Abstract: An irregular seismic data collection method is based on forward modeling. The method includes: fully collecting geological information of a work area to build a geological model, determining arrangement parameters of receiver points and shot points for regular high-density collection and irregular sparse collection, performing irregular optimization design on positions of the shot points based on forward modeling, performing irregular optimization design on positions of the receiver points based on forward modeling, and outputting a preferred irregular sparse observation system.

Abstract: The present disclosure provides a method and system for predicting a relative permeability curve based on machine learning. The present disclosure takes logging curve data as an input, and water saturation endpoint values as an output to establish a first relative permeability curve starting point model, and takes the logging curve data and a predicted water saturation starting value output from the first relative permeability curve starting point model as an input, and relative permeabilities under different water saturations as an output to establish a first relative permeability model, thereby obtaining a comprehensive prediction method for the relative permeability curve based on deep learning, and implying control mechanisms and parameters to a model.