Application of Machine Learning Method of Data-Driven Deep Learning Model to Predict Well Production Rate in the Shale Gas Reservoirs

Reservoir modeling to predict shale reservoir productivity is considerably uncertain and time consuming. Since we need to simulate the physical phenomenon of multi-stage hydraulic fracturing. To overcome these limitations, this paper presents an alternative proxy model based on data-driven deep lear...

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Veröffentlicht in:Energies (Basel) Jg. 14; H. 12; S. 3629
Hauptverfasser: Han, Dongkwon, Kwon, Sunil
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.06.2021
Schlagworte:
Artificial intelligence
Data analysis
Data mining
data-driven
Deep learning
deep neural network
Growth models
Hydraulic fracturing
Hydrocarbons
Machine learning
Methods
Neural networks
Neurons
Permeability
principal component analysis
Principal components analysis
Productivity
proxy model
Regression analysis
shale reservoir
Simulation
variable importance analysis
Variables
Visualization
ISSN:1996-1073, 1996-1073
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Zusammenfassung:Reservoir modeling to predict shale reservoir productivity is considerably uncertain and time consuming. Since we need to simulate the physical phenomenon of multi-stage hydraulic fracturing. To overcome these limitations, this paper presents an alternative proxy model based on data-driven deep learning model. Furthermore, this study not only proposes the development process of a proxy model, but also verifies using field data for 1239 horizontal wells from the Montney shale formation in Alberta, Canada. A deep neural network (DNN) based on multi-layer perceptron was applied to predict the cumulative gas production as the dependent variable. The independent variable is largely divided into four types: well information, completion and hydraulic fracturing and production data. It was found that the prediction performance was better when using a principal component with a cumulative contribution of 85% using principal component analysis that extracts important information from multivariate data, and when predicting with a DNN model using 6 variables calculated through variable importance analysis. Hence, to develop a reliable deep learning model, sensitivity analysis of hyperparameters was performed to determine one-hot encoding, dropout, activation function, learning rate, hidden layer number and neuron number. As a result, the best prediction of the mean absolute percentage error of the cumulative gas production improved to at least 0.2% and up to 9.1%. The novel approach of this study can also be applied to other shale formations. Furthermore, a useful guide for economic analysis and future development plans of nearby reservoirs.
Bibliographie:ObjectType-Article-1
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content type line 14
ISSN:1996-1073
1996-1073
DOI:10.3390/en14123629