Three-dimensional simulation method and system for dynamic expansion of pore fracturing based on digital twin

Abstract

The present invention provides a three-dimensional simulation method and system for dynamic expansion of pore fracturing based on digital twins, which relates to the field of digital twin technology. Specifically, by establishing a digital twin mapping relationship, geological spatial distribution data is converted into a three-dimensional geological basement model, fracturing operation time series operation data is converted into injection process control rules, and historical expansion feature data is converted into pore expansion constraint rules. Based on the above digital twin mapping relationship, a dynamic simulation framework is constructed. It uses the three-dimensional geological basement model as the spatial carrier, the injection process control rules as the time drive, and the pore expansion constraint rules as the basis for morphological evolution. After running the dynamic simulation framework, the fracturing fluid injection action is simulated according to the injection process control rules, the pressure field distribution inside the pore is synchronously calculated, and the expansion boundary is judged in combination with the pore expansion constraint rules. The three-dimensional morphological evolution process of the dynamic expansion of the pore is gradually generated, and the three-dimensional simulation results are finally output.

Description

Technical Field

[0001] The present invention relates to the field of digital twin technology, and in particular to a three-dimensional simulation method and system for dynamic expansion of pore fracturing based on digital twins. Background Art

[0002] In many engineering fields involving underground tunnel fracturing operations, such as oil and gas resource exploration and development, geothermal energy extraction and utilization, accurately grasping the dynamic expansion process of tunnel fracturing is of extremely critical significance. Traditionally, simulation methods for tunnel fracturing mainly rely on theoretical models and relatively simple numerical simulation methods. Theoretical models are usually constructed based on a series of idealized assumptions. These assumptions often have a large gap with the actual complex geological conditions. It is difficult to fully and truly reflect the comprehensive influence of multiple factors such as the diversity of geological spatial distribution, the dynamics of fracturing operation timing, and the uniqueness of historical expansion characteristics on the dynamic expansion of tunnel fracturing. Although existing numerical simulation methods can simulate the fracturing process to a certain extent, they generally lack the effective integration and full utilization of the full process data of tunnel fracturing, and are unable to accurately characterize the complex process of dynamic expansion of tunnel fracturing. Summary of the Invention

[0003] In view of the above-mentioned problems, in combination with the first aspect of the present invention, the present invention provides a three-dimensional simulation method for dynamic expansion of pore fracturing based on digital twins, the method comprising:

[0004] Acquire full-process data of channel fracturing, including geological spatial distribution data, fracturing operation time series data, and historical expansion characteristic data;

[0005] Establish a digital twin mapping relationship for pore fracturing, convert geological spatial distribution data into a three-dimensional geological basement model, convert fracturing operation time sequence data into injection process control rules, and convert historical expansion feature data into pore expansion constraint rules;

[0006] A dynamic simulation framework is constructed based on the digital twin mapping relationship, wherein the dynamic simulation framework uses a three-dimensional geological basement model as a spatial carrier, injection process control rules as a temporal driver, and pore expansion constraint rules as a morphological evolution basis;

[0007] Run the dynamic simulation framework to simulate the fracturing fluid injection action according to the injection process control rules, simultaneously calculate the pressure field distribution inside the pore, determine the expansion boundary based on the pore expansion constraint rules, and gradually generate the three-dimensional morphological evolution process of the dynamic pore expansion;

[0008] The output includes three-dimensional simulation results of time series pore morphology change data and expansion path data.

[0009] On the other hand, the present invention also provides a three-dimensional simulation system for dynamic expansion of pore fracturing based on digital twins, including a processor and a machine-readable storage medium, wherein the machine-readable storage medium is connected to the processor, the machine-readable storage medium is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the machine-readable storage medium to implement the above method.

[0010] Based on the above aspects, the present invention comprehensively obtains the full-process data of pore fracturing and establishes an accurate digital twin mapping relationship, thereby constructing a scientific and reasonable dynamic simulation framework. It can fully and comprehensively consider the synergistic influence of multiple factors on the dynamic expansion of pore fracturing, such as the complexity of geological spatial distribution, the dynamic nature of fracturing operation timing, and the uniqueness of historical expansion characteristics. Using the three-dimensional geological basement model as the spatial carrier, it can truly restore the underground geological environment; using the injection process control rules as the time drive, it can accurately simulate the timing process of the fracturing operation; using the pore expansion constraint rules as the basis for morphological evolution, it can accurately grasp the boundary conditions of the pore expansion, run the dynamic simulation framework, and accurately simulate the fracturing fluid injection action, synchronously and accurately calculate the pressure field distribution inside the pore, and accurately judge the pore expansion boundary, thereby generating a highly realistic and accurate three-dimensional morphological evolution process of the pore dynamic expansion, thereby providing more accurate, comprehensive and reliable simulation results for related engineering fields, greatly improving the depth of cognition and prediction accuracy of the dynamic expansion process of pore fracturing. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a schematic diagram of the execution flow of the three-dimensional simulation method of dynamic expansion of pore fracturing based on digital twin provided by the present invention.

[0012] Figure 2 It is a schematic diagram of exemplary hardware and software components of the digital twin-based three-dimensional simulation system for dynamic expansion of pore fracturing provided by the present invention. DETAILED DESCRIPTION

[0013] The present invention will be described in detail below with reference to the accompanying drawings. Figure 1 This is a flow chart of a three-dimensional simulation method for dynamic expansion of pore fracturing based on digital twins provided by an embodiment of the present invention. The following is a detailed introduction to the three-dimensional simulation method for dynamic expansion of pore fracturing based on digital twins.

[0014] Step S110: obtaining full-process data of duct fracturing, wherein the full-process data includes geological spatial distribution data, fracturing operation time sequence data and historical expansion characteristic data.

[0015] In this embodiment, the geological spatial distribution data can reflect the spatial characteristics of the geological structure around the channel, such as the distribution of different strata, the spatial arrangement of rocks, etc., which affect the expansion of the channel during the fracturing process. The fracturing operation time series data can show the specific operation of the fracturing operation over time, covering the start and stop time of fracturing fluid injection, flow adjustment at different stages, etc., reflecting the dynamic process of the fracturing operation. The historical expansion characteristic data is a summary of the channel expansion in previous similar channel fracturing projects, including the distribution of rupture points, expansion direction and rate, etc.

[0016] Step S111: Collect geological exploration data of the area where the tunnel is located. The geological exploration data includes the depth information of the stratum interface from the surface two-dimensional seismic exploration, the rock type distribution information from the downhole resistivity logging, and the initial tunnel geometry information captured by the in-hole imaging instrument. Through spatial coordinate conversion processing, the position information obtained by different detection methods is unified into a three-dimensional coordinate system with the hole mouth as the origin, generating continuous geological spatial distribution data.

[0017] To obtain geological spatial distribution data, a variety of geological exploration methods are required. Surface 2D seismic exploration utilizes the characteristics of seismic wave propagation underground to obtain information on the depth of strata. A seismic source transmits seismic waves into the ground. As these waves propagate, they are reflected and refracted when they encounter interfaces between different strata. Surface receivers receive the reflected waves and record their travel time. Since the propagation speed of seismic waves in different media is known, the relationship between travel time and speed can be used to calculate the distance between the strata interface and the surface, thereby determining the depth of the strata interface.

[0018] Downhole resistivity logging uses the principle that different rock types have different resistivities to obtain information about rock type distribution. Resistivity measuring devices are placed downhole to measure resistivity values ​​at various depths. A database of various rock types and their corresponding resistivity ranges is established. The measured resistivity values ​​are compared with the database to determine the rock type at that location.

[0019] The in-hole imager uses optical imaging technology to record the initial hole geometry. The imager moves along the inside of the hole, capturing a series of images. These images are stitched and processed to extract geometric parameters such as the hole diameter, length, and bend angle, thereby depicting the initial hole geometry.

[0020] Because different detection methods may use different coordinate systems, spatial coordinate transformation is required to integrate the position information obtained by these different methods. A three-dimensional coordinate system is established with the wellhead as the origin. For data acquired from surface two-dimensional seismic exploration, the position information of the formation interface is converted to this three-dimensional coordinate system based on the positions of the seismic source and receiver, as well as the measured angles and distances. For data from downhole resistivity logging and borehole imaging instruments, their position information is unified into a three-dimensional coordinate system with the wellhead as the origin using coordinate transformation formulas, based on their relative positions downhole and the measured coordinate system. This ultimately generates continuous geological spatial distribution data.

[0021] Step S112: Collect operation record data during the fracturing operation, which includes the time point when the fracturing fluid injection starts, the adjustment record of the pump truck flow rate during the injection process, the real-time monitoring record of the wellhead pressure, and the termination condition record of the injection stop. Through time axis calibration processing, the discrete operation time points are converted into continuous time series data arranged at intervals of seconds to generate the fracturing operation time series operation data.

[0022] During fracturing operations, detailed operational data must be collected and recorded. The start time of fracturing fluid injection is a critical metric, accurately recorded by a timing device installed on the injection equipment. During the injection process, the pump truck's flow rate is adjusted based on operational needs. The pump truck is equipped with a flow sensor that monitors and records flow rate changes in real time, creating a flow adjustment record.

[0023] Real-time monitoring and recording of wellhead pressure is also crucial data. A pressure sensor is installed at the wellhead to continuously measure wellhead pressure, transmitting and recording the data in real time. The termination conditions for injection stop clearly define the criteria for terminating the fracturing operation. These conditions may be related to factors such as the total injection flow rate or the wellhead pressure reaching a specific value, and these conditions are also recorded.

[0024] Because the time points in the operation log data are discrete, time axis calibration is required to facilitate subsequent analysis and simulation. The entire operation time is divided into second-level intervals, starting from the time the fracturing fluid injection begins. Discrete operation time points are mapped to second-level time intervals through linear interpolation or other appropriate interpolation methods, thereby converting the discrete operation time points into continuous time series data, generating the fracturing operation time series data.

[0025] Step S113: Organize the extended monitoring data of historical fracturing projects, which includes the distribution information of pore rupture points located by microseismic instruments, the pore extension length information measured by post-fracturing wellbore diameter measurement, and the fluid seepage direction information measured by oil testing. Through feature extraction processing, the spatial distribution law characteristics and time evolution law characteristics in the monitoring data are extracted as the direction preference characteristics and rate preference characteristics of the pore extension to generate historical extended feature data.

[0026] Organizing expansion monitoring data from historical fracturing projects is a crucial step in obtaining historical expansion characteristics. Microseismometers are used to locate the distribution of pore channel fracture points. During the fracturing process, pore channel fractures generate microseismic signals. Microseismometers detect the arrival time and intensity of these signals and use a location algorithm to determine the spatial location of the fracture points. By summarizing and analyzing the location information of all detected fracture points, the distribution of pore channel fracture points can be determined.

[0027] Post-fracturing caliper measurement is a method for obtaining information on the length of the pore channel. After the fracturing operation is completed, the pore channel diameter is measured using a caliper tool. By comparing the caliper data with the pre-fracturing caliper data, the pore channel extension length in all directions can be calculated.

[0028] Well testing can provide information on the direction of fluid flow. During the well testing phase after fracturing, the direction of fluid flow is determined by monitoring the flow of fluid in the well and analyzing the pressure changes and flow distribution of the fluid.

[0029] The above monitoring data are processed for feature extraction. For the spatial distribution regularity characteristics, the distribution pattern of the rupture points in three-dimensional space is analyzed, such as whether they are concentrated in certain specific areas or directions. For the temporal evolution regularity characteristics, the changing trends of the pore expansion length and fluid seepage direction over time are studied. The above regularity characteristics are further refined into the directional preference characteristics and rate preference characteristics of pore expansion. The directional preference characteristics reflect the directions in which the pores tend to develop during the expansion process, and the rate preference characteristics reflect the speed characteristics of the pore expansion, thereby generating historical expansion feature data.

[0030] Step S114: Cross-validation processing is performed on the geological spatial distribution data, the fracturing operation time series data and the historical expansion characteristic data. By comparing the rock type in the geological spatial distribution data with the fracture point distribution in the historical expansion characteristic data, and by comparing the pressure record in the fracturing operation time series data with the expansion rate in the historical expansion characteristic data, the consistency of the rock mechanical properties and the expansion law characteristics and the correlation between the injection pressure and the expansion rate are verified.

[0031] To ensure the accuracy and reliability of the acquired data, cross-validation processing is required for the geological spatial distribution data, the time series operation data of the fracturing operation, and the historical expansion characteristic data. The rock types in the geological spatial distribution data are compared with the distribution of fracture points in the historical expansion characteristic data. Different types of rocks have different mechanical properties, such as hardness and brittleness, which can affect the fracture of the pores. The distribution of rock types in the geological spatial distribution data and the distribution of fracture points in the historical expansion characteristic data are spatially matched and analyzed. If the frequency of pore fracture points in the past is high in an area where a certain rock type is distributed, it means that the mechanical properties of this rock type are consistent with the fracture and expansion laws of the pores.

[0032] Compare the pressure records in the fracturing operation time series data with the expansion rate in the historical expansion characteristic data. Injection pressure is a key factor affecting the pore expansion rate. Analyze the changes in wellhead pressure at different time periods in the fracturing operation time series data and observe the pore expansion rate in the corresponding time periods in the historical expansion characteristic data. If the pore expansion rate increases during periods of increased pressure, this indicates a positive correlation between injection pressure and pore expansion rate, verifying the rationality and relevance of the data.

[0033] Step S120: Establish a digital twin mapping relationship for pore fracturing, convert the geological spatial distribution data into a three-dimensional geological basement model, convert the fracturing operation time sequence data into injection process control rules, and convert the historical expansion feature data into pore expansion constraint rules.

[0034] After obtaining the full-process data, it is necessary to establish a digital twin mapping relationship for pore fracturing. The digital twin mapping relationship is to correspond the physical system in the real world with the virtual digital model, so that the digital model can accurately reflect the behavior and characteristics of the physical system. Converting the geological spatial distribution data into a three-dimensional geological basement model can intuitively display the geological environment where the pore is located in a three-dimensional form, providing an accurate geological space framework for subsequent simulations. Converting the fracturing operation sequence data into injection process control rules can accurately control and simulate the injection process of the fracturing fluid, ensuring that the simulation process is consistent with the actual operation process. Converting the historical expansion feature data into pore expansion constraint rules can reasonably constrain the expansion of the pore during the simulation process, making the simulation results more consistent with the actual situation.

[0035] Step S121: Perform three-dimensional modeling on the geological spatial distribution data, and convert the discrete stratum interface depth information and rock type distribution information into a continuous three-dimensional grid model through an interpolation algorithm. Each node of the three-dimensional grid model contains a stratum identification and a rock type identification, forming a three-dimensional geological basement model.

[0036] When processing 3D modeling of geological spatially distributed data, interpolation algorithms are required to convert the discrete information about bed boundary depths and rock type distribution into a continuous 3D mesh model. Interpolation algorithms estimate data values ​​at other locations based on known discrete data points. For bed boundary depth information, spatial interpolation methods such as kriging or spline interpolation are used. Based on known bed boundary depth data points, bed boundary depths at other locations in 3D space are estimated, making the bed boundary information continuous across the entire space.

[0037] Interpolation algorithms are also used to determine rock type distribution information. Using the known rock type locations as sample points, rock types at other locations are predicted. Classification interpolation methods can be used to classify unknown locations into different rock type categories based on the rock type and spatial location relationships of the sample points.

[0038] In the generated 3D mesh model, each node includes a stratigraphic identifier and a rock type identifier. The stratigraphic identifier is compared with the interpolated stratigraphic boundary depth information to determine the stratigraphic stratum to which the node belongs. The rock type identifier is determined based on the interpolated rock type distribution information, thus forming a 3D geological basement model containing detailed geological information.

[0039] Step S122: Perform rule extraction processing on the time series operation data of the fracturing operation. By analyzing the flow regulation records and pressure monitoring records in the time series, the functional relationship between the injection flow rate and time and the response relationship between the wellhead pressure and flow rate are analyzed, and the injection process control rules including the time-flow mapping table and the flow-pressure mapping table are generated.

[0040] When extracting rules from fracturing operation time series data, the primary focus is analyzing the flow control records and pressure monitoring records within the time series. To determine the functional relationship between injection flow and time, the flow values ​​and corresponding time points in the flow control records are analyzed. Curve fitting can be used to fit the flow-time data using an appropriate function type, such as linear, polynomial, or exponential. The resulting function can describe the temporal variation of injection flow.

[0041] To understand the relationship between wellhead pressure and flow rate, we analyzed the pressure values ​​and corresponding flow rates from the pressure monitoring records. We also used curve fitting to establish a functional relationship between pressure and flow rate. By analyzing and fitting a large amount of data, we determined the parameters of the function, ensuring that it accurately reflects the changes in wellhead pressure and flow rate.

[0042] Based on these two functional relationships, a time-flow mapping table and a flow-pressure mapping table are generated. The time-flow mapping table can be used to query the corresponding injection flow rate value based on the time value, while the flow-pressure mapping table can be used to query the corresponding wellhead pressure value based on the flow value. The time-flow mapping table and the flow-pressure mapping table constitute the injection process control rules, which are used to accurately control the injection of fracturing fluid during the simulation.

[0043] Step S123: Summarize the regular characteristics of the historical expansion feature data, analyze the main direction probability distribution and average expansion rate value of the channel expansion by counting the dominant direction in the fracture point distribution information and the expansion rate in the wellbore measurement information, and generate the channel expansion constraint rules including the main expansion direction probability threshold and the expansion rate benchmark value.

[0044] When summarizing regularity characteristics of historical expansion feature data, we first count the dominant directions in the distribution of fracture points. We convert the fracture point locations into directional vectors, and then count the frequency of fracture points in each direction. The direction with the highest frequency is considered the dominant direction. We can divide the space into multiple directional intervals, calculate the number of fracture points in each interval, and determine the dominant direction by comparing the numbers.

[0045] For the expansion rate in the wellbore measurement information, calculate the expansion rate for each measurement point. The expansion rate is equal to the increase in wellbore diameter after fracturing divided by the fracturing time. Perform a statistical analysis of the expansion rates at all measurement points and calculate the average to obtain the average expansion rate value.

[0046] Analyze the probability distribution of the primary direction of tunnel expansion. Based on the statistical results of the dominant direction, calculate the probability of tunnel expansion in each direction. Set the probability of the dominant direction higher, and reduce the probability of other directions accordingly. By analyzing and statistically analyzing a large amount of historical data, determine the primary expansion direction probability threshold. The primary expansion direction probability threshold is a critical value. When the expansion probability in a certain direction exceeds this threshold, it is considered the primary expansion direction.

[0047] The average expansion rate is used as the expansion rate benchmark. This benchmark serves as a reference for duct expansion speed. During the simulation, the expansion rate is adjusted based on specific circumstances. This generates a duct expansion constraint rule that includes a probability threshold for the main expansion direction and the expansion rate benchmark.

[0048] Step S124: Establish an association relationship between the three-dimensional geological basement model, the injection process control rules and the pore expansion constraint rules, associate the rock type identifier of the three-dimensional geological basement model with the main expansion direction probability threshold of the pore expansion constraint rule, and associate the flow-pressure mapping table of the injection process control rules with the spatial position of the three-dimensional geological basement model.

[0049] To ensure the synergistic effect of the 3D geological basement model, injection process control rules, and pore expansion constraint rules, a correlation between them is necessary. The rock type identifier of the 3D geological basement model is associated with the main expansion direction probability threshold of the pore expansion constraint rule. Different rock types have different effects on pore expansion, so different main expansion direction probability thresholds correspond to different rock types. Based on the rock type identifier, the corresponding main expansion direction probability threshold is found in the pore expansion constraint rule and the two are then correlated.

[0050] The flow-pressure mapping table for the injection process control rules is associated with the spatial position of the 3D geological basement model. The injection flow rate and wellhead pressure affect the pressure distribution within the channel, which is closely related to the channel's expansion. Based on the spatial position of the 3D geological basement model, the distance and relative position relationship between that location and the wellhead are determined. The pressure value at that location is calculated by combining the flow-pressure mapping table with the spatial position of the 3D geological basement model. This establishes an association between the flow-pressure mapping table and the spatial position of the 3D geological basement model, allowing the pressure distribution within the channel to be accurately calculated based on different spatial positions and injection flow rates during the simulation process.

[0051] Step S130: constructing a dynamic simulation framework based on the digital twin mapping relationship, wherein the dynamic simulation framework uses the three-dimensional geological basement model as the spatial carrier, the injection process control rules as the time drive, and the pore expansion constraint rules as the morphological evolution basis.

[0052] After establishing the digital twin mapping relationships, a dynamic simulation framework was constructed based on these relationships. This dynamic simulation framework uses a 3D geological basement model as a spatial carrier to simulate the pore expansion process in this 3D space. The 3D geological basement model provides accurate geological spatial information, including stratigraphic distribution and rock types, providing a realistic geological environment for the simulation.

[0053] Using the injection process control rules as a time-driven approach, the fracturing fluid injection process is simulated according to the actual fracturing operation time sequence. Based on the time-flow and flow-pressure mapping tables in the injection process control rules, the injection flow rate and wellhead pressure are determined within each time step to control the fracturing fluid injection process.

[0054] Using the pore expansion constraint rules as the basis for morphological evolution, reasonable constraints are placed on the pore expansion morphology during the simulation process. Based on the main expansion direction probability threshold and expansion rate baseline value in the pore expansion constraint rules, the pore expansion direction and speed within each time step are determined to ensure that the simulation results conform to the historical expansion law.

[0055] Step S131: Based on the three-dimensional geological basement model, the initial area of ​​the channel and the surrounding stratum area are divided. The shape of the initial area of ​​the channel is consistent with the geometric shape of the initial channel in the geological spatial distribution data, and the surrounding stratum area is divided into different mechanical property areas according to the rock type identification.

[0056] Regional division is performed based on the 3D geological basement model. First, the initial channel region is determined. This region's shape corresponds to the initial channel geometry captured by the borehole imager in the geological spatial distribution data. The portion of the 3D geological basement model corresponding to the initial channel geometry is marked as the initial channel region.

[0057] The surrounding stratigraphic area is divided into zones with different mechanical properties based on rock type identification. Different rock types have different mechanical properties, such as hardness and elastic modulus. Based on the rock type identification of each node in the 3D geological basement model, the surrounding stratigraphic area is divided into multiple zones. Each zone has the same or similar rock type and similar mechanical properties. This allows for more accurate simulation of pore expansion based on the mechanical properties of different zones during the simulation.

[0058] Step S132: Using the time-flow mapping table of the injection process control rule as a time-driven parameter, the time step of the simulation operation is set to be consistent with the time interval of the fracturing operation sequence data, so that the simulation time is synchronized with the actual operation time.

[0059] The injection process control rule's time-flow mapping table is used as a time-driven parameter. During the simulation, the injection flow rate within each time step is determined based on the time-flow mapping table. The simulation time step is set to match the time interval of the fracturing operation sequence data. If the fracturing operation sequence data is recorded in seconds, the simulation time step is also set to seconds. This ensures that the actual operation time corresponding to each time step is the same during the simulation, synchronizing the simulation time with the actual operation time and ensuring that the simulation process accurately reflects the actual fracturing operation process.

[0060] Step S133: The main expansion direction probability threshold and expansion rate reference value of the pore expansion constraint rule are used as morphological evolution parameters, and the expansion probability of each boundary point of the pore boundary is set to the main expansion direction probability threshold corresponding to the rock type where the boundary point is located, and the expansion rate is the product of the expansion rate reference value and the pressure value of the boundary point.

[0061] The main expansion direction probability threshold and expansion rate baseline of the pore expansion constraint rule are used as morphological evolution parameters. For each boundary point of the pore boundary, the corresponding main expansion direction probability threshold is found in the pore expansion constraint rule according to the rock type identifier of the boundary point. This probability threshold is then set as the expansion probability of the boundary point. As a result, boundary points of different rock types have different expansion probabilities during the simulation.

[0062] The expansion rate is calculated by multiplying the expansion rate baseline value by the pressure value at the boundary point. Within each time step, the pressure distribution within the pore channel is calculated according to the injection process control rules to determine the pressure value at the boundary point. Multiplying the expansion rate baseline value by the pressure value yields the expansion rate at that boundary point. The expansion rate determines the distance the boundary point expands within that time step, thus affecting the pore channel's expansion morphology.

[0063] Step S134: Establishing the operation logic flow of the dynamic simulation framework, the operation logic flow includes: in each time step, determining the current injection flow according to the time-flow mapping table, and calculating the pressure distribution inside the channel according to the flow-pressure mapping table; determining the pressure value of each point on the channel boundary according to the pressure distribution and rock type identification, and judging whether the boundary point meets the expansion condition in combination with the main expansion direction probability threshold; for the points that meet the expansion condition, calculating the expansion distance according to the product of the expansion rate reference value and the pressure value, and generating a new boundary point position; connecting all new boundary point positions into a new channel boundary, updating the channel area in the three-dimensional geological basement model, and entering the next time step.

[0064] Establish the operational logic of the dynamic simulation framework. At the beginning of each time step, the injection rate for the current time step is determined based on the time-flow mapping table in the injection process control rule. Based on this injection rate, the flow-pressure mapping table is queried to obtain the wellhead pressure. Then, based on the wellhead pressure and the channel geometry, a pressure propagation model is used to calculate the pressure distribution within the channel. The pressure propagation model takes into account factors such as channel length, diameter, and roughness, and solves fluid dynamics equations to obtain pressure values ​​at different locations within the channel.

[0065] The pressure values ​​at each point along the pore boundary are determined based on the pressure distribution and rock type identification. Different rock types have different pressure transmission and absorption characteristics. The pressure distribution is modified based on the rock type identification to obtain accurate pressure values ​​at the boundary points. The main expansion direction probability threshold is used to determine whether the boundary point meets the expansion conditions. If the pressure value at the boundary point is greater than the preset expansion trigger pressure and the expansion direction of the boundary point meets the main expansion direction probability threshold, the boundary point is considered to meet the expansion conditions.

[0066] For points that meet the expansion criteria, the expansion distance is calculated as the product of the expansion rate baseline and the pressure value. Starting from this boundary point, the expansion distance is extended along the expansion direction to calculate the new boundary point position. The new positions of all boundary points that meet the expansion criteria are connected to form a new channel boundary. This new channel boundary is updated to the channel area in the 3D geological basement model, completing the simulation for the current time step. The simulation then proceeds to the next time step and repeats the above steps until the simulation termination criteria are met.

[0067] Step S140: Run the dynamic simulation framework, simulate the fracturing fluid injection action according to the injection process control rules, synchronously calculate the pressure field distribution inside the pore, determine the expansion boundary in combination with the pore expansion constraint rules, and gradually generate the three-dimensional morphological evolution process of the dynamic expansion of the pore.

[0068] Step S141: Initialize the running time of the dynamic simulation framework to the initial time point, and set the initial boundary of the channel to the initial area boundary of the channel in the three-dimensional geological basement model.

[0069] Before starting the dynamic simulation framework, initialization is required. The runtime is set to the initial time point, which marks the start of the entire simulation process, much like the firing of the starting gun in a race, marking the beginning of the simulation. At the same time, the initial boundary of the channel is determined as the boundary of the initial channel region in the three-dimensional geological basement model. This is because the three-dimensional geological basement model is constructed based on previously acquired geological spatial distribution data, where the boundary of the initial channel region is precisely determined based on the initial channel geometry captured by the in-hole imaging device. Therefore, using this as the initial boundary ensures that the simulation starts from the actual initial state of the channel.

[0070] Step S142: Within the first time step, the initial injection flow rate is obtained according to the time-flow mapping table of the injection process control rule, and the initial pressure distribution inside the channel is calculated according to the flow-pressure mapping table. The initial pressure distribution is characterized by the highest pressure value at the orifice position and gradually decreases towards the bottom of the hole.

[0071] In the first time step, the initial injection rate is determined based on the time-flow mapping table within the injection process control rules. This table, derived from rule-based processing of the fracturing operation's sequential operational data, accurately records the injection rate at different times. By querying this table, the desired injection rate for the initial time point can be determined.

[0072] Next, the initial pressure distribution inside the channel is calculated based on the flow-pressure mapping table. The flow-pressure mapping table reflects the relationship between the wellhead pressure and the injection rate. Once the initial injection rate is determined, the corresponding wellhead pressure can be found from the table. Since the fracturing fluid is injected into the channel from the orifice, during the injection process, the fracturing fluid must overcome the channel's resistance and flow toward the bottom of the hole. As the distance from the orifice increases, the pressure gradually decreases. Therefore, the initial pressure distribution inside the channel is characterized by the highest pressure at the orifice and gradually decreases toward the bottom of the hole.

[0073] Step S1421: extract the injection flow rate value of the current time step, query the flow-pressure mapping table to obtain the corresponding wellhead pressure value.

[0074] To calculate the internal pressure distribution in the pores, the injection flow rate value for the current time step must first be extracted from the time-flow mapping table in the injection process control rules. This injection flow rate value is then used to query the flow-pressure mapping table. This mapping table, derived from analyzing the relationship between flow and pressure in a large amount of fracturing operation data, stores the wellhead pressure values ​​corresponding to different injection rates. By querying this table, the wellhead pressure value corresponding to the current injection rate can be obtained.

[0075] Step S1422: Based on the geometric shape of the initial area of ​​the pore, a pressure attenuation model along the length of the pore is established. The attenuation model assumes that the pressure value decays linearly from the pore mouth to the pore bottom.

[0076] A pressure attenuation model along the length of the pore is established based on the geometric shape of the initial area of ​​the pore. The geometric shape of the initial area of ​​the pore is accurately measured by an in-hole imager, which contains information such as the length, diameter, and curvature of the pore. In this attenuation model, it is assumed that the pressure value decays linearly from the pore mouth to the bottom of the hole. This is because under ideal conditions, during the flow of fracturing fluid in the pore, if the pore material is uniform and the diameter does not change much, and some complex fluid dynamic factors are ignored, the pressure loss is roughly proportional to the flow distance, so it can be approximately assumed that the pressure decays linearly along the length of the pore. The above assumptions can not only simplify the calculation process, but also reflect the actual pressure changes to a certain extent.

[0077] Step S1423: Calculate the pressure value at each position inside the channel based on the wellhead pressure value and the total length of the channel. The pressure value is equal to the wellhead pressure value minus the product of the pressure attenuation per unit length and the distance from the position to the orifice.

[0078] After establishing the pressure decay model, the pressure value at each location within the orifice is calculated based on the wellhead pressure and the total length of the orifice. First, the pressure decay per unit length is calculated, which is equal to the wellhead pressure divided by the total length of the orifice. Then, for any location within the orifice, the pressure value at that location is obtained by subtracting the product of the pressure decay per unit length and the distance from the orifice from the wellhead pressure. For example, locations closer to the orifice have a smaller pressure loss (multiplied by the pressure decay per unit length) due to their shorter distance to the orifice, resulting in a relatively higher pressure value. Locations farther from the orifice, on the other hand, have a greater pressure loss and a relatively lower pressure value, which is consistent with the fact that pressure gradually decreases from the orifice to the bottom of the hole.

[0079] Step S1424: performing spatial interpolation processing on the pressure values ​​to generate pressure distribution data consistent with the grid resolution of the three-dimensional geological basement model, wherein each grid node in the pressure distribution data corresponds to a pressure value.

[0080] In order to make the calculated pressure distribution data match the three-dimensional geological basement model, the pressure values ​​need to be spatially interpolated. The three-dimensional geological basement model is a three-dimensional grid model, and each grid node represents a specific spatial position. Since the pressure values ​​calculated previously may be discrete or the resolution is inconsistent with the three-dimensional geological basement model, it is necessary to use a spatial interpolation method to generate pressure distribution data consistent with the grid resolution of the three-dimensional geological basement model. There are many spatial interpolation methods, such as nearest neighbor interpolation and bilinear interpolation. Through these methods, the pressure value of each grid node in the three-dimensional geological basement model is estimated based on the known pressure value, so that each grid node in the pressure distribution data corresponds to an accurate pressure value.

[0081] Step S1425: Verify the rationality of the pressure distribution data by comparing the difference between the wellhead pressure value and the bottom hole pressure value to see if it conforms to the empirical range of pressure decay in historical fracturing operations, so that the pressure distribution calculation result matches the actual situation.

[0082] In order to ensure the accuracy and reliability of the pressure distribution data, it is necessary to verify its rationality. The verification method is to compare the difference between the wellhead pressure value and the bottom hole pressure value, and compare this difference with the empirical range of pressure decay in historical fracturing operations. A large amount of actual data on pressure decay has been accumulated in historical fracturing operations. These data reflect the reasonable range of pressure decay under similar geological conditions and channel characteristics. If the calculated difference between the wellhead pressure value and the bottom hole pressure value is within this empirical range, it means that the pressure distribution calculation result matches the actual situation and the pressure distribution data is reasonable; if the difference exceeds the empirical range, it is necessary to re-examine whether the assumptions of the pressure decay model are reasonable, whether there are errors in the calculation process, etc., and correct the pressure distribution calculation until the result is consistent with the actual situation.

[0083] Step S143: Traverse each boundary point of the initial boundary of the pore, obtain the rock type identifier of the boundary point and the corresponding main expansion direction probability threshold, calculate the product of the pressure value of the boundary point and the expansion rate reference value as the candidate expansion distance; if the pressure value of the boundary point is greater than the preset expansion starting pressure, retain the candidate expansion distance, otherwise set it to zero.

[0084] Each boundary point of the initial tunnel boundary is traversed, and during the traversal process, the rock type identifier and the corresponding primary expansion direction probability threshold are obtained for each boundary point. The rock type identifier is determined when constructing the 3D geological basement model and records the rock type at each grid node. The primary expansion direction probability threshold is derived from historical expansion feature data. Different rock types correspond to different primary expansion direction probability thresholds, which reflect the probability of tunnel expansion in various directions under the conditions of that rock type.

[0085] Next, the product of the pressure value at the boundary point and the expansion rate benchmark is calculated as the candidate expansion distance. The expansion rate benchmark is the average expansion rate obtained from historical expansion feature data and represents the average expansion speed of the pore under normal circumstances. The pressure value affects the expansion capacity of the pore; the higher the pressure, the easier the pore expands. Therefore, the pressure value is multiplied by the expansion rate benchmark to preliminarily estimate the possible expansion distance of the boundary point.

[0086] The pressure at this boundary point is then compared to a preset expansion trigger pressure. This preset expansion trigger pressure is a critical value determined based on extensive experiments and practical experience. Only when the pressure at the boundary point exceeds this critical value will the pore expansion occur. If the pressure at the boundary point is greater than the preset expansion trigger pressure, the candidate expansion distance is retained, indicating that the boundary point has the potential for expansion. If the pressure is less than or equal to the preset expansion trigger pressure, the candidate expansion distance is set to zero, indicating that the boundary point will not expand under the current conditions.

[0087] Step S1431: discretize the initial boundary of the duct into multiple boundary points, each boundary point corresponding to a grid node in the three-dimensional geological basement model.

[0088] In order to facilitate detailed analysis of the initial boundary of the pore, the initial boundary of the pore is discretized into multiple boundary points. The initial boundary of the pore is a continuous curve or surface, which is converted into a series of discrete points through discretization. Each boundary point corresponds to a grid node in the three-dimensional geological basement model. The advantage of this is that various information about the node stored in the three-dimensional geological basement model can be directly utilized, such as rock type identification, pressure value, etc. The discretization process can adopt the method of equal interval division, or it can be adaptively divided according to the geometric characteristics of the pore boundary to ensure that the shape and characteristics of the initial boundary of the pore can be accurately described.

[0089] Step S1432: For each boundary point, query the rock type identifier of the grid node where it is located, and obtain the main expansion direction probability threshold of the boundary point based on the association between the main expansion direction probability threshold of the pore expansion constraint rule and the rock type.

[0090] For each discrete boundary point, query the rock type identifier of the grid node where it is located. Each grid node in the three-dimensional geological basement model has a clear rock type identifier, and the rock type of the boundary point can be quickly determined by querying. Then, the main expansion direction probability threshold of the boundary point is obtained based on the correlation between the main expansion direction probability threshold and the rock type in the pore expansion constraint rule. The above correlation relationship is established when the regular feature induction processing of the historical expansion feature data is performed, which reflects the probability distribution of the pore expansion direction under different rock types. Through the above correlation query, a reasonable main expansion direction probability threshold can be determined for each boundary point.

[0091] Step S1433: query the pressure distribution data corresponding to the boundary point to obtain the pressure value of the boundary point.

[0092] Query the pressure distribution data corresponding to this boundary point. This data, obtained through a series of calculations and processing, records the pressure value at each grid node in the 3D geological basement model. Since each boundary point corresponds to a grid node, the pressure value at that boundary point can be directly obtained from the pressure distribution data. This pressure value is calculated based on the wellhead pressure, pore geometry, and a pressure decay model, and reflects the pressure at that boundary point under the current injection conditions.

[0093] Step S1434: multiplying the pressure value of the boundary point by the expansion rate reference value to obtain a candidate expansion distance of the boundary point, where the candidate expansion distance represents the possible expansion length of the boundary point in the current time step.

[0094] Multiply the pressure value at the boundary point by the expansion rate benchmark to obtain the candidate expansion distance for that boundary point. The expansion rate benchmark represents the average expansion speed of the pore, while the pressure value reflects the expansion capacity of the boundary point. The higher the pressure, the greater the possibility and distance of expansion. Therefore, multiplying the pressure value by the expansion rate benchmark provides a preliminary estimate of the possible expansion distance of the boundary point within the current time step. The candidate expansion distance is only a preliminary estimate; other factors must be considered to further determine whether the boundary point will actually expand.

[0095] Step S1435: Record the rock type identifier, main expansion direction probability threshold, pressure value and candidate expansion distance of each boundary point to form a boundary point expansion parameter list.

[0096] The rock type identification, main expansion direction probability threshold, pressure value and candidate expansion distance of each boundary point are recorded, and this information is compiled into a boundary point expansion parameter list. By analyzing the boundary point expansion parameter list, the boundary points that may be expanded can be screened out, and the expansion direction and expansion distance can be determined, thereby accurately simulating the expansion process of the channel.

[0097] Step S144: for the boundary points of the retained candidate extension distances, the extension direction is determined according to the main extension direction probability threshold, and the extension direction is preferably consistent with the direction with the highest main extension direction probability threshold, to generate a new boundary point position.

[0098] Boundary points with candidate expansion distances are selected from the boundary point expansion parameter list. These boundary points are points where expansion is likely to occur within the current time step. For these boundary points, their expansion directions are determined based on the main expansion direction probability threshold. The main expansion direction probability threshold includes the probability values ​​of multiple possible directions, reflecting the likelihood of the pore channel expanding in each direction under the conditions of the rock type. The direction with the highest main expansion direction probability threshold is preferentially selected as the expansion direction for the boundary point. This is because in historical expansion data, this direction is the direction in which the pore channel is most likely to expand. Following this principle can make the simulation results more consistent with reality.

[0099] Starting from this boundary point, the candidate extension distance is extended along the extension direction, and the new boundary point position is calculated using spatial geometry calculation methods. During the calculation process, it is necessary to consider the 3D spatial coordinate system of the channel and the initial position of the boundary point. The accurate coordinates of the new boundary point in 3D space are determined through methods such as vector operations.

[0100] Step S1441: Filter out boundary points whose pressure values ​​are greater than the expansion start pressure from the boundary point expansion parameter list as valid expansion points.

[0101] From the list of boundary point expansion parameters, select boundary points whose pressure values ​​exceed the expansion start pressure. The expansion start pressure is a critical threshold; only when the pressure value of the boundary point exceeds this threshold does the channel meet the basic conditions for expansion. Through screening, these boundary points that meet the conditions are designated as valid expansion points. These valid expansion points are the focus of subsequent expansion operations, representing the locations where channel expansion is likely to occur under the current pressure conditions.

[0102] Step S1442: For each valid expansion point, obtain its main expansion direction probability threshold, where the main expansion direction probability threshold includes probability values ​​of multiple possible directions, such as a probability value along the stratigraphic layer and a probability value perpendicular to the stratigraphic layer.

[0103] For each valid expansion point, a probability threshold for its primary expansion direction is obtained. This probability threshold is derived by summarizing the regularity of historical expansion feature data and includes probability values ​​for multiple possible directions. For example, this includes probability values ​​along stratigraphic layers and perpendicular to stratigraphic layers. These probability values ​​reflect the likelihood of the pore expanding in different directions given the rock type.

[0104] Step S1443: Select the direction with the highest main extension direction probability threshold as the extension direction of the valid extension point.

[0105] The direction with the highest probability value from the main expansion direction probability threshold is selected as the expansion direction for the valid expansion point. This is because, based on historical expansion data, this expansion direction is the direction in which the pore channel is most likely to expand under the conditions of this rock type. Selecting this expansion direction as the expansion direction makes the simulation results more consistent with the actual pore channel expansion patterns. Determining the expansion direction in this way can improve the accuracy and reliability of the simulation.

[0106] Step S1444: Taking the valid extension point as the starting point, the candidate extension distance is extended along the extension direction to calculate the new boundary point position coordinates.

[0107] Starting from the valid extension point, the candidate extension distance is extended along the determined extension direction. In three-dimensional space, spatial geometry and vector operations are used to calculate the new boundary point's position coordinates. First, a direction vector is determined based on the extension direction. This direction vector represents the direction of extension. Then, using the candidate extension distance as the vector's modulus, the extension is performed along the direction vector, starting from the valid extension point's coordinates. Through coordinate transformation and calculation, the new boundary point's accurate position coordinates in three-dimensional space are obtained.

[0108] Step S1445: Smoothing the new boundary point positions of adjacent valid extension points to make the new channel boundary shape continuous, and recording the coordinates of all new boundary point positions to form a new channel boundary coordinate set.

[0109] Smoothing is performed on the new boundary points of adjacent valid expansion points. In the actual channel expansion process, the channel boundary shape should be continuous. However, due to computational and discretization reasons, the new boundary points of adjacent valid expansion points may appear discontinuous. Through smoothing, such as using spline interpolation, the positions of adjacent new boundary points are fitted to make the new channel boundary shape continuous.

[0110] The coordinates of all new boundary points are recorded and organized into a new channel boundary coordinate set, which represents the new boundary of the channel after expansion in the current time step.

[0111] Step S145: Connect all new boundary point positions to form a new channel boundary, update the channel area in the three-dimensional geological basement model, and record the channel boundary data of the current time step.

[0112] Connect all the new boundary points to form a new channel boundary, which reflects the channel's expanded shape within the current time step. This new channel boundary is then updated to the channel region in the 3D geological basement model. The 3D geological basement model is the foundation of the entire simulation, and updating the channel region ensures that the model accurately reflects the channel's real-time state.

[0113] At the same time, the channel boundary data of the current time step is recorded. The channel boundary data includes the coordinate information of the new channel boundary, the geometric parameters of the channel, etc. By recording these channel boundary data, the process information of the dynamic expansion of the channel can be completely saved.

[0114] Step S146: Increase the running time by one time step, and repeat the steps of injection flow acquisition, pressure distribution calculation, expansion boundary judgment and channel boundary update until the injection termination condition in the injection process control rule is reached or the channel is extended to the boundary of the three-dimensional geological basement model.

[0115] Increasing the run time by one time step signifies the simulation enters the next time phase. The steps of obtaining the injection flow rate, calculating the pressure distribution, determining the expansion boundary, and updating the pore boundary are then repeated. Within the new time step, the new injection flow rate is obtained according to the time-flow mapping table of the injection process control rules. The pressure distribution within the pore is calculated based on the new injection flow rate. The expansion boundary is determined by combining the pressure distribution and the pore expansion constraint rules to determine which boundary points will be expanded. Finally, the pore boundary is updated based on the expansion results.

[0116] This process continues until the injection termination conditions specified in the injection process control rules are met or the pore channel reaches the boundary of the 3D geological basement model. These conditions are pre-set in the injection process control rules, such as the injection time, injection volume, or wellhead pressure. The simulation terminates when these conditions are met or the pore channel reaches the boundary of the 3D geological basement model. At this point, the 3D morphological evolution of the dynamic pore channel expansion has been fully simulated.

[0117] Step S150: Outputting a three-dimensional simulation result including time series pore morphology change data and expansion path data.

[0118] After the dynamic simulation framework is completed, the output is a three-dimensional simulation result containing time series of pore morphological change data and expansion path data. This three-dimensional simulation result is the final presentation of the entire simulation process.

[0119] Step S151: Collect the pore boundary coordinate set of each time step during the operation of the dynamic simulation framework, and arrange them in chronological order to form time series data of pore morphological changes.

[0120] The pore boundary coordinates are collected at each time step during the dynamic simulation framework. During the simulation, the pore boundary coordinates are recorded at each time step. These coordinate sets reflect the pore morphology at different times. These coordinate sets are arranged in chronological order to form a time series of pore morphological changes. This time series data allows for a clear understanding of the dynamic changes in pore morphology over time and the expansion of the pore at different stages.

[0121] Step S152: performing trajectory extraction processing on the duct boundary coordinate set in the time series data, calculating the position change of each boundary point at different time steps, and generating a path coordinate sequence of duct expansion.

[0122] Trajectory extraction is performed on the coordinate sets of the channel boundary in the time series data. For each boundary point, its position change at different time steps is analyzed. By comparing the coordinates of adjacent time steps, the displacement and movement direction of the boundary point are calculated. These position change information is concatenated in chronological order to generate a sequence of channel expansion path coordinates. This path coordinate sequence records the expansion path of each boundary point in detail, demonstrating the gradual expansion of the channel.

[0123] Step S153: Convert the time series pore morphology change data and expansion path data into a three-dimensional visualization format, wherein the three-dimensional visualization format supports dynamic display of the pore expansion process along the time axis in three-dimensional space.

[0124] Convert the time series of pore morphology change and expansion path data into a 3D visualization format. A 3D visualization format is a format that allows for intuitive display of data in three dimensions, such as common virtual reality (VR) and 3D animation formats. Using professional 3D visualization software, the pore morphology change and expansion path data are processed and converted, enabling a dynamic display of the pore expansion process in three dimensions along a timeline. This visualization allows users to more intuitively observe the pore morphology changes and expansion paths over time, gaining a deeper understanding of the dynamic process of pore fracturing.

[0125] Step S154: extracting key characteristic parameters from the simulation results, wherein the key characteristic parameters include the maximum extension length, the main extension direction, the extension rate of each time step, and the rock type distribution in the extension area.

[0126] Extract key characteristic parameters from the simulation results. Maximum expansion length refers to the maximum distance a pore extends in a certain direction during the entire simulation, reflecting the pore's expansion capacity and range. The primary expansion direction is the direction in which the pore is most likely to expand, based on the pore expansion path and expansion probability statistics.

[0127] The expansion rate at each time step refers to the speed at which the pores expand within each time step. By analyzing how the expansion rate changes over time, we can understand the dynamic characteristics of pore expansion. The rock type distribution in the expansion area records the rock types involved in the pore expansion process and their distribution. Different rock types have different effects on pore expansion, and analyzing the rock type distribution provides a basis for further research on the pore expansion mechanism.

[0128] Step S155: Perform statistical analysis on key characteristic parameters and generate a simulation analysis report containing a summary of extended regular characteristics.

[0129] Perform statistical analysis on the extracted key characteristic parameters. For the maximum extension length, calculate its mean, standard deviation, and other statistical quantities to understand its distribution. For the main extension directions, calculate the frequency of each direction to identify the most important extension direction. For the extension rate at each time step, analyze its changing trend, such as whether it increases or decreases over time. For the distribution of rock types in the extension area, calculate the proportion and distribution patterns of different rock types.

[0130] Based on the statistical analysis results, the regular characteristics of pore expansion are summarized, such as the differences in expansion rates under different rock types and the relationship between the main expansion direction and rock type. These analysis results and regular characteristics are compiled into a simulation analysis report. This simulation analysis report provides a scientific basis for optimizing and improving pore fracturing schemes, helping to improve fracturing effectiveness and resource extraction efficiency.

[0131] Figure 2 A schematic diagram illustrates exemplary hardware and software components of a digital twin-based three-dimensional simulation system 100 for dynamic expansion of pore-channel hydraulic fracturing, provided in some embodiments of the present application, that can implement the concepts of the present application. For example, a processor 120 can be used in the digital twin-based three-dimensional simulation system 100 for dynamic expansion of pore-channel hydraulic fracturing, and can be used to perform the functions of the present application.

[0132] The digital twin-based 3D simulation system 100 for dynamic expansion of duct fractures can be a general-purpose server or a special-purpose server, both of which can be used to implement the digital twin-based 3D simulation method for dynamic expansion of duct fractures of the present application. Although only one server is shown in this application, for convenience, the functions described in this application can be implemented in a distributed manner on multiple similar platforms to balance the processing load.

[0133] For example, the three-dimensional simulation system 100 for dynamic expansion of pore fracturing based on digital twins may include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and storage media 140 in different forms, such as a disk, ROM, or RAM, or any combination thereof. Exemplarily, the three-dimensional simulation system 100 for dynamic expansion of pore fracturing based on digital twins may also include program instructions stored in ROM, RAM, or other types of non-temporary storage media, or any combination thereof. The method of the present application can be implemented according to these program instructions. The three-dimensional simulation system 100 for dynamic expansion of pore fracturing based on digital twins also includes an I / O interface 150 between the computer and other input and output devices.

[0134] For ease of explanation, only one processor is described in the three-dimensional simulation system 100 for dynamic expansion of pore fracturing based on digital twins. However, it should be noted that the three-dimensional simulation system 100 for dynamic expansion of pore fracturing based on digital twins in this application may also include multiple processors, so the steps performed by one processor described in this application may also be performed jointly or individually by multiple processors. For example, if the processor of the three-dimensional simulation system 100 for dynamic expansion of pore fracturing based on digital twins executes step A and step B, it should be understood that step A and step B may also be performed jointly by two different processors or individually in one processor. For example, the first processor executes step A, the second processor executes step B, or the first processor and the second processor execute steps A and B together.

[0135] In addition, an embodiment of the present invention also provides a readable storage medium, in which computer-executable instructions are preset. When the processor executes the computer-executable instructions, the above-mentioned three-dimensional simulation method of dynamic expansion of duct fracturing based on digital twins is implemented.

[0136] It should be noted that in order to simplify the description of the present invention and thus help understand one or more embodiments of the invention, in the foregoing description of the embodiments of the present invention, multiple features are sometimes combined into one embodiment, figure or description thereof.

Claims

1. A three-dimensional simulation method for dynamic expansion of pore fracturing based on digital twins, characterized in that: The method comprises: Acquire full-process data of channel fracturing, including geological spatial distribution data, fracturing operation time series data, and historical expansion characteristic data; Establish a digital twin mapping relationship for channel fracturing, convert geological spatial distribution data into a three-dimensional geological basement model, convert fracturing operation time series operation data into injection process control rules, and convert historical expansion feature data into channel expansion constraint rules, wherein the fracturing operation time series operation data is subjected to rule extraction processing, and by analyzing the flow regulation records and pressure monitoring records in the time series, the functional relationship between the injection flow rate and the time change and the response relationship between the wellhead pressure and the flow rate are analyzed to generate injection process control rules including a time-flow mapping table and a flow-pressure mapping table, and the historical expansion feature data is subjected to regular feature induction processing, and by statistically analyzing the dominant direction in the fracture point distribution information and the expansion rate in the wellbore measurement information, the main direction probability distribution and the average expansion rate value of the channel expansion are analyzed to generate the channel expansion constraint rules including the main expansion direction probability threshold and the expansion rate reference value; A dynamic simulation framework is constructed based on the digital twin mapping relationship, wherein the dynamic simulation framework uses a three-dimensional geological basement model as a spatial carrier, injection process control rules as a temporal driver, and pore expansion constraint rules as a morphological evolution basis; Run the dynamic simulation framework to simulate the fracturing fluid injection action according to the injection process control rules, simultaneously calculate the pressure field distribution inside the pore, determine the expansion boundary based on the pore expansion constraint rules, and gradually generate the three-dimensional morphological evolution process of the dynamic pore expansion; The output includes three-dimensional simulation results of time series pore morphology change data and expansion path data.

2. The three-dimensional simulation method for dynamic expansion of pore fracturing based on digital twin according to claim 1, characterized in that: The method of obtaining the full process data of the pore fracturing includes: Collecting geological exploration data for the area where the tunnel is located. The geological exploration data includes information on the depth of strata from surface two-dimensional seismic exploration, rock type distribution information from downhole resistivity logging, and initial tunnel geometry information captured by an in-hole imager. Through spatial coordinate conversion processing, the position information obtained by different detection methods is unified into a three-dimensional coordinate system with the hole mouth as the origin, generating continuous geological spatial distribution data. Collecting operational record data during the fracturing operation, including the time point at which fracturing fluid injection begins, records of pump truck flow rate adjustments during the injection process, real-time monitoring of wellhead pressure, and records of termination conditions for injection cessation. Through time axis calibration processing, discrete operational time points are converted into continuous time series data arranged at second-level intervals to generate fracturing operation time series operational data; Organizing the extended monitoring data of historical fracturing projects, which includes information on the distribution of pore rupture points located by microseismic instruments, information on pore extension lengths from post-fracturing wellbore measurements, and information on fluid flow direction from well testing, and extracting spatial distribution patterns and temporal evolution patterns from the monitoring data through feature extraction processing as directional and rate preference patterns for pore extension to generate historical extended feature data; Cross-validation processing was performed on geological spatial distribution data, fracturing operation time series data and historical expansion characteristic data. By comparing the rock types in the geological spatial distribution data with the fracture point distribution in the historical expansion characteristic data, and by comparing the pressure records in the fracturing operation time series data with the expansion rate in the historical expansion characteristic data, the consistency of the rock mechanical properties and the expansion law characteristics and the correlation between the injection pressure and the expansion rate were verified.

3. The three-dimensional simulation method for dynamic expansion of pore fracturing based on digital twin according to claim 1, characterized in that: The digital twin mapping relationship of the pore fracturing is established to convert the geological spatial distribution data into a three-dimensional geological basement model, including: Performing 3D modeling on the geological spatial distribution data, converting the discrete stratum interface depth information and rock type distribution information into a continuous 3D grid model through an interpolation algorithm. Each node of the 3D grid model contains a stratum identification and a rock type identification, forming a 3D geological basement model. Among them, the association relationship between the three-dimensional geological basement model, the injection process control rules and the channel expansion constraint rules is established, the rock type identification of the three-dimensional geological basement model is associated with the main expansion direction probability threshold of the channel expansion constraint rule, and the flow-pressure mapping table of the injection process control rules is associated with the spatial position of the three-dimensional geological basement model.

4. The three-dimensional simulation method for dynamic expansion of pore fracturing based on digital twin according to claim 1, characterized in that: The dynamic simulation framework is constructed based on the digital twin mapping relationship. The dynamic simulation framework uses the three-dimensional geological basement model as the spatial carrier, the injection process control rules as the time drive, and the pore expansion constraint rules as the morphological evolution basis, including: Based on the three-dimensional geological basement model, the initial area of ​​the pore and the surrounding stratum area are divided. The shape of the initial area of ​​the pore is consistent with the initial pore geometry in the geological spatial distribution data. The surrounding stratum area is divided into different mechanical property areas according to the rock type identification. The time-flow mapping table of the injection process control rule is used as the time-driven parameter, and the time step of the simulation operation is set to be consistent with the time interval of the fracturing operation time series operation data, so that the simulation time is synchronized with the actual operation time; The main expansion direction probability threshold and expansion rate reference value of the pore expansion constraint rule are used as morphological evolution parameters. The expansion probability of each boundary point of the pore boundary is set as the main expansion direction probability threshold corresponding to the rock type where the boundary point is located, and the expansion rate is the product of the expansion rate reference value and the pressure value of the boundary point. A logical operation flow of the dynamic simulation framework is established, which includes: in each time step, determining the current injection flow according to the time-flow mapping table, and calculating the pressure distribution inside the channel according to the flow-pressure mapping table; determining the pressure value of each point on the channel boundary according to the pressure distribution and rock type identification, and judging whether the boundary point meets the expansion condition in combination with the main expansion direction probability threshold; for points that meet the expansion condition, calculating the expansion distance according to the product of the expansion rate baseline value and the pressure value to generate a new channel boundary; updating the new channel boundary to the three-dimensional geological basement model and entering the next time step.

5. The three-dimensional simulation method for dynamic expansion of pore fracturing based on digital twin according to claim 1, characterized in that: The dynamic simulation framework simulates the injection of fracturing fluid according to the injection process control rules, simultaneously calculates the pressure field distribution inside the pores, determines the expansion boundary in combination with the pore expansion constraint rules, and gradually generates the three-dimensional morphological evolution process of the dynamic expansion of the pores, including: Initialize the running time of the dynamic simulation framework to the initial time point, and set the initial boundary of the channel to the initial area boundary of the channel in the three-dimensional geological basement model; In the first time step, the initial injection flow rate is obtained according to the time-flow mapping table of the injection process control rule, and the initial pressure distribution inside the channel is calculated according to the flow-pressure mapping table. The initial pressure distribution shows that the pressure value is the highest at the orifice and gradually decreases towards the bottom of the hole. Traverse each boundary point of the initial boundary of the pore, obtain the rock type identifier and the corresponding main expansion direction probability threshold of the boundary point, and calculate the product of the pressure value of the boundary point and the expansion rate reference value as the candidate expansion distance; if the pressure value of the boundary point is greater than the preset expansion starting pressure, the candidate expansion distance is retained; otherwise, it is set to zero; For the boundary points of the retained candidate expansion distances, their expansion directions are determined according to the main expansion direction probability threshold, and the expansion direction is preferably consistent with the direction with the highest main expansion direction probability threshold, to generate a new boundary point position; Connect all new boundary point positions to form a new channel boundary, update the channel area in the three-dimensional geological basement model, and record the channel boundary data of the current time step; The running time is increased by one time step, and the steps of injection flow acquisition, pressure distribution calculation, expansion boundary judgment, and pore boundary update are repeated until the injection termination condition in the injection process control rule is reached or the pore extends to the boundary of the three-dimensional geological basement model.

6. The three-dimensional simulation method for dynamic expansion of pore fracturing based on digital twin according to claim 5, characterized in that: Calculating the pressure distribution inside the duct according to the flow-pressure mapping table includes: Extract the injection flow value of the current time step and query the flow-pressure mapping table to obtain the corresponding wellhead pressure value; Based on the geometric shape of the initial area of ​​the pore, a pressure attenuation model along the length of the pore is established. The attenuation model assumes that the pressure value decays linearly from the pore mouth to the pore bottom. Calculate the pressure value at each location inside the channel based on the wellhead pressure value and the total length of the channel. The pressure value is equal to the wellhead pressure value minus the product of the pressure attenuation per unit length and the distance from the location to the orifice. Performing spatial interpolation processing on the pressure values ​​to generate pressure distribution data consistent with the grid resolution of the three-dimensional geological basement model, wherein each grid node in the pressure distribution data corresponds to a pressure value; Verify the rationality of the pressure distribution data by comparing the difference between the wellhead pressure value and the bottom hole pressure value to see if it conforms to the empirical range of pressure decay in historical fracturing operations, so that the pressure distribution calculation results match the actual situation.

7. The three-dimensional simulation method for dynamic expansion of pore fracturing based on digital twin according to claim 5, characterized in that: The step of traversing each boundary point of the initial boundary of the pore, obtaining a rock type identifier of the boundary point and a corresponding main expansion direction probability threshold, and calculating the product of the pressure value of the boundary point and the expansion rate reference value as a candidate expansion distance includes: The initial boundary of the pore is discretized into multiple boundary points, each of which corresponds to a grid node in the three-dimensional geological basement model; For each boundary point, query the rock type identifier of the grid node where it is located, and obtain the main expansion direction probability threshold of the boundary point based on the correlation between the main expansion direction probability threshold of the pore expansion constraint rule and the rock type; Query the pressure distribution data corresponding to the boundary point to obtain the pressure value of the boundary point; Multiplying the pressure value of the boundary point by the expansion rate reference value to obtain a candidate expansion distance of the boundary point, where the candidate expansion distance represents the possible expansion length of the boundary point in the current time step; The rock type identification, main extension direction probability threshold, pressure value and candidate extension distance of each boundary point are recorded to form a boundary point extension parameter list.

8. The three-dimensional simulation method for dynamic expansion of pore fracturing based on digital twins according to claim 5, characterized in that: For the boundary points of the retained candidate expansion distances, determining their expansion directions according to the main expansion direction probability threshold, preferably selecting the expansion direction consistent with the direction with the highest main expansion direction probability threshold, and generating new boundary point positions, including: Filter out boundary points whose pressure values ​​are greater than the expansion start pressure from the boundary point expansion parameter list as valid expansion points; For each valid extension point, obtaining its main extension direction probability threshold, wherein the main extension direction probability threshold includes probability values ​​of multiple possible directions; Select the direction with the highest main expansion direction probability threshold as the expansion direction of the valid expansion point; Taking the valid extension point as the starting point, extend the candidate extension distance along the extension direction and calculate the new boundary point position coordinates; The new boundary point positions of adjacent valid extension points are smoothed to make the new channel boundary shape continuous, and the coordinates of all new boundary point positions are recorded to form a new channel boundary coordinate set.

9. The three-dimensional simulation method for dynamic expansion of pore fracturing based on digital twin according to claim 1, characterized in that: The output includes the three-dimensional simulation results of the time series pore morphology change data and the expansion path data, including: Collect the pore boundary coordinates of each time step during the operation of the dynamic simulation framework and arrange them in chronological order to form time series data of pore morphological changes; Perform trajectory extraction on the duct boundary coordinate set in the time series data, calculate the position change of each boundary point at different time steps, and generate the path coordinate sequence of the duct expansion; Converting the time series of pore morphology change data and the expansion path coordinate sequence into a three-dimensional visualization format, wherein the three-dimensional visualization format supports dynamic display of the pore expansion process along the time axis in three-dimensional space; Extracting key characteristic parameters from the simulation results, wherein the key characteristic parameters include maximum extension length, main extension direction, extension rate at each time step, and rock type distribution in the extension area; Perform statistical analysis on key characteristic parameters and generate a simulation analysis report containing a summary of extended regularity characteristics.

10. A three-dimensional simulation system for dynamic expansion of pore fracturing based on digital twins, characterized in that: It includes a processor and a memory, the memory is connected to the processor, the memory is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the memory to implement the three-dimensional simulation method of dynamic expansion of duct fracturing based on digital twins as described in any one of claims 1 to 9.

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