Prediction of complex diagenesis and the resulting variations in reservoir quality are critical for hydrocarbon exploration. In fact, the limitations of core accessibility and expensive costs of core-based experiments have posed challenges for establishment of diagenetic prediction model. Integrated petrographic and petrophysical analyses are served here to fully understand the dominant diagenetic features and their controls on reservoir quality. Six diagenetic facies representing distinct mineralogical compositions, diagenetic processes, and pore systems were then categorized. To upscale diagenetic features through correlating core diagenetic facies with geophysical well logs, linear discriminant analysis (LDA) was first employed to obtain eigenvectors that can best describe and distinguish diagenetic facies. The well logs that have stronger influence on the first and second eigenvectors and have less correlations with each other are selected to compress the feature space dimensions. The supervised self-organizing map (SSOM) predicable model was trained using dimensionally reduced well log database as input and core diagenetic facies as supervision to determine the nonlinear mapping relations between log response combination features and diagenetic facies group membership. The results showed that LDA-assisted SSOM model yielded higher accuracy as compared with commonly employed linear and non-linear predictable model. The supervised LDA-assisted SSOM method as provided here can be gainfully used to predict diagenetic facies via conventional well logs.

复杂成岩作用的预测以及由此产生的储层质量变化对油气勘探至关重要。实际上，核心可访问性的局限性和基于核心的实验的昂贵成本对建立成岩预测模型提出了挑战。这里提供岩相和岩石物理综合分析，以充分了解主要的成岩特征及其对储层质量的控制。然后对代表不同矿物学组成，成岩作用和孔隙系统的六个成岩相进行了分类。为了通过将岩心成岩相与地球物理测井资料相关联来扩大成岩特征，首先采用线性判别分析（LDA）来获得能最好地描述和区分成岩相的特征向量。选择对第一和第二特征向量有较大影响并且彼此之间具有较小相关性的测井记录以压缩特征空间尺寸。利用降维测井数据库作为输入，以岩心成岩相为监督训练了监督自组织图（SSOM）可预测模型，确定了测井响应组合特征与成岩相组员之间的非线性映射关系。结果表明，与常用的线性和非线性可预测模型相比，LDA辅助的SSOM模型具有更高的准确性。此处提供的监督LDA辅助SSOM方法可用于通过常规测井曲线预测成岩相。