Characteristics, classification and KNN-based evaluation of paleokarst carbonate reservoirs: A case study of Feixianguan Formation in northeastern Sichuan Basin, China

2023年 4卷 第3期
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Yang Ren Wei Wei Peng Zhu Xiuming Zhang Keyong Chen Yisheng Liu
The Feixianguan Formation reservoirs in northeastern Sichuan are mainly a suite of carbonate platform deposits. The reservoir types are diverse with high heterogeneity and complex genetic mechanisms. Pores, vugs and fractures of different genetic mechanisms and scales are often developed in association, and it is difficult to classify reservoir types merely based on static data such as outcrop observation, and cores and logging data. In the study, the reservoirs in the Feixianguan Formation are grouped into five types by combining dynamic and static data, that is, karst breccia-residual vuggy type, solution-enhanced vuggy type, fractured-vuggy type, fractured type and matrix type (non-reservoir). Based on conventional logging data, core data and formation microscanner image (FMI) data of the Qilibei block, northeastern Sichuan Basin, the reservoirs are classified in accordance with fracture-vug matching relationship. Based on the principle of cluster analysis, K-Nearest Neighbor (KNN) classification templates are established, and the applicability of the model is verified by using the reservoir data from wells uninvolved in modeling. Following the analysis of the results of reservoir type discrimination and the production of corresponding reservoir intervals, the contributions of various reservoir types to production are evaluated and the reliability of reservoir type classification is verified. The results show that the solution-enhanced vuggy type is of high-quality sweet spot reservoir in the study area with good physical property and high gas production, followed by the fractured-vuggy type, and the fractured and karst breccia-residual vuggy types are the least promising.
Carbonate reservoir; Reservoir type; Cluster analysis; K-Nearest Neighbor (KNN); Feixianguan Formation; Sichuan basin;
https://doi.org/10.1016/j.engeos.2023.100156