A phase-controlled porosity prediction method based on multi-threshold birch clustering

Abstract

The invention discloses a phase-controlled porosity prediction method based on multi-threshold Birch clustering, belonging to the field of oil and gas exploration and development, comprising the following steps: S1: normalize all data; S2: initialize M-Birch clustering Model; S3: Dynamically build CF Tree; S4: After the CF Tree is built, use the agglomeration method to cluster CF globally; S5: Under the constraints of sedimentary facies, train the ridge regression prediction model; S6: Get the optimal parameters On the basis of estimators, porosity predictions are calculated from unlabeled impedance data. On the basis of the existing phase-controlled porosity prediction technology, the present invention proposes a method combining multi-threshold Birch clustering and ridge regression algorithm to predict porosity, and takes the porosity data at the well point and the seismic acoustic impedance attribute as input, The improved multi-threshold BIRCH clustering algorithm (M-Birch) is applied to determine the sedimentary facies type, and the predicted porosity results are more accurate in the case of sparse well point data.

Description

technical field [0001] The invention relates to a phase-controlled porosity prediction method, which belongs to the field of oil and gas exploration and development, and more specifically relates to a phase-controlled porosity prediction method based on multi-threshold Birch clustering and ridge regression. Background technique [0002] Porosity is an important parameter for oil and gas prediction and reservoir evaluation. In the oil and gas exploration stage, the porosity can be used for lateral prediction of the reservoir. In the development stage, the porosity can be calibrated by logging to describe the reservoir. Accurately obtaining porosity is the key to reservoir stratigraphic interpretation and geological model establishment. [0003] Among the widely used lateral prediction technologies of reservoir physical properties, the more commonly used porosity prediction methods mainly include: directly using the Wyllie time-average equation to predict porosity for reservo...

AI Technical Summary

Problems solved by technology

[0003] Among the widely used lateral prediction technologies of reservoir physical properties, the more commonly used porosity prediction methods mainly include: directly using the Wyllie time-average equation to predict porosity for reservoirs with single lithology and little change, the principle is relatively simple, and the application Convenience, but the calculation of porosity only based on velocity parameters is prone to multiple solutions, which will affect the prediction results; well constraint inversion method, but different inversion methods have different requirements for well constraint conditions, improper use of well constraint conditions will seriously Affect the accuracy of inversion; function approximation method, this method mainly adopts multiple regression method, but when using this method, wells and pores with uniform distribution are required, which is not universal; porosity calculation method based on Biot-Gassmann equation , the disadvantage of this method is that many parameters need to be provided in advance. In addition to the parameters other than porosity in the Gassmann equation, stress, pore pressure, fluid hysteresis coefficient, and seismic wave attenuation coefficient are also required. Too many input parameters limit the Wide application of this method

Images