Potential for Prediction of Water Saturation Distribution in Reservoirs Utilizing Machine Learning Methods

Qitao Zhang, Chenji Wei, Yuhe Wang, Shuyi Du, Yuanchun Zhou and Hongqing Song
Additional contact information
Qitao Zhang: School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
Chenji Wei: Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China
Yuhe Wang: Department of Petroleum Engineering, Texas A&M University at Qatar, Doha 999043, Qatar
Shuyi Du: School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
Yuanchun Zhou: National & Local Joint Engineering Lab for Big Data Analysis and Computer Technology, Beijing 100190, China
Hongqing Song: School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China

Energies, 2019, vol. 12, issue 19, 1-21

Abstract: Machine learning technology is becoming increasingly prevalent in the petroleum industry, especially for reservoir characterization and drilling problems. The aim of this study is to present an alternative way to predict water saturation distribution in reservoirs with a machine learning method. In this study, we utilized Long Short-Term Memory (LSTM) to build a prediction model for forecast of water saturation distribution. The dataset deriving from monitoring and simulating of an actual reservoir was utilized for model training and testing. The data model after training was validated and utilized to forecast water saturation distribution, pressure distribution and oil production. We also compared standard Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) which are popular machine learning methods with LSTM for better water saturation prediction. The results show that the LSTM method has a good performance on the water saturation prediction with overall AARD below 14.82%. Compared with other machine learning methods such as GRU and standard RNN, LSTM has better performance in calculation accuracy. This study presented an alternative way for quick and robust prediction of water saturation distribution in reservoir.

Keywords: machine learning; water saturation; long short-term memory; artificial neural network; computational time (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/19/3597/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/19/3597/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2019:i:19:p:3597-:d:269173

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().