Abstract

As water flooding continues to advance in mature oil fields, conventional well logging curve responses exhibit anomalies, particularly in deep-seated beach-bar thin-layer sand bodies. These sand bodies exhibit strong vertical and planar heterogeneity, which hinders a clear understanding of their distribution and connectivity. This paper conducts a sensitivity analysis of the lower part of the 4th member of the Shahejie Formation in fault block C26, based on core data analysis and the integration of drilling, logging, recording, and production dynamic data. Natural potential and resistivity curves are selected as the sensitive curves for identifying sand bodies. Preliminary processing of well logging curves is carried out using the “standard layer constraint and multi-standardization method comparison” principle. Employing sedimentological research methods and the principles of wavelet transformation, the well logging curves within the study area undergo extraction of both high and low frequencies. This procedure accentuates details related to thin sand bodies and responses indicative of sedimentary cyclicity. Through a thoughtful amalgamation of multiple curves, the investigation achieves a systematic fusion of natural potential curves via multi-curve frequency division fusion, employing reconstruction optimization. This method adeptly mitigates interference stemming from water-flooded layers, effectively addressing challenges such as excessive calibration and ambiguous identification of sand bodies. As a result, a comprehensive analytical approach is established for assessing the distribution and connectivity of deep-seated, beach-bar thin sand bodies influenced by water-flooded layers, providing clarity on the connectivity relationships among the sand bodies. Additionally, in combination with the results of mercury injection experiments in the water-flooded layer segments, favorable reservoir criteria for the study area are determined, providing a scientific basis for adjusting future development plans.

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