Comparative Study on Supervised Learning Models for Productivity Forecasting of Shale Reservoirs Based on a Data-Driven Approach
| Autor: | Jihun Jung, Sunil Kwon, Dongkwon Han |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
clustering analysis
variables importance method Computer science 020209 energy 02 engineering and technology Machine learning computer.software_genre lcsh:Technology Field (computer science) Data-driven lcsh:Chemistry Hydraulic fracturing 020401 chemical engineering 0202 electrical engineering electronic engineering information engineering General Materials Science 0204 chemical engineering Cluster analysis lcsh:QH301-705.5 Instrumentation Fluid Flow and Transfer Processes lcsh:T business.industry Process Chemistry and Technology Supervised learning shale gas General Engineering lcsh:QC1-999 Computer Science Applications Random forest Support vector machine machine learning lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 data-driven Artificial intelligence Gradient boosting lcsh:Engineering (General). Civil engineering (General) business computer lcsh:Physics |
| Zdroj: | Applied Sciences Volume 10 Issue 4 Applied Sciences, Vol 10, Iss 4, p 1267 (2020) |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app10041267 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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