Fracturing construction injection parameter dynamic regulation method based on DAS monitoring

Abstract

The application discloses a fracturing construction injection parameter dynamic regulation and control method based on DAS monitoring and relates to the fracturing technical field. The method comprises the following steps: firstly, a plurality of samples are generated based on actual working conditions; then, an optimal state currently reached and an ideal optimal state are obtained based on a self-adaptive genetic algorithm; and injection parameters are adjusted according to the threshold of the optimal state currently reached and the ideal optimal state until the deviation between the optimal state currently reached and the ideal optimal state is less than the threshold, wherein the injection parameters at this time are optimal injection parameters. The application provides a mathematical model and a method for quantitatively characterizing a sand liquid injection profile in a horizontal well fracturing process, can help technical personnel in the field to understand the sand liquid injection profile in the horizontal well fracturing process, to clearly know the liquid and sand injection dynamics of each perforation cluster and to provide a scientific basis for accurate regulation and control of the horizontal well fracturing.

Description

Technical Field

[0001] This invention relates to the field of fracturing technology, and specifically to a method for dynamic control of fracturing injection parameters based on DAS monitoring. Background Technology

[0002] In the development of unconventional oil and gas resources, multi-cluster fracturing in horizontal wells is a core development technology, widely used due to its ability to effectively stimulate low-permeability reservoirs and enhance single-well productivity. The accuracy of fracture propagation is crucial for optimizing fracturing design, assessing reservoir stimulation volume, and improving recovery rates. However, the complex fracture network formed during fracturing exhibits significant heterogeneity. The propagation of fractures in each perforation cluster is influenced by factors such as changes in the geostress field, rock mechanical properties, and fracturing fluid loss, making accurate characterization difficult. Competition for fracture initiation among perforation clusters, inter-fracturing stage leakage, and cross-flow phenomena during fracturing lead to poor fracturing effects. Timely control measures such as variable displacement and temporary plugging are necessary during fracturing. However, diagnosing the effectiveness of these control measures remains a major technical challenge, making it difficult to guarantee the effectiveness of horizontal well fracturing and hindering the development efficiency of unconventional oil and gas wells. To achieve this goal, the key lies in real-time monitoring of the sand and fluid ingress dynamics of each perforation cluster and accurate evaluation of the injection profile.

[0003] Conventional monitoring methods primarily employ microseismic monitoring, tracer testing, and pressure transient analysis. While these methods can acquire some information about fracture propagation, they have significant limitations: microseismic monitoring has limited resolution, making it difficult to accurately diagnose fracture propagation; tracer testing requires the injection of special chemicals, posing environmental pollution risks and incurring high costs; and pressure transient analysis is limited by interference from complex fracture networks, resulting in multiple interpretations and failing to provide detailed analysis of fracture dynamics within each perforation cluster. Furthermore, existing methods struggle to monitor the dynamic distribution of fracturing fluid within each perforation cluster, leading to significant discrepancies between the interpretation of the injection profile and the actual fracture propagation morphology.

[0004] In recent years, Distributed Acoustic Sensing (DAS) technology has been widely used in the dynamic monitoring of multi-cluster fracturing in oil and gas wells. DAS technology, by installing fiber optic sensors downhole, can monitor downhole acoustic signals in real time during the fracturing process. By analyzing the frequency, amplitude, and distribution characteristics of downhole acoustic vibrations, the dynamics of fracture propagation and fluid transport can be indirectly reflected. The flow of fracturing fluid and proppant in complex fracture networks excites characteristic acoustic signals, whose propagation characteristics are closely related to fracture geometry, fracturing fluid injection volume, fluid flow velocity, and sand ratio. Therefore, the fracturing effect can be evaluated by monitoring acoustic data in the wellbore. However, DAS technology still faces multiple technical bottlenecks in monitoring the injection profile of fracturing fluid in horizontal wells. These bottlenecks are mainly manifested in the strong heterogeneity of the fractures in the perforation clusters formed during the fracturing process. The half-length of each perforation cluster fracture is affected by multiple factors such as in-situ stress interference, rock brittleness index, and fracturing fluid filtration effect. As a result, the relationship between the downhole DAS response and the sand and fluid injection volume of the perforation clusters during the fracturing process is still unclear. Furthermore, there is a lack of a calculation model for the injection state based on DAS data during the fracturing process. Currently, it is still impossible to quantitatively assess the sand and fluid injection state through DAS monitoring data.

[0005] Therefore, how to construct a high-precision injection state calculation model based on DAS acoustic data and realize the characteristic characterization of sand injection profile during multi-cluster fracturing in horizontal wells has become a key technical challenge for improving the fracturing effect of unconventional reservoirs and optimizing fracturing design.

[0006] In summary, existing dynamic monitoring technologies for horizontal wells have many shortcomings in evaluating sand injection profiles in segmented, multi-cluster fracturing horizontal wells, making it difficult to meet the needs of actual production. Therefore, there is an urgent need for a new method based on DAS technology to accurately assess the sand injection status during segmented, multi-cluster fracturing in horizontal wells, providing a scientific basis for precise control of fracturing stimulation. Summary of the Invention

[0007] To address at least one of the aforementioned problems, this invention provides a method for dynamic control of injection parameters during fracturing operations based on DAS monitoring.

[0008] The technical solution of this invention to solve the above problems is as follows: A method for dynamic control of injection parameters during fracturing based on DAS monitoring, comprising the following steps:

[0009] S1. Based on the limitations on fluid injection rate and sand ratio during fracturing operations, constraint conditions are constructed. At the same time, based on the constraint conditions, the fluid injection rate and sand ratio of each perforation cluster at time t are generated to construct an initial population. Simultaneously, the acoustic energy of each perforation cluster at time t within the fracturing section is obtained.

[0010] S2. Substitute the acoustic wave energy into the relationship model between acoustic wave energy and orifice inlet flow velocity to calculate the inlet flow velocity of each perforation cluster. Use the orifice diameter prediction model to obtain the orifice diameter of each perforation cluster. Based on the inlet flow velocity and orifice diameter, obtain the inlet flow velocity of each perforation cluster and calculate its fitness value. The relationship model is as follows: In the formula, y denoted as DAS energy; v is the flow rate of the fluid entering the perforation cluster; A and B are the model regression coefficients obtained experimentally.

[0011] S3. Based on the adaptive genetic algorithm, combined with the constraints and fitness values, the initial population is iteratively calculated to finally output the optimal liquid inlet amount for each perforation cluster.

[0012] S4. Calculate the ideal liquid inlet rate for each perforation cluster, and calculate the liquid inlet rate deviation by combining the optimal liquid inlet rate and the ideal liquid inlet rate.

[0013] S5. If the liquid inlet volume deviation is greater than the threshold, adjust the injection parameters and repeat S1~S4 until the liquid inlet volume deviation is less than the threshold.

[0014] The beneficial effects of this invention are as follows: This invention provides a mathematical model and method for quantitatively characterizing the sand injection profile during horizontal well fracturing, which can help those skilled in the art to understand the sand injection profile during horizontal well fracturing, clarify the fluid and sand injection dynamics of each perforation cluster, and provide a scientific basis for precise control of horizontal well fracturing. Attached Figure Description

[0015] Figure 1 This is a flowchart of a method according to an embodiment of the present invention. Detailed Implementation

[0016] The specific embodiments of the present invention will be clearly and completely described below with reference to examples. Obviously, the described examples are only some embodiments of the present invention, and not all embodiments.

[0017] like Figure 1 As shown, the method for dynamic control of injection parameters during fracturing operations based on DAS monitoring includes the following steps:

[0018] S1. Based on the limitations on fluid injection rate and sand ratio during fracturing operations, constraint conditions are constructed. At the same time, based on the constraint conditions, the fluid injection rate and sand ratio of each perforation cluster at time t are generated to construct an initial population. Simultaneously, the acoustic energy of each perforation cluster at time t within the fracturing section is obtained.

[0019] The constraints are primarily designed to address construction limitations and prevent the final result from failing to meet existing construction conditions. The specific constraints are as follows:

[0020]

[0021] In the formula, Indicates the first k Liquid inflow rate per perforation cluster; This indicates the total pumping volume for the corresponding fracturing stage; , The lower and upper limits of the liquid inflow parameters are restrictions imposed on construction parameters. S k Indicates the first k Sand ratio of a perforation cluster; , The lower and upper limits of the sand ratio parameter are the constraints for construction parameters.

[0022] In this step, the initial population is randomly generated based on the constraints described above, and its specific composition is as follows: P0 represents the initial population set. =(q N,1 ,q N,2 ,…,q N,m ,S N,1 ,S N,2 ,…,S N,m In the formula, Let q represent the Nth individual in the initial population. N,m express The influent volume S of the m-th cluster. N,m express The sand ratio of the m-th cluster in the initial population. Each individual in this initial population contains a complete set of liquid inflow and sand ratio state parameters.

[0023] S2. Substitute the acoustic wave energy into the relationship model between acoustic wave energy and orifice inlet flow rate to calculate the inlet flow rate of each perforation cluster. Use the orifice diameter prediction model to obtain the orifice diameter of each perforation cluster. Based on the inlet flow rate and orifice diameter, obtain the inlet flow rate of each perforation cluster and calculate its fitness value. The relationship model is as follows: In the formula, y denoted as DAS energy; v is the flow rate of the fluid entering the perforation cluster; A and B are the model regression coefficients obtained experimentally.

[0024] This step primarily provides fitness values ​​for the subsequent adaptive genetic algorithm, assisting in its iteration. Specifically, it includes the following steps:

[0025] After calculating the liquid inflow rate of each perforation cluster based on DAS energy, the pore size of each perforation cluster is obtained based on the pore size prediction model: In the formula, The diameter of the perforation hole; t For pumping time; This is an empirical coefficient, typically taken as 1.07 × 10⁻⁶. -13 m 2 ·s / kg;C This refers to the proppant concentration; v The fluid velocity at the perforation orifice is given. Those skilled in the art will know that, given the inlet fluid velocity and orifice diameter, the inlet fluid rate for each perforation cluster can be calculated. .

[0026] Match the influent volume: In the formula, The first result obtained after matching calculation and engineering constraint correction k The final liquid inflow rate of each perforation cluster; This is the reference injection rate for the k-th perforation cluster, calculated based on DAS measured acoustic energy data. =x· ; , The lower and upper limits of the liquid inlet flow rate are defined as construction parameter restrictions; the liquid inlet flow rate matching step involves using the liquid inlet flow rate calculated above. As a physical constraint, it is matched with the individuals in step 1 to facilitate the subsequent calculation of fitness values.

[0027] Calculate the objective function: In the formula, E This represents the objective function, which is dimensionless. Indicates the first k The calculated value of the liquid inlet volume per perforation cluster, in m³. 3 / min; This represents the result calculated based on the pump injection rate, the number of perforation clusters, and the wellbore flow conditions under the current constraints of fracturing operation parameters. k Liquid intake per perforation cluster;

[0028] Calculate fitness value: In the formula, F represents the fitness value. This represents a very small positive value, ensuring that the denominator is not 0.

[0029] The fitness value calculation method and the obtained fitness value in this step can be used for individual selection and parameter adjustment in subsequent adaptive genetic algorithms.

[0030] S3. Based on the adaptive genetic algorithm, combined with the constraints and fitness values, the initial population is iteratively calculated to finally output the optimal liquid inlet amount for each perforation cluster.

[0031] The adaptive genetic algorithm used in this step is as follows:

[0032] S31. Calculate the selection probability based on individual fitness. ,in accordance with From population Individuals are selected to form a mating pool. Elite individuals are retained, and the top 10% with the highest fitness directly enter the next generation. The remaining 90% of individuals undergo a roulette wheel selection process, selecting individuals equal to the initial population size to form a new population. The prototype;

[0033] Choose probability The calculation formula is:

[0034]

[0035] In the formula Choose the probability for the individual; For the first i Individual fitness of each individual; The maximum fitness of the current population; This represents the average fitness of the current population.

[0036] The formula for calculating the probability of a choice in roulette is:

[0037]

[0038] In the formula For the first i The probability of an individual being selected in roulette; For the first i Individual fitness of each individual.

[0039] S32, Regarding new populations Individuals in the prototype are randomly paired, and adaptive crossover probabilities are calculated. With probability Perform arithmetic crossover on paired individuals to produce offspring individuals;

[0040] Adaptive crossover probability The calculation formula is:

[0041]

[0042] In the formula For adaptive crossover probability; This is the upper limit of the crossover probability, typically set to 0.9; This is the lower bound for the crossover probability, typically set to 0.6; For the first i The fitness of each parent individual; This represents the average fitness of the current population. This represents the maximum fitness of the current population.

[0043] The formula for calculating the individual arithmetic crossover is:

[0044]

[0045] In the formula For those generated by crossover of parent individuals, in t The value of the first parameter for surviving offspring individuals in generation +1, in units of m. 3 / min; For adaptive crossover probability; For the first t In the generation, the parent individual i The value for the influent volume is in cubic meters (m). 3 / min; For the first t In the generation, the parent individual j The value for the influent volume is in cubic meters (m). 3 / min.

[0046] S33. Calculate the adaptive mutation probability for all individuals in the population after the crossover operation. With probability Randomly perturbing the genes of individual individuals eventually leads to the formation of a new population. ;

[0047] Adaptive mutation probability The calculation formula is:

[0048]

[0049] In the formula For adaptive mutation probability; This is the upper limit of the mutation probability, typically set to 0.1; This is the lower limit of the mutation probability, typically taken as 0.01; For the first i The fitness of each parent individual; This represents the average fitness of the current population. This represents the maximum fitness of the current population.

[0050] The formula for calculating random disturbances is:

[0051]

[0052]

[0053] In the formula For the first t +1 generation, no. m The new value after variation in fluid inflow for each individual, in m³. 3 / min; For adaptive mutation probability; For the first t Generation Zhong, No. mOriginal values ​​of fluid intake volume for each individual before variation, in m³ 3 / min; For the first t +1 generation, no. m New value of individual values ​​after variation in sand ratio, % For the first t Generation Zhong, No. m Original value of each individual in terms of sand ratio before variation, % , The upper and lower limits of the search space for the liquid inflow rate parameter, in meters. 3 / min; , The upper and lower limits of the search space for the sand ratio parameter are %; , is a random perturbation factor, and is a random number generated independently within [0,1].

[0054] S34. Calculate the new population The fitness value will and Compare and determine whether to accept the new population. If the population is positive, the new population is accepted and the process continues in S2; otherwise, it is rejected and the process returns to S4 to perform the crossover and mutation operation again.

[0055] S35. Repeat the above operation iteratively to obtain a series of population samples. , ,…, These samples constitute the set of possible injection state parameters under the current operating conditions. Whether the repeated operation ends depends on meeting one of the following three conditions: 1) The maximum fitness fluctuation of the population for 5 consecutive generations is less than 10. -5 ;2) Current population The error value corresponding to the optimal individual with the highest fitness. Less than 0.01m 3 / min; 3) The number of iterations reaches the maximum number of iterations. If any condition is not met, return to S2 to continue iterating, and finally output the optimal liquid distribution.

[0056] In conventional adaptive genetic algorithms, both global search capability and convergence efficiency are relatively low. In this embodiment, to address this issue, this step employs perturbation of the current individual's genes to improve both global search capability and convergence efficiency.

[0057]

[0058]

[0059] In the formula, For the firstt +1 generation, no. m New values ​​after variation in liquid inlet volume for each individual; For adaptive mutation probability; For the first t Generation Zhong, No. m Original values ​​before variation in fluid intake for each individual; For the first t +1 generation, no. m New values ​​of individual sand ratios after variation; For the first t Generation Zhong, No. m Original values ​​of individual individuals in terms of sand ratio before variation; , These are the upper and lower limits of the search space for the liquid inflow parameter; , This represents the upper and lower limits of the search space for the sand ratio parameter; , is a random perturbation factor, and is a random number generated independently within [0,1].

[0060] S4. Calculate the ideal liquid inlet rate for each perforation cluster, and calculate the liquid inlet rate deviation by combining the optimal liquid inlet rate and the ideal liquid inlet rate.

[0061] In this step, the formula for calculating the liquid inlet volume deviation is as follows: In the formula, D is the deviation of the liquid inlet volume; The output of step S3 is the first k The stage-wise liquid inflow rate of each perforation cluster, in m³. 3 / min; The target fluid injection rate is the amount of fluid required to achieve a more balanced fracturing effect across all perforation clusters under the assumption of sufficient fracturing stimulation.

[0062] The above-described control process, combined with step S3, is a repetitive control process: first, the actual liquid inflow state is obtained on-site; then, based on S3, the currently achievable optimal state is obtained; in this step, The formula in this step represents the ideal optimal state, while the formula in this step represents the difference between the current achievable optimal state and the ideal optimal state. If the difference between the two is too large (i.e., the liquid inlet deviation is too large), it means that the current state still needs to be optimized.

[0063] S5. If the liquid inlet deviation is greater than the threshold, adjust the injection parameters and repeat S1~S4 until the liquid inlet deviation is less than the threshold; take the injection parameters at this time as the optimal injection parameters.

[0064] As mentioned above, in S4, when the fluid injection rate deviation exceeds the threshold, it indicates that the current injection status needs optimization. Optimization typically involves adjusting injection parameters, including pump flow rate, pump pressure, and proppant-to-sand ratio. For perforation clusters with low fluid injection rates, methods such as increasing stage flow rate, adjusting the sand ratio, or changing the injection rhythm can guide subsequent fracturing fluid and proppant to be redistributed to the under-modified perforation clusters.

[0065] Regarding the threshold in this step, based on some engineering experience in the fluid inlet balance of each perforation cluster during on-site fracturing operations, and combined with a large number of experiments, the inventors found that setting the threshold to 0.1~0.3 is more appropriate. If higher precision is desired, the threshold can be set smaller, but relatively speaking, the requirements for construction parameters are higher.

[0066] The present invention has been disclosed above with preferred embodiments. However, those skilled in the art should understand that these embodiments are for illustrative purposes only and should not be construed as limiting the scope of the invention. Further improvements can be made without departing from the principles of the invention, and these improvements should also be considered as protections of the present invention.

Claims

1. A method for dynamic control of injection parameters during fracturing operations based on DAS monitoring, characterized in that, Includes the following steps: S1. Based on the limitations on fluid injection rate and sand ratio during fracturing operations, constraint conditions are constructed. At the same time, based on the constraint conditions, the fluid injection rate and sand ratio of each perforation cluster at time t are generated to construct an initial population. Simultaneously, the acoustic energy of each perforation cluster at time t within the fracturing section is obtained. S2. Substitute the acoustic wave energy into the relationship model between acoustic wave energy and orifice inlet flow rate to calculate the inlet flow rate of each perforation cluster. Use the orifice diameter prediction model to obtain the orifice diameter of each perforation cluster. Based on the inlet flow rate and orifice diameter, obtain the inlet flow rate of each perforation cluster and calculate its fitness value. The relationship model is as follows: In the formula, y denoted as DAS energy; v is the flow rate of the fluid entering the perforation cluster; A and B are the model regression coefficients obtained experimentally. S3. Based on the adaptive genetic algorithm, combined with the constraints and fitness values, the initial population is iteratively calculated to finally output the optimal liquid inlet amount for each perforation cluster. S4. Calculate the ideal liquid inlet rate for each perforation cluster, and calculate the liquid inlet rate deviation by combining the optimal liquid inlet rate and the ideal liquid inlet rate. S5. If the liquid inlet deviation is greater than the threshold, adjust the injection parameters and repeat S1~S4 until the liquid inlet deviation is less than the threshold; take the injection parameters at this time as the optimal injection parameters.

2. The method for dynamic control of injection parameters in fracturing operations based on DAS monitoring according to claim 1, characterized in that, In S1, the initial population is: P0 represents the initial population set. =(q N,1 ,q N,2 ,…,q N,m ,S N,1 ,S N,2 ,…,S N,m In the formula, Let q represent the Nth individual in the initial population. N,m express The influent volume S of the m-th cluster. N,m express The sand ratio of the m-th cluster in the array.

3. The method for dynamic control of injection parameters during fracturing operations based on DAS monitoring according to claim 1, characterized in that, In S1, the constraint condition is: In the formula, Indicates the first k Liquid inflow rate per perforation cluster; This indicates the total pumping volume for the corresponding fracturing stage; , The lower and upper limits of the liquid inflow parameters are restrictions imposed on construction parameters. , The lower and upper limits of the sand ratio parameter are the constraints for construction parameters.

4. The method for dynamic control of injection parameters in fracturing operations based on DAS monitoring according to claim 1, characterized in that, In S2, the fitness value is calculated as follows: The aperture diameters of each perforation cluster are obtained based on the aperture prediction model: In the formula, The diameter of the perforation hole; t For pumping time; This is an empirical coefficient; C This refers to the proppant concentration; Match the influent volume: In the formula, The first result obtained after matching calculation and engineering constraint correction k The final liquid inflow rate of each perforation cluster; This is the reference injection rate for the k-th perforation cluster, calculated based on DAS measured acoustic energy data. ; , The lower and upper limits of the liquid inflow parameters are restrictions imposed on construction parameters. Calculate the objective function: In the formula, E This represents the objective function, which is dimensionless. This represents the result calculated based on the pump injection rate, the number of perforation clusters, and the wellbore flow conditions under the current constraints of fracturing operation parameters. k Liquid intake per perforation cluster; Calculate fitness value: In the formula, F represents the fitness value. This represents a very small positive value, ensuring that the denominator is not 0.

5. The method for dynamic control of injection parameters in fracturing operations based on DAS monitoring according to claim 4, characterized in that, In S3, the termination condition for the iterative calculation is: the maximum fitness fluctuation of the population for five consecutive generations is less than 10. -5 ; or, the current population In the middle, the error value corresponding to the best individual Less than 0.01m 3 / min; or, the number of iterations reaches the maximum.

6. The method for dynamic control of injection parameters in fracturing operations based on DAS monitoring according to claim 1, characterized in that, In S3, the adaptive genetic algorithm uses perturbation of the current individual's genes to improve global search capability and convergence efficiency. In the formula, For the first t +1 generation, no. m New values ​​after variation in liquid inlet volume for each individual; For adaptive mutation probability; For the first t Generation Zhong, No. m Original values ​​before variation in fluid intake for each individual; For the first t +1 generation, no. m New values ​​of individual sand ratios after variation; For the first t Generation Zhong, No. m Original values ​​of individual individuals in terms of sand ratio before variation; , These are the upper and lower limits of the search space for the liquid inflow parameter; , This represents the upper and lower limits of the search space for the sand ratio parameter; , is a random perturbation factor, and is a random number generated independently within [0,1].

7. The method for dynamic control of injection parameters during fracturing operations based on DAS monitoring according to claim 1, characterized in that, The formula for calculating the liquid inlet volume deviation is as follows: In the formula, D is the deviation of the liquid inlet volume; The output of step S7 k The stage-wise liquid inflow rate of each perforation cluster; The target fluid injection rate is the amount of fluid required to achieve a more balanced fracturing effect across all perforation clusters under the assumption of sufficient fracturing stimulation.

8. The method for dynamic control of injection parameters in fracturing operations based on DAS monitoring according to claim 1, characterized in that, In S5, the injection parameters include pump flow rate, pump pressure, and proppant sand ratio.

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