There are a large number of low-contrast oil and gas reservoir in the Nanpu oilfield area, and the logging characterization is extremely similar to that of the water reservoir. The oil-bearing property of the reservoir cannot be accurately evaluated by using conventional logging methods because of the certain complexity and concealment of the reservoir. The genesis of low-contrast oil and gas reservoir was studied from two aspects: internal petrophysical characteristics and external environment of the reservoir, and fine lithology, high clay content, and intrusion of saline mud were identified as key factors. Aiming at the genesis of low-contrast oil and gas reservoir in the study area, natural gamma ray and apparent formation water resistivity are identified as effective parameters for the identification of low-contrast oil and gas reservoir. The probabilistic neural network method is used to enhance the identification ability of subtle oil and gas reservoir, solve the contradiction between electrical and oil-bearing properties, and form a low-contrast oil and gas reservoir evaluation technology with amplified electrical characteristics as the core.