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ORIGINAL RESEARCH article

Front. Earth Sci.

Sec. Georeservoirs

Volume 13 - 2025 | doi: 10.3389/feart.2025.1659387

This article is part of the Research TopicIntelligent Artificial Lift and Multiphase Flow in the Wellbore in the Oil and Gas Production SystemsView all 3 articles

The Research on Intelligent Evaluation Methods for Non-sealing Faults in Reservoirs Based on Well Test Analysis

Provisionally accepted
Yiyi  YangYiyi Yang1BOYING  LIBOYING LI2*Xudong  WangXudong Wang1Mingming  QiMingming Qi1Di  ZhaiDi Zhai1
  • 1CNOOC International Limited, Beijing, China
  • 2China University of Petroleum, Beijing, Beijing, China

The final, formatted version of the article will be published soon.

In practical reservoirs, there exist a large number of non-sealing faults with flow capacity, which significantly influence oilfield development patterns and wellbore pressure transient behavior.The quantitative evaluation of fault sealing capacity is of great importance for characterizing remaining oil distribution and reconstructing flow fields. However, existing fault analysis methods are primarily qualitative, with limitations in the quantitative characterization of fault sealing. Traditional numerical well-test interpretation models do not account for fluid flow within faults, leading to significant deviations in well-test data interpretation, increased model-solving difficulties, and challenges in achieving quantitative analysis of reservoir sealing. Therefore, based on the fault, fluid, and reservoir property characteristics of the X reservoir, this study establishes a composite reservoir well-test interpretation mathematical model considering skin effects and solves the model using the Boltzmann transformation. By applying the "partial" mirror superposition principle, the dynamic response characteristics of typical curves under different fault boundary conditions are analyzed, and a quantitative sealing evaluation method suitable for non-sealing faults is developed. Furthermore, by integrating XGBoost multi-output regression and PSO algorithms, an intelligent hybrid inversion framework for identifying non-sealing faults in composite reservoirs is constructed: the XGBoost model predicts initial fault characteristic parameters, while the PSO algorithm performs global optimization to refine XGBoost parameters, ultimately inverting the fault connectivity coefficient ( fD C ) and effective connected thickness ( b h ). The results indicate the presence of a non-sealing fault F1 between Well B30Y and Well B1A, with inverted values of 0.73 fD C = and 20.44 b h = , demonstrating strong fault connectivity. Additionally, the fitting trend of bottom-hole flowing pressure during shut-in periods in both wells verifies the validity and stability of the proposed model. The method presented in this study enables rapid, quantitative, and precise evaluation of non-sealing fault closure, providing robust technical support for subsequent remaining oil potential exploitation and development strategy optimization.

Keywords: Non-sealing fault, Well test analysis, Intelligent evaluation, Boltzmann transformation, "Partial" image method

Received: 04 Jul 2025; Accepted: 19 Aug 2025.

Copyright: © 2025 Yang, LI, Wang, Qi and Zhai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: BOYING LI, China University of Petroleum, Beijing, Beijing, China

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