A modeling method for carbonate fractured-vug reservoirs

Abstract

This invention provides a modeling method for fracture-vuggy carbonate reservoirs, relating to the field of seismic exploration technology for oil and gas in complex exploration areas. It fully considers the randomness in the spatial morphology and distribution of fracture-vuggy reservoirs formed by dissolution, tectonic compression, or extension in carbonate reservoirs. The method proposes using uniformly distributed random functions, elliptic complex exponential equations, and random walk (Brownian motion) model functions to simulate the randomness of the spatial morphology of reservoir development zones, the scale and spatial distribution characteristics of fractures and dissolution pores within the reservoir, respectively. These are represented by random arbitrary polygon combinations. The resulting Ordovician carbonate fracture-vuggy reservoir model more realistically reflects the spatial development characteristics of fracture-vuggy carbonate reservoirs.

Description

Technical Field

[0001] This invention relates to the field of seismic exploration technology for oil and gas in complex exploration areas, specifically to a modeling method for fracture-vuggy carbonate reservoirs. Background Technology

[0002] Carbonate fracture-vuggy reservoirs are one of the major oil and gas reservoir types in western my country. These reservoirs, having undergone multiple phases of tectonic fracturing, fracture, paleoweathering, and dissolution, exhibit spatial heterogeneity as highly heterogeneous carbonate fracture-vuggy systems, whether karst or fault-controlled, providing crucial sites for oil and gas migration and accumulation. Exploration practice shows that the seismic reflection characteristics of fracture-vuggy reservoirs are influenced by the reservoir's external shape, internal structure, fracture density, and subsequent dissolution alteration. The seismic response characteristics of these reservoirs are characterized by strong amplitude reflections along fault zones, such as "beaded" or "sheet-like" patterns. However, in deep-buried areas with low signal-to-noise ratios, the seismic response characteristics of carbonate reservoirs, especially fracture-vuggy reservoirs developed under strike-slip fault-controlled dissolution systems, vary greatly and are unique depending on their shape, structure, and scale, due to limitations in seismic data quality. Therefore, accurately identifying the seismic response characteristics of carbonate fracture-vuggy reservoirs under different seismic data conditions, establishing seismic identification models, and guiding and improving the accuracy of fracture-vuggy reservoir prediction are crucial for oil and gas exploration.

[0003] Numerical simulation based on reservoir models is a crucial technique for studying the seismic response characteristics of carbonate reservoirs. Its key component is establishing reservoir models that match the seismic response characteristics. These models can be broadly categorized into homogeneous medium (equivalent medium) modeling and stochastic medium modeling. Research has revealed that most numerical simulation techniques build upon the "homogeneous medium" model, establishing models of fracture-vuggy complexes with regular distributions, such as caverns of varying sizes, combinations of caverns in different orientations, vertical fractures, and transverse fractures, through well-seismic calibration. Simultaneously, stochastic modeling techniques for fracture-vuggy reservoirs based on mixed, exponential, and Gaussian elliptic autocorrelation functions have been developed, along with numerical simulation techniques for the seismic response characteristics of fracture-vuggy reservoirs. Furthermore, research focuses on numerical simulation algorithms and the analysis of seismic response characteristics of fracture-vuggy bodies, which can be summarized in the following aspects:

[0004] (1) Homogeneous medium (equivalent medium) modeling: The reservoir medium has a regular external shape and internal structure. By manually establishing fractured-vuggy reservoirs with regular shapes, filling them with equivalent or average velocity, density parameters, etc., the influence of longitudinal and lateral development scales, filling material type and background surrounding rock factors on seismic response characteristics of fractured-vuggy reservoirs is studied.

[0005] (2) Random medium simulation: Reservoir media have regular external shapes and random internal structures. The scale of non-uniform anomalies in various directions is usually represented by elliptical autocorrelation functions (exponential, Gaussian, or mixed types). Specifically, this includes: ① Establishing a carbonate fracture-vuggy reservoir based on a non-uniform random medium (based on Gaussian elliptical autocorrelation functions and geostatistics) according to the ratio of pore area to total area in the medium, and studying the kinematic and dynamic characteristics of the target seismic reflection wavefield; ② Multi-scale random modeling: Constructing autocorrelation functions by determining the parameter values ​​of the random medium, using Fourier transform to obtain the power spectral density function of the spatial random perturbation function, and combining the random phase function to calculate the inverse Fourier transform of the random power spectral density function, normalizing the spatial random perturbation function, and obtaining a discrete random medium model; ③ Based on random medium theory, assigning the elastic parameters of known pore inclusions to specific pore regions to obtain a seismic numerical model of the fracture-vuggy reservoir to be analyzed. ④ By combining Boolean simulation and multi-point geostatistics algorithms, a stochastic model of the fracture network is obtained, and its simulation accuracy is evaluated by comparing it with the geometric parameters such as trace length and strike of the real fracture field.

[0006] Forward modeling analysis based on the above modeling methods helps in understanding fractured and fracture-vuggy reservoirs, providing assistance and guidance for the exploration of this type of oil and gas reservoir. However, it also has certain limitations, mainly reflected in:

[0007] ①Most commonly used reservoir modeling is deterministic modeling, which is based on the reservoir information encountered by well drilling to establish a uniform and regular reservoir medium model. It does not consider the reservoir heterogeneity caused by sedimentation, tectonic fracturing and other effects and the randomness of the reservoir spatial profile. At the same time, most reservoir distribution is completed manually, which is inefficient.

[0008] ② Stochastic medium modeling is a reservoir modeling technique based on mixed, exponential, and Gaussian elliptical autocorrelation functions. It takes into account the uncertainty of the distribution of reservoir parameters outside the well point, reflecting the heterogeneity and randomness of reservoir distribution. However, it does not take into account the randomness and arbitrariness of the spatial distribution shape of large-scale fractured-vuggy reservoirs.

[0009] ③ The randomness of fracture-vuggy reservoirs developing along complex strike-slip fault surfaces (flower-shaped strike-slip faults with tensile and compressive-torsional properties) is considered, but models of fracture development zones or fracture zones with specific distribution ranges along faults are not taken into account.

[0010] Patent CN104977606A discloses a method for establishing a seismic numerical model of fractured-vuggy reservoirs. This method includes: a pore distribution model establishment step, where a pore distribution model of the fractured-vuggy reservoir to be analyzed is established based on random medium theory, including a background medium region and a pore region; a fracture elastic parameter determination step, where the fracture elastic parameters of the fractured-vuggy reservoir to be analyzed are determined based on the geometric parameters of the fractures; and a fractured-vuggy reservoir model establishment step, where the fracture elastic parameters are assigned to the background medium region, and the elastic parameters of known pore inclusions are assigned to the pore region, resulting in a seismic numerical model of the fractured-vuggy reservoir to be analyzed. This model reflects the anisotropic characteristics of the fracture medium and the scattering characteristics of random pores in the fractured-vuggy reservoir. However, this invention is based on random medium theory, which performs multiple random simulations of the morphology and location of a single pore reservoir under a regular fracture medium background to ultimately form a forward model. This model cannot reflect the spatial outline of the entire carbonate fractured-vuggy reservoir or the randomness within the reservoir itself. Patent CN112782757A decomposes a complex carbonate fracture-vuggy reservoir model into three parts: the stratigraphic framework, the reservoir's spatial structure (fracture-void development zones, fault development zones, etc.), and the distribution of rock physical parameters within the reservoir. Utilizing existing data and methods, it forms image matrices for each component of the complex model. By analyzing, extracting, and combining the features of these image matrices, along with random function matrices and related algorithms, it achieves precise and rapid modeling of the stratigraphic framework and stochastic simulation of the reservoir structure and internal rock physical parameters. The main steps are: ① Establishing the stratigraphic framework; ② Stochastic simulation of the reservoir structure; ③ Stochastic simulation of fracture (void) filling parameters; ④ Stochastic simulation of the complex reservoir medium. Finally, it obtains velocity and density data for the complex medium model used in forward modeling. This invention can achieve relative-scale stratigraphic framework, random reservoir contours, and random parameters of all grid points within the reservoir using binary data ("0", "1") from reservoir images of any shape (such as interpreted stratigraphic positions, faults, irregular contours, or even images of random walks (Brownian motion) or reservoir models generated by stochastic medium theory). However, this simulation is designed for reservoirs with relatively uniform physical properties (such as reservoir velocity varying between 85% and 95% of the surrounding rock, which is relatively stable). It is more suitable for small-scale reservoirs with fractures, dissolution pores, and pore types. It cannot reflect the large-scale, highly heterogeneous reservoirs with large fractures and pores that are often lost during drilling, such as the Lianglitag Formation and Yingshan Formation of the Ordovician in the Tarim Basin.

[0011] To address the problems existing in current carbonate fracture-vuggy reservoir modeling methods, it is essential to find a modeling method for carbonate fracture-vuggy reservoirs that conforms to the heterogeneity of carbonate fracture-vuggy reservoirs and is conducive to the study of reservoir seismic response characteristics. Summary of the Invention

[0012] This invention addresses the problems existing in the prior art by providing a modeling method for fractured-vuggy carbonate reservoirs. This method can accurately reflect the spatial development characteristics of carbonate reservoirs, effectively guide seismic data interpretation and reservoir prediction, improve the accuracy and quality of reservoir identification, and provide a basis and reference for subsequent reservoir classification evaluation, reserve calculation, trap identification and evaluation, and target selection.

[0013] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0014] This invention provides a method for modeling fractured-vuggy carbonate reservoirs, comprising the following steps:

[0015] S1: Based on the scale and spatial location of the carbonate rock random medium reservoir in the actual geological model framework, set the spatial range and number of walk points M of the random walk model and perform random walk; form the profile model Kl, calculate the external profile of the reservoir, and fill in the velocity and density parameters;

[0016] S2: Utilizing the characteristics of the elliptic complex exponential equation and its symmetry, and combining it with a uniformly distributed random function to simulate the random distribution of cracks or holes, a crack Frac model or a hole Karst model with an externally regular and internally randomly distributed structure is formed.

[0017] S3: Simulation of internal reservoir structure: Using the contour model Kl to constrain and extract the fracture Frac model and pore Karst model located inside, respectively generate fracture Frac model and pore Karst model under arbitrary irregular shape.

[0018] S4: Simulation of a random medium reservoir in carbonate rocks;

[0019] S5: Generate the final forward model: Overlay the simulated carbonate stochastic reservoir onto the velocity profile.

[0020] Furthermore, the generation of the Frac model for cracks and the Karst model for pores in step S3 includes the following steps:

[0021] By analyzing the crack length a, crack width b, and crack inclination angle... The average random function R(n) is constructed at the location of the crack center point P. Under the constraint of the relative density n of cracks or pores per unit area, the cracks or pores are simulated using the random elliptic complex exponential equation of formula (1), and the crack Frac model and the pore Karst model are generated.

[0022] Formula (1):

[0023] Where a·R(n)·Re(E) represents the length of the crack or hole; b·R(n)·IM(E) represents the width of the crack or hole; The dip angle of the crack is represented by L·R(n) + i·W·R(n); the position of the crack center point P is represented by E = e iθ θ = 0 - 2π; L is the horizontal length of the storage group, W is the vertical height of the storage group, i = 1 to N, where N is a non-zero integer; IM(E) is the imaginary part of the complex function E, and Re(E) is the imaginary part of the complex function E.

[0024] Furthermore, R(n) specifically refers to the average random function rand(n) in MATLAB software.

[0025] Furthermore, the carbonate rock stochastic reservoir simulation in step S4 includes fractured reservoir simulation, voided reservoir simulation, and / or fracture-voided reservoir simulation.

[0026] Furthermore, the simulation of the crack-type reservoir specifically includes: an overlay contour model Kl and a truncated internal crack Frac model.

[0027] Furthermore, the simulation of the porous reservoir specifically includes: an overlay contour model Kl and a cut-out internal hole Karst model.

[0028] Furthermore, the crack-void type reservoir specifically includes: a superimposed contour model Kl and a truncated internal crack Frac model and a void Karst model.

[0029] Furthermore, the velocity profile in step S7 is obtained from seismic data processing or inversion.

[0030] Furthermore, the carbonate rock random medium includes a rock framework and reservoir space.

[0031] Furthermore, the storage space comprises an aggregate of holes, crevices, or cracks.

[0032] Carbonate reservoirs can be divided into two parts: the rock skeleton and the reservoir space (an aggregate of pores, fractures, or fissures). The reservoir space is subject to randomness due to the degree of dissolution, the size and density of fractures formed by tectonic compression or extension. This method uses uniformly distributed random functions (characteristics: uniform, unbiased, independent, and uncorrelated), elliptic complex exponential equations, and random walk (Brownian motion) models to simulate the randomness of the spatial distribution characteristics of fractures (fracture length a, width b, angle θ, and fracture center point position P) and the randomness of the external shape of the dissolution pore reservoir aggregate, respectively. This method can more realistically reflect the development characteristics of carbonate reservoir space, mainly in the following aspects:

[0033] (1) Simulation of the external contour of karst reservoirs: Using the wandering model of the irregular movement of microparticles (Brownian motion model), the external contour of the dissolution pore type reservoir is stochastically simulated, reflecting the randomness of atmospheric freshwater or hydrothermal fluid along the weathering crust and fractures and fracture zones.

[0034] (2) Simulation of fractured and pore-type reservoirs mainly utilizes uniformly distributed random functions and elliptic complex exponential functions to characterize the randomness of the spatial distribution of fractures and pores. Based on constraints such as reservoir size, fracture (pore) density per unit area, maximum size of a single fracture (pore), and velocity density of the geological body, the elliptic complex exponential equation and its symmetry are used to simulate the internal structure of the reservoir space within a specific range:

[0035] ① Simulation of fractured reservoirs (a>>b)

[0036] The random simulation of single fracture length, width, dip angle, and center location (fracture distribution) aims to reflect the randomness of fractured reservoir development within a certain area. It can generate two types of fractured reservoirs: one is a simulation of randomly distributed internal fracture zones with regular shapes, which directly utilizes the simulation results of fractured reservoirs based on fracture density, reservoir size, etc., and fills in the framework background and reservoir petrophysical parameters; the other is a simulation of internal fracture zones with arbitrary irregular shapes based on parameters such as arbitrary boundaries of fracture development zones, fracture development density, velocity, and density, such as irregular random polygons formed by karst reservoir outer contour simulation, and typical flower-shaped strike-slip fault zones.

[0037] ② Simulation of dissolution cavities (a≈b)

[0038] The simulation of the randomness of pore size, shape, and distribution location is used to simulate the pores inside the reservoir and fill in the rock physical parameters under the constraints of reservoir external contour simulation.

[0039] (3) Fracture-pore type reservoir simulation: The simulation results of pore type reservoir and fracture type reservoir are superimposed to form a fracture-pore type reservoir model with arbitrary contour.

[0040] The technical effects achieved by this invention are:

[0041] Compared with existing methods for modeling stochastic reservoir media, this invention fully considers the randomness in the spatial morphology and scale of fracture-void reservoirs formed by dissolution, tectonic compression, or extension in carbonate reservoirs. This facilitates further analysis of the seismic response characteristics of fracture-void reservoirs of different morphologies, scales, types, and internal structures, effectively guiding the detailed interpretation of seismic data and the identification and description of reservoirs. This makes reservoir research more precise and accurate, laying the foundation for target optimization and well location deployment.

[0042] The advantages of the novel approach and methodology for analyzing strike-slip fault zones in deep carbonate rocks in this invention are as follows:

[0043] 1) This invention uses uniformly distributed random functions, elliptic complex exponential equations, and random walk models to simulate the randomness of the spatial distribution characteristics of fractures (cavities) inside the reservoir (such as fracture length a, width b, angle θ, and fracture center point position P) and the randomness of the external shape of the reservoir, respectively. The combination of the two can more realistically reflect the spatial development characteristics of carbonate reservoirs.

[0044] 2) This invention reflects the randomness of the external contour and internal structure of fractured and fractured-vuggy reservoirs, and reflects the anisotropic characteristics of carbonate reservoirs. It provides a reliable reservoir model that conforms to geological laws for forward modeling, which is conducive to the analysis and research of seismic response characteristics of various reservoirs and the establishment of reservoir seismic identification models.

[0045] 3) This invention can effectively guide the interpretation of seismic data and reservoir prediction, improve the accuracy and quality of reservoir identification, and provide a basis and reference for subsequent reservoir classification evaluation, reserve calculation, trap identification and evaluation, target selection, etc. Attached Figure Description

[0046] Figure 1 A schematic diagram of the forward modeling stochastic medium modeling technique for carbonate fractured-vuggy reservoirs provided in Embodiment 1 of the present invention;

[0047] Figure 2 For the stochastic modeling of carbonate fracture-vuggy reservoirs, a is the stochastic model 1 of the reservoir, b is the stochastic model 2 of the reservoir, c is the stochastic model 3 of the reservoir, d is the stochastic simulation of fractures inside the reservoir, e is the stochastic simulation of pores inside the reservoir, f is the stochastic simulation of fracture-pores inside the reservoir, g is the stochastic simulation of pore cluster development zone, and h is the stochastic simulation of fracture-pore development zone.

[0048] Figure 3 Forward modeling results of stochastic modeling for fracture-pore type reservoirs in carbonate rocks, where a is the stochastic model of fracture-pore type reservoirs based on the frame model, and b is the forward modeling simulation result;

[0049] Figure 4 For multi-scale crack simulation of strike-slip fracture zones, a represents random simulation of cracks of the same size but different densities and scales, b represents random simulation of cracks of the same density but different scales, and c represents random model of cracks in any strike-slip fracture zone.

[0050] Figure 5 The images show the forward modeling results of cracks and fracture zones. In image a, we show the stochastic models and forward modeling results for cracks of different sizes; in image b, we show... Figure 4 .c Forward modeling results. Detailed Implementation

[0051] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention.

[0052] Before further describing specific embodiments of the present invention, it should be understood that the scope of protection of the present invention is not limited to the specific embodiments described below; it should also be understood that the terminology used in the embodiments of the present invention is for describing specific embodiments and not for limiting the scope of protection of the present invention.

[0053] When numerical ranges are given in the embodiments, it should be understood that, unless otherwise stated in the invention, both endpoints of each numerical range and any value between the two endpoints may be selected. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0054] Example 1

[0055] Figure 1 The present invention provides a schematic diagram of the forward modeling stochastic medium modeling technique for carbonate fractured-vuggy reservoirs, which includes the following steps:

[0056] Step S1: Based on the reservoir size L×W and spatial location in the actual geological model framework, set the spatial range and number of points M of the random walk (Brownian motion) model, and perform a random walk to form the contour model K. l Calculate the external profile of the reservoir and fill in the velocity and density parameters. Figure 2 .a~ Figure 2 .c);

[0057] Step S2: Utilizing the complex exponential equation of an ellipse and its symmetry, combined with a uniformly distributed random function, the random distribution of cracks or pores is simulated to form a randomly distributed crack (pore) development zone. Figure 2 .g、 Figure 2 .h、 Figure 4 a、 Figure 4 .b).

[0058] The length a, width b, and dip angle of fractures in the reservoir were determined using actual seismic profiles and core interpretation measurement data. The approximate range of variation is determined, and an average random function R(n) is constructed based on parameters such as the location P of the crack center point determined by the scale range of the reservoir. Under the constraint of the relative density n of cracks (cavities) per unit area, the crack (cavity) is simulated using the random elliptic complex exponential equation of formula (1), and crack F is generated. rac and hole K arst Model.

[0059]

[0060] Where a·R(n)·Re(E) represents the length of the crack (hole); b·R(n)·IM(E) represents the width of the crack (hole); The dip angle of the crack is represented by L·R(n) + i·W·R(n); the position of the crack center point P is represented by E = e iθ θ = 0 - 2π. L is the horizontal length of the storage group, W is the vertical height of the storage group, i = 1 to N, where N is a non-zero integer; IM(E) is the imaginary part of the complex function E, and Re(E) is the imaginary part of the complex function E.

[0061] Step S3: Simulate the internal structure of the reservoir using the profile K l Constrain and cut off the internal crack F rac and hole K arst The model is used as input, along with typical fracture or reservoir development zone contours, to generate internal fractures F within arbitrary irregular bodies. rac Model and internal holes K arst Model;

[0062] Step S4: Simulation of cracked reservoir, superimposed profile K l Model and the extracted internal crack F rac Model( Figure 2 .d);

[0063] Step S5: Simulation of the porous reservoir, superimposed contour K l Model and the extracted internal hole K arst Model( Figure 2 .e);

[0064] Step S6: Simulation of crack-void type reservoir, superimposed profile K l Model and the extracted internal crack F rac and hole K arst Model( Figure 2 .f);

[0065] Step S7: Generate the final forward model, which combines the simulated carbonate rock stochastic reservoir with a superimposed framework model. Figure 3 a、 Figure 4 .c).

[0066] Step S8: Conduct forward modeling analysis based on stochastic models of various fractured-vuggy reservoirs and fault fracture zones in carbonate strata to guide production and scientific research. Figure 3 .b、 Figure 5 ).

[0067] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, and is not intended to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions made by those skilled in the art to the technical solution of the present invention do not depart from the essence and scope of the technical solution of the present invention.

Claims

1. A method for modeling fracture-vuggy carbonate reservoirs, characterized in that: Includes the following steps: S1: Based on the scale and spatial location of the carbonate rock random medium reservoir in the actual geological model framework, set the spatial range and number of walk points M of the random walk model and perform random walk; form the profile model Kl, calculate the external profile of the reservoir, and fill in the velocity and density parameters; S2: Utilizing the characteristics of the elliptic complex exponential equation and its symmetry, and combining it with a uniformly distributed random function to simulate the random distribution of cracks or holes, a crack Frac model or a hole Karst model with an externally regular and internally randomly distributed structure is formed. The generation of the Frac model of cracks and the Karst model of pores includes the following steps: By analyzing the crack length a, crack width b, and crack inclination angle... The average random function R(n) is constructed at the location of the crack center point P. Under the constraint of the relative density n of cracks or pores per unit area, the cracks or pores are simulated using the random elliptic complex exponential equation of formula (1), and the crack Frac model and the pore Karst model are generated. ； in, Characterizes the length of a crack or hole; Characterizes the width of a crack or hole; Characterizing the dip angle of the crack; Characterizes the location P of the crack center point; θ∈[0,2π]; L is the horizontal length of the storage group, W is the vertical height of the storage group; IM(E) is the imaginary part of the complex function E, and Re(E) is the real part of the complex function E; S3: Simulation of internal reservoir structure: Using the contour model Kl to constrain and extract the fracture Frac model and pore Karst model located inside, respectively generate fracture Frac model and pore Karst model under arbitrary irregular shape. S4: Simulation of a random medium reservoir in carbonate rocks; S5: Generate the final forward model: Overlay the simulated carbonate stochastic reservoir onto the velocity profile.

2. The modeling method according to claim 1, characterized in that: The carbonate rock stochastic reservoir simulation in step S4 includes fractured reservoir simulation, pore-type reservoir simulation, and / or fracture-pore-type reservoir simulation.

3. The modeling method according to claim 2, characterized in that: The simulation of the cracked reservoir specifically includes: the superimposed profile model Kl and the extracted internal crack Frac model.

4. The modeling method according to claim 2, characterized in that: The simulation of the porous reservoir specifically includes: the superimposed contour model Kl and the truncated internal hole Karst model.

5. The modeling method according to claim 2, characterized in that: The fracture-void type reservoir specifically includes: the superimposed contour model Kl and the extracted internal fracture Frac model and void Karst model.

6. The modeling method according to claim 1, characterized in that: The velocity profile in step S5 is obtained through seismic data processing.

7. The modeling method according to claim 1, characterized in that: The random medium of the carbonate rock includes a rock framework and reservoir space.

8. The modeling method according to claim 7, characterized in that: The storage space includes an aggregate of holes, crevices, or cracks.

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