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Improved methods for determining effective sandstone reservoirs and evaluating hydrocarbon enrichment in petroliferous basins

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  • Wang, Wenyang
  • Pang, Xiongqi
  • Chen, Zhangxin
  • Chen, Dongxia
  • Ma, Xinhua
  • Zhu, Weiping
  • Zheng, Tianyu
  • Wu, Keliu
  • Zhang, Kun
  • Ma, Kuiyou

Abstract

Geological uncertainty is the greatest risk in oil exploration. Most of the evaluation and risk assessment of exploration projects are characterized by the hydrocarbon enrichment evaluation of the target reservoir to avoid non-effective reservoirs. Based on experiments and detailed analysis of the hydrocarbon accumulation process, we propose a new criterion, namely reservoir hydrocarbon enrichment potential index, expressed as PI, for improved evaluation and assessment. According to the index PI, sandstone reservoirs are classified into four categories: Level IV, PI ≤ 0; Level III, 0 < PI ≤ 0.01; Level II, 0.01 < PI ≤ 0.1; Level I, 0.1 < PI ≤ 1. Level IV reservoirs are non-effective, whereas Level III, Level II, and Level I reservoirs are effective and their hydrocarbon enrichment potentials increase progressively. The recommended strategy is to prioritize the exploration of sandstone reservoirs in the order of Level I > Level II > Level III and avoid Level IV reservoirs. This strategy was applied to the Mesozoic sandstone in western Sichuan Depression, China, to demonstrate the method and workflow. Drilling success rate is positively correlated with the PI of the sandstone reservoir. The results of 153 wells drilled in western Sichuan Depression show the accuracy of the method to be 84.5%. Therefore, this method is useful for quick and effective scientific decisions on oil exploration projects, thus mitigating the risks in oil exploration projects.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:261:y:2020:i:c:s0306261919321452
    DOI: 10.1016/j.apenergy.2019.114457
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    1. 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).
    2. Wang, Wenyang & Pang, Xiongqi & Chen, Zhangxin & Chen, Dongxia & Wang, Yaping & Yang, Xuan & Luo, Bing & Zhang, Wang & Zhang, Xinwen & Li, Changrong & Wang, Qifeng & Li, Caijun, 2021. "Quantitative evaluation of transport efficiency of fault-reservoir composite migration pathway systems in carbonate petroliferous basins," Energy, Elsevier, vol. 222(C).

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