3results about How to "Reduce exploration and development costs" patented technology

The invention provides a method for identifying the water content of a reservoir based on light hydrocarbon logging. The method comprises the following steps: acquiring light hydrocarbon logging data of an oil and gas reservoir to be evaluated; selecting total hydrocarbon abundance, benzene and toluene / benzene in the light hydrocarbon logging data as light hydrocarbon parameters; establishing a two-dimensional atlas through the intersection of benzene and toluene / benzene in the light hydrocarbon parameters, establishing a three-dimensional atlas through the total hydrocarbon abundance and the change of benzene and toluene / benzene, dividing the boundaries of an oil layer, an oil-water layer and an oil-containing water layer, and quantitatively explaining and evaluating the fluid properties of the reservoir. According to the method, the light hydrocarbon logging technology is utilized, two-dimensional and three-dimensional interpretation maps are formed through intersection by selecting light hydrocarbon parameters such as total hydrocarbon abundance, benzene and methylbenzene, the interface and the water content of the reservoir fluid are quantitatively recognized, the coincidence rate of actual interpretation and evaluation of the reservoir fluid is increased, the exploration and development cost of an oil field is effectively reduced, and the purpose of improving the oil testing effect is achieved; the method is easy to operate, high in identification and evaluation accuracy, high in universality and wide in application range.

The invention discloses a three-dimensional space distribution optimization prediction method based on reservoir classification, and relates to the field of oil-gas exploration and development, and the method comprises the steps: obtaining the physical property data of a core sample in a target region, and classifying the physical property test points through a clustering analysis method; establishing a three-dimensional geologic model of a target area, and determining favorable main control factors and adverse main control factors which influence physical properties through spatial superposition of different types of reservoir bodies and physical property influence factors; a three-dimensional space influence factor superposition three-dimensional body is established through superposition of various physical property influence factor weight coefficients, physical properties are calculated through physical property influence factor homogenization numerical values and the weight coefficients, and three types of reservoir bodies are divided; and continuously verifying and training the precision degree of the prediction model by adding a new drilling well and a new type of influence factors, and predicting the reservoir type of the well-free region by using the corrected and optimized prediction model. The reservoir prediction precision can be improved, the visualization of the prediction result is enhanced, and the exploration and development efficiency is improved.

The application provides a reservoir rock slice fracture connectivity evaluation method and device based on semantic segmentation, equipment and a storage medium, relates to the technical field of reservoir evaluation in oil and gas field exploration and development, and aims to quickly and accurately calculate the reservoir rock slice fracture connectivity. The method comprises the following steps: inputting a plurality of reservoir rock slice images of a target area obtained into a fracture identification model to obtain fracture pixel data and matrix pixel data corresponding to each reservoir rock slice image, and performing binaryzation processing and thinning processing to obtain fracture pixel data under a topological form corresponding to each reservoir rock slice image, and each fracture pixel data under the topological form represents a node; based on the fracture pixel data under the topological form, the number of nodes of different connection types corresponding to each reservoir rock slice image is identified and counted, and then the average node number of the reservoir rock slice fracture of the target area is calculated to determine the fracture connectivity level of the target area.