Method, system, equipment and medium for evaluating pore type carbonate reservoir

Abstract

The invention discloses a porous carbonate reservoir evaluation method, system, equipment and medium, and the evaluation method constructs a reservoir elasticity factor K and a quality factor F which can reflect the quality of a carbonate reservoir, draws a cross plot through the reservoir elasticity factor K and the quality factor F, determines the value range of the reservoir, and obtains the value of the carbonate reservoir. Using a power formula fitting method to obtain expressions of the reservoir quality factor formula about speed and density, finally substituting the expressions into a reservoir quality factor data volume and a reservoir elastic factor data volume, and finally performing assignment to obtain a high-quality reservoir for predicting distribution of the high-quality reservoir in the seismic work area; the invention further comprises a system, equipment and a medium for implementing the method. According to the method, the reservoir quality of the porous carbonate rock can be quantitatively evaluated, and then a high-quality exploration and development target is optimized.

Description

Technical Field

[0001] The invention belongs to the technical field of carbonate oil and gas reservoir exploration and development, and particularly relates to a porous carbonate reservoir evaluation method, system, equipment and medium. Background Art

[0002] Carbonate oil and gas reservoirs occupy an important position in the world's discovered oil and gas resources and have great potential for exploration and development. At present, carbonate oil and gas reservoirs account for about 70% of the world's oil and gas resources, about 50% of proven recoverable reserves, and about 60% of total production. They are widely distributed in key oil and gas regions around the world such as Russia and China. From the perspective of reservoir storage space, carbonate reservoirs can be divided into four types: porous type, pore type, fracture pore type, and fracture-cavity type. Among them, porous carbonate reservoirs have the highest production and the largest reserves, and are the main reservoir type of large carbonate oil and gas reservoirs in the Middle East, Central Asia and the Americas. However, the development and evolution of carbonate reservoirs are jointly controlled by three major geological factors: structure, sedimentation and diagenesis. The pore formation and preservation mechanism is complex, the reservoir is highly heterogeneous, and prediction is difficult.

[0003] At present, the main evaluation methods for porous carbonate reservoirs are attribute method and inversion method. The attribute method is mainly based on pure mathematical transformation of seismic data, which does not have clear geological significance, cannot decouple factors such as fluid, matrix, pores, etc., and has poor prediction effect on porous carbonate reservoirs. The inversion method uses seismic data and known geological laws, drilling, and logging data as constraints to solve physical equations for the reservoir. For example, the patent application with publication number CN110887772B discloses a carbonate reservoir permeability identification method, system and device, which divides the pores of the carbonate reservoir into multiple pore types, establishes the correlation between each pore type and the range of pore structure parameters, and establishes a permeability model for each pore type. According to the pore type of the carbonate reservoir to be identified, the corresponding permeability model is selected for permeability calculation, thereby obtaining a detailed interpretation of the permeability of the carbonate reservoir to be identified, improving the recognition accuracy of the permeability, and providing an accurate data basis for the evaluation and development of oil and gas reservoirs. This method has rigorous mathematical derivation and clear geological significance. However, due to the complex pore and fluid characteristics of carbonate reservoirs, key reservoir parameters such as porosity and hydrocarbon saturation are not simply positively or negatively correlated with elastic parameters such as velocity and density obtained by seismic inversion. Directly using the inverted elastic parameters such as longitudinal waves, shear waves, density, etc. cannot reflect the quality of the reservoir. Summary of the invention

[0004] In order to overcome the defects of the above-mentioned prior art, the purpose of the present invention is to provide a porous carbonate reservoir evaluation method, system, equipment and medium, construct elastic factors and quality factors that can reflect the quality of carbonate reservoirs, which are used to predict the distribution of high-quality reservoirs in seismic work areas, and can quantitatively evaluate the reservoir quality of porous carbonate rocks, thereby selecting high-quality exploration and development targets.

[0005] In order to achieve the above technical objectives, the present invention includes the following technical solutions:

[0006] A porous carbonate reservoir evaluation method comprises the following steps:

[0007] Step 101, based on the well logging data, the P-wave velocity, S-wave velocity, density, porosity, oil and gas saturation, and dolomite content of the region are obtained; the seismic P-wave velocity, S-wave velocity, and seismic density of the well point are obtained through seismic data;

[0008] Step 102, the reservoir elasticity factor K is calculated according to the P-wave velocity, S-wave velocity, density and porosity obtained from the logging data by the following formula:

[0009]

[0010] Where ρ is the density, V P is the longitudinal wave velocity, V S is the shear wave velocity;

[0011] The reservoir quality factor F is calculated based on the P-wave velocity, S-wave velocity, density, porosity, oil and gas saturation, and dolomite content obtained from the logging data:

[0012]

[0013] Where ρ is the density, V p is the longitudinal wave velocity, V S is the shear wave velocity, θ is the porosity, ω is the oil and gas saturation, and γ is the dolomite content;

[0014] Step 103, using the reservoir elasticity factor K and the quality factor F obtained in step 102 to draw a cross-plot, and determine the value ranges of three types of reservoirs: high-quality reservoir, medium reservoir, and poor reservoir in the cross-plot;

[0015] Step 104, verifying whether the seismic P-wave velocity, seismic S-wave velocity, and seismic density at the well point location in step 101 are consistent with the P-wave velocity, S-wave velocity, and density obtained from the logging data. If they are inconsistent, the seismic velocity at the well point location is corrected to the logging velocity using a dispersion correction method to ensure that they are consistent with the logging velocity and density at the corresponding well location;

[0016] Step 105, in the cross-plot generated in step 103, the expression of the reservoir quality factor formula with respect to velocity and density is obtained by using the power formula fitting method:

[0017]

[0018] Among them, α and β are the formula fitting coefficients, ρ is the density, V P is the longitudinal wave velocity, V S is the shear wave velocity.

[0019] Step 106: convert the seismic shear wave velocity V corrected in step 104 into P , shear wave velocity V S Substitute the density ρ into the reservoir quality factor formula in step 105 to calculate and obtain the reservoir quality factor data body of the entire seismic work area;

[0020] Step 107, using the reservoir elasticity factor K formula of step 102 and the seismic shear wave velocity V corrected in step 104 P , shear wave velocity V S and density ρ, and similarly obtain the reservoir elasticity factor data volume of the entire seismic area;

[0021] Step 108, using the value ranges of the three types of reservoirs determined in step 103, assign a value of 1 to the data bodies generated in steps 106 and 107 within the value range, and assign a value of 0 to other positions, and then multiply each data point of the two newly generated data bodies, and the portion of the data body with a value of 1 is the high-quality reservoir.

[0022] In the cross-plot generated in step 103, the reservoir elasticity factor K is the horizontal coordinate, the reservoir quality factor F is the vertical coordinate, and the reference data points correspond to the porosity, oil and gas saturation, and dolomite content of the logging data. The reservoirs with high values ​​of these three parameters are high-quality reservoirs located in the upper left part of the cross-plot, the reservoirs with medium values ​​of these three parameters are medium reservoirs located in the middle of the cross-plot, and the poor reservoirs are located in the lower right part of the cross-plot; according to the logging reservoir classification standard, the value ranges of the three types of reservoirs are determined in the cross-plot; according to the logging reservoir classification standard, the value ranges of the three types of reservoirs are determined in the cross-plot.

[0023] A porous carbonate reservoir evaluation system comprises a processing instruction module for executing each step of the porous carbonate reservoir evaluation method.

[0024] A porous carbonate reservoir evaluation device, comprising:

[0025] Memory: a computer-readable device storing the above-mentioned porous carbonate reservoir evaluation method computer program;

[0026] Processor: used to implement the porous carbonate reservoir evaluation method when executing the computer program.

[0027] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for evaluating a porous carbonate reservoir can be implemented.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] (1) The reservoir elasticity factor K is calculated based on the P-wave velocity, S-wave velocity, density, and porosity obtained from logging data, which can accurately reflect the porosity and dolomite content of the reservoir without being affected by the fluid.

[0030] (2) The reservoir quality factor F is calculated based on the velocity, density, porosity, oil and gas saturation, and dolomite content obtained from logging data, which can fully reflect the reservoir quality.

[0031] In summary, the present invention constructs elastic factors and quality factors that reflect the quality of carbonate reservoirs, which more accurately and comprehensively reflect the porosity, lithology and oil and gas content characteristics of the reservoirs, and can be used to predict the distribution of high-quality reservoirs in seismic work areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a flow chart of a method for evaluating the quality of porous carbonate reservoirs according to an embodiment of the present invention.

[0033] Figure 2 It is the cross diagram of elasticity factor K and quality factor F.

[0034] Figure 3 This is the fitting diagram of the seismic reservoir quality factor formula. DETAILED DESCRIPTION

[0035] The following description will enable any person skilled in the art to utilize the present invention. The descriptive information provided in the specific embodiments and applications is only an example. Various extensions and combinations of the embodiments described herein will be apparent to those skilled in the art, and the general principles defined in the present invention may be applied to other embodiments and applications without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited to the embodiments shown, and the present invention covers the maximum scope consistent with the principles and features shown herein.

[0036] like Figure 1As shown, a flow chart of a porous carbonate reservoir quality evaluation method according to an embodiment of the present invention involves the entire implementation process of the present invention. In the specific implementation process, it is necessary to first input six parameters, namely, P-wave velocity, S-wave velocity, density, porosity, dolomite content, and oil and gas saturation obtained by conventional logging interpretation, and P-wave velocity, S-wave velocity, and density obtained by seismic inversion;

[0037] Using the input logging data, the reservoir elasticity factor and reservoir quality factor are calculated through the elasticity factor calculation formula and the quality factor calculation formula;

[0038] By using the formula fitting, the relationship between the elastic factor and the reservoir quality factor is obtained, that is, the seismic reservoir quality factor formula is obtained;

[0039] Finally, the reservoir quality factor formula of the seismic reservoir is used to combine the input P-wave velocity, S-wave velocity and density to obtain the reservoir quality factor data volume of the entire seismic work area, which is used to predict the distribution of high-quality reservoirs.

[0040] Specifically, the porous carbonate reservoir evaluation method of the present invention comprises the following steps:

[0041] Step 101, based on the well logging data, the P-wave velocity, S-wave velocity, density, porosity, oil and gas saturation, and dolomite content of the region are obtained; the seismic P-wave velocity, S-wave velocity, and seismic density of the well point are obtained through seismic data;

[0042] Step 102, the reservoir elasticity factor K is calculated according to the P-wave velocity, S-wave velocity, density and porosity obtained from the logging data by the following formula:

[0043]

[0044] Where ρ is the density, V P is the longitudinal wave velocity, V S is the shear wave velocity;

[0045] The reservoir quality factor F is calculated based on the P-wave velocity, S-wave velocity, density, porosity, oil and gas saturation, and dolomite content obtained from the logging data:

[0046]

[0047] Where ρ is the density, V P is the longitudinal wave velocity, V S is the shear wave velocity, θ is the porosity, ω is the oil and gas saturation, and γ is the dolomite content;

[0048] Step 103, using the reservoir elasticity factor K and quality factor F obtained in step 102, draw a cross-plot, such as Figure 2As shown, the reservoir elasticity factor K is the horizontal coordinate, the reservoir quality factor F is the vertical coordinate, and the comparison data points correspond to the porosity, oil and gas saturation, and dolomite content of the well logging data. The high-quality reservoir with high porosity, oil and gas saturation, and dolomite content is located in the upper left part of the cross-plot, the medium reservoir with medium values ​​of the three parameters is located in the middle of the cross-plot, and the poor reservoir is located in the lower right part of the cross-plot; according to the well logging reservoir classification standard, the value ranges of the three types of reservoirs can be determined in the cross-plot;

[0049] Step 104, verifying whether the seismic P-wave velocity, S-wave velocity, and density at the well point location in step 101 are consistent with the P-wave velocity, S-wave velocity, and density obtained from the logging data. If they are inconsistent, the seismic velocity at the well point location is corrected to the logging velocity using a commonly used dispersion correction method to ensure that they are consistent with the logging velocity and density at the corresponding well location;

[0050] Step 105, in the intersection diagram generated in step 103, refer to Figure 3 , the expression of the reservoir quality factor formula about velocity and density is obtained by using the power formula fitting method:

[0051]

[0052] Among them, α and β are the formula fitting coefficients, ρ is the density, V P is the longitudinal wave velocity, V S is the shear wave velocity.

[0053] Furthermore, in step 105, based on the data of this embodiment, the seismic reservoir quality factor is obtained by using a formula fitting, and the following formula can be obtained:

[0054] F=44.741K -2.14

[0055] Substituting the expression of elastic factor K into the above formula, the expression of reservoir quality factor with respect to velocity and density is obtained:

[0056]

[0057] Step 106: convert the seismic shear wave velocity V corrected in step 104 into P , shear wave velocity V S Substitute the density ρ into the reservoir quality factor formula in step 105 to calculate and obtain the reservoir quality factor data body of the entire seismic work area;

[0058] Step 107, using the reservoir elasticity factor K formula of step 102 and the seismic shear wave velocity V corrected in step 104 P , shear wave velocity V S and density ρ, and similarly obtain the reservoir elasticity factor data volume of the entire seismic area;

[0059] Step 108, using the value ranges of the three types of reservoirs determined in step 103, assign a value of 1 to the data bodies generated in steps 106 and 107 within the value range, and assign a value of 0 to other positions, and then multiply each data point of the two newly generated data bodies, and the portion of the data body with a value of 1 is the high-quality reservoir.

[0060] Through the above method, the P-wave velocity, S-wave velocity, and density data obtained from seismic inversion can be converted into two reservoir quality factors using a fitting relationship. In the intersection diagram composed of the two factors, the high-quality carbonate rock data points are concentrated in the upper left part of the intersection diagram, the poor reservoir data points are in the lower right part of the intersection diagram, and the medium reservoir is located between the two. Combined with the commonly used intersection analysis method, the distribution range of the high-quality reservoir can be predicted.

[0061] The present invention also includes a porous carbonate reservoir evaluation system, including a processing instruction module for executing each step in the above-mentioned porous carbonate reservoir evaluation method.

[0062] A porous carbonate reservoir evaluation device, comprising:

[0063] Memory: a computer-readable device storing the above-mentioned porous carbonate reservoir evaluation method computer program;

[0064] Processor: used to implement the porous carbonate reservoir evaluation method when executing the computer program.

[0065] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for evaluating a porous carbonate reservoir can be implemented.

[0066] The present invention can be implemented in any suitable form, including hardware, software, firmware or any combination thereof. The present invention can be implemented in selected parts according to the circumstances, such as computer software is executed in one or more data processors and digital signal processors. The elements and components of each embodiment of this paper can be implemented in any suitable manner physically, functionally, and logically. In fact, a function can be implemented in an independent unit, in a group of units, or as a part of other functional units. Therefore, the system and method can be implemented in an independent unit, or can be distributed between different units and processors physically and functionally.

[0067] Those skilled in the relevant art will recognize that there are many possible modifications and combinations of the embodiments of the present invention, although in slightly different forms, still using the same basic mechanisms and methods. For the purpose of explanation, the foregoing description refers to several specific embodiments. However, the above illustrative discussion is not intended to be exhaustive or limit the precise form invented herein. As shown in the foregoing, many modifications and variations are possible. The selected and described embodiments are used to explain the principles of the present invention and its practical application, so that those skilled in the art can best utilize the modifications and deformations of the present invention and various embodiments for specific applications.

Claims

1. A porous carbonate reservoir evaluation method, characterized in that: The following steps are involved: Step 101, based on the well logging data, the P-wave velocity, S-wave velocity, density, porosity, oil and gas saturation, and dolomite content of the region are obtained; the seismic P-wave velocity, S-wave velocity, and seismic density of the well point are obtained through seismic data; Step 102, the reservoir elasticity factor K is calculated according to the P-wave velocity, S-wave velocity, density and porosity obtained from the logging data by the following formula: Where ρ is the density, V P is the longitudinal wave velocity, V S is the shear wave velocity; The reservoir quality factor F is calculated based on the P-wave velocity, S-wave velocity, density, porosity, oil and gas saturation, and dolomite content obtained from the logging data: Where ρ is the density, V p is the longitudinal wave velocity, V S is the shear wave velocity, θ is the porosity, ω is the oil and gas saturation, and γ is the dolomite content; Step 103, using the reservoir elasticity factor K and the quality factor F obtained in step 102 to draw a cross-plot, and determine the value ranges of the three types of reservoirs: high-quality reservoirs, medium-quality reservoirs, and poor reservoirs in the cross-plot. Step 104, verifying whether the seismic P-wave velocity, seismic S-wave velocity, and seismic density at the well point location in step 101 are consistent with the P-wave velocity, S-wave velocity, and density obtained from the logging data. If they are inconsistent, the seismic velocity at the well point location is corrected to the logging velocity using a dispersion correction method to ensure that they are consistent with the logging velocity and density at the corresponding well location; Step 105, in the cross-plot generated in step 103, the expression of the reservoir quality factor formula with respect to velocity and density is obtained by using the power formula fitting method: Among them, α and β are the formula fitting coefficients, ρ is the density, V P is the longitudinal wave velocity, V S is the shear wave velocity. Step 106: convert the seismic shear wave velocity V corrected in step 104 into P , shear wave velocity V S Substitute the density ρ into the reservoir quality factor formula in step 105 to calculate and obtain the reservoir quality factor data body of the entire seismic work area; Step 107, using the reservoir elasticity factor K formula of step 102 and the seismic shear wave velocity V corrected in step 104 P , shear wave velocity V S and density ρ, and similarly obtain the reservoir elasticity factor data volume of the entire seismic area; Step 108, using the value ranges of the three types of reservoirs determined in step 103, assign a value of 1 to the data bodies generated in steps 106 and 107 within the value range, and assign a value of 0 to other positions, and then multiply each data point of the two newly generated data bodies, and the portion of the data body with a value of 1 is the high-quality reservoir.

2. A porous carbonate reservoir evaluation method according to claim 1, characterized in that: In the cross-plot generated in step 103, the reservoir elasticity factor K is the horizontal coordinate, the reservoir quality factor F is the vertical coordinate, and the reference data points correspond to the porosity, oil and gas saturation, and dolomite content of the logging data. The reservoirs with high values ​​of these three parameters are high-quality reservoirs located in the upper left part of the cross-plot, the reservoirs with medium values ​​of these three parameters are medium reservoirs located in the middle of the cross-plot, and the poor reservoirs are located in the lower right part of the cross-plot; according to the logging reservoir classification standard, the value ranges of the three types of reservoirs are determined in the cross-plot; according to the logging reservoir classification standard, the value ranges of the three types of reservoirs are determined in the cross-plot.

3. A porous carbonate reservoir evaluation system, comprising a processing instruction module for executing each step in a porous carbonate reservoir evaluation method as claimed in claim 1 or 2.

4. A porous carbonate reservoir evaluation device, comprising: Memory: a computer-readable device storing a computer program of a porous carbonate reservoir evaluation method according to claim 1 or 2; Processor: used to implement the porous carbonate reservoir evaluation method described in claim 1 or 2 when executing the computer program.

5. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, can implement the porous carbonate reservoir evaluation method according to claim 1 or 2.

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