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The temperature distribution model and its application to reservoir depth prediction in fault-karst carbonate reservoirs

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Abstract

The fault-karst carbonate reservoirs in Tarim Basin have previously been determined to have promising prospects for exploration and development. Large karst caves are the main storage space and flow channel, which are separated from one another, yet connected by fractures. During the drilling processes, when large karst caves are encountered, drilling break and mud leakage often occur. Such occurrences make it impractical to carry out normal logging operations or determine the depths of the reservoirs. Therefore, in order to address the difficulties in determining the reservoir depths during the development fault-karst reservoirs, this study presented a method by which to accurately predict reservoir depths by means of combining the relationships between the flow temperatures and rest temperatures within the well structures. A mathematical model describing the heat transfer was established for the purpose of obtaining the temperature distributions of the fluid within the karst caves and wellbores during stable oil production, and the model was verified using numerical simulations. In addition, a temperature distribution method was applied to the temperature predictions of wells in Shunbei Block of Tarim Basin, and the reservoir depths were predicted at the same time. Then, the research results were compared with the seismic section of the fault-karst. It was found that good application effects and consistency had been achieved.

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Funding

The Project Supported by the National Natural Science Foundation of China (Grant No.2016ZX05053) and the Jilin province science and technology development plan outstanding young talent project (Grant 20190103150JH).

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Correspondence to Guo Yuhang.

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Responsible Editor: Santanu Banerjee

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Lei, J., Pan, B., Guo, Y. et al. The temperature distribution model and its application to reservoir depth prediction in fault-karst carbonate reservoirs. Arab J Geosci 13, 676 (2020). https://doi.org/10.1007/s12517-020-05696-x

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  • DOI: https://doi.org/10.1007/s12517-020-05696-x

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