Predicting the surfactant-polymer flooding performance in chemical enhanced oil recovery: Cascade neural network and gradient boosting decision tree

Larestani, Aydin; Mousavi, Seyed Pezhman; Hadavimoghaddam, Fahimeh; Ostadhassan, Mehdi; Hemmati-Sarapardeh, Abdolhossein

doi:10.1016/j.aej.2022.01.023

Cited by 21 publications

(13 citation statements)

References 28 publications

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“… 39 Usually, CFBPNs can provide more accurate results with a lower number of hidden neurons in comparison to conventional ANNs. 40 However, the higher number of adjustable weights may result in higher computational costs. 41 Figure 4 depicts a typical CFBPN.…”

Section: Intelligent Methodsmentioning

confidence: 99%

“…In the cascade algorithm, each layer is linked to all previous layers, while each layer is linked only to its preceding layer in conventional ANNs . Usually, CFBPNs can provide more accurate results with a lower number of hidden neurons in comparison to conventional ANNs . However, the higher number of adjustable weights may result in higher computational costs …”

Section: Intelligent Methodsmentioning

confidence: 99%

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Compositional Modeling of the Oil Formation Volume Factor of Crude Oil Systems: Application of Intelligent Models and Equations of State

et al. 2022

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This communication primarily concentrates on developing reliable and accurate compositional oil formation volume factor ( B o ) models using several advanced and powerful machine learning (ML) models, namely, extra trees (ETs), random forest (RF), decision trees (DTs), generalized regression neural networks, and cascade-forward back-propagation network, alongside radial basis function and multilayer perceptron neural networks. Along with these models, seven equations of state (EoSs) were employed to estimate B o . The performance of the developed ML models and employed EoSs was assessed through various statistical and graphical evaluations. Overall, the ML models could provide much more accurate predictions in comparison to EoSs. However, the results indicated that tree-based models, specifically ET models, could outperform the other models and can be reliably applied for estimating B o . The most reliable ET model could predict B o with a total average error of 1.17%. Lastly, the outlier detection approach verified the dataset’s consistency detecting only 17 (out of 1224) data points as outliers for the proposed B o models.

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Section: Intelligent Methodsmentioning

confidence: 99%

Section: Intelligent Methodsmentioning

confidence: 99%

Compositional Modeling of the Oil Formation Volume Factor of Crude Oil Systems: Application of Intelligent Models and Equations of State

et al. 2022

Self Cite

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“…A DT is made up of three parts: a root (start) node, several internal (decision) nodes, and several leaf (terminal) nodes. The model's output is represented by the leaf (terminal) nodes, while new information is introduced into the network at its root node [15]. There are some "decision nodes" in between the "root" and "leaf" nodes.…”

Section: Decision Treementioning

confidence: 99%

Machine learning estimation of crude oil viscosity as function of API, temperature, and oil composition: Model optimization and design space

Zhang

Kang

2023

PLoS ONE

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Measurement of viscosity of crude oil is critical for reservoir simulators. Computational modeling is a useful tool for correlation of crude oil viscosity to reservoir conditions such as pressure, temperature, and fluid compositions. In this work, multiple distinct models are applied to the available dataset to predict heavy-oil viscosity as function of a variety of process parameters and oil properties. The computational techniques utilized in this work are Decision Tree (DT), MLP, and GRNN which were utilized in estimation of heavy crude oil samples collected from middle eastern oil fields. For the estimation of viscosity, the firefly algorithm (FA) was employed to optimize the hyper-parameters of the machine learning models. The RMSE error rates for the final models of DT, MLP, and GRNN are 40.52, 25.08, and 30.83, respectively. Also, the R2-scores are 0.921, 0. 978, and 0.933, respectively. Based on this and other criteria, MLP is chosen as the best model for this study in estimating the values of crude oil viscosity.

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“…Finally, the results of the SA show the positive/negative influence of input parameters on oil recovery and NPV. Larestani et al [180] developed various ML models using MLPs, Cascaded networks, RBFNNs, ANNs, SVRs, and DTs to predict the oil recovery factor and NPV, regarding several input parameters, similar to those of Karambeigi et al The authors also performed an SA to determine the most influential parameters. The results showed that the Cascade network outperformed the others, in terms of the recovery factor and NPV prediction accuracy.…”

Section: Static Machine Learning Modelsmentioning

confidence: 99%

Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II

Samnioti,

Gaganis

2023

Energies

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In recent years, Machine Learning (ML) has become a buzzword in the petroleum industry, with numerous applications which guide engineers in better decision making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in multiple modeling procedures, such as individual simulation runs, history matching and production forecast and optimization. However, all of these applications lead to considerable computational time and computer resource-associated costs, rendering reservoir simulators as not fast and robust enough, and thus introducing the need for more time-efficient and intelligent tools, such as ML models which are able to adapt and provide fast and competent results that mimic the simulator’s performance within an acceptable error margin. In a recent paper, the developed ML applications in a subsurface reservoir simulation were reviewed, focusing on improving the speed and accuracy of individual reservoir simulation runs and history matching. This paper consists of the second part of that study, offering a detailed review of ML-based Production Forecast Optimization (PFO). This review can assist engineers as a complete source for applied ML techniques in reservoir simulation since, with the generation of large-scale data in everyday activities, ML is becoming a necessity for future and more efficient applications.

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Predicting the surfactant-polymer flooding performance in chemical enhanced oil recovery: Cascade neural network and gradient boosting decision tree

Cited by 21 publications

References 28 publications

Compositional Modeling of the Oil Formation Volume Factor of Crude Oil Systems: Application of Intelligent Models and Equations of State

Compositional Modeling of the Oil Formation Volume Factor of Crude Oil Systems: Application of Intelligent Models and Equations of State

Machine learning estimation of crude oil viscosity as function of API, temperature, and oil composition: Model optimization and design space

Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II

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