Less well region porosity prediction method based on statistical rock physical modeling and less well region porosity prediction system based on statistical rock physical modeling

Abstract

The invention discloses a less well region porosity prediction method based on statistical rock physical modeling and a less well region porosity prediction system based on statistical rock physical modeling. The method can comprise the steps that a rock physical model is established based on the geological data and the logging curve; the porosity increment is set as multiple constant values within the preset range based on the rock physical model, and velocity, impedance and elasticity parameter curves of the porosity with the addition of the porosity increment are calculated; the result of the porosity with the addition of the corresponding porosity increment is defined as a class of object phase, a cross-plot of the velocity, impedance and elasticity parameter curves is established andthe corresponding probability density function of the class of object phase is calculated; and pre-stack seismic inversion is performed based on the logging data and the pre-stack seismic gather data,the velocity, impedance and elasticity parameter inversion data volumes are acquired, the inversion data volumes are converted by using the probability density function and the porosity probability volume corresponding to each class of object phase is predicted. The porosity prediction work is effectively performed in the less well region so that the porosity prediction precision of the less wellregion can be enhanced and powerful technical support can be provided for exploration and development.

Description

technical field [0001] The invention relates to the field of oil and gas exploration and development, and more specifically, to a porosity prediction method and system based on statistical petrophysical modeling in areas with few wells. Background technique [0002] Porosity is an important index to evaluate reservoir performance, so the prediction of reservoir porosity is an important scientific problem in geophysical exploration. Usually, the porosity prediction method is mainly carried out by using statistical methods, through the intersecting of the logging curves of P-wave velocity, shear-wave velocity, density, wave impedance, and elastic parameters with the porosity, and the distribution of data points is selected to be relatively concentrated, that is, the correlation between the two Better logging curves, fitting the relationship between the two, and obtaining the porosity data body by inverting the parameters and using the relationship to convert, and then accompli...

AI Technical Summary

Problems solved by technology

However, the problem with this method is that porosity is only one aspect of the factors affected by compressional wave velocity, shear wave velocity, density, wave impedance, and elastic parameter logging curves, and the method of fitting formulas cannot eliminate rock components at different depths. Due to the influence of factors such as differences in pore structure and pore structure types, the uncertainty of porosity prediction by this method is relatively high

Other porosity prediction methods mainly include multiple linear regression methods and nonlinear prediction methods (neural network, support vector machine, etc.). The linear and nonlinear relationship of the data volume, apply the linear and nonlinear relationship to the entire seismic and inversion data volume to predict the porosity data volume, the defect of this type of method is that there is a certain requirement for the number of drilling samples, only when the number of drilling wells It can only work when the requirements are met, so this method is not suitable for relevant research in areas with few wells

Images