Method for modeling dynamic permeability of a porous carbonate reservoir, related methods and devices

Abstract

The present application relates to a kind of pore type carbonate reservoir dynamic permeability modeling method, related method and device.The method includes: obtaining the rock type model established according to the single well lithofacies division of pore type carbonate reservoir, under the constraint of the rock type model, dissolution intensity model is established;According to the single well effective permeability and single well static permeability of pore type carbonate reservoir, the single well permeability correction multiple is calculated;According to the single well permeability correction multiple, under the constraint of the dissolution intensity model, using random simulation method, permeability multiple model is established;According to the permeability multiple model, the static permeability model established according to the single well static permeability is corrected, and dynamic permeability model is obtained.Based on the method, pore type carbonate reservoir numerical simulation history matching can improve the efficiency of history matching.

Description

Technical Field

[0001] This invention relates to a method, related methods, and apparatus for modeling dynamic permeability in porous carbonate reservoirs. Background Technology

[0002] In reservoir numerical simulation, permeability modeling is one of the most fundamental and core tasks. The spatial distribution of permeability determines the flow patterns of fluids, pressure propagation, and the distribution of remaining oil. The accuracy of permeability modeling directly affects the reliability of reservoir numerical simulation results and the decision-making process for development strategies. Carbonate reservoirs have complex geological genesis and strong reservoir heterogeneity, making history fitting in reservoir numerical simulations difficult and time-consuming. Among these challenges, the calibration of the permeability model is one of the key factors affecting the efficiency of history fitting.

[0003] Currently, the data used for permeability modeling mainly comes from two sources: (1) static permeability obtained from core experiments and well logging interpretation; and (2) effective permeability obtained from production logging and well test data interpretation. Existing permeability modeling methods mainly rely on the static permeability obtained from core experiments and well logging interpretation to establish a static permeability model. This model is then imported into the reservoir numerical simulation dynamic model for reservoir numerical simulation history fitting. During history fitting, the correction of the static permeability model is performed directly in the reservoir numerical simulation dynamic model using the effective permeability obtained from production logging and well test data interpretation. Summary of the Invention

[0004] To improve the efficiency of historical fitting in numerical simulation of porous carbonate reservoirs, this invention provides a method, related methods, and apparatus for modeling dynamic permeability of porous carbonate reservoirs.

[0005] In a first aspect, the present invention provides a method for modeling the dynamic permeability of porous carbonate reservoirs, the method comprising:

[0006] Obtain a rock type model based on the lithofacies classification of a single well in a porous carbonate reservoir, and establish a dissolution intensity model under the constraints of the rock type model.

[0007] Calculate the permeability correction factor for a single well based on the effective permeability and static permeability of a single well in a porous carbonate reservoir.

[0008] Based on the single-well permeability correction factor, and under the constraints of the dissolution intensity model, a permeability factor model is established using a stochastic simulation method.

[0009] Based on the permeability multiple model, the static permeability model established based on the static permeability of a single well is corrected to obtain the dynamic permeability model.

[0010] In one or more optional embodiments of this application, the step of obtaining a rock type model established based on the lithofacies classification of a single well in a porous carbonate reservoir, and establishing a dissolution intensity model under the constraints of the rock type model, includes:

[0011] Obtain a rock type model established by stochastic simulation based on the lithofacies classification of single wells in porous carbonate reservoirs, and import the rock type model into the dynamic model of numerical simulation of porous carbonate reservoirs.

[0012] The dissolution intensity curves of the reservoir at different depths in a single well were obtained based on the classification criteria for dissolution intensity of porous carbonate reservoirs.

[0013] The dissolution intensity of the reservoir is assigned a value, and the dissolution intensity of a single well at different depths of the reservoir is assigned a value accordingly, so as to obtain the dissolution intensity curve of a single well at different depths of the reservoir after assignment.

[0014] The dissolution intensity curves of the single wells in the reservoir at different depths are imported into the dynamic model of the reservoir numerical simulation. Based on the rock type model, the dissolution intensity of each rock type in the single well of the reservoir is divided by taking the mode, so as to obtain the single well dissolution intensity curve under the constraint of rock type.

[0015] Based on the single-well dissolution intensity curve under the constraints of the rock type, the dissolution intensity model is established using a stochastic simulation method.

[0016] In one or more optional embodiments of this application, the step of calculating the single-well permeability correction factor based on the single-well effective permeability and single-well static permeability of a porous carbonate reservoir includes:

[0017] To obtain the effective permeability of a single well at any depth in a porous carbonate reservoir, obtained through production logging and well testing.

[0018] The average static permeability of a single well at any depth is obtained by arithmetically averaging the static permeability obtained from core experiments and well logging interpretation at any depth in the porous carbonate reservoir.

[0019] The ratio of the effective permeability to the average static permeability of the corresponding depth segment of the single well is calculated to obtain the permeability correction factor of the corresponding depth segment of the single well.

[0020] In one or more optional embodiments of this application, the step of establishing a permeability factor model using a stochastic simulation method based on the single-well permeability correction factor and under the constraint of the dissolution intensity model includes:

[0021] Based on the spatial distribution of the dissolution intensity model, the spatial correlation between the permeability correction factors at different depths of a single well is obtained through variogram analysis.

[0022] Based on the spatial correlation between the permeability correction factors at different depths of the single well, a permeability factor model is established using a stochastic simulation method.

[0023] In one or more optional embodiments of this application, the step of correcting the static permeability model established based on the static permeability of a single well to obtain a dynamic permeability model according to the permeability multiple model includes:

[0024] Based on the aforementioned permeability factor model, the static permeability model is corrected using the following formula to obtain the dynamic permeability model:

[0025] P D＝ M×P S ,

[0026] In the formula, P D Let M represent the dynamic permeability model, and P represent the permeability multiple model. S This represents the static permeability model.

[0027] Secondly, this invention provides a method for optimizing a dynamic permeability model of porous carbonate reservoirs, the method comprising:

[0028] The dynamic permeability model is imported into the reservoir numerical simulation dynamic model, and the reservoir numerical simulation dynamic model is historically fitted to obtain the historical fitting results of the reservoir numerical simulation dynamic model.

[0029] Obtain the corrected permeability factor model determined based on the historical fitting results of the reservoir numerical simulation dynamic model;

[0030] Based on the modified permeability factor model, a new dynamic permeability model is obtained. The process of historical fitting of the reservoir numerical simulation dynamic model is then repeated until the historical fitting result of the reservoir numerical simulation dynamic model meets the requirements, thus obtaining the optimized dynamic permeability model.

[0031] In one or more optional embodiments of this application, the step of re-executing the process of historical fitting of the reservoir numerical simulation dynamic model according to the modified permeability factor model until the historical fitting result of the reservoir numerical simulation dynamic model meets the requirements, thereby obtaining an optimized dynamic permeability model, includes:

[0032] Based on the modified permeability multiple model, the static permeability model established based on the static permeability of a single well is corrected to obtain the modified dynamic permeability model;

[0033] The modified dynamic permeability model is imported into the reservoir numerical simulation dynamic model, and the reservoir numerical simulation dynamic model is re-historically fitted to obtain a new reservoir numerical simulation dynamic model history fitting result.

[0034] Obtain the new corrected permeability factor model determined based on the historical fitting results of the new reservoir numerical simulation dynamic model, and continue to perform the above process of historical fitting of the reservoir numerical simulation dynamic model based on the new corrected permeability factor model until the historical fitting results of the reservoir numerical simulation dynamic model meet the requirements, thus obtaining the optimized dynamic permeability model.

[0035] In one or more optional embodiments of this application, before importing the dynamic permeability model into the reservoir numerical simulation dynamic model, performing history fitting on the reservoir numerical simulation dynamic model, and obtaining the history fitting result of the reservoir numerical simulation dynamic model, the method further includes:

[0036] The dynamic permeability model is coarsened.

[0037] Thirdly, the present invention provides a dynamic permeability modeling device for porous carbonate reservoirs, comprising:

[0038] The dissolution intensity module is used to obtain a rock type model based on the lithofacies classification of a single well in a porous carbonate reservoir, and to establish a dissolution intensity model under the constraints of the rock type model.

[0039] The correction factor module is used to calculate the permeability correction factor of a single well based on the effective permeability and static permeability of a single well in a porous carbonate reservoir.

[0040] The permeability correction factor module is used to establish a permeability correction factor model using a stochastic simulation method under the constraints of the dissolution intensity model, based on the single well permeability correction factor.

[0041] The dynamic model module is used to correct the static permeability model established based on the static permeability of the single well according to the permeability multiple model, so as to obtain the dynamic permeability model.

[0042] Fourthly, the present invention provides a device for optimizing the dynamic permeability model of porous carbonate reservoirs, comprising:

[0043] The dissolution intensity module is used to obtain a rock type model based on the lithofacies classification of a single well in a porous carbonate reservoir, and to establish a dissolution intensity model under the constraints of the rock type model.

[0044] The correction factor module is used to calculate the permeability correction factor of a single well based on the effective permeability and static permeability of a single well in a porous carbonate reservoir.

[0045] The permeability correction factor module is used to establish a permeability correction factor model using a stochastic simulation method under the constraints of the dissolution intensity model, based on the single well permeability correction factor.

[0046] The dynamic model module is used to correct the static permeability model established based on the static permeability of the single well according to the permeability multiple model, so as to obtain the dynamic permeability model.

[0047] The model import module is used to import the dynamic permeability model into the reservoir numerical simulation dynamic model, perform historical fitting on the reservoir numerical simulation dynamic model, and obtain the historical fitting results of the reservoir numerical simulation dynamic model.

[0048] The model correction module is used to obtain the corrected permeability factor model determined based on the historical fitting results of the reservoir numerical simulation dynamic model.

[0049] The model iteration module is used to obtain a new dynamic permeability model based on the modified permeability factor model, and to re-execute the process of historical fitting of the reservoir numerical simulation dynamic model until the historical fitting result of the reservoir numerical simulation dynamic model meets the requirements, thereby obtaining the optimized dynamic permeability model.

[0050] Fifthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program / instruction thereon, which, when executed by a processor, implements the dynamic permeability modeling method for porous carbonate reservoirs as described in the first aspect above, and / or the dynamic permeability model optimization method for porous carbonate reservoirs as described in the second aspect.

[0051] In a sixth aspect, embodiments of the present invention provide a computer program product, including a computer program / instruction that, when executed by a processor, implements the dynamic permeability modeling method for porous carbonate reservoirs as described in the first aspect above, and / or the dynamic permeability model optimization method for porous carbonate reservoirs as described in the second aspect.

[0052] In a seventh aspect, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the dynamic permeability modeling method for porous carbonate reservoirs as described in the first aspect above, and / or the dynamic permeability model optimization method for porous carbonate reservoirs as described in the second aspect.

[0053] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0054] This invention provides a method for dynamic permeability modeling of porous carbonate reservoirs. It involves acquiring a rock type model based on the lithofacies classification of single wells in the porous carbonate reservoir. Under the constraints of this rock type model, a dissolution intensity model is established. Based on the effective permeability and static permeability of each single well in the reservoir, a single-well permeability correction factor is calculated. Under the constraints of this dissolution intensity model, a permeability factor model is established using a stochastic simulation method. The static permeability model is then corrected using the permeability factor model to obtain the dynamic permeability model. Because this invention uses a permeability factor model to calculate the dynamic permeability model, when performing history fitting of porous carbonate reservoirs based on the dynamic permeability model, only the permeability factor model needs to be corrected to quickly and systematically update the dynamic permeability model. Furthermore, since the permeability factor model is established under the constraints of the dissolution intensity model, geological genesis is effectively applied to the establishment of the dynamic permeability model, enabling a more accurate characterization of reservoir heterogeneity. The dynamic permeability modeling method for porous carbonate reservoirs provided by this invention solves the problems of long time consumption and low accuracy in directly correcting static permeability in the dynamic model of reservoir numerical simulation, and improves the efficiency of history fitting. Attached Figure Description

[0055] The accompanying drawings are provided to further illustrate the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application, and do not constitute a limitation on the technical solutions of this application. In the accompanying drawings:

[0056] Figure 1 A schematic diagram of the dynamic permeability modeling method for thick porous carbonate reservoirs provided in this application embodiment;

[0057] Figure 2 A typical single-well rock type and dissolution intensity histogram provided for embodiments of this application;

[0058] Figure 3 The corrosion intensity model provided in the embodiments of this application;

[0059] Figure 4 Typical single-well permeability correction factor and permeability curves before and after correction are provided for embodiments of this application;

[0060] Figure 5 The penetration rate multiple model provided for embodiments of this application;

[0061] Figure 6 A comparison chart of dynamic permeability changes obtained using existing permeability modeling methods and the dynamic permeability modeling method of the present invention is provided for embodiments of this application.

[0062] Figure 7 The roughened dynamic permeability model provided for embodiments of this application;

[0063] Figure 8 A schematic diagram of the process for optimizing the dynamic permeability model of thick porous carbonate reservoirs provided in this application embodiment;

[0064] Figure 9 A comparison chart of the historical fitting effect of reservoir daily oil production before and after correction of the permeability factor model provided in the embodiments of this application;

[0065] Figure 10 A comparison chart of historical fitting effects of daily oil production of typical single wells before and after correction of the permeability factor model provided in the embodiments of this application;

[0066] Figure 11 A schematic diagram of the dynamic permeability modeling and model optimization process for thick porous carbonate reservoirs provided in this application embodiment;

[0067] Figure 12 A schematic diagram of the dynamic permeability modeling device for thick porous carbonate reservoirs provided in this application embodiment;

[0068] Figure 13 A schematic diagram of the process for optimizing the dynamic permeability model of a thick porous carbonate reservoir provided in this application embodiment. Detailed Implementation

[0069] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. However, this description is exemplary and not restrictive, and those skilled in the art can implement this application in various forms without being limited to the embodiments set forth herein. These embodiments are provided to enable a more thorough understanding of this disclosure and to fully convey the scope of this disclosure to those skilled in the art.

[0070] The embodiments, features, and elements disclosed in this application can also be combined with any conventional features or elements to form a unique inventive scheme as defined by the claims. Any feature or element of any embodiment can also be combined with features or elements from other inventive schemes to form another unique inventive scheme as defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in this application can be implemented individually or in any suitable combination.

[0071] The inventors discovered that when performing history fitting on a dynamic model of a porous carbonate reservoir numerical simulation, the correction of the static permeability model mainly involves directly modifying the effective permeability within the dynamic model. However, directly modifying the static permeability model within the dynamic model cannot quickly and systematically update the static permeability model, nor can it effectively incorporate reservoir geological genesis, such as rock type models, and cannot accurately characterize reservoir heterogeneity. This results in time-consuming and low-accurate history fitting, impacting its efficiency. Based on this, the inventors, through further research, have provided a method, related methods, and apparatus for dynamic permeability modeling of porous carbonate reservoirs.

[0072] Example 1

[0073] Embodiment 1 of this invention provides a method for modeling the dynamic permeability of porous carbonate reservoirs, referring to... Figure 1 As shown, the method may include the following steps S101-S104:

[0074] S101: Obtain the rock type model established based on the lithofacies classification of the single well in the porous carbonate reservoir, and establish the dissolution intensity model under the constraints of the rock type model.

[0075] S102: Calculate the permeability correction factor for a single well based on the effective permeability and static permeability of a single well in a porous carbonate reservoir.

[0076] S103: Based on the single-well permeability correction factor, and under the constraints of the dissolution intensity model, a permeability factor model is established using a stochastic simulation method.

[0077] S104: Based on the permeability multiple model, the static permeability model established based on the static permeability of the single well is corrected to obtain the dynamic permeability model.

[0078] This invention provides a method for dynamic permeability modeling of porous carbonate reservoirs. It obtains a rock type model based on the lithofacies classification of single wells in the porous carbonate reservoir. Under the constraints of this rock type model, a dissolution intensity model is established. Based on the effective permeability and static permeability of a single well in the reservoir, a single-well permeability correction factor is calculated. Under the constraints of this dissolution intensity model, a permeability factor model is established using a stochastic simulation method. The static permeability model is then corrected using the permeability factor model to obtain the dynamic permeability model. Because this invention uses a permeability factor model to calculate the dynamic permeability model, when performing history fitting of porous carbonate reservoirs based on the dynamic permeability model, only the permeability factor model needs to be corrected to quickly and systematically update the dynamic permeability model. Furthermore, since the permeability factor model is established under the constraints of the dissolution intensity model, geological genesis is well applied to the establishment of the dynamic permeability model, enabling a more accurate characterization of reservoir heterogeneity. The dynamic permeability modeling method for porous carbonate reservoirs provided by this invention solves the problems of long time consumption and low accuracy in directly correcting static permeability in the dynamic model of reservoir numerical simulation, and improves the efficiency of history fitting.

[0079] In this embodiment, the process of establishing the dissolution intensity model in step S101 includes the following steps:

[0080] Obtain a rock type model established by stochastic simulation based on the lithofacies classification of single wells in porous carbonate reservoirs, and import the rock type model into the dynamic model of numerical simulation of porous carbonate reservoirs.

[0081] The dissolution intensity curves of the reservoir at different depths in a single well were obtained based on the classification criteria for dissolution intensity of porous carbonate reservoirs.

[0082] The dissolution intensity of the reservoir is assigned a value, and the dissolution intensity of a single well at different depths of the reservoir is assigned a value accordingly, so as to obtain the dissolution intensity curve of a single well at different depths of the reservoir after assignment.

[0083] The dissolution intensity curves of the single wells in the reservoir at different depths are imported into the dynamic model of the reservoir numerical simulation. Based on the rock type model, the dissolution intensity of each rock type in the single well of the reservoir is divided by taking the mode, so as to obtain the single well dissolution intensity curve under the constraint of rock type.

[0084] Based on the single-well dissolution intensity curve under the constraints of the rock type, the dissolution intensity model is established using a stochastic simulation method.

[0085] Dissolution is a key controlling factor for the permeability of carbonate reservoirs. In this embodiment, a dissolution intensity model was established by classifying dissolution intensity. The dissolution intensity model can accurately characterize the differences in reservoir quality, distinguish between strong dissolution zones and high-permeability zones and weak dissolution zones and low-permeability zones, clarify the "sweet spot" of the reservoir, and also reveal the fluid flow path. In areas with high dissolution intensity, dominant seepage channels may be formed, affecting oil and gas migration and development dynamics.

[0086] In this embodiment, the dissolution intensity model can be established using a stochastic simulation method, such as the sequential Gaussian method. Of course, other methods in the prior art can also be used to establish the dissolution intensity model; no specific limitation is made here. The calculation formula for the sequential Gaussian method is as follows:

[0087]

[0088] In the formula, Y * (u) represents the dissolution intensity value of other grids, y(u) a ) represents the dissolution intensity value of the known grid at the well point, a and i represent counting flags, and n represents the number of known grid points. λ a For interpolation weights, μ represents the Lagrange multiplier, and C(v) x,y -v x',y' ) represents the covariance of dissolution intensity between two different grid locations (x, y) and (x′, y′).

[0089] In one specific embodiment, taking a thick-layered porous carbonate reservoir in a certain region as an example, based on the lithofacies classification results of a single well in the reservoir, a rock type model is established using a stochastic simulation method, and this rock type model is imported into the reservoir numerical simulation dynamic model. Based on the core experiment and thin section analysis results of the reservoir, four different intensities of dissolution are classified: strong, moderately strong, moderate, and weak, assigned values ​​of 4, 3, 2, and 1 respectively. Correspondingly, dissolution intensities at different depths in a single well are assigned values, resulting in dissolution intensity curves at different depths in the reservoir. These dissolution intensity curves are then imported into the reservoir numerical simulation dynamic model. Both the rock type curve and the dissolution intensity curve are loaded simultaneously in each well. Each rock type may correspond to multiple dissolution intensities. The dissolution intensity corresponding to each rock type is obtained by taking the mode of these multiple dissolution intensities. The typical dissolution intensity curve of a single well under rock type constraints obtained after taking the mode is shown below. Figure 2As shown, the first column from right to left represents the rock type curves, where RT1 and RT3 represent different rock types. The second column from right to left represents the dissolution intensity curves, where level-2 and level-4 represent dissolution intensities 2 and 4, respectively. For example, before taking the mode, rock type RT3 corresponds to two dissolution intensities, level-2 and level-3. The dissolution intensity level-2, which has the highest proportion, is taken as the dissolution intensity for that rock type. In the special case where the proportions of dissolution intensities are the same, the value is assigned based on the dissolution intensity with the highest proportion among rock types in the same stratigraphic layer of adjacent wells. Thus, the single-well dissolution intensity curve under the constraint of the reservoir rock type can be obtained. A reservoir dissolution intensity model is established using the sequential Gaussian method for the single-well dissolution intensity curve under the constraint of the reservoir rock type. The resulting reservoir dissolution intensity model is shown below. Figure 3 As shown.

[0090] In this embodiment, the process of calculating the single-well permeability correction factor in step S102 includes the following steps:

[0091] To obtain the effective permeability of a single well at any depth in a porous carbonate reservoir, obtained through production logging and well testing.

[0092] The average static permeability of a single well at any depth is obtained by arithmetically averaging the static permeability obtained from core experiments and well logging interpretation at any depth in the porous carbonate reservoir.

[0093] The ratio of the effective permeability to the average static permeability of the corresponding depth segment of the single well is calculated to obtain the permeability correction factor of the corresponding depth segment of the single well.

[0094] In one specific embodiment, the permeability correction factor is calculated for 30 test wells in a thick porous carbonate reservoir in a certain region.

[0095] First, obtain the effective permeability from the measurement depth data based on production logging and well testing data.

[0096] Next, based on the core test and well logging interpretation results, the average static permeability of the corresponding measurement depth section was calculated using the following formula:

[0097]

[0098] In the formula, P A P represents the average static permeability at the measurement depth. i represents the static permeability of the i-th sampling point in the static permeability curve of the measurement depth segment, and N represents the number of sampling points in the static permeability curve of the measurement depth segment.

[0099] Finally, based on the effective permeability and average static permeability of the measurement depth range, the permeability correction factor for the measurement depth range is calculated using the following formula:

[0100]

[0101] In the formula, M represents the permeability correction factor for the measurement depth range, and P... E P represents the effective permeability at the measurement depth. A This represents the average static permeability at the measured depth.

[0102] The permeability correction factors for these 30 wells were calculated using the method described above, and the specific data are shown in Table 1 below. The permeability correction factor curve for a typical single well in Table 1 is shown below. Figure 4 As shown, Figure 4 The first column from right to left is the corrected permeability, i.e., the effective permeability; the second column from right to left is the permeability before correction, i.e., the static permeability; and the third column from right to left is the permeability correction factor.

[0103] Table 1. Statistics of Permeability Correction Factors

[0104] hashtag Top depth number Bottom depth number Permeability correction factor P1 3 16 0.1 P2 3 5 0.5 P3 3 15 0.5 P4 3 15 0.4 P5 3 15 0.4 P6 3 10 0.2 P7 3 12 0.2 P8 4 10 0.5 P9 4 8 0.5 P10 3 8 0.5 P11 3 13 0.2 P12 3 8 0.2 P13 3 3 0.4 P14 3 10 0.2 P15 3 3 0.2 P16 5 13 0.2 P17 3 12 0.2 P18 4 9 0.2 P19 4 12 0.2 P20 4 9 0.2 P21 4 9 0.2 P22 3 7 0.3 P23 3 8 0.3 P24 3 12 0.5 P25 3 14 0.3 P26 3 7 0.2 P27 3 8 0.4 P28 3 9 0.2 P29 3 12 0.2 P30 3 5 0.8

[0105] In this embodiment, the process of establishing the permeability multiple model in step S103 includes the following steps:

[0106] Based on the spatial distribution of the dissolution intensity model, the spatial correlation between the permeability correction factors at different depths of a single well is obtained through variogram analysis.

[0107] Based on the spatial correlation between the permeability correction factors at different depths of the single well, a permeability factor model is established using a stochastic simulation method.

[0108] In this embodiment, by establishing a permeability factor model, the dynamic permeability model can be rapidly and systematically updated when it is being modified, thus solving the problem of time-consuming history fitting in reservoir numerical simulation. When establishing the permeability factor model, dual constraints of rock type and dissolution intensity are adopted, giving the permeability factor model geological significance and improving the accuracy of history fitting in reservoir numerical simulation.

[0109] In this embodiment, the aforementioned penetration rate multiple model can be established using a stochastic simulation method, such as the ordered Gaussian method. Of course, other methods in the prior art can also be used to establish the penetration rate multiple model; no specific limitations are imposed here.

[0110] In one specific embodiment, the permeability correction factors of 30 wells in a thick-layered porous carbonate reservoir in a certain region are imported into the reservoir numerical simulation dynamic model. Each of the existing permeability correction factor grids for the 30 wells has a corresponding dissolution intensity. Based on the analysis of the corresponding dissolution intensity variation function, the spatial correlation between permeability correction factors in the same grid is determined. Then, the ordered inertial Gaussian method is used to establish a permeability factor model. The established permeability factor model is as follows: Figure 5 As shown.

[0111] In this embodiment, the process of calculating the dynamic permeability model in step S104 includes the following steps:

[0112] Based on the aforementioned permeability factor model, the static permeability model is corrected using the following formula to obtain the dynamic permeability model:

[0113] P D＝ M×P S ,

[0114] In the formula, P D Let M represent the dynamic permeability model, and P represent the permeability multiple model. S This represents the static permeability model.

[0115] The dynamic permeability model in this embodiment is established under the constraints of the dissolution intensity model, which well incorporates geological genesis and can more accurately characterize the heterogeneity of the reservoir. For example... Figure 6 As shown, where, Figure 6 a. A graph showing the change in dynamic permeability obtained using the dynamic permeability modeling method provided in this invention. Figure 6 b. A graph showing the dynamic permeability changes obtained using existing permeability modeling methods. Figure 6 a. It can reflect the slope of the dynamic permeability range from poor to good, indicating that the dynamic permeability model obtained through the embodiments of this invention can better distinguish rock properties and reflect the heterogeneity of the reservoir. Figure 6 b. The higher the static permeability, the higher the dynamic permeability. This does not reflect the slope of the dynamic permeability range from poor to good, and therefore cannot reflect the heterogeneity of the reservoir.

[0116] In this embodiment, the static permeability model is established using a stochastic simulation method based on the static permeability of a single well. For example, the sequential Gaussian method can be used. Of course, other methods in the prior art can also be used to establish the static permeability model, and no specific limitation is made here.

[0117] Example 2

[0118] Embodiment 2 of this invention provides a method for optimizing the dynamic permeability model of porous carbonate reservoirs, referring to... Figure 8 As shown, based on the dynamic permeability model obtained in Example 1, the method may further include the following steps S105-S107:

[0119] S105: Import the dynamic permeability model into the reservoir numerical simulation dynamic model, perform historical fitting on the reservoir numerical simulation dynamic model, and obtain the historical fitting result of the reservoir numerical simulation dynamic model.

[0120] S106: Obtain the corrected permeability factor model determined based on the historical fitting results of the reservoir numerical simulation dynamic model;

[0121] S107: Based on the modified permeability multiple model, a new dynamic permeability model is obtained. The process of historical fitting of the reservoir numerical simulation dynamic model is re-executed until the historical fitting result of the reservoir numerical simulation dynamic model meets the requirements, and the optimized dynamic permeability model is obtained.

[0122] This invention provides a method for optimizing the dynamic permeability model of porous carbonate reservoirs. It involves obtaining a rock type model based on the lithofacies classification of single wells in the porous carbonate reservoir. Under the constraints of this rock type model, a dissolution intensity model is established. Based on the effective permeability and static permeability of each single well in the reservoir, a single-well permeability correction factor is calculated. Under the constraints of this dissolution intensity model, a permeability factor model is established using a stochastic simulation method. The static permeability model is then corrected using the permeability factor model to obtain a dynamic permeability model. Based on this dynamic permeability model, a history fit is performed on the reservoir's numerical simulation dynamic model. The corrected dynamic permeability model is obtained based on the history fit results, and history fit is performed again. Through iterative execution of the correction-fit process, the history fit results are made to meet the requirements, resulting in an optimized dynamic permeability model. The dynamic permeability model optimization method for porous carbonate reservoirs provided by this invention can correct the dynamic permeability model based on the permeability factor model, enabling rapid optimization of the dynamic permeability model and thus improving the efficiency of history fitting in reservoir numerical simulations.

[0123] In this embodiment, after obtaining the dynamic permeability model based on step S104 and before executing step S105, the method further includes:

[0124] The dynamic permeability model is coarsened.

[0125] In this embodiment, the model complexity is reasonably simplified by coarsening the model, which can effectively solve the contradiction between high-precision geological models and limited computing resources.

[0126] In one specific embodiment, the dynamic permeability model of a porous carbonate reservoir in a certain region is coarsened to obtain a coarsened dynamic permeability model, as follows: Figure 7 As shown.

[0127] In this embodiment, the corrected permeability multiple model in step S106 is obtained in the following way:

[0128] Analyze the reasons for the differences between the calculation results of the reservoir numerical simulation dynamic model and historical observation data;

[0129] Based on the reasons for the discrepancies, determine whether the overall or local contradiction in the penetration rate correction factor is valid.

[0130] Following the hierarchical adjustment of indicators from reservoir to region to well group to single well, the overall or local contradictions of the permeability correction factor are adjusted to make the understanding of dissolution intensity, production dynamics and permeability correction factor consistent, thus obtaining the corrected permeability factor model.

[0131] In this embodiment, when correcting the permeability multiple model, the dissolution intensity model, the permeability multiple model, and the dynamic permeability model are correlated. When obtaining the corrected permeability multiple model, these three models can be flexibly adjusted according to the accuracy of the historical fitting results of single-well production indicators, thereby improving the efficiency of historical fitting of the reservoir numerical simulation dynamic model. Furthermore, these models can be verified and improved through the dynamic changes in single-well production. For specific implementation methods, those skilled in the art can refer to the detailed description in the prior art, which will not be repeated here.

[0132] In this embodiment, the optimized dynamic penetration rate model in step S107 is obtained in the following way:

[0133] A new dynamic permeability model is calculated based on the revised permeability factor model. The reservoir numerical simulation dynamic model is then subjected to history fitting based on the new dynamic permeability model to obtain the history fitting results of the new reservoir numerical simulation dynamic model.

[0134] Based on the historical fitting results of the new reservoir numerical simulation dynamic model, identify the reasons for the differences between the calculation results of the new reservoir numerical simulation dynamic model and the historical observation data;

[0135] Based on the reasons for the discrepancies, the modified penetration rate model was further revised.

[0136] The reservoir numerical simulation dynamic model was fitted to history again based on the revised permeability factor model.

[0137] Repeat the above correction-fitting process until the historical fitting results of the reservoir numerical simulation dynamic model meet the requirements, and obtain the optimized dynamic permeability model.

[0138] In this embodiment of the invention, the optimized dynamic permeability model corrects the static permeability model, which is more in line with the understanding of reservoir production dynamics, making the prediction results of reservoir production dynamics more reliable, reducing the uncertainty of dynamic prediction in reservoir development and production, and helping to rationally formulate subsequent development strategies such as capacity building and measure selection, thereby reducing development risks and costs.

[0139] In one specific embodiment, a historical fitting was performed on the numerical simulation dynamic model of a thick-layered porous carbonate reservoir in a certain region. The calculation results of the numerical simulation dynamic model of the carbonate reservoir were compared with historical observation data. The permeability ratio model was adjusted in a targeted manner according to different situations. If the calculation results of reservoir indicators had large errors, the entire permeability ratio model needed to be corrected. If the calculation results of indicators in some areas had large errors, only the permeability ratio of those areas needed to be corrected. If the calculation results of indicators in some well groups had errors, only the permeability ratio of those well groups needed to be corrected. If the fitting error of a single well was large, the permeability ratio of that well and the surrounding wells needed to be corrected in conjunction with the dynamic analysis of the well group. Through repeated corrections, the fitting error of reservoir indicators was reduced to less than 5%. The fitting results of the daily oil production of the reservoir are shown in [the figure]. Figure 9 Wells with a single-well index fitting error of less than 5% accounted for 66%, and wells with a single-well index fitting error between 5% and 10% accounted for 23%. Typical single-well fitting results are shown below. Figure 10 , Figure 10 a. The fitted result of daily oil production per well before correction to the permeability factor model. Figure 10 b. The fitting results of daily oil production per well after correction to the permeability factor model. It should be noted that permeability is only one key parameter affecting the historical fitting results. When correcting the permeability factor model, porosity, saturation, pressure, oil-water connectivity, and production dynamics are also considered for simultaneous correction. For specific implementation methods, those skilled in the art can refer to the detailed descriptions in existing technologies, which will not be repeated here. The historical fitting evaluation criteria for numerical simulation of carbonate reservoirs are divided into three categories according to error magnitude: error less than 5% is good, error between 5% and 10% is average, and error greater than 10% is poor. The historical fitting results of the reservoir numerical simulation dynamic model meet the requirements when the percentage of all single wells achieving a good level exceeds 60%, and the percentage achieving an average level exceeds 85%.

[0140] In one specific embodiment, in order to provide a clearer and more complete description of the dynamic permeability modeling and optimization method for porous carbonate reservoirs provided by the present invention, so that those skilled in the art can implement the technical solution of the present invention, the following refers to... Figure 11The dynamic permeability modeling and optimization process of a thick porous carbonate reservoir in a certain region is described in detail below:

[0141] First, based on the lithofacies classification of a single well in a thick porous carbonate reservoir in a certain region, a dissolution intensity model constrained by the rock type model of the reservoir is established according to step S101.

[0142] Next, based on the static permeability and effective permeability of a single well in the reservoir, the permeability correction factor for a single well in the reservoir is calculated according to step S102. Then, based on the permeability correction factor for a single well in the reservoir, under the constraint of the dissolution intensity model, a permeability factor model is established using the sequential inertia Gaussian method according to step S103.

[0143] Next, based on the static permeability of a single well in the reservoir, a static permeability model for the reservoir is established using the sequential inertial Gaussian method.

[0144] Next, based on the permeability multiple model and static permeability model of the reservoir, the dynamic permeability model of the reservoir is calculated according to step S104.

[0145] Next, based on this dynamic permeability model, the reservoir numerical simulation history is fitted according to step S105, i.e. Figure 11 The numerical simulation is used to obtain the historical fitting results of the reservoir, and then the corrected permeability factor model is obtained according to step S106.

[0146] Finally, following step S107, the correction-fitting process is iteratively executed to ensure that the historical fitting results of the numerical simulation of the reservoir meet the requirements, thereby obtaining the optimized dynamic permeability model of the reservoir.

[0147] The results of applying the method of this invention to a thick porous carbonate reservoir in a certain region demonstrate that the method is simple and easy to use, has a fast model update speed and high accuracy, and has high practical application value. It can be widely applied to porous carbonate reservoirs through integrated geological-reservoir projects, and has good economic and social benefits.

[0148] Example 3

[0149] Based on the same inventive concept, this invention also provides a dynamic permeability modeling device for porous carbonate reservoirs, referring to... Figure 12 As shown, it includes:

[0150] The dissolution intensity module 101 is used to obtain a rock type model established based on the lithofacies classification of a single well in a porous carbonate reservoir, and to establish a dissolution intensity model under the constraints of the rock type model.

[0151] The correction factor module 102 is used to calculate the single-well permeability correction factor based on the single-well effective permeability and single-well static permeability of porous carbonate reservoirs.

[0152] The permeability correction factor module 103 is used to establish a permeability correction factor model using a stochastic simulation method under the constraints of the dissolution intensity model, based on the single well permeability correction factor.

[0153] The dynamic model module 104 is used to correct the static permeability model established based on the static permeability of the single well according to the permeability multiple model, so as to obtain the dynamic permeability model.

[0154] Example 4

[0155] Based on the same inventive concept, this invention also provides a device for optimizing the dynamic permeability model of porous carbonate reservoirs, referring to... Figure 13 As shown, it includes:

[0156] The dissolution intensity module 101 is used to obtain a rock type model established based on the lithofacies classification of a single well in a porous carbonate reservoir, and to establish a dissolution intensity model under the constraints of the rock type model.

[0157] The correction factor module 102 is used to calculate the single-well permeability correction factor based on the single-well effective permeability and single-well static permeability of porous carbonate reservoirs.

[0158] The permeability correction factor module 103 is used to establish a permeability correction factor model using a stochastic simulation method under the constraints of the dissolution intensity model, based on the single well permeability correction factor.

[0159] The dynamic model module 104 is used to correct the static permeability model established based on the static permeability of the single well according to the permeability multiple model, so as to obtain the dynamic permeability model.

[0160] The model import module 105 is used to import the dynamic permeability model into the reservoir numerical simulation dynamic model, perform historical fitting on the reservoir numerical simulation dynamic model, and obtain the historical fitting result of the reservoir numerical simulation dynamic model.

[0161] The model correction module 106 is used to obtain the corrected permeability ratio model determined based on the historical fitting results of the reservoir numerical simulation dynamic model.

[0162] The model iteration module 107 is used to obtain a new dynamic permeability model based on the modified permeability multiple model, and to re-execute the process of historical fitting of the reservoir numerical simulation dynamic model until the historical fitting result of the reservoir numerical simulation dynamic model meets the requirements, thereby obtaining the optimized dynamic permeability model.

[0163] Example 5

[0164] Based on the same inventive concept, embodiments of the present invention provide a computer-readable storage medium storing a computer program / instruction thereon, which, when executed by a processor, implements the dynamic permeability modeling method for porous carbonate reservoirs as described in Embodiment 1 above, and / or the dynamic permeability model optimization method for porous carbonate reservoirs as described in Embodiment 2.

[0165] Example 6

[0166] Based on the same inventive concept, embodiments of the present invention provide a computer program product, including a computer program / instruction, which, when executed by a processor, implements the dynamic permeability modeling method for porous carbonate reservoirs as described in Embodiment 1 above, and / or the dynamic permeability model optimization method for porous carbonate reservoirs as described in Embodiment 2.

[0167] Example 7

[0168] Based on the same inventive concept, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the dynamic permeability modeling method for porous carbonate reservoirs as described in Embodiment 1 above, and / or the dynamic permeability model optimization method for porous carbonate reservoirs as described in Embodiment 2.

[0169] In this embodiment, when describing representative embodiments, the specification may have presented the method and / or process as a specific sequence of steps. However, the method or process should not be limited to the specific order of steps described herein, to the extent that the method or process does not depend on the specific order of steps described herein. Other sequences of steps are also possible, as understood by those skilled in the art. Therefore, the specific order of steps set forth in the specification should not be construed as a limitation of the claims. Furthermore, the claims for the method and / or process should not be limited to the steps performed in the written order, and those skilled in the art will readily understand that these orders can be varied and still remain within the spirit and scope of the embodiments of the invention.

[0170] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0171] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer program products according to embodiments of the invention. Those skilled in the art will understand that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0172] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0173] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

Claims

1. A method for modeling dynamic permeability of porous carbonate reservoirs, characterized in that, The method includes: Obtain a rock type model based on the lithofacies classification of a single well in a porous carbonate reservoir, and establish a dissolution intensity model under the constraints of the rock type model. Calculate the permeability correction factor for a single well based on the effective permeability and static permeability of a single well in a porous carbonate reservoir. Based on the single-well permeability correction factor, and under the constraints of the dissolution intensity model, a permeability factor model is established using a stochastic simulation method. Based on the permeability multiple model, the static permeability model established based on the static permeability of a single well is corrected to obtain the dynamic permeability model.

2. The method for modeling dynamic permeability of porous carbonate reservoirs as described in claim 1, characterized in that, The process involves obtaining a rock type model based on the lithofacies classification of a single well in a porous carbonate reservoir, and establishing a dissolution intensity model under the constraints of the rock type model, including: Obtain a rock type model established by stochastic simulation based on the lithofacies classification of single wells in porous carbonate reservoirs, and import the rock type model into the dynamic model of numerical simulation of porous carbonate reservoirs. The dissolution intensity curves of the reservoir at different depths in a single well were obtained based on the classification criteria for dissolution intensity of porous carbonate reservoirs. The dissolution intensity of the reservoir is assigned a value, and the dissolution intensity of a single well at different depths of the reservoir is assigned a value accordingly, so as to obtain the dissolution intensity curve of a single well at different depths of the reservoir after assignment. The dissolution intensity curves of the single wells in the reservoir at different depths are imported into the dynamic model of the reservoir numerical simulation. Based on the rock type model, the dissolution intensity of each rock type in the single well of the reservoir is divided by taking the mode, so as to obtain the single well dissolution intensity curve under the constraint of rock type. Based on the single-well dissolution intensity curve under the constraints of the rock type, the dissolution intensity model is established using a stochastic simulation method.

3. The method for modeling dynamic permeability of porous carbonate reservoirs as described in claim 1, characterized in that, The calculation of the single-well permeability correction factor based on the effective permeability and static permeability of a single well in a porous carbonate reservoir includes: To obtain the effective permeability of a single well at any depth in a porous carbonate reservoir, obtained through production logging and well testing. The average static permeability of a single well at any depth is obtained by arithmetically averaging the static permeability obtained from core experiments and well logging interpretation at any depth in the porous carbonate reservoir. The ratio of the effective permeability to the average static permeability of the corresponding depth segment of the single well is calculated to obtain the permeability correction factor of the corresponding depth segment of the single well.

4. The method for modeling dynamic permeability of porous carbonate reservoirs as described in claim 3, characterized in that, The step of establishing a permeability factor model using a stochastic simulation method based on the single-well permeability correction factor, under the constraints of the dissolution intensity model, includes: Based on the spatial distribution of the dissolution intensity model, the spatial correlation between the permeability correction factors at different depths of a single well is obtained through variogram analysis. Based on the spatial correlation between the permeability correction factors at different depths of the single well, a permeability factor model is established using a stochastic simulation method.

5. The method for modeling dynamic permeability of porous carbonate reservoirs as described in claim 1, characterized in that, The process of correcting the static permeability model established based on the static permeability of a single well according to the permeability multiple model to obtain the dynamic permeability model includes: Based on the aforementioned permeability factor model, the static permeability model is corrected using the following formula to obtain the dynamic permeability model: P D＝ M×P S , In the formula, P D Let M represent the dynamic permeability model, and P represent the permeability multiple model. S This represents the static permeability model.

6. A method for optimizing the dynamic permeability model of porous carbonate reservoirs, characterized in that, include: The dynamic permeability model is imported into the reservoir numerical simulation dynamic model, and the reservoir numerical simulation dynamic model is historically fitted to obtain the historical fitting results of the reservoir numerical simulation dynamic model. Obtain the corrected permeability factor model determined based on the historical fitting results of the reservoir numerical simulation dynamic model; Based on the modified permeability factor model, a new dynamic permeability model is obtained. The process of historical fitting of the reservoir numerical simulation dynamic model is then repeated until the historical fitting result of the reservoir numerical simulation dynamic model meets the requirements, thus obtaining the optimized dynamic permeability model.

7. The method for optimizing the dynamic permeability model of porous carbonate reservoirs as described in claim 6, characterized in that, The process of re-executing the history fitting of the reservoir numerical simulation dynamic model based on the modified permeability factor model, until the history fitting result of the reservoir numerical simulation dynamic model meets the requirements, to obtain the optimized dynamic permeability model, includes: Based on the modified permeability multiple model, the static permeability model established based on the static permeability of a single well is corrected to obtain the modified dynamic permeability model; The modified dynamic permeability model is imported into the reservoir numerical simulation dynamic model, and the reservoir numerical simulation dynamic model is re-historically fitted to obtain a new reservoir numerical simulation dynamic model history fitting result. Obtain the new corrected permeability factor model determined based on the historical fitting results of the new reservoir numerical simulation dynamic model, and continue to perform the above process of historical fitting of the reservoir numerical simulation dynamic model based on the new corrected permeability factor model until the historical fitting results of the reservoir numerical simulation dynamic model meet the requirements, thus obtaining the optimized dynamic permeability model.

8. The method for optimizing the dynamic permeability model of porous carbonate reservoirs as described in claim 7, characterized in that, Before importing the dynamic permeability model into the reservoir numerical simulation dynamic model, performing history fitting on the reservoir numerical simulation dynamic model, and obtaining the history fitting result of the reservoir numerical simulation dynamic model, the method further includes: The dynamic permeability model is coarsened.

9. A dynamic permeability modeling device for porous carbonate reservoirs, characterized in that, include: The dissolution intensity module is used to obtain a rock type model based on the lithofacies classification of a single well in a porous carbonate reservoir, and to establish a dissolution intensity model under the constraints of the rock type model. The correction factor module is used to calculate the permeability correction factor of a single well based on the effective permeability and static permeability of a single well in a porous carbonate reservoir. The permeability correction factor module is used to establish a permeability correction factor model using a stochastic simulation method under the constraints of the dissolution intensity model, based on the single well permeability correction factor. The dynamic model module is used to correct the static permeability model established based on the static permeability of the single well according to the permeability multiple model, so as to obtain the dynamic permeability model.

10. A device for optimizing the dynamic permeability model of porous carbonate reservoirs, characterized in that, include: The dissolution intensity module is used to obtain a rock type model based on the lithofacies classification of a single well in a porous carbonate reservoir, and to establish a dissolution intensity model under the constraints of the rock type model. The correction factor module is used to calculate the permeability correction factor of a single well based on the effective permeability and static permeability of a single well in a porous carbonate reservoir. The permeability correction factor module is used to establish a permeability correction factor model using a stochastic simulation method under the constraints of the dissolution intensity model, based on the single well permeability correction factor. The dynamic model module is used to correct the static permeability model established based on the static permeability of the single well according to the permeability multiple model, so as to obtain the dynamic permeability model. The model import module is used to import the dynamic permeability model into the reservoir numerical simulation dynamic model, perform historical fitting on the reservoir numerical simulation dynamic model, and obtain the historical fitting results of the reservoir numerical simulation dynamic model. The model correction module is used to obtain the corrected permeability factor model determined based on the historical fitting results of the reservoir numerical simulation dynamic model. The model iteration module is used to obtain a new dynamic permeability model based on the modified permeability factor model, and to re-execute the process of historical fitting of the reservoir numerical simulation dynamic model until the historical fitting result of the reservoir numerical simulation dynamic model meets the requirements, thereby obtaining the optimized dynamic permeability model.

11. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instruction is executed by the processor, it implements the dynamic permeability modeling method for porous carbonate reservoirs according to any one of claims 1-5, and / or the dynamic permeability model optimization method for porous carbonate reservoirs according to any one of claims 6-8.

12. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the dynamic permeability modeling method for porous carbonate reservoirs according to any one of claims 1-5, and / or the dynamic permeability model optimization method for porous carbonate reservoirs according to any one of claims 6-8.

13. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the dynamic permeability modeling method for porous carbonate reservoirs according to any one of claims 1-5, and / or the dynamic permeability model optimization method for porous carbonate reservoirs according to any one of claims 6-8.

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