A data-driven shale gas production forecasting method based on the multi-objective random forest regression

Xue, Liang; Liu, Yuetian; Xiong, Yifei; Li, Yanli; Cui, Xuehui; Lei, Gang

doi:10.1016/j.petrol.2020.107801

Cited by 117 publications

(36 citation statements)

References 46 publications

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“…However, due to the complex hydraulic fracture network and the gas flow mechanism, the physics-based forecasting model does not yet exist and therefore the data-based model provides an alternative way of dealing with the production forecasting problem. The tool thus obtained, based on the random-forest method, showed the ability to make reasonable predictions of gas production when the physical model is not yet fully available [24]. Dealing with the problem of optimization of the lay-out of wind power generation parks to maximize the generation of electric energy.…”

Section: Examples Of Data-driven Approaches For Solving Problemsmentioning

confidence: 99%

Data Driven: An Overview and Practical Measures for Organizations

Oliveira¹,

Braga²

2021

IJAERS

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Data is considered a primary resource for innovation. The existence of a large amount of available data, as well as technological tools capable of explore them, allows companies to extract information that can be used to create and implement new ideas and new projects. To this end, the details regarding the care that organizations should have with data are explored. The difficulties regarding the adoption of datadriven approach and some measures to implement this type of decisionmaking approach are also discussed. Some examples of data-driven approaches for diverse industries and products are shown. A real example of prediction model for decision making that is based on industrial data is also discussed. This example shows the difficulties in the preparation of data for the building of these models, which confirms that most of the time spent in the construction of predictive models it is due to this step. The use of the data-driven approach allows organizations to obtain superior results in their processes, thus becoming a tremendous competitive advantage and a special strategic factor in a highly competitive market, regardless of the field of activity.

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Section: Examples Of Data-driven Approaches For Solving Problemsmentioning

confidence: 99%

Data Driven: An Overview and Practical Measures for Organizations

Oliveira¹,

Braga²

2021

IJAERS

View full text Add to dashboard Cite

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“… 18 Xue et al (2020) built a data-driven proxy model based on the multiobjective random forest method to forecast dynamic behavior of shale gas production. 19 Zhong et al (2020) proposed a deep convolutional generative neural network (CDC-GAN)-based data-driven proxy model to predict the field oil production of reservoir developed by the waterflooding technology. 20 Deng and Pan (2021) designed and implemented the echo state network (ESN)-based data-driven proxy model to complete predicting tasks for waterflooding fields.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Proxy Model for Forecasting of Cumulative Oil Production during the Steam-Assisted Gravity Drainage Process

Liu

et al. 2021

ACS Omega

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The purpose of this study is to develop a data-driven proxy model for forecasting of cumulative oil (Cum-oil) production during the steam-assisted gravity drainage process. During the model building process, an artificial neural network (ANN) is used to offer a complementary and computationally efficient tool for the physics-driven model, and the von Bertalanffy performance indicator is used to bridge the physics-driven model with the ANN. After that, the accuracy of the model is validated by blind-testing cases. Average absolute percentage error of related parameters of the performance indicator in the testing data set is 0.77%, and the error of Cum-oil production after 20 years is 0.52%. The results illustrate that the integration of performance indicator and ANN makes it possible to solve time series problems in an efficient way. Besides, the data-driven proxy model could be applied to fast parametric studies, quick uncertainty analysis with the Monte Carlo method, and average daily oil production prediction. The findings of this study could help for better understanding of combination of physics-driven model and data-driven model and illustrate the potential for application of the data-driven proxy model to help reservoir engineers, making better use of this significant thermal recovery technology for oil sands or heavy oil reservoirs.

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“…It was concluded that random forest has the best performance and these models were useful for designing hydraulic fracture treatments. Liang et al [16] used multi-objective random forest to predict dynamic production data. However, there remains a challenge to choose the right method for production prediction.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Production Prediction Model and Its Application in Duvernay Formation

et al. 2021

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The production of a single gas well is influenced by many geological and completion factors. The aim of this paper is to build a production prediction model based on machine learning technique and identify the most important factor for production. Firstly, around 159 horizontal wells were collected, targeting the Duvernay Formation with detailed geological and completion records. Secondly, the key factors were selected using grey relation analysis and Pearson correlation. Then, three statistical models were built through multiple linear regression (MLR), support vector regression (SVR), gaussian process regression (GPR). The model inputs include fluid volume, proppant amount, cluster counts, stage counts, total horizontal lateral length, gas saturation, total organic carbon content, condensate-gas ratio. The model performance was assessed by root mean squared errors (RMSE) and R-squared value. Finally, sensitivity analysis was applied based on best performance model. The analysis shows following conclusions: (1) GPR model shows the best performance with the highest R-squared value and the lowest RMSE. In the testing set, the model shows a R-squared of 0.8 with a RMSE of 280.54 × 104 m3 in the prediction of cumulative gas production within 1st 6 producing months and gives a R-squared of 0.83 with a RMSE of 1884.3 t in the prediction of cumulative oil production within 1st 6 producing months (2) Sensitivity analysis based on GPR model indicates that condensate-gas ratio, fluid volume, and total organic carbon content are the most important features to cumulative oil production within 1st 6 producing months. Fluid volume, Stages, and total organic carbon content are the most significant factors to cumulative gas production within 1st 6 producing months. The analysis progress and results developed in this study will assist companies to build prediction models and figure out which factors control well performance.

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A data-driven shale gas production forecasting method based on the multi-objective random forest regression

Cited by 117 publications

References 46 publications

Data Driven: An Overview and Practical Measures for Organizations

Data Driven: An Overview and Practical Measures for Organizations

Data-Driven Proxy Model for Forecasting of Cumulative Oil Production during the Steam-Assisted Gravity Drainage Process

Machine Learning-Based Production Prediction Model and Its Application in Duvernay Formation

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