Comparison of different machine learning algorithms for predicting the SAGD production performance

Huang, Ziteng; Chen, Zhangxin

doi:10.1016/j.petrol.2021.108559

Cited by 34 publications

(9 citation statements)

References 13 publications

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“…Several attributes are almost exclusive in sparse feature space, i.e. they hardly take nonzero values simultaneously [91]. A typical example of exclusive features is One-hot encoded features.…”

Section: (4) Lightgbmmentioning

confidence: 99%

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

2022

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Ensemble learning techniques have achieved state-of-the-art performance in diverse machine learning applications by combining the predictions from two or more base models. This paper presents a concise overview of ensemble learning, covering the three main ensemble methods: bagging, boosting, and stacking, their early development to the recent state-of-the-art algorithms. The study focuses on the widely used ensemble algorithms, including random forest, adaptive boosting (AdaBoost), gradient boosting, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost). An attempt is made to concisely cover their mathematical and algorithmic representations, which is lacking in the existing literature and would be beneficial to machine learning researchers and practitioners.

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Section: (4) Lightgbmmentioning

confidence: 99%

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

2022

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“…The reservoir properties of the basic infill well cases are derived from the previous studies. [27,32] The number of grid blocks used in this model is 50 Â 5 Â 30. The grid sizes along the x-axis and z-axis are 1 m each, while the grid size along the y-axis is 200 m. A pair of horizontal wells, an injector and a producer, is set on one edge of the model in the 50th grid block on the x-axis.…”

Section: Simulated Model Constructionmentioning

confidence: 99%

“…In this study, numerous different GBDT algorithms are employed and compared, including XGBoost, LightGBM, and CatBoost, which have been introduced and applied in the previous studies. [27,32,36] 2.5.3 | Support vector machines (SVM)…”

Section: Gradient Boosting Decision Tree (Gbdt)mentioning

confidence: 99%

“…The introduction of those GBDT algorithms hyperparameters has been detailed in previous studies. [27,32,36] F I G U R E 8 An example training process of a gradient boosting decision tree algorithms (GBDT) algorithm.…”

Section: Gbdt Modelsmentioning

confidence: 99%

“…Those studies paid much attention to the impact of reservoir heterogeneity, [12][13][14][15][16][17][18] optimization, [19][20][21][22][23] production performance prediction, [24][25][26][27][28] and clustering, [29][30][31] which have significantly improved the ability to predict a SAGD process. However, studies of a data-driven model applied to infill wells in a SAGD process are still rare.…”

Section: Introductionmentioning

confidence: 99%

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Integration of machine learning and data analysis for the SAGD production performance with infill wells

2023

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There have been numerous studies on predicting the production performance of the steam assisted gravity drainage (SAGD) process by data‐driven models with different machine learning algorithms since their introduction into industry. Similar efforts on SAGD infill wells, nevertheless, remain rare for this advanced alteration in improving the classical SAGD performance. On the other hand, predictive tools to optimize an infill well start time is useful in maximizing bitumen production and minimizing its costs. In this paper, a series of SAGD infill well models are constructed with selected ranges of operational conditions. Three SAGD infill well production performance indicators, namely, an increased ratio (), a total steam–oil ratio (SORtotal), and a stolen ratio () for each SAGD infill well, are calculated based on simulated infill well cases and control models. Five different machine learning algorithms (an artificial neural network [ANN] algorithm, three gradient boosting decision tree [GBDT] algorithms, and a support vector machine [SVM] algorithm) are trained, tested, and evaluated for their effectiveness in predicting those three indicators as output parameters, given seven SAGD relevant parameters as input parameters. Comparisons of different data sets show that the ANN is the best in predicting all three performance indicators under different infill well start times among all the above machine learning algorithms, while the GBDT algorithms have a better ability to learn a variation trend in the SAGD infill well performance.

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A Method for Evaluating the Suitability of CO2 Injection in Oil Reservoirs Based on Multi-model Coupled Machine Learning Algorithm

Su,

Tian,

Jiao

et al. 2024

Springer Series in Geomechanics and Geoengineering

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Comparison of different machine learning algorithms for predicting the SAGD production performance

Cited by 34 publications

References 13 publications

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

Integration of machine learning and data analysis for the SAGD production performance with infill wells

A Method for Evaluating the Suitability of CO2 Injection in Oil Reservoirs Based on Multi-model Coupled Machine Learning Algorithm

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