在高度非均质性的储层中，岩石物理表征一直是一个挑战。完整论文中介绍的案例研究描述了一种确定电学性质的机器学习 (ML) 技术。该方法结合了哥伦比亚北部 Mamey 油田的测井、岩石类型和岩相以及数字岩心分析，该油田的储层由层间泥岩和极细至细砂岩组成，被三角洲环境包围，顶部覆盖着与切谷沉积物相关的交错层状砂岩。所得结果表明，该技术可用于估计与图像和计算机断层扫描中识别的纹理变化相关的 Archie 参数 m 和 n 的连续曲线。
多年来，业界一直在审查易于开采的油藏，而忽略了那些稍微复杂一些的油藏。
Petrophysical characterization in reservoirs with high heterogeneity is a consistent challenge. The case study presented in the complete paper describes a machine-learning (ML) technique to determine electrical properties. The methodology combines logs, rock types, and facies and digital core analyses from the Mamey field in northern Colombia, a reservoir composed of interlaminated mudstones and very-fine to fine sandstones enclosed in a deltaic environment and capped by cross-stratification sandstones associated with incised valley deposits. The results obtained indicate that the technique is feasible for estimating a continuous curve of the Archie parameters m and n associated with the textural changes identified in images and computed tomography.
For many years, the industry has reviewed easily extracted reservoirs and neglected those that were slightly more complex.
