventional oil reservoirs

Zhang

Sun

et al. 2023

Front. Earth Sci.

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Unconventional reservoirs are rich in petroleum resources. Reservoir fluid property identification for these reservoirs is an essential process in unconventional oil reservoir evaluation methods, which is significant for enhancing the reservoir recovery ratio and economic efficiency. However, due to the mutual interference of several factors, 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 accuracy. In this paper, we propose a new ensemble learning model that combines 12 base learners using the multiverse optimizer to improve the accuracy of reservoir fluid identification for unconventional reservoirs. The experimental results show that the overall classification accuracy of the adaptive ensemble learning by opposite multiverse optimizer (AIL-OMO) is 0.85. Compared with six conventional reservoir fluid identification models, AIL-OMO achieved high accuracy on classifying dry layers, oil–water layers, and oil layers, with accuracy rates of 94.33%, 90.46%, and 90.66%. For each model, the identification of the water layer is not accurate enough, which may be due to the classification confusion caused by noise interference in the logging curves of the water layer in unconventional reservoirs.

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