Natural fracture time-varying effect simulation method based on fractured low-permeability reservoir

Abstract

The invention relates to the field of oilfield development, in particular to a natural fracture time-varying effect simulation method based on a fractured low-permeability reservoir. The method mainly solves the problems that an existing crack description method is low in crack development trend description precision and cannot effectively guide dual-medium numerical simulation. Comprising the following steps that a microsphere focusing resistivity curve and a sound wave time difference curve are obtained, and a single well crack recognition mode is established; correcting the single-well and single-layer fracture interpretation strength, and establishing a single-well fracture strength model; simulating to obtain a crack strength attribute body; establishing a crack probability distribution model; establishing a relation model of the micro-crack opening time and the water injection pressure; and establishing a natural fracture conductivity multiplied factor calculation formula, and correcting the conductivity at the opening time point of the micro-fracture to realize time-varying effect simulation of the natural fracture. According to the method, time-varying effect simulation of natural cracks and microcracks can be realized, numerical simulation and residual oil quantification precision are improved, targeted accurate potential tapping of residual oil is guided, and a block development effect is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of oilfield development, in particular to a natural fracture time-varying effect simulation method based on fractured low-permeability reservoirs. BACKGROUND

[0002] At present, when describing fractures, mainly qualitative and quantitative methods are used for description. In terms of qualitative description, mainly four aspects are analyzed, including core description, XRMI logging, microseismic monitoring and water breakthrough characteristics of oil wells. First, whether the fractures are developed is determined by observing the coring well, but the number of coring wells is small, and the coring well position may not be in the fracture development area, resulting in one-sidedness of the description result. Second, the fracture development direction and number are determined by XRMI logging data, and the description result is also one-sided due to the small number of logging. Third, the stress direction is determined according to the microseismic monitoring result, so as to determine the fracture development direction, and this method can only assist in determining the fracture development direction. Fourth, the fracture development direction is roughly determined according to the analysis of the water breakthrough direction of the oil well, and this method can only be used as an auxiliary analysis method. In summary, through the above four methods, the fracture development direction and number can only be qualitatively determined, and the permeability and other properties of the fracture development area cannot be quantitatively described, which cannot effectively guide the dual medium numerical simulation.

[0003] In terms of quantitative description, on the basis of qualitatively analyzing whether the block is developed with natural fractures and the approximate development direction of the fractures, the single well fracture interpretation is carried out by comprehensively using logging, three-dimensional seismic and other data, and on this basis, the natural fracture three-dimensional model is established by well-seismic collection to quantitatively represent the permeability and other property values of the fractures in different directions. There are two problems. On the one hand, only static data is used in the modeling process without combining production dynamics and other data, resulting in low precision of single well fracture interpretation and fracture development trend description between wells. On the other hand, the logging data used can only reflect the geological characteristics at the initial stage of development, resulting in that the fracture model established cannot reflect the water breakthrough phenomenon caused by micro-fracture opening when guiding the dual medium numerical simulation, thereby affecting the quantitative precision of numerical simulation of remaining oil, and leading to poor effect of tapping potential. SUMMARY

[0004] In order to overcome the low precision of the existing fracture description method in describing the fracture development trend, and the fact that the influence of micro-fracture opening is not considered in the numerical simulation process, which cannot effectively guide the dual medium numerical simulation, the present application provides a natural fracture time-varying effect simulation method based on fractured low-permeability reservoirs. The natural fracture time-varying effect simulation method based on fractured low-permeability reservoirs can realize the simulation of natural fracture micro-fracture time-varying effect, improve the numerical simulation and quantitative precision of remaining oil, guide the targeted and precise tapping of remaining oil, and improve the development effect of the block.

[0005] The technical scheme of the present application is: a natural fracture time-varying effect simulation method based on fractured low-permeability oil reservoirs, comprising:

[0006] S1, obtaining a microsphere focused resistivity curve and an acoustic travel time curve, establishing a single-well fracture identification mode, and interpreting single-well fractures;

[0007] S2, for the single-well fracture interpretation result, eliminating the influence of mud and diameter expansion on fracture identification by comparison with natural gamma and caliper curves, and correcting the single-well and single-layer fracture interpretation intensity by using liquid production and water injection profile data, so as to establish a single-well fracture intensity model;

[0008] S3, on the basis of the single-well fracture intensity model, extracting fracture-related attributes and determining the weight parameters of each attribute, and simulating a fracture intensity attribute volume according to the discretized single-well fracture intensity curve;

[0009] S4, on the basis of the fracture intensity attribute volume, establishing a fracture probability distribution model according to the regional fracture development characteristics, and correcting the distribution trend of effective interwell fractures to obtain a fracture equivalent permeability field;

[0010] S5, establishing a relationship model between microfracture opening time and water injection pressure;

[0011] S6, establishing a natural fracture conductivity multiplier calculation formula, correcting the conductivity at the microfracture opening time point to realize the simulation of the time-varying effect of natural fractures.

[0012] Further, in step S1, according to the comparison between the sampling points of the microsphere focused resistivity curve and the acoustic travel time curve and the adjacent upper and lower points, it is determined that the resistivity reduction and the acoustic travel time increase in the curve are caused by fracture response, so as to establish a single-well fracture identification mode.

[0013] Further, in step S1, according to the comparison between the sampling points of the microsphere focused resistivity curve and the acoustic travel time curve and the adjacent upper and lower points, four modes of the two curve shapes are determined respectively, and the four curve shape modes are:

[0014] Concave mode: the observation point value is greater than the upper and lower sampling points;

[0015] Convex mode: the observation point value is less than the upper and lower sampling points;

[0016] Pah mode: the observation point value is less than the upper sampling point but greater than the lower sampling point;

[0017] Nah mode: the observation point value is greater than the upper sampling point but less than the lower sampling point.

[0018] Further, the four curve shape modes of the resistivity curve and the acoustic travel time curve are arranged and combined to determine the mode caused by fracture response.

[0019] Further, in the mode caused by the fracture response, the resistivity decreases and the acoustic travel time increases.

[0020] Further, the relevant attributes in the step S3 include fault distance, ant body, curvature body and sand body thickness attribute, the correlation of each attribute with the single well fracture development strength is analyzed, the neural network algorithm is applied to analyze the matrix net-to-gross ratio, porosity, fault distance, ant body, curvature body and single well fracture strength curve, and reasonable weight parameters of each attribute are determined.

[0021] Further, the single well fracture development strength is strong within 200m of the fault, and the single well fracture development strength is negatively correlated with the sandstone thickness.

[0022] Further, in the step S4, on the basis of the fracture probability distribution model, the distribution trend of the effective fracture between wells is corrected by adding block water content, development status and tracer data, and the fracture equivalent permeability field is obtained by using the Oda analytical equivalent method.

[0023] Further, in the step S5, according to the relationship between the water absorption thickness of the water well and the different injection pressures, with the increase of the injection pressure, the oil layer water absorption thickness gradually increases, when all the sublayers in the oil layer participate in water absorption, the water absorption thickness is the largest, and with the continuous increase of the injection pressure, the reservoir water absorption thickness begins to decrease, and the relationship model between the micro-fracture opening time and the injection pressure is established by counting the water well water absorption profile data.

[0024] Further, in the step S6, first, the relationship between the micro-fracture opening width and the rock physical parameters and the pore fluid pressure is established:

[0025]

[0026] In the formula, w is the width after the micro-fracture opening, m; E is the rock elastic modulus, MPa; v is the Poisson's ratio; p is the pore fluid pressure, MPa; H is the oil layer thickness, m; δ θθ is the micro-fracture opening stress, MPa, which is determined by the water absorption thickness change curve.

[0027] Further, according to the relationship between the micro-fracture opening width and the rock physical parameters and the pore fluid pressure, the natural fracture conductivity multiplier factor calculation formula is obtained:

[0028]

[0029] In the formula, w0 is the initial width of the micro-fracture, m.

[0030] The present application has the following beneficial effects: due to the above-mentioned scheme, the present application improves the single well fracture interpretation and natural fracture modeling precision through rich production profile, water absorption profile and oil-water well production dynamic data; the time model of natural fracture micro-fracture opening is established through the production data analysis; the time-varying effect simulation of natural fracture micro-fracture is realized by adjusting the fracture conductivity at the micro-fracture opening time, thereby improving the numerical simulation and remaining oil quantification precision of the fractured low permeability reservoir, guiding the remaining oil targeted precise potential tapping, and improving the block development effect. BRIEF DESCRIPTION OF DRAWINGS

[0031] Figure 1 is a flow chart of the present application;

[0032] Figure 2 is a concave-convex dash line 4 mode chart for fracture identification provided in the embodiment of the present application;

[0033] Figure 3 is a 20 combination mode chart for identifying natural fractures by a conventional logging mode method provided in the embodiment of the present application;

[0034] Figure 4 is a single well fracture interpretation correction result chart provided in the embodiment of the present application;

[0035] Figure 5 is a fracture intensity chart for correcting the water absorption profile provided in the embodiment of the present application;

[0036] Figure 6 is a single well fracture interpretation intensity model chart provided in the embodiment of the present application;

[0037] Figure 7 is a fracture intensity and ant body intersection chart provided in the embodiment of the present application;

[0038] Figure 8 is a fracture intensity and sand body thickness intersection chart provided in the embodiment of the present application;

[0039] Figure 9 is a multi-attribute fusion attribute correlation coefficient chart provided in the embodiment of the present application;

[0040] Figure 10 is a fracture intensity body chart provided in the embodiment of the present application;

[0041] Figure 11 is a fracture attribute model chart provided in the embodiment of the present application;

[0042] Figure 12 is a water absorption thickness change curve chart provided in the embodiment of the present application;

[0043] Figure 13 is a micro-fracture opening time and water injection pressure relationship scatter chart provided in the embodiment of the present application;

[0044] Figure 14 The water cut fitting curve of the 90-72 well is provided in the embodiment of the present application.

[0045] Figure 15 The oil saturation map without considering fracture opening is provided in the embodiment of the present application.

[0046] Figure 16 The oil saturation map considering fracture opening is provided in the embodiment of the present application.

[0047] Figure 17 The columnar graph of the water injection thickness and the number of layers in the profile control well area is provided in the embodiment of the present application. DETAILED DESCRIPTION

[0048] The present application will be described in detail below with reference to the drawings and embodiments, and the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0049] As shown in Figure 1 , a natural fracture time-varying effect simulation method based on fractured low-permeability oil reservoirs includes:

[0050] S1, obtaining the microsphere focused resistivity curve and the acoustic travel time curve, establishing a single well fracture identification mode, and interpreting the single well fracture.

[0051] This process is based on the previous oil well curve interpretation, aiming at the problem of large fracture strength in single well interpretation, using the production and water injection profile monitoring data, according to the production and water injection thickness ratio of different layers of single well, correcting the fracture strength of interpretation, and improving the accuracy of single well fracture description.

[0052] First, the sensitive logging curve is optimized. The microsphere focusing reflects the flushing zone resistivity, which is less affected by surrounding rock and oil and gas. The acoustic travel time reflects the physical properties of the reservoir, which is less affected by the borehole. The microsphere focusing resistivity and high-resolution acoustic travel time curve are combined, and the fracture identification is performed by point-by-point mode according to the sampling points.

[0053] Second, the single well fracture identification mode is established. According to the analysis of the logging curve characteristics, the sampling points and the adjacent points above and below the resistivity curve and the acoustic travel time curve are compared, and their shapes can be summarized into four modes: concave, convex, stroke and nashi. The specific shape is shown in the flushing zone resistivity curve in Figure 2 , wherein:

[0054] Concave mode: the observation point value is greater than the upper and lower sampling points.

[0055] Convex mode: the observed point value is less than the upper neighbor and greater than the lower neighbor;

[0056] Punch mode: the observed point value is less than the upper neighbor but greater than the lower neighbor;

[0057] Punch mode: the observed point value is less than the upper neighbor but greater than the lower neighbor;

[0058] According to the four modes of the well logging curves, 20 kinds of theoretical combination modes of the target sampling points of the resistivity and acoustic interval transit time curves and their upper and lower adjacent points are established, as shown in Table 1. Figure 3 According to the fact that the cracks in the rock cause the decrease of the resistivity and the increase of the acoustic interval transit time, it is determined that seven of the modes are possibly caused by the crack response, which guides the single well crack interpretation. Figure 3 In the 2nd, 3rd, 4th, 11th, 12th, 17th and 19th modes in Table 1, the resistivity decreases, and the acoustic interval transit time increases or remains unchanged, so the cracks possibly exist in the rock.

[0059] Thirdly, the crack calculation parameter control. According to the uplink and downlink change gradient of the microsphere focusing and high resolution acoustic interval transit time curve, the instrument measurement error and the crack response are distinguished.

[0060] The uplink gradient formula is:

[0061] TIDU RXO_UP = Abs((RXO (n-1) -RXO) / RXO) (1)

[0062] TIDU AC_UP = Abs((AC (n-1) -AC) / AC) (2)

[0063] The downlink gradient formula is:

[0064] TIDU RXO_DOWN = Abs((RXO-RXO (n+1) ) / RXO) (3)

[0065] TIDU AC_DOWN = Abs((AC-AC (n+1) ) / AC) (4)

[0066] In the formula, TIDU RXO_UP is the uplink gradient of the flushed zone resistivity; RXO is the flushed zone resistivity, Ω.m; TIDU AC_UP is the uplink gradient of the acoustic interval transit time; AC is the acoustic interval transit time, μs / m; TIDU RXO_DOWN is the uplink and downlink gradient of the flushed zone resistivity; TIDU AC_DOWN is the downlink gradient of the acoustic interval transit time.

[0067] The gradient of acoustic travel time minimum to identify the fracture is generally 0.003, and the gradient of flushed zone resistivity minimum to identify the fracture is generally 0.01.

[0068] S2, for single well fracture interpretation result, by comparing with natural gamma, caliper curve, eliminating the influence of mud, diameter expansion on fracture identification, see Figure 4 . On the basis of single fracture interpretation, increase the production and water absorption profile data, according to the physical property, relative water absorption and production fluid, the single well, single layer fracture interpretation intensity is corrected, the interpretation precision is improved, so as to establish single well fracture intensity model, see Figure 5 、 Figure 6 .

[0069] S3, on the basis of single well fracture intensity model, extract fracture related attributes, and determine the weight parameter of each attribute, according to the discrete single well fracture intensity curve, the fracture intensity attribute volume is simulated.

[0070] This process is based on the previous well seismic combination fracture three-dimensional modeling, aiming at the low accuracy of interwell fracture prediction, increasing the block water content, development status and tracer data, correcting the distribution trend of effective interwell fracture, improving the prediction accuracy of interwell fracture distribution trend.

[0071] First, fracture related attribute extraction. The fault distance, ant body, curvature body and sand thickness attribute are extracted by using seismic data volume, and the correlation between each attribute and single well fracture development intensity is analyzed. The analysis shows that the fracture intensity within 200m of fault is large; it is negatively correlated with sand thickness, which means that the thinner the sand thickness, the greater the probability of single well fracture development; there is a certain correlation with ant body, see Figure 7 、 Figure 8 .

[0072] Second, fracture intensity volume simulation. The neural network algorithm is used to analyze the NTG (net to gross ratio), porosity, fault distance, ant body, curvature body and single well fracture intensity curve of matrix, and determine the reasonable weight parameter of each attribute. The discrete single well fracture intensity curve is used as hard data, and the sequential Gaussian algorithm is used to simulate the fracture intensity attribute volume, see Figure 9 、 Figure 10 .

[0073] S4, on the basis of fracture intensity attribute volume, according to the regional fracture development characteristics, the fracture probability distribution model is established, on the basis of which, by increasing the block water content, development status and tracer data, the distribution trend of effective interwell fracture is corrected, and the equivalent permeability field of fracture is obtained by using Oda analytical equivalent method, which lays a foundation for double medium numerical simulation, see Figure 11 .

[0074] S5, the relationship model between micro fracture opening time and water injection pressure is established.

[0075] The research results show that part of the reservoir with developed natural fractures is dominant fracture and part is recessive fracture. During the water injection development of low permeability oilfield, the pressure of water injection well is increased. When the oil reservoir pressure reaches a certain high pressure value and remains for a certain time, the recessive micro-fracture will gradually open, which results in the sharp increase of fracture permeability. This is the time-varying effect of fracture. This phenomenon is dynamically reflected in both water injection well and production well.

[0076] At the end of water injection well, according to the statistical data of water injection thickness and water injection pressure, with the increase of water injection pressure, the thickness of oil reservoir water absorption gradually increases. When all the small layers in the oil reservoir participate in water absorption, the water absorption thickness is the largest. With the continuous increase of water injection pressure, the water absorption thickness of the reservoir begins to decrease, which indicates that the high pressure of the oil reservoir leads to the opening of the recessive micro-fracture, which makes the water absorption of single layer of the fracture developed reservoir advance, and the water absorption thickness decreases, see Figure 12 .

[0077] From the actual production dynamic situation, the micro-fracture is opened under the joint action of pressure and time. Through the statistics of a large amount of water injection profile data, the relationship model between micro-fracture opening time and water injection pressure is established, which lays the foundation for the simulation of natural fracture time-varying effect, see Figure 13 .

[0078] S6, the natural fracture conductivity multiplier calculation formula is established, and the natural fracture time-varying effect simulation is realized by correcting the conductivity at the micro-fracture opening time point.

[0079] The relationship between the opening width of micro-fracture and the rock physical parameters and the fluid pressure in the pore is established by applying rock mechanics theory:

[0080]

[0081] The natural fracture conductivity multiplier calculation formula is obtained:

[0082]

[0083] In the formula, w is the width of micro-fracture after opening, m; w0 is the initial width of micro-fracture, m; E is the elastic modulus of rock, MPa; v is Poisson's ratio; p is the fluid pressure in the pore, MPa; H is the thickness of oil reservoir, m; δ θθ is the micro-fracture opening stress, MPa, which is determined by the water absorption thickness curve, δ θθ = 11 MPa.

[0084] Based on the matrix and natural fracture model, the dual medium numerical simulation is carried out to improve the numerical simulation accuracy. On this basis, considering the opening of natural fracture micro-fracture, the formula (6) is applied to determine the natural fracture conductivity multiplication factor, and the time-varying effect simulation of natural fracture is realized by correcting the conductivity at the micro-fracture opening time point, further improving the numerical simulation accuracy and realizing the accurate simulation of fracture plane interference type remaining oil.

[0085] Through the simulation of the time-varying effect of natural fractures, the fitting accuracy of single well water cut is greatly improved, and the description of fracture plane interference type remaining oil caused by the opening of natural fracture micro-fracture is more accurate, which better guides the precise tapping of remaining oil.

[0086] Embodiment:

[0087] Taking the well of 90-72 as an example, the water cut of the well rises sharply after water breakthrough, assuming that the matrix / fracture permeability is unchanged, the water breakthrough time can basically be fitted, but the rising amplitude of water cut is obviously lower than the actual; if the fracture permeability is increased at the initial stage of water injection, the water breakthrough time of the oil well will be greatly advanced, which is also not in line with the actual situation; according to the natural fracture micro-fracture opening time model, the micro-fracture opening time of the well is 28 months after water injection, and by enlarging the fracture conductivity by 15 times at the micro-fracture opening time point, the fitting curve accuracy of the water cut of the well is increased from 66.65% to 85.21%, which is increased by 18.56 percentage points, and the water breakthrough time is reduced by 6.5 months. Figure 14 .

[0088] Considering the further increase of the permeability difference between natural fractures and matrix after the opening of natural fractures, the channeling of injected water along the fractures is obviously enhanced, and the interference of fractures on matrix seepage on the plane is intensified, compared with not considering the opening of micro-fractures, the difference of oil saturation between matrix and fractures is increased by 2.43 percentage points. Figure 15 、 16 .

[0089] For the fracture plane interference type remaining oil, deep profile control is carried out to block the water drive dominant channel formed by the large pore channel of the fracture, so as to expand the water drive swept volume. The well group of 78-78 in the block is optimized to carry out 10 injection and 28 production deep profile control test, and the water absorption thickness and layer percentage are increased by 12.5 and 13.9 percentage points respectively before and after profile control, the oil layer producing condition is improved, the water drive dominant channel is controlled, the production fluid structure is optimized, and the water drive dominant channel is controlled. Figure 17 .

[0090] Having described various embodiments of the application, it is to be understood that the above description is meant not to limit and not to encompass all of the possible embodiments covered by the claims. Many modifications and variations of this application can be apparent to those of ordinary skill in the art without departing from the spirit and scope of the described embodiments. It is intended that the scope of the application should only be limited by the appended claims.

Claims

1. A method for simulating the time-varying effects of natural fractures in fractured, low-permeability reservoirs, characterized in that... include: S1. Obtain the microsphere focusing resistivity curve and acoustic transit time curve, establish a single-well fracture identification mode, and interpret the single-well fracture; S2. Based on the interpretation results of single-well fractures, the influence of clay and diameter enlargement on fracture identification is eliminated by comparing with natural gamma and well diameter curves. The interpretation strength of single-well and single-layer fractures is corrected by using production and water absorption profile data, thereby establishing a single-well fracture strength model. S3. Based on the single-well fracture strength model, extract fracture-related attributes and determine the weight parameters of each attribute. Based on the discretized single-well fracture strength curve, simulate the fracture strength attribute body. S4. Based on the fracture strength attribute body, according to the regional fracture development characteristics, establish a fracture probability distribution model, correct the distribution trend of effective fractures between wells, and obtain the fracture equivalent permeability field. S5. Establish a model relating microcrack initiation time to water injection pressure; S6. Establish a formula for calculating the conductivity multiplier of natural fractures, and simulate the time-varying effect of natural fractures by correcting the conductivity at the micro-crack opening time point.

2. The method for simulating the time-varying effects of natural fractures in fractured, low-permeability reservoirs according to claim 1, characterized in that: In step S1, by comparing the sampling points of the microsphere focused resistivity curve and the acoustic transit time curve with the adjacent points above and below, it is determined that the decrease in resistivity and the increase in acoustic transit time in the curve are caused by the fracture response, thereby establishing a single-well fracture identification mode.

3. The method for simulating the time-varying effects of natural fractures in fractured low-permeability reservoirs according to claim 2, characterized in that: In step S1, based on the comparison between the sampling points of the microsphere focusing resistivity curve and the acoustic transit time curve and their adjacent points, four modes of the two curve shapes are determined. The four curve shape modes are as follows: Concave mode: The observed value is greater than the upper and lower sampling values; Convex mode: The observed value is less than the upsampling and downsampling values; Skip mode: The observed value is less than the upsampled value, but greater than the downsampled value; Pressing down mode: The observed value is greater than the upsampled value, but less than the downsampled value.

4. The method for simulating the time-varying effects of natural fractures in fractured, low-permeability reservoirs according to claim 3, characterized in that: The four curve morphology patterns of the resistivity curve and the acoustic time difference curve are arranged and combined to determine the mode caused by the crack response.

5. The method for simulating the time-varying effects of natural fractures in fractured, low-permeability reservoirs according to claim 4, characterized in that: In the mode caused by the crack response, resistivity decreases and acoustic transit time increases.

6. The method for simulating the time-varying effects of natural fractures in fractured low-permeability reservoirs according to claim 1, characterized in that: The relevant attributes in step S3 include fault distance, ant body, curvature body, and sand body thickness. The correlation between each attribute and the fracture development intensity of a single well is analyzed. A neural network algorithm is applied to analyze the matrix net-to-gross ratio, porosity, fault distance, ant body, curvature body, and fracture intensity curves of a single well to determine reasonable weight parameters for each attribute.

7. The method for simulating the time-varying effects of natural fractures in fractured, low-permeability reservoirs according to claim 6, characterized in that: The intensity of fracture development in a single well is high within 200m of the fault; the intensity of fracture development in a single well is negatively correlated with the thickness of the sandstone.

8. The method for simulating the time-varying effects of natural fractures in fractured low-permeability reservoirs according to claim 1, characterized in that: In step S4, based on the fracture probability distribution model, the distribution trend of effective fractures between wells is corrected by adding block water content, development status and tracer data, and the Oda analytical equivalent method is used to obtain the fracture equivalent permeability field.

9. The method for simulating the time-varying effects of natural fractures in fractured, low-permeability reservoirs according to claim 1, characterized in that: In step S5, based on the relationship between the water absorption thickness of the well and different injection pressures, as the injection pressure increases, the water absorption thickness of the oil layer gradually increases. When all sub-layers in the oil layer participate in water absorption, the water absorption thickness is at its maximum. As the injection pressure continues to increase, the reservoir water absorption thickness begins to decrease. By statistically analyzing the water absorption profile data of the well, a model is established to show the relationship between the microfracture opening time and the injection pressure.

10. The method for simulating the time-varying effects of natural fractures in fractured low-permeability reservoirs according to claim 1, characterized in that: In step S6, the relationship between the microcrack opening width and the rock physical properties and the fluid pressure within the pores is first established: This leads to the formula for calculating the conductivity multiplier of natural cracks: Where: w is the width of the microcrack after opening, m; w0 is the initial width of the microcrack, m; E is the elastic modulus of the rock, MPa; ν is Poisson's ratio; p is the fluid pressure inside the pores, MPa; H is the oil layer thickness, m; δ θθ The initiation stress of the microcrack, in MPa, is determined by the water absorption thickness variation curve.

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