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Open AccessArticle

Comparative Study on Supervised Learning Models for Productivity Forecasting of Shale Reservoirs Based on a Data-Driven Approach

1
Department of Energy and Mineral Resources Engineering, Dong-A University, 37, Nakdong-daero 550beon-gil, Saha-gu, Busan 49315, Korea
2
Department of Research and Development, IHK, 35, Gonghang-daero 81-gil, Gangseo-gu, Seoul 07556, Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(4), 1267; https://doi.org/10.3390/app10041267
Received: 13 January 2020 / Revised: 3 February 2020 / Accepted: 10 February 2020 / Published: 13 February 2020
Due to the rapid development of shale gas, a system has been established that can utilize a considerable amount of data using the database system. As a result, many studies using various machine learning techniques were carried out to predict the productivity of shale gas reservoirs. In this study, a comprehensive analysis is performed for a machine learning method based on data-driven approaches that evaluates productivity for shale gas wells by using various parameters such as hydraulic fracturing and well completion in Eagle Ford shale gas field. Two techniques are used to improve the performance of the productivity prediction machine learning model developed in this study. First, the optimal input variables were selected by using the variables importance method (VIM). Second, cluster analysis was used to analyze the similarities in the datasets and recreate the machine learning models for each cluster to compare the training and test results. To predict productivity, we used random forest (RF), gradient boosting tree (GBM), and support vector machine (SVM) supervised learning models. Compared to other supervised learning models, RF, which is applied with the VIM, has the best prediction performance. The retraining model through cluster analysis has excellent predictive performance. The developed model and prediction workflow are considered useful for reservoir engineers planning of field development plan. View Full-Text
Keywords: shale gas; machine learning; data-driven; variables importance method; clustering analysis shale gas; machine learning; data-driven; variables importance method; clustering analysis
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Han, D.; Jung, J.; Kwon, S. Comparative Study on Supervised Learning Models for Productivity Forecasting of Shale Reservoirs Based on a Data-Driven Approach. Appl. Sci. 2020, 10, 1267.

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