Method for identifying dominant percolation channels in extra-high water cut stage based on time-varying reservoir properties

Abstract

The application provides a method for identifying dominant seepage channels in an extra-high water cut stage based on reservoir physical property time variation, relates to the field of dynamic geology and reservoir characterization, calibrates permeability and water phase relative permeability time variation parameters through a core multiple PV multiple water drive experiment, constructs a suitable physical property time variation model and embeds a three-dimensional oil-water two-phase numerical simulation model, solves a global dynamic seepage field evaluation index after production history fitting and calibration; fuses production dynamics, logging, tracer and other multi-source monitoring data, dynamically corrects a discrimination threshold and corrects a channeling parameter, preliminarily locates a potential dominant seepage channel; and finally completes channel grading quantitative characterization and precision verification through a multi-level evaluation system. The application can effectively distinguish a primary high-permeability zone and a water drive secondary dominant channel, significantly improves channeling channel identification precision in an extra-high water cut stage, and can provide reliable technical support for profile control, water plugging, water control and oil stabilization and water drive recovery improvement in an old oilfield.

Description

Technical Field

[0001] This invention relates to the field of dynamic geology and reservoir characterization, and in particular to a method for identifying dominant seepage channels during ultra-high water cut periods based on time-varying reservoir properties. Background Technology

[0002] Currently, domestic technologies for identifying dominant seepage channels in water-driven old oil reservoirs during the ultra-high water-cut stage mostly rely on static geological property parameters, numerical simulations of reservoirs with constant parameters, and single dynamic monitoring data for identification. These technologies have long suffered from shortcomings such as inaccurate mechanism characterization, low identification accuracy, and poor channel differentiation capabilities. They are unable to adapt to the dynamic evolution and development characteristics of reservoir properties during the ultra-high water-cut stage, specifically exhibiting the following core defects: 1. Traditional methods assume that reservoir permeability, relative permeability, and other physical properties remain constant throughout the development process. However, in ultra-high water-cut old oil reservoirs, long-term water injection scouring, particle migration, and stress can lead to irreversible dynamic alterations in reservoir porosity and permeability, as well as rock wettability. Constant property models cannot characterize these time-varying mechanisms, resulting in distorted flow field simulations and fundamentally causing errors in identifying dominant channels.

[0003] 2. Existing technologies rely solely on initial static physical properties to identify high-permeability areas, confusing the primary high-permeability zones formed by sedimentation with secondary dominant channels created by water-driven modification. Ineffective water circulation during ultra-high water cut periods mainly originates from later water-driven modification channels; primary high-permeability zones may not necessarily have water channeling issues. Traditional identification methods easily lead to insufficient targeting and poor effectiveness of profile control measures.

[0004] 3. Existing channel identification thresholds are mostly fixed empirical values, without dynamic correction based on the time-varying laws of reservoir properties. In the ultra-high water-cut stage, the oil-water seepage patterns and reservoir heterogeneity change significantly compared to the early development stage. Static thresholds cannot adapt to the dynamic seepage field characteristics, which easily leads to problems such as missed detection of hidden secondary channels and underestimation of channel development.

[0005] 4. Conventional identification methods rely solely on single production or logging data, failing to integrate and verify multi-source data such as numerical simulation of dynamic flow fields, on-site monitoring, tracers, and well testing. The limitations of single data dimensions prevent a comprehensive depiction of reservoir property changes and inter-well flow characteristics, resulting in insufficient accuracy in identifying the spatial morphology, development scale, and flow intensity of dominant channels. This fails to meet the engineering requirements for precise water control and potential tapping in ultra-high water-cut reservoirs.

[0006] Therefore, a method for identifying dominant seepage channels during ultra-high water cut periods based on time-varying reservoir properties is needed to solve the above problems. Summary of the Invention

[0007] The purpose of this invention is to provide a method for identifying dominant seepage channels during ultra-high water cut periods based on the time-varying properties of reservoirs. This method involves calibrating the time-varying parameters of reservoir permeability and relative permeability through core water drive experiments, and constructing a time-varying mathematical model of properties suitable for different reservoir types. The time-varying model is embedded into a three-dimensional oil-water two-phase numerical simulation model, and after calibration with historical production data, a dynamic seepage field and time-varying evaluation indicators across the entire grid are obtained. Combining multi-source monitoring data such as production dynamics, well logging, and tracer data, the method dynamically corrects the discrimination threshold based on the time-varying laws of properties, calibrates inter-well crossflow parameters, and initially locates potential dominant seepage channels. Finally, a multi-level evaluation system is used to complete the graded quantitative characterization and accuracy verification of dominant channels. This method solves the problems of distorted seepage mechanism description and poor adaptability of the discrimination system in traditional static identification methods. The multi-source data coupling verification significantly improves the accuracy of identifying the spatial location, development scale, and crossflow intensity of dominant channels.

[0008] To achieve the above objectives, this invention provides a method for identifying dominant seepage channels during ultra-high water cut periods based on time-varying reservoir properties. This method is applicable to long-term water-driven sandstone, conglomerate, and fractured old oil reservoirs with a water cut of not less than 90%, and includes the following steps: S1: Through long core multi-PV multiple continuous water drive scour experiments, the dynamic evolution parameters of reservoir properties of the target oil reservoir were calibrated, a time-varying mathematical model of permeability coupled with cumulative water phase flux and critical scour flux was established, and a time-varying correction model of relative permeability corresponding to wettability reversal during the ultra-high water cut period was constructed to obtain the dynamic evolution law of reservoir permeability and relative water phase permeability with the water injection development process. S2: Based on the three-dimensional static geological model of the target block, a time-varying mathematical model of permeability and a time-varying correction model of relative permeability are assigned to each grid. A three-dimensional oil-water two-phase numerical simulation model of dynamic evolution of physical properties is constructed. The key fitting parameters of the time-varying permeability model and the time-varying correction model of relative permeability are globally calibrated by combining the historical production data of the block. A time-varying numerical simulation model characterizing the dynamic seepage characteristics of reservoirs during the ultra-high water cut period is obtained. S3: Based on the calibrated time-varying numerical simulation model, three types of time-varying flow field discrimination indices at the grid scale are dynamically calculated to characterize the spatial distribution characteristics of the dynamic seepage field after water drive modification; the three types of time-varying flow field discrimination indices include permeability amplification factor, oil-to-flow ratio (OFR), and inter-well dynamic seepage resistance coefficient. S4: Combining production dynamic data, well logging monitoring data, inter-well tracer inversion data, and well test monitoring data, the traditional discrimination threshold is corrected based on the time-varying evolution characteristics of physical properties during the ultra-high water cut period. In the three-dimensional time-varying numerical simulation model, a candidate grid set of potential dominant seepage channels is initially screened, and potential dominant seepage channels in the vertical layers and between planar wells are initially located. S5: Using the candidate grid set as the evaluation object, a multi-level fuzzy comprehensive evaluation system is constructed based on three types of time-varying flow field discrimination indicators. The discrimination threshold modified in S4 is used as the boundary parameter of the evaluation level. The multi-level fuzzy comprehensive evaluation system is constructed to classify the development level of dominant seepage channels. Grids outside the candidate grid set are judged as undeveloped or no longer participating in the evaluation. The parameters of channel permeability, channel width, crossflow multiple, and proportion of ineffective water circulation are quantitatively obtained. The accuracy of the identification results is verified by the actual measurement of tracer and the feedback of profile adjustment construction. Finally, the identification and three-dimensional spatial distribution of dominant seepage channels in the ultra-high water cut period are completed.

[0009] Preferably, in S1, for conventional sandstone reservoirs, a time-varying mathematical model of permeability controlled by water injection flushing is adopted. The specific expression and complete parameter definition are as follows: ; In the formula, The reservoir dynamic absolute permeability at development time t; The initial static absolute permeability of the reservoir; The reservoir scour expansion coefficient; From the start of water injection to The cumulative water phase flux in the grid at any given time; The critical scour flux of the reservoir is the critical water washing threshold that initiates the stripping of reservoir cement and particle migration. when > At that time, the reservoir permeability underwent irreversible expansion and modification; when ≤ The reservoir permeability remains at its initial state. For fractured and highly stress-sensitive reservoirs, a fluid-structure interaction time-varying mathematical model for permeability is adopted, which takes into account both water injection scour and formation stress deformation. The specific expression is defined as follows: ; In the formula, For water injection flushing expansion function; This is the correction function for the effective stress of the formation; This represents the effective stress of the formation.

[0010] Preferably, the time-varying correction model for relative permeability corresponding to the wettability reversal during the ultra-high water cut period is as follows: ; In the formula, For a moment Water saturation level is Dynamic relative permeability of the water phase at that time; For the initial development stage, the water saturation is The static relative permeability of the aqueous phase at that time; This is the wettability reversal correction factor.

[0011] Preferably, the permeability amplification factor is used to quantify the increase in reservoir permeability before and after waterflooding, and the calculation formula is as follows: The threshold for classifying physical property modifications corresponding to the permeability amplification factor is: ≥3 indicates a strongly modified reservoir, 1.5≤ <3 indicates a moderately modified reservoir, 1< <1.5 indicates a weakly modified reservoir.

[0012] Preferably, the formula for calculating the oil-to-flow ratio (OFR) is as follows: ; In the formula, The viscosity of underground crude oil; Formation water viscosity; Let be time t, and water saturation be The dynamic relative permeability of the oil phase at that time; the core discrimination threshold for the dominant seepage channel during the ultra-high water cut period was set to OFR < 0.002.

[0013] Preferably, by combining the time-varying evolution law of reservoir properties, the thresholds of various dynamic discrimination indicators are corrected, and the layers with monthly water cut increase >1.5%, annual oil production decrease >10%, single-layer water absorption ratio >60%, and inter-well pressure transmission time shortened by more than 50% are identified as potential dominant seepage channels. Combining multi-period saturation logging and oxygen activation logging monitoring data, the physical property modification layers with water cut saturation <0.3 and significant increase in porosity and permeability after water drive are identified. Based on the relative permeability time-varying correction model and permeability time-varying correction model constructed in S1, a numerical simulation model of inter-well tracer migration is established. The inter-well tracer breakthrough curve is fitted as the target to invert the crossflow multiple of the dominant channel. This crossflow multiple is coupled with production dynamic data, logging monitoring data, and well test monitoring data, and combined with the corrected discrimination thresholds to complete the preliminary identification of potential dominant seepage channels.

[0014] Preferably, the classification and calibration of the dominant seepage channel level is as follows: Type I strong developmental advantage pathway: ≥3, OFR < 0.001, crossflow ratio > 5, ineffective water circulation ratio ≥ 95%; Category II moderate developmental dominance pathway: 1.5≤ <3, 0.001≤OFR<0.002, 2<crossflow ratio≤5, indicating the existence of localized ineffective water crossflow; Category III Weak Developmental Potential Pathways: 1 < <1.5 and crossflow ratio ≤2 indicate an early warning channel for continuously rising water content.

[0015] Preferably, it is applied to a channel identification system, which specifically includes a physical property time-varying parameter calibration module, a time-varying numerical simulation module, a multi-source data coupling identification module, and a channel hierarchical characterization and verification module.

[0016] Preferably, the input and output terminals of the time-varying numerical simulation module are connected to the time-varying parameter calibration module and the multi-source data coupling identification module, respectively; the output terminal of the multi-source data coupling identification module is connected to the channel hierarchical characterization and verification module.

[0017] Preferably, after receiving the multi-PV multiple continuous water-driven flushing experimental data, the input terminal of the physical property time-varying parameter calibration module outputs the permeability time-varying model, the relative permeability time-varying correction model, and the calibrated time-varying characteristic parameters.

[0018] Therefore, the present invention employs the above-mentioned method for identifying dominant seepage channels during ultra-high water cut periods based on time-varying reservoir properties, and the beneficial technical effects are as follows: (1) This invention relies on core water-drive scour experiments to calibrate permeability and relative permeability time-varying parameters, and constructs a physical property time-varying model adapted to different reservoir characteristics. It can accurately characterize the irreversible physical property modification process of reservoir caused by long-term water injection scour, wettability reversal, and stress deformation during ultra-high water cut. It effectively solves the problems of distortion in traditional numerical model seepage field calculation and inaccurate mechanism characterization, and provides a real and reliable dynamic numerical basis for subsequent identification of dominant channels.

[0019] (2) This invention relies on time-varying indicators such as the permeability amplification factor and dynamic oil-to-channel ratio after water flooding to identify channels. It takes the magnitude of water flooding property modification as the core identification basis, which can effectively distinguish between the primary high-permeability zone formed by sedimentation and the secondary dominant crossflow channel formed by water injection modification.

[0020] (3) Based on the time-varying law of reservoir properties, the present invention dynamically corrects the channel discrimination threshold, which breaks through the limitation that the traditional fixed empirical threshold cannot adapt to the changes in the seepage mechanism in the high water-cut stage. It can accurately identify the hidden and weakly modified secondary dominant channels, and truly restore the development intensity and spatial distribution characteristics of the dominant channels. The discrimination results are more in line with the actual situation of the mine.

[0021] (4) This invention integrates dynamic numerical simulation flow field data, production dynamic data, well logging monitoring data, inter-well tracer and well test multi-source data, corrects crossflow parameters through time-varying models and multi-dimensional coupling discrimination, makes up for the defects of insufficient identification dimension and large error of single data, accurately shapes the development scale of advantageous channels, crossflow intensity and three-dimensional spatial distribution, and the identification accuracy is significantly better than traditional methods, which can meet the field engineering needs of precise water control and oil stabilization and efficient remaining oil tapping in ultra-high water-cut old oilfields. Attached Figure Description

[0022] Figure 1This is a flowchart of the method for identifying dominant seepage channels during ultra-high water cut periods based on time-varying reservoir properties, as described in this invention. Figure 2 This is a schematic diagram showing the connection relationship between the modules in the channel identification system of this invention. Detailed Implementation

[0023] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0024] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0025] Example 1 like Figure 1 As shown, this invention provides a method for identifying dominant seepage channels during ultra-high water cut periods based on time-varying reservoir properties. This method is applicable to long-term water-driven sandstone, conglomerate, and fractured old oil reservoirs with a water cut of not less than 90%, and includes the following steps: S1: Through long core multi-PV multiple continuous water drive scour experiments, the dynamic evolution parameters of reservoir properties of the target oil reservoir were calibrated, a time-varying mathematical model of permeability coupled with cumulative water phase flux and critical scour flux was established, and a time-varying correction model of relative permeability corresponding to wettability reversal during the ultra-high water cut period was constructed to obtain the dynamic evolution law of reservoir permeability and relative water phase permeability with the progress of water injection development.

[0026] S1 employs a time-varying mathematical model for permeability controlled by water injection flushing, specifically for conventional sandstone reservoirs. The detailed expression and complete parameter definitions are as follows: ; In the formula, The reservoir dynamic absolute permeability at development time t; The initial static absolute permeability of the reservoir; The reservoir scour expansion coefficient; From the start of water injection to The cumulative water phase flux in the grid at any given time; The critical scour flux of the reservoir is the critical water washing threshold that initiates the stripping of reservoir cement and particle migration. when > At that time, the reservoir permeability underwent irreversible expansion and modification; when ≤ At that time, the reservoir permeability remains in its initial state; For fractured and highly stress-sensitive reservoirs, a fluid-structure interaction time-varying mathematical model for permeability is adopted, which takes into account both water injection scour and formation stress deformation. The specific expression is defined as follows: ; In the formula, For water injection flushing expansion function; This is the correction function for the effective stress of the formation; This represents the effective stress of the formation.

[0027] The two functions are coupled in a product form. The water injection flushing expansion function dominates the long-term irreversible evolution trend of permeability, which increases monotonically with the development process. The formation effective stress correction function dominates the short-term reversible fluctuation of permeability, which changes dynamically with formation pressure. The superposition of the two functions can fully characterize the dual time-varying physical property characteristics of fractured and highly stress-sensitive reservoirs under long-term water-drive expansion and short-term stress fluctuation adjustment, which is closer to the actual seepage law of complex reservoirs than the single flushing model.

[0028] ; When the cumulative water flux does not reach the critical flushing flux, the reservoir pore structure is not substantially altered, the function value is always equal to 1, and the permeability remains at the initial level. When the cumulative water flux exceeds the critical threshold, long-term water flooding causes cementitious material to fall off, pore throats to widen, and large channels to develop. The function value continues to increase with the degree of water washing, and the reservoir permeability shows an irreversible linear expansion trend, which precisely corresponds to the formation mechanism of the secondary dominant channels of water flooding during the ultra-high water cut period.

[0029] ; In the formula, This represents the initial effective stress of the reservoir, corresponding to the effective stress state under the original formation pressure during the early stages of development. The reservoir stress sensitivity coefficient is determined by core permeability stress sensitivity experiments under different confining pressure conditions. This coefficient is significantly higher in fractured reservoirs than in matrix sandstone reservoirs, characterizing the sensitivity of permeability to stress changes.

[0030] When the effective stress of the formation increases (e.g., a decrease in formation pressure or compaction of overlying stress), pores and fractures are compressed, the seepage channels narrow, the function value is less than 1 when the physical properties are in the water-bearing stage, and permeability decreases. When the effective stress of the formation decreases (e.g., water injection to increase pressure or an increase in pore pressure), pores and fractures rebound and open, the function value is greater than 1 when the physical properties are in the water-bearing stage, and permeability shows a reversible rebound. For conventional homogeneous sandstone reservoirs, the stress sensitivity coefficient is extremely small, and the function value approaches 1 when the physical properties are in the water-bearing stage, ensuring the universality and compatibility of the model system.

[0031] The time-varying correction model for relative permeability corresponding to the wettability reversal during the ultra-high water cut period is constructed as follows: ; In the formula, For a moment Water saturation level is Dynamic relative permeability of the water phase at that time; For the initial development stage, the water saturation is The static relative permeability of the aqueous phase at that time; This is the wettability reversal correction factor.

[0032] S2: Based on the three-dimensional static geological model of the target block, a time-varying mathematical model of permeability and a time-varying correction model of relative permeability are assigned to each grid. A three-dimensional oil-water two-phase numerical simulation model of dynamic evolution of physical properties is constructed. The key fitting parameters of the time-varying permeability model and the time-varying correction model of relative permeability are globally calibrated by combining the historical production data of the block, so as to obtain a time-varying numerical simulation model that characterizes the dynamic seepage characteristics of reservoirs during the ultra-high water cut period.

[0033] S3: Based on the calibrated time-varying numerical simulation model, three types of time-varying flow field discrimination indices at the grid scale are dynamically calculated to characterize the spatial distribution characteristics of the dynamic seepage field after water-drive modification.

[0034] Three types of time-varying flow field discrimination indicators include permeability amplification factor, oil-to-flow ratio (OFR), and inter-well dynamic seepage resistance coefficient; The permeability amplification factor is used to quantify the increase in reservoir permeability before and after waterflooding. The calculation formula is as follows: The threshold for classifying physical property modifications corresponding to the permeability amplification factor is: ≥3 indicates a strongly modified reservoir, 1.5≤ <3 indicates a moderately modified reservoir, 1< <1.5 indicates a weakly modified reservoir.

[0035] The formula for calculating the oil-to-flow ratio (OFR) is as follows: ; In the formula, The viscosity of underground crude oil; The viscosity is the formation water viscosity; the core discrimination threshold for dominant seepage channels during the ultra-high water cut period is set to OFR < 0.002.

[0036] S4: Combining production dynamic data, well logging monitoring data, inter-well tracer inversion data, and well test monitoring data, the traditional discrimination threshold is corrected based on the time-varying evolution characteristics of physical properties during the ultra-high water cut period. In the three-dimensional time-varying numerical simulation model, a candidate grid set of potential dominant seepage channels is initially screened to preliminarily locate the potential dominant seepage channels in the vertical layers and between planar wells.

[0037] By combining the time-varying evolution law of reservoir properties, the thresholds of various dynamic discrimination indicators were corrected. Layers with monthly water cut increase >1.5%, annual oil production decrease >10%, single-layer water absorption ratio >60%, and inter-well pressure transmission time shortened by more than 50% were identified as potential dominant seepage channels. Based on multi-period saturation logging and oxygen activation logging monitoring data, physical property modification layers with water cut saturation <0.3 and significant porosity-permeability increase after water drive were identified. A numerical simulation model of inter-well tracer migration was established based on the relative permeability time-varying correction model and the permeability time-varying correction model constructed in S1. The inter-well tracer breakthrough curve was fitted as the target to invert the crossflow factor of the dominant channel. This crossflow factor was coupled with production dynamic data, logging monitoring data, and well test monitoring data, and combined with the corrected discrimination thresholds to complete the preliminary identification of potential dominant seepage channels.

[0038] The classification and calibration of dominant seepage channels are as follows: Type I strong developmental advantage pathway: ≥3, OFR < 0.001, crossflow ratio > 5, ineffective water circulation ratio ≥ 95%; Category II moderate developmental dominance pathway: 1.5≤ <3, 0.001≤OFR<0.002, 2<crossflow ratio≤5, indicating the existence of localized ineffective water crossflow; Category III Weak Developmental Potential Pathways: 1 < <1.5 and crossflow ratio ≤2 indicate an early warning channel for continuously rising water content.

[0039] S5: Using the candidate grid set as the evaluation object, a multi-level fuzzy comprehensive evaluation system is constructed based on three types of time-varying flow field discrimination indicators. The discrimination threshold modified in S4 is used as the boundary parameter of the evaluation level. The multi-level fuzzy comprehensive evaluation system is constructed to classify the development level of dominant seepage channels. Grids outside the candidate grid set are judged as undeveloped or no longer participating in the evaluation. The parameters of channel permeability, channel width, crossflow multiple, and ineffective water circulation ratio are quantitatively obtained. The accuracy of the identification results is verified by the actual measurement of tracer and the feedback of profile adjustment construction. Finally, the accurate identification and three-dimensional spatial distribution of dominant seepage channels during the ultra-high water cut period are completed.

[0040] This solution is applied to a channel identification system, which specifically includes a physical property time-varying parameter calibration module, a time-varying numerical simulation module, a multi-source data coupling identification module, and a channel hierarchical characterization and verification module.

[0041] The time-varying parameter calibration module is used to fit the dynamic evolution parameters of reservoir permeability and relative permeability through core water drive experiments and construct a time-varying mathematical model. The time-varying numerical simulation module is used to construct a three-dimensional reservoir numerical model containing dynamic physical property parameters, and to complete the production history fitting and dynamic flow field calculation. The multi-source data coupling and identification module is used to integrate dynamic and static monitoring data, correct the discrimination threshold, and initially locate the dominant channel; The channel classification characterization and verification module is used to classify advantageous channels, perform quantitative parameter calculations, and verify on-site accuracy.

[0042] like Figure 2 As shown, the input and output terminals of the time-varying numerical simulation module are connected to the time-varying parameter calibration module and the multi-source data coupling identification module, respectively; the output terminal of the multi-source data coupling identification module is connected to the channel hierarchical characterization and verification module.

[0043] After receiving the experimental data from the multi-PV multiple continuous water-driven flushing, the input terminal of the physical property time-varying parameter calibration module outputs the permeability time-varying model, the relative permeability time-varying correction model, and the calibrated time-varying characteristic parameters.

[0044] The time-varying numerical simulation module outputs time-series dynamic flow field data, including global grid dynamic permeability field, permeability amplification factor, dynamic oil-to-flow ratio, and dynamic seepage resistance coefficient. The multi-source data coupling identification module outputs the corrected dominant channel discrimination threshold, the corrected crossflow ratio parameter, and the preliminarily located longitudinal and planar potential dominant seepage channel development areas; The channel classification characterization and verification module outputs quantitative parameters of the dominant seepage channels, data on the proportion of ineffective water circulation, the three-dimensional spatial distribution results of the dominant channels, and verification results of the identification accuracy.

[0045] Example 2 A high water-cut sandstone oil reservoir block in eastern my country, developed for 22 years, was selected. The average reservoir porosity is 28.6%, the initial permeability is 50-800 mD, and the overall water cut of the block is stable at around 93%. This method was used to identify the dominant channels. The specific process is as follows: Through continuous waterflooding experiments with long core samples from sealed core wells at multiple PV ratios, the block scour expansion coefficient α = 0.42 and the wettability reversal correction coefficient β = 0.085PV were calibrated. -1 Critical scour flux of reservoir =1.8PV; Construct a time-varying mathematical model of permeability and a time-varying corrected model of relative permeability of water phase adapted to this block, and restore the dynamic evolution law of reservoir properties after long-term waterflooding from the mechanism level.

[0046] The larger α is, the looser the reservoir cementation and the weaker the water washing resistance. Under long-term water drive, the pore throat is more easily scoured and widened, the permeability increases faster, and the more likely it is to form secondary dominant channels. The smaller the α value, the denser the reservoir cementation, the stronger the resistance to water washing, the weaker the effect of water flooding on pore structure modification, and the slower the permeability expansion.

[0047] The aforementioned calibrated permeability time-varying mathematical model and the water phase relative permeability time-varying correction model were jointly embedded into the three-dimensional oil-water two-phase numerical model. The fitting and calibration were completed by combining the block's production history data of nearly 10 years. The final production fitting accuracy reached 96.8% and the water cut fitting accuracy reached 95.2%. The time-varying evaluation indicators such as the global grid permeability amplification factor, dynamic oil-to-flow ratio, and dynamic seepage resistance were dynamically solved, providing an accurate dynamic flow field basis for channel identification.

[0048] Based on the calibrated time-varying numerical simulation model, the permeability amplification factor of the reservoir during the ultra-high water cut period is dynamically calculated step-by-step and grid-by-grid. The three core indicators are: dynamic oil-to-flow ratio (OFR), and inter-well dynamic seepage resistance coefficient. Calculation results show that the permeability of the grid in the long-term water-washed formations of the block has generally increased, and the modified area... The OFR is distributed between 1.2 and 4.6, with the OFR in the high crossflow region generally below 0.002, which is highly consistent with the development characteristics of ineffective water cycle during the ultra-high water cut period, accurately depicting the heterogeneous distribution characteristics of the dynamic seepage field after water drive.

[0049] Combining block production dynamics data, multi-period saturation logging, oxygen activation logging, inter-well tracer monitoring, and well test data, and relying on a time-varying correction model to correct the traditional static discrimination threshold, dominant flow channels with monthly water cut increases exceeding 1.5% and single-layer water absorption exceeding 60% were selected. Combined with logging data, strong water-washing stimulation layers with water cut saturation <0.3 were identified. By correcting the tracer breakthrough curve using a time-varying model, the inter-well dominant channel flow multiple distribution was found to be between 2.3 and 6.8, initially locating 5 horizontal and 8 vertical potential dominant seepage channel development layers.

[0050] A multi-level fuzzy comprehensive evaluation system was constructed based on three types of time-varying discriminant indicators. The system was then used to classify the channels, identifying two Class I channels with strong developmental dominance, four Class II channels with moderate developmental dominance, and seven Class III channels with weak developmental potential. The quantitative identification parameters of typical channels in the blocks are shown in Table 1. Among these, Class I channels... With a water flow rate ≥3.0, OFR <0.001, and crossflow multiple >5.0, the ineffective water circulation accounts for up to 96.2%, making it the main ineffective water crossflow channel in the block. The identification results were verified by subsequent on-site profile control and tracer retesting data. The channel location and development level highly matched the on-site engineering feedback, achieving an identification accuracy of 94.7%. This successfully enabled the accurate identification and three-dimensional spatial depiction of the secondary dominant seepage channels generated by water drive during the ultra-high water cut period.

[0051] Table 1

[0052] As shown in the table above, the permeability amplification factor of the secondary dominant channels driven by water is greater than 1.5, with low dynamic oil-to-flow ratio, high cross-flow factor, and significant ineffective water circulation characteristics; while the permeability amplification factor of the primary high-permeability zone is only 1.18, with no obvious traces of water-driven modification, low proportion of ineffective water circulation, and does not belong to the target of water blocking treatment, thus verifying the ability of this method to accurately distinguish between the two types of channels.

[0053] Through on-site profile control and subsequent tracer retesting, the spatial location and development level of the dominant channels identified by this invention matched the actual situation on site with a rate of 94.7%, which is more than 25% more accurate than the traditional static identification method. The proportion of ineffective water circulation in the identified Class I secondary channels reached as high as 96.2%, providing a reliable geological basis for subsequent precise water blocking, water control and oil stabilization measures, fully demonstrating the technical advantages and on-site application value of this solution.

[0054] It is worth noting that all contents not described in detail in this invention are existing technologies and are well known to those skilled in the art.

[0055] Therefore, this invention employs the aforementioned method for identifying dominant seepage channels during ultra-high water cut periods based on the time-varying properties of reservoirs. Through core waterflooding experiments, time-varying parameters of reservoir permeability and relative permeability are calibrated, and a time-varying mathematical model adaptable to different reservoir types is constructed. This time-varying model is embedded into a three-dimensional oil-water two-phase numerical simulation model, and after calibration with historical production data, a dynamic seepage field and time-varying evaluation indicators across the entire grid are obtained. Combining multi-source monitoring data such as production dynamics, well logging, and tracer data, the discrimination threshold is dynamically corrected based on the time-varying property laws, and inter-well crossflow parameters are adjusted to initially locate potential dominant seepage channels. Finally, a multi-level evaluation system is used to complete the graded quantitative characterization and accuracy verification of dominant channels.

[0056] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for identifying dominant seepage channels during ultra-high water cut periods based on time-varying reservoir properties, applicable to long-term water-driven sandstone, conglomerate, and fractured old oil reservoirs with a water cut of not less than 90%, characterized in that... Includes the following steps: S1: Through long core multi-PV multiple continuous water drive scour experiments, the dynamic evolution parameters of reservoir properties of the target oil reservoir were calibrated, a time-varying mathematical model of permeability coupled with cumulative water phase flux and critical scour flux was established, and a time-varying correction model of relative permeability corresponding to wettability reversal during the ultra-high water cut period was constructed to obtain the dynamic evolution law of reservoir permeability and relative water phase permeability with the water injection development process. S2: Based on the three-dimensional static geological model of the target block, a time-varying mathematical model of permeability and a time-varying correction model of relative permeability are assigned to each grid. A three-dimensional oil-water two-phase numerical simulation model of dynamic evolution of physical properties is constructed. The key fitting parameters of the time-varying permeability model and the time-varying correction model of relative permeability are globally calibrated by combining the historical production data of the block. A time-varying numerical simulation model characterizing the dynamic seepage characteristics of reservoirs during the ultra-high water cut period is obtained. S3: Based on the calibrated time-varying numerical simulation model, three types of time-varying flow field discrimination indices at the grid scale are dynamically calculated to characterize the spatial distribution characteristics of the dynamic seepage field after water drive modification; the three types of time-varying flow field discrimination indices include permeability amplification factor, oil-to-flow ratio (OFR), and inter-well dynamic seepage resistance coefficient. S4: Combining production dynamic data, well logging monitoring data, inter-well tracer inversion data, and well test monitoring data, the traditional discrimination threshold is corrected based on the time-varying evolution characteristics of physical properties during the ultra-high water cut period. In the three-dimensional time-varying numerical simulation model, a candidate grid set of potential dominant seepage channels is initially screened, and potential dominant seepage channels in the vertical layers and between planar wells are initially located. S5: Taking the candidate grid set as the evaluation object, a multi-level fuzzy comprehensive evaluation system is constructed based on three types of time-varying flow field discrimination indicators. The discrimination threshold modified by S4 is used as the boundary parameter of the evaluation level to construct a multi-level fuzzy comprehensive evaluation system and classify the development level of the dominant seepage channel. Grids outside the candidate grid set are deemed undeveloped or no longer eligible for evaluation; parameters such as channel permeability, channel width, crossflow multiple, and proportion of ineffective water circulation are quantitatively obtained; the accuracy of the identification results is verified through tracer measurements and profile adjustment construction feedback; and finally, the identification and three-dimensional spatial distribution of dominant seepage channels during the ultra-high water cut period are completed.

2. The method for identifying dominant seepage channels during ultra-high water cut periods based on time-varying reservoir properties according to claim 1, characterized in that, S1 employs a time-varying mathematical model for permeability controlled by water injection flushing, specifically for conventional sandstone reservoirs. The detailed expression and complete parameter definitions are as follows: ； In the formula, The reservoir dynamic absolute permeability corresponding to development time t; The initial static absolute permeability of the reservoir; The reservoir scour expansion coefficient; From the start of water injection to The cumulative water phase flux in the grid at any given time; The critical scour flux of the reservoir is the critical water washing threshold that initiates the stripping of reservoir cement and particle migration. when > At that time, the reservoir permeability underwent irreversible expansion and modification; when ≤ At that time, the reservoir permeability remains in its initial state; For fractured and highly stress-sensitive reservoirs, a fluid-structure interaction time-varying mathematical model for permeability is adopted, which takes into account both water injection scour and formation stress deformation. The specific expression is defined as follows: ； In the formula, For water injection flushing expansion function; This is the correction function for the effective stress of the formation. This represents the effective stress of the formation.

3. The method for identifying dominant seepage channels during ultra-high water cut periods based on time-varying reservoir properties according to claim 2, characterized in that, The time-varying correction model for relative permeability corresponding to the wettability reversal during the ultra-high water cut period is constructed as follows: ； In the formula, For a moment Water saturation level is Dynamic relative permeability of the water phase at that time; For the initial development stage, the water saturation is The static relative permeability of the aqueous phase at that time; This is the wettability reversal correction factor.

4. The method for identifying dominant seepage channels during ultra-high water cut periods based on time-varying reservoir properties according to claim 3, characterized in that, The permeability amplification factor is used to quantify the increase in reservoir permeability before and after waterflooding. The calculation formula is as follows: The threshold for classifying physical property modifications corresponding to the permeability amplification factor is: ≥3 indicates a strongly modified reservoir, 1.5≤ <3 indicates a moderately modified reservoir, 1< <1.5 indicates a weakly modified reservoir.

5. The method for identifying dominant seepage channels during ultra-high water cut periods based on time-varying reservoir properties according to claim 4, characterized in that, The formula for calculating the oil-to-flow ratio (OFR) is as follows: ； In the formula, The viscosity of underground crude oil; Formation water viscosity; Let be time t, and water saturation be The dynamic relative permeability of the oil phase at that time; the core discrimination threshold for the dominant seepage channel during the ultra-high water cut period was set to OFR < 0.

002.

6. The method for identifying dominant seepage channels during ultra-high water cut periods based on time-varying reservoir properties according to claim 5, characterized in that, By combining the time-varying evolution law of reservoir properties, the thresholds of various dynamic discrimination indicators were corrected. Layers with monthly water cut increase >1.5%, annual oil production decrease >10%, single-layer water absorption ratio >60%, and inter-well pressure transmission time shortened by more than 50% were identified as potential dominant seepage channels. Based on multi-period saturation logging and oxygen activation logging monitoring data, physical property modification layers with water cut saturation <0.3 and significant increase in porosity and permeability after water drive were identified. A numerical simulation model of inter-well tracer migration was established based on the relative permeability time-varying correction model and the permeability time-varying correction model constructed in S1. The inter-well tracer breakthrough curve was fitted as the target to invert the crossflow multiple of the dominant channel. The crossflow multiple was coupled with production dynamic data, logging monitoring data, and well test monitoring data, and combined with the corrected discrimination thresholds to complete the preliminary identification of potential dominant seepage channels.

7. The method for identifying dominant seepage channels during ultra-high water cut periods based on time-varying reservoir properties according to claim 6, characterized in that, The classification and calibration of dominant seepage channels are as follows: Type I strong developmental advantage pathway: ≥3, OFR < 0.001, crossflow ratio > 5, ineffective water circulation ratio ≥ 95%; Category II moderate developmental dominance pathway: 1.5≤ <3, 0.001≤OFR<0.002, 2<crossflow ratio≤5, indicating the existence of localized ineffective water crossflow; Category III Weak Developmental Potential Pathways: 1 < <1.5 and crossflow ratio ≤2 indicate an early warning channel for continuously rising water content.

8. The method for identifying dominant seepage channels during ultra-high water cut periods based on time-varying reservoir properties according to claim 7, characterized in that, It is applied to a channel identification system, which specifically includes a physical property time-varying parameter calibration module, a time-varying numerical simulation module, a multi-source data coupling identification module, and a channel hierarchical characterization and verification module.

9. The method for identifying dominant seepage channels during ultra-high water cut periods based on time-varying reservoir properties according to claim 8, characterized in that, The input and output terminals of the time-varying numerical simulation module are connected to the time-varying parameter calibration module and the multi-source data coupling identification module, respectively; the output terminal of the multi-source data coupling identification module is connected to the channel hierarchical characterization and verification module.

10. The method for identifying dominant seepage channels during ultra-high water cut periods based on time-varying reservoir properties according to claim 9, characterized in that, After receiving the experimental data from the multi-PV multiple continuous water-driven flushing, the input terminal of the physical property time-varying parameter calibration module outputs the permeability time-varying model, the relative permeability time-varying correction model, and the calibrated time-varying characteristic parameters.

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