CN-121993191-A - Compressibility evaluation method based on multisource fracture imaging logging data fusion

CN121993191ACN 121993191 ACN121993191 ACN 121993191ACN-121993191-A

Abstract

The invention relates to a compressibility evaluation method based on multi-source fracture imaging logging data fusion, which comprises the steps of multi-source logging data acquisition and well wall fracture identification, cross-scale acquisition of near well fracture information and far well fracture information is achieved, a fracture basic data set is formed, a multi-source logging data fusion and fracture three-dimensional network is constructed, well wall fracture parameters of a near well area and far well area fracture imaging results are fused to represent fracture space spread in a target detection range with a well shaft as a center, rock mechanical parameters based on sound waves and density logging are calculated, brittleness index and compressibility index are calculated, brittleness index, fracture network density, development degree and ground stress difference ratio are integrated, a reservoir fracture comprehensive compressibility evaluation model is constructed, and fracture compressibility evaluation is completed. According to the invention, the near well electric imaging data and the far well acoustic imaging data are fused, the rock mechanical parameters are calculated in a fused mode, and the problem that in the prior art, the crack characteristics and the rock mechanical parameters are mutually cracked is effectively avoided.

Inventors

TANG JIZHOU
WANG HANCHENG
JIA YUCHENG
ZHANG JUAN
ZHAO LUANXIAO
ZHAO SHIJIE
WANG ZI
ZHOU JUN
REN GUOHUI
LIU ZIPING
CHEN WEIHUA
LI JUNLUN
YU LEI
LI BENCHI

Assignees

同济大学

Dates

Publication Date: 20260508
Application Date: 20260327

Claims (7)

1. A compressibility evaluation method based on multisource fracture imaging logging data fusion is characterized by comprising the following steps: The method comprises the steps of S1, collecting multi-source logging data and well wall crack identification, collecting electric imaging logging, acoustic logging and density logging data, firstly converting abstract electric signals into visual well wall geological feature images by using an electric imaging logging imaging method, carrying out well wall crack information identification and parameter extraction to realize fine description of near-well area cracks, layer arrangement and holes, extracting reflection waves in vibration crack acoustic waves by using acoustic logging data, extracting far-well area crack features by using shear waves to realize cross-scale acquisition of near-well crack and far-well crack information, and forming a crack basic data set; Step S2, constructing a multi-source well logging data fusion and crack three-dimensional network, fusing well wall crack parameters of a near well region and crack imaging results of a far well region, introducing acoustic logging and density logging data as physical constraint conditions, performing space prolongation on one-dimensional crack characteristics acquired at the well wall along radial and longitudinal directions, and constructing the three-dimensional network covering a near well zone and a far well zone so as to characterize crack space spread in a target detection range by taking a well shaft as a center; S3, calculating rock mechanical parameters based on acoustic wave and density logging; And S4, calculating a brittleness index and a compressibility index, integrating the brittleness index, the fracture network density, the development degree and the ground stress difference ratio, forming a compressibility comprehensive index section for representing whether the stratum is easy to be transformed by fracturing through weighted fusion or an empirical drawing, constructing a reservoir fracture comprehensive compressibility evaluation model, and completing evaluation of fracture compressibility.
2. The method for evaluating the compressibility based on multi-source fracture imaging logging data fusion according to claim 1, wherein the method is characterized in that the method S1 specifically comprises the following steps: Firstly, an electric imaging logging imaging method is used, the micro resistivity of a stratum is intensively measured through a plurality of groups of micro electrode arrays which are closely attached to a well wall, collected data are converted into gray level or color images which are unfolded according to depth and azimuth angle after being processed and normalized, abstract electric signals are converted into a well wall geological feature map, and fine description of cracks, layers and holes is realized; and extracting fracture characteristics of a far well region by using a dipole transverse wave far detection method, wherein shear waves are formed by expansion, shearing and translation transformation of a basic function, and the shear wave basic function is obtained by using a shear wave The definition is as follows: ; Wherein R+ represents a positive real number set, S is a shearing matrix for determining a signal direction, S is a shearing coefficient, R represents a real number set, t is a translation parameter, R 2 represents a determining coefficient, ψ is composed of a wavelet function ψ 1 and a collision function ψ 2 , and a representation form in a wavelet function frequency domain And representation in the frequency domain of the collision function The relationship is as follows: ; Wherein ω 1 and ω 2 represent angular frequencies in any two-dimensional space; the anisotropic expansion matrix a is: ; wherein a is the scale factor, ψ a,s,t is in the form of the Fourier domain The following are provided: ; Where i is an imaginary unit, it is assumed that the function f is square integrable and its continuous shear wave transformation function SH f is: ; Taking the extracted reflected signals as input, setting coordinates of an instrument transmitting probe and a receiving probe, establishing an observation system for sound wave remote detection, and storing forward wave fields at each moment The position of the receiver is used as an initial value, the wave equation is utilized to reversely extend the wave field of the receiver at each moment, the same differential format as the forward extension is adopted, the wave field of each time step is gradually extended to the moment t=0, and the wave field of each time step is recorded; And using a cross-correlation imaging condition to sum the cross-correlation of the forward extended sound source wave field and the reverse time extrapolated wave field of the receiver at all moments to obtain a sound wave crack imaging result.
3. The method for evaluating the compressibility based on the multi-source fracture imaging logging data fusion according to claim 2, wherein when the multi-source logging data in S1 is acquired, FMI electric imaging logging is firstly carried out on a target well to acquire well wall imaging data, preprocessing is carried out on the FMI electric imaging logging data, and the preprocessing comprises noise suppression, image enhancement and standardization processing.
4. The method for evaluating the compressibility based on multi-source fracture imaging logging data fusion according to claim 3, wherein the method for identifying and extracting the parameters of the well wall fracture information in S1 is characterized in that an image segmentation and edge detection algorithm is adopted to identify the fracture of the well wall imaging image, distinguish natural fractures from induced fractures, and extract the occurrence parameters and the geometric parameters of the fracture, wherein the occurrence parameters comprise the inclination angle and the trend of the fracture, the geometric parameters comprise the length and the opening of the fracture, and finally, the quantitative description result of the near-well region fracture is obtained.
5. The method for evaluating the compressibility based on the multi-source fracture imaging logging data fusion according to claim 4, wherein the step S2 is specifically as follows: And fusing well wall crack parameters of a near well region with crack imaging results of a far well region, introducing acoustic logging and density logging data as physical constraint conditions, performing spatial extension on one-dimensional crack characteristics acquired at the well wall along radial and longitudinal directions through interpolation and extrapolation algorithms, constructing a multi-source logging data fusion and crack three-dimensional network covering the near well region and the far well region, and characterizing crack spatial distribution characteristics and communication relations in a certain detection range by taking a well shaft as a center.
6. The method for evaluating the compressibility based on the multi-source fracture imaging logging data fusion according to claim 5, wherein the step S3 is specifically as follows: Calculating dynamic Young's modulus by using acoustic logging longitudinal wave time difference, transverse wave time difference data and density logging data, and converting dynamic parameters into static Young's modulus for fracturing design by combining regional empirical formulas or rock core calibration data; the dynamic young's modulus is obtained by shear and longitudinal wave acoustic time differences and densities, calculating the longitudinal wave time difference: ; Wherein V p is the longitudinal wave velocity and fat is the measured acoustic time difference; Effective stress coefficient: ; Wherein ρ m is the density value of the stratum, ρ395 is the density of the skeletal rock material, taken from the dense sandstone ρ m =2.65g/cm 3 and from other lithology, V mp is the longitudinal wave velocity of the skeletal material, dense sandstone V mp =5.95 km/s, from manual input of other lithology, V ms is the shear wave velocity of the skeletal material, V ms =3.0 km/s is taken for the dense sandstone, V p is the longitudinal wave velocity of the stratum, V s is the shear wave velocity of the stratum; the dynamic elastic modulus was calculated using the following equation: ; Wherein E d is dynamic elastic modulus, ZDEN is density, DTC is longitudinal acoustic time difference t, and DTS is transverse acoustic time difference; the static elastic modulus Es is: 。
7. The method for evaluating the compressibility based on the multi-source fracture imaging logging data fusion according to claim 6, wherein the step S4 is specifically as follows: The brittleness index is based on a uniaxial full stress strain curve, and is provided by linearizing the full stress-strain curve and splitting the whole curve into three linear sections, namely an elastic section, a plastic section and a peak rear section, so as to simplify the curve; the brittleness index B is calculated as follows: ; wherein dW * d is the pre-peak index, dW f is the post-peak index, dW e(A) is the slope of the elastic section stress-strain curve, dW e(B) is the slope of the plastic section stress-strain curve, and dW e(C) is the slope of the post-peak section stress-strain curve; Based on brittleness index and stratum stress difference coefficient of multi-source logging data, constructing a comprehensive reservoir fracture compressibility evaluation model, and evaluating the gas storage by using the compressibility evaluation model; The fracture comprehensive compressibility evaluation model is as follows: ; ; ; ; Wherein σ H is the maximum horizontal principal stress, σ h is the minimum horizontal principal stress, Δσ is the formation stress difference coefficient, Δσ （min） is the minimum value of the ground stress difference coefficient, Δσ （max） is the maximum value of the formation stress difference coefficient, B is the brittleness index, B （min） is the minimum value of the brittleness index, B （max） is the maximum value of the brittleness index, B n is the normalized brittleness index, Δσ n is the normalized formation stress difference coefficient, and F new is the compressibility coefficient; Yield stress is the formation fracture pressure: ; Wherein, sigma c is yield stress, P f stratum fracture pressure, sigma H is maximum horizontal ground stress, sigma h is minimum horizontal ground stress, P p stratum pore pressure, alpha is effective stress coefficient, dimensionless and S t is uniaxial tensile strength.

Description

Compressibility evaluation method based on multisource fracture imaging logging data fusion Technical Field The invention relates to the technical field of oil and gas field development fracturing engineering, in particular to a method for constructing a well three-dimensional fracture network model based on multi-source logging data fusion such as electric imaging and acoustic imaging and quantitatively evaluating reservoir fracturing property by synthesizing rock mechanical parameters. Background In fracturing reformation of unconventional reservoirs such as tight oil gas, shale gas and the like, the fracturing property of the reservoir directly determines the yield increasing effect. The accurate evaluation of the fracking property depends on two major core elements, namely, the space development characteristics (geometric form, density and trend) of a natural fracture system in the stratum and the rock mechanical properties (brittleness and ground stress field) of the stratum. At present, the industry mainly faces two technical bottlenecks, namely, the first problem of data island. The electric imaging logging (such as FMI) can identify the well wall crack with high precision, but the detection depth is shallow, and the acoustic wave far detection imaging (such as dipole transverse wave far detection of DSI) can detect crack reflectors within the range of tens of meters around the well, but the resolution of near-well wall microcracks is insufficient. The two data are independently interpreted, and a unified and continuous space crack model cannot be formed. Second, evaluate index fracture problem. The prior method often calculates the brittleness index or counts the crack density independently, and lacks a quantitative fusion evaluation model combining the space geometrical characteristics of the crack and the mechanical response characteristics of the rock. Determination of fracture design parameters (e.g., perforation location, cluster spacing, construction pressure) remains too empirical and lacks direct guidance from subsurface three-dimensional geomechanical models. Disclosure of Invention The invention aims to provide a compressibility evaluation method based on multi-source fracture imaging logging data fusion, which is used for solving the problem of data island or evaluation index fracture of the existing reservoir fracturing evaluation method. The technical scheme adopted by the invention for solving the technical problems is that the compressibility evaluation method based on multi-source fracture imaging logging data fusion comprises the following steps: The method comprises the steps of S1, collecting multi-source logging data and well wall crack identification, collecting electric imaging logging, acoustic logging and density logging data, firstly converting abstract electric signals into visual well wall geological feature images by using an electric imaging logging imaging method, carrying out well wall crack information identification and parameter extraction to realize fine description of near-well area cracks, layer arrangement and holes, extracting reflection waves in vibration crack acoustic waves by using acoustic logging data, extracting far-well area crack features by using shear waves to realize cross-scale acquisition of near-well crack and far-well crack information, and forming a crack basic data set; Step S2, constructing a multi-source well logging data fusion and crack three-dimensional network, fusing well wall crack parameters of a near well region and crack imaging results of a far well region, introducing acoustic logging and density logging data as physical constraint conditions, performing space prolongation on one-dimensional crack characteristics acquired at the well wall along radial and longitudinal directions, and constructing the three-dimensional network covering a near well zone and a far well zone so as to characterize crack space spread in a target detection range by taking a well shaft as a center; S3, calculating rock mechanical parameters based on acoustic wave and density logging; And S4, calculating a brittleness index and a compressibility index, integrating the brittleness index, the fracture network density, the development degree and the ground stress difference ratio, forming a compressibility comprehensive index section for representing whether the stratum is easy to be transformed by fracturing through weighted fusion or an empirical drawing, constructing a reservoir fracture comprehensive compressibility evaluation model, and completing evaluation of fracture compressibility. The scheme S1 specifically includes: Firstly, an electric imaging logging imaging method is used, the micro resistivity of a stratum is intensively measured through a plurality of groups of micro electrode arrays which are closely attached to a well wall, collected data are converted into gray level or color images which are unfolded according to depth and azimuth angle a