A productivity prediction system and a productivity correction method for developing a low-permeability oil reservoir based on a multi-stage fractured horizontal well

Abstract

The application provides a productivity prediction system and a productivity correction method for developing low-permeability oil reservoirs based on multi-stage fracturing horizontal wells. Downhole temperature and pressure data and fracture closure pressure of the low-permeability oil reservoir horizontal well during multi-stage fracturing are collected. The phase distribution of the fracturing fluid in the near-wellbore zone of the horizontal well is determined. The fracture propagation characteristics generated by the volume expansion of the phase transition induced fracturing fluid on the natural fracture activation are determined by the phase distribution and the fracture closure pressure. The erosion intensity of the fracture wall surface caused by the phase transition impact of the fracturing fluid is determined by the energy release characteristics in the microseismic data. The fracture branches are fractally reconstructed according to the erosion intensity and the fracture propagation characteristics, and the flow conductivity attenuation degree of the fracture is obtained. The effective permeability is corrected according to the flow conductivity attenuation degree, and the productivity of the low-permeability oil reservoir is predicted based on the corrected effective permeability. The above scheme can correct the effective permeability of the low-permeability oil reservoir in the case of the complication of the fracture network caused by the fracturing fluid.

Description

Technical Field

[0001] The present application relates to productivity prediction of low permeability oil reservoirs, and more specifically, to a productivity prediction system and productivity correction method for developing low permeability oil reservoirs based on multi-stage fracturing horizontal wells. Background Art

[0002] The development of low-permeability reservoirs is a key direction for improving oil and gas recovery rates. In the development of low-permeability reservoirs, multi-stage fracturing horizontal wells improve seepage conditions by forming complex fracture networks. Its production capacity prediction needs to comprehensively consider fracture morphology, seepage characteristics and reservoir parameters.

[0003] Existing prediction methods for low-permeability reservoirs consider the low-permeability matrix as a bedrock system and the hydraulic fractures as diversion channels. The production capacity is solved by establishing a matrix-fracture coupled seepage equation, or each hydraulic fracture is considered as an independent seepage unit and the total production capacity is calculated by the superposition principle. However, the above methods have difficulty in dealing with complex fracture networks and stress-sensitive effects. During the fracturing process, even slight changes in the injection pressure in the near-wellbore area will cause drastic changes in the physical properties of the fracturing fluid, thereby causing uncontrolled damage to the low-permeability reservoir, making the fracture network with multi-stage fracturing more complex. At the same time, highly fractal fractures will accelerate the dissipation of the injection pressure, resulting in a decrease in the seepage capacity of the hydraulic fractures as diversion channels. Therefore, how to correct the effective permeability of low-permeability reservoirs when the fracturing fluid complicates the fracture network has become a difficult problem faced by the industry. Summary of the Invention

[0004] The present application provides a productivity prediction system and productivity correction method for developing low-permeability oil reservoirs based on multi-stage fracturing horizontal wells, which can correct the effective permeability of low-permeability oil reservoirs when the fracturing fluid complicates the fracture network.

[0005] In a first aspect, the present application provides a productivity correction method for developing a low-permeability reservoir based on multi-stage fracturing horizontal wells, which is used in a low-permeability reservoir productivity prediction system to correct the permeability of a low-permeability reservoir in a near zone before prediction. The method comprises pre-injecting a supercritical fluid as a fracturing fluid into a horizontal well in the low-permeability reservoir for multi-stage fracturing, comprising the following steps:

[0006] collecting downhole temperature and pressure data and fracture closure pressure of the horizontal well in the low permeability oil reservoir during multi-stage fracturing;

[0007] Regulating the injection parameters of the fracturing fluid based on the downhole temperature and pressure data, and determining the phase distribution of the fracturing fluid in the near-wellbore zone of the horizontal well, and determining the fracture extension characteristics caused by the phase change-induced volume expansion of the fracturing fluid on the activation of the natural fracture through the phase distribution and the fracture closure pressure;

[0008] Acquiring microseismic data of the low-permeability oil reservoir, determining the erosion intensity of the fracture wall caused by the phase change impact of the fracturing fluid based on the energy release characteristics of the microseismic data, performing fractal reconstruction on the fracture branches based on the erosion intensity and the fracture extension characteristics, and obtaining the conductivity attenuation of the fracture branch structure affected by the phase change impact;

[0009] The effective permeability of the low permeability oil reservoir in the near-wellbore area is corrected according to the conductivity attenuation, and the productivity of the low permeability oil reservoir is predicted based on the corrected effective permeability.

[0010] In some embodiments, regulating the injection parameters of the fracturing fluid based on the downhole temperature and pressure data, and determining the phase distribution of the fracturing fluid in the near-wellbore area of ​​the horizontal well specifically includes:

[0011] regulating the injection parameters of the fracturing fluid according to the downhole temperature and pressure data;

[0012] Drawing a phase pressure distribution diagram based on the downhole temperature and pressure data;

[0013] The phase distribution of the fracturing fluid in the near-wellbore area of ​​the horizontal well is determined by the phase pressure distribution diagram.

[0014] In some embodiments, determining the fracture extension characteristics caused by the volume expansion of the phase change induced fracturing fluid on the activation of the natural fracture through the phase distribution and the fracture closure pressure specifically includes:

[0015] determining a phase change region of the fracturing fluid according to the phase distribution;

[0016] determining a plurality of volume expansion pressures of the fracturing fluid within the phase change region;

[0017] determining an activated region of a natural fracture based on all volume expansion pressures and the fracture closure pressure;

[0018] The fracture extension characteristics of the low permeability oil reservoir are determined based on the activation zone.

[0019] In some embodiments, determining the erosion intensity of the fracture wall caused by the phase change impact of the fracturing fluid based on the energy release characteristics in the microseismic data specifically includes:

[0020] determining an energy mutation event of the fracturing fluid based on the microseismic data;

[0021] The erosion intensity of the fracture caused by the phase change impact of the fracturing fluid is determined based on the energy mutation event.

[0022] In some embodiments, fractal reconstruction of the crack branches is performed based on the erosion intensity and the crack extension characteristics to obtain the conductivity attenuation of the crack branch structure affected by the phase change impact, specifically comprising:

[0023] constructing a three-dimensional crack image of the crack according to the crack extension characteristics;

[0024] Mapping the erosion intensity to corresponding crack branch coordinate points in the three-dimensional crack image;

[0025] The cracks are reconstructed based on all the crack branch coordinate points to obtain the crack surface shape of the crack branch structure affected by the impact;

[0026] Determining, based on the three-dimensional crack image and the crack surface, a crack channel width variation characteristic of the crack branching structure under the influence of an impact;

[0027] The channel width variation characteristics are used to determine the conductivity attenuation of the crack under the influence of the impact on the crack branch structure.

[0028] In some embodiments, correcting the effective permeability of the low permeability oil reservoir in the near-wellbore area according to the conductivity attenuation specifically includes:

[0029] Obtaining the effective permeability of the low-permeability oil reservoir in the near-wellbore area;

[0030] converting the conductivity attenuation into a permeability correction coefficient;

[0031] The effective permeability of the low-permeability oil reservoir in the near-wellbore area is corrected according to the permeability correction coefficient.

[0032] In some embodiments, predicting the productivity of the low permeability reservoir based on the corrected effective permeability specifically includes:

[0033] Obtaining a prediction model for the production of the low permeability oil reservoir;

[0034] The productivity of the low-permeability oil reservoir is predicted based on the prediction model and the corrected effective permeability.

[0035] In some embodiments, a distributed optical fiber temperature and pressure sensing system is used to continuously arrange sensing fibers along the horizontal wellbore to collect downhole temperature and pressure data and fracture closure pressure of horizontal wells in low permeability reservoirs during multi-stage fracturing in real time.

[0036] In some embodiments, microseismic data of the low permeability reservoir is acquired by a fiber optic distributed acoustic sensing system deployed along a horizontal wellbore.

[0037] In a second aspect, the present application provides a productivity prediction system for developing low-permeability oil reservoirs based on multi-stage fracturing horizontal wells. The productivity prediction system for developing low-permeability oil reservoirs based on multi-stage fracturing horizontal wells includes a productivity correction unit, and the productivity correction unit includes:

[0038] A collection module, used to collect downhole temperature and pressure data and fracture closure pressure of the horizontal well of the low permeability oil reservoir during multi-stage fracturing;

[0039] a processing module for regulating injection parameters of the fracturing fluid based on the downhole temperature and pressure data, determining the phase distribution of the fracturing fluid in the near-wellbore zone of the horizontal well, and determining, through the phase distribution and the fracture closure pressure, fracture extension characteristics resulting from the activation of natural fractures by volume expansion of the fracturing fluid induced by phase change;

[0040] The processing module is further configured to obtain microseismic data of the low-permeability oil reservoir, determine the erosion intensity of the fracture wall caused by the phase change impact of the fracturing fluid based on the energy release characteristics in the microseismic data, perform fractal reconstruction on the fracture branches based on the erosion intensity and the fracture extension characteristics, and obtain the conductivity attenuation of the fracture branch structure affected by the phase change impact;

[0041] An execution module is used to correct the effective permeability of the low permeability oil reservoir in the near-wellbore area according to the conductivity attenuation, and predict the productivity of the low permeability oil reservoir based on the corrected effective permeability.

[0042] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores code, and the processor is configured to obtain the code and execute the above-mentioned productivity correction method for developing low-permeability oil reservoirs based on multi-stage fracturing horizontal wells.

[0043] In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned productivity correction method for developing low-permeability oil reservoirs based on multi-stage fracturing horizontal wells.

[0044] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0045] In the productivity prediction system and productivity correction method for developing low-permeability oil reservoirs based on multi-stage fracturing horizontal wells provided in the present application, first, downhole temperature and pressure data and fracture closure pressure of the horizontal wells of the low-permeability oil reservoir during multi-stage fracturing are collected; the injection parameters of the fracturing fluid are regulated based on the downhole temperature and pressure data, and the phase distribution of the fracturing fluid in the near-wellbore area of ​​the horizontal well is determined, and the fracture extension characteristics caused by the phase change induced volume expansion of the fracturing fluid on the activation of natural fractures are determined through the phase distribution and the fracture closure pressure; microseismic data of the low-permeability oil reservoir are obtained, and the erosion intensity of the fracture wall caused by the phase change impact of the fracturing fluid is determined through the energy release characteristics in the microseismic data, and the fracture branches are fractally reconstructed according to the erosion intensity and the fracture extension characteristics to obtain the conductivity attenuation of the fracture branch structure affected by the phase change impact; the effective permeability of the low-permeability oil reservoir in the near-wellbore area is corrected according to the conductivity attenuation, and the productivity of the low-permeability oil reservoir is predicted based on the corrected effective permeability.

[0046] It can be seen that in the process of the productivity correction method for developing low-permeability oil reservoirs based on multi-stage fracturing horizontal wells, the present application first collects the downhole temperature and pressure data and the crack closure pressure of the horizontal wells of the low-permeability oil reservoir when performing multi-stage fracturing; based on the downhole temperature and pressure data, the injection parameters of the fracturing fluid are regulated, and the phase distribution of the fracturing fluid in the near-wellbore area of ​​the horizontal well is determined. The phase distribution refers to the spatial distribution of the fracturing fluid in different phases (such as supercritical state, gas phase, liquid phase) in the near-wellbore area of ​​the horizontal well, which is used to characterize the changes in the fracturing fluid in the near-wellbore area, so as to facilitate the subsequent quantification of the extension characteristics of the fractures; the fracture extension characteristics caused by the phase change induced fracturing fluid volume expansion on the natural fracture activation are determined through the phase distribution and the fracture closure pressure, wherein the fracture extension The expansion characteristics refer to the characteristics of the natural fractures in the low permeability reservoir when the volume expansion of the fracturing fluid expands the fractures into micro-fractures during the fracturing process of the low permeability reservoir, including the above-mentioned branch density and main fracture length, which are used to characterize the geometric morphology and complexity of the micro-fractures generated by the phase change expansion of the fracturing fluid in the natural fractures; obtaining the microseismic data of the low permeability reservoir, determining the erosion intensity of the fracture wall caused by the phase change impact of the fracturing fluid through the energy release characteristics in the microseismic data, fractally reconstructing the fracture branches based on the erosion intensity and the fracture extension characteristics, and obtaining the conductivity attenuation of the fracture branch structure affected by the phase change impact; correcting the effective permeability of the low permeability reservoir in the near-wellbore area based on the conductivity attenuation, and predicting the production capacity of the low permeability reservoir based on the corrected effective permeability. The above scheme can correct the effective permeability of the low permeability reservoir when the fracturing fluid complicates the fracture network. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1is an exemplary flow chart of a productivity correction method for developing a low-permeability reservoir based on multi-stage fractured horizontal wells according to some embodiments of the present application;

[0048] Figure 2 is an exemplary flow chart for determining crack extension characteristics according to some embodiments of the present application;

[0049] Figure 3 is a schematic diagram of the engineering principle of capturing elastic wave signals according to some embodiments of the present application;

[0050] Figure 4 is a schematic structural diagram of a capacity correction unit according to some embodiments of the present application;

[0051] Figure 5 It is a structural schematic diagram of a computer device for implementing a productivity correction method for developing low-permeability oil reservoirs based on multi-stage fracturing horizontal wells according to some embodiments of the present application. DETAILED DESCRIPTION

[0052] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0053] refer to Figure 1 This figure is an exemplary flow chart of a method for correcting the productivity of a low-permeability reservoir developed by multi-stage fracturing horizontal wells according to some embodiments of the present application. The method for correcting the productivity of a low-permeability reservoir developed by multi-stage fracturing horizontal wells mainly includes the following steps:

[0054] In step 101, downhole temperature and pressure data and fracture closure pressure of a horizontal well in a low permeability oil reservoir during multi-stage fracturing are collected.

[0055] In a specific implementation, first, a distributed fiber optic temperature and pressure sensing system (such as Silixa Ultima™) is used to continuously arrange sensing fibers along the horizontal wellbore (one is set every 100 meters) to collect real-time downhole temperature and pressure of the horizontal well in the low-permeability oil reservoir during multi-stage fracturing. The set of all collected temperatures and pressures is used as the downhole temperature and pressure data of the horizontal well in the low-permeability oil reservoir during multi-stage fracturing. The temperature and pressure are sampled synchronously at a sampling frequency of 10 Hz. Secondly, during the fracturing pump stop stage of the multi-stage fracturing process, a bottomhole pressure gauge is used to record the curve of the bottomhole pressure drop over time. Then, a function analysis method (such as the G function analysis method) is used to extract the inflection point of the pressure drop rate in the above curve as the closure feature point from fracture opening to closure. The closure pressure value of each fracturing stage is then inverted in combination with the formation stress model. The set of all obtained closure pressure values ​​is used as the fracture closure pressure of the horizontal well in the low-permeability oil reservoir. In other embodiments, other methods can also be used for determination, which are not limited here.

[0056] In step 102, the injection parameters of the fracturing fluid are regulated based on the downhole temperature and pressure data, and the phase distribution of the fracturing fluid in the near-wellbore area of ​​the horizontal well is determined. The fracture extension characteristics caused by the phase change-induced volume expansion of the fracturing fluid on the activation of natural fractures are determined through the phase distribution and the fracture closure pressure.

[0057] In some embodiments, the following steps may be used to control the injection parameters of the fracturing fluid based on the downhole temperature and pressure data and determine the phase distribution of the fracturing fluid in the near-wellbore area of ​​the horizontal well:

[0058] regulating the injection parameters of the fracturing fluid according to the downhole temperature and pressure data;

[0059] Drawing a phase pressure distribution diagram based on the downhole temperature and pressure data;

[0060] The phase distribution of the fracturing fluid in the near-wellbore area of ​​the horizontal well is determined by the phase pressure distribution diagram.

[0061] In a specific implementation, the injection parameters of the fracturing fluid can be regulated according to the downhole temperature and pressure data in the following manner, namely: obtaining the critical temperature and critical pressure of the fracturing fluid in a supercritical state from the National Basic Discipline Public Science Data Center; when it is detected that the downhole temperature data reaches the critical temperature of the fracturing fluid in a supercritical state, controlling the heating system to increase the injection temperature of the fracturing fluid, wherein the increased temperature is generally 5-10°C, so as to ensure that the fracturing fluid at the bottom of the well always maintains a supercritical state; at the same time, adjusting the injection pressure of the fracturing fluid based on the pressure data, so that the pressure at the bottom of the well is stabilized at more than 1.2 times the critical pressure, wherein, when When the temperature and pressure of the fracturing fluid approach their critical points, even slight changes in temperature or pressure may cause the phase of the fracturing fluid to change. Therefore, adjusting the parameters of the injected fracturing fluid based on the monitored bottom hole pressure and temperature can avoid frequent changes in the phase of the fracturing fluid near the bottom hole (such as liquid, gas, supercritical state, etc.) due to temperature and pressure fluctuations. The injection parameters refer to the parameters when injecting the fracturing fluid, including injection pressure and injection temperature. The injection pressure refers to the pressure when injecting the fracturing fluid, and the injection temperature refers to the temperature when injecting the fracturing fluid. In other embodiments, other methods can also be used for determination, which is not limited here.

[0062] In a specific implementation, the phase pressure distribution diagram based on the downhole temperature and pressure data can be achieved in the following manner: using phase analysis software to input the downhole temperature and pressure data into the phase analysis software, and at the same time combining the state equation of supercritical fluid (such as the Peng-Robinson model) to calculate the phase boundary curve of the fracturing fluid; dividing the horizontal wellbore into grid nodes with a spacing of 100m, and marking the phase type of the fracturing fluid (i.e., supercritical state, gas phase, liquid phase) at each grid node according to the above phase boundary curve, and using the matrix laboratory (Matrix In the present invention, a three-dimensional geological model of a low-permeability oil reservoir is established with the wellbore center of a horizontal well as the origin, and the three-dimensional geological model and the phase boundary curve are imported into MATLAB to generate a phase pressure distribution diagram under three-dimensional temperature and pressure coordinates, wherein the phase pressure distribution diagram is a distribution diagram describing the phase state of the fracturing fluid as the pressure and temperature change, with the horizontal axis being the temperature, the vertical axis being the pressure, and the vertical axis being the depth. The phase pressure distribution diagram is used to intuitively display the phase type of the fracturing fluid at different positions and its corresponding pressure and temperature distribution; in other embodiments, other methods can also be used for determination, which is not limited here.

[0063] In specific implementation, the phase distribution of the fracturing fluid in the near-wellbore area in the horizontal well is determined by the phase pressure distribution diagram in the following manner, namely: select a grid node from the phase pressure distribution diagram as the selected grid node, use the ratio of the pressure at the selected grid node to the critical pressure as the comparative pressure of the selected grid node, use the ratio of the temperature at the selected grid node to the critical temperature as the comparative temperature of the selected grid node, and continue to determine the comparative pressures and comparative temperatures at the remaining grid nodes; circle a circular area with a diameter of 200m around the low-permeability oil reservoir with the horizontal well as the center in the phase pressure distribution diagram as the near-wellbore area, set multiple grid points in the near-wellbore area at intervals of 100m and mark them in the phase pressure distribution diagram, and apply the radial distance of the horizontal well, the grid nodes to the seepage-heat conduction coupling model (such as the fractured rock seepage-heat transfer coupling model), and the relative humidity of the horizontal well and the relative humidity of the horizontal well are calculated. The injection rate of the fracturing fluid and the thickness of the low permeability oil reservoir are input into the above model as known parameters, and the pressure and temperature at each grid node in the near-well zone are inverted, thereby obtaining the comparative temperature and comparative pressure at each grid node in the near-well zone; all grid nodes in the horizontal well and the near-well zone whose comparative temperature is greater than the supercritical temperature threshold and whose comparative pressure is greater than the supercritical pressure threshold are marked as supercritical states, and all grid nodes in the horizontal well and the near-well zone whose temperature is less than the supercritical temperature threshold and greater than the gaseous temperature threshold and whose pressure is less than the supercritical pressure threshold and greater than the gaseous pressure threshold are marked as phase transition states; all grid nodes in the horizontal well and the near-well zone whose temperature is less than the gaseous temperature threshold and whose pressure is less than the gaseous pressure threshold are marked as gaseous states; thereby displaying all the obtained supercritical states, phase transition states, and gaseous states in the phase pressure distribution diagram using different colors, and obtaining

[0064] Phase distribution of the fracturing fluid in the near-wellbore zone of a horizontal well; in other embodiments, other methods may be used to determine the phase distribution, which is not limited here.

[0065] It should be noted that when the proportion of grid nodes in the supercritical state is greater than or equal to 85%, it is determined that the fracturing fluid has an effective phase distribution in the low permeability reservoir. Otherwise, the injection parameters of the fracturing fluid are readjusted until the phase stability requirements are met; the supercritical temperature threshold, supercritical pressure threshold, gas temperature threshold, and gas temperature threshold can all be set according to actual operating conditions. In this application, the supercritical temperature threshold is set to 1.0, the supercritical pressure threshold is set to 1.2, the gas temperature threshold is set to 0.95, and the gas temperature threshold is set to 0.9; the phase distribution refers to the spatial distribution of the fracturing fluid in different phases (such as supercritical state, gas phase, and liquid phase) in the near-wellbore area of ​​the horizontal well, which is used to characterize the changes in the fracturing fluid in the near-wellbore area, so as to facilitate the subsequent quantification of the extension characteristics of the fracture.

[0066] In some embodiments, reference Figure 2As shown in FIG. 1 , this figure is an exemplary flow chart for determining fracture extension characteristics in some embodiments of the present application. In this embodiment, the fracture extension characteristics caused by the volume expansion of the phase change-induced fracturing fluid on the activation of the natural fracture can be determined by the phase distribution and the fracture closure pressure using the following steps:

[0067] First, in step 1021, the phase change region of the fracturing fluid is determined according to the phase distribution;

[0068] Next, in step 1022, a plurality of volume expansion pressures of the fracturing fluid in the phase change region are determined;

[0069] Then, in step 1023, the activation area of ​​the natural fracture is determined based on all volume expansion pressures and the fracture closure pressure;

[0070] Finally, in step 1024 , the fracture extension characteristics of the low permeability reservoir are determined based on the activated region.

[0071] In specific implementation, the phase change region of the fracturing fluid can be determined based on the phase distribution in the following manner, namely: the region consisting of grid nodes in the phase pressure distribution diagram corresponding to all phase transition states in the phase distribution is used as the phase change region of the fracturing fluid, and the percentage of the phase change region in the total volume of the near-wellbore zone is calculated, wherein the phase change region describes the region where the phase of the fracturing fluid changes in the near-wellbore zone; in other embodiments, other methods can also be used for determination, which are not limited here.

[0072] In a specific implementation, determining multiple volume expansion pressures of the fracturing fluid in the phase change region can be achieved in the following manner, namely: selecting a grid node from the phase change region as the selected grid node, substituting the temperature and pressure monitored in real time at the selected grid node into the gas state equation (such as the van der Waals equation), calculating the expansion ratio of the fracturing fluid at the selected grid node when it transitions from the supercritical state to the gas phase (taking a baseline value of 3.0), and then multiplying the expansion ratio by the injection pressure of the fracturing fluid as the volume expansion pressure of the fracturing fluid at the selected grid node, continuing to determine the volume expansion pressures of the remaining grid nodes, and taking all the obtained volume expansion pressures as the volume expansion pressure of the fracturing fluid in the phase change region, wherein the volume expansion pressure refers to the additional pressure generated by volume expansion when the fracturing fluid transitions from the supercritical state to the gas phase in the phase change region; in other embodiments, other methods can also be used for determination, which are not limited here.

[0073] In specific implementation, the activation area can be determined based on all volume expansion pressures and the crack closure pressure in the following manner, namely: first, a grid node is selected from all grid nodes in the phase transition state as the selected grid node, and the volume expansion pressure at the selected grid node is compared with the closure pressure value at the fracturing section position corresponding to the selected grid node in the crack closure pressure. When the volume expansion pressure exceeds 1.2 times the closure pressure value, the grid node is marked as a primary activation point. When the volume expansion pressure is between 1.0 and 1.2 times the closure pressure value, the comparison temperature of the selected grid node is combined to determine: if If the contrast temperature of the selected grid node is greater than the supercritical temperature threshold, the selected grid node is directly marked as a secondary activation point; secondly, the spatial area formed by all consecutive adjacent activation points is used as the activation area, wherein the primary activation points constitute the core activation zone, and the secondary activation points constitute the potential activation zone; wherein, the activation area refers to the area where, during the fracturing process, the pressure generated by the volume expansion caused by the phase change of the fracturing fluid overcomes the crack closure pressure, thereby keeping the crack open and further expanding. The activation area is divided into two parts: the core activation zone and the potential activation zone; in other embodiments, other methods can also be used for determination, which is not limited here.

[0074] In specific implementation, the fracture extension characteristics of the low permeability reservoir can be determined based on the activation area in the following manner, namely: first, a fracturing section is selected as the selected fracturing section, and the largest connected area composed of grid nodes in the activation area of ​​the selected fracturing section is extracted, so as to generate a three-dimensional fracture trajectory in the connected area through three-dimensional geological modeling software (such as Petrel) combined with a centerline extraction algorithm, and then calculate the curve integral distance of the fracture trajectory as the main fracture length of the selected fracturing section; secondly, a characteristic line extraction technology based on curvature extreme points is applied on the surface of the activation area of ​​the selected fracturing section to identify surface protrusion structures, and calculate the projection length of each protrusion structure (along the main normal direction) and base aperture ratio (ratio of protrusion height to base width), all protrusion structures with a projected length greater than or equal to 5m, an angle with the main fracture greater than 15°, and a base aperture ratio greater than or equal to 0.3 are regarded as effective branch fractures; finally, the number of all effective branch fractures is counted, and the ratio of the number of effective branch fractures to the total area of ​​the activated area of ​​the selected fracturing segment is used as the branch density of the selected fracturing segment, and the main fracture length and branch density of the remaining fracturing segments are further determined. The obtained main fracture length and branch density of each fracturing segment are used as the fracture extension characteristics of the low permeability reservoir; in other embodiments, other methods can also be used for determination, which are not limited here.

[0075] It should be noted that the fracture extension characteristics in this application refer to the characteristics of the fractures when the natural fractures in the low permeability reservoir are affected by the volume expansion of the fracturing fluid to extend the microcracks during the fracturing process of the low permeability reservoir, including the above-mentioned branch density and main fracture length, which are used to characterize the geometric morphology and complexity of the microcracks generated by the phase change expansion of the fracturing fluid in the natural fractures, so as to facilitate the subsequent reconstruction of the fractures.

[0076] In step 103, microseismic data of the low-permeability oil reservoir is obtained, and the erosion intensity of the fracture wall caused by the phase change impact of the fracturing fluid is determined through the energy release characteristics in the microseismic data. The fracture branches are fractally reconstructed according to the erosion intensity and the fracture extension characteristics to obtain the conductivity attenuation of the fracture branch structure affected by the phase change impact.

[0077] In the specific implementation, first, the fiber optic distributed acoustic wave sensing system deployed along the horizontal wellbore (a monitoring point is arranged every 100 meters) is used to capture the elastic wave signals generated by rock fracture during the multi-stage fracturing process in real time. Figure 3 As shown, the figure is a schematic diagram of the engineering principle of capturing elastic wave signals in some embodiments of the present application; the P wave (longitudinal wave) and S wave (transverse wave) components in the elastic wave signal are separated by polarization analysis technology, and the arrival time and amplitude of each monitoring point are recorded; secondly, based on the time difference positioning principle, the arrival time and amplitude of each monitoring point are input as known parameters into the P / S wave velocity model, thereby inverting the spatial coordinates of all rupture points; finally, the energy release value of each rupture point is calculated according to the distance between each rupture point and the horizontal well and the amplitude attenuation model (such as the fractional-order constant Q model), and the positions of all rupture points are marked in the phase pressure distribution diagram, and all rupture points located in the phase change area are regarded as valid rupture points, so that the set consisting of the position coordinates, energy, and rupture time corresponding to each valid rupture point is used as the microseismic data of the low permeability oil reservoir, wherein one rupture point corresponds to one position coordinate, energy, and rupture time; in other embodiments, other methods can also be used for determination, which is not limited here.

[0078] In some embodiments, determining the erosion intensity of the fracture wall caused by the phase change impact of the fracturing fluid based on the energy release characteristics in the microseismic data can be achieved by the following steps:

[0079] determining an energy mutation event of the fracturing fluid based on the microseismic data;

[0080] The erosion intensity of the fracture wall caused by the phase change impact of the fracturing fluid is determined based on the energy mutation event.

[0081] In specific implementation, the energy mutation event of the fracturing fluid can be determined according to the microseismic data in the following manner, namely: first, the microseismic event data set is imported into MATLAB, and the energy change rate and spatial energy density gradient between adjacent rupture points are calculated, wherein the energy change rate is the change of energy in time, and the spatial energy density gradient is the change of energy in space; secondly, all rupture points in the microseismic event data set are sorted according to time, and a rupture point is selected from the microseismic event data set as the selected rupture point. If the energy change rate and spatial energy density gradient of the selected rupture point are relative to the rupture points to the left of the selected rupture point, If the energy change rate and spatial energy density gradient increase of the point are both greater than 50%, the selected fracture point is marked as an energy sudden event, and the remaining fracture points are further marked; finally, all fracture points marked as energy sudden events are verified by a support vector machine classifier to determine the causal relationship between the event and the phase change impact of the fracturing fluid, and an energy sudden event including the event position, peak energy, and duration of each fracture point is obtained, wherein the energy sudden event refers to a rock fracture event that is triggered by the phase change impact of the fracturing fluid and exhibits an abnormally high energy release rate and spatial gradient in a microseismic event; in other embodiments, other methods may also be used for determination, which are not limited here.

[0082] In specific implementation, the erosion intensity of the fracture wall caused by the phase change impact of the fracturing fluid based on the energy mutation event can be determined in the following manner, namely: extracting the duration and peak energy of each rupture point in the energy mutation event to MATLAB to draw an integral curve of energy time, thereby obtaining the erosion energy of each rupture point by integral calculation; inputting all erosion energies into a calibration curve of the erosion energy-erosion depth relationship established by a laboratory core erosion experiment to obtain the erosion depth of each rupture point, selecting a fracturing section as the selected fracturing section, and taking the average of the erosion depths of all rupture points in the selected fracturing section as the erosion intensity value of the selected fracturing section, continuing to determine the erosion intensity values ​​of the remaining fracturing sections, and taking the set of all obtained erosion intensity values ​​as the erosion intensity of the fracture wall caused by the phase change impact of the fracturing fluid; in other embodiments, other methods can also be used for determination, which are not limited here.

[0083] It should be noted that the erosion strength value in this application refers to the average degree of erosion of the crack wall in the fracturing section. It is a dimensionless value, 1 represents maximum erosion, and 0 represents no erosion. The erosion strength refers to the overall erosion effect of the phase change impact of the fracturing fluid on the natural crack wall, which is used to reflect the erosion ability of the fracturing fluid on the crack wall.

[0084] In some embodiments, fractal reconstruction of the crack branches based on the erosion intensity and the crack extension characteristics to obtain the conductivity attenuation of the crack branch structure affected by the phase change impact can be achieved by the following steps:

[0085] constructing a three-dimensional crack image of the crack according to the crack extension characteristics;

[0086] Mapping the erosion intensity to corresponding crack branch coordinate points in the three-dimensional crack image;

[0087] The cracks are reconstructed based on all the crack branch coordinate points to obtain the crack surface shape of the crack branch structure affected by the impact;

[0088] Determining, based on the three-dimensional crack image and the crack surface, a crack channel width variation characteristic of the crack branching structure under the influence of an impact;

[0089] The channel width variation characteristics are used to determine the conductivity attenuation of the crack under the influence of the impact on the crack branch structure.

[0090] In a specific implementation, constructing a three-dimensional fracture image of a fracture based on the fracture extension characteristics can be achieved in the following manner: inputting the fracture extension characteristics into three-dimensional geological modeling software (such as Petrel), and determining the main fracture axis of each fracturing stage based on the lengths of all main fractures in the fracture extension characteristics in combination with surface modeling software. Based on the center position of the horizontal well as the reference point, nodes are set at intervals of 1 meter along the extension direction of each main fracture. Then, starting points of each branch fracture are generated on both sides of the main fracture axis in the software based on a branch density parameter, where the spacing between the starting points is inversely proportional to the branch density. Then, using adaptive triangulated mesh generation technology, the nodes of the main fracture axis and the starting points of each branch fracture are used as primary control points. Secondary interpolation points are inserted between adjacent primary control points based on the local curvature variation between adjacent primary control points. Finally, all nodes are connected using a Delaunay triangulation algorithm to obtain a three-dimensional fracture image of the fracture. The three-dimensional fracture image is a three-dimensional image that characterizes the spatial geometry and interconnectedness of the main fracture and branch fractures. In other embodiments, other methods can also be used for determination, which are not limited here.

[0091] In a specific implementation, mapping the erosion intensity to the corresponding fracture branch coordinate points in the fracture three-dimensional image can be achieved in the following manner, namely: coordinate modeling of the fracture three-dimensional image is performed according to three-dimensional geological modeling software, and the three-dimensional coordinates of the nodes on the main fracture axis, the starting points of the branch fractures, and the intersection coordinate points of the main fracture and the branch fractures in the fracture three-dimensional image are extracted. The above-extracted coordinate points are all used as fracture branch coordinate points, a fracturing section is selected as the selected fracturing section, the erosion intensity value of the selected fracturing section is used as the benchmark intensity value, and the coordinates of all fracture points in the selected fracturing section are used as control nodes, thereby establishing a mapping rule according to a spatial distance weight distribution algorithm: first, calculating the Euclidean distance from each fracture branch coordinate point in the selected fracturing section to each control node, and secondly, substituting all the Euclidean distances and benchmark intensity values ​​into the calculation formula of the erosion intensity to calculate the erosion intensity of each fracture branch coordinate point, wherein the calculation formula of the erosion intensity can be: erosion intensity of the fracture branch coordinate point = benchmark intensity value*(1 / The minimum distance in the Euclidean distance) * distance attenuation coefficient, where the distance attenuation coefficient is an exponential attenuation coefficient; wherein the crack branch coordinate point refers to the specific position coordinates of the crack branch in the three-dimensional crack image, including the node on the main crack axis, the starting point of the branch crack, and the intersection coordinate point of the main crack and the branch crack; in other embodiments, other methods can also be used for determination, which is not limited here.

[0092] In specific implementation, the cracks are reconstructed based on all the crack branch coordinate points, and the crack morphology of the crack branch structure affected by the impact can be obtained in the following way, namely: a crack branch coordinate point is selected as the selected crack branch coordinate point, and the selected crack branch coordinate point is judged. If the erosion intensity corresponding to the selected crack branch is greater than or equal to 0.7, a depression with a diameter of 1 meter offset in the normal direction of the wall of the natural crack is generated with the selected crack branch coordinate point as the center. If the erosion intensity corresponding to the selected crack branch is greater than or equal to 0.5 and less than 0.7, three secondary microcracks are extended with the selected crack branch coordinate point as the center along the connection direction of the starting point of the branch crack connected to the selected crack branch coordinate point. In the embodiment, the secondary microcracks are tree-like bifurcated structures with a width of 0.1 to 0.5 mm; finally, all the secondary microcracks are connected to the natural cracks in the phase pressure distribution diagram through a network manifold algorithm, and the natural cracks in the phase pressure distribution diagram are marked as gray, the secondary microcracks connected to the natural cracks are marked as red, and the stress connection zone between the natural cracks and the secondary microcracks is marked as yellow, thereby obtaining the crack morphology of the crack branch structure affected by the impact, wherein the crack morphology describes the morphological characteristics of the crack surface after the branch structure of the natural crack is impacted, including geometric and topological characteristics such as the width, depth, roughness, and branch structure of the crack; in other embodiments, other methods can also be used for determination, which are not limited here.

[0093] In a specific implementation, determining the channel width variation characteristics of the fracture branch structure under the influence of impact based on the three-dimensional fracture image and the fracture appearance can be achieved in the following manner: selecting a fracture branch coordinate point as a selected fracture branch coordinate point, extracting the cross section of the fracture channel at the position corresponding to the selected fracture branch coordinate point in the three-dimensional fracture image as the original cross section, extracting the cross section of the fracture channel at the selected fracture branch coordinate point at the fracture appearance as the impacted cross section, and calculating the channel width variation rate at the selected fracture branch coordinate point, wherein the channel width variation rate can be calculated as follows: channel width variation rate = impacted cross section / original cross section, continuing to determine the channel width variation rates of the remaining fracture branch coordinate points, and taking the set of all obtained channel width variation rates as the channel width variation characteristics of the fracture branch structure under the influence of impact; wherein the channel width variation rate is a parameter value describing the degree of change in the channel width of a natural fracture under the impact of a fracturing fluid phase change, and the channel width variation characteristics refer to the overall characteristics of the channel width variation of the fracture branch structure of a natural fracture under the influence of impact during a multi-stage fracturing process; in other embodiments, other methods can also be used for determination, which are not limited here.

[0094] In a specific implementation, determining the conductivity attenuation of a fracture in a fracture branching structure under the influence of an impact using the channel width variation characteristic can be achieved in the following manner: initializing a conductivity attenuation model, using the channel width variation characteristic as an initialization parameter of the conductivity attenuation model, and using all main fracture lengths in the fracture extension characteristic as constraint parameters of the conductivity attenuation model. Then, using the conductivity attenuation model, the conductivity attenuation of the fracture in the fracture branching structure under the influence of an impact is obtained. The conductivity attenuation model is a conductivity attenuation model established using a machine learning algorithm (e.g., a decision tree, a neural network, etc.). For example, the following model is obtained: conductivity attenuation = channel width variation characteristic (i.e., initialization parameter) * A + main fracture length (i.e., constraint parameter) * B, where A and B are weight coefficients. A and B can be determined by fitting a historical dataset for training conductivity attenuation using a multivariate linear regression method (e.g., the least squares method). In other embodiments, other methods can also be used for determination, which are not limited here.

[0095] It should be noted that the conductivity attenuation in this application is a parameter value that describes the degree of damage to the conductivity of the fracture caused by the phase change impact of the fracturing fluid. It is used to reflect the degree to which the natural fracture is affected by the phase change impact of the fracturing fluid and thus the collection of the reservoir, so as to facilitate the subsequent correction of the effective permeability of the low-permeability reservoir in the near-well area.

[0096] In step 104, the effective permeability of the low permeability reservoir in the near-wellbore area is corrected according to the conductivity attenuation, and the productivity of the low permeability reservoir is predicted based on the corrected effective permeability.

[0097] In some embodiments, the effective permeability of the low-permeability oil reservoir in the near-wellbore area is corrected according to the conductivity attenuation by the following steps:

[0098] Obtaining the effective permeability of the low-permeability oil reservoir in the near-wellbore area;

[0099] converting the conductivity attenuation into a permeability correction coefficient;

[0100] The effective permeability of the low-permeability oil reservoir in the near-wellbore area is corrected according to the permeability correction coefficient.

[0101] In specific implementation, the effective permeability of the low-permeability oil reservoir in the near-well zone can be obtained in the following manner, namely: obtaining the permeability data of the low-permeability oil reservoir from the storage layer evaluation report of the low-permeability oil reservoir before multi-stage fracturing, converting the permeability data into the effective permeability of the near-well zone of the low-permeability oil reservoir through the wellbore radial flow model, verifying the accuracy of the permeability through historical multi-stage fracturing construction data, and if the error between the current permeability and the historical permeability is greater than 15%, starting the core flow experiment to calibrate the current permeability, thereby obtaining the effective permeability of the low-permeability oil reservoir in the near-well zone; wherein, the permeability data is data describing the ability of the low-permeability oil reservoir to allow fluid to pass through, and the effective permeability is a parameter value describing the effective conduction capacity of the low-permeability oil reservoir to the fluid when the phase change expansion of the fracturing fluid affects the natural fracture; in other embodiments, other methods can also be used for determination, which are not limited here.

[0102] In a specific implementation, the conductivity attenuation can be converted into a permeability correction coefficient by establishing a linear mapping model between conductivity attenuation and permeability correction coefficient based on the conductivity-permeability conversion relationship in the petroleum and natural gas industry standard (SY / T 6380-2022). When the conductivity attenuation is greater than 1, the linear mapping model can be: correction coefficient = conductivity attenuation * 1.2; when the conductivity attenuation is less than or equal to 1, the linear mapping model can be: correction coefficient = conductivity attenuation * 0.8, thereby obtaining the permeability correction coefficient. The permeability correction coefficient is a parameter value used to describe the correction of the effective permeability of a low-permeability reservoir. In other embodiments, other methods can be used for determination, which are not limited here.

[0103] In specific implementation, the effective permeability of the low-permeability oil reservoir in the near-well zone can be corrected according to the permeability correction coefficient in the following manner, namely: the permeability correction coefficient is substituted into the correction formula to correct the effective permeability, wherein the correction formula can be: corrected effective permeability = effective permeability * permeability correction coefficient; in other embodiments, other methods can also be used for determination, which are not limited here.

[0104] In some embodiments, the production capacity of the low permeability reservoir can be predicted based on the corrected effective permeability by using the following steps:

[0105] Obtaining a prediction model for the production of the low permeability oil reservoir;

[0106] The productivity of the low-permeability oil reservoir is predicted based on the prediction model and the corrected effective permeability.

[0107] In a specific implementation, the prediction model for the production of the low-permeability oil reservoir can be obtained in the following manner, namely: using oil reservoir numerical simulation software (such as an oil reservoir simulator) as a basic platform, loading a reservoir geological model into the platform to establish the spatial structure of the low-permeability oil reservoir, and loading a black oil model (Black Oil Model) to quantify the flow pattern of complex hydrocarbons in the low-permeability oil reservoir, so that the integrated system formed by the black oil model and the reservoir geological model is used as the prediction model for the production of the low-permeability oil reservoir, wherein the input parameters of the prediction model are the corrected effective permeability and the target production system, and the output is the monthly production of the low-permeability oil reservoir; in other embodiments, other methods can also be used for determination, which are not limited here.

[0108] In a specific implementation, the production capacity of the low permeability reservoir can be predicted based on the prediction model and the corrected effective permeability in the following manner, namely: the corrected effective permeability and the target production system are both imported as input parameters into the prediction model, the prediction model is run to perform transient simulation calculations (time step of 1 day), and the monthly oil production of the low permeability reservoir within a one-month prediction period is output; in other embodiments, other methods can also be used for determination, which are not limited here.

[0109] In addition, in another aspect of the present application, in some embodiments, the present application provides a productivity prediction system for developing low permeability reservoirs based on multi-stage fractured horizontal wells, the system including a productivity correction unit, referring to Figure 4 , which is a schematic diagram of the structure of a production capacity correction unit according to some embodiments of the present application. The production capacity correction unit includes: a collection module 401, a processing module 402 and an execution module 403, which are described as follows:

[0110] Acquisition module 401, in this application, acquisition module 401 is mainly used to collect downhole temperature and pressure data and fracture closure pressure of the horizontal well of the low permeability oil reservoir during multi-stage fracturing;

[0111] Processing module 402, in the present application, is used to control the injection parameters of the fracturing fluid based on the downhole temperature and pressure data, and determine the phase distribution of the fracturing fluid in the near-wellbore zone of the horizontal well, and determine the fracture extension characteristics caused by the phase change-induced volume expansion of the fracturing fluid on the activation of the natural fracture through the phase distribution and the fracture closure pressure;

[0112] It should be noted that the processing module 402 in the present application is also used to obtain microseismic data of the low-permeability oil reservoir, determine the erosion intensity of the fracture wall caused by the phase change impact of the fracturing fluid through the energy release characteristics in the microseismic data, perform fractal reconstruction of the fracture branch according to the erosion intensity and the fracture extension characteristics, and obtain the conductivity attenuation of the fracture branch structure affected by the phase change impact;

[0113] The execution module 403 in this application is mainly used to correct the effective permeability of the low permeability oil reservoir in the near-wellbore area according to the conductivity attenuation, and predict the production capacity of the low permeability oil reservoir based on the corrected effective permeability.

[0114] In addition, the present application also provides a computer device, which includes a memory and a processor, wherein the memory stores code, and the processor is configured to obtain the code and execute the above-mentioned productivity correction method for developing low-permeability oil reservoirs based on multi-stage fracturing horizontal wells.

[0115] In some embodiments, reference Figure 5 , which is a schematic diagram of the structure of a computer device for implementing a method for correcting the productivity of a low permeability reservoir developed by multi-stage fracturing horizontal wells according to some embodiments of the present application. The method for correcting the productivity of a low permeability reservoir developed by multi-stage fracturing horizontal wells in the above embodiment can be achieved by Figure 5 The computer device 500 shown in FIG. 5 is implemented as shown in FIG. 5 . The computer device 500 includes at least one processor 501 , a communication bus 502 , a memory 503 , and at least one communication interface 504 .

[0116] The processor 501 may be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0117] The communication bus 502 may be used to transmit information between the aforementioned components.

[0118] The memory 503 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, a random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto. The memory 503 may be independent and connected to the processor 501 via the communication bus 502. The memory 503 may also be integrated with the processor 501.

[0119] The memory 503 is used to store program code for executing the solution of the present application, and is controlled by the processor 501. The processor 501 is used to execute the program code stored in the memory 503. The program code may include one or more software modules. The method used in the above embodiment can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0120] The communication interface 504 uses any device such as a transceiver to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area network (WLAN), etc.

[0121] In a specific implementation, as an example, a computer device may include multiple processors, each of which may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. A processor herein may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0122] The aforementioned computer device can be a general-purpose computer device or a dedicated computer device. In a specific implementation, the computer device can be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. The embodiments of this application do not limit the type of computer device.

[0123] In addition, the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned productivity correction method for developing low-permeability oil reservoirs based on multi-stage fracturing horizontal wells.

[0124] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0125] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A productivity correction method for developing low-permeability reservoirs based on multi-stage fracturing horizontal wells, which is used in a low-permeability reservoir productivity prediction system to correct the permeability of low-permeability reservoirs in the near zone before prediction, wherein: Supercritical fluid is pre-injected as a fracturing fluid into a horizontal well in a low permeability oil reservoir to perform multi-stage fracturing, characterized in that the method comprises the following steps: collecting downhole temperature and pressure data and fracture closure pressure of the horizontal well in the low permeability oil reservoir during multi-stage fracturing; Regulating the injection parameters of the fracturing fluid based on the downhole temperature and pressure data, and determining the phase distribution of the fracturing fluid in the near-wellbore zone of the horizontal well, and determining the fracture extension characteristics caused by the phase change-induced volume expansion of the fracturing fluid on the activation of the natural fracture through the phase distribution and the fracture closure pressure; Acquiring microseismic data of the low-permeability oil reservoir, determining the erosion intensity of the fracture wall caused by the phase change impact of the fracturing fluid based on the energy release characteristics of the microseismic data, performing fractal reconstruction on the fracture branches based on the erosion intensity and the fracture extension characteristics, and obtaining the conductivity attenuation of the fracture branch structure affected by the phase change impact; The effective permeability of the low permeability oil reservoir in the near-wellbore area is corrected according to the conductivity attenuation, and the productivity of the low permeability oil reservoir is predicted based on the corrected effective permeability.

2. The method according to claim 1, wherein Regulating the injection parameters of the fracturing fluid based on the downhole temperature and pressure data, and determining the phase distribution of the fracturing fluid in the near-wellbore area of ​​the horizontal well specifically includes: regulating the injection parameters of the fracturing fluid according to the downhole temperature and pressure data; Drawing a phase pressure distribution diagram based on the downhole temperature and pressure data; The phase distribution of the fracturing fluid in the near-wellbore area of ​​the horizontal well is determined by the phase pressure distribution diagram.

3. The method according to claim 1, wherein Determining the fracture extension characteristics caused by the volume expansion of the phase change induced fracturing fluid on the activation of the natural fracture through the phase distribution and the fracture closure pressure specifically includes: determining a phase change region of the fracturing fluid according to the phase distribution; determining a plurality of volume expansion pressures of the fracturing fluid within the phase change region; determining an activated region of a natural fracture based on all volume expansion pressures and the fracture closure pressure; The fracture extension characteristics of the low permeability oil reservoir are determined based on the activation zone.

4. The method according to claim 1, wherein Determining the erosion intensity of the fracture wall by the phase change impact of the fracturing fluid through the energy release characteristics in the microseismic data specifically includes: determining an energy mutation event of the fracturing fluid based on the microseismic data; The erosion intensity of the fracture caused by the phase change impact of the fracturing fluid is determined based on the energy mutation event.

5. The method according to claim 1, wherein The fractal reconstruction of the crack branches is performed according to the erosion intensity and the crack extension characteristics to obtain the conductivity attenuation of the crack branch structure affected by the phase change impact, which specifically includes: constructing a three-dimensional crack image of the crack according to the crack extension characteristics; Mapping the erosion intensity to corresponding crack branch coordinate points in the three-dimensional crack image; The cracks are reconstructed based on all the crack branch coordinate points to obtain the crack surface shape of the crack branch structure affected by the impact; Determining, based on the three-dimensional crack image and the crack surface, a crack channel width variation characteristic of the crack branching structure under the influence of an impact; The channel width variation characteristics are used to determine the conductivity attenuation of the crack under the influence of the impact on the crack branch structure.

6. The method according to claim 1, wherein Correcting the effective permeability of the low permeability oil reservoir in the near-wellbore area according to the conductivity attenuation specifically includes: Obtaining the effective permeability of the low-permeability oil reservoir in the near-wellbore area; converting the conductivity attenuation into a permeability correction coefficient; The effective permeability of the low-permeability oil reservoir in the near-wellbore area is corrected according to the permeability correction coefficient.

7. The method according to claim 1, wherein The production capacity of the low permeability reservoir is predicted based on the corrected effective permeability, specifically including: Obtaining a prediction model for the production of the low permeability oil reservoir; The productivity of the low-permeability oil reservoir is predicted based on the prediction model and the corrected effective permeability.

8. The method according to claim 1, wherein By continuously arranging sensing optical fibers along the horizontal wellbore through a distributed optical fiber temperature and pressure sensing system, downhole temperature and pressure data and fracture closure pressure of horizontal wells in low-permeability reservoirs during multi-stage fracturing are collected in real time.

9. The method according to claim 1, wherein Microseismic data of the low-permeability oil reservoir is obtained through an optical fiber distributed acoustic wave sensing system arranged along the horizontal wellbore.

10. A productivity prediction system for developing low-permeability oil reservoirs based on multi-stage fracturing horizontal wells, the productivity prediction system for developing low-permeability oil reservoirs based on multi-stage fracturing horizontal wells includes a productivity correction unit, characterized in that: The production capacity correction unit includes: A collection module, used to collect downhole temperature and pressure data and fracture closure pressure of the horizontal well of the low permeability oil reservoir during multi-stage fracturing; a processing module for regulating injection parameters of the fracturing fluid based on the downhole temperature and pressure data, determining the phase distribution of the fracturing fluid in the near-wellbore zone of the horizontal well, and determining, through the phase distribution and the fracture closure pressure, fracture extension characteristics resulting from the activation of natural fractures by volume expansion of the fracturing fluid induced by phase change; The processing module is further configured to obtain microseismic data of the low-permeability oil reservoir, determine the erosion intensity of the fracture wall caused by the phase change impact of the fracturing fluid based on the energy release characteristics in the microseismic data, perform fractal reconstruction on the fracture branches based on the erosion intensity and the fracture extension characteristics, and obtain the conductivity attenuation of the fracture branch structure affected by the phase change impact; An execution module is used to correct the effective permeability of the low permeability oil reservoir in the near-wellbore area according to the conductivity attenuation, and predict the productivity of the low permeability oil reservoir based on the corrected effective permeability.

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