A method for simulating dissolution intensity of offshore deep reservoir

Abstract

The present application relates to the technical field of reservoir diagenetic strength prediction characterization, and more particularly to a simulation method for offshore deep reservoir dissolution strength, which is characterized in that, according to the relationship between the parameters of diagenetic fluid channels and the reservoir dissolution strength, the diagenetic fluid channels are mathematically characterized, a reservoir dissolution factor calculation model under the guidance of fault parameters is constructed, the model can calculate the dissolution strength in the vertical direction of a single well, and finally, the longitudinal distribution curve of the dissolution factor calculated by the model is combined with seismic data to perform inversion simulation, spatial simulation and prediction of the reservoir dissolution strength, so that quantitative evaluation of the reservoir dissolution effect can be realized. The present application constructs a dissolution factor prediction calculation model based on the quantitative relationship between the dissolution strength and the diagenetic fluid channels, and performs calculation from the cause mechanism of dissolution, so that the rationality and accuracy of the characterization are improved.

Description

Technical Field

[0001] This invention relates to the technical field of reservoir diagenetic strength prediction and characterization, and more specifically, to a method for simulating the dissolution intensity of deep marine reservoirs. Background Technology

[0002] During burial, reservoir sediments undergo various geological processes such as sedimentation, diagenesis, and tectonic uplift. Reservoir permeability is influenced by sediment grain size and composition, as well as diagenetic processes like compaction, cementation, and dissolution. Generally, dissolution is beneficial to reservoirs; the stronger the dissolution, the higher the permeability. However, in some deep-sea, highly heterogeneous, low-permeability sandstones affected by deep hydrothermal activity, dissolution has a negative impact, resulting in decreased permeability. Under similar sedimentary, compaction, and cementation backgrounds, dissolution determines reservoir permeability and is the primary factor influencing it.

[0003] Existing technology discloses a method and apparatus for determining the geophysical two-dimensional characterization of micro-dissolution porosity. The method first identifies the development zone of micro-dissolution porosity, then studies the well logging response characteristics of micro-dissolution porosity by combining well logging curves and nuclear magnetic resonance (NMR) porosity curves, stratifies the target area according to depth, and then determines the petrophysical scale. Next, based on the petrophysical scale, the well logging characteristic curves of micro-dissolution porosity are obtained, and the distribution characteristics of micro-dissolution porosity are determined through seismic waveform indication inversion. This scheme can determine the distribution characteristics of micro-dissolution porosity on a two-dimensional profile; however, this scheme relies solely on well logging data for dissolution rate calculation, resulting in low characterization accuracy. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies that rely solely on well logging data to calculate the low accuracy of dissolution rate characterization, and to provide a method for simulating the dissolution intensity of deep offshore reservoirs. Based on the quantitative relationship between dissolution intensity and diagenetic fluid channels, a characterization model is constructed to improve the rationality and accuracy of the characterization.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0006] A method for simulating the dissolution intensity of deep marine reservoirs is provided, comprising the following steps:

[0007] S1: Based on the parameters of the diagenetic fluid channel, a model for predicting dissolution factors is constructed;

[0008] S2: Comprehensively evaluate the relationship between various parameters of the diagenetic fluid channel and the reservoir dissolution intensity, classify each parameter 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: Based on the fault distribution trend, simulated wells were set up in the study area. The parameters of the diagenetic fluid channels around the simulated wells were measured at vertical intervals according to the seismic profile. The parameters were then substituted into the reservoir dissolution factor calculation model to obtain the longitudinal distribution curve of the dissolution factor.

[0011] S5: Collaborate with seismic data from different frequency bands to perform artificial intelligence inversion. Using the Grey Wolf optimization algorithm, establish a mapping relationship between dissolution factors and seismic frequency attributes. Under the constraints of the set dissolution factor curves of simulated wells and real wells, use the changes in seismic waveforms to simulate the spatial distribution of reservoir dissolution rate.

[0012] This invention provides a method for simulating the dissolution intensity of deep marine reservoirs. Based on the relationship between various parameters of the diagenetic fluid channel and the reservoir dissolution intensity, the method mathematically characterizes the diagenetic fluid channel and constructs a reservoir dissolution factor calculation model guided by fault parameters. This model can calculate the vertical dissolution intensity of a single well. Finally, the vertical distribution curve of the dissolution factor calculated by the model is combined with seismic data for inversion simulation, enabling spatial simulation and prediction of reservoir dissolution intensity. This allows for a quantitative evaluation of reservoir dissolution. This invention constructs a characterization model based on the quantitative relationship between dissolution intensity and the diagenetic fluid channel, and performs calculations starting from the etiological mechanism of dissolution, thus improving the rationality and accuracy of the characterization.

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

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

[0015] In the formula, Z represents the reservoir dissolution factor at the target location; A represents the distance from the target location to the fault F, the secondary conduit for diagenetic fluids. n The distance; B represents the secondary conduit fault F of the diagenetic fluid at the target location. n The connection with the main diagenetic fluid conduit fault F; C represents the secondary diagenetic fluid conduit fault F near the target location. n The vertical distance from the main diagenetic fluid conduit fault F; D represents the secondary diagenetic fluid conduit fault F near the target location. n The quantity; E represents the quantitative score value of the difference in reservoir dissolution intensity controlled by the hanging wall and footwall 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 fault of the main channel of diagenetic fluid; F n This indicates a fault that communicates with F, i.e., a secondary channel fault for diagenetic fluids.

[0016] Preferably, in step S2, 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 second most 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 resulting fuzzy evaluation matrix is:

[0017]

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

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

[0020] Preferably, parameters A and 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 presence of diagenetic fluids directly connected to F is abundant, which is conducive to reservoir dissolution. n If F is connected to another element through three or more faults, the relationship value is set to 1; when F... n F is connected to F through two faults, and the relationship value is set to 1.1; when F n F is connected via a fault line, with a relationship value set to 1.2; when F n Communicate directly with F, with the relationship value set to 1.3. At this point, the width of the fault at the connection point needs to be considered, which is measured on the seismic profile.

[0022] For parameter E, the hanging wall of the fault is highly active and prone to small faults, which is conducive to the dissolution of the reservoir by diagenetic fluids. Therefore, the value near the hanging wall is set to 2, and the value near the footwall is set to 1.

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

[0024]

[0025] In the formula, Z 测井 Φ represents the dissolution factor calculated from well logging. N-D Φ represents neutron-density porosity, i.e., total reservoir porosity; S Φ represents acoustic transit time porosity, i.e., the primary porosity of the reservoir; 实测 This represents the measured porosity, obtained from routine physical property tests; Φ D Indicates density porosity; ΦN Δt represents neutron porosity; Δt represents acoustic transit time; Δt ma Δt represents the propagation time of the gliding wave within the rock skeleton. f C represents the propagation time of the gliding wave in the porous fluid; p This represents the compaction correction coefficient.

[0026] Preferably, the compaction correction coefficient C p It is related to the burial depth, age and region of the strata, 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 from the quantitative analysis of the porosity of the cast thin section.

[0028] Preferably, the calculation process for the dissolution rate obtained from the quantitative analysis of the porosity of the cast thin sheet is as follows: the proportion of porosity to the total porosity is determined by the area estimation method, and the total porosity of the dissolved porosity is calculated:

[0029]

[0030] In the formula, Z 定量 Φ represents the porosity obtained from quantitative porosity calculations. 溶蚀 Indicates the proportion of dissolved porosity within the thin section; Φ 总 This indicates the total face rate.

[0031] Preferably, it is applied to reservoirs where dissolution has a negative effect on reservoir properties and where faults are developed in the surrounding area. n The reservoir.

[0032] Compared with existing technologies, the method for simulating the dissolution intensity of deep marine reservoirs in this invention has the following advantages:

[0033] This invention constructs a calculation model for predicting dissolution factors based on the quantitative relationship between dissolution intensity and diagenetic fluid channels. The model can calculate the vertical dissolution intensity of a single well. This invention performs calculations based on the causal mechanism of dissolution, thereby improving the rationality and accuracy of the characterization. Attached Figure Description

[0034] Figure 1 This is a flowchart of the method for simulating the dissolution intensity of deep marine reservoirs in an embodiment of the present invention;

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

[0036] Figure 3 This is a seismic profile of gas well R1 in Embodiment 2 of the present invention;

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

[0038] Figure 5 This is a seismic profile of gas well R2 in Embodiment 2 of the present invention;

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

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

[0041] Figure 8 This is a planar prediction diagram of the dissolution intensity of gas field R in Embodiment 2 of the present invention. Detailed Implementation

[0042] The present invention will be further described below with reference to specific embodiments.

[0043] Example 1

[0044] A method for simulating the dissolution intensity of deep marine reservoirs, such as Figure 1 As shown, it includes the following steps:

[0045] S1: Based on the parameters of the diagenetic fluid channel, a model for predicting dissolution factors is constructed;

[0046] S2: Comprehensively evaluate the relationship between various parameters of the diagenetic fluid channel and the reservoir dissolution intensity, classify each parameter 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: Based on the fault distribution trend, simulated wells were set up in the study area. The parameters of the diagenetic fluid channels around the simulated wells were measured at 1m intervals vertically according to the seismic profile. The parameters were then substituted into the reservoir dissolution factor calculation model to obtain the longitudinal distribution curve of the dissolution factor.

[0049] S5: Collaborate with seismic data from different frequency bands to perform artificial intelligence inversion. Using the Grey Wolf optimization algorithm, establish a mapping relationship between dissolution factors and seismic frequency attributes. Under the constraints of the set dissolution factor curves of simulated wells and real wells, use the changes in seismic waveforms to simulate the spatial distribution of reservoir dissolution rate.

[0050] The aforementioned method for simulating the dissolution intensity of deep marine reservoirs mathematically characterizes the diagenetic fluid channels based on the relationship between various parameters of the diagenetic fluid channels and the reservoir dissolution intensity. It then constructs a reservoir dissolution factor calculation model guided by fault parameters. This model can calculate the vertical dissolution intensity of a single well. Finally, it uses the vertical distribution curve of the dissolution factor calculated by the model combined with seismic data for inversion simulation, enabling spatial simulation and prediction of reservoir dissolution intensity. This allows for a 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 from the perspective of the dissolution mechanism, improving the rationality and accuracy of the characterization.

[0051] Diagenetic fluids can dissolve and modify reservoirs. Reservoirs near channel faults are more susceptible to the influence of diagenetic fluids. The greater the intensity of the modification 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 the pore channels to be blocked, thereby reducing the reservoir permeability.

[0052] Based on the dissolution rate obtained from quantitative porosity experiments and the dissolution factor derived from single-well logging responses, the distribution of reservoir dissolution intensity was studied in conjunction with the properties and relationships of diagenetic fluid conduit faults. The study found that reservoir dissolution intensity is related to the distance, connectivity, number of conduits, and hanging wall / footwall position of adjacent diagenetic fluid conduit faults. Specifically, the closer a location is to the main diagenetic fluid conduit fault connecting to the mantle, the stronger the reservoir dissolution. When a secondary diagenetic fluid conduit fault is directly connected to the main diagenetic fluid conduit fault connecting to the mantle, and the wider the fault at the connection point, the greater the reservoir dissolution intensity. The shorter the distance between a secondary conduit fault and the main conduit fault near a location, the greater the reservoir dissolution intensity. The more diagenetic fluid conduit faults near a location, the greater the reservoir dissolution intensity. Because the hanging wall of a fault is more active and more prone to fracture zones, which facilitates dissolution, the closer to the hanging wall of a diagenetic fluid conduit fault, the stronger the dissolution intensity. Both the dissolution factor and the dissolution rate are parameters characterizing the intensity of reservoir dissolution.

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

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

[0055] In the formula, Z represents the reservoir dissolution factor at the target location; A represents the distance from the target location to the fault F, the secondary conduit for diagenetic fluids. n The distance; B represents the secondary conduit fault F of the diagenetic fluid at the target location. n The connection with the main diagenetic fluid conduit fault F; C represents the secondary diagenetic fluid conduit fault F near the target location. n The vertical distance from the main diagenetic fluid conduit fault F; D represents the secondary diagenetic fluid conduit fault F near the target location. nThe quantity; E represents the quantitative score value of the difference in reservoir dissolution intensity controlled by the hanging wall and footwall 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 fault of the main channel of diagenetic fluid; F n This indicates a fault that communicates with F, i.e., a secondary channel fault for diagenetic fluids.

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

[0057]

[0058] In step S3, the normalized diagenetic fluid channel parameters are substituted into the fuzzy evaluation matrix, and SPSS software is used for calculation to obtain the fuzzy comprehensive evaluation results: m1, m2, m3, m4, and m5, forming the calculation model for 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 presence of diagenetic fluids directly connected to F is abundant, which is conducive to reservoir dissolution. n If F is connected to another element through three or more faults, the relationship value is set to 1; when F... n F is connected to F through two faults, and the relationship value is set to 1.1; when F n F is connected via a fault line, with a relationship value set to 1.2; when F n Communicate directly with F, with the relationship value set to 1.3. At this point, the width of the fault at the connection point needs to be considered, which is measured on the seismic profile.

[0062] For parameter E, the hanging wall of the fault is highly active and prone to small faults, which is conducive to the dissolution of the reservoir by diagenetic fluids. Therefore, the value near the hanging wall is set to 2, and the value near the footwall is set to 1.

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

[0064]

[0065] In the formula, Z 测井 Φ represents the dissolution factor calculated from well logging.N-D Φ represents neutron-density porosity, i.e., total reservoir porosity; S Φ represents acoustic transit time porosity, i.e., the primary porosity of the reservoir; 实测 This represents the measured porosity, obtained from routine physical property tests; Φ D Indicates density porosity; Φ N Δt represents neutron porosity; Δt represents acoustic transit time, obtained from acoustic logging measurements; Δt ma Δt represents the propagation time of the sliding wave within the rock skeleton, measured by well logging. f This represents the propagation time of the sliding wave in the pore fluid, measured by well logging; C p This represents the compaction correction coefficient.

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

[0067] The dissolution factor calculated from well logging needs to be calibrated and corrected using the dissolution rate obtained from quantitative analysis of the porosity of the cast thin section.

[0068] The calculation process for the dissolution rate obtained from the quantitative analysis of porosity in cast thin sheets is as follows: the proportion of porosity to the total porosity is determined by the area estimation method, and the total porosity of the dissolved porosity is statistically calculated.

[0069]

[0070] In the formula, Z 定量 Φ represents the porosity obtained from quantitative porosity calculations. 溶蚀 Indicates the proportion of dissolved porosity within the thin section; Φ 总 This indicates the total face rate.

[0071] This embodiment applies to reservoirs where dissolution has a negative impact on reservoir properties and where faults are developed in the surrounding area. n The reservoir.

[0072] Example 2

[0073] This embodiment is similar to Embodiment 1, except that the low-permeability gas field R has a large number of faults in its deep, low-permeability reservoir. Specifically, a large fault F1 connecting to the mantle develops in the northern part of the gas field, serving as a channel for diagenetic fluid migration, while fault F2 develops in the southern part, serving as a hydrocarbon supply channel. Several other faults in the region are connected to it, all acting as channels for diagenetic fluid migration. Blind wells R1, R2, R3, and R7 have been drilled in this area. Figure 2 As shown.

[0074] The dissolution factor of a single well was calculated using logging parameters, and the compaction correction coefficient for this area was set to 1. Quantitative porosity analysis was performed on existing cast thin sections to calculate the dissolution rate of the thin sections, which was then used to correct the dissolution factor of the single well.

[0075] Measurements and studies of regional fault parameters revealed that the dissolution factor of a single well is inversely proportional to the distance from the fault connecting to the mantle; the greater the distance, the lower the dissolution rate. Figures 3 to 6 As shown; simultaneously, when the nearest fault is directly connected to a fault communicating with the mantle, the dissolution rate is higher than that of a fault communicating with the mantle through more than one connected fault; the shorter the distance between the fault connections, the higher the dissolution rate; the number of faults developing around the fault communicating with the mantle is positively correlated with the dissolution rate, the more faults, the higher the dissolution rate; there are more fracture zones on the hanging wall of the fault, and under the same conditions, the reservoir closer to the hanging wall of the fault has a greater dissolution intensity. Based on the fault parameters, a fuzzy weighting method is used to obtain a reservoir dissolution factor calculation model, and the predicted dissolution rate calculated by the model is compared with the average dissolution factor calculated by well logging.

[0076] Applying the reservoir dissolution factor calculation model at the target location, interpolation well placement is performed within the region, such as... Figure 7 As shown, the planar distribution of dissolution rate intensity is obtained using seismic inversion, as follows: Figure 8 As shown in Table 1, the logging dissolution factor with porosity quantitative correction was verified by inversion using blind spots and blind wells R3 that were not involved in the learning process.

[0077] Table 1 Comparison of the predicted and calculated dissolution rates of blind wells in gas field R.

[0078]

[0079] As shown in Table 1, the predicted dissolution rate calculated by the model and the average dissolution factor calculated by well logging have a consistency of up to 93.01%.

[0080] Example 3

[0081] This embodiment is similar to Embodiment 1, except that it uses a case study of the deep, low-permeability reservoir in the low-permeability gas field S. Gas field S has a large fault extending into the mantle, where dissolution negatively impacts reservoir properties. Blind wells S1, S2, S3, S4, and S6 were drilled in this area. Fault parameters within the area were studied, and the dissolution factor calculated from individual wells was used to determine how fault parameters affect the intensity of dissolution. The verification results are shown in Table 2.

[0082] Table 2 Comparison of the predicted and calculated dissolution rates of blind wells in gas field S.

[0083]

[0084] As shown in Table 2, the predicted dissolution rate calculated by the reservoir dissolution factor calculation model at the target location is largely consistent with the average value of the logging dissolution factor after quantitative correction of porosity, with an overall consistency of over 80%.

[0085] In the specific implementation of the above embodiments, the technical features can be combined in any non-contradictory way. For the sake of brevity, not all possible combinations of the above technical features are described. However, as long as the combination of these technical features is not contradictory, it 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 implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for simulating the dissolution intensity of deep marine reservoirs, characterized in that, Includes the following steps: S1: Based on the parameters of the diagenetic fluid channel, a model for predicting dissolution factors is constructed; The parameters of the diagenetic fluid conduit are obtained through a comprehensive analysis of the relationship between the wellpoint dissolution factor and the fault, whereby the wellpoint dissolution factor is derived from well logging parameter calculations. In the formula, Z 测井 Φ represents the dissolution factor calculated from well logging. N-D Φ represents neutron-density porosity, i.e., total reservoir porosity; S Φ represents acoustic transit time porosity, i.e., the primary porosity of the reservoir; 实测 This represents the measured porosity, obtained from routine physical property tests; Φ D Indicates density porosity; Φ N Δt represents neutron porosity; Δt represents acoustic transit time; Δt ma Δt represents the propagation time of the gliding wave within the rock skeleton. f C represents the propagation time of the gliding wave in the porous fluid; p Indicates the compaction correction coefficient; The model for the dissolution factor prediction function is as follows: Z = m1A + m2B + m3C + m4D + m5E; In the formula, Z represents the reservoir dissolution factor at the target location; A represents the distance from the target location to the fault F, the secondary conduit for diagenetic fluids. n The distance; B represents the secondary conduit fault F of the diagenetic fluid at the target location. n The connection with the main diagenetic fluid conduit fault F; C represents the secondary diagenetic fluid conduit fault F near the target location. n The vertical distance from the main diagenetic fluid conduit fault F; D represents the secondary diagenetic fluid conduit fault F near the target location. n The quantity; E represents the quantitative score value of the difference in reservoir dissolution intensity controlled by the hanging wall and footwall 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 fault of the main channel of diagenetic fluid; F n This indicates a fault that communicates with F, i.e., a secondary channel fault for diagenetic fluids; S2: Comprehensively evaluate the relationship between various parameters of the diagenetic fluid channel and the reservoir dissolution intensity, classify each parameter 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: Based on the fault distribution trend, simulated wells were set up in the study area. The parameters of the diagenetic fluid channels around the simulated wells were measured at vertical intervals according to the seismic profile. The parameters were then substituted into the reservoir dissolution factor calculation model to obtain the longitudinal distribution curve of the dissolution factor. S5: Collaborate with seismic data from different frequency bands to perform artificial intelligence inversion. Using the Grey Wolf optimization algorithm, establish a mapping relationship between dissolution factors and seismic frequency attributes. Under the constraints of the set dissolution factor curves of simulated wells and real wells, use the changes in seismic waveforms to simulate the spatial distribution of reservoir dissolution rate.

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

3. The method for simulating the dissolution intensity of deep marine reservoirs according to claim 2, characterized in that, In step S3, the normalized diagenetic fluid channel parameters are substituted into the fuzzy evaluation matrix, and SPSS software is used for calculation to obtain the fuzzy comprehensive evaluation results: m1, m2, m3, m4, and m5, forming the calculation model for the reservoir dissolution factor at the target location: Z=0.4309A+0.1787B+0.1781C+0.0721D+0.1408E.

4. The method for simulating the dissolution intensity of deep marine reservoirs according to claim 3, 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 presence of diagenetic fluids directly connected to F is abundant, which is conducive to reservoir dissolution. n If F is connected to another element through three or more faults, the relationship value is set to 1; when F... n F is connected to F through two faults, and the relationship value is set to 1.1; when F n F is connected via a fault line, with a relationship value set to 1.2; when F n Communicate directly with F, with the relationship value set to 1.

3. At this point, the width of the fault at the connection point needs to be considered, which is measured on the seismic profile. For parameter E, the hanging wall of the fault is highly active and prone to small faults, which is conducive to the dissolution of the reservoir by diagenetic fluids. Therefore, the value near the hanging wall is set to 2, and the value near the footwall is set to 1.

5. The method for simulating the dissolution intensity of deep marine reservoirs according to claim 1, characterized in that, Compaction correction coefficient C p It is related to the burial depth, age and region of the strata, and is obtained by porosity correction.

6. The method for simulating the dissolution intensity of deep marine reservoirs according to claim 1, characterized in that, The dissolution factor calculated from well logging needs to be calibrated and corrected using the dissolution rate obtained from quantitative analysis of the porosity of the cast thin section.

7. The method for simulating the dissolution intensity of deep marine reservoirs according to claim 6, characterized in that, The calculation process for the dissolution rate obtained from the quantitative analysis of porosity in cast thin sheets is as follows: the proportion of porosity to the total porosity is determined by the area estimation method, and the total porosity of the dissolved porosity is statistically calculated. In the formula, Z 定量 Φ represents the porosity obtained from quantitative porosity calculations. 溶蚀 This indicates the proportion of dissolved pores within the thin section; Φ 总 This indicates the total face rate.

8. The method for simulating the dissolution intensity of deep marine reservoirs according to any one of claims 1 to 7, characterized in that, Applications of dissolution that negatively impact reservoir properties and are located near fault zones F n The reservoir.

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