Application of a Reservoir Classification Method Based on Core Data from Offshore Tight Reservoirs: A Case Study of the Liushagang Formation in the Beibu Basin

Several methods are currently used to test offshore tight reservoirs. However, the effectiveness of these applications varies among wells, and some exhibit unclear reservoir classifications. These issues lead to difficulties in decision-making during tests and result in higher testing costs. Therefore, to address this issue, this study used reservoirs in the Liushagang Formation of the Beibu Basin as the research object and employed core data to apply the multi-stage FZI method. This method computes the FZI and its cumulative probability, classifying the target reservoir into seven distinct types. According to the Winland R-35 method, the target reservoir was classified into five distinct types. Seven characteristic parameters were selected based on the mercury injection experimental data. The K-means clustering method was then used to classify the target reservoirs into two types. The conclusions were that, in this formation, there is predominantly low to extra-low porosity and extra-low to ultra-low permeability. According to relationship models, logged porosity can be used to calculate effective permeability. Combining the multi-stage FZI method with the K-means clustering method for reservoir classification is recommended. This integrated approach facilitates a more comprehensive analysis of the characteristics of offshore low-permeability tight reservoirs at both macro and micro scales after classification. This research provides key insights for enhancing offshore well production.