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Modeling of asphaltic sludge formation during acidizing process of oil well reservoir using machine learning methods

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  • Shakouri, Sina
  • Mohammadzadeh-Shirazi, Maysam

Abstract

Considering the global need for fossil fuels and its limited resources, maximum production from oil reservoirs is important. Acid treatment is a common method to stimulate oil reservoirs, but acid and oil interaction may form undesirable asphaltic sludge, and the prediction of this phenomenon by using machine learning models can be useful for field application. In this study, multi-layer perceptron (MLP), extreme gradient boosting (XGBoost), random forest (RF), and categorical boosting (CatBoost) as four machine learning models were employed to estimate the weight of asphaltic sludge formed. To this end, a data set containing 199 experimental data for seven different oil samples including a wide range of SARA fractions was used. The input parameters of the models included oil properties, acid properties, and the content of protective additives. The statistical analysis indicated that the MLP model has the highest accuracy with the coefficient of determination (R2) of 0.9517. In addition, the impact analysis of the input variables showed that the ferric ion concentration has the highest impact on asphaltic sludge formation with a relevance factor of 0.2755. Finally, using the leverage method, only 4 outlier data points were identified, which proved the validity of the model.

Suggested Citation

  • Shakouri, Sina & Mohammadzadeh-Shirazi, Maysam, 2023. "Modeling of asphaltic sludge formation during acidizing process of oil well reservoir using machine learning methods," Energy, Elsevier, vol. 285(C).
    • Handle: RePEc:eee:energy:v:285:y:2023:i:c:s036054422302827x
    DOI: 10.1016/j.energy.2023.129433
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    References listed on IDEAS

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    1. Hui, Gang & Chen, Zhangxin & Wang, Youjing & Zhang, Dongmei & Gu, Fei, 2023. "An integrated machine learning-based approach to identifying controlling factors of unconventional shale productivity," Energy, Elsevier, vol. 266(C).
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    Cited by:

    1. Chen, Zherui & Zhang, Yue & Sun, Jingyue & Tian, Yuxuan & Liu, Weiguo & Chen, Cong & Dai, Sining & Song, Yongchen, 2024. "The influence of cyclodextrin on hydrophobicity of pipeline and asphalt distribution: A green and efficient corrosion inhibitor," Energy, Elsevier, vol. 297(C).

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