IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v351y2023ics030626192301200x.html
   My bibliography  Save this article

A novel method for petroleum and natural gas resource potential evaluation and prediction by support vector machines (SVM)

Author

Listed:
  • Wang, Qiaochu
  • Chen, Dongxia
  • Li, Meijun
  • Li, Sha
  • Wang, Fuwei
  • Yang, Zijie
  • Zhang, Wanrong
  • Chen, Shumin
  • Yao, Dongsheng

Abstract

Petroleum and natural gas resources (PNGR) are some of the major forms of fossil energy that are important for the development of industry and energy security. Along with the growing demand of petroleum consumption and the requirement for enhancing drilling success rate, reducing the exploration risk and saving exploration cost, prediction method for PNGR potential with high accuracy and wide practicability is needed. However, the existing PNGR evaluation and prediction methods based on traditional statistical principles are far from meeting the requirements of the present petroleum exploration and exploitation. Therefore, this study introduces a novel method for PNGR potential prediction by applying support vector machines (SVM) in the context of the rapid development of artificial intelligence and machine learning. This novel machine learning methodology first proposed a combination of support vector classification (SVC) for hydrocarbon accumulation probability prediction and then support vector regression (SVR) for reserve abundance prediction. The combining use of classification and regression model can fully utilize the professional knowledge of petroleum geology and the powerful data processing capabilities of machine learning algorithms and hence significantly improve the performance of the method. Furthermore, the dataset is set based on petroleum geology knowledge with the feature variables of source rock, sandstone reservoirs, sealing capacity and hydrocarbon migration, whose distribution are predictable and thus ensures the predictive effect in practical petroleum exploration. The results show that the testing accuracy of the hydrocarbon accumulation probability evaluation model by SVC ranges from 80% to 100% with an average of 88.92%. The performance of the SVR model for evaluating reserve abundance also performs well with the highest correlation coefficient of 0.767. In addition, several validation ways are applied for testing the reliability and stability of the model. For a hold-out test for a new zone, the model provides precise prediction of hydrocarbon accumulation probability and reserve abundance with an accuracy of 72.5% and a correlation coefficient of 0.744. The evaluation metric of the F1-score shows an average of 0.91 for the SVC models, the 4-fold cross-validation shows an average correlation coefficient of 0.663 for SVR model, which indicates the good performance of the SVC and SVR model. To conclude, this study not only provides an intelligent ML method system for PNGR potential precisely evaluation and prediction with the combination of SVC and SVR which is firstly used by application of ML in petroleum industry field, but is also significant for the application of ML in petroleum and natural gas exploration and exploitation.

Suggested Citation

  • Wang, Qiaochu & Chen, Dongxia & Li, Meijun & Li, Sha & Wang, Fuwei & Yang, Zijie & Zhang, Wanrong & Chen, Shumin & Yao, Dongsheng, 2023. "A novel method for petroleum and natural gas resource potential evaluation and prediction by support vector machines (SVM)," Applied Energy, Elsevier, vol. 351(C).
  • Handle: RePEc:eee:appene:v:351:y:2023:i:c:s030626192301200x
    DOI: 10.1016/j.apenergy.2023.121836
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030626192301200X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2023.121836?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ciulla, G. & D'Amico, A., 2019. "Building energy performance forecasting: A multiple linear regression approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    2. Wang, Jianzhou & Heng, Jiani & Xiao, Liye & Wang, Chen, 2017. "Research and application of a combined model based on multi-objective optimization for multi-step ahead wind speed forecasting," Energy, Elsevier, vol. 125(C), pages 591-613.
    3. Ma, Kuiyou & Pang, Xiongqi & Pang, Hong & Lv, Chuanbing & Gao, Ting & Chen, Junqing & Huo, Xungang & Cong, Qi & Jiang, Mengya, 2022. "A novel method for favorable zone prediction of conventional hydrocarbon accumulations based on RUSBoosted tree machine learning algorithm," Applied Energy, Elsevier, vol. 326(C).
    4. Niu, Wente & Sun, Yuping & Zhang, Xiaowei & Lu, Jialiang & Liu, Hualin & Li, Qiaojing & Mu, Ying, 2023. "An ensemble transfer learning strategy for production prediction of shale gas wells," Energy, Elsevier, vol. 275(C).
    5. Jamei, Mehdi & Ali, Mumtaz & Karbasi, Masoud & Xiang, Yong & Ahmadianfar, Iman & Yaseen, Zaher Mundher, 2022. "Designing a Multi-Stage Expert System for daily ocean wave energy forecasting: A multivariate data decomposition-based approach," Applied Energy, Elsevier, vol. 326(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Fuwei & Chen, Dongxia & Li, Meijun & Chen, Zhangxin & Wang, Qiaochu & Jiang, Mengya & Rong, Lanxi & Wang, Yuqi & Li, Sha & Iltaf, Khawaja Hasnain & Wanma, Renzeng & Liu, Chen, 2024. "A novel method for predicting shallow hydrocarbon accumulation based on source-fault-sand (S-F-Sd) evaluation and ensemble neural network (ENN)," Applied Energy, Elsevier, vol. 359(C).

    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.
    1. Arkar, C. & Žižak, T. & Domjan, S. & Medved, S., 2020. "Dynamic parametric models for the holistic evaluation of semi-transparent photovoltaic/thermal façade with latent storage inserts," Applied Energy, Elsevier, vol. 280(C).
    2. Zonggui Yao & Chen Wang, 2018. "A Hybrid Model Based on A Modified Optimization Algorithm and An Artificial Intelligence Algorithm for Short-Term Wind Speed Multi-Step Ahead Forecasting," Sustainability, MDPI, vol. 10(5), pages 1-33, May.
    3. 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).
    4. Tonglin Fu & Chen Wang, 2018. "A Hybrid Wind Speed Forecasting Method and Wind Energy Resource Analysis Based on a Swarm Intelligence Optimization Algorithm and an Artificial Intelligence Model," Sustainability, MDPI, vol. 10(11), pages 1-24, October.
    5. Wang, Chen & Zhang, Shenghui & Liao, Peng & Fu, Tonglin, 2022. "Wind speed forecasting based on hybrid model with model selection and wind energy conversion," Renewable Energy, Elsevier, vol. 196(C), pages 763-781.
    6. Wu, Zhuochun & Xiao, Liye, 2019. "A structure with density-weighted active learning-based model selection strategy and meteorological analysis for wind speed vector deterministic and probabilistic forecasting," Energy, Elsevier, vol. 183(C), pages 1178-1194.
    7. Jianzhou Wang & Chunying Wu & Tong Niu, 2019. "A Novel System for Wind Speed Forecasting Based on Multi-Objective Optimization and Echo State Network," Sustainability, MDPI, vol. 11(2), pages 1-34, January.
    8. Hui Wang & Jingxuan Sun & Jianbo Sun & Jilong Wang, 2017. "Using Random Forests to Select Optimal Input Variables for Short-Term Wind Speed Forecasting Models," Energies, MDPI, vol. 10(10), pages 1-13, October.
    9. Zhineng Hu & Jing Ma & Liangwei Yang & Liming Yao & Meng Pang, 2019. "Monthly electricity demand forecasting using empirical mode decomposition-based state space model," Energy & Environment, , vol. 30(7), pages 1236-1254, November.
    10. Shiyi Song & Hong Leng & Ran Guo, 2022. "Multi-Agent-Based Model for the Urban Macro-Level Impact Factors of Building Energy Consumption on Different Types of Land," Land, MDPI, vol. 11(11), pages 1-24, November.
    11. Zhu, Yuli & Jiang, Bo & Zhu, Jiangong & Wang, Xueyuan & Wang, Rong & Wei, Xuezhe & Dai, Haifeng, 2023. "Adaptive state of health estimation for lithium-ion batteries using impedance-based timescale information and ensemble learning," Energy, Elsevier, vol. 284(C).
    12. Yang, Wendong & Wang, Jianzhou & Niu, Tong & Du, Pei, 2019. "A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting," Applied Energy, Elsevier, vol. 235(C), pages 1205-1225.
    13. Tian, Chengshi & Hao, Yan & Hu, Jianming, 2018. "A novel wind speed forecasting system based on hybrid data preprocessing and multi-objective optimization," Applied Energy, Elsevier, vol. 231(C), pages 301-319.
    14. Shiyi Song & Hong Leng & Han Xu & Ran Guo & Yan Zhao, 2020. "Impact of Urban Morphology and Climate on Heating Energy Consumption of Buildings in Severe Cold Regions," IJERPH, MDPI, vol. 17(22), pages 1-24, November.
    15. Niu, Wente & Lu, Jialiang & Sun, Yuping & Zhang, Xiaowei & Li, Qiaojing & Cao, Xu & Liang, Pingping & Zhan, Hongming, 2024. "Techno-economic integration evaluation in shale gas development based on ensemble learning," Applied Energy, Elsevier, vol. 357(C).
    16. Hu, Jianming & Heng, Jiani & Wen, Jiemei & Zhao, Weigang, 2020. "Deterministic and probabilistic wind speed forecasting with de-noising-reconstruction strategy and quantile regression based algorithm," Renewable Energy, Elsevier, vol. 162(C), pages 1208-1226.
    17. He, Qingqing & Wang, Jianzhou & Lu, Haiyan, 2018. "A hybrid system for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 226(C), pages 756-771.
    18. Jun Hao & Xiaolei Sun & Qianqian Feng, 2020. "A Novel Ensemble Approach for the Forecasting of Energy Demand Based on the Artificial Bee Colony Algorithm," Energies, MDPI, vol. 13(3), pages 1-25, January.
    19. Wenninger, Simon & Kaymakci, Can & Wiethe, Christian, 2022. "Explainable long-term building energy consumption prediction using QLattice," Applied Energy, Elsevier, vol. 308(C).
    20. Yang, Zhongshan & Wang, Jian, 2018. "A combination forecasting approach applied in multistep wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm," Applied Energy, Elsevier, vol. 230(C), pages 1108-1125.

    Corrections

    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:eee:appene:v:351:y:2023:i:c:s030626192301200x. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.