Method for simulating corrosion strength of offshore deep reservoir

Abstract

The invention relates to the technical field of reservoir diagenesis strength prediction and characterization, in particular to an offshore deep reservoir corrosion strength simulation method, which comprises the following steps of: performing mathematical characterization on a diagenesis fluid channel according to the relationship between each parameter of the diagenesis fluid channel and the reservoir corrosion strength, and constructing a reservoir corrosion factor calculation model under the guidance of fault parameters; the model can calculate the corrosion strength of a single well in the vertical direction, and finally inversion simulation is carried out by combining a corrosion factor longitudinal distribution curve calculated by the model with seismic data, so that reservoir corrosion strength space simulation prediction is carried out, and quantitative evaluation of the reservoir corrosion effect can be realized. According to the method, the corrosion factor prediction calculation model is constructed based on the quantitative relationship between the corrosion strength and the diagenetic fluid channel, calculation is carried out from the origin mechanism of corrosion, and the characterization reasonability and precision are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of reservoir diagenetic strength prediction and characterization, and more specifically, to a method for simulating the dissolution strength of offshore deep reservoirs. Background Art

[0002] During the burial process, reservoir sediments will experience various geological processes such as sedimentation, diagenesis, and tectonic uplift. The permeability of the reservoir will be affected by the particle size and composition of the sediments, as well as by diagenetic processes such as compaction, cementation, and dissolution. Under normal circumstances, dissolution is beneficial to the reservoir. The stronger the dissolution, the higher the permeability of the reservoir. However, in some deep offshore highly heterogeneous low-permeability sandstones, affected by deep hydrothermal fluids, dissolution has a negative effect on the reservoir, which is manifested as a decrease in permeability after the reservoir is dissolved. Under the same sedimentation, compaction, and cementation background, dissolution determines the permeability of the reservoir and is the main factor affecting permeability.

[0003] The prior art discloses a method and device for determining the geophysical two-dimensional characterization of microscopic dissolution pores. The method first determines the microscopic dissolution pore development area, then combines the well logging curve and the nuclear magnetic logging porosity curve to study the logging response characteristics of the microscopic dissolution pores, stratifies the target area according to the depth, and then determines the rock physical quantity plate; secondly, the microscopic dissolution pore logging characteristic curve is obtained according to the rock physical quantity plate, and the distribution characteristics of the microscopic dissolution pores are determined by inversion of seismic waveform indications. This scheme can determine the distribution characteristics of microscopic dissolution pores on a two-dimensional profile, but this scheme only relies on logging data to calculate the dissolution rate, and the characterization accuracy is low. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art that the dissolution rate characterization accuracy is low only by relying on logging data to calculate, and to provide a method for simulating the dissolution intensity of deep offshore reservoirs. A characterization model is constructed based on the quantitative relationship between the dissolution intensity and the diagenetic fluid channel to improve the rationality and accuracy of the characterization.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A method for simulating the dissolution intensity of an offshore deep reservoir is provided, comprising the following steps:

[0007] S1: Based on the parameters of diagenetic fluid channels, a dissolution factor prediction function model is constructed;

[0008] S2: Comprehensively evaluate the relationship between various parameters of diagenetic fluid channels and reservoir dissolution intensity, classify various parameters and give weight index values, and obtain the fuzzy evaluation matrix by arranging the weight indexes and using the hierarchical analysis method;

[0009] S3: Substitute the normalized diagenetic fluid channel parameters into the fuzzy evaluation matrix to obtain the fuzzy comprehensive evaluation results and form a reservoir dissolution factor calculation model;

[0010] S4: According to the fault distribution trend, simulated wells are set in the study area. The diagenetic fluid channel parameter values ​​around the simulated wells are measured at vertical intervals according to the seismic profile, and the values ​​are substituted into the reservoir dissolution factor calculation model to obtain the vertical distribution curve of the dissolution factor;

[0011] S5: Coordinated artificial intelligence inversion of seismic data of different frequency bands, using the Gray Wolf optimization algorithm to establish a mapping relationship between the dissolution factor and the seismic frequency division attribute. Under the constraints of the set dissolution factor curves of simulated wells and real wells, the spatial distribution of reservoir dissolution rate is simulated using seismic waveform changes.

[0012] The method for simulating the dissolution intensity of deep offshore reservoirs of the present invention mathematically characterizes the diagenetic fluid channel according to the relationship between the parameters of the diagenetic fluid channel and the reservoir dissolution intensity, and constructs a reservoir dissolution factor calculation model under the guidance of fault parameters. The model can calculate the vertical dissolution intensity of a single well, and finally uses the longitudinal distribution curve of the dissolution factor calculated by the model in combination with seismic data for inversion simulation, and performs spatial simulation prediction of reservoir dissolution intensity, which can realize quantitative evaluation of reservoir dissolution. The present invention constructs a characterization model based on the quantitative relationship between dissolution intensity and diagenetic fluid channels, performs calculations based on the genetic mechanism of dissolution, and improves the rationality and accuracy of characterization.

[0013] Preferably, in step S1, the corrosion factor prediction function model is:

[0014] Z=m1A+m2B+m3C+m4D+m5E;

[0015] Where Z represents the reservoir dissolution factor at the target location; A represents the distance from the target location to the secondary channel fault of the diagenetic fluid F. n B represents the secondary channel fault F of the diagenetic fluid at the target location n The connection relationship with the main channel fault F of the diagenetic fluid; C represents the secondary channel fault F of the diagenetic fluid near the target location n The vertical distance from the fault F of the main channel of diagenetic fluid; D represents the fault F of the secondary channel of diagenetic fluid near the target location. n E represents the quantitative score of the difference in reservoir dissolution intensity between the upper and lower walls of the fault; m1, m2, m3, m4, and m5 represent the evaluation values ​​of parameters A, B, C, D, and E, respectively; F represents the main channel fault of the diagenetic fluid; F n It indicates the fault communicating with F, i.e. the secondary channel fault of diagenetic fluid.

[0016] Preferably, in step S2, the parameters A, B, C, D, and E are divided into three categories, the most important parameter A is given a weight index value of 5, the secondary important parameters B, C, and E are given a weight index value of 2, and the generally important parameter D is given a weight index value of 1. The fuzzy evaluation matrix obtained is:

[0017]

[0018] Preferably, in step S3, the normalized diagenetic fluid channel parameters are substituted into the fuzzy judgment matrix, and the SPSS software is used for calculation to obtain the fuzzy comprehensive evaluation results: m1, m2, m3, m4 and m5, and the calculation model of the reservoir dissolution factor at the target position is formed as follows:

[0019] Z=0.4309A+0.1787B+0.1781C+0.0721D+0.1408E.

[0020] Preferably, parameter A and parameter C are obtained from seismic profile measurements;

[0021] For parameter B, in order to quantify the relationship between fault and dissolution factor, since F n The diagenetic fluids generated by direct communication with F are more, which is conducive to reservoir dissolution. n Communicate with F through three or more faults, and the relationship value is set to 1; when F n Communicate with F through two faults, and the relationship value is set to 1.1; when F n Communicate with F through a fault, and the relationship value is set to 1.2; when F n Directly communicate with F, the relationship value is set to 1.3. At this time, the width of the fault at the connection needs to be considered, and the width is measured on the seismic section;

[0022] For parameter E, the upper wall of the fault is highly active and prone to small faults, which is conducive to the dissolution of reservoirs by diagenetic fluids. Therefore, the data close to the upper wall is set to 2, and the data close to the lower wall is set to 1.

[0023] Preferably, in step S1, the parameters of the diagenetic fluid channel are obtained by comprehensive analysis of the relationship between the well point dissolution factor and the fault, wherein the well point dissolution factor is derived from the calculation of the well logging parameters:

[0024]

[0025] In the formula, Z 测井 represents the dissolution factor calculated by well logging; Φ N-D represents the neutron-density porosity, i.e. the total porosity of the reservoir; Φ S represents the sonic time porosity, i.e. the original porosity of the reservoir; Φ 实测 Indicates the measured porosity, obtained from conventional physical property experiments; Φ D Indicates density porosity; ΦN represents neutron porosity; Δt represents the acoustic time difference; Δt ma represents the propagation time of the sliding wave in the rock skeleton; Δt f represents the propagation time of the sliding wave in the pore fluid; C p Represents the compaction correction factor.

[0026] Preferably, the compaction correction factor C p It is related to the burial depth, age and region of the stratum, and is obtained by porosity correction.

[0027] Preferably, the dissolution factor calculated by well logging needs to be calibrated and corrected using the dissolution rate obtained by quantitative analysis of the pores in the casting thin section.

[0028] Preferably, the calculation process of the dissolution rate obtained by quantitative analysis of the pores in the casting thin section is as follows: the proportion of pores to the total surface area ratio is determined by the area estimation method, and the total surface area ratio of the dissolution pores is calculated:

[0029]

[0030] In the formula, Z 定量 represents the dissolution surface ratio obtained by quantitative calculation of pores; Φ 溶蚀 Indicates the proportion of dissolved pores in the thin section; Φ 总 represents the total face rate.

[0031] Preferably, it is applied to the areas where dissolution has a negative effect on reservoir properties and where faults are developed around n of the reservoir.

[0032] Compared with the prior art, the method for simulating the dissolution intensity of deep offshore reservoirs of the present invention has the following beneficial effects:

[0033] The present invention constructs a dissolution factor prediction calculation model based on the quantitative relationship between dissolution intensity and diagenetic fluid channels. The model can calculate the vertical dissolution intensity of a single well. The present invention performs calculation based on the genetic mechanism of dissolution to improve the rationality and accuracy of characterization. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a flow chart of a method for simulating the dissolution intensity of offshore deep reservoirs in an embodiment of the present invention;

[0035] Figure 2 This is a schematic diagram of fault type distribution of the gas field R in the second embodiment of the present invention;

[0036] Figure 3 This is a seismic profile diagram of the gas well R1 in the second embodiment of the present invention;

[0037] Figure 4This is a graph showing the variation of the dissolution rate of gas well R1 with the fault strike in Example 2 of the present invention;

[0038] Figure 5 This is a seismic profile diagram of the gas well R2 in the second embodiment of the present invention;

[0039] Figure 6 This is a graph showing the variation of the dissolution rate of gas well R2 with the fault strike in Example 2 of the present invention;

[0040] Figure 7 This is a grid diagram of numerical simulation of R dissolution in gas field in Example 2 of the present invention;

[0041] Figure 8 This is a plane prediction diagram of the dissolution intensity of gas field R in Example 2 of the present invention. DETAILED DESCRIPTION

[0042] The present invention will be further described below in conjunction with specific implementation modes.

[0043] Embodiment 1

[0044] A method for simulating the dissolution intensity of deep offshore reservoirs, such as Figure 1 As shown, the following steps are included:

[0045] S1: Based on the parameters of diagenetic fluid channels, a dissolution factor prediction function model is constructed;

[0046] S2: Comprehensively evaluate the relationship between various parameters of diagenetic fluid channels and reservoir dissolution intensity, classify various parameters and give weight index values, and obtain the fuzzy evaluation matrix by arranging the weight indexes and using the hierarchical analysis method;

[0047] S3: Substitute the normalized diagenetic fluid channel parameters into the fuzzy evaluation matrix to obtain the fuzzy comprehensive evaluation results and form a reservoir dissolution factor calculation model;

[0048] S4: According to the fault distribution trend, simulated wells are set in the study area. The diagenetic fluid channel parameter values ​​around the simulated wells are measured at vertical intervals of 1 m according to the seismic profile, and the parameters are substituted into the reservoir dissolution factor calculation model to obtain the vertical distribution curve of the dissolution factor;

[0049] S5: Coordinated artificial intelligence inversion of seismic data of different frequency bands, using the Gray Wolf optimization algorithm to establish a mapping relationship between the dissolution factor and the seismic frequency division attribute. Under the constraints of the set dissolution factor curves of simulated wells and real wells, the spatial distribution of reservoir dissolution rate is simulated using seismic waveform changes.

[0050] The above-mentioned offshore deep reservoir dissolution intensity simulation method mathematically characterizes the diagenetic fluid channel according to the relationship between the various parameters of the diagenetic fluid channel and the reservoir dissolution intensity, and constructs a reservoir dissolution factor calculation model under the guidance of fault parameters. The model can calculate the vertical dissolution intensity of a single well, and finally uses the longitudinal distribution curve of the dissolution factor calculated by the model combined with seismic data for inversion simulation to perform spatial simulation prediction of reservoir dissolution intensity, which can realize quantitative evaluation of reservoir dissolution. This embodiment constructs a characterization model based on the quantitative relationship between dissolution intensity and diagenetic fluid channels, and performs calculations based on the genetic mechanism of dissolution to improve the rationality and accuracy of the characterization.

[0051] Diagenetic fluids will dissolve and transform reservoirs. Reservoirs close to channel faults are more susceptible to the impact of diagenetic fluids. The greater the intensity of reservoir transformation by diagenetic fluids, the easier it is for potassium feldspar in the reservoir to be dissolved under the action of high-temperature hydrothermal fluids, causing pore channels to be blocked, thereby reducing the permeability of the reservoir.

[0052] Based on the dissolution rate obtained from the pore quantitative experiment, combined with the dissolution factor obtained from the single well logging response, and combined with the properties and relationships of the diagenetic fluid channel faults, the distribution of reservoir dissolution intensity was studied. The study found that the reservoir dissolution intensity is related to the distance, connection mode, number of channels and the position of the upper and lower walls of the adjacent diagenetic fluid channel faults; among them, the closer the distance between a certain position in space and the main diagenetic fluid channel fault that communicates with the mantle, the stronger the reservoir dissolution; the diagenetic fluid secondary channel fault is directly connected to the main diagenetic fluid channel fault that communicates with the mantle, and the larger the fault width at the connection, the greater the reservoir dissolution intensity; the shorter the distance between the secondary channel fault and the main channel fault near a certain position in space, the greater the reservoir dissolution intensity; the more diagenetic fluid channel faults there are near a certain position in space, the greater the reservoir dissolution intensity; because the upper wall of the fault is more active, it is easier to produce more fracture zones, which is conducive to dissolution. Therefore, the closer to the upper wall of the diagenetic fluid channel fault, the stronger the dissolution intensity. Both the dissolution factor and the dissolution rate are parameters that characterize the dissolution intensity of reservoirs.

[0053] In step S1, the corrosion factor prediction function model is:

[0054] Z=m1A+m2B+m3C+m4D+m5E;

[0055] Where Z represents the reservoir dissolution factor at the target location; A represents the distance from the target location to the secondary channel fault of the diagenetic fluid F. n B represents the secondary channel fault F of the diagenetic fluid at the target location n The connection relationship with the main channel fault F of the diagenetic fluid; C represents the secondary channel fault F of the diagenetic fluid near the target location n The vertical distance from the fault F of the main channel of diagenetic fluid; D represents the fault F of the secondary channel of diagenetic fluid near the target location. nE represents the quantitative score of the difference in reservoir dissolution intensity between the upper and lower walls of the fault; m1, m2, m3, m4, and m5 represent the evaluation values ​​of parameters A, B, C, D, and E, respectively; F represents the main channel fault of the diagenetic fluid; F n It indicates the fault communicating with F, i.e. the secondary channel fault of diagenetic fluid.

[0056] In step S2, the parameters A, B, C, D, and E are divided into three categories. The most important parameter A is given a weight index value of 5, the secondary important parameters B, C, and E are given a weight index value of 2, and the generally important parameter D is given a weight index value of 1. The fuzzy evaluation matrix obtained is:

[0057]

[0058] In step S3, the normalized diagenetic fluid channel parameters are substituted into the fuzzy judgment matrix, and the SPSS software is used for calculation to obtain the fuzzy comprehensive evaluation results: m1, m2, m3, m4 and m5, forming the calculation model of the reservoir dissolution factor at the target location:

[0059] Z=0.4309A+0.1787B+0.1781C+0.0721D+0.1408E.

[0060] Parameters A and C are obtained from seismic profile measurements;

[0061] For parameter B, in order to quantify the relationship between fault and dissolution factor, since F n The diagenetic fluids generated by direct communication with F are more, which is conducive to reservoir dissolution. n Communicate with F through three or more faults, and the relationship value is set to 1; when F n Communicate with F through two faults, and the relationship value is set to 1.1; when F n Communicate with F through a fault, and the relationship value is set to 1.2; when F n Directly communicate with F, the relationship value is set to 1.3. At this time, the width of the fault at the connection needs to be considered, and the width is measured on the seismic section;

[0062] For parameter E, the upper wall of the fault is highly active and prone to small faults, which is conducive to the dissolution of reservoirs by diagenetic fluids. Therefore, the data close to the upper wall is set to 2, and the data close to the lower wall is set to 1.

[0063] In step S1, the parameters of the diagenetic fluid channel are obtained by comprehensive analysis of the relationship between the well point dissolution factor and the fault, wherein the well point dissolution factor is derived from the calculation of the well logging parameters:

[0064]

[0065] In the formula, Z 测井 represents the dissolution factor calculated by well logging; ΦN-D represents the neutron-density porosity, i.e. the total porosity of the reservoir; Φ S represents the sonic time porosity, i.e. the original porosity of the reservoir; Φ 实测 Indicates the measured porosity, obtained from conventional physical property test; Φ D Indicates density porosity; Φ N represents neutron porosity; Δt represents the acoustic time difference, which is measured by acoustic logging; Δt ma Indicates the propagation time of the sliding wave in the rock skeleton, measured by well logging; Δt f represents the propagation time of the sliding wave in the pore fluid, measured by well logging; C p Represents the compaction correction factor.

[0066] Compaction correction factor C p It is related to the burial depth, age and region of the stratum, and is obtained by porosity correction. Different regions have specific empirical values.

[0067] The dissolution factor calculated by logging needs to be calibrated and corrected using the dissolution rate obtained from the quantitative analysis of the pores in the casting thin section.

[0068] The calculation process of the dissolution rate obtained from the quantitative analysis of the pores in the casting thin section is as follows: the proportion of pores to the total surface area ratio is determined by the area estimation method, and the total surface area ratio of the dissolution pores is calculated:

[0069]

[0070] In the formula, Z 定量 represents the dissolution surface ratio obtained by quantitative calculation of pores; Φ 溶蚀 Indicates the proportion of dissolved pores in the thin section; Φ 总 represents the total face rate.

[0071] This embodiment is applied to the case where dissolution has a negative effect on reservoir properties and there are faults around n of the reservoir.

[0072] Embodiment 2

[0073] This embodiment is similar to the first embodiment, except that the deep low-permeability reservoir of the low-permeability gas field R has large-scale faults. A large fault F1 connecting the mantle is developed in the north of the gas field, which is a migration channel for diagenetic fluids. A fault F2 is developed in the south, which is a hydrocarbon supply channel. There are multiple faults connected to it in the region, all of which are channels for diagenetic fluid migration. Blind wells R1, R2, R3 and R7 are opened in the region. Figure 2 shown.

[0074] The single well dissolution factor was calculated using logging parameters, and the compaction correction coefficient in this area was taken as 1. The existing casting thin sections were subjected to quantitative pore analysis, and the thin section dissolution rate was calculated to correct the single well dissolution factor.

[0075] Through the measurement and study of regional fault parameters, it is found that the single well dissolution factor is inversely proportional to the distance between the mantle fault and the fault. The greater the distance, the lower the dissolution rate. Figures 3 to 6 As shown; at the same time, when the nearest fault is directly connected to the fault communicating with the mantle, the dissolution rate is higher than that of the fault communicating with the mantle through more than one connected fault; when the distance of the fault connection is shorter, the dissolution rate is higher; the number of faults communicating with the mantle developed around it is positively correlated with the dissolution rate, and the more faults there are, the higher the dissolution rate; there are more broken zones on the upper wall of the fault, and under the same conditions, the reservoir dissolution intensity is greater near the upper wall of the fault. Comprehensive fault parameters, the fuzzy weight method is used to obtain the reservoir dissolution factor calculation model, and the predicted dissolution rate calculated by the model is compared with the average dissolution factor calculated by logging.

[0076] The target location reservoir dissolution factor calculation model is used to interpolate and arrange wells in the region, such as Figure 7 As shown in Figure 2, the plane distribution of the dissolution rate intensity is obtained by using seismic inversion, as shown in Figure 2. Figure 8 As shown in Table 1, the well logging dissolution factor with quantitative correction of pore volume is used to verify the inversion results using the blind spot and blind well R3 that are not involved in the learning:

[0077] Table 1 Comparison of the predicted dissolution rate and the calculated value of the blind well in gas field R

[0078]

[0079] As shown in Table 1, the predicted dissolution rate calculated by the model is compared with the average dissolution factor calculated by logging, and the agreement is as high as 93.01%.

[0080] Embodiment 3

[0081] This embodiment is similar to the first embodiment, except that an example is conducted to verify the deep low-permeability reservoir of the low-permeability gas field S. The gas field S has a large fault deep into the mantle, and the dissolution has a negative effect on the reservoir properties. Blind wells S1, S2, S3, S4 and S6 are opened in this area. The fault parameters in the area are studied, and combined with the dissolution factor calculated from a single well, it is found that the fault parameters affect the change in dissolution intensity; the verification results are shown in Table 2:

[0082] Table 2 Comparison of the predicted dissolution rate and the calculated value of the blind well in gas field S

[0083]

[0084] As shown in Table 2, the predicted dissolution rate calculated by the target position reservoir dissolution factor calculation model is consistent with the average value of the logging dissolution factor after pore quantitative correction, and the overall consistency is higher than 80%.

[0085] In the specific contents of the above-mentioned specific implementation methods, the various technical features can be combined in any non-contradictory manner. In order to make the description concise, not all possible combinations of the above-mentioned technical features are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0086] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A method for simulating the dissolution intensity of deep offshore reservoirs, characterized in that: The following steps are involved: S1: Based on the parameters of diagenetic fluid channels, a dissolution factor prediction function model is constructed; S2: Comprehensively evaluate the relationship between various parameters of diagenetic fluid channels and reservoir dissolution intensity, classify various parameters and give weight index values, and obtain the fuzzy evaluation matrix by arranging the weight indexes and using the hierarchical analysis method; S3: Substitute the normalized diagenetic fluid channel parameters into the fuzzy evaluation matrix to obtain the fuzzy comprehensive evaluation results and form a reservoir dissolution factor calculation model; S4: According to the fault distribution trend, simulated wells are set in the study area. The diagenetic fluid channel parameter values ​​around the simulated wells are measured at vertical intervals according to the seismic profile, and the values ​​are substituted into the reservoir dissolution factor calculation model to obtain the vertical distribution curve of the dissolution factor; S5: Coordinated artificial intelligence inversion of seismic data of different frequency bands, using the Gray Wolf optimization algorithm to establish a mapping relationship between the dissolution factor and the seismic frequency division attribute. Under the constraints of the set dissolution factor curves of simulated wells and real wells, the spatial distribution of reservoir dissolution rate is simulated using seismic waveform changes.

2. The method for simulating the dissolution intensity of deep offshore reservoirs according to claim 1, characterized in that: In step S1, the corrosion factor prediction function model is: Z=m1A+m2B+m3C+m4D+m5E; Where Z represents the reservoir dissolution factor at the target location; A represents the distance from the target location to the secondary channel fault of the diagenetic fluid F. n B represents the secondary channel fault F of the diagenetic fluid at the target location n The connection relationship with the main channel fault F of the diagenetic fluid; C represents the secondary channel fault F of the diagenetic fluid near the target location n The vertical distance from the fault F of the main channel of diagenetic fluid; D represents the fault F of the secondary channel of diagenetic fluid near the target location. n E represents the quantitative score of the difference in reservoir dissolution intensity between the upper and lower walls of the fault; m1, m2, m3, m4, and m5 represent the evaluation values ​​of parameters A, B, C, D, and E, respectively; F represents the main channel fault of the diagenetic fluid; F n It indicates the fault communicating with F, i.e. the secondary channel fault of diagenetic fluid.

3. The method for simulating the dissolution intensity of deep offshore reservoirs according to claim 2, characterized in that: In step S2, the parameters A, B, C, D, and E are divided into three categories. The most important parameter A is given a weight index value of 5, the secondary important parameters B, C, and E are given a weight index value of 2, and the generally important parameter D is given a weight index value of 1. The fuzzy evaluation matrix obtained is:

4. The method for simulating the dissolution intensity of deep offshore reservoirs according to claim 3, characterized in that: In step S3, the normalized diagenetic fluid channel parameters are substituted into the fuzzy judgment matrix, and the SPSS software is used for calculation to obtain the fuzzy comprehensive evaluation results: m1, m2, m3, m4 and m5, forming the calculation model of the reservoir dissolution factor at the target location: Z=0.4309A+0.1787B+0.1781C+0.0721D+0.1408E.

5. The method for simulating the dissolution intensity of deep offshore reservoirs according to claim 4, characterized in that: Parameters A and C are obtained from seismic profile measurements; For parameter B, in order to quantify the relationship between fault and dissolution factor, since F n The diagenetic fluids generated by direct communication with F are more, which is conducive to reservoir dissolution. n Communicate with F through three or more faults, and the relationship value is set to 1; when F n Communicate with F through two faults, and the relationship value is set to 1.1; when F n Communicate with F through a fault, and the relationship value is set to 1.2; when F n Directly communicate with F, the relationship value is set to 1.

3. At this time, the width of the fault at the connection needs to be considered, and the width is measured on the seismic section; For parameter E, the upper wall of the fault is highly active and prone to small faults, which is conducive to the dissolution of reservoirs by diagenetic fluids. Therefore, the data close to the upper wall is set to 2, and the data close to the lower wall is set to 1.

6. The method for simulating the dissolution intensity of deep offshore reservoirs according to claim 1, characterized in that: In step S1, the parameters of the diagenetic fluid channel are obtained by comprehensive analysis of the relationship between the well point dissolution factor and the fault, wherein the well point dissolution factor is derived from the calculation of the well logging parameters: In the formula, Z 测井 represents the dissolution factor calculated by well logging; Φ N-D represents the neutron-density porosity, i.e. the total porosity of the reservoir; Φ S represents the sonic time porosity, i.e. the original porosity of the reservoir; Φ 实测 Indicates the measured porosity, obtained from conventional physical property experiments; Φ D Indicates density porosity; Φ N represents neutron porosity; Δt represents the acoustic time difference; Δt ma represents the propagation time of the sliding wave in the rock skeleton; Δt f represents the propagation time of the sliding wave in the pore fluid; C p Represents the compaction correction factor.

7. The method for simulating the dissolution intensity of offshore deep reservoirs according to claim 6, characterized in that: Compaction correction factor C p It is related to the burial depth, age and region of the stratum, and is obtained by porosity correction.

8. The method for simulating the dissolution intensity of offshore deep reservoirs according to claim 6, characterized in that: The dissolution factor calculated by logging needs to be calibrated and corrected using the dissolution rate obtained from the quantitative analysis of the pores in the casting thin section.

9. The method for simulating the dissolution intensity of deep offshore reservoirs according to claim 8, characterized in that: The calculation process of the dissolution rate obtained from the quantitative analysis of the pores in the casting thin section is as follows: the proportion of pores to the total surface area ratio is determined by the area estimation method, and the total surface area ratio of the dissolution pores is calculated: In the formula, Z 定量 represents the dissolution surface ratio obtained by quantitative calculation of pores; Φ 溶蚀 It indicates the proportion of dissolved pores in the thin section; Φ 总 represents the total face rate.

10. The method for simulating the dissolution intensity of offshore deep reservoirs according to any one of claims 1 to 9, characterized in that: Applied to the reservoir where dissolution has a negative effect on reservoir properties and faults are developed around it. n of the reservoir.

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