Development of a data-driven forecasting tool for hydraulically fractured, horizontal wells in tight-gas sands

Kulga, Burak; Artun, Emre; Ertekin, Turgay

doi:10.1016/j.cageo.2017.03.009

Cited by 40 publications

(15 citation statements)

References 49 publications

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“…Thus, in recent years, researchers have begun to develop alternative models to replace reservoir simulation and DCA model [19][20][21][22]. For example, some studies have proposed an advanced, fast analytical model based on complex analysis methods [23,24].…”

Section: Introductionmentioning

confidence: 99%

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

Han

Kwon

2021

Energies

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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.

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Section: Introductionmentioning

confidence: 99%

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

Han

Kwon

2021

Energies

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“…14 Kulga et al (2017) proposed an artificial neural network (ANN)-based forecast model to predict daily gas production from tight-gas sand formation and found that the ANN model has a good performance. 15 Amirian et al ( 2018) employed artificial and computational intelligence (ACI)-based learning algorithms to realize performance forecasting for polymer flooding in heavy oil reservoirs. 16 Sagheer et al (2019) built a deep long shortterm memory (LSTM) network, in order to solve time series prediction problem of petroleum production.…”

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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“…A practical alternative is to use a proxy model, which is well suited for repeated calculations. There are two main types of surrogate models, where one is the reduced physical model (Wilson and Durlofsky, 2013;Pouladi et al, 2017), and the other one is the data-driven model (Zhou et al, 2014;Kulga et al, 2017;Wang and Chen, 2019b;Wang et al, 2021;Xue et al, 2021). The data-driven model can quickly establish a mathematical model approaching the accuracy of the numerical simulation model by sampling the reservoir numerical simulator.…”

Section: Introductionmentioning

confidence: 99%

“…It is reported that GA recursive sampling assisted a dynamically updated Artificial Neural Network (ANN) method to optimize production (Golzari et al, 2015). A datadriven forecasting technique is built based on ANN to complement the simulator-based model (Kulga et al, 2017). The oil production models are developed based on Random Forests (RFs), Support Vector Regression (SVR), and Gradient-Boosting Machine (GBM) (Schuetter et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

A Novel Shale Gas Production Prediction Model Based on Machine Learning and Its Application in Optimization of Multistage Fractured Horizontal Wells

Wang

Qiao

et al. 2021

Front. Earth Sci.

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Shale gas production prediction and horizontal well parameter optimization are significant for shale gas development. However, conventional reservoir numerical simulation requires extensive resources in terms of labor, time, and computations, and so the optimization problem still remains a challenge. Therefore, we propose, for the first time, a new gas production prediction methodology based on Gaussian Process Regression (GPR) and Convolution Neural Network (CNN) to complement the numerical simulation model and achieve rapid optimization. Specifically, through sensitivity analysis, porosity, permeability, fracture half-length, and horizontal well length were selected as influencing factors. Second, the n-factorial experimental design was applied to design the initial experiment and the dataset was constructed by combining the simulation results with the case parameters. Subsequently, the gas production model was built by GPR, CNN, and SVM based on the dataset. Finally, the optimal model was combined with the optimization algorithm to maximize the Net Present Value (NPV) and obtain the optimal fracture half-length and horizontal well length. Experimental results demonstrated the GPR model had prominent modeling capabilities compared with CNN and Support Vector Machine (SVM) and achieved the satisfactory prediction performance. The fracture half-length and well length optimized by the GPR model and reservoir numerical simulation model converged to almost the same values. Compared with the field reference case, the optimized NPV increased by US$ 7.43 million. Additionally, the time required to optimize the GPR model was 1/720 of that of numerical simulation. This work enriches the knowledge of shale gas development technology and lays the foundation for realizing the scale-benefit development for shale gas, so as to realize the integration of geological engineering.

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Development of a data-driven forecasting tool for hydraulically fractured, horizontal wells in tight-gas sands

Cited by 40 publications

References 49 publications

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

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

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

A Novel Shale Gas Production Prediction Model Based on Machine Learning and Its Application in Optimization of Multistage Fractured Horizontal Wells

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