| CPC G01V 1/30 (2013.01) [G01V 2210/6169 (2013.01)] | 6 Claims |
|
1. A seismic quantitative prediction method for shale total organic carbon (TOC) based on sensitive parameter volumes, wherein the seismic quantitative prediction method comprises:
step (1) determining a target stratum for a TOC content to be measured in a stratum; obtaining logging data of the target stratum and post stack three-dimensional (3D) seismic data; determining M numbers of depths at equal intervals on the target stratum and obtaining a TOC content of a core at each of the M numbers of depths; wherein the logging data comprise a plurality of logging curves;
step (2) performing a correlation analysis on each of the plurality of logging curves and the TOC contents at the M numbers of depths to obtain a correlation coefficient between each logging curve and the TOC contents; setting a threshold and retaining the logging curves with the correlation coefficient greater than the threshold as sensitive parameters; and the number of the sensitive parameters being N, and the sensitive parameters being labeled as first to Nth sensitive parameters;
step (3) constructing sample data:
constructing the sample data at each depth of the target stratum, wherein the sample data at a jth depth of the M numbers of depths is Lj, Lj={L1j, L2j, . . . , Lij, . . . , LNj}, where Lij represents a value of the ith sensitive parameter at the jth depth, i=1˜N, and j=1˜M;
step (4) establishing a radial basis function (RBF) neural network, and training the RBF neural network with the sample data as an input and the TOC content at the depth corresponding to the sample data as an output to obtain a RBF neural network prediction model;
step (5) for the first to Nth sensitive parameters, using each sensitive parameter as a constraint, obtaining sensitive parameter volumes by performing inversion based on the post stack 3D seismic data; and the sensitive parameter volumes being labeled as first to Nth sensitive parameter volumes;
step (6) constructing prediction samples, comprising steps (61)˜(65);
step (61) forming a three-dimensional data volume of P×Q×H for each sensitive parameter volume, each sensitive parameter volume having a same size, and a line number, a trace number, and a sampling point of each sensitive parameter volume being P, Q, and H, respectively;
step (62) organizing the first sensitive parameter volume into a two-dimensional matrix of K×H, and converting the two-dimensional matrix into a one-dimensional array of 1×L wherein K=P×Q, and L=K×H;
step (63), processing the second to the Nth sensitive parameter volumes into one-dimensional arrays respectively; and in the one-dimensional arrays, elements at same positions correspond to same coordinate values;
step (64) taking the one-dimensional arrays corresponding to the first to the Nth sensitive parameter volumes as data from first to Nth rows of a matrix, respectively, to form a prediction matrix of N×L; and
step (65) extracting each column of the data from the prediction matrix to form a prediction sample, and the number of the prediction sample being L;
step (7) inputting the L numbers of prediction samples into the RBF neural network prediction model, outputting L numbers of TOC values, and for each TOC value, using coordinates of the prediction sample corresponding to the TOC value as coordinates of the TOC value to obtain a TOC value with coordinates, thereby obtaining a one-dimensional TOC array; the coordinates comprising a line number, a trace number, and a sampling point;
step (8) transforming the one-dimensional TOC array to form a three-dimensional TOC data volume of P×Q×H according to the coordinates, thereby predicting a TOC content of the target stratum; and
step (9) performing, based on the TOC content of the target stratum, shale gas exploration and development on the target stratum.
|