CN103760081A

CN103760081A - Gas reservoir prediction method and system for carbonate reservoir based on pore structure characteristics

Info

Publication number: CN103760081A
Application number: CN201310752436.1A
Authority: CN
Inventors: 巴晶; 于豪; 张研; 李劲松; 张光荣; 陈骁; 秦楠; 徐光成
Original assignee: Petrochina Co Ltd
Current assignee: Petrochina Co Ltd
Priority date: 2013-12-31
Filing date: 2013-12-31
Publication date: 2014-04-30
Anticipated expiration: 2033-12-31
Also published as: CN103760081B

Abstract

The invention provides a gas reservoir prediction method and a gas reservoir prediction system for a carbonate reservoir based on pore structure characteristics, wherein the method comprises the following steps: collecting a rock sample of a target reservoir section of a carbonate reservoir; performing geological slice identification on the rock sample to obtain basic rock parameters, wherein the basic rock parameters comprise rock components, pore shapes, face porosity and sedimentary facies bands; carrying out pore permeability measurement on the rock sample to obtain pore permeability basic parameters, wherein the pore permeability basic parameters comprise porosity, permeability and density; constructing a rock dry skeleton model according to the rock basic parameters, the hole permeability basic parameters and the differential equivalent medium model; carrying out fluid replacement on the rock dry skeleton model to generate a rock physical plate; acquiring pre-stack seismic inversion data of a carbonate reservoir; and (4) intersecting the pre-stack seismic inversion data with a rock physical chart to obtain a prediction result of the porosity and the gas saturation of the carbonate reservoir. And accurate quantitative prediction of the gas reservoir is realized.

Description

Gas reservoir prediction method and system for carbonate reservoir based on pore structure characteristics

Technical Field

The invention relates to the technical field of oil and gas exploration, in particular to a carbonate reservoir prediction technology, and specifically relates to a gas reservoir prediction method and a gas reservoir prediction system for a carbonate reservoir based on pore structure characteristics.

Background

According to the statistics of USGS in 2000, the recoverable resource amount of marine carbonate rock oil gas accounts for 72 percent of the total recoverable resource amount of oil gas in a global range, and is in a leading position. The carbonate rock to be proved has far more oil and gas reserves than clastic rock and has huge exploration potential. The biological reef is used as an excellent carbonate reservoir and is a key point and a difficult point of oil and gas exploration.

The carbonate reservoir has a complex pore structure and strong heterogeneity, and can greatly influence the seismic wave velocity. It has been found through research that carbonate reservoirs partitioned by pore type (e.g., pore voids, solution-molded pores, intergranular voids, intragranular voids, etc.) decrease in velocity with increasing porosity, but different pore types do not exhibit significant regularity. The carbonate reservoirs (such as acicular pores, flat pores, round pores and the like) divided according to the pore structures have the speed reduced along with the increase of the porosity, the speed change rule of different pore structures is obvious, and the speed difference can exceed 1500m/s under the condition of giving one porosity value. Therefore, the complexity of the pore structure of the carbonate reservoir can cause great difference of the rock elasticity characteristics, and is a key factor for determining the rock elasticity characteristics.

In the current oil geophysical exploration industrialization technology, the conventional petrophysical analysis-based oil gas detection method utilizes a petrophysical chart based on a single pore form to predict the whole region, and is mainly suitable for reservoirs with single pore forms. The carbonate reservoir pore structure is complex, the heterogeneity is strong, and when the reservoir pore form transverse change is obvious, the single-well rock physical modeling has the applicability problem in the whole area.

The Gassmann equation models the propagation of elastic waves in a porous medium at low frequencies, and at higher frequencies, the underlying assumption of the Gassmann equation does not hold and does not describe the propagation of waves in a porous medium containing a fluid. Biot establishes the fundamental kinetic theory of elastic waves involving fluid rocks, the essence of which is to relate the wave characteristics (velocity and attenuation) of fluid-saturated rocks to the rock matrix, the rock skeleton (dry rock) and the fluid, for the entire frequency range. Basic assumptions of Biot theory include: (1) rock pore media (matrix and framework) are macroscopically homogeneous and isotropic; (2) all pores are communicated with each other and have the same particle size; (3) the wavelength is much larger than the average size of the rock particles; (4) the relative motion between the rock matrix and the pore fluid follows Darcy's law; (5) the pore fluid and the rock matrix do not chemically interact with each other. Biot's theory is essentially consistent with the Gassmann equation as the frequency approaches zero. In subsequent studies, the theory was generalized to anisotropic media, but the process was so complex that it required the input of parameters that were generally difficult to obtain, that they were not widely used.

The traditional Biot-Gassmann theory is researched aiming at a seismic wave propagation mechanism in a porous medium with fluid in pores, but mainly considers that the uniform pore structure of one fluid is saturated, the uneven distribution of mineral components, solid particles, the pore structure and fluid components in rocks is ignored, and the complex condition of an actual reservoir cannot be described. White et al analyzed the effect of locally distributed bubbles inside water-bearing rocks on seismic wave propagation. Dutta et al improved the White theory to match the predicted compressional velocity at the low frequency limit with the analysis of classical Biot theory. Johnson proposes a branching function method to realize the simulation of the propagation law of unsaturated medium waves in different frequency bands. Muller, Gurevich, Toms and the like consider that numerical simulation of one-dimensional and three-dimensional random unsaturated media can also provide reasonable description and prediction for wave response of actual rocks. Dvorkin et al proposed the BISQ theory based on Biot flow and jet flow inside fluid-containing rocks. The neyman-constructor et al introduced fluid desaturation in the BISQ model. The influences of fluid non-saturation on wave propagation in the BISQ model are researched by Nioni-constructure, Barre crystal and the like, but the hypothesis of the result is that gas and water are completely and uniformly mixed. Liu xuan and so on have studied the seismic wave propagation law in the heterogeneous, unsaturated rock model of globular patch and horizontal alternating lamellar, but it is unfavorable to the application to calculate loaded down with trivial details.

In summary, the following problems mainly exist in the prior art solutions:

(1) the pore structure of a carbonate reservoir development area is complex, and the conventional rock physical model based on a single pore structure has applicability in the whole area;

(2) carbonate reservoirs are strong in heterogeneity, and the distribution states of underground rocks and fluids cannot be accurately described by a conventional fluid replacement method based on uniform mixing of fluids.

Disclosure of Invention

In order to overcome the problems in the prior art, the invention provides a gas reservoir prediction method and a gas reservoir prediction system for a carbonate reservoir based on pore structure characteristics, wherein corresponding rock skeleton models are respectively established for different pore structures developed in different geological sedimentary facies of carbonate rock aiming at the influence of the rock pore structure of the carbonate reservoir on the establishment of a rock physical model; aiming at the condition that the fluid of the carbonate reservoir is partially saturated, the fluid is replaced by simulating the state that the pores are homogeneous and the fluid is non-uniformly distributed, and accurate quantitative prediction of the gas reservoir is carried out.

One of the purposes of the invention is to provide a gas reservoir prediction method of a carbonate reservoir based on pore structure characteristics, which comprises the following steps: collecting a rock sample of a target reservoir section of a carbonate reservoir; performing geological slice identification on the rock sample to obtain basic rock parameters, wherein the basic rock parameters comprise rock components, pore shapes, face porosity and sedimentary facies bands; carrying out pore permeability measurement on the rock sample to obtain pore permeability basic parameters, wherein the pore permeability basic parameters comprise porosity, permeability and density; analyzing a pore structure according to the pore shape and the sedimentary facies belt, and constructing a rock dry skeleton model according to the rock basic parameters, the pore permeability basic parameters and the differential equivalent medium model; performing fluid replacement on the rock dry skeleton model to generate a rock physical plate; acquiring pre-stack seismic inversion data of a carbonate reservoir; and intersecting the pre-stack seismic inversion data with the rock physical chart to obtain a prediction result of the porosity and the gas saturation of the carbonate reservoir.

One of the objects of the present invention is to provide a gas reservoir prediction system for carbonate reservoirs based on pore structure characteristics, comprising: the rock sample collecting device is used for collecting a rock sample of a target reservoir section of the carbonate reservoir; the geological thin slice identification device is used for carrying out geological thin slice identification on the rock sample to obtain basic rock parameters, wherein the basic rock parameters comprise rock components, pore shapes, face porosity and sedimentary facies bands; the pore-permeability measuring device is used for carrying out pore-permeability measurement on the rock sample to obtain pore-permeability basic parameters, and the pore-permeability basic parameters comprise porosity, permeability and density; the rock dry skeleton member device is used for analyzing a pore structure according to the pore shape and the sedimentary facies belt and constructing a rock dry skeleton model according to the rock basic parameters, the pore permeation basic parameters and the differential equivalent medium model; the rock physical plate generating device is used for carrying out fluid replacement on the rock dry skeleton model to generate a rock physical plate; the pre-stack seismic inversion data acquisition device is used for acquiring pre-stack seismic inversion data of the carbonate reservoir; and the gas saturation prediction device is used for intersecting the pre-stack seismic inversion data with the rock physical chart to obtain a prediction result of the porosity and the gas saturation of the carbonate reservoir.

The invention has the beneficial effects that the gas reservoir prediction method and the gas reservoir prediction system of the carbonate reservoir based on the pore structure characteristics are provided, the carbonate reservoir has strong heterogeneity, the rock physical modeling based on the single pore structure cannot well take account of the heterogeneity, and the technical scheme of the invention aims at solving the significant difficulties in the prior art and achieving the following purposes:

1. the complex microscopic pore structure of a carbonate reservoir development area is fully considered, the technical means such as geological slice identification, core experiment measurement and the like are utilized, the basis is provided for the division of pore structure characteristics from the perspective of geological analysis and experiment observation, and a rock skeleton model is respectively established for different pore structures developed in different sedimentary facies zones.

2. And designing fluid detection methods of reservoirs with different sedimentary phases based on a targeted rock physical model obtained by pore structure characteristic analysis.

In order to make the aforementioned and other objects, features and advantages of the invention comprehensible, preferred embodiments accompanied with figures are described in detail below.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a flowchart of a first embodiment of a method for predicting a gas reservoir of a carbonate reservoir based on pore structure characteristics according to an embodiment of the present invention;

fig. 2 is a flowchart of a second embodiment of a gas reservoir prediction method for a carbonate reservoir based on pore structure characteristics according to an embodiment of the present invention;

fig. 3 is a flowchart of a third embodiment of a gas reservoir prediction method for a carbonate reservoir based on pore structure characteristics according to an embodiment of the present invention;

FIG. 4 is a detailed flowchart of step S107 in FIG. 1;

fig. 5 is a flowchart of a fourth embodiment of a gas reservoir prediction method for a carbonate reservoir based on pore structure characteristics according to an embodiment of the present invention;

FIG. 6 is a block diagram of a first embodiment of a system for predicting gas reservoirs in carbonate reservoirs based on pore structure characteristics according to an embodiment of the present invention;

fig. 7 is a structural block diagram of a second embodiment of a gas reservoir prediction system for a carbonate reservoir based on pore structure characteristics according to an embodiment of the present invention;

fig. 8 is a structural block diagram of a third embodiment of a gas reservoir prediction system for a carbonate reservoir based on pore structure characteristics according to an embodiment of the present invention;

fig. 9 is a block diagram illustrating a specific structure of a gas saturation prediction apparatus 700 in a gas reservoir prediction system for a carbonate reservoir based on pore structure characteristics according to an embodiment of the present invention;

fig. 10 is a structural block diagram of a fourth embodiment of a gas reservoir prediction system for a carbonate reservoir based on pore structure characteristics according to an embodiment of the present invention;

FIG. 11 is a schematic of Met22 well reservoir rock slices (vugs);

FIG. 12 is a schematic of Met21 well reservoir rock slices (fractures);

FIG. 13 is a schematic illustration of a rock sample collected for a target reservoir section of a carbonate reservoir;

FIG. 14 is a schematic view of a CT scan of a rock sample acquired of a target reservoir section of a carbonate reservoir;

FIG. 15 is a plot of porosity versus density;

FIG. 16 is a graph of porosity versus permeability;

FIG. 17 is a schematic view of a conventional ultrasonic measurement system;

FIG. 18 is a graph of compressional and shear wave velocity versus porosity of rock in the dry state;

FIG. 19 is a graph of compressional and shear wave velocity versus porosity for a rock at water saturation;

FIG. 20 is a plot of core sample porosity versus longitudinal to transverse wave velocity ratio;

FIG. 21 is a graph of longitudinal wave impedance and longitudinal and transverse wave velocity ratio of a core sample;

FIG. 22 is a graph of the effect of reservoir rock pore structure on compressional velocity;

FIG. 23 is a graph of a petrophysical plate based on a pore-dissolving structure intersected with M22 well side channel seismic data;

FIG. 24 is a graph of a petrophysical plate based on fracture pore structure intersecting with M21 well side channel seismic data;

FIG. 25 is a schematic representation of the results of two-dimensional line porosity seismic inversion across Met22 and Met3 wells;

FIG. 26 is a schematic representation of seismic inversion results of gas saturation of two-dimensional lines through Met22 and Met3 wells;

FIG. 27 is a schematic of the Met22 well porosity curve and gas test interval;

FIG. 28 is a schematic of the Met3 well porosity curve and gas test interval;

FIG. 29 is a schematic representation of results of seismic inversion of two-dimensional line porosity across Zen21 and Met21 wells;

FIG. 30 is a schematic representation of seismic inversion results of gas saturation across Zen21 and Met21 well two-dimensional lines;

FIG. 31 is a schematic diagram of a Zen21 well porosity curve and a gas test interval;

fig. 32 is a schematic of Met21 well porosity curve and gas test interval.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Carbonate reservoirs are strong in heterogeneity, and rock physical modeling based on a single pore structure cannot well take account of the heterogeneity. Aiming at the problems of physical modeling of carbonate reservoir rock and quantitative prediction of gas reservoir, the invention provides a gas reservoir prediction method and system of carbonate reservoir based on pore structure characteristics, FIG. 1 is a specific flow chart of the method, and as can be seen from FIG. 1, the method comprises the following steps:

s101: a rock sample of a target reservoir section of a carbonate reservoir is collected. In particular embodiments, conventional seismic exploration methods may be employed to collect rock samples of a target reservoir interval of a carbonate reservoir.

S102: and carrying out geological slice identification on the rock sample to obtain basic rock parameters, wherein the basic rock parameters comprise rock components, pore shapes, face porosity and sedimentary facies bands. The identification of geological thin slice is a method for identifying rock and mineral under the polarization microscope, and is characterized by that the rock or mineral specimen is ground into thin slice, under the polarization microscope the crystal characteristics of mineral can be observed, its optical property can be measured, the mineral composition of rock can be defined, its structure and structure can be studied, the generation sequence of mineral can be analyzed, the rock type and its causative characteristics can be defined, and the name of rock can be made. The thin slice identification method is a frequently used research means in the geological oil and gas exploration work.

S103: and carrying out pore permeability measurement on the rock sample to obtain pore permeability basic parameters, wherein the pore permeability basic parameters comprise porosity, permeability and density. Reference herein to density includes dry rock density, water saturated sample density and rock particle matrix density, and generally rock density is referred to as dry rock density. Dry rock density refers to the density of the rock pore space when it is air, water saturated sample density refers to the density of the rock pore space when it is filled with water, and rock particle matrix density refers to the density of the particle components that make up the rock, not including the pore space.

The core experiment measurement is to obtain more accurate reservoir rock information including porosity, permeability, mineral composition, density, longitudinal and transverse wave velocity and the like by directly measuring the underground core. The information is helpful for understanding the relation between the reservoir elasticity characteristics and the physical property parameters and guiding the establishment of the reservoir rock theoretical model.

The hole seepage measurement is one of the rock core experiment measurements, and the hole seepage measurement means that a sample is put into a vacuum oven for drying treatment, the mass of the sample is measured by an electronic balance, and the error is within 0.01 g. The porosity phi and permeability kappa of the sample were measured using a pore-permeation linked measurement system (helium method). Both the porosity and the permeability of the sample change under the measured pressure. Since the volume change of the mineral particles is small, the volume change of the rock after being pressed can be approximately equal to the change of the pore volume, namely, when the pore pressure is not changed, the change of the porosity along with the confining pressure can be measured by the pore pressure fluid.

Measuring the diameter and length of the core cylinder by using a vernier caliper, and calculating the volume V_bThe mass of the dried sample was measured by an electronic balance as W_drySo the dry rock density is:

the water-saturated sample density was:

ρ_wet=ρ_dry+φρ_w

where φ is the rock porosity, ρ_wIs the density of water.

The rock particle matrix density is:

the porosity and the permeability are measured by a helium method by using a pore permeability measuring instrument, helium is filled into rock pores, the filling amount can be measured by an instrument, and the porosity is calculated; the amount of gas passing through the rock per unit time is known as the permeability.

S104: and analyzing the pore structure according to the pore shape and the sedimentary facies belt, and constructing a rock dry skeleton model according to the rock basic parameters, the pore permeability basic parameters and the differential equivalent medium model.

In this step, pore structure analysis may be performed first based on the pore shape and the sedimentary phase belt. When the aperture is flat, the aspect ratio is less than 0.1, the aperture is a crack type aperture, and when the aspect ratio is large, the aperture is an erosion aperture. Given the rock composition and pore space, the differential equivalent medium model can be used to estimate the equivalent elastic modulus of the rock skeleton, i.e. rock skeleton modeling.

Differential Equivalent Medium (DEM) theory simulates a biphasic mixture by gradually adding an inclusion phase to a solid mineral phase. Assuming that the solid mineral is phase 1 and the inclusion is phase 2, the starting state is only phase 1 without phase 2, after which phase 2 is added stepwise until the desired content of the ingredients is reached. In general, an inclusion of material 1 as the main phase and gradually adding material 2 will result in different equivalent properties compared to an inclusion of material 2 as the main phase and gradually adding material 1. For multiple inclusion shapes or multiple inclusion components, the equivalent modulus depends not only on the volume content of the final components, but also on the order of inclusion addition. The process of gradual addition of inclusions to the solid mineral phase is an ideal experiment, and the evolution of rock porosity in nature is very complex.

Bulk modulus K of constructed rock dry skeleton model^*And shear modulus mu^*The coupled differential equation set of (a) is:

(1 - y) \frac{{dK}^{*} (y)}{dy} = [K_{2} - K^{*} (y)] P^{(* 2)} (y)

wherein, K₂Is the bulk modulus, μ, of the pores₂Is the shear modulus of the pores, y is the content of pores, P, Q is a geometric factor, initial condition K^*(0)=K₁、μ^*(0)=μ₁，K₁Is the bulk modulus, μ, of the original mineral constituent₁Is the shear modulus of the original mineral constituent. K1 represents the initial main phase, i.e. the original state; k2 represents added inclusions, which can be generally considered as pores. For fluid inclusions and empty inclusions, y is equal to the porosity φ, P and Q are the geometric factors given in Table 1 for some inclusion shapes, and subscripts m and i refer to the background material and the inclusion material, respectively.

TABLE 1

Wherein,

α is the pore aspect ratio.

As described above is the process of rock skeleton modeling of the present invention. And investigating rock information of the target reservoir, including basic parameters such as lithology, mineral composition, argillaceous content, burial depth, pore type, diagenesis, temperature and pressure. Pore structure parameters (surface porosity, pore aspect ratio, scale factor, connectivity coefficient and the like) are obtained by technical means such as geological slice identification, logging data and experimental measurement data (ultrasonic measurement, nano CT scanning and the like). And respectively establishing corresponding rock skeleton models by utilizing the differential equivalent medium models according to different pore structures.

In the above technical solutions, it is an important step of the present invention to respectively establish corresponding rock skeleton models according to different pore structures developed by different sedimentary facies belts. The geological slice identification and the rock core experiment measurement respectively provide theoretical basis and data reference for the division of the pore structure from a geological angle and an experiment angle. The microscopic pore structure characteristics of the rock and the corresponding sedimentary facies zones can be determined through the microscopic observation of the rock slice, the rock skeleton elastic parameters of the reservoir can be obtained through the ultrasonic measurement of the core sample, and the important support is provided for the quantitative prediction of the gas reservoir.

S105: and carrying out fluid replacement on the rock dry skeleton model to generate a rock physical plate. In a specific embodiment, this step may be achieved by: and based on the rock dry skeleton model, carrying out fluid replacement by using a Biot-Rayleigh equation system to generate rock physical charts based on different pore structures.

In 2004, Pride, Berryman, etc. proposed a dual-pore medium theory (referred to as a dual-pore medium for short) to analyze the propagation and attenuation rules of elastic waves in unsaturated media, in which case the "dual pores" correspond to the gas-containing pores and water-containing pores in unsaturated rocks, respectively. In order to further derive a wave propagation equation with simple format, few parameters and physical realizability of each parameter to meet the requirements of practical application and industrial production, Barcrystal and the like derive a Biot-Rayleigh equation (B-R equation for short) for describing the seismic wave propagation rule of unsaturated rocks on the basis of a Biot theoretical framework and are successfully applied to the engineering problem of practical gas reservoir exploration.

Barcrystal and the like describe local fluid flow of bubbles in unsaturated rocks under longitudinal wave excitation by adopting a Rayleigh theory, and derive a dual-pore medium wave propagation equation, namely a Biot-Rayleigh equation, from the Hamilton principle of classical mechanics. The equation can simulate the conditions of one type of fluid and two types of frameworks; it is also possible to simulate the case of one type of matrix, two types of fluid, i.e. the inclusion is completely identical to the solid matrix of the background phase, the main difference between the two coming from the differences in density, elastic modulus and viscosity of the water and gas inside the pore space.

The Biot-Rayleigh equation is as follows:

wherein u = [ ]₁,u₂,u₃]、

U^{(1)} = [U_{1}^{(1)}, U_{2}^{(1)}, U_{3}^{(1)}], U^{(2)} = [U_{1}^{(2)}, U_{2}^{(2)}, U_{3}^{(2)}]

Respectively, the spatial vector displacements of the three components are indicated, the

indices

1, 2, 3 indicate the three directions of the vector space,

representing local fluid deformation increments generated during seismic wave excitation.

<math> <mrow> <msub> <mi>e</mi> <mi>ij</mi> </msub> <mo>=</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <mrow> <mo>(</mo> <mfrac> <msub> <mrow> <mo>&PartialD;</mo> <mi>u</mi> </mrow> <mi>i</mi> </msub> <mrow> <mo>&PartialD;</mo> <msub> <mi>x</mi> <mi>j</mi> </msub> <mo></mo> </mrow> </mfrac> <mo>+</mo> <mfrac> <msub> <mrow> <mo>&PartialD;</mo> <mi>u</mi> </mrow> <mi>j</mi> </msub> <msub> <mrow> <mo>&PartialD;</mo> <mi>x</mi> </mrow> <mi>i</mi> </msub> </mfrac> <mo>)</mo> </mrow> <mo>,</mo> <msubsup> <mi>ξ</mi> <mi>ij</mi> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msubsup> <mo>=</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <mrow> <mo>(</mo> <mfrac> <mrow> <mo>&PartialD;</mo> <msubsup> <mi>U</mi> <mi>i</mi> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msubsup> </mrow> <msub> <mrow> <mo>&PartialD;</mo> <mi>x</mi> </mrow> <mi>j</mi> </msub> </mfrac> <mo>+</mo> <mfrac> <mrow> <mo>&PartialD;</mo> <msubsup> <mi>U</mi> <mi>j</mi> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msubsup> </mrow> <msub> <mrow> <mo>&PartialD;</mo> <mi>x</mi> </mrow> <mi>i</mi> </msub> </mfrac> <mo>)</mo> </mrow> <msub> <mi>δ</mi> <mi>ij</mi> </msub> <mo>,</mo> <msubsup> <mi>ξ</mi> <mi>ij</mi> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </msubsup> <mo>=</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <mrow> <mo>(</mo> <mfrac> <mrow> <mo>&PartialD;</mo> <msubsup> <mi>U</mi> <mi>i</mi> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </msubsup> </mrow> <msub> <mrow> <mo>&PartialD;</mo> <mi>x</mi> </mrow> <mi>j</mi> </msub> </mfrac> <mo>+</mo> <mfrac> <mrow> <mo>&PartialD;</mo> <msubsup> <mi>U</mi> <mi>j</mi> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </msubsup> </mrow> <msub> <mrow> <mo>&PartialD;</mo> <mi>x</mi> </mrow> <mi>i</mi> </msub> </mfrac> <mo>)</mo> </mrow> <msub> <mi>δ</mi> <mi>ij</mi> </msub> <mo>,</mo> </mrow> </math>

x₁，x₂And x₃Respectively representing the coordinates of three directions. Phi is a₁And phi₂Denotes the absolute porosity of both types of pores, the total porosity of the rock phi = phi₁+φ₂；φ₁₀Phi and phi₂₀Respectively representing the local porosity in two regions, phi if the rock contains only one skeleton but is saturated with two fluids₁₀=φ₂₀=φ。ρ_f1And η₁Indicating the density and viscosity of the background phase fluid. R₀Denotes the bubble radius, κ₁₀Representing the rock permeability.

A、N、Q₁、R₁、Q₂、R₂Represents six Biot elastic parameters in a double-pore medium, and can be explicitly calculated and estimated according to rock physical parameters of bases such as the elastic modulus of a rock skeleton, the volume modulus of a fluid, the porosity, the elastic modulus of a solid matrix and the like; rho₁₁、ρ₁₂、ρ₁₃、ρ₂₂And rho₃₃Represents five density parameters in a dual pore medium, which can also be explicitly calculated and estimated based on the density of solid particles in the rock, the density of pore fluid, the porosity, and the composition ratio of different pore structures, assuming a spherical approximation assumption for individual rock particles inside the rock. Methods for calculating and estimating various parameters are known in the art, such as patent numbers 201310308550.5 and 201210335739.9, the present invention is not described in detail. The solving process of the Biot-Rayleigh equation is also described in detail in the above two patents and thus is not described in detail here. In the above formula, ξ is the fluid displacement increment under seismic wave extrusion, b represents the Biot dissipation parameter,

where η, φ and κ represent fluid viscosity, porosity and permeability, respectively. For a porous medium containing a two-phase fluid, b₁、b₂Respectively show the Biot dissipation coefficients in the water-containing pores and the air-containing pores,(

subscripts

1, 2 correspond to two types of pores).

For example, after a Biot-Rayleigh equation is solved by adopting a plane wave analysis method, the longitudinal wave velocity, the transverse wave velocity and the density can be obtained, and various rock physical charts can be manufactured based on the three parameters. The rock physical plate generated based on the Biot-Rayleigh equation in the step comprises the rock physical plate generated according to various sensitive parameters. The types of sensitive parameters mentioned herein include: longitudinal wave velocity, transverse wave velocity, density, longitudinal-transverse wave velocity ratio, poisson's ratio, elastic parameters, young's modulus, and the like.

S106: acquiring pre-stack seismic inversion data of a carbonate reservoir;

s107: and intersecting the pre-stack seismic inversion data with the rock physical chart to obtain a prediction result of the porosity and the gas saturation of the carbonate reservoir. Fig. 4 is a specific flowchart of step S107, and as can be seen from fig. 4, the step specifically includes:

s401: acquiring longitudinal wave impedance from the pre-stack seismic inversion data;

s402: the longitudinal wave impedance is taken as a transverse axis;

s403: acquiring a longitudinal wave velocity ratio and a transverse wave velocity ratio from the pre-stack seismic inversion data;

s404: taking the longitudinal and transverse wave velocity ratio as a vertical axis;

s405: mapping the petrophysical plate into a coordinate system consisting of the horizontal axis and the vertical axis;

s406: and adjusting the coordinates of the rock physical plate to obtain a prediction result of the porosity and the gas saturation of the target reservoir section of the carbonate reservoir.

In a specific implementation mode, a rock physical map and prestack seismic inversion data scatter points are intersected, wherein the seismic inversion data mainly comprise longitudinal wave impedance and longitudinal and transverse wave velocity ratio data, under a longitudinal wave impedance (X axis) -longitudinal and transverse wave velocity ratio (Y axis) coordinate system, a grid longitudinal line of the rock physical map represents predicted porosity (increasing from right to left), a transverse line represents gas saturation (increasing from top to bottom), the matching degree of a scatter point mapping position and the map is observed, the rock physical map is adjusted, and the porosity and the gas saturation of a target reservoir stratum are inverted.

Fig. 2 is a flowchart of a second embodiment of a gas reservoir prediction method for a carbonate reservoir based on pore structure characteristics according to an embodiment of the present invention, as can be seen from fig. 2, steps S101 to S104 in fig. 1 are the same as steps S201 to S204 in fig. 2, and steps S105 to S107 in fig. 1 are the same as steps S209 to S211 in fig. 2, which are not repeated herein, and the method further includes:

s205: acquiring a geological report of a carbonate reservoir;

s206: acquiring logging data of a carbonate reservoir;

s207: acquiring logging data of a carbonate reservoir;

s208: and correcting the rock dry skeleton model according to the geological report, the logging data and the logging data. Geological reports, well log data, and well log data may provide the age of the subsurface formations, reservoir information (e.g., lithology, mineral composition, location of specific intervals of gas and water layers, etc.), and are generally considered reliable. In the process of modeling the dry rock skeleton, the data needs to be referred to, so that the model is more reasonable and better accords with the underground real situation. The geological report gives an overview of the large-scale geological condition of the work area, the logging information records the lithology and mineral composition of each stratum rock in the small range around the well, and input parameters must be set according to the information in the modeling of the dry skeleton. For example, the invention applies that the lithologic character of the carbonate reservoir rock in the work area is limestone, the main mineral component of the limestone is calcite, and a small amount of argillaceous substances are attached, so that the dry skeleton modeling is carried out by the calcite and the argillaceous substances. The logging data records information of the velocity, density, porosity and the like of the formation rock, and the information is all reference data which is required to input and correct input parameters in the dry skeleton modeling. These data reflect information about the subsurface rock intuitively, and without this information it is not possible to accurately build a dry skeleton model.

Fig. 3 is a flowchart of a third embodiment of a gas reservoir prediction method for a carbonate reservoir based on pore structure characteristics according to an embodiment of the present invention, as can be seen from fig. 3, steps S101 to S105 in fig. 1 are the same as steps S301 to S305 in fig. 3, and steps S106 to S107 in fig. 1 are the same as steps S309 to S310 in fig. 3, which are not repeated herein, and the method further includes:

s306: and carrying out ultrasonic measurement on the rock sample to obtain measurement results, wherein the measurement results comprise a drying longitudinal wave velocity, a drying transverse wave velocity, a water-containing longitudinal wave velocity and a water-containing transverse wave velocity.

The single fluid saturation experiment is to saturate a rock sample with a single fluid and measure the change relation of the rock longitudinal and transverse wave velocity with pressure and temperature in the state.

The highest pressure of the ultrasonic measurement experiment equipment is 20000psi, the highest temperature is 150-200 ℃, and the ultrasonic measurement experiment equipment is suitable for measuring the longitudinal and transverse wave velocity, anisotropy, resistivity and the like of various rocks such as sandstone, mudstone, carbonate rock, oil sand and the like. The ultrasonic measuring system consists of a digital oscilloscope, a pulse transmitting and receiving device, a high-temperature high-pressure container, a measuring probe, digital mercury, a temperature controller and the like. Fig. 17 is a schematic diagram of a conventional ultrasonic measurement system, and as can be seen from fig. 17, except for the high-temperature and high-pressure vessel, the remaining equipment can be divided into four subsystems: the device comprises a signal acquisition system, a pore pressure system, a confining pressure system and a heating and cooling system. Pore pressure and confining pressure are isolated by using a rubber sleeve wrapping the rock core sample, so that the pore pressure and the formation pressure of rock under the underground condition can be simulated. The heating and cooling system is used to control the temperature of the sample to the formation temperature.

Ultrasonic velocity measurement is performed using a pulse transmission technique. The electric signal transmitted by the pulse transmitter is converted into ultrasonic wave through a longitudinal wave transducer or a transverse wave transducer, the ultrasonic wave penetrates through the rock core and is received by the other transducer, and finally the ultrasonic wave is transmitted to an oscilloscope for signal acquisition. The longitudinal wave and transverse wave signals are picked up, and the longitudinal wave and transverse wave speeds can be obtained through travel time correction and conversion. The first arrival pick-up error is controlled within 0.03 mu s.

S307: preferred sensitive parameters are determined based on said measurements. Rock parameters under two states of drying and water saturation are measured through experiments, and obtained measurement results comprise drying longitudinal wave velocity, drying transverse wave velocity, water-containing longitudinal wave velocity and water-containing transverse wave velocity. These parameters are merged with each other, and if the data points in the two states are clearly distinguished, the merged parameters are the preferred sensitive parameters. Such as: if the difference between the measured dry longitudinal wave velocity and the measured water-containing longitudinal wave velocity is large, the longitudinal wave velocity can be directly used as a preferred sensitive parameter, and if the difference is not large, the longitudinal wave velocity cannot be used as the sensitive parameter; if the difference between the velocity ratio of the longitudinal and transverse waves (or some other elastic parameter) calculated on the basis of the measurement results is large in the dry and water-containing cases, the velocity ratio of the longitudinal and transverse waves (or some other elastic parameter) can be taken as a preferred sensitive parameter.

S308: and selecting the preferred rock physical plate from the rock physical plates according to the preferred sensitive parameters. The optimal sensitive parameters obtained by experimental measurement can provide reference for the parameters of rock physical modeling, and the modeling precision is improved.

In other embodiments of the invention, the equivalent medium theory is used to give rock skeleton information, and the equivalent elastic parameters, preferably sensitive elastic parameters, of the rock saturated with other fluids are calculated according to the known saturation or dryness condition of one fluid. The petrophysical plate was calculated using the Biot-Rayleigh equation.

Fig. 5 is a flowchart of a fourth implementation manner of the gas reservoir prediction method for a carbonate reservoir based on pore structure characteristics according to the embodiment of the present invention, as can be seen from fig. 5, steps S101 to S107 in fig. 1 are the same as steps S501 to S507 in fig. 5, and are not repeated here, and the method further includes:

s508: acquiring original logging data of a carbonate reservoir;

s509: determining a logging interpretation result according to the original logging data;

s510: and verifying the prediction results of the porosity and the gas saturation according to the well logging interpretation result. And comparing the gas layer position of the prediction result with the gas layer position in the well logging interpretation result, and if the gas layer position is basically consistent, the prediction is accurate and reasonable.

According to the gas reservoir prediction method for the carbonate reservoir based on the pore structure characteristics, provided by the invention, the basic rock parameters including the porosity, the permeability, the mineral content, the pore structure characteristics and the like are obtained through a geological report, logging data, geological slice identification and core experiment measurement results, and a dry rock skeleton model corresponding to different pore structures of the reservoir is generated; based on a rock dry skeleton model, carrying out fluid replacement by using a Biot-Rayleigh equation system to generate rock physical charts based on different pore structures; and intersecting pre-stack seismic inversion data of different pore structure development zones with corresponding rock physical templates, and inverting the porosity and the gas saturation of the target reservoir from the rock physical templates by using a mapping method.

Fig. 6 is a structural block diagram of a first embodiment of the gas reservoir prediction system, and as can be seen from fig. 6, the system includes:

the rock sample collecting device 100 is used for collecting a rock sample of a target reservoir section of a carbonate reservoir. In particular embodiments, conventional seismic exploration methods may be employed to collect rock samples of a target reservoir interval of a carbonate reservoir.

And the geological thin slice identification device 200 is used for performing geological thin slice identification on the rock sample to obtain basic rock parameters, wherein the basic rock parameters comprise rock components, pore shapes, face porosity and sedimentary facies bands. The identification of geological thin slice is a method for identifying rock and mineral under the polarization microscope, and is characterized by that the rock or mineral specimen is ground into thin slice, under the polarization microscope the crystal characteristics of mineral can be observed, its optical property can be measured, the mineral composition of rock can be defined, its structure and structure can be studied, the generation sequence of mineral can be analyzed, the rock type and its causative characteristics can be defined, and the name of rock can be made. The thin slice identification method is a frequently used research means in the geological oil and gas exploration work.

And the pore-permeability measuring device 300 is used for carrying out pore-permeability measurement on the rock sample to obtain pore-permeability basic parameters, wherein the pore-permeability basic parameters comprise porosity, permeability and density. Reference herein to density includes dry rock density, water saturated sample density and rock particle matrix density, and generally rock density is referred to as dry rock density. Dry rock density refers to the density of the rock pore space when it is air, water saturated sample density refers to the density of the rock pore space when it is filled with water, and rock particle matrix density refers to the density of the particle components that make up the rock, not including the pore space.

the water-saturated sample density was:

ρ_wet=ρ_dry+φρ_w

where φ is the rock porosity, ρ_wIs the density of water.

The rock particle matrix density is:

And the rock dry skeleton member device 400 is used for analyzing the pore structure according to the pore shape and the sedimentary facies belt and constructing a rock dry skeleton model according to the rock basic parameters, the pore seepage basic parameters and the differential equivalent medium model. In this step, pore structure analysis may be performed first based on the pore shape and the sedimentary phase belt. When the aperture is flat, the aspect ratio is less than 0.1, the aperture is a crack type aperture, and when the aspect ratio is large, the aperture is an erosion aperture. Given the rock composition and pore space, the differential equivalent medium model can be used to estimate the equivalent elastic modulus of the rock skeleton, i.e. rock skeleton modeling.

(1 - y) \frac{{dK}^{*} (y)}{dy} = [K_{2} - K^{*} (y)] P^{(* 2)} (y)

wherein, K₂Is porousBulk modulus, μ₂Is the shear modulus of the pores, y is the content of pores, P, Q is a geometric factor, initial condition K^*(0)=K₁、μ^*(0)=μ₁，K₁Is the bulk modulus, μ, of the original mineral constituent₁Is the shear modulus of the original mineral constituent. K1 represents the initial main phase, i.e. the original state; k2 represents added inclusions, which can be generally considered as pores. For fluid inclusions and empty inclusions, y is equal to the porosity φ, P and Q are the geometric factors given in Table 1 for some inclusion shapes, and subscripts m and i refer to the background material and the inclusion material, respectively.

And the rock physical plate generating device 500 is used for performing fluid replacement on the rock dry skeleton model to generate a rock physical plate. In a specific embodiment, this step may be achieved by: and based on the rock dry skeleton model, carrying out fluid replacement by using a Biot-Rayleigh equation system to generate rock physical charts based on different pore structures.

The Biot-Rayleigh equation is as follows:

wherein u = [ ]₁,u₂,u₃]、

U^{(1)} = [U_{1}^{(1)}, U_{2}^{(1)}, U_{3}^{(1)}], U^{(2)} = [U_{1}^{(2)}, U_{2}^{(2)}, U_{3}^{(2)}]

indices

1, 2, 3 indicate the three directions of the vector space,

representing local fluid deformation produced during seismic wave excitationAnd (4) increasing.

x₁，x₂And x₃Respectively representing the coordinates of three directions. Phi is a₁And phi₂Denotes the absolute porosity of both types of pores, the total porosity of the rock phi = phi₁+φ₂；φ₁₀Phi and phi₂₀Respectively representing the local porosity in two regions, phi if the rock contains only one skeleton but is saturated with two fluids₁₀=φ₂₀=φ。ρ_f1And η₁To representDensity and viscosity of the background phase fluid. R₀Denotes the bubble radius, κ₁₀Representing the rock permeability.

A、N、Q₁、R₁、Q₂、R₂Represents six Biot elastic parameters in a double-pore medium, and can be explicitly calculated and estimated according to rock physical parameters of bases such as the elastic modulus of a rock skeleton, the volume modulus of a fluid, the porosity, the elastic modulus of a solid matrix and the like; rho₁₁、ρ₁₂、ρ₁₃、ρ₂₂And rho₃₃Represents five density parameters in a dual pore medium, which can also be explicitly calculated and estimated based on the density of solid particles in the rock, the density of pore fluid, the porosity, and the composition ratio of different pore structures, assuming a spherical approximation assumption for individual rock particles inside the rock. The calculation and estimation methods of each parameter are known techniques, such as the schemes mentioned in the invention patents with patent numbers 201310308550.5 and 201210335739.9, and the present invention is not described in detail. The solving process of the Biot-Rayleigh equation is also described in detail in the above two patents and thus is not described in detail here. In the above formula, ξ is the fluid displacement increment under seismic wave extrusion, b represents the Biot dissipation parameter,

where η, φ and κ represent fluid viscosity, porosity and permeability, respectively. For a porous medium containing a two-phase fluid, b₁、b₂Respectively show the Biot dissipation coefficients in the water-containing pores and the air-containing pores,

(

subscripts

1, 2 correspond to two types of pores).

The pre-stack seismic inversion data acquisition device 600 is used for acquiring pre-stack seismic inversion data of a carbonate reservoir;

and the gas saturation prediction device 700 is used for intersecting the pre-stack seismic inversion data with the rock physical chart to obtain a prediction result of the porosity and the gas saturation of the carbonate reservoir. Fig. 9 is a block diagram illustrating a specific structure of a gas saturation prediction apparatus 700, and as can be seen from fig. 9, the gas saturation prediction apparatus specifically includes:

a total wave impedance obtaining module 701, configured to obtain longitudinal wave impedance from the pre-stack seismic inversion data;

a horizontal axis setting module 702, configured to set the longitudinal wave impedance as a horizontal axis;

a longitudinal-transverse wave velocity ratio obtaining module 703, configured to obtain a longitudinal-transverse wave velocity ratio from the pre-stack seismic inversion data;

a longitudinal axis setting module 704, configured to set the longitudinal-to-transverse wave velocity ratio as a longitudinal axis;

a mapping module 705, configured to map the petrophysical plate into a coordinate system composed of the horizontal axis and the vertical axis;

and the gas saturation prediction module 706 is configured to adjust the coordinates of the petrophysical plate to obtain a prediction result of the porosity and the gas saturation of the target reservoir section of the carbonate reservoir.

Fig. 7 is a structural block diagram of a second embodiment of a gas reservoir prediction system for a carbonate reservoir based on pore structure characteristics according to an embodiment of the present invention, and as can be seen from fig. 7, the system further includes in the second embodiment:

a geological report acquisition device 800 for acquiring a geological report of a carbonate reservoir;

a logging data acquisition device 900 for acquiring logging data of a carbonate reservoir;

the logging data acquisition device 1100 is used for acquiring logging data of a carbonate reservoir;

and the rock dry skeleton model correcting device 1200 is used for correcting the rock dry skeleton model according to the geological report, the logging data and the logging data. Geological reports, well log data, and well log data may provide the age of the subsurface formations, reservoir information (e.g., lithology, mineral composition, location of specific intervals of gas and water layers, etc.), and are generally considered reliable. In the process of modeling the dry rock skeleton, the data needs to be referred to, so that the model is more reasonable and better accords with the underground real situation.

The geological report gives an overview of the large-scale geological condition of the work area, the logging information records the lithology and mineral composition of each stratum rock in the small range around the well, and input parameters must be set according to the information in the modeling of the dry skeleton. For example, the invention applies that the lithologic character of the carbonate reservoir rock in the work area is limestone, the main mineral component of the limestone is calcite, and a small amount of argillaceous substances are attached, so that the dry skeleton modeling is carried out by the calcite and the argillaceous substances. The logging data records information of the velocity, density, porosity and the like of the formation rock, and the information is all reference data which is required to input and correct input parameters in the dry skeleton modeling. These data reflect information about the subsurface rock intuitively, and without this information it is not possible to accurately build a dry skeleton model.

Fig. 8 is a structural block diagram of a third embodiment of a gas reservoir prediction system for a carbonate reservoir based on pore structure characteristics according to an embodiment of the present invention, and as can be seen from fig. 8, the system further includes, in the third embodiment:

the ultrasonic measurement device 1300 is configured to perform ultrasonic measurement on the rock sample to obtain measurement results, where the measurement results include a drying longitudinal wave velocity, a drying transverse wave velocity, a water-containing longitudinal wave velocity, and a water-containing transverse wave velocity. The single fluid saturation experiment is to saturate a rock sample with a single fluid and measure the change relation of the rock longitudinal and transverse wave velocity with pressure and temperature in the state.

And a preferred sensitive parameter selecting device 1400, configured to determine a preferred sensitive parameter based on the measurement result. Rock parameters under two states of drying and water saturation are measured through experiments, and obtained measurement results comprise drying longitudinal wave velocity, drying transverse wave velocity, water-containing longitudinal wave velocity and water-containing transverse wave velocity. These parameters are merged with each other, and if the data points in the two states are clearly distinguished, the merged parameters are the preferred sensitive parameters. Such as: if the difference between the measured dry longitudinal wave velocity and the measured water-containing longitudinal wave velocity is large, the longitudinal wave velocity can be directly used as a preferred sensitive parameter, and if the difference is not large, the longitudinal wave velocity cannot be used as the sensitive parameter; if the difference between the velocity ratio of the longitudinal and transverse waves (or some other elastic parameter) calculated on the basis of the measurement results is large in the dry and water-containing cases, the velocity ratio of the longitudinal and transverse waves (or some other elastic parameter) can be taken as a preferred sensitive parameter.

And the rock physical plate selecting device 1500 is used for selecting the preferred rock physical plate from the rock physical plates according to the preferred sensitive parameters. The optimal sensitive parameters obtained by experimental measurement can provide reference for the parameters of rock physical modeling, and the modeling precision is improved.

Fig. 10 is a structural block diagram of a fourth embodiment of a gas reservoir prediction system for a carbonate reservoir based on pore structure characteristics according to an embodiment of the present invention, and as can be seen from fig. 10, the system further includes, in the fourth embodiment:

the logging data acquisition device 1600 is used for acquiring original logging data of the carbonate reservoir;

the well logging interpretation result determining device 1700 is used for determining the well logging interpretation result according to the original well logging data;

and the predicted result verifying device 1800 is used for verifying the predicted results of the porosity and the gas saturation according to the well logging interpretation result. And comparing the gas layer position of the prediction result with the gas layer position in the well logging interpretation result, and if the gas layer position is basically consistent, the prediction is accurate and reasonable.

According to the gas reservoir prediction system of the carbonate reservoir based on the pore structure characteristics, rock basic parameters including porosity, permeability, mineral content, pore structure characteristics and the like are obtained through geological reports, logging data, geological slice identification and core experiment measurement results, and rock dry skeleton models corresponding to different pore structures of the reservoir are generated; based on a rock dry skeleton model, carrying out fluid replacement by using a Biot-Rayleigh equation system to generate rock physical charts based on different pore structures; and intersecting pre-stack seismic inversion data of different pore structure development zones with corresponding rock physical templates, and inverting the porosity and the gas saturation of the target reservoir from the rock physical templates by using a mapping method.

The technical solution of the present invention will be described in detail with reference to specific examples. The research aims at the Jurashike system carbonate rock gas reservoir in the army river basin Mejiwai area.

Identification of geological lamellae

FIG. 11 shows the Met22 well reservoir rock slice microscopic identification result, and it can be known from FIG. 11 that: the brilliant sand-dust limestone takes medium sand as main material, has thicker particles, better development of erosion holes and higher porosity, facilitates the gathering of oil gas in a storage space, and comprehensively judges that a sedimentary facies belt of the brilliant sand-dust limestone is positioned in an open terrace facies;

FIG. 12 shows the identification result of Met21 well reservoir rock slice under the mirror, and it can be known from FIG. 12 that: the mud brilliant sand bits limestone is based on fine sand, and the granule is thinner, and the pore development is relatively poor, and most pores and cracks have been filled to the calcite, develop the structure seam, and the porosity is lower, and the reservoir space is unfavorable for the gathering of oil gas, synthesizes and judges that its deposit facies is located the limitation terrace facies.

In conclusion, the rock development of the reservoir layers of the two wells belongs to different sedimentary facies zones, the erosion holes are developed on the open plateau facies, the structural seams are formed on the plateau facies, and the difference of pore structures is obvious.

(II) core Experimental measurements

Fig. 13 is a schematic diagram of rock samples collected from a target reservoir section of a carbonate reservoir, and as can be seen from fig. 13, the experimentally measured core samples are 15 blocks, 13 Met22 wells and 2 Met21 wells. The core samples are all drilled in the direction perpendicular to the stratum and processed into cylinders with the length of 50mm and the diameter of 38 mm. And polishing the top and the bottom of the core cylinder by using abrasive paper to ensure that the core cylinder is smooth and parallel, and the length change is less than 0.1 mm. The main mineral components of the sample comprise calcite, a small amount of dolomite and uneven grain size.

Fig. 14 is a nanoct scan of a core sample, where black represents porosity, gray represents limestone matrix, and white represents filler calcite.

FIG. 15 is a plot of porosity versus density for core samples, with both samples having a porosity of less than 2% and a density of greater than 2.6g/cm for Met21 wells³And compared with the density, the sample porosity of the Met22 well is more than 2%, the sample density decreases along with the increase of the porosity, and the linear relationship is better.

FIG. 16 is a graph showing the relationship between the porosity and the permeability of a core sample, wherein the permeability of two samples in a Met21 well is poor, the permeability of only two samples in a Met22 well is poor, the porosity of the rest samples is high, the permeability is good, and the porosity and the permeability have good correlation.

The core sample was subjected to ultrasonic measurement at a temperature of 20 ℃, a pore pressure of 22MPa, and a confining pressure of 52MPa, and fig. 17 is a multifunctional ultrasonic measurement system.

FIG. 18 is a graph of the relationship between the longitudinal and transverse wave velocities and the porosity of the rock in a dry state, wherein the longitudinal and transverse wave velocities decrease with the increase of the porosity, the longitudinal wave velocity changes from 6.199km/s to 5.145km/s, and the transverse wave velocity changes from 3.266km/s to 2.872 km/s.

FIG. 19 is a diagram of the relationship between the longitudinal and transverse wave velocities and the porosity of the rock in a water saturation state, wherein the longitudinal wave velocity decreases with the increase of the porosity, the longitudinal wave velocity changes from 6.323km/s to 5.458km/s, and the transverse wave velocity changes from 3.264km/s to 2.833 km/s.

FIG. 20 is a plot of core sample porosity versus longitudinal to transverse wave velocity ratio.

FIG. 21 is a graph of longitudinal wave impedance versus longitudinal and transverse wave velocity ratio for core samples. The Vp/Vs of the dry-state core sample is basically between 1.8 and 1.9, the Vp/Vs of the water-saturated-state core sample is basically between 1.9 and 2.0, the increase of the Vp/Vs is obvious compared with the dry state, and the change rule of the Vp/Vs is not obviously influenced by the porosity (from low porosity to high porosity), which shows that the Vp/Vs can effectively distinguish the fluid saturation state in the rock and can be used as a sensitive parameter for fluid detection.

Fig. 22 is a graph showing the influence of the pore structure of the reservoir core sample on the velocity of longitudinal waves, and as the porosity increases, both the solution pores and the fractures have great influence on the velocity of seismic waves, but the solution pores have a relatively slow influence on the velocity, and the fractures have a relatively sensitive influence on the velocity.

(III) rock physical modeling and quantitative prediction of gas reservoir

The petrophysical parameters adopted by the petrophysical modeling are as follows: calcite bulk modulus 76.8GPa, shear modulus 32GPa, water bulk modulus 2.51GPa, gas volume modulus 1.44 multiplied by 10⁵Pa, water viscosity of 0.001Pa s, gas viscosity of 0.000022Pa s, and average density of limestone matrix of 2.7g/cm³Water density 1.04g/cm³Air density of 0.01g/cm³ Average bubble size 2 mm. The aspect ratio of the solution pores is 0.62, and the aspect ratio of the cracks is 0.02.

FIG. 23 is a cross-plot of a petrophysical plate based on a pore-dissolving structure with M22 well sidetrack seismic data, where the square scatters represent gas layers, the diamond scatters represent water layers, and the triangle scatters represent non-reservoir layers. The square scattering points and the diamond scattering points fall between 6% and 14% of the predicted porosity of the plate, and the triangular scattering points are below 6% of the predicted porosity and consistent with the geological analysis result of the taken interval; the basic set of square scatter points is distributed at the lower part of the plate, namely, the position with higher predicted gas saturation, and the basic set of diamond scatter points is distributed at the upper part of the plate, namely, the position with higher predicted water saturation.

FIG. 24 is a graph of a petrophysical plate based on fracture pore structure intersecting with M21 well side channel seismic data. The square scattering points and the diamond scattering points fall between 7% and 13% of the predicted porosity of the plate, and the triangular scattering points are below 7% of the predicted porosity and consistent with geological analysis results; the square scatter points are basically gathered at the lower part of the plate, namely, the positions with higher predicted gas saturation, and the diamond scatter points are basically gathered at the upper part of the plate, namely, the positions with higher predicted water saturation.

FIG. 25 is a schematic diagram of the results of predicting porosity and gas saturation based on a dissolved hole model for two-dimensional line seismic data of over-Met 22 and Met3 wells, FIG. 26 is the results of predicting gas saturation based on a dissolved hole model for two-dimensional line seismic data of over-Met 22 and Met3 wells, and the porosity of the Met22 wells reaches 10% between 1860ms and 1880ms, and the gas saturation is higher. The porosity of the Met3 well reaches 8 percent between 1870ms and 1890ms, and the gas saturation is higher.

Fig. 27 is a Met22 well logging porosity curve and a gas test interval, fig. 28 is a Met3 well logging porosity curve and a gas test interval, the gas test result of a Met22 well in an interval from 2670 m to 2679m is 67.5 ten thousand square/day, the average porosity of the interval corresponding to the logging curve from 1863 ms to 1867ms is 12%, and the logging interpretation result is a gas layer. The gas test result of the Met3 well in the interval of 2710-2730 m is 76.5 ten thousand square/day, the average porosity of the interval corresponding to the logging curve 1879-1887 ms is 10%, and the logging interpretation result is a gas layer. The well logging porosity of the two wells is identical to the predicted porosity, the gas testing layer sections are all in the range of the predicted layer sections, and the gas testing result is identical to the predicted gas saturation.

FIG. 29 is a porosity prediction result of Zen21 and Met21 well two-dimensional line seismic data based on a fracture model, FIG. 30 is a gas saturation prediction result of Zen21 and Met21 well two-dimensional line seismic data based on a fracture model, the porosity of a Zen21 well reaches 12% between 1920ms and 1940ms, only the upper end of a target stratum is displayed in gas, and the gas saturation is low. The Met21 well reached 8% porosity between 1815ms and 1845ms, and no gas was seen.

Fig. 31 is a Zen21 well logging porosity curve and a gas testing interval, fig. 32 is a Met21 well logging porosity curve and a gas testing interval, the gas testing result of a Zen21 well in a 2785-2805 m interval is 0.96 ten thousand square/day, the average porosity of the corresponding logging curve 1924-1932 ms interval is 13%, and the logging interpretation result is a gas-water layer. The well logging porosity of the well is matched with the predicted porosity, the gas testing layer section is in the range of the predicted layer section, and the gas testing result is matched with the predicted gas saturation. The gas test result of the Met21 well in the 2730-2789 m interval is a dry layer, the average porosity of the corresponding logging curve 1820-1840 ms interval is 5%, and the logging interpretation result is a gas difference layer. The porosity of the well is slightly smaller than the predicted porosity, the gas testing interval is in the range of the predicted interval, and the gas testing result is identical with the predicted gas saturation.

In conclusion, according to the gas reservoir prediction method and system for the carbonate reservoir based on the pore structure characteristics, the established petrophysical model has the characteristics of being more accurate and more targeted, and the problems of petrophysical modeling and gas saturation prediction of different pore structure development zones in the same research area can be solved. The scheme is an important extension of the existing rock physical modeling method, and the beneficial effects of the scheme are mainly embodied in the following aspects:

1. different pore structures of reservoir development of different sedimentary facies zones in the same research area are considered in the rock skeleton modeling process for the first time, and corresponding rock skeleton models are respectively established aiming at the different pore structure development zones. The conventional rock physical modeling method based on a single pore structure is only suitable for work areas with unobvious pore structure changes, and the conventional modeling method has applicability to carbonate reservoir development areas with strong heterogeneity.

2. The method provides a basis for dividing pore structure characteristics from two aspects of geological slice identification (geological angle) and core experiment measurement (experiment angle). The identification of the thin slice can directly observe the micro-pore structure characteristics of the rock from the lower part of the mirror; the core experiment can directly measure the relation between the rock physical property and the wave response of the fluid, and preferably selects sensitive parameters, thereby providing a reliable basis for quantitative prediction of the gas reservoir.

It will be understood by those skilled in the art that all or part of the processes of the methods of the above embodiments may be implemented by a computer program, which can be stored in a general computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.

Those skilled in the art will also appreciate that the various functions performed in the exemplary embodiments of the present invention are implemented as hardware or software, depending upon the particular application and design requirements of the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present embodiments.

The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims

1. A gas reservoir prediction method of a carbonate reservoir based on pore structure characteristics is characterized by specifically comprising the following steps:

collecting a rock sample of a target reservoir section of a carbonate reservoir;

performing geological slice identification on the rock sample to obtain basic rock parameters, wherein the basic rock parameters comprise rock components, pore shapes, face porosity and sedimentary facies bands;

carrying out pore permeability measurement on the rock sample to obtain pore permeability basic parameters, wherein the pore permeability basic parameters comprise porosity, permeability and density;

analyzing a pore structure according to the pore shape and the sedimentary facies belt, and constructing a rock dry skeleton model according to the rock basic parameters, the pore permeability basic parameters and the differential equivalent medium model;

performing fluid replacement on the rock dry skeleton model to generate a rock physical plate;

acquiring pre-stack seismic inversion data of a carbonate reservoir;

and intersecting the pre-stack seismic inversion data with the rock physical chart to obtain a prediction result of the porosity and the gas saturation of the carbonate reservoir.

2. The method as claimed in claim 1, wherein the rock dry skeleton model constructed according to the rock basic parameters, the pore permeability basic parameters and the differential equivalent medium model is as follows:

(1 - y) \frac{{dK}^{*} (y)}{dy} = [K_{2} - K^{*} (y)] P^{(* 2)} (y)

wherein, K₂Is the bulk modulus, μ, of the pores₂Is the shear modulus of the pores, y is the content of pores, P, Q is a geometric factor, initial condition K^*(0)=K₁、μ^*(0)=μ₁，K₁Is the bulk modulus, μ, of the original mineral constituent₁Is the shear modulus, K, of the original mineral constituent^*Bulk modulus, μ, for a rock dry skeleton model^*The shear modulus of the rock dry skeleton model.

3. The method of claim 2, further comprising:

acquiring a geological report of a carbonate reservoir;

acquiring logging data of a carbonate reservoir;

and correcting the rock dry skeleton model according to the geological report, the logging data and the logging data.

4. The method as claimed in claim 1 or 3, wherein the fluid substitution of the rock dry skeleton model to generate the petrophysical layout comprises:

and based on the rock dry skeleton model, carrying out fluid replacement by using a Biot-Rayleigh equation system to generate rock physical charts based on different pore structures.

5. The method of claim 4, wherein the Biot-Rayleigh equation system is:

wherein u = [ ]₁,u₂,u₃]、

U^{(1)} = [U_{1}^{(1)}, U_{2}^{(1)}, U_{3}^{(1)}], U^{(2)} = [U_{1}^{(2)}, U_{2}^{(2)}, U_{3}^{(2)}]

Respectively, the spatial vector displacements of the three components are indicated, the indices 1, 2, 3 indicate the three directions of the vector space,

representing the increment of local fluid deformation generated in the process of seismic wave excitation, ξ is the increment of fluid displacement under the extrusion of seismic waves, b₁、b₂Respectively representing the Biot dissipation coefficients in the water-containing pores and the air-containing pores;

x₁、x₂and x₃Respectively representing the coordinates of three directions, phi₁、φ₂Denotes the absolute porosity of both types of pores, the total porosity of the rock phi = phi₁+φ₂，φ₁₀Phi and phi₂₀Respectively shows the local porosity in two regions, the rock interior only contains one skeleton, but is saturated with two fluids, then phi₁₀=φ₂₀=φ，ρ_f1And η₁Denotes the density and viscosity, R, of the background phase fluid₀Denotes the bubble radius, κ₁₀Indicating rock permeability, A, N, Q₁、R₁、Q₂、R₂Six Biot elastic parameters, rho, in a two-well medium₁₁、ρ₁₂、ρ₁₃、ρ₂₂And rho₃₃Five density parameters are shown in the dual-pore medium.

6. The method of claim 5, further comprising:

carrying out ultrasonic measurement on the rock sample to obtain measurement results, wherein the measurement results comprise a drying longitudinal wave velocity, a drying transverse wave velocity, a water-containing longitudinal wave velocity and a water-containing transverse wave velocity;

determining a preferred sensitive parameter based on said measurement;

and selecting the preferred rock physical plate from the rock physical plates according to the preferred sensitive parameters.

7. The method as claimed in claim 1 or 6, wherein the step of intersecting the pre-stack seismic inversion data with the petrophysical map to obtain the prediction result of the porosity and gas saturation of the carbonate reservoir comprises:

acquiring longitudinal wave impedance from the pre-stack seismic inversion data;

the longitudinal wave impedance is taken as a transverse axis;

acquiring a longitudinal wave velocity ratio and a transverse wave velocity ratio from the pre-stack seismic inversion data;

taking the longitudinal and transverse wave velocity ratio as a vertical axis;

mapping the petrophysical plate into a coordinate system consisting of the horizontal axis and the vertical axis;

and adjusting the coordinates of the rock physical plate to obtain a prediction result of the porosity and the gas saturation of the target reservoir section of the carbonate reservoir.

8. The method of claim 7, further comprising:

acquiring original logging data of a carbonate reservoir;

determining a logging interpretation result according to the original logging data;

and verifying the prediction results of the porosity and the gas saturation according to the well logging interpretation result.

9. A gas reservoir prediction system of a carbonate reservoir based on pore structure characteristics is characterized by specifically comprising:

the rock sample collecting device is used for collecting a rock sample of a target reservoir section of the carbonate reservoir;

the geological thin slice identification device is used for carrying out geological thin slice identification on the rock sample to obtain basic rock parameters, wherein the basic rock parameters comprise rock components, pore shapes, face porosity and sedimentary facies bands;

the pore-permeability measuring device is used for carrying out pore-permeability measurement on the rock sample to obtain pore-permeability basic parameters, and the pore-permeability basic parameters comprise porosity, permeability and density;

the rock dry skeleton member device is used for analyzing a pore structure according to the pore shape and the sedimentary facies belt and constructing a rock dry skeleton model according to the rock basic parameters, the pore permeation basic parameters and the differential equivalent medium model;

the rock physical plate generating device is used for carrying out fluid replacement on the rock dry skeleton model to generate a rock physical plate;

the pre-stack seismic inversion data acquisition device is used for acquiring pre-stack seismic inversion data of the carbonate reservoir;

and the gas saturation prediction device is used for intersecting the pre-stack seismic inversion data with the rock physical chart to obtain a prediction result of the porosity and the gas saturation of the carbonate reservoir.

10. The system of claim 9, wherein the rock dry skeleton model constructed by the rock dry skeleton member device is:

(1 - y) \frac{{dK}^{*} (y)}{dy} = [K_{2} - K^{*} (y)] P^{(* 2)} (y)

11. The system of claim 10, further comprising:

the geological report acquisition device is used for acquiring a geological report of the carbonate reservoir;

the logging data acquisition device is used for acquiring logging data of the carbonate reservoir;

and the rock dry skeleton model correction device is used for correcting the rock dry skeleton model according to the geological report, the logging data and the logging data.

12. The system according to claim 9 or 11, wherein said petrophysical plate generating means is arranged to:

13. The system of claim 12, wherein the Biot-Rayleigh equation system is:

wherein u = [ ]₁,u₂,u₃]、

U^{(1)} = [U_{1}^{(1)}, U_{2}^{(1)}, U_{3}^{(1)}], U^{(2)} = [U_{1}^{(2)}, U_{2}^{(2)}, U_{3}^{(2)}]

14. The system of claim 13, further comprising:

the ultrasonic measurement device is used for carrying out ultrasonic measurement on the rock sample, and the measurement results comprise a drying longitudinal wave velocity, a drying transverse wave velocity, a water-containing longitudinal wave velocity and a water-containing transverse wave velocity;

the preferred sensitive parameter selecting device is used for determining preferred sensitive parameters based on the measuring result;

and the rock physical plate selecting device is used for selecting the preferred rock physical plate from the rock physical plates according to the preferred sensitive parameters.

15. The system according to claim 9 or 14, wherein the gas saturation prediction means comprises:

the total wave impedance acquisition module is used for acquiring longitudinal wave impedance from the pre-stack seismic inversion data;

the transverse axis setting module is used for taking the longitudinal wave impedance as a transverse axis;

the longitudinal and transverse wave velocity ratio acquisition module is used for acquiring the longitudinal and transverse wave velocity ratio from the pre-stack seismic inversion data;

the longitudinal axis setting module is used for taking the longitudinal and transverse wave velocity ratio as a longitudinal axis;

the mapping module is used for mapping the rock physical plate into a coordinate system consisting of the transverse axis and the longitudinal axis;

and the gas saturation prediction module is used for adjusting the coordinates of the rock physical plate to obtain a prediction result of the porosity and the gas saturation of the target reservoir section of the carbonate reservoir.

16. The system of claim 15, further comprising:

the logging data acquisition device is used for acquiring original logging data of the carbonate reservoir;

the well logging interpretation result determining device is used for determining a well logging interpretation result according to the original well logging data;

and the prediction result verifying device is used for verifying the prediction results of the porosity and the gas saturation according to the well logging interpretation result.