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<p>In this paper, eight conventional logging curves were calculated by applying the rescaled extreme difference (R/S)-finite difference (FD) method; then, Bayesian theory was introduced to objectively optimize the fracture sensitivity curves. Finally, the composite fracture identification index (I) is constructed. The fracture prediction results were validated using core and XRMI<sup>®</sup> imaging logging. Research indicates that four conventional logging curves, namely AC, DEN, RS, and RD, are highly sensitive to fractures and are employed to construct the composite fracture identification index I. The lower limit for data when using the index (I) for fracture prediction is 0.1. When the I value ranges from 0 to 0.1, the formation can be considered fracture-free. The reasonable range for fracture prediction is 0.1–1.0, with higher values indicating greater fracture prediction accuracy. Applying this method to carry out single-well fracture predictions can improve the fracture prediction accuracy to 72.72%. This approach effectively improves the fracture prediction accuracy for a single well. This method is effective for single-well fracture predictions for wells with little coring information and for wells where coring and XRMI<sup>®</sup> information are not available.</p>

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Research on methods for identifying fractures in carbonate reservoirs based on the R/S-FD method and Bayesian theory: a case study of the Tofutai area, Tarim Basin

  • 期刊论文

  • 发表日期：

    2026年2月23日

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  • Peng-Yuan Han,
  • Wen-Long Ding,
  • Hai-Long Ma,
  • Zhen Wang,
  • Ting-Feng Liu,
  • Juan Zhang

摘要

In this paper, eight conventional logging curves were calculated by applying the rescaled extreme difference (R/S)-finite difference (FD) method; then, Bayesian theory was introduced to objectively optimize the fracture sensitivity curves. Finally, the composite fracture identification index (I) is constructed. The fracture prediction results were validated using core and XRMI® imaging logging. Research indicates that four conventional logging curves, namely AC, DEN, RS, and RD, are highly sensitive to fractures and are employed to construct the composite fracture identification index I. The lower limit for data when using the index (I) for fracture prediction is 0.1. When the I value ranges from 0 to 0.1, the formation can be considered fracture-free. The reasonable range for fracture prediction is 0.1–1.0, with higher values indicating greater fracture prediction accuracy. Applying this method to carry out single-well fracture predictions can improve the fracture prediction accuracy to 72.72%. This approach effectively improves the fracture prediction accuracy for a single well. This method is effective for single-well fracture predictions for wells with little coring information and for wells where coring and XRMI® information are not available.