Review of Machine Learning Methods for Steady State Capacity and Transient Production Forecasting in Oil and Gas Reservoir
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Wang, Chang & Zheng, Jianqin & Liang, Yongtu & Wang, Bohong & Klemeš, Jiří Jaromír & Zhu, Zhu & Liao, Qi, 2022. "Deeppipe: An intelligent monitoring framework for operating condition of multi-product pipelines," Energy, Elsevier, vol. 261(PB).
- Wang, Ke & Li, Haitao & Wang, Junchao & Jiang, Beibei & Bu, Chengzhong & Zhang, Qing & Luo, Wei, 2017. "Predicting production and estimated ultimate recoveries for shale gas wells: A new methodology approach," Applied Energy, Elsevier, vol. 206(C), pages 1416-1431.
- Zhou, Guangzhao & Guo, Zanquan & Sun, Simin & Jin, Qingsheng, 2023. "A CNN-BiGRU-AM neural network for AI applications in shale oil production prediction," Applied Energy, Elsevier, vol. 344(C).
- Zha, Wenshu & Liu, Yuping & Wan, Yujin & Luo, Ruilan & Li, Daolun & Yang, Shan & Xu, Yanmei, 2022. "Forecasting monthly gas field production based on the CNN-LSTM model," Energy, Elsevier, vol. 260(C).
- Du, Jian & Zheng, Jianqin & Liang, Yongtu & Lu, Xinyi & Klemeš, Jiří Jaromír & Varbanov, Petar Sabev & Shahzad, Khurram & Rashid, Muhammad Imtiaz & Ali, Arshid Mahmood & Liao, Qi & Wang, Bohong, 2022. "A hybrid deep learning framework for predicting daily natural gas consumption," Energy, Elsevier, vol. 257(C).
- Du, Jian & Zheng, Jianqin & Liang, Yongtu & Ma, Yunlu & Wang, Bohong & Liao, Qi & Xu, Ning & Ali, Arshid Mahmood & Rashid, Muhammad Imtiaz & Shahzad, Khurram, 2024. "A deep learning-based approach for predicting oil production: A case study in the United States," Energy, Elsevier, vol. 288(C).
- Ziwei Mei & Zhentao Shi, 2022. "On LASSO for High Dimensional Predictive Regression," Papers 2212.07052, arXiv.org, revised Jan 2024.
- Wang, Kejun & Qi, Xiaoxia & Liu, Hongda & Song, Jiakang, 2018. "Deep belief network based k-means cluster approach for short-term wind power forecasting," Energy, Elsevier, vol. 165(PA), pages 840-852.
- Göncü, Ahmet & Kuzubaş, Tolga U. & Saltoğlu, Burak, 2024. "Predicting oil prices: A comparative analysis of machine learning and image recognition algorithms for trend prediction," Finance Research Letters, Elsevier, vol. 67(PB).
- Fang, Wenchao & Chen, Sheng & Shi, Shuo, 2022. "Dynamic characteristics and real-time control of a particle-to-sCO2 moving bed heat exchanger assisted by BP neural network," Energy, Elsevier, vol. 256(C).
- Hou, Guolian & Wang, Junjie & Fan, Yuzhen, 2024. "Multistep short-term wind power forecasting model based on secondary decomposition, the kernel principal component analysis, an enhanced arithmetic optimization algorithm, and error correction," Energy, Elsevier, vol. 286(C).
- Mei, Ziwei & Shi, Zhentao, 2024. "On LASSO for high dimensional predictive regression," Journal of Econometrics, Elsevier, vol. 242(2).
- Min, Chao & Wen, Guoquan & Gou, Liangjie & Li, Xiaogang & Yang, Zhaozhong, 2023. "Interpretability and causal discovery of the machine learning models to predict the production of CBM wells after hydraulic fracturing," Energy, Elsevier, vol. 285(C).
- Fuquan Song & Heying Ding & Yongzheng Wang & Shiming Zhang & Jinbiao Yu, 2023. "A Well Production Prediction Method of Tight Reservoirs Based on a Hybrid Neural Network," Energies, MDPI, vol. 16(6), pages 1-22, March.
- Evan D. Sherwin & Jeffrey S. Rutherford & Zhan Zhang & Yuanlei Chen & Erin B. Wetherley & Petr V. Yakovlev & Elena S. F. Berman & Brian B. Jones & Daniel H. Cusworth & Andrew K. Thorpe & Alana K. Ayas, 2024. "US oil and gas system emissions from nearly one million aerial site measurements," Nature, Nature, vol. 627(8003), pages 328-334, March.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Xianlin Ma & Long Zhang & Jie Zhan & Shilong Chang, 2024. "Application of Dual-Stage Attention Temporal Convolutional Networks in Gas Well Production Prediction," Mathematics, MDPI, vol. 12(24), pages 1-16, December.
- Chengwang Liao & Ziwei Mei & Zhentao Shi, 2024. "Nickell Meets Stambaugh: A Tale of Two Biases in Panel Predictive Regressions," Papers 2410.09825, arXiv.org.
- Moreno, Sinvaldo Rodrigues & Seman, Laio Oriel & Stefenon, Stefano Frizzo & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2024. "Enhancing wind speed forecasting through synergy of machine learning, singular spectral analysis, and variational mode decomposition," Energy, Elsevier, vol. 292(C).
- Zhou, Wei & Li, Xiangchengzhen & Qi, ZhongLi & Zhao, HaiHang & Yi, Jun, 2024. "A shale gas production prediction model based on masked convolutional neural network," Applied Energy, Elsevier, vol. 353(PA).
- Gou, Liangjie & Yang, Zhaozhong & Min, Chao & Yi, Duo & Li, Xiaogang & Kong, Bing, 2024. "A novel domain adaptation method with physical constraints for shale gas production forecasting," Applied Energy, Elsevier, vol. 371(C).
- Wang, Jun & Cao, Junxing, 2024. "Reservoir properties inversion using attention-based parallel hybrid network integrating feature selection and transfer learning," Energy, Elsevier, vol. 304(C).
- Zhan Gao & Ji Hyung Lee & Ziwei Mei & Zhentao Shi, 2024. "Econometric Inference for High Dimensional Predictive Regressions," Papers 2409.10030, arXiv.org, revised Nov 2024.
- Du, Jian & Zheng, Jianqin & Liang, Yongtu & Ma, Yunlu & Wang, Bohong & Liao, Qi & Xu, Ning & Ali, Arshid Mahmood & Rashid, Muhammad Imtiaz & Shahzad, Khurram, 2024. "A deep learning-based approach for predicting oil production: A case study in the United States," Energy, Elsevier, vol. 288(C).
- Shi, Changfeng & Zhi, Jiaqi & Yao, Xiao & Zhang, Hong & Yu, Yue & Zeng, Qingshun & Li, Luji & Zhang, Yuxi, 2023. "How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning," Energy, Elsevier, vol. 269(C).
- Cheng, Fang & Liu, Hui, 2024. "Multi-step electric vehicles charging loads forecasting: An autoformer variant with feature extraction, frequency enhancement, and error correction blocks," Applied Energy, Elsevier, vol. 376(PB).
- Tong, Jianfeng & Liu, Zhenxing & Zhang, Yong & Zheng, Xiujuan & Jin, Junyang, 2023. "Improved multi-gate mixture-of-experts framework for multi-step prediction of gas load," Energy, Elsevier, vol. 282(C).
- Yang, Mao & Wang, Da & Xu, Chuanyu & Dai, Bozhi & Ma, Miaomiao & Su, Xin, 2023. "Power transfer characteristics in fluctuation partition algorithm for wind speed and its application to wind power forecasting," Renewable Energy, Elsevier, vol. 211(C), pages 582-594.
- Cheng, Runkun & Yang, Di & Liu, Da & Zhang, Guowei, 2024. "A reconstruction-based secondary decomposition-ensemble framework for wind power forecasting," Energy, Elsevier, vol. 308(C).
- Feiyu Chen & Linghui Sun & Boyu Jiang & Xu Huo & Xiuxiu Pan & Chun Feng & Zhirong Zhang, 2025. "A Review of AI Applications in Unconventional Oil and Gas Exploration and Development," Energies, MDPI, vol. 18(2), pages 1-30, January.
- Shengli Liao & Xudong Tian & Benxi Liu & Tian Liu & Huaying Su & Binbin Zhou, 2022. "Short-Term Wind Power Prediction Based on LightGBM and Meteorological Reanalysis," Energies, MDPI, vol. 15(17), pages 1-21, August.
- Shi, Wenrui & Zhang, Chaomo & Jiang, Shu & Liao, Yong & Shi, Yuanhui & Feng, Aiguo & Young, Steven, 2022. "Study on pressure-boosting stimulation technology in shale gas horizontal wells in the Fuling shale gas field," Energy, Elsevier, vol. 254(PB).
- Cen, Xiao & Chen, Zengliang & Chen, Haifeng & Ding, Chen & Ding, Bo & Li, Fei & Lou, Fangwei & Zhu, Zhenyu & Zhang, Hongyu & Hong, Bingyuan, 2024. "User repurchase behavior prediction for integrated energy supply stations based on the user profiling method," Energy, Elsevier, vol. 286(C).
- Feng, Zhanyu & Zhang, Jian & Jiang, Han & Yao, Xuejian & Qian, Yu & Zhang, Haiyan, 2024. "Energy consumption prediction strategy for electric vehicle based on LSTM-transformer framework," Energy, Elsevier, vol. 302(C).
- Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
- Nguyen, Phong & Carey, J. William & Viswanathan, Hari S. & Porter, Mark, 2018. "Effectiveness of supercritical-CO2 and N2 huff-and-puff methods of enhanced oil recovery in shale fracture networks using microfluidic experiments," Applied Energy, Elsevier, vol. 230(C), pages 160-174.
More about this item
Keywords
machine learning; steady-state capacity prediction; transient production forecasting; spatio-temporal analysis; physics-informed neural networks;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:18:y:2025:i:4:p:842-:d:1588724. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.