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A novel method for favorable zone prediction of conventional hydrocarbon accumulations based on RUSBoosted tree machine learning algorithm

Author

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  • Ma, Kuiyou
  • Pang, Xiongqi
  • Pang, Hong
  • Lv, Chuanbing
  • Gao, Ting
  • Chen, Junqing
  • Huo, Xungang
  • Cong, Qi
  • Jiang, Mengya

Abstract

The prediction of favorable zone (FZ) is the most important step for conventional hydrocarbon accumulations (CHAs) exploration. Recently, the method of coupling multiple hydrocarbon accumulation (HA) elements is widely used to predict the distribution of FZ in the petroleum exploration field. However, the forming mechanism of CHAs is extremely complicated, which causes difficulty in accurately describing the relationship between multiple HA elements and HA probability (HAP). Hence, it is difficult to predict the distribution of FZ quantitatively and credibly using traditional methods. This study proposes a method for predicting FZ for CHAs based on random undersampling boosted (RUSBoosted) tree machine learning (ML) algorithm. First, the characteristics of data in the petroleum exploration field are clarified, and a suitable ML algorithm is selected. Second, the theory and knowledge of the petroleum exploration field is integrated into the data, a HAP prediction model for CHAs is constructed, and then the method for FZ prediction is proposed. Further, the method is applied to Jin 93 Well Block for predicting FZ of CHAs. Finally, this study discussed the difference in performance among models constructed by the RUSBoosted tree and other five ML algorithms and the difference in training results between the original geological data and preprocessed geological data on the RUSBoosted tree ML algorithm. Results show that, currently, datasets in the petroleum exploration field are small and unbalanced, and the RUSBoosted tree ML algorithm has excellent training results on it. Compared with the original geological data, the performance of the HAP prediction model constructed by preprocessed geological data is improved. On a Jin 93 Well Block dataset, the HAP prediction model constructed by the RUSBoosted tree ML algorithm belongs to a good prediction model, and FZ of CHAs predicted by this HAP prediction model agree well with CHAs discovered areas. The results of this study provide an idea for intelligently predicting the distribution of FZ of CHAs and are of great significance to the development of intelligent petroleum exploration technology.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:326:y:2022:i:c:s0306261922012405
    DOI: 10.1016/j.apenergy.2022.119983
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    References listed on IDEAS

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    1. Wang, Wenyang & Pang, Xiongqi & Chen, Zhangxin & Chen, Dongxia & Ma, Xinhua & Zhu, Weiping & Zheng, Tianyu & Wu, Keliu & Zhang, Kun & Ma, Kuiyou, 2020. "Improved methods for determining effective sandstone reservoirs and evaluating hydrocarbon enrichment in petroliferous basins," Applied Energy, Elsevier, vol. 261(C).
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    Cited by:

    1. 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).
    2. 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).
    3. 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).
    4. Yi, Jun & Qi, ZhongLi & Li, XiangChengZhen & Liu, Hong & Zhou, Wei, 2024. "Spatial correlation-based machine learning framework for evaluating shale gas production potential: A case study in southern Sichuan Basin, China," Applied Energy, Elsevier, vol. 357(C).

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