Investigation and Optimization of EOR Screening by Implementing Machine Learning Algorithms

Su, Shengshuai; Zhang, Na; Wang, Peng; Jia, Shun; Zhang, Acacia; Wang, Han; Zhang, Min

doi:10.3390/app132212267

Cited by 4 publications

(1 citation statement)

References 69 publications

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“…They found that the RF performed the best with an accuracy of 0.91. Su et al 18 used the RF, ANN, NB, SVM, and DT for EOR screening of 13 EOR techniques as a function of porosity, permeability, depth, gravity, temperature, viscosity, net thickness, and initial oil saturation. They used 956 EOR experiences to train, validate, and test their models.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of power-law committee machine to combine five machine learning algorithms for enhanced oil recovery screening

Yousefzadeh,

Kazemi,

Al-Maamari

2024

Sci Rep

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One of the main challenges in screening of enhanced oil recovery (EOR) techniques is the class imbalance problem, where the number of different EOR techniques is not equal. This problem hinders the generalization of the data-driven methods used to predict suitable EOR techniques for candidate reservoirs. The main purpose of this paper is to propose a novel approach to overcome the above challenge by taking advantage of the Power-Law Committee Machine (PLCM) technique optimized by Particle Swam Optimization (PSO) to combine the output of five cutting-edge machine learning methods with different types of learning algorithms. The PLCM method has not been used in previous studies for EOR screening. The machine learning models include the Artificial Neural Network (ANN), CatBoost, Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The CatBoost is used for the first time in this work for screening of EOR methods. The role of the PSO is to find the optimal values for the coefficients and exponents of the power-law model. In this study, a bigger dataset than those in previous studies, including 2563 successful worldwide EOR experiences, was gathered. A bigger dataset improves the generalization of the data-driven methods and prevents overfitting. The hyperparameters of the individual machine-learning models were tuned using the fivefold cross-validation technique. The results showed that all the individual methods could predict the suitable EOR method for unseen cases with an average score of 0.868. Among the machine learning models, the KNN and SVM had the highest scores with a value of 0.894 and 0.892, respectively. Nonetheless, after combining the output of the models using the PLCM method, the score of the predictions improved to 0.963, which was a substantial increase. Finally, a feature importance analysis was conducted to find out the most influential parameters on the output. The novelty of this work is having shown the ability of the PLCM technique to construct an accurate model to overcome the class-imbalance issue in EOR screening by utilizing different types of data-driven models. According to feature importance analysis, oil gravity and formation porosity were recognized as the most influential parameters on EOR screening.

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Section: Literature Reviewmentioning

confidence: 99%

Application of power-law committee machine to combine five machine learning algorithms for enhanced oil recovery screening

Yousefzadeh,

Kazemi,

Al-Maamari

2024

Sci Rep

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Advanced EOR screening methodology based on LightGBM and random forest: A classification problem with imbalanced data

Seyyedattar,

Afshar,

Zendehboudi

et al. 2024

Can J Chem Eng

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In an unstable oil market with volatile prices due to various natural and geopolitical factors, it is crucial for oil‐producing companies to enhance the value of their assets by improving the recovery factors of petroleum reservoirs. Primary recovery through natural depletion or artificial lift and secondary recovery using waterflooding and immiscible gas injection typically recover no more than 10%–40% of the available reserves. A significant portion of the hydrocarbons remain unproduced if enhanced oil recovery (EOR) methods are not implemented. EOR projects are extremely costly, complex, and usually have long lead times from the decision‐making and design phases to pilot and full‐field implementations. Therefore, oil and gas operator companies need reliable insights into the best possible EOR options from the early stages of any field development planning. Since screening potential EOR choices is the first step in deciding future production scenarios, a smart EOR screening tool can add significant value by streamlining the EOR decision‐making process. In this study, we developed an EOR screening tool based on two advanced machine learning classification algorithms, random forest and light gradient boosting machine (LightGBM). These tree‐based ensemble learning classifiers were trained on an extensive dataset of 1384 worldwide EOR implementations, encompassing various reservoir conditions and reservoir rock and fluid properties as the feature space, to predict the EOR type as the class label. Considering EOR screening as a classification problem, an essential aspect of model development would be addressing the data imbalance of EOR datasets. To tackle this issue, the adaptive synthetic (ADASYN) sampling method was used to reduce classification bias by oversampling the training sets to achieve uniform class distributions. We designed an iterative model development procedure in which the classifiers were trained and tested on various training and test subsets split by stratified random sampling. For each classifier, the classification results at each iteration were used to build the confusion matrix and calculate model evaluation metrics (accuracy, precision, recall, and F1–score), which were then averaged over all independent runs to provide a fair assessment of classification performance. Moreover, binary receiver operating characteristic (ROC) curves were used to evaluate the classifier predictions and improvements obtained by oversampling. The results showed that both random forest and LightGBM classifiers made accurate class predictions, with LightGBM achieving slightly better classification performance in each modelling scenario (with or without oversampling). In both cases, the oversampling of the training dataset resulted in significant improvement of the classifiers, as evidenced by higher values of the evaluation metrics, leading to considerably more accurate EOR type predictions; specifically, oversampling boosted the prediction accuracy of the random forest model from 78.3% to 89.5% and the LightGBM model from 77.5% to 90.2%. Additionally, feature importance rankings provided valuable insights into which input variables had the greatest impact on model development.

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Estimating oil recovery efficiency of carbonated water injection with supervised machine learning paradigms and implications for uncertainty analysis

Turkson,

Md Yusof,

Fjelde

et al. 2025

Chemometrics and Intelligent Laboratory Systems

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Investigation and Optimization of EOR Screening by Implementing Machine Learning Algorithms

Cited by 4 publications

References 69 publications

Application of power-law committee machine to combine five machine learning algorithms for enhanced oil recovery screening

Application of power-law committee machine to combine five machine learning algorithms for enhanced oil recovery screening

Advanced EOR screening methodology based on LightGBM and random forest: A classification problem with imbalanced data

Estimating oil recovery efficiency of carbonated water injection with supervised machine learning paradigms and implications for uncertainty analysis

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