Method for identifying water logging grades of oil reservoir by using neural network analogue cross plot

Abstract

The invention discloses a method for identifying water logging grades of an oil reservoir by using a neural network analogue cross plot. In the method, the conventional cross plot technology is improved by a neural network algorithm, so nonlinear identification and quantitative analysis functions of the cross plot are realized, and a back propagation (BP) neural network algorithm is used and the method comprises the following steps of screening object characteristic parameters, selecting network structure parameters, training a neural network model, testing the network model, and establishing a neural network analogue cross plot layout. The method specifically comprises the following steps of: according to various characteristics of oil, gas and water layers in reservoirs, accurately selecting parameter samples which can best reflect the characteristics of the oil, gas and water layers in the reservoirs from parameters calculated during well logging or the well logging curves relevant to oil and gas interpretation by a statistics method; selecting appropriate weight values and threshold values by the BP neural network algorithm to establish the network model, and training the model and checking errors; and judging the fluid type or water logging degree of the reservoir with the depth according to projective points of identification vectors which are obtained by network output on a plane.

Description

Technical field: [0001] The invention relates to a method for identifying the water-flooded level of an oil layer by simulating a intersection graph with a neural network, that is, using the network topology structure of a neural network to construct a cross-graph to perform water-flooded layer classification for oil field logging water-flooded layer interpretation and logging oil, gas, and water layers method of identification. Background technique: [0002] Crossplot recognition technology is widely used in oil and gas exploration, and it plays an important role in checking the quality of logging data, selecting interpretation parameters, determining lithology, testing interpretation results and evaluating formation fluid types. In terms of petrophysics, we can make petrophysical quantity boards through crossplots, and use them to predict lithology; in terms of seismic AVO (Amplitude Versus Offset, amplitude variation with offset) technology application, through crossplot ...

AI Technical Summary

Problems solved by technology

At the same time, in the traditional crossplot analysis, rough description or manual delineation is generally used for the division of the crossplot domain. The method itself has great uncertainty, especially when there are many data points. Sample points with different attributes overlap, making it difficult to accurately and quickly judge and identify the category to which the sample points belong

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