Porosity Prediction Based on Stochastic Modeling and Facies-Controlled Dataset Constrained by Seismic Attribute

Porosity is a critical petrophysical property for reservoir characterization. While conventional porosity inversion involves complex processes and factors, deep learning methods offer a more intelligent alternative. However, existing training dataset modeling strategies are inadequate for complex geological formations, whereas seismic facies-controlled modeling method enables fine characterization of underground structures. In order to achieve intelligent and fine porosity prediction, we propose a facies-controlled porosity prediction method constrained by seismic attributes. First, stochastic modeling is used to generate heterogeneous background models, enhancing the spatial variability of the dataset to reduce discomfort. Second, sensitive seismic attributes are selected as facies labels to construct seismogram and porosity training sets with facies-controlled significance. Finally, a designed neural network establishes the intrinsic relationship between seismogram and porosity, enabling petrophysical properties for other seismic sections in the same area. Validation using reservoir model data confirms the method’s feasibility and finer resolution compared to conventional method, offering enhanced accuracy in reservoir prediction. Field data from tight sandstone further demonstrates the superiority in reservoir characterization.