Data-driven model for predicting production periods in the SAGD process

Huang, Ziteng; Yang, Min; Yang, Bo; Wei, Liu; Chen, Zhangxin

doi:10.1016/j.petlm.2021.12.006

Cited by 8 publications

(7 citation statements)

References 15 publications

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“…The machine learning algorithms did not fully learn a potential relationship between data points, as shown in the ANN curves in Figures 13–15. As proven in previous studies, [ 27,32 ] the increment of a value selection can improve the predictive ability of data‐driven models. As a result, setting smaller or more random step sizes of each input parameter can yield better results.…”

Section: Resultsmentioning

confidence: 84%

“…The introduction of those GBDT algorithms hyperparameters has been detailed in previous studies. [ 27,32,36 ]…”

Section: Resultsmentioning

confidence: 99%

“…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 \times 5 \times 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: Methodologiesmentioning

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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Section: Resultsmentioning

confidence: 84%

“…The introduction of those GBDT algorithms hyperparameters has been detailed in previous studies. [ 27,32,36 ]…”

Section: Resultsmentioning

confidence: 99%

“…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 \times 5 \times 30

Section: Methodologiesmentioning

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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“…The generation of each tree in the boosting procedure can improve the poor learning of the previous tree [ 44 , 45 ]. Table 6 presents the hyperparameters and the search ranges of the XGBoost model in this study [ 46 , 47 ].…”

Section: Tree-based Machine Learning Architecture To Predict Impedanc...mentioning

confidence: 99%

“…By using an ensemble learning strategy, the CatBoost approach takes advantage of the combination of weaker regression models to form a robust regression model. Table 7 illustrates hyperparameters and the search ranges of the CatBoost model in this study [ 47 , 49 , 50 , 51 ].…”

Section: Tree-based Machine Learning Architecture To Predict Impedanc...mentioning

confidence: 99%

Tree-Based Machine Learning Models with Optuna in Predicting Impedance Values for Circuit Analysis

Lai

Lin

et al. 2023

Micromachines

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The transmission characteristics of the printed circuit board (PCB) ensure signal integrity and support the entire circuit system, with impedance matching being critical in the design of high-speed PCB circuits. Because the factors affecting impedance are closely related to the PCB production process, circuit designers and manufacturers must work together to adjust the target impedance to maintain signal integrity. Five machine learning models, including decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), categorical boosting (CatBoost), and light gradient boosting machine (LightGBM), were used to forecast target impedance values. Furthermore, the Optuna algorithm is used to determine forecasting model hyperparameters. This study applied tree-based machine learning techniques with Optuna to predict impedance. The results revealed that five tree-based machine learning models with Optuna can generate satisfying forecasting accuracy in terms of three measurements, including mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2). Meanwhile, the LightGBM model with Optuna outperformed the other models. In addition, by using Optuna to tune the parameters of machine learning models, the accuracy of impedance matching can be increased. Thus, the results of this study suggest that the tree-based machine learning techniques with Optuna are a viable and promising alternative for predicting impedance values for circuit analysis.

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