CN110501745B - Solid-liquid two-phase stripping hydrocarbon detection method - Google Patents

Solid-liquid two-phase stripping hydrocarbon detection method Download PDF

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CN110501745B
CN110501745B CN201910797653.XA CN201910797653A CN110501745B CN 110501745 B CN110501745 B CN 110501745B CN 201910797653 A CN201910797653 A CN 201910797653A CN 110501745 B CN110501745 B CN 110501745B
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rock
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hydrocarbon detection
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CN110501745A (en
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张岩
侯志强
赵军
姜勇
李键
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China Oilfield Services Ltd Shanghai Branch
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
    • G01V1/282—Application of seismic models, synthetic seismograms
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
    • G01V1/30—Analysis
    • G01V1/306—Analysis for determining physical properties of the subsurface, e.g. impedance, porosity or attenuation profiles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
    • G01V1/36—Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
    • G01V1/362—Effecting static or dynamic corrections; Stacking

Abstract

The invention providesA solid-liquid two-phase stripping hydrocarbon detection method; the detection method comprises the following steps: deducing an approximate mathematical analytical expression containing rock pore structure factors, porosity and fluid modulus; eliminating the influence of porosity on fluid identification in a Gassmann equation, and constructing a hydrocarbon detection factor; determining the longitudinal-transverse wave velocity ratio coefficient gamma of the dry rock under the maximum correlation degree of Russell fluid factor rho f and water saturationdry(ii) a Finally obtaining the hydrocarbon detecting section of elastic space-time varying reservoir two-phase stripping. The detection method provided by the invention can flexibly realize the separation of solid-liquid two-phase components of the rock, and the elastic information of the fluid item and the framework item in the prestack AVO information is directly extracted through inversion so as to improve the accuracy and reliability of the identification of different fluid properties in a strong heterogeneity reservoir stratum, thereby greatly reducing the risk in the oil-gas exploration, development, design and deployment process.

Description

Solid-liquid two-phase stripping hydrocarbon detection method
Technical Field
The invention belongs to the technical field of geophysical exploration, and relates to a reservoir oil-gas detection method, in particular to a solid-liquid two-phase stripping hydrocarbon detection method, and particularly relates to a seismic reservoir fluid identification method based on prestack information, which is applied to the field of oil-gas resource exploration and development.
Background
Seismic reservoir fluid property identification has been a hot spot and a huge challenge for research topic in the geophysical field at home and abroad. The method is mainly embodied in the aspects of strong elasticity space-time variation characteristics of a macroscopic underground reservoir, coupling relations among components of microscopic rocks, non-uniqueness of seismic reflection response characteristics, uncertainty in a mathematical method solving process and the like. With the increasing demand of exploration precision, the development of the seismic reservoir fluid identification technology gradually steps to a high precision. The development process of the method is successively changed from post-stack to pre-stack, from single-phase elastic theory to multi-phase pore medium theory, from mathematical drive to model drive, from indirect combination to direct inversion and the like. Higher requirements are put on the precision, reliability and stability of the calculation method. At the present stage, the mapping relation between pore fluid and seismic reflection response characteristics is fully considered, and the technical scheme of sensitive fluid identification factor construction formed under the seismic rock physical theory framework and direct inversion based on pre-stack seismic information is the most effective means.
According to different mathematical bases, fluid factors can be roughly divided into three categories: one is based on the parameters of a single-phase elastic medium theory; secondly, parameters based on the multi-phase rock pore medium theory; and the third is based on the combination parameters of the sensitive amplification effect. The first fluid factor has definite physical meaning, can effectively reflect the sensitivity of the change of a single component of the rock, but is easily influenced by practical data to reduce the flexibility of identification capability; the second fluid factor is based on the seismic rock physical theory, fully considers the restriction relationship among rock components, and can effectively identify reservoir fluid with mature compaction consolidation; however, due to the influence of sensitive distortion including solid matrix, porosity, pore microstructure and the like, the identification degree of complex areas with low compaction consolidation degree is reduced and the false appearance is easy to generate; determining a fluid angle or a pure mathematical high-order operation by the third fluid factor based on intersection statistical analysis; but the physical meaning is unclear, the selection of the fluid angle excessively depends on manual picking, and the noise is raised while the effective signals are highlighted by high-order operation.
The prestack seismic inversion is the most direct way for accurately and stably realizing fluid factor acquisition, and mainly comprises prestack elastic parameter inversion and prestack elastic impedance inversion. The pre-stack elastic parameter inversion can obtain three basic parameters: longitudinal wave impedance, transverse wave impedance, and density. Any elastic parameter and its derivatives can be formed by mathematical operations. As is well known, the problems of 'undercharacterization' and 'ill-conditioned solution' commonly exist in the seismic inversion method, and the prediction precision is greatly reduced due to accumulated errors.
Therefore, a new two-phase stripping hydrocarbon detection method based on direct extraction of prestack AVO information needs to be developed to meet application requirements.
Disclosure of Invention
The invention aims to provide a solid-liquid two-phase stripping hydrocarbon detection method; the detection method provided by the invention can flexibly realize the separation of solid-liquid two-phase components of the rock, and the elastic information of the fluid item and the framework item in the prestack AVO information is directly extracted through inversion so as to improve the accuracy and reliability of the identification of different fluid properties in a strong heterogeneity reservoir stratum, thereby greatly reducing the risk in the oil-gas exploration, development, design and deployment process.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the invention provides a solid-liquid two-phase stripping hydrocarbon detection method, which comprises the following steps:
(1) deducing an approximate mathematical analytical expression containing rock pore structure factors, porosity and fluid modulus based on a fluid item f of a Gassmann equation;
(2) eliminating the influence of the porosity in the Gassmann equation on fluid identification by adopting the shear modulus mu capable of directly reflecting the influence of the porosity of the rock, and constructing a hydrocarbon detection factor;
(3) method for determining longitudinal and transverse wave velocity ratio coefficient gamma of dry rock under maximum correlation degree of Russell fluid factor rhof and water saturation by utilizing self-adaptive correlation scanningdry；
(4) Combining three AVO approximate formulas provided by Russell, directly extracting intraformational solid-liquid two-phase elasticity characteristic information from reflection information of a fluid item f and a shear modulus mu interface of AVO attributes through well control colored inversion, and performing parameter combination on an elasticity data body of the fluid item f and the shear modulus mu obtained through inversion to finally obtain a hydrocarbon detection profile of elasticity time-space variant reservoir two-phase stripping.
The invention fully considers the restriction relationship among the solid skeleton, multiphase fluid, rock pore and pore structure factor in the porous elastic medium; under the theoretical framework of a seismic rock physical porous elastic medium, the influence of the porosity in a fluid item of a Gassmann equation is eliminated to the maximum extent, and a high-sensitivity hydrocarbon detection factor is constructed; and directly extracting the elasticity information of a fluid item f and a shear modulus mu from the prestack AVO attribute body by utilizing well control colored inversion, and finally obtaining a hydrocarbon detection data body stripped in a solid-liquid two-phase mode; therefore, reservoir fluid distribution rule information with high precision, high stability and high reliability can be provided for reservoir describers.
The method is based on the mathematical representation of the reflection coefficient of the fluid factor, and a fluid factor data volume and related elastic parameters can be directly obtained through an inversion means; the method eliminates the underdetermined obstacle of calculating the wavelet or the reflection coefficient from the realization of the method, effectively avoids the problems of 'ill-conditioned solution' and the like in the seismic inversion method, and provides accurate, stable and reliable technical support for identifying the seismic reservoir fluid.
The invention can be widely applied to the field of oil reservoir description of oil-gas exploration and development earthquake, improves the accuracy of earthquake hydrocarbon detection, and further reduces the risk in oil-gas exploration, development and deployment.
The skeleton term s ═ C · μ, and C ═ γdry 2(γdrySquare of) is a constant term coefficient, and thus, the shear modulus μ and the framework term s may be equivalent.
In the invention, the derivation process of the approximate mathematical analytic expression in the step (1) is as follows:
under the low frequency assumption, the fluid term f according to the standard Gassmann equation is known as:
Figure BDA0002181416220000041
wherein f represents the Gassmann equation fluid term; β represents the Biot coefficient; m represents a bulk modulus; kdryRepresents the dry rock bulk modulus; kmDenotes the matrix bulk modulus; kfRepresents the mixed fluid modulus; p represents a pore structure coefficient; phi denotes the rock porosity.
a. For the Biot coefficient β, the following processing is performed:
approximation of theory K from dry rockdry＝Km·(1-φ)pTherefore, the following steps are carried out: kdry/Km＝(1-φ)p<1，(Kdry<Km<Ksat)；
To (1-phi)pPerforming a first order Taylor expansion approximation yields: (1-phi)p≈1-P·φ；
By
Figure BDA0002181416220000042
It is known that β ≈ 1- (1-P · Φ) ═ P · Φ.
b. The bulk modulus M was treated as follows:
from the above derivation:
Figure BDA0002181416220000043
due to Kf<<KmThe following can be obtained:
Figure BDA0002181416220000044
combining beta and M to obtain:
f＝β2M≈P2·φ·Kf。
the invention obtains the rigidity parameter P, the porosity phi and the fluid bulk modulus K in the fluid item f by derivationfWherein the bulk modulus K of the fluid in the fluid term ffIs the most direct parameter for hydrocarbon detection, but is generally difficult to obtain. Fluid item f contains KfAnd are readily available; however, the fluid item f also contains parameters reflecting the skeleton influence, such as the rigidity parameter P and the porosity phi, and the existence of the parameters influences the sensitivity of the fluid item f to hydrocarbon detection, so that the influence needs to be eliminated or reduced as much as possible. The shear modulus μ is introduced, which can directly reflect the influence of the skeleton (in other words, the skeleton influence of the rigidity parameter P and the porosity Φ, etc. is included in μ). Therefore, the hydrocarbon detection factor RK is obtained by a construction method of the ratiofIn which only K is reflected as much as possiblefFluid information, the purpose of solid-liquid two-phase stripping is achieved.
In the invention, the method for eliminating the influence of the porosity on the fluid identification in the Gassmann equation in the step (2) is to introduce mu to eliminate the influence of the porosity.
Wherein mu is an elastic parameter capable of directly reflecting the influence of the porosity of the rock.
Preferably, the hydrocarbon detection factor in step (2) is constructed by the following method:
the formula of Gassmann fluid term of Russell is derived as follows:
Figure BDA0002181416220000051
where ρ represents density; f represents the fluid term of the Gassmann equation; s represents a skeletal item; zPRepresenting the longitudinal wave impedance; zSRepresents the transverse wave impedance; gamma raydryRepresenting the longitudinal-transverse wave velocity ratio coefficient of the dry rock;
and the number of the first and second electrodes,
Figure BDA0002181416220000052
wherein s represents a skeletal item; kdryRepresents the dry rock bulk modulus; μ represents a shear modulus; vPRepresenting the velocity of longitudinal waves; vSRepresents the shear wave velocity;
the following equations are taken together:
Figure BDA0002181416220000061
Figure BDA0002181416220000062
wherein f represents the Gassmann equation fluid term;
namely:
Figure BDA0002181416220000063
the following can be obtained:
Figure BDA0002181416220000064
then:
Figure BDA0002181416220000065
i.e. constructing said hydrocarbon detection factor RKfThe following were used:
Figure BDA0002181416220000066
wherein the hydrocarbon detection factor RKfIs rotanal-KfI.e. the bulk modulus of the rotating fluid.
In the invention, the longitudinal-transverse wave velocity ratio coefficient gamma of the dry rockdryThe determination method of (2) is as follows:
at gammadry 2Within the range of the value change (0 to + ∞), change gamma with a change interval step of 0.001dry 2Value of will be different from gammadry 2Performing cross-correlation operation on the rho f factor and the water saturation Sw under the value condition, determining the value with the maximum correlation degree by using a self-adaptive scanning method and outputting the corresponding longitudinal-transverse wave velocity ratio coefficient gamma of the dry rockdry。
In the invention, the method for obtaining the hydrocarbon profile of the elastic space-time varying reservoir two-phase stripping in the step (4) comprises the following steps:
(I) using the three-term AVO approximation proposed by Russell as shown below, by combining the dry rock longitudinal and transverse wave velocity ratio coefficient γdryConverting the angle gathers into AVO attribute bodies, namely reflectivity information of the fluid item f and the shear modulus mu;
Figure BDA0002181416220000071
(II) under the constraint of a structural horizon, performing mathematical interpolation modeling by using logging data to provide inverted low-frequency trend information; the interface relative information of the fluid item f and the shear modulus mu is converted into in-layer absolute information by carrying out colored inversion, so that the direct extraction of the solid-liquid two-phase elastic characteristic information is completed;
and (III) performing parameter combination according to the hydrocarbon detection factor form obtained in the step (3) to obtain a hydrocarbon detection data volume for solid-liquid two-phase stripping.
The method is used for hydrocarbon detection by utilizing the finally calculated hydrocarbon detection data body.
Compared with the prior art, the invention has the following beneficial effects:
(1) the method is based on the mathematical representation of the reflection coefficient of the fluid factor, and a fluid factor data volume and related elastic parameters can be directly obtained through an inversion means; the method eliminates the underdetermined obstacle of calculating the wavelet or the reflection coefficient from the realization of the method, effectively avoids the problems of 'ill-conditioned solution' and the like in the seismic inversion method, and provides accurate, stable and reliable technical guarantee for identifying the seismic reservoir fluid;
(2) the detection method provided by the invention can flexibly realize the separation of solid-liquid two-phase components of the rock, and the elastic information of the fluid item and the framework item in the prestack AVO information is directly extracted through inversion so as to improve the accuracy and reliability of the identification of different fluid properties in a strong heterogeneity reservoir stratum, thereby greatly reducing the risk in the oil-gas exploration, development, design and deployment process.
Drawings
FIG. 1 is a schematic diagram of eliminating the effect of porosity on fluid identification in the Gassmann equation.
FIG. 2 is a graph of the preferred dry rock longitudinal and transverse wave velocity ratio coefficient γ using an adaptive scanning methoddryFigure (a).
FIG. 3 is a plot of data elasticity curves versus hydrocarbon detection factors.
Fig. 4A is a plot of the data fluid term f crossing shear modulus.
FIG. 4B is a graph of hydrocarbon detection factor versus shear modulus.
FIG. 5 is a comparative analysis of fluid sensitivity for different elastic parameters.
Wherein, 1-poisson's ratio; 2-Lame constant; 3-shear modulus; 4-Lami times density; 5-shear density; 6-bulk modulus; 7-longitudinal wave modulus; 8-longitudinal wave impedance; 9-transverse wave impedance; 10-fluid factor; 11-a fluid item; 12-a hydrocarbon detection factor; 13-spreading the impedance.
FIG. 6 is a post-stack seismic waveform display section through example well A and well B.
FIG. 7 is a Sw hydrocarbon profile of water saturation inverted through the Extended Elastic Impedance (EEI) of example well A and well B.
FIG. 8 is a direct extraction of a New Hydrocarbon Retention factor RK from example well A and well B based on prestack AVO informationfAnd (4) section.
FIG. 9 is an Extended Elastic Impedance (EEI) and new hydrocarbon detectivity RK for an example well AfThe section is a partially enlarged view.
Wherein the left image is a partially enlarged cross-sectional view of the Extended Elastic Impedance (EEI) of the example well A and the right image is the new hydrocarbon detection factor RKfIs enlarged partially.
Detailed Description
The technical solution of the present invention is further explained by the following embodiments. It should be understood by those skilled in the art that the examples are only for the understanding of the present invention and should not be construed as the specific limitations of the present invention.
The solid-liquid dual-phase stripping hydrocarbon detection method based on pre-stack AVO information direct extraction provided by the invention takes seismic rock physics theory as guidance, eliminates the influence of the porosity in Gassmann fluid item f on the fluid identification capability thereof by introducing the shear modulus mu capable of directly reflecting the influence of the porosity of rock, and further constructs a new high-sensitivity hydrocarbon detection factor; combining three AVO approximate formulas provided by Russell, and directly extracting solid-liquid biphase characteristic information in the layer from fluid item f and shear modulus mu interface reflection information of AVO attribute through colored inversion; in the process, the longitudinal and transverse wave velocity ratio coefficient gamma of the dry rock under the maximum correlation degree of Russell fluid factor rho f and water saturation is determined by using a self-adaptive correlation scanning methoddryTo ensure the accuracy and reliability of AVO attribute generation; and finally obtaining the hydrocarbon detection profile of the elastic space-time varying reservoir two-phase stripping through the mathematical combination of the two elastic parameters.
Example 1
The embodiment provides a solid-liquid two-phase stripping hydrocarbon detection method as follows:
(1) an approximate mathematical analytical expression comprising rock pore structure factors, porosity and fluid modulus is deduced based on a fluid item f of a Gassmann equation, and the specific process is as follows:
under the low frequency assumption, the fluid term f according to the standard Gassmann equation is known as:
Figure BDA0002181416220000091
wherein f represents the Gassmann equation fluid term; β represents the Biot coefficient; m represents a bulk modulus; kdryRepresents the dry rock bulk modulus; kmDenotes the matrix bulk modulus; kfRepresents the mixed fluid modulus; p represents a pore structure coefficient; phi represents the rock porosity;
a. for the Biot coefficient β, the following processing is performed:
approximation of theory K from dry rockdry＝Km·(1-φ)pTherefore, the following steps are carried out: kdry/Km＝(1-φ)p<1，(Kdry<Km<Ksat)；
To (1-phi)pPerforming a first order Taylor expansion approximation yields: (1-phi)p≈1-P·φ；
By
Figure BDA0002181416220000101
It can be seen that β ≈ 1- (1-P · Φ) ═ P · Φ;
b. the bulk modulus M was treated as follows:
from the above derivation:
Figure BDA0002181416220000102
due to Kf<<KmThe following can be obtained:
Figure BDA0002181416220000103
combining beta and M to obtain:
f＝β2M≈P2·φ·Kf。
(2) the method is characterized in that the influence of the porosity in a Gassmann equation on fluid identification is eliminated by adopting the shear modulus mu capable of directly reflecting the influence of the porosity of the rock, and a hydrocarbon detection factor is constructed, and the method specifically comprises the following steps:
mu can directly reflect the elastic parameter of the influence of rock porosity, so the influence of the porosity on the fluid identification in the Gassmann equation is eliminated by introducing mu.
The hydrocarbon detection factor is constructed as follows:
the formula of Gassmann fluid term of Russell is derived as follows:
Figure BDA0002181416220000104
where ρ represents density; f represents the fluid term of the Gassmann equation; s represents a skeletal item; zPRepresenting the longitudinal wave impedance; zSRepresents the transverse wave impedance; gamma raydryRepresenting the longitudinal-transverse wave velocity ratio coefficient of the dry rock;
and the number of the first and second electrodes,
Figure BDA0002181416220000111
wherein s represents a skeletal item; kdryRepresents the dry rock bulk modulus; μ represents a shear modulus; vPRepresenting the velocity of longitudinal waves; vSRepresents the shear wave velocity;
the following equations are taken together:
Figure BDA0002181416220000112
Figure BDA0002181416220000113
wherein f represents the Gassmann equation fluid term;
namely:
Figure BDA0002181416220000114
the following can be obtained:
Figure BDA0002181416220000115
then:
Figure BDA0002181416220000121
i.e. constructing said hydrocarbon detection factor RKfThe following were used:
Figure BDA0002181416220000122
wherein RKfThe new hydrocarbon detection factor can be also referred to as new hydrocarbon detection factor for short.
(3) Method for determining longitudinal and transverse wave velocity ratio coefficient gamma of dry rock under maximum correlation degree of Russell fluid factor rhof and water saturation by utilizing self-adaptive correlation scanningdryThe determination method is as follows:
at gammadry 2Within the range of the value change (0 to + ∞), change gamma with a change interval step of 0.001dry 2Value of will be different from gammadry 2Performing cross-correlation operation on the rho f factor and the water saturation Sw under the value condition, determining the value with the maximum correlation degree by using a self-adaptive scanning method and outputting the corresponding longitudinal-transverse wave velocity ratio coefficient gamma of the dry rockdry。
(4) Combining three AVO approximate formulas provided by Russell, directly extracting intraformational solid-liquid two-phase elasticity characteristic information from reflection information of a fluid item f and a shear modulus mu interface of AVO attributes through well control colored inversion, and performing parameter combination on an elasticity data body of the fluid item f and the shear modulus mu obtained through inversion to finally obtain a hydrocarbon detection profile of elasticity time-space variation reservoir two-phase stripping, wherein the method comprises the following steps:
(I) utilize the followingThe three-term AVO approximation proposed by Russell is shown by combining the dry rock aspect ratio coefficient γdryConverting the angle gathers into AVO attribute bodies, namely reflectivity information of the fluid item f and the shear modulus mu;
Figure BDA0002181416220000123
(II) under the constraint of a structural horizon, performing mathematical interpolation modeling by using logging data to provide inverted low-frequency trend information; the interface relative information of the fluid item f and the shear modulus mu is converted into in-layer absolute information by carrying out colored inversion, so that the direct extraction of the solid-liquid two-phase elastic characteristic information is completed;
and (III) performing parameter combination according to the hydrocarbon detection factor form obtained in the step (3) to obtain a hydrocarbon detection data volume for solid-liquid two-phase stripping.
FIG. 1 is a schematic diagram of eliminating the influence of porosity on fluid identification in the Gassmann equation in step (2). The Gassmann fluid item f contains information such as porosity, pore microstructure and fluid properties, and the shear modulus mu (framework item s ═ C · mu, C ═ gamma) capable of directly reflecting the influence of rock porosity is introduceddry 2(γdrySquare of) is a constant term coefficient, and thus the shear modulus μ and the skeleton term s may be equal), the abnormal interference thereof is reduced as much as possible.
Fig. 2 to 9 are related graphs of the solid-liquid two-phase stripping hydrocarbon detection method based on the pre-stack AVO information direct extraction, and the following describes in detail the specific implementation process of the solid-liquid two-phase stripping hydrocarbon detection method based on the pre-stack AVO information direct extraction according to the present invention with reference to the following graphs:
the main oil-gas-bearing layer in the research area is concentrated at the middle and deep part, the transverse heterogeneity changes violently, and the impedance superposition among gas sand, water sand and mudstone is seriously influenced due to the compaction and consolidation action in the longitudinal direction. Reservoir parameter time-space variations present a significant challenge to fluid identification.
The research area has two exploratory wells A and B with measured data, wherein the well A is used as a test well, and the well B is used as a verification well. Logging petrophysical separation by local zoneAnalysis determined that the Gassmann fluid item f had a higher degree of fluid discrimination but still had a certain elastic overlap interval. In order to further eliminate the distortion influence of porosity factors on the fluid pore term f, a new hydrocarbon detection factor RK is constructedf. The longitudinal and transverse wave velocity ratio coefficient gamma of the dry rock is needed in the physical analysis of the logging rock and the calculation process of the subsequent AVO attributedryThe value is usually obtained by using a statistical fit of a log intersection analysis, and a large error generally exists to influence the accuracy of the elastic parameter inversion. The invention adopts a self-adaptive scanning method to optimize the longitudinal and transverse wave velocity ratio coefficient gamma of dry rockdry. FIG. 2 is a diagram of the optimization of the longitudinal-transverse wave velocity ratio coefficient gamma of dry rock by using an adaptive scanning methoddryA drawing; when gamma is, as shown in FIG. 2dry 2At 2.3, the Russell fluid factor ρ f is up to about 85% of maximum correlation with water saturation. And the Gassmann fluid item f obtained by the method also has the maximum correlation coefficient under the same condition, and accuracy guarantee is provided for the calculation of the subsequent AVO attribute fluid item f. FIG. 3 shows the velocity ratio coefficient gamma of the longitudinal and transverse waves of the dry rockdryThe calculated hydrocarbon detectivity is highly correlated with a comparison of the water saturation curve of the well log interpretation result. FIG. 4A is an intersection plot of the fluid term f and shear modulus μ of an example well A, and FIG. 4B is the hydrocarbon detection factor RKfCross plot with shear modulus. It can be seen from fig. 4A that the shear modulus is essentially indistinguishable from the gas and water layers, which the Gassmann fluid item f can identify, but that there is some overlap in the numerical ranges; and the newly constructed hydrocarbon detection factor has obviously reduced overlapping range of a gas layer and a water layer, and the distinguishing discrimination is enhanced, as shown in figure 4B.
For further statistical analysis and quantitative comparison of the sensitivity of the different elasticity parameters to fluid identification, fluid index coefficients for thirteen elasticity parameters were calculated (table 1). It is noted that the fluid indicator coefficient is the ratio of the absolute value of the mean difference between the elastic parameters of the gas and water layers in the investigation region to the standard deviation of the elastic parameter of the gas layer. This coefficient essentially describes how sensitive a certain elastic parameter is to fluid identification, the larger the value the more sensitive. It can be seen from the statistical analysis of fig. 5 that different elasticity parameters have different sensitivities. The new hydrocarbon detection factor (12) based on the porous elastic medium theory, the Gassmann fluid item f (11) and the Russell fluid factor rho f (10) are listed in the first three positions, and the fluid identification sensitivity is higher. And the elasticity parameter based on the single-phase medium theory is generally low in sensitivity, and the fluid identification has large uncertainty.
FIG. 6 is a stacked seismic section through well A and well B of the study area with the well logs inserted as well log interpreted gas saturation curves. Converting the original CRP gather data into angle gather data by combining the velocity field information of the research area, and combining the longitudinal and transverse wave velocity ratio coefficient gamma of the dry rock optimized by self-adaptive scanning on the basis of three-term AVO approximate expression which is derived by Russell and contains a fluid term f, a shear modulus mu and a density term rhodryCalculate AVO attribute volume. And (3) establishing a low-frequency model of a fluid term f, a shear modulus mu and a density term rho by using the well A as a test well through mathematical interpolation under the constraint of a geological horizon. And under the constraint of low-frequency trend, carrying out well control colored inversion to convert the interface reflection information of the AVO attribute into in-layer elastic parameter information, and directly extracting the fluid item f and the shear modulus mu. Finally, the flow prediction section of the newly constructed hydrocarbon detection factor is obtained through the mathematical combination (ratio) of the two.
FIG. 7 is a profile of extensional elastic impedance inversion fluid saturation across A-well and B-well, and FIG. 8 is a profile of dual phase stripped hydrocarbon detection across A-well and B-well based on pre-stack AVO information direct extraction. The two methods are better matched with the gas saturation curve provided by the single well logging interpretation result. The statistical accuracies are respectively about 78% for the former A-well and 76% for the former B-well; the latter is about 90% for A-well and about 84% for B-well. However, the two partial detail depictions have differences, fig. 9 is a partial enlarged view of a well bypass of the well a, wherein dark color indicates higher gas saturation, and the gas saturation gradually decreases and the water saturation gradually increases along with the process of lightening the color. The square arrow indicates that the position well logging is explained as the gas and water layers, the inversion result of the gas and water layers is displayed as a pure gas layer, and the inversion result of the gas and water layers clearly depicts the phenomenon of the gas and water layers. The oval arrows indicate that the position log is interpreted as a high pore gas layer, the former does not invert into the gas layer, and the latter clearly depicts the distribution of fluid within the reservoir.
Therefore, the method can provide reservoir fluid distribution rule information with high precision, high stability and high reliability for geological reservoir personnel. The risk in the oil and gas exploration and development design is greatly reduced, and powerful technical support is provided for future fine reservoir description.
TABLE 1
Figure BDA0002181416220000151
Figure BDA0002181416220000161
The applicant states that the present invention is illustrated by the above examples to the solid-liquid two-phase stripping hydrocarbon detection method of the present invention, but the present invention is not limited to the above process steps, i.e. it does not mean that the present invention must rely on the above process steps to be carried out. It will be apparent to those skilled in the art that any modification of the present invention, equivalent substitutions of selected materials and additions of auxiliary components, selection of specific modes and the like, which are within the scope and disclosure of the present invention, are contemplated by the present invention.

Claims (1)

1. The solid-liquid two-phase stripping hydrocarbon detection method is characterized by comprising the following steps:
(1) and (3) deriving an approximate mathematical analytic expression containing rock pore structure factors, porosity and fluid modulus based on the fluid term f of the Gassmann equation, wherein the derivation process is as follows:
under the low frequency assumption, the fluid term f according to the standard Gassmann equation is known as:
Figure FDA0002919393970000011
wherein f represents the Gassmann equation fluid term; β represents the Biot coefficient; m represents a bulk modulus; kdryExpressing bulk modulus of dry rock；KmDenotes the matrix bulk modulus; kfRepresents the mixed fluid modulus; p represents a pore structure coefficient; phi represents the rock porosity;
a. for the Biot coefficient β, the following processing is performed:
approximation of theory K from dry rockdry＝Km·(1-φ)pTherefore, the following steps are carried out: kdry/Km＝(1-φ)p<1，Kdry<Km<Ksat；
To (1-phi)pPerforming a first order Taylor expansion approximation yields: (1-phi)p≈1-P·φ；
By
Figure FDA0002919393970000012
It can be seen that β ≈ 1- (1-P · Φ) ═ P · Φ;
b. the bulk modulus M was treated as follows:
from the above derivation:
Figure FDA0002919393970000013
due to Kf<<KmThe following can be obtained:
Figure FDA0002919393970000021
combining beta and M to obtain:
f＝β2M≈P2·φ·Kf；
(2) the method comprises the following steps of eliminating the influence of the porosity on fluid identification in a Gassmann equation by adopting the shear modulus mu capable of directly reflecting the influence of the porosity of the rock, and constructing a hydrocarbon detection factor, wherein the construction method comprises the following steps:
the formula of Gassmann fluid term of Russell is derived as follows:
Figure FDA0002919393970000022
where ρ represents density; f represents the fluid term of the Gassmann equation; s represents a skeletal item; zPRepresenting the longitudinal wave impedance; zSRepresents the transverse wave impedance; gamma raydryRepresenting the longitudinal-transverse wave velocity ratio coefficient of the dry rock;
and the number of the first and second electrodes,
Figure FDA0002919393970000023
wherein s represents a skeletal item; kdryRepresents the dry rock bulk modulus; μ represents a shear modulus; vPRepresenting the velocity of longitudinal waves; vSRepresents the shear wave velocity;
the following equations are taken together:
Figure FDA0002919393970000024
Figure FDA0002919393970000031
wherein f represents the Gassmann equation fluid term;
namely:
Figure FDA0002919393970000032
the following can be obtained:
Figure FDA0002919393970000033
i.e. constructing said hydrocarbon detection factor RKfThe following were used:
Figure FDA0002919393970000034
(3) method for determining longitudinal and transverse wave velocity ratio coefficient gamma of dry rock under maximum correlation degree of Russell fluid factor rhof and water saturation by utilizing self-adaptive correlation scanningdryThe determination method is as follows:
at gammadry 2Within the range of the value change, change gamma with a change interval step of 0.001dry 2Value of will be different from gammadry 2Performing cross-correlation operation on the rho f factor and the water saturation Sw under the value condition, determining the value with the maximum correlation degree by using a self-adaptive scanning method and outputting the corresponding longitudinal-transverse wave velocity ratio coefficient gamma of the dry rockdry；
(4) Combining three AVO approximate formulas provided by Russell, directly extracting intraformational solid-liquid two-phase elasticity characteristic information from reflection information of a fluid item f and a shear modulus mu interface of AVO attributes through well control colored inversion, and performing parameter combination on an elasticity data body of the fluid item f and the shear modulus mu obtained through inversion to finally obtain a hydrocarbon detection profile of elasticity time-space variation reservoir two-phase stripping, wherein the method comprises the following steps:
(I) using the three-term AVO approximation proposed by Russell as shown below, by combining the dry rock longitudinal and transverse wave velocity ratio coefficient γdryConverting the angle gathers into AVO attribute bodies, namely reflectivity information of the fluid item f and the shear modulus mu;
Figure FDA0002919393970000041
(II) under the constraint of a structural horizon, performing mathematical interpolation modeling by using logging data to provide inverted low-frequency trend information; the interface relative information of the fluid item f and the shear modulus mu is converted into in-layer absolute information by carrying out colored inversion, so that the direct extraction of the solid-liquid two-phase elastic characteristic information is completed;
and (III) performing parameter combination according to the hydrocarbon detection factor form obtained in the step (3) to obtain a hydrocarbon detection data volume for solid-liquid two-phase stripping.
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