The activity of synsedimentary faults plays an important role in controlling the distribution of sand bodies in basins and furthermore the porosity and permeability of reservoirs. We have used fault interpretation, the method of image and granularity size analysis, and the seismic pumping effect to investigate the control of the activity of the Kongdong fault on the development degree of the dissolution pores and grain size, further studying the controlling mechanism of the activity of synsedimentary faults on reservoir quality (porosity and permeability). The results showed that the slip rate of synsedimentary faults is one of the main factors in controlling reservoir quality. The slip rate controls the accommodation space and hydrodynamic conditions and it furthermore controls the grain size. The higher the slip rate, the bigger the grain size in the downthrow wall of synsedimentary faults; the seismic pump produced by synsedimentary faults activity also controls the development degree of dissolution pores. The development degree of dissolution pores in the downthrown wall of synsedimentary faults is greater than that in the upthrown wall. Dissolution pores are more developed in areas with a large slip rate of synsedimentary faults. Porosity increases gradually with the increase of plane porosity of dissolution pores, whereas the changes of permeability are not obvious.

Inverse mapping from geophysics to geology is a difficult problem due to the inherent uncertainty of geophysical data and the spatially heterogeneous patterns (structure) in geology. We improve conditional facies simulation based on GANs (GANSim), a new Earth model simulation method based on generative adversarial networks (GANs), to bridge the gap between remotely sensed geophysical information and geology, by introducing a specially designed loss function and an input architecture for probability maps representing geophysical interpretation. After training, the GANSim is then used to produce multiple geological facies models conditioned to the input geophysics-interpreted probability maps alone or together with input well observations and global features. By evaluation, the generated facies models are realistic, diversified, and consistent with all input conditions. We demonstrate that the GAN learns the implicit geological pattern knowledge from the training data set and the knowledge of conditioning to inputs from human-defined explicit functions. Given the commonality of remotely sensed geophysical information, sparse measurements, and global features, GANSim should be applicable to many problems of geosciences.