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

Author

Listed:
  • Qitao Zhang

    (School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
    National & Local Joint Engineering Lab for Big Data Analysis and Computer Technology, Beijing 100190, 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
    National & Local Joint Engineering Lab for Big Data Analysis and Computer Technology, Beijing 100190, China)

  • Yuanchun Zhou

    (National & Local Joint Engineering Lab for Big Data Analysis and Computer Technology, Beijing 100190, China
    Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China)

  • Hongqing Song

    (School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
    National & Local Joint Engineering Lab for Big Data Analysis and Computer Technology, Beijing 100190, China)

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.

Suggested Citation

  • Qitao Zhang & Chenji Wei & Yuhe Wang & Shuyi Du & Yuanchun Zhou & Hongqing Song, 2019. "Potential for Prediction of Water Saturation Distribution in Reservoirs Utilizing Machine Learning Methods," Energies, MDPI, vol. 12(19), pages 1-21, September.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:19:p:3597-:d:269173
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    Citations

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    Cited by:

    1. Haochen Wang & Yafeng Ju & Kai Zhang & Chengcheng Liu & Hongwei Yin & Zhongzheng Wang & Zhigang Yu & Ji Qi & Yanzhong Wang & Wenzheng Zhou, 2023. "Saturation and Pressure Prediction for Multi-Layer Irregular Reservoirs with Variable Well Patterns," Energies, MDPI, vol. 16(6), pages 1-25, March.
    2. Baozhong Wang & Jyotsna Sharma & Jianhua Chen & Patricia Persaud, 2021. "Ensemble Machine Learning Assisted Reservoir Characterization Using Field Production Data–An Offshore Field Case Study," Energies, MDPI, vol. 14(4), pages 1-20, February.

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