Hakiki Farizal, Akbar Muhammad Nur Ali, Muttaqin Zaki
National Yang Ming Chiao Tung University, Disaster Prevention and Water Environment Research Center, Hsincu 30010, Taiwan.
National Yang Ming Chiao Tung University, Civil Engineering Department, Hsincu 30010, Taiwan.
ACS Omega. 2024 Jul 25;9(32):34636-34649. doi: 10.1021/acsomega.4c02879. eCollection 2024 Aug 13.
Rock typing is a vital step in oil and gas reservoir development to achieve predictions of hydrocarbon reserves, recovery, and underground storage capacity for CO or hydrogen. To address inaccurate initial hydrocarbon-in-place prediction and improper rock property distribution in a reservoir model, a recent rock typing method, pore geometry and structure (PGS), has revealed a more accurate prediction on connate water saturation and better grouping of capillary pressure. However, the current state still needs physical interpretations of the PGS rock typing. We have compiled thousands of experimentally measured hydraulic properties, such as permeability within 12 orders of magnitude, porosity ϕ up to 0.9, specific surface area within 4 orders of magnitude, and pore size ranges around 3 orders of magnitude. We conduct the first-ever holistic physical interpretations of the PGS rock typing using gathered data combined with analytical theory and the Kozeny-Carman equation. Surprisingly, our physics-inspired data-driven study reveals advanced findings on the PGS rock typing. These include (i) why PGS method prevails over the hydraulic flow unit rock typing, (ii) explanations to distinguish between causality and indirect relationships among hydraulic properties, rock type number, and electrical resistivity, (iii) a proposed novel method: permeability prediction from the resistivity and rock type number relationship, and (iv) a suggestion and criticism on how to avoid a recursive prediction on permeability.
岩石分类是油气藏开发中的关键步骤，有助于预测碳氢化合物储量、采收率以及二氧化碳或氢气的地下储存能力。为解决油藏模型中初始原地碳氢化合物预测不准确和岩石属性分布不合理的问题，一种最新的岩石分类方法——孔隙几何结构（PGS），在预测原生水饱和度方面更为准确，且能更好地对毛管压力进行分组。然而，目前仍需对PGS岩石分类进行物理解释。我们收集了数千个实验测量的水力性质数据，如渗透率跨越12个数量级、孔隙度ϕ高达0.9、比表面积跨越4个数量级以及孔径范围约为3个数量级。我们利用收集的数据结合解析理论和柯曾尼-卡曼方程，首次对PGS岩石分类进行了全面的物理解释。令人惊讶的是，我们基于物理启发的数据驱动研究揭示了关于PGS岩石分类的先进发现。这些发现包括：（i）PGS方法优于水力流动单元岩石分类的原因；（ii）解释如何区分水力性质、岩石类型数量和电阻率之间的因果关系和间接关系；（iii）提出一种新方法：根据电阻率和岩石类型数量关系预测渗透率；（iv）关于如何避免对渗透率进行递归预测的建议和批评。
