Permeability has a dominant influence on the flow properties of a natural
fluid. Lattice Boltzmann simulator determines permeability from the nano and
micropore network. The simulator holds millions of flow dynamics calculations
with its accumulated errors and high consumption of computing power. To
efficiently and consistently predict permeability, we propose a morphology
decoder, a parallel and serial flow reconstruction of machine learning
segmented heterogeneous Cretaceous texture from 3D micro computerized
tomography and nuclear magnetic resonance images. For 3D vision, we introduce
controllable-measurable-volume as new supervised segmentation, in which a
unique set of voxel intensity corresponds to grain and pore throat sizes. The
morphology decoder demarks and aggregates the morphologies boundaries in a
novel way to produce permeability. Morphology decoder method consists of five
novel processes, which describes in this paper, these novel processes are: (1)
Geometrical 3D Permeability, (2) Machine Learning guided 3D Properties
Recognition of Rock Morphology, (3) 3D Image Properties Integration Model for
Permeability, (4) MRI Permeability Imager, and (5) Morphology Decoder (the
process that integrates the other four novel processes).

通过利用形态解码器结合机器学习、三维显微成像分割、核磁共振成像等手段，能够有效、准确地预测渗透率。