Quantitative Evaluation Methods and Applications for Gravel Characteristics Distribution in Conglomerate Reservoirs

Conglomerate reservoirs often exhibit chaotic internal structures and strong heterogeneity due to the influence of gravel, which seriously restricts the balanced initiation of multiple clusters and the balanced expansion of artificial fractures in the volume fracturing section of horizontal wells. Therefore, clarifying the distribution pattern of gravel in conglomerate reservoirs is of great significance for the design and parameter optimization of horizontal well segmentation and clustering. This work conducts research on the interpretation results of imaging logging, establishes a characterization model for the distribution characteristics of gravel around horizontal wells, develops gravel feature recognition and analysis software for conglomerate reservoirs using image processing technology, and effectively obtains the morphology of gravel in imaging logging. Based on this, a correlation model between conventional logging and imaging logging is constructed to predict the distribution of gravel in horizontal wells without imaging logging. Using the Kriging interpolation method, a “point line surface” gravel distribution prediction method is proposed. Through three methods of imaging logging, downhole eagle-eye camera, and on-site coring, the model accuracy is found to be greater than 80%, guiding segmented clustering to avoid high gravel areas. During the fracturing process, the wellhead pressure is lower than that of adjacent wells, enabling greater fluid savings per well. The production effect is better than that of adjacent wells in the same block, providing a reference for the study of gravel distribution characteristics in conglomerate oil reservoirs.