Fault trap oil-bearing height seismic prediction method

Abstract

A fault trap oil-bearing height seismic prediction method comprises the following steps of 1, establishing an seismic attribute prediction fault trap oil-bearing height geological model of a researcharea; 2, determining the seismic attribute prediction fault trap oil-bearing height key parameters; 3, determining the quantitative relationship among the seismic attributes, rock burial depth, rock argillaceous content and rock displacement pressure by using a neural network method; 4, calculating the lithologic displacement pressure of a fault-reservoir; and 5, converting the displacement pressure difference into a liquid column height, comparing the liquid column height with the trap height, and determining a fault trap oil-bearing height value. The method has the following technical effects that 1) the quantitative calculation of the fault trap oil-bearing height in a well-less or well-free control area is realized; 2) the stratum pressure in the reservoir is directly calculated by using the seismic data, the data is easy to obtain, the parameter values are more, and the prediction result is more accurate; and 3) the fault plugging height is calculated by using the space change fault distance value, so that the calculation precision is improved.

Description

technical field [0001] The invention belongs to the technical field of petroleum exploration and development, and in particular relates to a seismic prediction method for fault trap oil-bearing height. Background technique [0002] The oil-bearing height of fault traps is an important parameter for evaluating reservoir potential, and is of great significance to reserve calculation, well location deployment, and development plan optimization. The oil-bearing height of a fault-trap reservoir depends on the displacement pressure of the reservoir, the dip angle of the fault, and the sealability of the fault. At present, the methods for calculating the sealing height of faults mainly focus on referring to the experience values ​​of similar work areas or calculating based on measured data and empirical formulas. In most cases, there are large errors in the method of referring to the experience value of the type of work area; the quantitative calculation of the trap height is gene...

AI Technical Summary

Problems solved by technology

In most cases, the method of referring to the experience value of the type of work area has a large error; the quantitative calculation of the trap height is generally carried out in the exploration stage, and the measured data is also small, which cannot meet the needs of quantitative calculation

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