Enhanced oil recovery by nanoparticles ﬂooding: From numerical modeling improvement to machine learning prediction

Alwated, Budoor; El-Amin, Mohamed F.

doi:10.46690/ager.2021.03.06

Cited by 25 publications

(10 citation statements)

References 34 publications

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“…Wang et al [25] predicted the first annual productivity of subwells based on RF, GBDT, Linear Regression and neural network, among which RF and GBDT had significantly better prediction effect than the latter. Alwated et al [26] mainly studied the machine learning technology, including random forest, gradient boosting regression and decision tree to predict the migration of fluid in porous media. However, the GBDT algorithm has not been fully demonstrated its ability to predict single-layer production, and complex geological conditions have not been fully considered.…”

Section: Gbdtmentioning

confidence: 99%

A Data-Driven Oil Production Prediction Method Based on the Gradient Boosting Decision Tree Regression

Ma¹,

Zhao²,

Zhao³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Accurate prediction of monthly oil and gas production is essential for oil enterprises to make reasonable production plans, avoid blind investment and realize sustainable development. Traditional oil well production trend prediction methods are based on years of oil field production experience and expertise, and the application conditions are very demanding. With the rapid development of artificial intelligence technology, big data analysis methods are gradually applied in various sub-fields of the oil and gas reservoir development. Based on the data-driven artificial intelligence algorithm Gradient Boosting Decision Tree (GBDT), this paper predicts the initial single-layer production by considering geological data, fluid PVT data and well data. The results show that the GBDT algorithm prediction model has great accuracy, significantly improving efficiency and strong universal applicability. The GBDT method trained in this paper can predict production, which is helpful for well site optimization, perforation layer optimization and engineering parameter optimization and has guiding significance for oilfield development.

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Section: Gbdtmentioning

confidence: 99%

A Data-Driven Oil Production Prediction Method Based on the Gradient Boosting Decision Tree Regression

Ma¹,

Zhao²,

Zhao³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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“…First, we briefly analyze the steam-channeling mechanisms during steam injection and confirm the dominant parameters which affect the degree of steam breakthrough. Next, we show the criteria for the evaluation of steam channeling and the detailed procedure to construct the machine-learning identification model [40]. Based on this machine-learning model, we then test the feasibility and accuracy of our model in a realistic reservoir.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Assisted Identification of Steam Channeling after Cyclic Steam Stimulation in Heavy-Oil Reservoirs

Liu

Jiao

et al. 2023

Geofluids

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Cyclic steam stimulation (CSS) is one efficient technology for enhancing heavy-oil recovery. However, after multiple cycles, steam channeling severely limits the thermal recovery because high-temperature steam preferentially breaks through to the producers. To solve the issues of steam breakthrough, it is essentially important and necessary to recognize steam channeling. In this work, a machine-learning-assisted identification model, based on a random-forest ensemble algorithm, is developed to predict the occurrence of steam channeling during steam huff-and-puff processes. The set of feature attributes is constructed based on the permeability ratio, steam quality, and steam-injection speed, which provides the reference for the construction of the training-sample set, steam-channeling reconstruction set, and prediction set. Based on the realistic data, the Pearson correlation coefficient is implemented to confirm the linear correlation among different characteristics; thus, the dimension reduction of the characteristic parameters is achieved. The random oversampling method is adopted to treat the unbalanced training-sample set. Our results show that this model can accurately describe the current state of steam channeling and predict steam propagation in the following cycles.

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“…With the widespread adoption of artificial intelligence in various fields, experts and scholars have proposed new approaches to efficiently and accurately identify lithofacies based on geological data combined with machine-learning methods. 1,2 The origin of machine learning can be traced back to the 1950s, with early machine-learning algorithms, including linear regression and perceptron models. In the 1970s, machine learning was applied to speech and image recognition, such as k-nearest neighbors.…”

Section: Introductionmentioning

confidence: 99%

“…They are significantly influenced by human factors, have a low interpretation efficiency, and are limited by the number of mineral components in the formation, making them unsuitable for use in complex lithologic reservoirs. With the widespread adoption of artificial intelligence in various fields, experts and scholars have proposed new approaches to efficiently and accurately identify lithofacies based on geological data combined with machine‐learning methods 1,2 …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Lithofacies identification of shale formation based on mineral content regression using LightGBM algorithm: A case study in the Luzhou block, South Sichuan Basin, China

Liu,

Zhu,

Zhai

et al. 2023

Energy Science & Engineering

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Lithofacies form the basis for evaluating shale gas fields and play an important role in gas reservoir enrichment. The accurate identification of shale lithofacies is key for exploration and development. Based on well‐logged data, the accuracy of mineral content prediction using machine‐learning regression models is not ideal. Therefore, feature derivation was introduced to enhance the correlation between minerals and lithofacies and improve the data expression ability. Four machine‐learning models for mineral regression were established based on feature‐derived data sets: LightGBM, XGBoost, artificial neural network, and support vector machine. By calculating the evaluation metrics of each model, we found that LightGBM had the best prediction performance. To compare and confirm the accuracy of the model in identifying lithofacies, this study established a new method, MT‐LightGBM, which combines the LightGBM mineral content regression model with mineral ternary diagrams to identify lithofacies. By using the MT‐LightGBM model and LightGBM classification models to identify the target lithofacies, it was found that the accuracy of lithofacies identification of MT‐LightGBM reached 94%. This accuracy is high and is of great significance for understanding and evaluating underground shale reservoirs.

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Enhanced oil recovery by nanoparticles ﬂooding: From numerical modeling improvement to machine learning prediction

Cited by 25 publications

References 34 publications

A Data-Driven Oil Production Prediction Method Based on the Gradient Boosting Decision Tree Regression

A Data-Driven Oil Production Prediction Method Based on the Gradient Boosting Decision Tree Regression

Machine-Learning-Assisted Identification of Steam Channeling after Cyclic Steam Stimulation in Heavy-Oil Reservoirs

Lithofacies identification of shale formation based on mineral content regression using LightGBM algorithm: A case study in the Luzhou block, South Sichuan Basin, China

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