An injection parameter optimization method for biologic agent to degrade reservoir fracturing fluid damage

Abstract

This invention provides a method for optimizing injection parameters to degrade reservoir fracturing fluid damage using biological agents, belonging to the field of oil and gas field development technology. This invention screens the optimal bio-enzyme compound system and establishes an initial database, determines the applicable concentration window, constructs a molecular weight degradation rate prediction model and a reservoir damage index prediction model, verifies the model using a microfluidic chip experiment to establish a micro-dynamic model, establishes a two-layer game optimization model based on performance evaluation criteria, and uses a genetic algorithm framework to solve the objectives of maximizing the upper-layer molecular weight degradation rate and minimizing the lower-layer reservoir damage index. The optimal slug size, injection rate, and bio-enzyme concentration are output, solving the technical problem of the inability to achieve systematic synergistic optimization between molecular weight degradation efficiency and reservoir damage control when bio-enzyme compound systems degrade fracturing fluid.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas field development technology, and specifically relates to a method for optimizing injection parameters to degrade reservoir fracturing fluid damage with biological agents. Background Technology

[0002] In oil and gas reservoir fracturing operations, guar gum fracturing fluid is widely used as a proppant-carrying medium for supporting and modifying formation fractures. After fracturing, residual guar gum must be degraded using a bio-enzyme system to restore reservoir permeability. Traditional bio-enzyme degradation technologies mainly rely on single enzymes or simple enzyme blends, using empirical concentration ratios and injection parameter settings to break down the fracturing fluid. This lack of quantitative characterization of the molecular chain breakage mechanism and pore structure evolution during degradation is problematic. Existing degradation processes lack a systematic understanding of the coupling relationship between molecular-scale degradation efficiency and macroscopic reservoir damage. Engineers often prioritize rapid breakdown while neglecting the impact of residual particle size distribution on pore throat blockage, or overemphasize permeability recovery, leading to insufficient molecular weight degradation and the risk of secondary polymerization. This makes it difficult to establish a scientific balance among multiple interdependent performance indicators and to finely control injection parameters according to specific reservoir conditions. In other words, existing technologies have a technical problem in that the degradation of fracturing fluid by bio-enzyme compound systems cannot achieve systematic synergistic optimization between molecular weight degradation efficiency and reservoir damage control. Summary of the Invention

[0003] In view of this, the present invention provides an injection parameter optimization method for bio-agent degradation of reservoir fracturing fluid damage, which can solve the technical problem in the prior art that the degradation of fracturing fluid by bio-enzyme compound system cannot achieve systematic synergistic optimization between molecular weight degradation efficiency and reservoir damage control.

[0004] This invention is implemented as follows: It provides a method for optimizing injection parameters to degrade reservoir fracturing fluid damage using biological agents, comprising the following steps: Based on the temperature, salinity, and pH of the target reservoir, a bio-enzyme compound system with optimal degradation effect is selected; viscosity-concentration tests at different concentration gradients are conducted on the selected bio-enzyme compound system to determine the optimal concentration window; a multi-factor experiment is designed to construct a degradation effect prediction model; a degradation-displacement experiment is conducted using a one-dimensional high-temperature, high-pressure displacement experimental system to establish a reservoir damage index prediction model; CT scan data from fracturing reservoir cores are extracted to design a microfluidic chip, and a micro-dynamic model is constructed for verification; a performance evaluation standard for the bio-enzyme compound system is established, and an upper-layer optimization model aiming to maximize molecular weight degradation rate and a lower-layer optimization model aiming to minimize the reservoir damage index are constructed. These are solved using a two-layer game optimization algorithm to output the optimal slug size, optimal injection rate, and optimal bio-enzyme compound system concentration, achieving synergistic optimization of molecular weight degradation efficiency and reservoir damage control.

[0005] The step of screening out the bio-enzyme compound system with the best degradation effect specifically involves experimentally testing the change over time of the debonding effect of multiple bio-enzyme compound systems formed by compounding multiple groups of different bio-enzyme main agents in a 1:1 mass concentration ratio on guar gum fracturing fluid.

[0006] The main bio-enzyme includes cellulase, mannanase, xylanase, and amylase.

[0007] The 1:1 mass concentration ratio of the compound refers to mixing the two bio-enzyme main agents when their mass concentrations are equal. The synergistic degradation mechanism of the bio-enzyme compound system is that a single enzyme degrades only one chemical bond in the guar gum molecular chain, while enzymes with different functions in the compound system attack the guar gum molecular chain from multiple sites simultaneously.

[0008] Specifically, the step of determining the optimal concentration window involves measuring the change curve of the apparent viscosity of the guar gum fracturing fluid over time at various concentrations, and using a laser particle size analyzer to measure the particle size distribution of solid particles in the broken gum residue. Based on the viscosity decrease rate and residue particle size data, the optimal concentration window suitable for the target reservoir is determined.

[0009] The optimal bio-enzyme compound system for degradation refers to the bio-enzyme compound system that exhibits the fastest viscosity reduction rate in guar gum fracturing fluid within 4 hours; preferably, the optimal bio-enzyme compound system for degradation refers to the bio-enzyme compound system that exhibits the fastest viscosity reduction rate in guar gum fracturing fluid within 4 hours and has the smallest residue particle size.

[0010] Specifically, the step of constructing the degradation effect prediction model involves designing a multi-factor experiment for the selected bio-enzyme compound system, considering factors such as temperature, concentration of the bio-enzyme compound system, pH value, mineralization, and slug ratio. Gel permeation chromatography is used to determine the molecular weight of guar gum in the broken gel solution, and the molecular weight degradation rate is calculated. A degradation effect prediction model is then constructed, using temperature, concentration of the bio-enzyme compound system, pH value, mineralization, and slug ratio as input parameters and the molecular weight degradation rate as the output.

[0011] The molecular weight degradation rate is used to quantify the degree of breakage of guar gum molecular chains. The formula for calculating the molecular weight degradation rate is as follows: The molecular weight degradation rate is equal to the difference between the molecular weight of guar gum before breakage and the molecular weight of guar gum after breakage, divided by the product of the molecular weight of guar gum before breakage and the molecular weight of standard guar gum.

[0012] Specifically, the step of establishing the reservoir damage index prediction model involves using a one-dimensional high-temperature and high-pressure displacement experimental system to simulate formation conditions and conduct degradation and displacement experiments. This includes measuring the initial permeability of the core sample before and after degradation treatment, calculating the permeability recovery rate, and using nuclear magnetic resonance (NMR) technology to measure the permeability of the core sample before and after degradation treatment. Spectral distribution, based on displacement pressure difference, injection flow rate and Correlation analysis of spectral distribution changes was conducted to establish a reservoir damage index prediction model with displacement pressure difference and injection flow rate as input parameters and permeability recovery rate as output.

[0013] The operation process of the one-dimensional high-temperature and high-pressure displacement experimental system includes cleaning the original fluid and organic matter in the core, drying the core to constant weight in a constant-temperature oven, vacuuming and saturating the dried core with formation water, measuring the initial permeability, injecting guar gum fracturing fluid into the core so that it is retained in the core pores and forms a filter cake, injecting a bio-enzyme compound system to fully react with the retained guar gum fracturing fluid, and measuring the permeability again after the reaction is completed.

[0014] The core parameter of the reservoir damage index prediction model is the permeability recovery rate, which is calculated as follows: the permeability recovery rate is equal to the permeability of the core after degradation treatment divided by the permeability of the core before degradation treatment.

[0015] The nuclear magnetic resonance technique, mentioned above, measures the nuclear magnetic resonance (NMR) of rock cores. Spectral distribution characterizes changes in pore structure. In spectral distribution Relaxation time is directly proportional to pore size; smaller pores correspond to shorter relaxation times. Relaxation time, large pore size corresponds to long relaxation time Relaxation time.

[0016] Specifically, the step of constructing and verifying the microdynamic model involves combining a high-speed microscopic imaging system to observe the retention state of guar gum fracturing fluid in a microfluidic chip and the dynamic process of degradation of the bio-enzyme compound system. The video data is then digitally processed to extract the retention area shrinkage rate and the migration trajectory of the residue particles. A microdynamic model is constructed, and the prediction results of the microdynamic model are compared and verified with the prediction results of the degradation effect prediction model.

[0017] The performance evaluation criteria include four constraints: apparent viscosity reduced to 5... The following conditions must be met: breaking time less than 4 hours, residue content less than 300%. , residue particle size Less than 100 Degradation schemes that do not meet any of the above constraints will be directly eliminated.

[0018] The constraints of the upper-level optimization model include the concentration range of the bio-enzyme compound system. Temperature range is pH range is Mineralization range is The slug ratio range is The units are %, ℃, dimensionless, and Dimensionless.

[0019] The two-layer game optimization algorithm adopts a genetic algorithm framework. The initial population is randomly generated within the feasible range of each injection process parameter. The population evolves through selection, crossover and mutation operations. The fitness function comprehensively evaluates the weighted sum of the upper-level objective function and the lower-level objective function. When the change of the optimal solution is less than 0.001 for 10 consecutive generations, the iteration terminates and the optimal slug size, optimal injection speed and optimal concentration of bioenzyme compound system are output.

[0020] The beneficial effects of this invention are:

[0021] This invention proposes a method for optimizing injection parameters to degrade fracturing fluid damage using bio-based agents. An upper-layer optimization model aims to maximize the molecular weight degradation rate to ensure sufficient chain breakage of guar gum molecules, while a lower-layer optimization model aims to minimize the reservoir damage index to ensure effective pore structure recovery. The two models establish a dynamic feedback mechanism through a coupling term between slug volume and injection flow rate. This method utilizes gel permeation chromatography to quantitatively characterize molecular weight changes, combines nuclear magnetic resonance (NMR) to monitor pore structure evolution, and verifies the accuracy of the degradation kinetic model at the microscale using microfluidic chip experiments. This organically links the molecular-scale chemical degradation process with the macro-scale physical displacement process, avoiding the performance bias caused by single-objective optimization in traditional methods, and achieving a synergistic improvement in degradation efficiency and reservoir protection. In summary, this invention solves the technical problem mentioned in the background art of the inability to achieve systematic synergistic optimization between molecular weight degradation efficiency and reservoir damage control when using bio-enzyme compound systems to degrade fracturing fluids. Attached Figure Description

[0022] Figure 1 This is a flowchart of the method of the present invention.

[0023] Figure 2 This is a particle size distribution diagram of the solid particles in the embodiment.

[0024] Figure 3 This is a diagram of the high-temperature and high-pressure displacement experimental apparatus used in the embodiment.

[0025] Figure 4 The biological enzyme system in the examples before and after the reaction Spectral distribution diagram.

[0026] Figure 5 This is a schematic diagram of the microfluidic chip in the embodiment.

[0027] Figure 6 This is an image of the degradation process of the bioenzyme system in the examples.

[0028] Figure 7 The image shows the residual fracturing fluid after 1 hour of degradation by the bio-enzyme system in the example. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0030] like Figure 1 The diagram shown is a flowchart of a method for optimizing injection parameters to degrade reservoir fracturing fluid damage using biological agents, provided by this invention. This method includes the following steps:

[0031] S1. Based on the temperature, salinity and pH of the target reservoir, the effects of multiple bio-enzyme compound systems with different bio-enzyme main agents mixed in a 1:1 mass concentration ratio on the gel breaking effect of guar gum fracturing fluid over time were tested. Viscosity change data were recorded, and the bio-enzyme compound system with the best degradation effect was screened out. All experimental data were recorded to form an initial database.

[0032] S2. Viscosity tests were conducted on the selected bio-enzyme compound system at different concentration gradients. The apparent viscosity of the guar gum fracturing fluid at each concentration was measured over time. The particle size distribution of solid particles in the broken gum residue was measured using a laser particle size analyzer. Based on the viscosity decrease rate and residue particle size data, the optimal concentration window suitable for the target reservoir was determined.

[0033] S3. For the selected bio-enzyme compound system, a multi-factor experiment was designed with temperature, bio-enzyme compound system concentration, pH value, mineralization degree, and slug ratio. The molecular weight of guar gum in the broken liquid was determined by gel permeation chromatography, the molecular weight degradation rate was calculated, and a degradation effect prediction model was constructed with temperature, bio-enzyme compound system concentration, pH value, mineralization degree, and slug ratio as input parameters and molecular weight degradation rate as output.

[0034] S4. Degradation and displacement experiments were conducted using a one-dimensional high-temperature and high-pressure displacement experimental system to simulate formation conditions. The initial permeability of the core samples before and after degradation treatment was measured, and the permeability recovery rate was calculated. Nuclear magnetic resonance (NMR) technology was used to determine the permeability of the core samples before and after degradation treatment. Spectral distribution, based on displacement pressure difference, injection flow rate and Correlation analysis of spectral distribution changes was conducted to establish a reservoir damage index prediction model with displacement pressure difference and injection flow rate as input parameters and permeability recovery rate as output.

[0035] S5. CT scan data of fractured reservoir cores are extracted to design a microfluidic chip. A high-speed microscopic imaging system is used to observe the retention state of guar gum fracturing fluid in the microfluidic chip and the dynamic process of degradation of the bio-enzyme compound system. The video data is digitally processed to extract the retention area shrinkage rate and the migration trajectory of residue particles. A micro-dynamic model is constructed, and the prediction results of the micro-dynamic model are compared and verified with the prediction results of the degradation effect prediction model.

[0036] S6. Establish performance evaluation criteria for the bio-enzyme compound system based on apparent viscosity, gel breaking time, residue content, and residue particle size. Using the performance evaluation criteria as constraints, construct an upper-layer optimization model with the goal of maximizing molecular weight degradation rate and a lower-layer optimization model with the goal of minimizing reservoir damage index. Solve the upper-layer optimization model and the lower-layer optimization model through a two-layer game optimization algorithm to output the optimal slug size, optimal injection rate, and optimal bio-enzyme compound system concentration.

[0037] The bio-enzyme main agents include cellulase, mannanase, xylanase, and amylase. The 1:1 mass concentration ratio of the compound refers to mixing the two bio-enzyme main agents when their mass concentrations are equal. The synergistic degradation mechanism of the bio-enzyme compound system is that a single enzyme degrades only one chemical bond in the guar gum molecular chain, while enzymes with different functions in the compound system attack the guar gum molecular chain from multiple sites simultaneously, resulting in a faster and more thorough degradation effect. The main chain of the guar gum molecular chain is mannan, and the side chain contains galactose.

[0038] The initial database includes temperature, salinity, pH value, bio-enzyme main agent combination, compounding ratio, total concentration, apparent viscosity, ruptured fluid state, rupture time, and viscosity decrease rate over 4 hours. The viscosity change data includes the apparent viscosity values ​​of guar gum fracturing fluid measured at different time points. The bio-enzyme compounding system with the best degradation effect refers to the bio-enzyme compounding system that has the fastest viscosity decrease rate of guar gum fracturing fluid over 4 hours.

[0039] The process of determining the optimal concentration window is as follows: the concentration of the bio-enzyme compound system corresponding to the fastest viscosity decrease rate, the smallest residue particle size, and the most concentrated particle size distribution is the optimal concentration. The optimal concentration window is formed within the range of fluctuation above and below the optimal concentration value. The laser particle size analyzer measures the particle size distribution of solid particles through the principle of laser scattering. Solid particles of different sizes scatter laser light at different angles. Particle size distribution data is obtained by analyzing the intensity distribution of scattered light.

[0040] The degradation effect prediction model establishes a mapping relationship from input parameters to molecular weight degradation rate using multi-factor experimental data. The molecular weight degradation rate is used to quantify the degree of breakage of guar gum molecular chains, and the calculation formula for the molecular weight degradation rate is as follows:

[0041] ;

[0042] In the formula Degradation rate by molecular weight The molecular weight of guar gum before it breaks down is measured in units of: , The molecular weight of guar gum after breaking is given by [unit]. , The standard molecular weight unit for guar gum is... Dimensionless processing is achieved by dividing by the standard guar gum molecular weight.

[0043] The operation process of the one-dimensional high-temperature and high-pressure displacement experimental system includes cleaning the original fluid and organic matter in the core, drying the core to constant weight in a constant-temperature oven, vacuuming and saturating the dried core with formation water, measuring the initial permeability, injecting guar gum fracturing fluid into the core so that it is retained in the core pores and forms a filter cake, injecting a bio-enzyme compound system to fully react with the retained guar gum fracturing fluid, and measuring the permeability again after the reaction is completed.

[0044] The core parameter of the reservoir damage index prediction model is the permeability recovery rate, which is calculated using the following formula:

[0045] ;

[0046] In the formula For penetration recovery rate, The permeability of the core after degradation treatment is expressed in units of . , The permeability of the core before degradation treatment is in units of The nuclear magnetic resonance technique is used to measure the nuclear magnetic resonance (NMR) of rock cores. Spectral distribution characterizes changes in pore structure, the In spectral distribution Relaxation time is directly proportional to pore size; smaller pores correspond to shorter relaxation times. Relaxation time, large pore size corresponds to long relaxation time Relaxation time.

[0047] based on The mechanism of spectral distribution and the formula for estimating permeability recovery rate are expressed as follows:

[0048] ;

[0049] In the formula The permeability recovery rate is based on nuclear magnetic resonance data. Permeability after degradation treatment, calculated based on nuclear magnetic resonance data. The permeability before degradation treatment is calculated based on nuclear magnetic resonance data. The porosity after degradation treatment, Porosity before degradation treatment For reference porosity used in dimensionless calculation, The average value after degradation treatment The unit of relaxation time is , Average before degradation treatment The unit of relaxation time is , For reference The unit of relaxation time is Used for dimensionless transformation.

[0050] The microdynamic model is used to quantitatively characterize the decay of the guar gum fracturing fluid retention area over time. The equations of the microdynamic model are as follows:

[0051] ;

[0052] The integral form of the microdynamic model equations is expressed as follows:

[0053] ;

[0054] In the formula for The unit of dwell area at any given time is , The initial retention area is in units of , The degradation rate constant is The time unit is , The reference time unit is Used for dimensionless determination. The micro-dynamic model determines the degradation rate constant by fitting experimental observation data and predicts that the retention area exhibits an exponential decay.

[0055] The performance evaluation criteria include four constraints: apparent viscosity reduced to 5... The following conditions must be met: Break-off time less than 4 seconds. Residue content less than 300 , residue particle size Less than 100 Degradation schemes that do not meet any of the above constraints are directly eliminated, while all degradation schemes that meet all four constraints proceed to the next optimization step. The residue particle size... It refers to the particle diameter corresponding to when the cumulative particle size distribution reaches 90%.

[0056] The upper-level optimization model aims to maximize the molecular weight degradation rate, and its objective function is expressed as follows:

[0057] ;

[0058] In the formula For the upper-level objective function, The molecular weight degradation rate is calculated by the degradation effect prediction model. The maximum molecular weight degradation rate is used for dimensionless degradation. The range of values ​​for the slug volume weighting coefficient is as follows: , The unit of volume for slugs is , The reference slug volume unit is Used for dimensionless transformation The concentration sensitivity coefficient has a range of values ​​of 100. , The concentration of the biological enzyme compound system is expressed in %. For reference, the concentration of the biological enzyme compound system is expressed in % for dimensionless purposes.

[0059] The constraints of the upper-level optimization model include the concentration range of the bio-enzyme compound system. The unit is %, and the temperature range is The unit is ℃, and the pH range is [missing information]. Mineralization range is Units are The slug ratio range is .

[0060] The lower-level optimization model aims to minimize the reservoir damage index, which is defined as 1 minus the permeability recovery rate. The objective function of the lower-level optimization model is expressed as follows:

[0061] ;

[0062] In the formula For the lower-level objective function, The permeability recovery rate is calculated by the reservoir damage index prediction model. The maximum permeability recovery rate is used for dimensionless conversion. The range of values ​​for the displacement pressure differential weighting coefficient is as follows: , The unit of displacement pressure difference is , For reference, the unit of displacement pressure difference is... Used for dimensionless transformation The unit of injection flow is , The unit of injection flow is for reference. Used for dimensionless transformation.

[0063] The constraints of the lower-level optimization model include an injection speed range of [missing information]. Units are The slug size range is Units are The It is a multiple of the pore volume.

[0064] The coupling term of the two-layer game optimization model is the product of the slug volume and the injection flow rate. The slug volume determined by the upper optimization model directly affects the injection flow rate optimization in the lower optimization model. At the same time, the permeability recovery rate obtained by the lower optimization model feeds back to adjust the molecular weight degradation rate target of the upper optimization model. The two models reach Nash equilibrium through iterative solution.

[0065] The two-layer game optimization algorithm adopts a genetic algorithm framework. The initial population is randomly generated within the feasible range of each injection process parameter. Population evolution is carried out through selection, crossover, and mutation operations. The fitness function comprehensively evaluates the weighted sum of the upper-level objective function and the lower-level objective function. The crossover operation generates offspring by exchanging parameter gene fragments of parent individuals in a binary encoding manner. During the iteration process, the four constraints of the performance evaluation criteria are satisfied simultaneously. When the change of the optimal solution is less than 0.001 for 10 consecutive generations, the iteration is terminated and the optimal slug size, optimal injection speed, and optimal concentration of the bioenzyme complex system are output.

[0066] The initial population of the two-level game optimization algorithm is within the slug size range of Units are Injection speed range is Units are The concentration range of the bio-enzyme compound system is: The units are randomly generated within a % range, the population size is 100 individuals, the crossover probability is 0.8, the mutation probability is 0.05, and the maximum number of iterations is 200 generations.

[0067] The specific implementation methods of the above steps are described in detail below.

[0068] The specific implementation of step S1 involves first determining the experimental conditions based on the geological parameters of the target reservoir. The temperature is set to match the formation temperature of the target reservoir. The salinity and pH value are prepared with reference to formation water test data. Four bio-enzymes—cellulase, mannanase, xylanase, and amylase—are selected for pairwise compounding experiments. Each compound is mixed at a 1:1 mass concentration ratio, meaning the mass concentrations of the two enzymes are equal. The apparent viscosity change of the guar gum fracturing fluid is continuously monitored using a rotational viscometer under the set temperature conditions. Viscosity values ​​are recorded every 30 minutes until 4 hours. The results are then calculated... The viscosity decrease rate within 4 hours was used to screen out the bio-enzyme compound system with the best degradation effect. The synergistic degradation mechanism of this compound system is that a single enzyme can only degrade one chemical bond in the guar gum molecular chain, while the enzymes with different functions in the compound system can attack the guar gum molecular chain from multiple sites on the mannan backbone and galactose side chain at the same time, thus producing a faster and more thorough degradation effect. All experimental data, including temperature, mineralization, pH value, bio-enzyme main agent combination, compounding ratio, total concentration, apparent viscosity, state of the broken liquid, breaking time, and viscosity decrease rate within 4 hours, were entered into the initial database to provide data support for subsequent analysis.

[0069] The specific implementation of step S2 involves designing a concentration gradient experiment for the screened bio-enzyme compound system. The concentration range is set from 0.1% to 1.0%, with a gradient interval of 0.1%. Under each concentration condition, the apparent viscosity of the guar gum fracturing fluid changes over time. After the experiment, the broken-up liquid is collected, and the particle size distribution of the solid phase is measured using a laser particle size analyzer. The laser particle size analyzer works based on the principle of laser scattering. Solid phase particles of different sizes scatter laser light at different angles. By analyzing the intensity distribution of scattered light, particle size distribution data can be obtained. The focus is on the dual indicators of viscosity decrease rate and residue particle size. When the viscosity decrease rate reaches the fastest value at a certain concentration and the residue particle size is the smallest and the particle size distribution is the most concentrated, this concentration is the optimal concentration. The optimal concentration window is formed within a range of 0.1% above and below the optimal concentration value. This window ensures both the degradation rate and controls the residue particle size, thereby providing concentration boundary conditions for subsequent injection parameter optimization.

[0070] The specific implementation of step S3 involves designing a multi-factor orthogonal experiment for the screened bio-enzyme compound system. The experimental factors include: temperature set to three levels within the range of 80℃ to 110℃; concentration of the bio-enzyme compound system set to three levels within the optimal concentration window; pH value set to three levels within the range of 6.5 to 8.5; and mineralization set to 10000. Up to 20000 Three levels within the range were selected, with slug ratios of 1:1, 1:2, and 2:1. The molecular weight of guar gum in the broken solution was determined by gel permeation chromatography (GPC). GPC can separate polymers based on molecular weight. The change in the molecular weight of guar gum before and after breaking reflects the degree of molecular chain breakage. The molecular weight degradation rate was calculated by dividing the difference between the molecular weight before and after breaking by the product of the molecular weight before breaking and the molecular weight of standard guar gum, and then multiplying by 100%. Dividing by the molecular weight of standard guar gum achieves dimensionless processing. A neural network algorithm was used to construct a degradation effect prediction model from five input parameters (temperature, concentration of the bio-enzyme compound system, pH value, mineralization, and slug ratio) to the molecular weight degradation rate output. The neural network can capture the complex relationship between input parameters and degradation effect through multi-layer nonlinear mapping. The model training used a backpropagation algorithm to continuously adjust the network weights until the prediction error converged to an acceptable range.

[0071] The specific implementation of step S4 involves using a one-dimensional high-temperature and high-pressure displacement experimental system to simulate formation conditions and conduct a degradation and displacement experiment. First, the original fluids and organic matter in the core are cleaned. The core is then dried to constant weight in a constant-temperature oven to ensure complete dryness. After drying, the core is vacuumed and saturated with formation water, and the initial permeability is measured. Guarnitrogen fracturing fluid is injected into the core, allowing it to remain in the core pores and form a filter cake. Subsequently, a bio-enzyme compound system is injected to fully react with the retained guarnitrogen fracturing fluid. After the reaction, the permeability is measured again, and the permeability recovery rate is calculated as the ratio of the core permeability after degradation treatment to the core permeability before degradation treatment. Simultaneously, nuclear magnetic resonance (NMR) technology is used to measure the permeability of the core before and after degradation treatment. Spectral distribution, nuclear magnetic resonance (NMR) technology is used to determine the spectral distribution of rock cores. Relaxation time characterizes changes in pore structure; smaller pores correspond to shorter relaxation times. Relaxation time, large pore size corresponds to long relaxation time Relaxation time, based on The mechanism of spectral distribution and the estimation of permeability recovery rate are based on the fourth power and average of porosity changes. The form of the square product of the relaxation time changes is analyzed by considering the displacement pressure difference, injection flow rate, and... To investigate the correlation of spectral distribution changes, a reservoir damage index prediction model was established using a support vector machine regression algorithm. The model takes displacement pressure difference and injection flow rate as input parameters and permeability recovery rate as output. The support vector machine can handle nonlinear relationships and has good generalization ability through kernel function mapping.

[0072] The specific implementation of step S5 involves extracting CT scan data from the fracturing reservoir core to obtain real pore network structure information. A microfluidic chip is designed based on the geometric features of the pore network. The microchannel network on the chip can reproduce the pore connectivity and scale distribution of the reservoir core. A high-speed microscopic imaging system is used to observe in real time the retention state of the guar gum fracturing fluid in the microfluidic chip and the dynamic process of degradation of the bio-enzyme compound system. A high-speed camera acquires video data at a rate of 30 frames per second. The video data undergoes digital processing, including image segmentation, edge recognition, and area calculation, to extract the shrinkage rate of the retention area over time. The migration trajectory of the residue particles was investigated, and a microdynamic model was constructed to quantitatively characterize the decay law of the guar gum fracturing fluid retention area over time. The model equation describes the degradation rate constant, which is proportional to the current retention area with a negative proportionality coefficient, as the derivative of the retention area with respect to time. The integral form shows that the ratio of the retention area to the initial retention area decays exponentially. The degradation rate constant was determined by nonlinear least squares fitting experimental observation data. The prediction results of the microdynamic model were compared and verified with the prediction results of the degradation effect prediction model. If the prediction trends of the two are consistent and the error is less than 10%, the model is considered reliable.

[0073] The specific implementation of step S6 involves first establishing performance evaluation criteria for the bio-enzyme compound system based on apparent viscosity, gel breaking time, residue content, and residue particle size. Specific constraints include reducing the apparent viscosity to 5... The following conditions must be met: Break-off time less than 4 seconds. Residue content less than 300 , residue particle size Less than 100 ,in This refers to the particle diameter corresponding to a cumulative particle size distribution reaching 90%. Using the aforementioned performance evaluation criteria as constraints, a two-layer optimization model is constructed. The upper-layer optimization model aims to maximize the molecular weight degradation rate. The objective function consists of a normalized term for the molecular weight degradation rate and a composite adjustment term for the slug volume and the concentration of the bio-enzyme compound system. In the composite adjustment term, the slug volume weight coefficient reflects the influence of slug size on the degradation effect, with a value ranging from 0.1 to 0.5. The concentration sensitivity coefficient reflects the influence of bio-enzyme concentration on the cost-effectiveness balance, with a value ranging from 0.5 to 2.0. The constraints of the upper-layer optimization model include a bio-enzyme compound system concentration range of 0.3% to 0.7%, a temperature range of 80℃ to 110℃, a pH range of 6.5 to 8.5, and a mineralization range of 10000. Up to 20000 The slug ratios are 1:1, 1:2, and 2:1. The lower-layer optimization model aims to minimize the reservoir damage index, defined as 1 minus the permeability recovery rate. The objective function consists of a reservoir damage term and a composite adjustment term for displacement pressure differential and injection flow rate. The displacement pressure differential weight coefficient in the composite adjustment term reflects the influence of pressure differential on reservoir damage and ranges from 0.2 to 0.8. The constraints of the lower-layer optimization model include an injection rate range of 0.5. Up to 1.5 The slug size range is 0.5. Up to 1.2 The two-layer model is coupled through the product of slug volume and injection flow rate. The slug volume determined by the upper-layer optimization model directly affects the injection flow rate optimization in the lower-layer optimization model. Simultaneously, the permeability recovery rate obtained from the lower-layer optimization model provides feedback to adjust the molecular weight degradation rate target of the upper-layer optimization model. A genetic algorithm framework is used to solve the two-layer game optimization model. The genetic algorithm, by simulating natural selection and genetic mechanisms, can find the global optimum in complex multi-objective optimization problems. The initial population size is set to 100 individuals randomly generated within the feasible range of slug size, injection rate, and concentration of the bio-enzyme complex system. The crossover probability is set to 0.8, representing 80% of the individuals in the population. Individuals participate in crossover operations to generate new individuals. The mutation probability is set to 0.05 to maintain population diversity and prevent premature convergence. The crossover operation generates offspring by exchanging parameter gene fragments of parent individuals through binary encoding. The fitness function comprehensively evaluates the weighted sum of the upper-level objective function and the lower-level objective function. During the iteration process, the four constraints of the performance evaluation criteria are satisfied simultaneously. When the change of the optimal solution is less than 0.001 for 10 consecutive generations, the algorithm is considered to have converged and the iteration is terminated. The final output is the optimal slug size, the optimal injection rate, and the optimal concentration of the bioenzyme complex system that satisfy the two-layer objectives. The maximum number of iterations is set to 200 generations to ensure that the algorithm has sufficient search space.

[0074] It should be noted that the key technical idea of ​​this invention is the synergistic degradation mechanism of the bio-enzyme compound system. Traditional single-enzyme degradation methods can only target one chemical bond in the guar gum molecular chain, resulting in slow and incomplete degradation. However, by using multiple functional enzymes in a mass concentration ratio, the guar gum molecular chain can be attacked simultaneously from multiple sites on the mannan backbone and galactose side chain. Through the synergistic effect between enzymes, the degradation rate is significantly accelerated and the degradation thoroughness is improved. The technical advantage of this mechanism is that it shortens the gum breaking time and reduces the particle size of the residue, thereby reducing damage to the reservoir.

[0075] The second key technical approach involves characterizing pore structure and estimating permeability recovery rate based on nuclear magnetic resonance (NMR) technology. Traditional methods, which evaluate degradation effects solely through macroscopic permeability testing, struggle to reveal the changing patterns of microscopic pore structure. However, NMR technology, used to measure the pore structure of rock cores... Spectral distribution can quantitatively characterize the pore size distribution and its evolution before and after degradation, based on porosity changes and average porosity. The mechanism-based permeability recovery rate estimation formula established by relaxation time variation links changes in micropore structure with the recovery of macroscopic permeability performance. The advantage of this technical approach is that it reveals the influence of bioenzyme degradation on reservoir properties from the microscopic mechanism level, providing a scientific basis for optimizing injection parameters.

[0076] The third key technical approach is the coupled solution mechanism of the two-layer game optimization algorithm. Traditional optimization methods usually treat degradation effect and reservoir damage as a single objective, which leads to the loss of one aspect. However, the two-layer game optimization algorithm constructs a coupled optimization model with maximizing the molecular weight degradation rate and minimizing the reservoir damage index as the upper and lower objectives, respectively. The dynamic coupling of the two models is achieved through the product of slug volume and injection flow rate. The optimization result of the upper layer directly affects the optimization input of the lower layer, and the optimization result of the lower layer feeds back to adjust the upper layer objective. The two models reach a Nash equilibrium state through iterative solution. The advantage of this technical approach is that it achieves multi-objective synergistic optimization of degradation effect and reservoir protection, avoiding the local optimum problem caused by single-objective optimization.

[0077] The synergistic effect of the above three key technical approaches is reflected in the synergistic degradation mechanism of the bio-enzyme complex system, which improves degradation efficiency at the microscopic molecular level; the pore structure characterization of nuclear magnetic resonance technology, which reveals the mechanism of degradation's impact on the reservoir at the mesoscopic scale; and the two-layer game optimization algorithm, which achieves the global optimal configuration of injection parameters at the macroscopic engineering level. The three approaches construct a complete optimization system at the microscopic, mesoscopic, and macroscopic scales, respectively. Through multi-scale synergy, a comprehensive technical effect of fast degradation rate, small residue particle size, and low reservoir damage is achieved. Compared with traditional single-scale optimization methods, it has stronger scientific validity and engineering applicability.

[0078] It should be noted that this invention also solves the following technical problem: traditional bioenzyme degradation methods lack quantitative screening methods for the adaptability of enzyme compound systems under different reservoir conditions, which often leads to large fluctuations in degradation efficiency during field applications. This invention establishes an initial database containing multiple factors such as temperature, salinity, and pH value, and combines laser particle size analyzer to determine the particle size distribution of residues and gel permeation chromatography to determine molecular weight changes, thus constructing a multi-scale characterization system from macroscopic viscosity to microscopic molecular structure. This system can quickly screen out the enzyme compound scheme with the best degradation effect and determine the optimal concentration window for specific reservoir conditions, thereby improving the adaptability and stability of the bioenzyme system under different geological environments. Furthermore, existing technologies for monitoring changes in pore structure during degradation mainly rely on permeability testing, a macroscopic indicator, which cannot reveal the distribution patterns and transport mechanisms of residue particles in the pore throat space. This invention uses nuclear magnetic resonance (NMR) technology to determine the relaxation time spectrum distribution before and after degradation, and combines it with a high-speed microscopic imaging system based on a microfluidic chip to observe the decay process of the guar gum retention area and the transport trajectory of residue particles in real time. This establishes a verification closed loop from macroscopic displacement experiments to microscopic dynamic models, achieving a refined characterization of degradation behavior at the pore scale and providing reliable microscopic mechanism support for optimizing injection parameters.

[0079] Specifically, the principle of this invention is as follows: The core of this invention in solving the aforementioned technical problems lies in establishing a multi-level degradation evaluation system from the molecular scale to the reservoir scale and achieving parameter collaborative decision-making through a game-theoretic optimization algorithm. Traditional methods rely solely on macroscopic indicators such as apparent viscosity and gel breaking time for evaluation, failing to reveal the intrinsic relationship between the degree of guar gum molecular chain breakage and the risk of pore blockage. This invention, however, quantifies the effect of enzyme-catalyzed reactions on the breakage of the guar gum backbone and side chains through molecular weight degradation rate, and characterizes the distribution of residue particles in the pore throat space through permeability recovery rate combined with NMR spectroscopy. The design logic of the two-layer optimization model conforms to the physicochemical nature of the degradation process. The upper-layer model ensures that the enzyme complex system can simultaneously attack the guar gum molecular chain from multiple sites to generate sufficiently small degradation fragments. The lower-layer model controls the migration trajectory of residue particles by optimizing the displacement pressure difference and injection flow rate to prevent secondary pore throat blockage. When the two-layer model reaches Nash equilibrium through iterative solution, it ensures both sufficient molecular weight degradation and effective control of reservoir damage, thus resolving the contradiction between degradation efficiency and reservoir protection from a mechanistic perspective.

[0080] The following provides a specific embodiment 1 of the present invention, and the specific implementation of each step in this embodiment 1 is described in detail below.

[0081] The specific implementation of step S1 is as follows: First, the experimental conditions are determined based on the geological parameters of the target reservoir. The temperature is set to be consistent with the formation temperature of the target reservoir. The salinity and pH value are prepared with reference to formation water test data. Four biological enzymes—cellulase, mannanase, xylanase, and amylase—are selected for pairwise compounding experiments. Each compounding combination is mixed at a 1:1 mass concentration ratio, that is, the mass concentrations of the two enzymes are equal before mixing. The apparent viscosity change of the guar gum fracturing fluid is continuously monitored using a rotational viscometer under the set temperature conditions. The viscosity value is recorded every 30 minutes until 4 hours. The formula for calculating the viscosity decrease rate after 4 hours is expressed as follows:

[0082] ;

[0083] In the formula, The viscosity decrease rate over 4 hours. The initial apparent viscosity is in units of Using a rotational viscometer at a rotation speed of 100 Measurement at shear rate The apparent viscosity after 4 hours is expressed in units of . The viscosity was measured at the same shear rate. The optimal bio-enzyme compound system for degradation was selected by calculating the viscosity decrease rate over 4 hours. The formula for calculating the viscosity decrease rate is as follows:

[0084] ;

[0085] In the formula, For the viscosity decrease rate, The time unit is , for The unit of apparent viscosity at time t is , The reference viscosity unit is The value used for dimensionless transformation is usually taken as 100. The synergistic degradation mechanism of this compound system lies in the fact that a single enzyme can only degrade one type of chemical bond in the guar gum molecular chain, while enzymes with different functions in the compound system can simultaneously attack the guar gum molecular chain from multiple sites on the mannan backbone and galactose side chain, thus producing a faster and more thorough degradation effect. All experimental data, including temperature, mineralization, pH value, combination of biological enzymes, compounding ratio, total concentration, apparent viscosity, state of the broken gel, breaking time, and viscosity reduction rate after 4 hours, were entered into the initial database to provide data support for subsequent analysis.

[0086] The specific implementation of step S2 involves designing a concentration gradient experiment for the screened bio-enzyme compound system. The concentration range is set from 0.1% to 1.0%, with a gradient interval of 0.1%. At each concentration condition, the apparent viscosity of the guar gum fracturing fluid is measured over time. After the experiment, the broken-up residue is collected, and the solid particle size distribution is determined using a laser particle size analyzer. The laser particle size analyzer operates based on the principle of laser scattering; solid particles of different sizes scatter laser light at different angles. By analyzing the intensity distribution of the scattered light, particle size distribution data can be obtained. The formula for calculating the residue particle size concentration evaluation index is as follows:

[0087] ;

[0088] In the formula, As an evaluation index for residue particle size distribution, The unit of particle diameter is the particle size distribution that corresponds to a cumulative particle size distribution of 90%. , The unit of particle diameter is the particle size distribution that corresponds to a cumulative particle size distribution of 10%. , The unit of particle diameter is the particle size distribution that corresponds to a cumulative particle size distribution of 50%. That is, median particle size, The reference particle size unit is The value used for dimensionless transformation is usually taken as 50. The focus is on both viscosity reduction rate and residue particle size. The optimal concentration is the concentration at which the viscosity reduction rate reaches its fastest value and the residue particle size is the smallest and the particle size distribution is the most concentrated. The optimal concentration window is defined as the range of fluctuation within 0.1% above and below the optimal concentration value. This window ensures both the degradation rate and controls the residue particle size, thus providing concentration boundary conditions for subsequent injection parameter optimization.

[0089] The specific implementation of step S3 involves designing a multi-factor orthogonal experiment for the screened bio-enzyme compound system. The experimental factors include: temperature set to three levels within the range of 80℃ to 110℃; concentration of the bio-enzyme compound system set to three levels within the optimal concentration window; pH value set to three levels within the range of 6.5 to 8.5; and mineralization set to 10000. Up to 20000 Three levels within the range were used, with slug ratios set to 1:1, 1:2, and 2:1. The molecular weight of guar gum in the lysate was determined by gel permeation chromatography. Gel permeation chromatography can separate polymers based on molecular weight. The change in the molecular weight of guar gum before and after lysing reflects the degree of molecular chain breakage. The formula for calculating the molecular weight degradation rate is as follows:

[0090] ;

[0091] In the formula, Degradation rate by molecular weight The molecular weight of guar gum before it breaks down is measured in units of: Measured by gel permeation chromatography. The molecular weight of guar gum after breaking is given by [unit]. Measured using the same instruments and mobile phase conditions, The standard molecular weight unit for guar gum is... The empirical value used for dimensionless processing is A neural network algorithm was used to construct a degradation effect prediction model that outputs molecular weight degradation rate from five input parameters: temperature, concentration of bio-enzyme compound system, pH value, mineralization degree, and slug ratio. The neural network can capture the complex relationship between input parameters and degradation effect through multi-layer nonlinear mapping. The model training uses the backpropagation algorithm to continuously adjust the network weights until the prediction error converges to an acceptable range.

[0092] The specific implementation of step S4 involves using a one-dimensional high-temperature and high-pressure displacement experimental system to simulate formation conditions and conduct a degradation and displacement experiment. First, the original fluids and organic matter in the core are cleaned. The core is then dried to constant weight in a constant-temperature oven to ensure complete dryness. After drying, the core is vacuumed and saturated with formation water before initial permeability is measured. Guarnitrogen fracturing fluid is injected into the core, allowing it to remain in the core pores and form a filter cake. Subsequently, a bio-enzyme compound system is injected to fully react with the retained guarnitrogen fracturing fluid. After the reaction is complete, permeability is measured again. The formula for calculating the permeability recovery rate is as follows:

[0093] ;

[0094] In the formula, For penetration recovery rate, The permeability of the core after degradation treatment is expressed in units of . Using a gas permeability meter at a confining pressure of 5 Determined under the conditions, The permeability of the core before degradation treatment is in units of The measurements were taken under the same confining pressure conditions. Simultaneously, nuclear magnetic resonance (NMR) technology was used to determine the core samples before and after degradation treatment. Spectral distribution, nuclear magnetic resonance (NMR) technology is used to determine the spectral distribution of rock cores. Relaxation time characterizes changes in pore structure; smaller pores correspond to shorter relaxation times. Relaxation time, large pore size corresponds to long relaxation time The relaxation time and porosity calculation formulas are expressed as follows:

[0095] ;

[0096] In the formula, Porosity for The total number of discrete time points in the spectral distribution For the first The NMR signal amplitude at each time point This represents the NMR signal amplitude corresponding to the rock skeleton. Average. The formula for calculating relaxation time is as follows:

[0097] ;

[0098] In the formula, For average The unit of relaxation time is , For the first The corresponding time points The unit of relaxation time is .based on The mechanism of spectral distribution and the formula for estimating permeability recovery rate are expressed as follows:

[0099] ;

[0100] In the formula, The permeability recovery rate is based on nuclear magnetic resonance data. Permeability after degradation treatment, calculated based on nuclear magnetic resonance data. The permeability before degradation treatment is calculated based on nuclear magnetic resonance data. The porosity after degradation treatment is obtained using the aforementioned porosity calculation formula. The porosity before degradation treatment is obtained using the aforementioned porosity calculation formula. The reference porosity used for dimensionless conversion is 0.15 by default. The average value after degradation treatment The unit of relaxation time is Through the aforementioned average The formula for calculating relaxation time is obtained. Average before degradation treatment The unit of relaxation time is Through the aforementioned average The formula for calculating relaxation time is obtained. For reference The unit of relaxation time is The default value for dimensionless conversion is 100. By analyzing the displacement pressure difference, injection flow rate and... To investigate the correlation of spectral distribution changes, a reservoir damage index prediction model was established using a support vector machine regression algorithm. The model takes displacement pressure difference and injection flow rate as input parameters and permeability recovery rate as output. The support vector machine can handle nonlinear relationships and has good generalization ability through kernel function mapping.

[0101] The specific implementation of step S5 involves extracting CT scan data from the fracturing reservoir core to obtain real pore network structure information. A microfluidic chip is designed based on the geometric features of the pore network. The microchannel network on the chip can reproduce the pore connectivity and scale distribution of the reservoir core. A high-speed microscopic imaging system is used to observe in real time the retention state of the guar gum fracturing fluid in the microfluidic chip and the dynamic process of degradation of the bio-enzyme compound system. A high-speed camera acquires video data at a rate of 30 frames per second. The video data undergoes digital processing, including image segmentation, edge recognition, and area calculation. The shrinkage rate of the retention area over time and the migration trajectory of the residue particles are extracted. The formula for calculating the shrinkage rate of the retention area is as follows:

[0102] ;

[0103] In the formula, The shrinkage rate of the retention area, for The unit of dwell area at any given time is , for The unit of dwell area at any given time is , and The unit for two adjacent observation times is , The reference area unit is For dimensionless transformation, the value is usually taken as the initial retention area. A microdynamic model was constructed to quantitatively characterize the decay of the guar gum fracturing fluid retention area over time. The equations of the microdynamic model are as follows:

[0104] ;

[0105] In the formula, for The unit of dwell area at any given time is , The initial retention area is in units of The boundary of the guar gum retention area in the microfluidic chip was identified using image processing software, and the area of ​​the closed region was calculated. The microscopic degradation rate constant was obtained by fitting experimental data. The time microelement unit is , The reference time unit is The value used for dimensionless transformation is typically 60. The integral form of the microdynamic model equations is as follows:

[0106] ;

[0107] In the formula, The time unit is The meanings of the remaining parameters are the same as described above. The microscopic degradation rate constant was determined by nonlinear least squares fitting experimental observation data. The objective function for fitting is expressed as follows:

[0108] ;

[0109] In the formula, This represents the total number of time points observed in the experiment. For the first The experimental measurements of the retention area at each time point were in units of: , For the first Each observation time point is a unit of time. The prediction results of the microdynamic model are compared and verified with those of the degradation effect prediction model. If the prediction trends of the two are consistent and the error is less than 10%, the model is considered reliable.

[0110] The specific implementation of step S6 involves first establishing performance evaluation criteria for the bio-enzyme compound system based on apparent viscosity, gel breaking time, residue content, and residue particle size. Constraints include an apparent viscosity reduced to 5... The following conditions must be met: Break-off time less than 4 seconds. Residue content less than 300 , residue particle size Less than 100 ,in This refers to the particle diameter corresponding to a cumulative particle size distribution reaching 90%. Using the above performance evaluation criteria as constraints, a two-layer optimization model is constructed. The upper-layer optimization model aims to maximize the molecular weight degradation rate. The objective function of the upper-layer model is expressed as follows:

[0111] ;

[0112] In the formula, For the upper-level objective function, The molecular weight degradation rate was calculated using a degradation effect prediction model. The maximum molecular weight degradation rate for dimensionless degradation was determined to be 85% based on historical experimental data from an initial database. The slug volume weighting coefficient, ranging from 0.1 to 0.5, reflects the contribution of slug size to degradation efficiency. The unit of volume for slugs is The calculation formula is in The unit for slug size is That is, the multiple of pore volume. The unit for core pore volume is The calculation formula is in The unit for core radius is , The unit for core length is , Core porosity was determined using the helium gas method or nuclear magnetic resonance method. The reference slug volume unit is The default value for dimensionless transformation is 1. , The concentration sensitivity coefficient, ranging from 0.5 to 2.0, reflects the cost-benefit balance as the concentration of the biological enzyme increases. The concentration of the biological enzyme compound system is expressed in %. For reference, the concentration unit of the bio-enzyme compound system is % (%), and the default value for dimensionless calculation is 0.5%. The constraints of the upper-level optimization model include a bio-enzyme compound system concentration range of 0.3% to 0.7%, a temperature range of 80℃ to 110℃, a pH range of 6.5 to 8.5, and a mineralization range of 10000. Up to 20000 The slug ratio ranges from 1:1, 1:2, and 2:1. The lower-layer optimization model aims to minimize the reservoir damage index, which is defined as 1 minus the permeability recovery rate. The objective function of the lower-layer optimization model is expressed as follows:

[0113] ;

[0114] In the formula, For the lower-level objective function, The permeability recovery rate is calculated using the reservoir damage index prediction model. The maximum permeability recovery rate, used for dimensionless calculation, was empirically valued at 0.95 based on historical data from displacement experiments. The displacement pressure differential weighting coefficient ranges from 0.2 to 0.8, reflecting the sensitivity of pressure differential to reservoir microfracture propagation. The unit of displacement pressure difference is The pressure difference between the inlet and outlet pressures is obtained by real-time monitoring using the pressure sensor of the displacement experimental system. For reference, the unit of displacement pressure difference is... The default value for dimensionless transformation is 2. , The unit of injection flow is Measured by a high-precision flow meter. The unit of injection flow is for reference. The default value for dimensionless transformation is 1. The constraints of the lower-level optimization model include an injection rate range of 0.5. Up to 1.5 The slug size range is 0.5. Up to 1.2 ,in The two-layer model is coupled through the product of slug volume and injection flow rate, with the slug volume determined by the upper optimization model directly affecting the injection flow rate optimization in the lower optimization model. Simultaneously, the permeability recovery rate obtained from the lower optimization model provides feedback to adjust the molecular weight degradation rate objective of the upper optimization model. A genetic algorithm framework is used to solve the two-layer game optimization model. The genetic algorithm, by simulating natural selection and genetic mechanisms, can find the global optimum in complex multi-objective optimization problems. The initial population size is set to 100 individuals, randomly generated within the feasible range of slug size, injection rate, and concentration of the bio-enzyme complex system. The crossover probability is set to 0.8, indicating that 80% of the individuals in the population participate in the crossover operation to generate new individuals. The mutation probability is set to 0.05 to maintain population diversity and prevent premature convergence. The crossover operation generates offspring by exchanging parameter gene fragments of parent individuals using binary encoding. The fitness function comprehensively evaluates the weighted sum of the upper and lower objective functions. The fitness function calculation formula is as follows:

[0115] ;

[0116] In the formula, For the fitness function value, The weight coefficient for the upper-level objective function is set to 0.6 by default. The weight coefficients for the lower-level objective function are set to 0.4 by default. , The term is used to transform the lower-level minimization objective into a maximization form to unify the optimization direction. During the iteration process, four constraints of the performance evaluation criteria are simultaneously satisfied. The algorithm is considered converged and the iteration terminates when the change in the optimal solution over 10 consecutive generations is less than 0.001. The convergence criterion is stated as follows:

[0117] ;

[0118] In the formula, Let the current iteration algebra be... For historical iteration algebra, For the first The optimal fitness function value of the generation. For the first The optimal fitness function value is obtained for each generation. The final output is the optimal slug size, optimal injection rate, and optimal concentration of the bioenzyme complex system that satisfy the dual-layer objective. The maximum number of iterations is set to 200 generations to ensure that the algorithm has sufficient search space.

[0119] The principle and effect of the viscosity decrease rate calculation formula are explained below. This formula quantifies the rate of degradation of guar gum fracturing fluid by the bio-enzyme compound system by the ratio of the change in apparent viscosity per unit time to the initial viscosity. The numerator term... The absolute decrease in viscosity is expressed in units of denominator The dimensionless processing achieved by multiplying time and reference viscosity makes guar gum samples with different initial viscosities comparable. This index can directly reflect the degradation efficiency of different bio-enzyme compound systems within the same time period. The larger the value, the faster the degradation rate and the shorter the gel breaking time, providing a quantitative evaluation basis for screening the optimal bio-enzyme compound system.

[0120] The principle and effect of the formula for calculating the residue particle size concentration evaluation index are explained below. This formula characterizes the dispersion of the residue particle size distribution by dividing the difference between the 90th and 10th quantile values ​​of the cumulative particle size distribution by the product of the 50th quantile value and the reference particle size. The numerator term... The unit reflecting the span of particle size distribution is denominator The dimensionless processing unit is achieved by multiplying the median particle size by the reference particle size. The smaller the value of this index, the more concentrated the particle size distribution of the residue is, which is more conducive to the migration and discharge of the residue in the formation. It provides a standard for controlling the particle size of the residue to determine the optimal concentration window of the bio-enzyme compound system.

[0121] The principle and effect of the formula for calculating molecular weight degradation rate are explained below. This formula quantifies the degree of guar gum molecular chain breakage by using the ratio of the difference in molecular weight of guar gum before and after breakage to the product of the molecular weight before breakage and the standard molecular weight. The molecular term... The absolute decrease in molecular mass is expressed in units of . denominator Dimensionless processing units are achieved by multiplying the molecular mass before depolymerization with the standard molecular mass. By introducing the standard molecular weight of guar gum as a normalization parameter, the comparability between different batches of guar gum samples was achieved. This index can directly reflect the depolymerization effect of the bio-enzyme compound system on guar gum macromolecules. The larger the value, the more thorough the molecular chain breakage and the better the degradation effect. This provides a quantitative evaluation basis for screening the optimal bio-enzyme compound system and optimizing the injection parameters.

[0122] The principle and effect of the porosity calculation formula are explained below. This formula is based on nuclear magnetic resonance. The ratio of the sum of signal amplitudes at each time point in the spectrum to the signal amplitude of the rock skeleton is used to characterize the proportion of the core pore space to the total volume, where the numerator term... The sum of the NMR signals corresponding to all pores, denominator term The total volume of the core is represented by pore signal and skeleton signal. Since the signal amplitude is proportional to the fluid volume, this ratio directly reflects the porosity. This formula can quickly obtain the core porosity through non-invasive nuclear magnetic resonance testing, avoiding the damage to the core caused by the traditional saturation weighing method, and providing a quantitative means to evaluate the changes in pore structure before and after bio-enzyme degradation treatment.

[0123] To better understand and implement this invention, Example 2, a specific application scenario, is provided below: A technical team conducted research on the degradation and optimization of fracturing fluid in a deep, tight sandstone reservoir in western China. The reservoir has a temperature of 105℃, a formation water salinity of 18000 mg / L, and a pH of 7.8, representing a typical high-temperature, high-salinity reservoir environment. Due to the reservoir flowback rate of only 35% after previous fracturing operations, a large amount of guar gum fracturing fluid remained in the reservoir pores, causing severe permeability damage. Therefore, there is an urgent need to develop an efficient bio-enzyme degradation technology.

[0124] The technical team first selected cellulase, mannanase, xylanase, and amylase as candidate bio-enzymes based on reservoir conditions, and then combined them in pairs at a 1:1 mass concentration ratio, forming a total of six compound systems. The gel-breaking effect of these compound systems was evaluated at 105℃. By measuring the change in apparent viscosity of the guar gum fracturing fluid over 4 hours, it was found that the mannanase-xylanase compound system performed best, with a viscosity reduction rate of 92.3% over 4 hours, far exceeding the other compound systems. This is because the guar gum molecular chain is composed of mannan, with side chains containing galactose and other substituents. Mannanase can effectively cleave the β-1,4 glycosidic bonds on the main chain, while xylanase can synergistically degrade the xylan components in the side chains and impurities. Both simultaneously attack the guar gum molecular chain from different sites, producing a significant synergistic degradation effect. The technical team used a temperature of 105℃, a mineralization of 18000 mg / L, a pH of 7.8, and a bio-enzyme combination of mannanase and xylanase in a 1:1 ratio, with a total concentration of 0.5% and an initial apparent viscosity of 520. Data such as the state of the breaking liquid being a pale yellow transparent liquid, the breaking time being 2.8 hours, and the viscosity reduction rate being 92.3% after 4 hours were entered into the initial database.

[0125] For the selected mannanase-xylanase compound system, the technical team designed viscosity-concentration tests at five concentration gradients: 0.2%, 0.4%, 0.6%, 0.8%, and 1.0%. The results showed that when the concentration of the compound system was below 0.4%, the gel breaking time exceeded 5 hours, which could not meet the requirements for on-site construction. When the concentration was above 0.8%, although the gel breaking speed was faster, the economic efficiency was poor and the residue content increased slightly. The solid particles in the gel breaking residue were analyzed using a laser particle size analyzer. Figure 2 As shown, the residue particle size distribution was found to be most ideal at a concentration of 0.6%. The value is 78 The particle size is concentrated in the range of 20 to 90 mm. The range, while at a concentration of 0.4%. Reaching 135 At a concentration of 0.8% For 89 Taking into account both the viscosity decrease rate and the particle size distribution characteristics of the residue, the optimal concentration window for this compound system is determined to be 0.4% to 0.8%, with a recommended concentration of 0.6%.

[0126] To establish a predictive model for degradation effects, the technical team designed a five-factor, three-level orthogonal experiment. The temperature levels were 90℃, 105℃, and 120℃; the concentration of the bio-enzyme compound system was 0.4%, 0.6%, and 0.8%; the pH levels were 6.8, 7.8, and 8.8; the mineralization levels were 12000 mg / L, 18000 mg / L, and 24000 mg / L; and the slub ratio was 1:1, 1:2, and 2:1. A total of 27 experiments were completed. Gel permeation chromatography was used to determine the molecular weight of guar gum before and after gel breaking. for Standard guar gum molecular weight for Molecular weight after gel breaking The range of variation under different conditions is to The molecular weight degradation rate was calculated. The percentage varied between 45.8% and 73.5%. A random forest prediction model was constructed based on 27 sets of experimental data, with 180 decision trees, a maximum tree depth of 12, and a minimum number of split samples of 4. Model parameters were optimized through cross-validation. After model training, feature importance analysis showed that temperature had a weight of 0.38, the concentration of the bio-enzyme complex system had a weight of 0.31, the slug ratio had a weight of 0.18, pH value had a weight of 0.09, and mineralization had a weight of 0.04, indicating that temperature and concentration are the dominant factors controlling degradation efficiency.

[0127] The technical team used a one-dimensional high-temperature and high-pressure displacement experimental system to conduct reservoir damage assessment experiments, such as... Figure 3 As shown. The experiment used a natural rock core with a length of 80 mm and a diameter of 25 mm. First, the core was washed with a mixture of toluene and ethanol to remove crude oil and organic residues. Then, it was dried in a 120℃ constant-temperature oven for 48 hours until constant weight. The dried core was then placed in a vacuum saturation device and evacuated for 6 hours. Subsequently, simulated formation water was injected for saturation. The formation water composition was as follows: 15200mg / L 2100mg / L 680 mg / L, total mineralization 18000 mg / L. After saturation, under a confining pressure of 20... Initial permeability was measured at a temperature of 105℃. It is 38.6 Then, guar gum fracturing fluid was injected at a flow rate of 0.3 mL / min, with an injection volume of 0.5 times the pore volume. After pumping was stopped, the mixture was aged for 24 hours to allow the fracturing fluid to fully retain in the pores and form a filter cake. Subsequently, a 0.6% mannanase-xylanase mixture was injected at a flow rate of 0.8 mL / min, with an injection volume of 1.0 times the pore volume, and the reaction time was set to 4 hours. After the reaction was completed, the permeability was measured again. It is 35.2 The permeability recovery rate was calculated. The value was 0.912. Changes in displacement pressure differential were recorded during the experiment; the displacement pressure differential before degradation treatment was 1.38. After degradation treatment, the displacement pressure differential decreased to 0.95. This indicates a significant reduction in pore blockage. The technical team further used nuclear magnetic resonance (NMR) technology to determine the core samples before and after degradation treatment. Spectral distribution, such as Figure 4 As shown, before degradation treatment The relaxation time is mainly distributed in the range of 0.5 to 20 ms, with a high proportion of small pores after degradation treatment. The relaxation time distribution shifts towards longer relaxation times. The proportion of pores longer than 10 ms increased from 28% to 47%, indicating that the residues in the small pores were effectively removed. Based on The porosity before degradation treatment was calculated from the spectral distribution data. The porosity after degradation treatment is 0.152. The value is 0.158, and the reference porosity is selected. Dimensionlessized to 0.16, average before degradation treatment Relaxation time The average time was 8.3 ms after degradation treatment. Relaxation time The time is 12.7ms, and a reference time is selected. Relaxation time Dimensionless values ​​were applied for 10 ms, and the permeability recovery rate based on nuclear magnetic resonance data was calculated. The value was 0.928, which is in good agreement with the measured permeability recovery rate of 0.912. The technical team will adjust the displacement pressure difference, injection flow rate, and other parameters accordingly. Data such as spectral distribution changes and permeability recovery rate were used as training samples to construct a reservoir damage index prediction model, as shown in Table 1.

[0128] Table 1. Reservoir damage assessment parameters under different degradation schemes

[0129]

[0130] The technical team designed a microfluidic chip based on CT scan data of the target reservoir core, such as... Figure 5 As shown, the chip is fabricated using polydimethylsiloxane material. The reservoir pore network structure is replicated onto the chip surface using a soft photolithography process, with a main channel width of 200 μm. The width of the tributary channel is 50 to 120 mm. The depth of the channel is 80. The technical team injected the guar gum fracturing fluid into the microfluidic chip and then stopped the pump, allowing it to accumulate in the channels. The initial accumulation area was... It is 15.8 Subsequently, a 0.6% concentration of mannanase-xylanase complex system was injected, and images of the degradation process were continuously acquired at a frequency of 25 frames per second using a high-speed microscopic imaging system, such as... Figure 6 As shown in the image, the video data was analyzed using image processing software to extract the lingering area at different times. After 10 minutes, the lingering area decreased to 12.4. It dropped to 9.7 after 20 minutes. It dropped to 7.5 after 30 minutes. It dropped to 5.8 after 60 minutes. ,like Figure 7 As shown. The experimental data were substituted into the microscopic dynamic model equations for fitting, and a reference time was selected. The degradation rate constant was obtained by dimensionless conversion over a period of 10 min. The correlation coefficient between the model's predicted curve and the experimental data reached 0.987, with a value of 0.165, validating the model's accuracy. Microscopic observations also revealed that the particle size of the residue particles formed by the breakage of guar gum molecular chains during degradation ranged from 5 to 95 μm. During this process, these particles gradually migrated out of the pores with the fluid without secondary blockage. The technical team compared the prediction results of the micro-dynamic model with the results of the macro-degradation experiment and found that the retention area shrank by 63.3% within 1 hour at the micro-scale, which was highly consistent with the viscosity decrease rate of 65.8% within 1 hour in the macro-experiment, proving that the micro-dynamic model can accurately reflect the macro-degradation effect.

[0131] The technical team established a performance evaluation standard based on four indicators: apparent viscosity, breaking time, residue content, and residue particle size. The standard requires that the apparent viscosity decrease to 5 after breaking. The following parameters are required: breaking time less than 4 hours, residue content less than 300 mg / L, and residue particle size. Less than 100 The 27 experimental schemes were tested one by one. Five schemes were eliminated because the gel breaking time exceeded 4 hours, and two schemes were eliminated due to the size of the residual particle size. More than 100 Eliminated, the remaining 20 schemes met the performance evaluation criteria and entered the optimization phase. The technical team constructed a two-layer game-theoretic optimization model. The upper-layer optimization model aimed to maximize the molecular weight degradation rate, while the lower-layer optimization model aimed to minimize the reservoir damage index. The two models achieved coordinated optimization through a coupling term between slug volume and injection flow rate. In the upper-layer optimization model, the slug volume weight coefficient... Take 0.3, concentration sensitivity coefficient Take 1.2, maximum molecular weight degradation rate Take 73.5%, referencing slug volume. Take 1.0 Reference concentration of biological enzyme compound system Take 0.6%. In the lower-level optimization model, the displacement pressure difference weighting coefficient... Take 0.5, maximum permeability recovery rate Take 0.928, referencing the displacement pressure difference. Take 1.2 Reference Injection Traffic The injection rate was set at 0.8 mL / min. The technical team used a genetic algorithm to solve the two-layer game optimization model. The population size was set at 100 individuals, with a crossover probability of 0.8, a mutation probability of 0.05, and a maximum number of iterations of 200 generations. The initial population was randomly generated within the range of slug size 0.5 to 1.2 times the pore volume, injection rate 0.5 to 1.5 mL / min, and bio-enzyme complex concentration 0.3% to 0.7%. The fitness function comprehensively evaluated the weighted sum of the upper and lower objective functions, with a weight ratio of 6:4, reflecting an optimization strategy that prioritizes degradation effect while also considering reservoir protection. After 156 iterations, the optimal solution changed by less than 0.001 for 10 consecutive generations. The algorithm converged and output the optimal injection process parameters as follows: slug size 0.95 times the pore volume, injection rate 0.72 mL / min, and bio-enzyme complex concentration 0.58%.

[0132] Verification experiments were conducted according to the optimized process parameters. The gel breaking time was 3.2 hours, and the apparent viscosity after gel breaking was 3.8. The residue content is 245 mg / L, and the residue particle size is... For 83 All indicators met the performance evaluation standards. The permeability recovery rate reached 0.935, an increase of 2.5 percentage points compared to before optimization, and the reservoir damage index decreased to 0.065. This optimization scheme was successfully applied to field fracturing operations, effectively solving the permeability damage problem caused by the retention of guar gum fracturing fluid in high-temperature and high-salinity reservoirs.

[0133] The advancement of this invention over traditional degradation methods lies in the establishment of a multi-level optimization system from the molecular scale to the reservoir scale. Traditional methods rely solely on experience to adjust the dosage of biological enzymes and injection parameters, failing to quantify the coupled influence of different factors on the degradation effect. This invention achieves accurate prediction of the synergistic effects of multiple factors such as temperature, concentration, and pH by constructing a degradation effect prediction model. It restores the correlation between changes in microscopic pore structure and macroscopic permeability through a reservoir damage index prediction model. It reveals the dynamic mechanism of residue removal through a microscopic kinetic model. Through a two-layer game optimization algorithm, it minimizes reservoir damage while ensuring efficient gel breaking, achieving the optimal balance between degradation efficiency and reservoir protection. This provides a systematic technical solution for the degradation of fracturing fluids in complex oil reservoirs.

[0134] It should be noted that the variables involved in this invention are explained in detail in Tables 2 and 3.

[0135] Table 2. Variable Explanation Table (Part 1)

[0136]

[0137] Table 3. Variable Explanation Table (Part Two)

[0138]

[0139] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing injection parameters to degrade reservoir fracturing fluid damage using biological agents, characterized in that, The method comprises the following steps: According to the temperature, salinity and pH value of the target reservoir, the biological enzyme compound system with the best degradation effect is screened out; the biological enzyme compound system screened out is subjected to viscosity concentration test experiment of different concentration gradients to determine the optimal concentration window; a multi-factor experiment is designed to construct a degradation effect prediction model; a one-dimensional high temperature and high pressure displacement experiment system is used to carry out a degradation displacement experiment to establish a reservoir damage index prediction model; CT scan data of the fractured reservoir core is extracted to design a microfluidic chip to construct a micro-kinetics model for verification; a performance evaluation standard of the biological enzyme compound system is established, an upper optimization model with the maximum molecular mass degradation rate as the target and a lower optimization model with the minimum reservoir damage index as the target are constructed, and the optimal slug size, optimal injection speed and optimal biological enzyme compound system concentration are output by solving the double-layer game optimization algorithm to realize the collaborative optimization of molecular mass degradation efficiency and reservoir damage control; The step of constructing the degradation effect prediction model is specifically that, for the biological enzyme compound system screened out, a multi-factor experiment of temperature, biological enzyme compound system concentration, pH value, salinity and slug ratio is designed, gel permeation chromatography is used to determine the molecular mass of the broken gel liquid, the molecular mass degradation rate is calculated, and a degradation effect prediction model with temperature, biological enzyme compound system concentration, pH value, salinity and slug ratio as input parameters and the molecular mass degradation rate as output is constructed. The step of establishing the reservoir damage index prediction model is specifically: performing a degradation displacement experiment by using a one-dimensional high-temperature and high-pressure displacement experiment system to simulate formation conditions, measuring initial permeability of the core before degradation treatment and permeability after degradation treatment, calculating a permeability recovery rate, measuring pore structure distribution of the core before degradation treatment and after degradation treatment by using a nuclear magnetic resonance technology, performing a correlation analysis based on changes in the displacement pressure difference, the injection flow rate and the pore structure distribution, and establishing a reservoir damage index prediction model taking the displacement pressure difference and the injection flow rate as input parameters and taking the permeability recovery rate as output. The step of establishing the reservoir damage index prediction model is specifically: performing a degradation displacement experiment by using a one-dimensional high-temperature and high-pressure displacement experiment system to simulate formation conditions, measuring initial permeability of the core before degradation treatment and permeability after degradation treatment, calculating a permeability recovery rate, measuring pore structure distribution of the core before degradation treatment and after degradation treatment by using a nuclear magnetic resonance technology, performing a correlation analysis based on changes in the displacement pressure difference, the injection flow rate and the pore structure distribution, and establishing a reservoir damage index prediction model taking the displacement pressure difference and the injection flow rate as input parameters and taking the permeability recovery rate as output.​ The step of constructing the micro-kinetics model for verification is specifically that, the retention state of the guanidium gel fracturing fluid in the microfluidic chip and the dynamic process of the biological enzyme compound system degradation are observed by combining a high-speed microscopic imaging system, the video data is digitally processed to extract the retention area shrinkage rate and the residual particle migration trajectory, a micro-kinetics model is constructed, and the prediction result of the micro-kinetics model is compared and verified with the prediction result of the degradation effect prediction model. The objective function of the upper optimization model is expressed as follows: ； In the formula, is the molecular mass degradation rate, is the maximum molecular mass degradation rate, is the slug volume weight coefficient, the value range is , is the slug volume, is the reference slug volume, is the concentration sensitivity coefficient, the value range is , is the biological enzyme complex system concentration, is the reference biological enzyme complex system concentration; The objective function of the lower optimization model is expressed as follows: ； In the formula, is the permeability recovery rate, is the maximum permeability recovery rate, is the displacement pressure difference weight coefficient, and the value range is , is the displacement pressure difference, is the reference displacement pressure difference, is the injection flow rate, is the reference injection flow rate.

2. The method of claim 1, wherein, The step of screening out the biological enzyme compound system with the best degradation effect is specifically that, the broken gel effect of a plurality of biological enzyme compound systems formed by different biological enzyme main agents in a 1:1 mass concentration ratio on the guanidium gel fracturing fluid is tested according to the time variation law.

3. The method of claim 2, wherein, The biological enzyme main agent comprises cellulase, mannanase, xylanase and amylase.

4. The method of claim 3, wherein, The double-layer game optimization algorithm is solved by using a genetic algorithm framework.

5. The method of claim 4, wherein, The step of determining the optimal concentration window is specifically that, the apparent viscosity of the guanidium gel fracturing fluid is determined according to the time variation curve at each concentration, the particle size distribution of the solid phase particles in the broken gel residual liquid is determined by using a laser particle size analyzer, and the optimal concentration window suitable for the target reservoir is determined based on the viscosity drop rate and the residual particle size data.

6. The method of claim 5, wherein, The biological enzyme compound system with the best degradation effect refers to the biological enzyme compound system with the fastest viscosity drop rate on the guanidium gel fracturing fluid within 4 hours.

7. The method of claim 6, wherein, The molecular mass degradation rate is used for quantifying the degree of rupture of the molecular chain of the guanidium gum, and the calculation formula of the molecular mass degradation rate is expressed as follows: the molecular mass degradation rate equals the difference between the molecular mass of the guanidium gum before breaking and the molecular mass of the guanidium gum after breaking divided by the product of the molecular mass of the guanidium gum before breaking and the standard molecular mass of the guanidium gum.

8. The method of claim 7, wherein, The operation process of the one-dimensional high-temperature and high-pressure displacement experiment system comprises the following steps: cleaning original fluid and organic matter in the core, drying the core to constant weight in a constant-temperature oven, vacuumizing and saturating the dried core with formation water, measuring the initial permeability, injecting the guanidium gum fracturing fluid into the core to make it stay in the core pore and form a filter cake, injecting the biological enzyme compound system and the guanidium gum fracturing fluid to fully react, and measuring the permeability again after the reaction is completed.

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