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Posterior probability-based hydraulic unit division and prediction: A case study

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  • Yu, Peng

Abstract

Hydraulic units (HUs) with analogous petrophysical and flow characteristics are commonly employed to describe reservoirs. However, their prediction performances would be greatly influenced if the relationship between well-logging parameters and HUs is not well revealed. In this study, the model broke through the dimensionality of the traditional model and expanded the dimensionality of the logging intersection space to three dimensions. Specifically, HUs of cored samples were divided into 5 classes based on flow zone indicators, and the Bayes theorem was used to write a VB program based on the posterior probability to determine the HU class in the 3D cube. Scheme B (RT-2 & AC & SH) exhibited good prediction performances when applied to predict the 30% cored data, with an overall accuracy rate of 90.06%, which exceeded that of the artificial neural network (80.45%). Therefore, scheme B was applied to predict HUs of un-cored wells. Then, sequential instruction simulation was conducted on HUs of inter wells in the Petrel software, based on which well section analysis was performed to identify the relationship between lithology and predicted HUs. HUs of wells with similar production time were dynamically verified based on the perforation thickness data of HUs, which were combined with the production performance data to confirm the distribution rationality of predicted HUs along the well trajectory. When planning infill wells, injection and production wells should be deployed in the same HU class to ensure good connectivity. HU#2 and HU#3 with relatively good quality occupy more than 26% (7.78% + 18.93%) of the total geological space, and they should be the main targets in the future remaining oil exploitation. More attention should be paid in the future to refine the interpretation results of well-logging parameters.

Suggested Citation

  • Yu, Peng, 2022. "Posterior probability-based hydraulic unit division and prediction: A case study," Energy, Elsevier, vol. 246(C).
    • Handle: RePEc:eee:energy:v:246:y:2022:i:c:s0360544222002109
    DOI: 10.1016/j.energy.2022.123307
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    References listed on IDEAS

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