Nano tackifying fluid and supercritical CO2 fluid synergistic fracturing method

Abstract

The invention relates to a nano tackifying fluid and supercritical CO2 fluid synergistic fracturing method. In the target stratum development area, nano tackifying fluid and supercritical CO2 fluid are adopted for collaborative fracturing, sand-carrying fluid and displacing fluid are combined to form a fracture network, and the corresponding relation between the construction parameter combination and the fracture flow conductivity and the corresponding relation between the construction parameter combination and the fracturing construction fluid amount are established; based on the corresponding relation, a multi-target optimization model with the fracture conductivity maximization and the fracturing construction liquid amount minimization as the target is constructed, and through population initialization, non-dominated sorting, crowding distance calculation, crossover variation selection and iterative updating, a construction parameter optimization result is output. The shale reservoir fracture flow conductivity and the fracture network complexity can be improved, the construction liquid amount is reduced, and the fracturing transformation effect and economical efficiency are improved.

Description

Technical Field

[0001] This application relates to the field of oil and gas extraction technology, and in particular to a synergistic fracturing method using nano-viscosifying fluid and supercritical CO2 fluid. Background Technology

[0002] my country is rich in shale oil resources, and hydraulic fracturing to create complex artificial fracture networks is one of the key technologies for achieving efficient and economical shale oil development. However, due to the high clay mineral content of shale oil, traditional water-based fracturing and hydraulic fracturing in shale oil reservoir fracturing have drawbacks such as formation damage and low flowback rates. To overcome these problems, supercritical CO2 fracturing technology has emerged. In addition to advantages such as low reservoir damage, high flowback rate, and water conservation, supercritical CO2 fluid, due to its ultra-low viscosity (0.12-0.16 mPa·s), can more easily transmit flowing pressure to the fracture tip, thereby invading and connecting the micro-fracture system to form a complex fracture network.

[0003] However, supercritical CO2 fracturing still faces some challenges. Firstly, its low viscosity results in a relatively insufficient capacity to form primary fractures, which may affect fracturing effectiveness. Secondly, the poor viscoelasticity of supercritical CO2 fluid makes it difficult to effectively deliver proppant to the depths of micro-fractures, and the conductivity of branch fractures needs further improvement.

[0004] In recent years, nano-viscosity-enhancing fluids (nano-viscosity-modified slickwater fracturing fluids) have emerged, exhibiting superior viscoelasticity, proppant-carrying capacity, and main fracture-creating ability. In contrast, the energy consumed in supercritical CO2 fracturing fracture propagation is primarily consumed by rock fracture, while nano-viscosity-enhancing fluids have relatively high viscosity, resulting in a fracture propagation mechanism dominated by liquid viscosity. The combined use of these two technologies can overcome the shortcomings of pure supercritical CO2 fracturing, increasing the complexity of the artificial fracture network and the fracture conductivity in shale oil. Furthermore, shale oil reservoirs have well-developed micro- and nano-pores, low formation pressure coefficients, and high crude oil flow resistance. Combining nano-viscosity-enhancing fluids with supercritical CO2 can reduce surface tension, increase formation pressure, and enhance the efficiency of nano-percolation oil displacement. Therefore, the combined fracturing technology of nano-viscosity-enhancing fluids and supercritical CO2 will become one of the important means to achieve increased and stable shale oil production in the future. Clarifying the construction process of synergistic fracturing using nano-viscosity-enhancing fluids and supercritical CO2, and providing methods for optimizing construction parameters, is of great significance for shale oil reservoir stimulation. Summary of the Invention

[0005] To overcome, to some extent, the difficulty in determining the construction parameters for synergistic fracturing using nano-viscosifying fluid and supercritical CO2 fluid, this application provides a method for synergistic fracturing using nano-viscosifying fluid and supercritical CO2.

[0006] The proposed solution is as follows:

[0007] A synergistic fracturing method combining nano-viscosifying fluid and supercritical CO2 includes: S1. In the target formation development area, nano-viscosifying fluid and supercritical CO2 fluid are used for synergistic fracturing, and a fracture network is formed by combining proppant-carrying fluid and displacement fluid. The correspondence between different combinations of construction parameters and fracture conductivity and fracturing fluid volume is established. S2. Based on the fracture conductivity and fracturing fluid volume corresponding to different combinations of construction parameters, a multi-objective optimization model is constructed with the optimization objectives of maximizing fracture conductivity and minimizing fracturing fluid volume. The multi-objective optimization model uses construction parameters as decision variables. S3. Using at least two of the construction parameters as parameters to be optimized, set the initial population size of the multi-objective optimization model and generate an initial population; each individual in the initial population corresponds to a set of construction parameter combinations; the construction parameters include: the discharge rate, liquid volume, viscosity and density of the nano-thickening fluid, the discharge rate, liquid volume, viscosity and density of the supercritical CO2 fluid, and the concentration, density and particle size of the proppant. S4. Calculate the fracture conductivity and fracturing fluid volume for each individual in the population. S5. Based on the fracture conductivity and fracturing fluid volume of each individual body, perform rapid non-dominated sorting and crowding distance calculation for each individual body, and select the parent individual based on the calculation results. S6. Perform crossover and mutation operations on the parent individuals to generate offspring individuals; S7. Merge parent individuals with offspring individuals to generate a new generation of population; S8. Repeat steps S4-S7 until the preset iteration termination condition is met. S9. Output the optimal solution set of the multi-objective optimization model as the optimization result of the construction parameters; the optimal solution set includes the optimization solutions corresponding to different combinations of construction parameters.

[0008] Preferably, in the target formation development area, synergistic fracturing is performed using nano-viscosifying fluid and supercritical CO2 fluid, combined with proppant-carrying fluid and displacement fluid to form a fracture network, including: S11. Inject nano-viscosity-enhancing fluid into the target formation development area to form a main fracture in the target formation development area; S12. Inject supercritical CO2 fluid into the target formation development area to activate branch fractures based on the main fracture; S13. Repeat the injection of nano-thickening fluid and supercritical CO2 fluid alternately according to the preset number of cycles to form an initial crack network; S14. Inject sand-carrying fluid into the target formation development area to support the initial fracture network and form a supporting fracture network; S15. Obtain the fracture permeability and fracture width of the supporting fracture network, and calculate the fracture conductivity. S16. Inject displacement fluid into the target formation development area to replace the sand-carrying fluid in the wellbore and form the final fracture network.

[0009] Preferably, the method further includes: Adjust the combination of construction parameters and repeat steps S11-S16 to establish the correspondence between different combinations of construction parameters and fracture conductivity and fracturing fluid volume.

[0010] Preferably, obtaining the fracture permeability of the supporting fracture network includes: The fracture permeability of the proppant fracture network is calculated based on the rock porosity, the specific surface area of ​​the proppant particles, and a preset constant in the target stratum development area.

[0011] Preferably, obtaining the crack width includes: The fracture width supporting the fracture network is calculated based on the Poisson's ratio and Young's modulus of the rock in the target stratum development area, as well as the net pressure and fracture height within the fracture.

[0012] Preferably, calculating the fracture conductivity includes: The product of the fracture permeability and fracture width of the supporting fracture network is calculated as the fracture conductivity.

[0013] Preferably, each individual in the population is represented by a parameter combination that includes all parameters to be optimized, and the dimension of the input parameters corresponds to the number of parameters to be optimized.

[0014] Preferably, each individual body is rapidly sorted and its congestion distance is calculated based on its corresponding fracture conductivity and fracturing fluid volume, and a parent individual is selected based on the calculation results, including: The non-dominance level of each body is determined based on the fracture conductivity and fracturing fluid volume of each body. Calculate crowding distance for individuals in the same non-dominant rank; Individuals with lower non-dominance levels and larger crowding distances are preferred as parent individuals.

[0015] Preferably, the method further includes: Output an optimized chart to characterize the distribution features of different optimization parameter values.

[0016] Preferably, the method further includes: The optimized combination of construction parameters is compared and verified with the original combination of construction parameters, and the verification results are output.

[0017] The technical solution provided in this application may include the following beneficial effects: This application utilizes a synergistic fracturing approach combining nano-thickening fluid and supercritical CO2 fluid, and establishes an optimization mechanism for the combination of construction parameters based on fracture conductivity and fracturing fluid volume. This mechanism enables quantitative optimization of key parameters before fracturing operations or during the design phase, effectively addressing the problem that parameter selection in synergistic fracturing relies heavily on experience and struggles to balance fracture creation effectiveness and construction costs. Specifically, the nano-thickening fluid enhances the ability to create main fractures, improves the fluid's proppant carrying capacity, and enhances proppant delivery, while the supercritical CO2 fluid facilitates entry into micro-fractures and increases the complexity of the fracture network. The synergistic effect of these two components helps to simultaneously improve the extension capacity of the main fracture and the communication capacity of branch fractures, thereby enhancing the overall conductivity of the fracture. Furthermore, this application constructs a multi-objective optimization model aimed at maximizing fracture conductivity and minimizing fracturing fluid volume, and outputs the optimal solution set by combining rapid non-dominated sorting, congestion distance calculation, cross-mutation, and iterative updates. This allows for the selection of better combinations of construction parameters under various construction constraints, making fracturing design more scientific, precise, and feasible. Therefore, it can not only improve the effect of shale oil reservoir stimulation and the complexity of artificial seam mesh, but also reduce the input of ineffective liquids, improve the economic efficiency of construction, and provide reliable parameter optimization support for the increase and stability of shale oil reservoir production.

[0018] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0020] Figure 1 This is a schematic flowchart of a synergistic fracturing method using nano-thickening fluid and supercritical CO2 fluid provided in one embodiment of this application; Figure 2 This is a schematic diagram of the process of using nano-viscosifying fluid and supercritical CO2 fluid for synergistic fracturing in the target formation development area, according to an embodiment of this application. Detailed Implementation

[0021] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0022] Example 1 Figure 1 This is a schematic flowchart of a synergistic fracturing method using nano-thickening fluid and supercritical CO2 fluid according to an embodiment of this application. (Refer to...) Figure 1 A synergistic fracturing method combining nano-viscosifying fluid and supercritical CO2 fluid, comprising: S1. In the target formation development area, nano-viscosifying fluid and supercritical CO2 fluid are used for synergistic fracturing, and a fracture network is formed by combining proppant-carrying fluid and displacement fluid. The correspondence between different combinations of construction parameters and fracture conductivity and fracturing fluid volume is established. S2. Based on the fracture conductivity and fracturing fluid volume corresponding to different combinations of construction parameters, a multi-objective optimization model is constructed with the optimization objectives of maximizing fracture conductivity and minimizing fracturing fluid volume. The multi-objective optimization model uses construction parameters as decision variables. S3. Using at least two of the construction parameters as parameters to be optimized, set the initial population size of the multi-objective optimization model and generate an initial population; each individual in the initial population corresponds to a set of construction parameter combinations; the construction parameters include: the discharge rate, liquid volume, viscosity and density of the nano-thickening fluid, the discharge rate, liquid volume, viscosity and density of the supercritical CO2 fluid, and the concentration, density and particle size of the proppant. S4. Calculate the fracture conductivity and fracturing fluid volume for each individual in the population. S5. Based on the fracture conductivity and fracturing fluid volume of each individual body, perform rapid non-dominated sorting and crowding distance calculation for each individual body, and select the parent individual based on the calculation results. S6. Perform crossover and mutation operations on the parent individuals to generate offspring individuals; S7. Merge parent individuals with offspring individuals to generate a new generation of population; S8. Repeat steps S4-S7 until the preset iteration termination condition is met. S9. Output the optimal solution set of the multi-objective optimization model as the optimization result of the construction parameters; the optimal solution set includes the optimization solutions corresponding to different combinations of construction parameters.

[0023] For ease of understanding, the following explains some key terms in this embodiment: Synergistic fracturing of nano-viscosifying fluids and supercritical CO2 refers to the injection of a composite nano-viscosifying fluid and supercritical CO2 fluid into the target formation development area to achieve formation modification. The nano-viscosifying fluid consists of a nanoemulsion CNI-A and an emulsion-like viscosity-modifying and drag-reducing agent CNI-B. The nanoemulsion CNI-A, composed of surfactants and nano-oil cores, forms a spatial network structure with the physically crosslinked viscosity-modifying and drag-reducing agent CNI-B through hydrophobic association, thus achieving a viscosity-enhancing effect. The nano-viscosifying fluid exhibits specific properties in viscoelasticity, proppant carrying capacity, and main fracture-forming ability. The supercritical CO2 fluid refers to carbon dioxide in a supercritical state, characterized by low viscosity and strong diffusivity, which can activate micro-fractures. The synergistic effect of these two components aims to form a complex fracture network and enhance conductivity.

[0024] A fracture network refers to an interconnected system of fractures formed during hydraulic fracturing through fluid injection and rock fracturing. This network includes main fractures and branch fractures extending from them, and its morphology and connectivity directly affect the seepage efficiency of oil and gas.

[0025] Fracture conductivity is an indicator of the efficiency of fluid transport through fracture channels, usually expressed as the product of fracture permeability and fracture width. A high conductivity value results in lower resistance to oil and gas flowing from the formation into the wellbore, thus improving production efficiency.

[0026] The volume of fracturing fluid refers to the total volume of all injected fluids used in the entire fracturing operation, including nano-thickening fluid, supercritical CO2 fluid, proppant-carrying fluid, and displacement fluid. Controlling this volume is significant for reducing construction costs and minimizing environmental impact.

[0027] Multi-objective optimization models are mathematical optimization methods used to find a set of compromise solutions when multiple conflicting optimization objectives exist. In this method, the model aims to simultaneously maximize fracture conductivity and minimize the amount of fracturing fluid.

[0028] Fast nondominated sorting is a method for stratifying individuals in a population within a multi-objective optimization algorithm. It classifies individuals into different nondominated levels based on their performance across all objectives, with lower levels indicating that the individual is close to the Pareto front.

[0029] Crowding distance is a metric for measuring the density of individuals in a multi-objective optimization population. For individuals of the same non-dominant class, a large crowding distance indicates that the solutions around that individual are sparse, which helps maintain population diversity during the selection process.

[0030] This embodiment provides a method for synergistic fracturing of nano-viscosifying fluid and supercritical CO2.

[0031] Specifically, in step S1, synergistic fracturing using nano-viscosifying fluid and supercritical CO2 fluid is employed in the target formation development area. This is combined with proppant-carrying fluid and displacement fluid to form a fracture network, establishing a correlation between different combinations of construction parameters and fracture conductivity and fracturing fluid volume. Synergistic fracturing can be implemented using various fluid injection strategies. For example, a staged injection approach can be adopted, injecting one fluid first to achieve a specific purpose, followed by the injection of another fluid to fulfill its function. Alternatively, both fluids can be injected simultaneously using specific injection equipment to achieve a mixing effect. Prop-carrying fluid is typically injected after fracture formation to provide structural support. Subsequently, displacement fluid is injected to replace the proppant-carrying fluid, ensuring wellbore cleanliness and preventing proppant deposition. The correlation between different combinations of construction parameters and fracture conductivity and fracturing fluid volume can be established through numerical simulations, laboratory experiments, or field tests. For instance, by running a series of numerical simulations, the corresponding fracture geometry, permeability, and total fluid volume are calculated for different combinations of injection rates, fluid volumes, and viscosity parameters, thereby constructing this correlation.

[0032] Furthermore, in step S2, based on the fracture conductivity and fracturing fluid volume corresponding to different combinations of construction parameters, a multi-objective optimization model is constructed with the optimization objectives of maximizing fracture conductivity and minimizing fracturing fluid volume. This model can use a weighted sum method to merge multiple objective functions into a single objective function, or it can use a method based on the Pareto optimality concept to find a set of non-dominated solutions. This model is designed to quantify and balance the relationship between fracture conductivity and fracturing fluid volume to guide the subsequent optimization process.

[0033] Based on this, in step S3, at least two of the construction parameters are selected as parameters to be optimized, and an initial population size is set to generate an initial population. Each individual in this initial population corresponds to a set of construction parameter combinations. Construction parameters may include the displacement, volume, viscosity, and density of the nano-viscosifying fluid; the displacement, volume, viscosity, and density of the supercritical CO2 fluid; and the concentration, density, and particle size of the proppant. The selection of parameters to be optimized can be based on parameters that significantly affect the fracturing effect. For example, the displacement of the nano-viscosifying fluid and the volume of the supercritical CO2 fluid can be selected as parameters to be optimized. The initial population can be generated through random sampling methods, for example, uniform random sampling within a preset range of each parameter to ensure the diversity of the initial solution.

[0034] Subsequently, in step S4, the fracture conductivity and fracturing fluid volume corresponding to each individual in the population are calculated. For each combination of construction parameters in the initial population, the corresponding fracture conductivity and fracturing fluid volume are calculated according to the correspondence established in step S1. This calculation can quickly obtain the results by calling a pre-trained surrogate model or consulting a pre-calculated database.

[0035] In step S5, the fracture conductivity and fracturing fluid volume corresponding to each individual are used to perform rapid non-dominated sorting and crowding distance calculations, and parent individuals are selected based on the calculation results. Rapid non-dominated sorting stratifies individuals in the population according to their dominance relationships in the multi-objective space, forming different non-dominated fronts. Crowding distance is used to measure the sparsity between individuals on the same non-dominated front. Based on these calculation results, various selection strategies can be used to determine parent individuals; for example, mechanisms such as tournament selection or roulette wheel selection can be used to select individuals from the current population to generate the next generation.

[0036] Further, in step S6, crossover and mutation operations are performed on the parent individual to generate offspring individuals. The crossover operation generates new offspring individuals by exchanging or combining some parameters of two parent individuals, for example, using single-point crossover or uniform crossover. The mutation operation introduces random perturbations into the parameters of the offspring individuals to explore new solution spaces and avoid getting trapped in local optima, for example, by making small-range random adjustments to a certain parameter value.

[0037] In step S7, the parent individual is merged with the offspring individual to generate a new generation population. By merging the parent and offspring individuals, a larger candidate population is formed. Subsequently, selection is performed again from this merged population to determine the members of the next generation population, typically retaining high-performing and diverse individuals, thereby controlling the population size and driving the optimization process.

[0038] Repeat steps S4-S7 until the preset iteration termination condition is met. This termination condition can be set to reaching the maximum number of iterations, for example, running 100 or 200 generations. Alternatively, convergence can be determined when the optimal solution set no longer changes significantly over multiple generations.

[0039] Finally, in step S9, the optimal solution set is output as the result of the construction parameter optimization. This optimal solution set is typically presented in the form of a Pareto front, which contains multiple non-dominated solutions that achieve different balances between maximizing fracture conductivity and minimizing fracturing fluid volume. These optimized solutions provide multiple options for on-site construction, allowing for decision-making based on actual needs.

[0040] It should be noted that the optimization algorithm used in this embodiment is the NSGA-II optimization algorithm, and its output Pareto optimal solution set is shown in Table 1 below: Table 1. Example of Pareto optimal solution set

[0041] This method utilizes synergistic fracturing of nano-thickening fluid and supercritical CO2 fluid, and establishes an optimization mechanism for the combination of construction parameters around fracture conductivity and fracturing fluid volume. This allows for quantitative optimization of key parameters before fracturing operations or during the design phase, effectively addressing the problem that parameter selection in synergistic fracturing relies heavily on experience and struggles to balance fracture creation effectiveness and construction cost. Specifically, the nano-thickening fluid enhances the ability to create main fractures, improves the fluid's proppant carrying capacity, and enhances proppant delivery, while the supercritical CO2 fluid facilitates entry into micro-fractures and increases the complexity of the fracture network. Their synergistic effect helps to simultaneously improve the extension capacity of the main fracture and the communication capacity of branch fractures, thereby enhancing the overall conductivity of the fracture. Furthermore, this application constructs a multi-objective optimization model aimed at maximizing fracture conductivity and minimizing fracturing fluid volume, and outputs the optimal solution set by combining rapid non-dominated sorting, congestion distance calculation, cross-mutation, and iterative updates. This allows for the selection of better combinations of construction parameters under various construction constraints, making fracturing design more scientific, precise, and feasible. Therefore, it can not only improve the effect of shale oil reservoir stimulation and the complexity of artificial seam mesh, but also reduce the input of ineffective liquids, improve the economic efficiency of construction, and provide reliable parameter optimization support for the increase and stability of shale oil reservoir production.

[0042] Example 2 Reference Figure 2 In the target formation development area, synergistic fracturing using nano-viscosifying fluid and supercritical CO2 fluid is employed, combined with proppant-carrying fluid and displacement fluid to form a fracture network, including: S11. Inject nano-viscosity-enhancing fluid into the target formation development area to form the main fracture in the target formation development area; S12. Inject supercritical CO2 fluid into the target formation development area to activate branch fractures based on the main fracture; S13. Repeat the injection of nano-thickening fluid and supercritical CO2 fluid alternately according to the preset number of cycles to form an initial crack network; S14. Inject sand-carrying fluid into the target formation development area to support the initial fracture network and form a supporting fracture network. S15. Obtain the fracture permeability and fracture width of the supporting fracture network, and calculate the fracture conductivity. S16. Inject displacement fluid into the target formation development area to replace the sand-carrying fluid in the wellbore and form the final fracture network.

[0043] First, 100-300 cubic meters of nano-viscosifying fluid are injected into the target formation development area at a flow rate of 0.5-2.0 cubic meters per minute to form a main fracture. This step aims to utilize the high viscosity and rheological properties of the nano-viscosifying fluid to preferentially open and extend one or more main fracture channels in the target formation, laying the foundation for subsequent fracture network expansion. The injection flow rate, volume, viscosity, and density of the nano-viscosifying fluid must be precisely controlled according to formation characteristics and fracturing design to ensure the effective formation and extension of the main fracture.

[0044] Subsequently, 100-300 cubic meters of supercritical CO2 fluid are injected into the target formation development area at a rate of 0.5-2.0 cubic meters per minute to activate branch fractures based on the main fracture. Supercritical CO2 fluid, with its low viscosity, high diffusivity, and good wettability, can penetrate into microfractures and natural fractures surrounding the main fracture. Through stress disturbance and fluid intrusion, it activates and expands these secondary fractures, thereby forming a complex, multidirectional network of branch fractures on both sides of the main fracture. The precise setting of parameters such as the supercritical CO2 fluid's flow rate, volume, viscosity, and density is crucial for the effective activation of the branch fractures.

[0045] Based on this, the injection of nano-viscosifying fluid and supercritical CO2 fluid is alternately repeated according to a preset number of cycles (2-10 times) to form an initial fracture network. This alternating injection strategy can fully leverage the synergistic advantages of the two fluids: the nano-viscosifying fluid is responsible for the continuous extension and support of the main fracture, while the supercritical CO2 fluid continuously activates and expands branch fractures. Through multiple cycles, the complexity and coverage of the fracture network can be effectively increased, forming a denser and more interconnected initial fracture network. Determining the preset number of cycles requires comprehensive consideration of formation conditions, fluid characteristics, and the desired fracture network morphology.

[0046] Next, 100-200 cubic meters of proppant-carrying fluid (proppant ratio of 5%-30%) are injected into the target formation development area at a flow rate of 2.0-4.0 cubic meters per minute to support the initial fracture network, forming a supported fracture network. After the initial fracture network is formed, fluid containing proppant-carrying fluid is injected to transport proppant particles into the fractures. Under the action of formation closure stress, the supported fractures remain open, preventing fracture closure. The selection of parameters such as the concentration, density, and particle size of the proppant directly affects the conductivity and long-term stability of the supported fracture network.

[0047] Furthermore, the fracture permeability and fracture width of the proppant fracture network are obtained, and the fracture conductivity is calculated. This step aims to quantify the fluid conduction performance of the formed fracture network. Fracture permeability characterizes the ease with which fluid fills the fractures through the proppant, while fracture width reflects the channel size for fluid flow. These parameters are key indicators for evaluating fracturing effectiveness and guiding subsequent optimization decisions.

[0048] Obtaining the crack width includes: Based on the Poisson's ratio and Young's modulus of the rocks in the target formation development area, as well as the net pressure and fracture height within the fractures, the fracture width of the supporting fracture network is calculated using the following formula: ; in, The crack width is expressed in meters (m). is Poisson's ratio of the rock; E is Young's modulus of the rock, in Pa; This represents the net pressure within the crack, expressed in Pa. The value is the crack height, in meters (m).

[0049] Obtain the fracture permeability of the supporting fracture network, including: Based on the rock porosity, proppant particle specific surface area, and preset constants in the target formation development area, the fracture permeability of the propped fracture network is calculated using the following formula: ; in, The unit for supporting fracture permeability is mD; Rock porosity; This is a preset constant; The specific surface area of ​​the proppant particles is expressed in cm⁻¹.

[0050] Calculating the fracture conductivity includes: The product of the fracture permeability and fracture width in the supporting fracture network is used to calculate the fracture conductivity, and the formula is as follows: ; in, The crack conductivity is expressed in mD·m.

[0051] Finally, a displacement fluid is injected into the target formation development area to replace the proppant-carrying fluid, ensuring a clean wellbore and preventing proppant deposition, thus forming the final fracture network. The displacement fluid is typically a low-viscosity fluid, and its main function is to displace the proppant-carrying fluid in the wellbore into the fractures, ensuring a clean wellbore and preventing proppant deposition.

[0052] Furthermore, the methods also include: Adjust the combination of construction parameters and repeat steps S11-S16 to establish the correspondence between different combinations of construction parameters and fracture conductivity and fracturing fluid volume.

[0053] Specifically, adjusting the combination of construction parameters refers to systematically changing the key parameters affecting fracturing effectiveness. These parameters may include the flow rate, volume, viscosity, and density of the nano-thickening fluid; the flow rate, volume, viscosity, and density of the supercritical CO2 fluid; and the concentration, density, and particle size of the proppant. A series of representative parameter combinations can be generated using pre-defined experimental design methods, such as orthogonal experiments, uniform design, or response surface methodology. Each combination represents a specific fracturing operation scheme. For each adjusted combination of construction parameters, steps S11 to S16, which involve forming the fracture network and calculating fracture conductivity, must be executed completely. This means that, firstly, a nano-thickening fluid is injected into the target formation development area to form a main fracture; then, supercritical CO2 fluid is injected to activate branch fractures based on the main fracture; then, the injection of nano-thickening fluid and supercritical CO2 fluid is alternately repeated according to a preset number of cycles to form an initial fracture network; subsequently, proppant-carrying fluid is injected into the target formation development area to support the initial fracture network, forming a supporting fracture network; the fracture permeability and fracture width of the supporting fracture network are obtained, and the fracture conductivity is calculated; finally, displacement fluid is injected into the target formation development area to replace the proppant-carrying fluid in the wellbore into the fractures, ensuring wellbore cleanliness and preventing proppant deposition, forming the final fracture network. By repeating the above process for each parameter combination, the fracture conductivity and fracturing fluid volume data under that combination can be obtained. Through the above adjustments and repetitions, a series of data points can be collected, each containing a specific combination of construction parameters and its corresponding fracture conductivity and fracturing fluid volume. These data points together constitute the mapping relationship between the combination of construction parameters and the fracture conductivity and fracturing fluid volume. This correspondence can be represented by a data table, a database, or a predictive model built using methods such as regression analysis and machine learning, used to describe how input parameters affect output performance metrics.

[0054] Example 3 It should be noted that each individual in the population is represented by a parameter combination that includes all parameters to be optimized, and the dimension of the input parameters corresponds to the number of parameters to be optimized.

[0055] Specifically, in genetic or evolutionary algorithms, an "individual" is a candidate solution in the solution space, and the "encoding representation" is the transformation of this candidate solution into a data structure that the algorithm can process. It is emphasized here that each individual must completely contain all the parameters that need to be optimized. This means that if there are N parameters to be optimized, then each individual's data structure must have N fields or elements, corresponding to the values ​​of these N parameters. For example, a real-number encoding method can be used, directly treating the value of each parameter to be optimized as a gene locus of the individual. This is suitable for continuous parameters, such as an individual containing parameters like displacement, volume, viscosity, and density of nano-thickening fluids, displacement, volume, viscosity, and density of supercritical CO2 fluids, and concentration, density, and particle size of proppant, which can be directly represented as a real-number vector. Alternatively, binary encoding, integer encoding, or a hybrid encoding method can also be used, but it is necessary to ensure that the value range and precision of each parameter can be accurately represented, and the encoding method should facilitate subsequent genetic operations such as crossover and mutation.

[0056] Meanwhile, the "input parameter dimension" refers to the number of parameters that the algorithm identifies and operates on when processing each individual. This application emphasizes that this dimension must be consistent with the total number of parameters that actually need to be optimized. This means that if the optimization model needs to adjust 10 parameters, then the algorithm must also identify and operate on these 10 parameters when processing each individual, no more and no less. This correspondence is usually achieved by defining the structure or length of the individual during the algorithm initialization phase. For example, by defining a structure or class to represent the individual, which contains member variables equal to the number of parameters to be optimized, or by using an array or list to store the parameter values ​​of the individual, the length of which is the number of parameters to be optimized.

[0057] It should be noted that a fast non-dominated sort and crowding distance calculation are performed on each individual, and parent individuals are selected based on the calculation results, including: The non-dominance level of each body is determined based on the fracture conductivity and fracturing fluid volume of each body. Calculate crowding distance for individuals in the same non-dominant rank; Individuals with lower non-dominance levels and larger crowding distances are preferred as parent individuals.

[0058] In this embodiment, firstly, the non-dominated class of each individual is determined based on its fracture conductivity and fracturing fluid volume. The non-dominated class is an important indicator used in multi-objective optimization to evaluate the performance of individuals. For any two individuals in the population, if one individual is not inferior to the other in all objectives and is superior to the other in at least one objective, then that individual is said to dominate the other. By comparing the dominance relationships among all individuals in the population, individuals can be classified into different non-dominated classes. Individuals not dominated by any other individual are assigned to the first non-dominated class, and these individuals are then removed from the population. This process is repeated for the remaining individuals to determine the second, third, and so on, until all individuals are assigned a class. The lower the non-dominated class, the closer the individual is to the Pareto front in the objective space, and the better its overall performance.

[0059] Secondly, crowding distances are calculated for individuals within the same non-dominated rank. Crowding distance is a measure of the density of an individual's distribution in the objective space, designed to maintain population diversity. For each individual within the same non-dominated rank, calculating its crowding distance typically involves determining its distance from its neighbors across each objective function dimension. Specifically, for each objective (e.g., fracture conductivity and fracturing fluid volume), all individuals within that non-dominated rank are sorted according to the objective function value. Then, for each individual, its crowding distance contribution to that objective is the difference in objective function value between its two immediate neighbors. For sorted boundary individuals, their crowding distance is typically set to infinity or a very large value to ensure they are prioritized, thus preserving boundary diversity of the solution set. Finally, a total crowding distance is the sum of its crowding distance contributions across all objective functions. A larger crowding distance indicates a sparser solution environment around the individual, and a greater contribution to maintaining population diversity.

[0060] Finally, individuals with lower non-dominance levels and larger crowding distances are preferentially selected as parents. When selecting parents, individuals are first sorted according to their non-dominance levels, with lower levels having higher priority. If two individuals have the same non-dominance level, they are then sorted according to their crowding distance, with greater distances having higher priority. This dual sorting mechanism allows for the selection of superior individuals with good distribution characteristics from the current population as parents for subsequent crossover and mutation operations.

[0061] Example 4 It should be noted that the method also includes: Output an optimized chart to characterize the distribution features of different optimization parameter values.

[0062] Specifically, outputting an optimization chart refers to presenting the optimization results obtained by the optimization algorithm, especially the optimal solution set, in a graphical way. This optimization chart can take various forms, such as two-dimensional or three-dimensional scatter plots, contour plots, and surface plots, with the aim of transforming complex numerical data into intuitive visual information. For example, a two-dimensional scatter plot can be constructed, where one axis represents fracture conductivity and the other represents fracturing fluid volume. Each point in the plot represents an optimization solution, that is, the fracture conductivity and fracturing fluid volume corresponding to a specific combination of construction parameters. In this way, the trade-offs between different optimization objectives can be clearly shown.

[0063] Characterizing the distribution features of different optimization parameter values ​​refers to visually displaying, through the aforementioned optimization charts, the range, central tendency, dispersion, and interrelationships of various construction parameters (such as the displacement, volume, viscosity, and density of nano-thickening fluids, the displacement, volume, viscosity, and density of supercritical CO2 fluids, and the concentration, density, and particle size of proppant) on the optimal solution set or Pareto front during the optimization process. For example, on a Pareto front plot, points with different parameter value ranges can be color-coded or distinguished by shape, thus visually observing the distribution of specific parameters in different optimization solutions. Furthermore, auxiliary charts such as parameter edge distribution plots or scatter plots can be used to further analyze the distribution characteristics of the parameters.

[0064] It should be noted that the method also includes: The optimized combination of construction parameters is compared and verified with the original combination of construction parameters, and the verification results are output.

[0065] Specifically, the optimized combination of construction parameters is the theoretically optimal solution calculated using a multi-objective optimization model, aiming to maximize fracture conductivity while minimizing the amount of fluid used in fracturing. The original combination of construction parameters typically refers to those used in actual oil and gas field development based on experience or traditional methods, without being processed by this optimization method. Comparative verification aims to evaluate the actual effectiveness and reliability of the optimized scheme.

[0066] For example, numerical simulation software, such as a professional fracturing simulator or reservoir numerical simulator, can be used to input the optimized parameter combination and the original parameter combination into the model for simulation calculation, and compare the key indicators in the simulation results, such as fracture conductivity, oil and gas production, and fracturing fluid flowback rate.

[0067] In addition, small-scale field trials can be conducted where conditions permit. Representative wells can be selected, and fracturing operations can be performed using both optimized and original parameter combinations. Subsequent production data (such as initial production, cumulative production, and decline patterns) can then be compared and analyzed. The output of the validation results aims to present the comparative validation conclusions in a clear and intuitive manner, providing decision-makers with a basis for evaluating the effectiveness and feasibility of the optimization scheme. Output formats may include, but are not limited to: generating a detailed validation report containing comparative data, charts, analytical conclusions, and recommendations; displaying the comparison of key indicators before and after optimization in the form of charts (e.g., bar charts, line charts, radar charts) or tables on a visualization interface, such as the percentage increase in fracture conductivity, the percentage reduction in fracturing fluid volume, and the increase in economic benefits; or integrating the validation results into oil and gas field production management or decision support systems so that users can intuitively understand the advantages and potential risks of the optimization scheme.

[0068] By comparing and verifying the optimized construction parameter combination with the original combination using the above technical solution, and outputting the verification results, the practical applicability and reliability of the optimization results can be effectively addressed. This allows technicians to intuitively evaluate the actual improvement effect of the optimized scheme compared to traditional or experience-based schemes. For example, whether it significantly reduces the fluid volume under the same fracture conductivity, or significantly improves the fracture conductivity under the same fluid volume. This verification process enhances confidence in the optimization results, provides a solid basis for on-site construction decisions, avoids the risks that may arise from blindly applying theoretical optimization results, and thus ensures the economy and effectiveness of fracturing operations.

[0069] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0070] It should be noted that in the description of this application, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this application, unless otherwise stated, "a plurality of" means at least two.

[0071] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the function involved, as will be understood by those skilled in the art to which embodiments of this application pertain.

[0072] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0073] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0074] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0075] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0076] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0077] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for synergistic fracturing of nano-viscosifying fluid and supercritical CO2 fluid, characterized in that, include: S1. In the target formation development area, nano-viscosifying fluid and supercritical CO2 fluid are used for synergistic fracturing, and a fracture network is formed by combining proppant-carrying fluid and displacement fluid. The correspondence between different combinations of construction parameters and fracture conductivity and fracturing fluid volume is established. S2. Based on the fracture conductivity and fracturing fluid volume corresponding to different combinations of construction parameters, a multi-objective optimization model is constructed with the optimization objectives of maximizing fracture conductivity and minimizing fracturing fluid volume; the multi-objective optimization model uses construction parameters as decision variables. S3. Using at least two of the construction parameters as parameters to be optimized, set the initial population size of the multi-objective optimization model and generate an initial population; each individual in the initial population corresponds to a set of construction parameter combinations; the construction parameters include: the discharge rate, liquid volume, viscosity and density of the nano-thickening fluid, the discharge rate, liquid volume, viscosity and density of the supercritical CO2 fluid, and the concentration, density and particle size of the proppant. S4. Calculate the fracture conductivity and fracturing fluid volume for each individual in the population. S5. Based on the fracture conductivity and fracturing fluid volume of each individual body, perform rapid non-dominated sorting and crowding distance calculation for each individual body, and select the parent individual based on the calculation results. S6. Perform crossover and mutation operations on the parent individuals to generate offspring individuals; S7. Merge the parent individuals with the offspring individuals to generate a new generation population; S8. Repeat steps S4-S7 until the preset iteration termination condition is met. S9. Output the optimal solution set of the multi-objective optimization model as the optimization result of the construction parameters; the optimal solution set includes the optimization solutions corresponding to different combinations of construction parameters.

2. The method according to claim 1, characterized in that, In the target formation development area, synergistic fracturing using nano-viscosifying fluid and supercritical CO2 fluid, combined with proppant-carrying fluid and displacement fluid to form a fracture network, including: S11. Inject nano-viscosity-enhancing fluid into the target formation development area to form a main fracture in the target formation development area; S12. Inject supercritical CO2 fluid into the target formation development area to activate branch fractures based on the main fracture; S13. Repeat the injection of nano-thickening fluid and supercritical CO2 fluid alternately according to the preset number of cycles to form an initial crack network; S14. Inject sand-carrying fluid into the target formation development area to support the initial fracture network and form a supporting fracture network; S15. Obtain the fracture permeability and fracture width of the supporting fracture network, and calculate the fracture conductivity. S16. Inject displacement fluid into the target formation development area to replace the sand-carrying fluid in the wellbore and form the final fracture network.

3. The method according to claim 2, characterized in that, The method further includes: Adjust the combination of construction parameters and repeat steps S11-S16 to establish the correspondence between different combinations of construction parameters and fracture conductivity and fracturing fluid volume.

4. The method according to claim 2, characterized in that, Obtain the fracture permeability of the supporting fracture network, including: The fracture permeability of the proppant fracture network is calculated based on the rock porosity, the specific surface area of ​​the proppant particles, and a preset constant in the target stratum development area.

5. The method according to claim 2, characterized in that, To obtain the crack width, the following steps are required: The fracture width supporting the fracture network is calculated based on the Poisson's ratio and Young's modulus of the rock in the target stratum development area, as well as the net pressure and fracture height within the fracture.

6. The method according to claim 2, characterized in that, Calculating the fracture conductivity includes: The product of the fracture permeability and fracture width of the supporting fracture network is calculated as the fracture conductivity.

7. The method according to claim 1, characterized in that, Each individual in the population is represented by a parameter combination that includes all parameters to be optimized, with the dimension of the input parameters corresponding to the number of parameters to be optimized.

8. The method according to claim 1, characterized in that, Based on the fracture conductivity and fracturing fluid volume of each individual body, rapid non-dominated sorting and congestion distance calculation are performed on each body, and parent bodies are selected based on the calculation results, including: The non-dominance level of each body is determined based on the fracture conductivity and fracturing fluid volume of each body. Calculate crowding distance for individuals in the same non-dominant rank; Individuals with lower non-dominance levels and larger crowding distances are preferred as parent individuals.

9. The method according to claim 1, characterized in that, The method further includes: Output an optimized chart to characterize the distribution features of different optimization parameter values.

10. The method according to claim 1, characterized in that, The method further includes: The optimized combination of construction parameters is compared and verified with the original combination of construction parameters, and the verification results are output.

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