A method for gas layer identification using array acoustic logging data

Abstract

The invention discloses a method for identifying gas layers using array acoustic logging data. The array acoustic logging data is combined with density curves to calculate rock mechanics parameters of formation rocks, and then the calculated rock mechanics parameters of formation rocks are displayed in the same track respectively. , the two curves in each track use the same scale, and the enveloping position indicates gas content, in which the bulk modulus and Lame constant adopt the reverse scale of 0~17; finally, the calculated parameters are normalized and the comprehensive content is calculated. Gas index SGI, according to the relative size of the comprehensive gas index SGI to judge whether the formation contains gas and the relative value of the gas content. The method of the present invention improves the identification accuracy, increases the ease of use, and expands the scope of application. It is not only suitable for carbonate rock formations, but also solves the problem that gas layers in carbonate rock formations are difficult to accurately identify, and at the same time has a good recognition effect on sandy mudstone formations. Also very good.

Description

technical field [0001] The invention belongs to the technical field of petroleum and natural gas exploration and development, and in particular relates to a method for identifying gas layers using array acoustic logging data. Background technique [0002] Due to the large variation of pore types in carbonate formations, many composite reservoirs of pores, caves and fractures, and strong pore heterogeneity, the response error of the three-porosity curve to rock porosity is large, and the response of resistivity to reservoir fluid properties is weak. It is difficult to accurately judge reservoir fluid properties based on well logging data. [0003] Using the monopole and dipole modes of array acoustic logging tools such as MPAL and XMAC, the characteristic parameters such as the P-wave and S-wave velocity of the formation can be measured in both hard and soft formations. When the formation contains gas, the P-wave velocity drops significantly. It basically has no effect on th...

AI Technical Summary

Problems solved by technology

[0002] Due to the large variation of pore types in carbonate formations, many composite reservoirs of pores, caves and fractures, and strong pore heterogeneity, the response error of the three-porosity curve to rock porosity is large, and the response of resistivity to reservoir fluid properties is weak. Logging data, it is difficult to accurately judge the properties of reservoir fluid

[0003] Using the monopole and dipole modes of array acoustic logging tools such as MPAL and XMAC, the characteristic parameters such as the P-wave and S-wave velocity of the formation can be measured in both hard and soft formations. When the formation contains gas, the P-wave velocity drops significantly. It basically has no effect on the shear wave velocity. At this time, the ratio of the longitudinal wave velocity to the transverse wave will decrease. Therefore, the gas layer can also be judged by the change of these parameters, but it is necessary to make a chart, which is inconvenient to use, and each parameter has a different response sensitivity to the gas layer. , increasing the uncertainty of gas layer identification

Images