Lu Hao, Ma Zike, Li Hu, Yan Penglei, Liu Xian, Liu Jiale, Ma Cheng, Kang Bo, Zhang Boning, Zhao Yulong, Zhang Liehui
Southwest Petroleum University, Chengdu, 610500, Sichuan Province, China.
School of Earth Sciences and Technology, Southwest Petroleum University, Chengdu, 610500, Sichuan Province, China.
Sci Rep. 2025 Jul 22;15(1):26580. doi: 10.1038/s41598-025-99466-y.
Ultra-deep carbonate reservoirs in the Permian Qixia Formation of the Sichuan Basin, particularly those exceeding 7000 m in depth, have emerged as a significant focus for exploration and development. However, production capacities between adjacent wells can vary by up to two orders of magnitude due to the multi-scale heterogeneity of pore connectivity, which poses challenges for accurately predicting well performance. This study utilizes thin-section analysis, high-pressure mercury injection (HPMI), scanning electron microscopy (SEM), and other techniques to investigate pore connectivity in ultra-deep reservoirs. It also explores methods for evaluating the connectivity of multi-scale pore networks in the Qixia Formation. The analysis reveals distinct permeability contribution patterns and connectivity characteristics across different reservoir types. Results indicate that the carbonate reservoirs in the Qixia Formation are predominantly composed of dolomite, with intercrystalline pores, dissolution pores, and fractures constituting the primary pore types. The pore-throat size distribution exhibits significant heterogeneity, as evidenced by a multi-peak distribution curve. Approximately 25% of the reservoirs contain well-connected pores, and a threshold radius (r) is identified as a key parameter for assessing connectivity. Overall, pore connectivity within the reservoirs is limited, with fractures playing a critical role in linking isolated pore spaces. This study introduces the parameter SH, which quantitatively evaluates the connectivity of multi-scale pore networks and distinguishes the abundance of fractures within the reservoir using a boundary value of 7%. By analyzing fluid seepage patterns, a permeability contribution model for the four identified reservoir types is established, providing a robust framework for assessing reservoir connectivity. These findings offer valuable insights for predicting production capacities and optimizing development strategies in ultra-deep carbonate reservoirs.
四川盆地二叠系栖霞组超深层碳酸盐岩储层，尤其是深度超过7000米的储层，已成为勘探开发的重要焦点。然而，由于孔隙连通性的多尺度非均质性，相邻井之间的产能差异可达两个数量级，这给准确预测油井性能带来了挑战。本研究利用薄片分析、高压压汞法（HPMI）、扫描电子显微镜（SEM）等技术，研究超深层储层的孔隙连通性。同时，探索栖霞组多尺度孔隙网络连通性的评价方法。分析揭示了不同储层类型的渗透率贡献模式和连通性特征。结果表明，栖霞组碳酸盐岩储层主要由白云岩组成，晶间孔、溶蚀孔和裂缝为主要孔隙类型。孔喉尺寸分布呈现出显著的非均质性，表现为多峰分布曲线。约25%的储层孔隙连通性良好，确定了一个临界半径（r）作为评估连通性的关键参数。总体而言，储层内部的孔隙连通性有限，裂缝在连接孤立孔隙空间方面起着关键作用。本研究引入参数SH，定量评价多尺度孔隙网络的连通性，并以7%的边界值区分储层内裂缝的丰度。通过分析流体渗流模式，建立了四种已识别储层类型的渗透率贡献模型，为评估储层连通性提供了有力框架。这些发现为预测超深层碳酸盐岩储层的产能和优化开发策略提供了有价值的见解。
