In the daily practice of Formation Evaluation for porosity calculation considering the shale volume is common the adoption of simple hypotheses about the physical properties of the reservoir rock constituents. In this paper, it is argued that an accurate estimate of clay mineral physical properties in the reservoir rock are essential for a realistic porosity calculation, once it may be different from the physical properties of clay minerals present in nearby shale layers. Geologically, the assumption of similarity between the clay mineral physical properties in the reservoir rock and adjacent shales properties means to presume a depositional continuity in sedimentary process and disregarded the innumerous postpositional processes involved in the sedimentary basin. By applying the angular competitive neural network to the Density-Neutron method to identify the clay mineral in the reservoir rock based on all identifiable clay minerals in the oil field, it is shown that a particular clay mineral signals a characteristic angular pattern in the Density-Neutron cross-plot. The methodology is presented with synthetic data and evaluated with actual well logs and core analysis from a borehole drilled in the Namorado oil field, Campos basin, Brazil.

在用于孔隙度计算的地层评估的日常实践中，考虑页岩体积是常见的，采用关于储层岩石成分物理性质的简单假设。本文认为，一旦储层岩石中粘土矿物的物理性质与附近页岩层中粘土矿物的物理性质不同，那么准确估算储层岩石的物理性质就至关重要。在地质学上，假设储层岩石中的粘土矿物物理性质与相邻页岩性质之间具有相似性，则意味着在沉积过程中假定沉积连续性，而无视沉积盆地中涉及的无数后沉积过程。通过将角竞争神经网络应用于密度-中子方法，基于油田中所有可识别的粘土矿物来识别储层岩石中的粘土矿物，结果表明，一种特定的粘土矿物在密度-密度信号中指示出特征的角度模式。中子交叉图。该方法使用综合数据进行介绍，并通​​过在巴西坎波斯盆地Namorado油田钻的井眼中的实际测井和岩心分析进行评估。