| CPC G01V 11/002 (2013.01) | 5 Claims |
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1. A pore pressure prediction method for a fine-grained hybrid sedimentary rock based on variable P-wave velocity, comprising:
determining a functional relationship between a variable P-wave velocity and effective stress;
establishing an effective stress influence term prediction model, comprising:
based on measured data of a P-wave velocity and an effective stress of core, using an optimization method to predict parameters A and B in a power function and parameters A, B, C, D, and E in the power function+S function respectively by dividing the fine-grained hybrid sedimentary rock into a mud-grade hybrid sedimentary rock and a sand-grade hybrid sedimentary reservoir, and obtaining effective stress influence term function f1(σ) and f2(σ) of the mud-grade hybrid sedimentary rock and the sand-grade hybrid sedimentary reservoir respectively;
power function+S function is shown in Equation (1):
Vp=V0+AσB+c/(1+(D/σ)E)=V0+f2(σ) (1)
for the mud-grade hybrid sedimentary rock, the function relationship between the P-wave velocity and the effective stress is denoted by a power function; the power function is shown in Equation (2):
Vp=V0+AσB=V0+f1(σ) (2)
where, Vp is a P-wave velocity of rock, m/s; V0 is an acoustic velocity background value, m/s; σ is the effective stress, MPa; A, B, C, D, and E are parameter variables, the parameter variables A, B, C, D, and E are obtained by a fitting; f1 and f2 are effective stress influence items of the mud-grade hybrid sedimentary rock and the sand-grade hybrid sedimentary reservoir, respectively;
establishing a logging evaluation model of a P-wave background value; comprising:
combining with measured pore pressure data of a fine-grained hybrid sedimentary rock formation in a work area to calculate an effective stress influence term value, then, deducing a P-wave velocity background value by using an acoustic logging curve of the measured pore pressure corresponding to a depth point; counting a relationship between the P-wave velocity background value and a neutron curve, a density curve, an acoustic logging curve, a resistivity curve, a natural gamma ray curve, and a buried depth, and establishing a logging evaluation model of the acoustic velocity background value; and
predicting a pore pressure of a variable acoustic velocity background value; comprising:
based on the acoustic velocity background value V0 and the effective stress influence term function f1(σ) or f2(σ), combined with a lithology logging identification method, calculating an effective stress value of a target layer by using the neutron curve, the density curve, the acoustic logging curve, the resistivity curve, the natural gamma ray curve and the buried depth, and then obtaining a pore pressure distribution curve, depicting a vertical and planar distribution of the pore pressure, and realizing a prediction of the pore pressure with the variable acoustic velocity background value, wherein the function relationship between the P-wave velocity and the effective stress is determined by:
carrying out a core sampling of the fine-grained hybrid sedimentary rock formation, and carrying out effective stress and P-wave velocity tests to measure a change of the P-wave velocity of rock under different effective stresses, analyzing the functional relationship between the P-wave velocity and the effective stress according to an intersection diagram of the P-wave velocity and the effective stress, and determining a functional relationship for two types of reservoirs, comprising the mud-grade hybrid sedimentary rock and the sand-grade hybrid sedimentary reservoir respectively; and
sampling the fine-grained hybrid sedimentary rock in a study area when the effective stress and P-wave velocity tests are carried out, a sample covers a main lithology, and the sample is a regular cylinder; carrying out the P-wave velocity test on the sample under different effective stresses, and establishing a change curve of the P-wave velocity of the rock with the effective stress, and setting an effective stress point uniformly.
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