Abstract: The Xihu Sag in the East China Sea Shelf Basin is a large Mesozoic-Cenozoic oil and gas-bearing sag with abundant oil and gas resources. However, coal-bearing strata are widely developed in this area. In the Pingbei slope zone, the oil and gas-bearing Pinghu Formation strata develop tide-influenced deltaic deposits, characterized by thin interbedded layers of sandstone, mudstone, and coal. The coal seams are thin and develop along with sand bodies. The lithology is mainly dominated by sandstone-mudstone interlayers interbedded with coal, featuring thin single layers, multiple layers, and rapid lateral changes. The thin coal layers in the coal seam section show abnormal features in logging curves, including low velocity, low density, high neutron values, and high resistivity. When conventional acoustic logging curves are used for inversion, the accuracy of sand body prediction is reduced. Therefore, eliminating the influence of coal seams and accurately identifying sand bodies has become an urgent issue. Based on an analysis of the logging curve characteristics of coal-bearing sections, a fitting method for acoustic logging curves in coal measure strata was proposed. Using drilling data, logging observations, and core analysis, the strata were divided into coal-bearing sections and non-coal sections. For non-coal sections, a petrophysical model was constructed for logging curve fitting, which was commonly applied in conventional clastic rock analysis. For coal-bearing sections, fitting was carried out using statistical regression techniques based on empirical formula methods. Subsequently, the results for coal-bearing and non-coal sections were matched and combined. The fitted acoustic primary wave velocity curve corrected the abnormal values caused by borehole collapse in coal seams. The correlation coefficient between the original curve and the fitted curve was 0.82. The fitted and corrected velocity curve was then used for inversion to delineate sand bodies. Application in a gasfield showed that the fitted and corrected acoustic primary wave velocity curve based on this method effectively predicted sand bodies in inversion, and the prediction results were consistent with drilling data, proving useful for identifying lithologic structural traps. This study provides an effective method for reservoir prediction in thin coal measure strata. By separately fitting the acoustic logging curves of coal-bearing and non-coal sections, the interference from coal seams is eliminated, and high-precision sand body prediction is achieved.

Key words: acoustic logging curve fitting, thin coal measure strata, coal seam section, statistical regression, inversion