Fractured well perforation cluster flow inversion method and system based on distributed optical fiber acoustic vibration

Abstract

The invention discloses a fractured well perforation cluster flow inversion method and system based on distributed optical fiber acoustic vibration. The method comprises the following steps: establishing a response calibration model between a unit flow velocity and an acoustic energy index; a distributed optical fiber acoustic vibration system is deployed in a fractured well shaft; acquiring and processing an acoustic vibration signal; the acoustic energy index is input into the calibration model to calculate the volume flow; and outputting a perforation cluster flow distribution diagram. According to the method, inversion processing and result output of the volume flow of the multiple perforation clusters in the fractured well shaft can be achieved, estimation of the volume flow of the multiple perforation clusters in the fractured well shaft is achieved, and technical support is provided for production monitoring, productivity diagnosis and fine regulation and control in the field of oil and gas reservoir development.

Description

Technical Field

[0001] The present invention relates to a method and system for inverse flow rate of perforation clusters in fractured wells based on distributed fiber optic acoustic vibration, belonging to the technical field of oil and gas reservoir development. Background Art

[0002] At present, distributed fiber optic acoustic vibration technology has been gradually popularized and applied in oil and gas engineering due to its advantages such as full well section coverage, large amount of collected data, and passive monitoring. By arranging optical fibers along the wellbore and combining with a ground demodulation device, it is possible to realize the perception of the acoustic vibration response at the position of perforation clusters inside the wellbore, providing technical support for fractured well monitoring. With the gradual deployment of distributed fiber optic acoustic vibration systems, their potential for evaluating perforation clusters during production is becoming increasingly prominent.

[0003] With the wide application of distributed fiber optic acoustic vibration systems in fractured well monitoring, new challenges have also emerged. Due to the weak energy of the wellbore sound source and the low signal-to-noise ratio of the sound signal, at present, it mainly relies on manual experience judgment or qualitative trend analysis, and it is still impossible to realize the quantitative inverse flow rate of perforation clusters based on the sound signal.

[0004] Therefore, a method and system for inverse flow rate of perforation clusters in fractured wells based on distributed fiber optic acoustic vibration are proposed. By combining experimental modeling and system deployment, a mapping relationship between unit flow rate and acoustic energy is constructed to realize the estimation of the flow rates of multiple perforation clusters during the production stage of fractured wells, providing reliable data support for the dynamic management of oil reservoir development. Summary of the Invention

[0005] The object of the present invention is to provide a method and system for inverse flow rate of perforation clusters in fractured wells based on distributed fiber optic acoustic vibration, which is used for processing distributed fiber optic vibration data during the production stage of fractured wells, realizing the identification and quantitative inverse flow rate of the volume flow rates of multiple perforation clusters in the wellbore, and solving the technical limitations of the distributed fiber optic acoustic vibration system in signal modeling, inverse flow rate method, and identification of perforation cluster contributions in the prior art.

[0006] To achieve the above object, the first aspect embodiment of the present invention proposes a method for inverse flow rate of perforation clusters in fractured wells based on distributed fiber optic acoustic vibration, and the method includes the following steps:

[0007] S100, establishing a response calibration model between unit flow rate and acoustic energy index;

[0008] S200, deploying a distributed fiber optic acoustic vibration system in the fractured wellbore;

[0009] S300, acquiring and processing acoustic vibration signals;

[0010] S400, inputting the acoustic energy index into the calibration model to calculate the volume flow rate;

[0011] S500, output the flow distribution map of perforation clusters.

[0012] According to the inversion method of the embodiments of the present invention, the acoustic response characteristics of a fractured well can be reflected, and the volumetric flow rate of each perforation cluster can be quantitatively estimated.

[0013] Further, in step S100, the response calibration model between the unit flow velocity and the acoustic energy index is:

[0014] Construct a wellbore simulation device under a controlled experimental environment, and set multiple groups of experimental parameter combinations of structure and fluid conditions to form multiple modeling samples. The experimental parameters include: different perforation aperture sizes (such as 3 mm, 4 mm, 5 mm), different numbers of perforations (such as 6 holes, 8 holes, 10 holes per cluster), and single-phase liquid types (such as clear water, 1% polymer solution, crude oil, etc.). The flow velocity setting should be adjustable, and the temperature and pressure conditions should be kept stable.

[0015] Under each group of parameter combination conditions, collect vibration signals on the outer wall of the wellbore tubing through a distributed optical fiber; the signals are processed by band-pass filtering from 20 Hz to 2000 Hz to reduce the interference of background noise and structural resonance, and the acoustic energy index E of each group of signals is extracted using a fixed time window a , and the calculation formula is as follows:

[0016]

[0017] where, E a is the acoustic energy index, with the unit of g; x i is the vibration signal amplitude value of the i-th sampling point within the time window, and n is the total number of sampling points within this window; the sampling frequency is not less than 1000 Hz to ensure that at least 1000 effective sample points are collected within each 1-second time window to meet the time resolution requirements; the window length is set between 1 second and 5 seconds, and the sliding step is 1 second.

[0018] To ensure the representativeness of the signals, it is recommended that the sampling points for each acoustic index be selected from the DAS sampling points within 1 meter above and below the center of the perforation cluster. Align the perforation cluster numbers with the sampling point numbers in combination with the well depth design or construction drawings so that each cluster corresponds to a unique acoustic response input.

[0019] After the above processing, the extracted E a is non-linearly fitted with the unit flow velocity v, aperture d, and number of perforations N under this group of parameters to construct a power-law model:

[0020] v = a · (E a ) b · (d) c · (N) e

[0021] Among them, a, b, c, and e are model fitting parameters; N is the number of perforations; v is the unit flow velocity with the unit of m / s; d is the pore diameter with the unit of mm.

[0022] The non - linear power - law model is non - linearly regression - fitted by the least - squares method. Through the input of multiple groups of combined data, parameter regression of the model is carried out to obtain the parameter values of a, b, c, and e.

[0023] The applicable conditions of the model are as follows: the fluid in the well section is a single - phase liquid, and the combination of on - site structural parameters is within the modeling sample space or its acceptable extrapolation range; this model can not only construct a general fitting path for different well sections, but also be customized and calibrated according to the specific conditions of each well, with flexible structural expansion ability.

[0024] The beneficial effect of this step is: by constructing a mathematical mapping relationship between the unit flow velocity and the vibration response under controlled conditions and combining with structural parameters to form a prediction model, the method enhances the adaptability and interpretability of the acoustic energy index; this step provides a core modeling basis for subsequent flow rate inversion with high precision, deployability, and engineering - scale promotion.

[0025] Further, in step S200, the deployment of the distributed fiber - optic acoustic vibration system in the fractured - wellbore is as follows:

[0026] Under the condition permitted by the completion structure of the fractured well, the distributed fiber - optic sensing optical cable is laid on the outer wall of the wellbore tubing; the optical cable can be mechanically fixed to the pipe string by bonding, card slots, winding and other structural methods to ensure effective coupling between the optical cable and the metal structure in the high - temperature and high - pressure environment after completion, and avoid signal loss caused by gaps, floating, or loosening.

[0027] The optical cable is preferably a single - mode communication optical fiber with good frequency response characteristics and spatial stability; the laying length of the optical cable preferably covers the entire perforation section area, and the well - depth positions of each perforation cluster are clearly defined in the laying design.

[0028] The spatial interval between each sampling point of the fiber - optic acoustic vibration system is not greater than 1 meter, which is used to ensure the independence and distinguishability of the vibration responses at different perforation cluster positions; the demodulation device uses a distributed acoustic wave demodulator, and its sampling frequency is set to 1000 Hz and above to meet the requirements of the time resolution for the calculation of the acoustic energy index.

[0029] To avoid response aliasing, the phenomenon of excessive bending and uneven attachment should be minimized during the laying of the optical cable. When necessary, casing scanning can be carried out to assist in confirming the coupling state; the optical cable laying data output in this step and the original vibration signals provided by the demodulation acquisition system will be used as the original input basis for the acoustic energy index of each cluster subsequently.

[0030] The beneficial effects of this step are as follows: By precisely deploying distributed optical fibers inside the fractured well and optimizing the coupling strength between the fibers and the pipe string through pasting or winding, combined with a demodulation system with high spatial resolution (not higher than 1 m) and high sampling frequency (≥1000 Hz), stable acquisition of the acoustic responses in multiple perforation cluster regions in the wellbore is achieved. This deployment scheme can ensure that there is an independent, continuous, and real vibration observation basis at each perforation cluster location, providing a high-fidelity and low-interference data source for subsequent extraction of acoustic energy indicators and inversion of the perforation cluster volume flow rate, and significantly improving the response stability and on-site adaptability of the entire system.

[0031] Further, in step S300, the acquisition and processing of the acoustic vibration signal are as follows:

[0032] During the production stage of the fractured well, distributed optical fibers deployed in the wellbore are used to collect acoustic signals in multiple perforation cluster regions in the wellbore. The acoustic signals are the vibration response time series data of the well sections corresponding to each perforation cluster, and the recording form is the time-varying sequence of the vibration amplitudes of the optical fiber sampling points arranged along the well depth direction.

[0033] To extract representative acoustic energy index signals, the following processing flow should be performed on the original DAS data: Vibration signals are collected from the outer wall of the wellbore through distributed optical fibers. The signals are processed by band-pass filtering from 20 Hz to 2000 Hz to reduce the interference of background noise and structural resonance, and a fixed time window is used to extract the acoustic energy index of each group of signals, denoted as the on-site acoustic energy index E′ a , and the calculation formula is as follows:

[0034]

[0035] where x i is the vibration signal amplitude value of the i-th sampling point within the time window, n is the total number of sampling points within this window; the sampling frequency is not lower than 1000 Hz to ensure that at least 1000 effective sample points are collected within each 1-second time window, meeting the time resolution requirements; the window length is set between 1 second and 5 seconds, and the sliding step is 1 second.

[0036] It should be noted that: The on-site acoustic energy index E′ a extracted in this step a is consistent with the index E used in the modeling stage in terms of the extraction method and window definition, and only the variable symbols are distinguished due to different application stages.

[0037] The beneficial effects of this step are as follows: By performing band-pass filtering, window segmentation, and acoustic energy extraction on the distributed optical fiber vibration signals collected on-site, acoustic energy characteristic indicators of multiple perforation clusters are constructed, and the input stability of the flow rate inversion model is enhanced.

[0038] Further, in step S400, the calculation of the volume flow rate by inputting the on-site acoustic energy index into the calibration model is as follows:

[0039] Taking the on-site acoustic energy index E' corresponding to each perforation cluster position obtained in step S300 a , as the input variable, substituting it into the power-law model between the unit flow velocity and the acoustic energy index established in step S100, and calculating the unit flow velocity v of each cluster i .

[0040] To ensure the engineering feasibility of this inversion process, the required structural parameters include: the aperture d of a single hole i and the number of perforations N in each cluster i . This structural parameter information can be obtained through the following methods: Obtain the designed aperture and number of perforations in each cluster according to the perforation design construction drawings, perforating gun model, and standard parameter table; Combine the completion report and depth segmentation data to pair the structural parameters with the well depth coordinates; If further verification is required, means such as casing imaging logging and wellbore caliper analysis can be used to assist in identification.

[0041] After the structural parameters are determined, input them together with the acoustic energy index into the power-law model to calculate the unit flow velocity. The calculation formula is as follows:

[0042]

[0043] where a, b, c, and e are the coefficients obtained by simulation fitting in step S100; N i is the number of perforations in the i-th perforation cluster; v i is the unit flow velocity of the i-th perforation cluster, with the unit of m / s; d i is the aperture of the i-th perforation cluster, with the unit of mm; E' a,i is the on-site acoustic energy index of the i-th perforation cluster, with the unit of g.

[0044] Subsequently, calculate the volume flow rate Q in combination with the structural parameters of this perforation cluster i , and its calculation method is as follows:

[0045]

[0046] where Q i is the volume flow rate of the i-th perforation cluster, with the unit of m 3 / s; v i is the flow velocity of the i-th perforation cluster, with the unit of m / s; N i is the number of perforations in the i-th perforation cluster, and d i is the aperture of the i-th perforation cluster, with the unit of mm.

[0047] The beneficial effects of this step are as follows: This step combines the acoustical response indicators actually extracted on-site with the established unit flow rate prediction model, and uses the structural parameters to complete the mapping process of the unit flow rate and the volume flow rate, realizing the quantitative estimation of the volume flow rates of multiple perforation clusters. This method has clear logic and reliable parameter sources, and has strong engineering adaptability, providing a key basis for subsequent production regulation and optimization.

[0048] Further, in step S500, the output perforation cluster flow distribution map is as follows:

[0049] Normalize the volume flow rate Q of each perforation cluster calculated in step S400 i to obtain the normalized volume flow rate index Q' i , and output the result in graphical form for constructing the wellbore perforation cluster response distribution map; so that on-site operators can visually identify and judge the production contributions of different perforation clusters.

[0050] The normalized volume flow rate Q' i is calculated by normalizing with the total flow rate of the entire well section, and the formula is:

[0051]

[0052] where: Q i is the volume flow rate of the i-th perforation cluster, with the unit of m 3 / s; Q' i is the normalized flow rate value of the i-th perforation cluster for subsequent graphical display, with the unit of dimensionless ratio; j represents the number variable of each cluster participating in the summation.

[0053] The normalization result Q' i can be output in the form of a bar chart or a profile diagram. In the bar chart, the horizontal axis represents the perforation cluster number or the well depth, and the vertical axis represents the normalized flow rate value; in the profile diagram, the horizontal axis represents the well depth, and the vertical axis shows the normalized flow rate accumulated from the finger tip, reflecting the contribution degree of the production.

[0054] The beneficial effects of this step are as follows: By performing total normalization on the volume flow rate results of each perforation cluster and presenting them in the form of a bar chart or a profile diagram, the visualization expression of the contribution degrees of each perforation cluster in the wellbore is realized. The normalized flow rate index enhances the contrast and readability between different clusters, providing data support for post-fracture regulation and measure deployment.

[0055] To achieve the above object, an embodiment of the second aspect of the present invention further provides a fracture well perforation cluster flow rate inversion system based on distributed fiber optic acoustic vibration. The system includes a processor, a memory, and a computer program stored in the memory and executable on the processor, which is used to implement the steps in a fracture well perforation cluster flow rate inversion method based on distributed fiber optic acoustic vibration. The fracture well perforation cluster flow rate inversion system based on distributed fiber optic acoustic vibration runs on computing devices such as desktop computers, laptops, and palm computers.

[0056] Through the operation of this system, the inversion processing of the volume flow rates of multiple perforation clusters in a fracture well and the output of structured results can be completed. It can process and model the distributed fiber optic acoustic acquisition data to estimate the flow rates of multiple perforation clusters in the fracture wellbore, output a perforation cluster flow rate distribution map, and provide stable technical support for on-site production monitoring, productivity diagnosis, and fine regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 Shown is a flowchart of a fracture well perforation cluster flow rate inversion method based on distributed fiber optic acoustic vibration;

[0058] Figure 2 Shown is a normalized perforation cluster flow rate distribution map;

[0059] Figure 3 Shown is a structural diagram of a fracture well perforation cluster flow rate inversion system based on distributed fiber optic acoustic vibration. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0061] Figure 1 Shown is a flowchart of a fracture well perforation cluster flow rate inversion method based on distributed fiber optic acoustic vibration.

[0062] Referring to Figure 1 , the present invention provides a fracture well perforation cluster flow rate inversion method based on distributed fiber optic acoustic vibration. The method includes the following steps:

[0063] S100, establish a response calibration model between the unit flow rate and the acoustic energy index;

[0064] S200, deploy a distributed fiber optic acoustic vibration system in the fracture wellbore;

[0065] S300, acquire and process the acoustic vibration signals;

[0066] S400, input the acoustic energy index into the calibration model to calculate the volume flow rate;

[0067] S500, output the flow rate distribution map of the perforation clusters.

[0068] According to the inversion method of the embodiment of the present invention, the flow response characteristics in the fractured well can be reflected, and the volume flow rate of each perforation cluster can be quantitatively estimated.

[0069] Further, in step S100, the response calibration model between the unit flow velocity and the acoustic energy index is:

[0070] Build a wellbore simulation device in the laboratory environment, set multiple groups of combined conditions that conform to the actual working conditions to form modeling samples. The experiment is set under constant temperature and normal pressure conditions, with the temperature controlled at 25°C. The experimental liquid uses the same single-phase crude oil as on-site, and three aperture sizes (3 mm, 4 mm, 5 mm) and three perforation numbers (6 holes, 8 holes, 10 holes per cluster) are respectively set corresponding to the design of the on-site perforation clusters; to improve the stability and wide adaptability of model fitting, under each combination of aperture and hole number, set 9 different flow velocity control gears, which are 0.1 m / s, 0.3 m / s, 0.5 m / s, 0.7 m / s, 0.9 m / s, 1.1 m / s, 1.3 m / s, 1.5 m / s, and 1.7 m / s respectively, and inject liquids respectively to construct the flow field; control the flow velocity of each cluster through a variable frequency pump, and jointly monitor through a high-precision mass flowmeter and a rotor flowmeter to ensure that the set flow velocity remains constant and stable.

[0071] Under each group of experimental combined conditions, closely fix the distributed optical fiber on the outer wall of the perforation channel, and collect the external vibration signal of the channel at a sampling frequency of 1000 Hz; the collected signal is processed in the frequency domain through a band-pass filter of 20 Hz to 2000 Hz to suppress background noise and structural resonance interference; then segment and extract the signal with a 3-second time window, and slide it at a 1-second step length to extract the acoustic energy index E of this window a , and the calculation formula is as follows:

[0072]

[0073] Among them, E a is the acoustic energy index, with the unit of g; x i is the vibration signal amplitude value of the i-th sampling point within the time window, and n is the total number of sampling points within this window; in the experiment, ensure that each window contains at least 3000 effective sample points.

[0074] To ensure the representativeness of the signal, the sampling points for acoustic indicators are selected from the DAS sampling points within 1 meter above and below the center of the perforation cluster. Align the perforation cluster numbers with the sampling point numbers in combination with the well depth design or construction drawings, so that each cluster corresponds to a unique acoustic response input.

[0075] After the above processing, the extracted E a is non-linearly fitted with the unit flow velocity v, pore diameter d, and number of perforations N under this set of parameters to construct a power-law model:

[0076] v = a·(E a ) b ·(d) c ·(N) e

[0077] where a, b, c, and e are model fitting parameters; N is the number of perforations; v is the unit flow velocity with the unit of m / s; d is the pore diameter with the unit of mm.

[0078] The non-linear power-law model is non-linearly regression-fitted by the least squares method. Through the input of multiple groups of combined data, the parameters of the model are regressed, and finally the fitting parameter values are obtained as: a = 0.95, b = 1.32, c = 0.85, and e = 0.58.

[0079] Further, in step S200, the deployment of the distributed fiber optic acoustic vibration system in the fractured wellbore is as follows:

[0080] Under the condition that the completion structure of the target fractured well permits, lay the distributed fiber optic sensing optical cable along the outer wall of the wellbore tubing; the optical cable is fixed on the pipe string by means of structural adhesive bonding and clamp limiting to ensure that a stable coupling is formed between the optical cable and the metal structure under the high-temperature and high-pressure working conditions after completion, and to avoid affecting the signal quality due to gaps, floating, or loosening.

[0081] The optical cable uses a single-mode communication optical fiber, which has good anti-interference and spatial stability. The fiber optic system sets the spatial resolution to 1 meter, and the acoustic signals at each point are independently collected and analyzed, with resolvability; the signal demodulation system sets the sampling frequency to 1000 Hz to ensure that 1000 sample points are collected per second, meeting the requirements for the extraction resolution of the acoustic energy index within the time window; according to the fracturing construction drawings and the designed positions of the perforation clusters, complete the well depth calibration and fiber optic identification to ensure that the center positions of 9 perforation clusters are accurately covered by the fiber optic sampling points.

[0082] To improve the accuracy of the collected signal, the system is configured with a band-pass filter to perform frequency-domain processing on the signal, and the filtering range is from 20 Hz to 2000 Hz to effectively suppress background noise and structural resonance interference; the collected signal will be segmented and slid in a fixed time window in subsequent steps and extracted as the on-site acoustic energy index.

[0083] Further, in step S300, the obtaining and processing of the acoustic vibration signal are as follows:

[0084] During the production stage of the fractured well, using the distributed optical fiber deployed in the wellbore, acoustic signal acquisition is carried out on multiple perforation cluster areas in the wellbore. The acoustic signal is the vibration response time series data of the well sections corresponding to each perforation cluster, and the recording form is the time-varying sequence of the vibration amplitude of the optical fiber sampling points arranged along the well depth direction.

[0085] In order to extract a representative acoustic energy index signal, the following processing flow should be performed on the original DAS data: collect vibration signals on the outer wall of the wellbore through the distributed optical fiber; the signal is processed by a band-pass filter from 20 Hz to 2000 Hz to reduce the interference of background noise and structural resonance, and the sampling frequency is set to 1000 Hz, the window length is 3 seconds, and the sliding step length is 1 second to collect the on-site acoustic energy index E′ a , and the calculation formula is as follows:

[0086]

[0087] where x i is the vibration signal amplitude value of the i-th sampling point within the time window, and n is the total number of sampling points within this window.

[0088] Taking this embodiment as an example, through the fracturing construction drawings and the designed positions of the perforation clusters, obtain the well depth information of 9 perforation cluster areas in the wellbore, and in combination with the actual layout of the distributed optical fiber, determine the optical fiber sampling points within 1 meter of the center of each perforation cluster as the target sampling window, so as to obtain the on-site acoustic energy indexes E′ a,1 to E′ a,9 corresponding to the positions of 9 perforation clusters respectively, which are used as the input for the subsequent unit flow rate inversion model.

[0089] Further, in step S400, the inputting the on-site acoustic energy index into the calibration model to calculate the volume flow rate is as follows:

[0090] Take the on-site acoustic energy indexes E′ a,1 to E′ a,9 corresponding to the positions of each perforation cluster obtained in step S300 as input variables, substitute them into the power-law model between the unit flow rate and the acoustic energy index established in step S100, and calculate the unit flow rates v1 to v9 of each cluster.

[0091] According to the perforation design construction drawings, the perforating gun model and the standard parameter table, obtain the designed aperture diameters d1 to d9 of each cluster of perforations and the number of perforations N1 to N9 of each cluster; in combination with the completion report and the depth segmentation data, pair the structural parameters with the well depth coordinates.

[0092] After the structural parameters are determined, they are input into the power-law model together with the acoustic energy index to calculate the unit flow velocity. The calculation formula is as follows:

[0093]

[0094] where a, b, c, and e are the coefficients obtained by fitting in step S100; N i is the number of perforations in the i-th perforation cluster; v i is the unit flow velocity of the i-th perforation cluster, with the unit of m / s; d i is the aperture of the i-th perforation cluster, with the unit of mm; E′ a,i is the on-site acoustic energy index of the i-th perforation cluster, with the unit of g.

[0095] Subsequently, the volume flow rates Q1 to Q9 of each cluster are calculated by combining the structural parameters of the perforation cluster. The calculation method is as follows:

[0096]

[0097] where Q i is the volume flow rate of the i-th perforation cluster, with the unit of m 3 / s; v i is the flow velocity of the i-th perforation cluster, with the unit of m / s; N i is the number of perforations in the i-th perforation cluster, d i is the aperture of the i-th perforation cluster, with the unit of mm.

[0098] Furthermore, in step S500, the output perforation cluster flow rate distribution map is as follows:

[0099] The volume flow rate Q of each perforation cluster calculated in step S400 i is normalized to obtain the normalized volume flow rate index Q′ i , and the result is output in a graphical manner to construct a wellbore perforation cluster response distribution map for intuitive identification and quantitative judgment of the production contributions of different perforation clusters by on-site operators.

[0100] The normalized volume flow rate Q′ i is calculated by normalizing with the total flow rate of the entire well section. Through this calculation, the normalized volume flow rates Q′1 to Q′9 are obtained. The formula is:

[0101]

[0102] where: Q i is the volume flow rate of the i-th perforation cluster, with the unit of m 3 / s; Q′ iis the normalized flow rate value of the i-th perforation cluster, used for subsequent graphical display, with the unit of dimensionless ratio; j represents the number variable of each cluster participating in the summation.

[0103] In this embodiment, the normalized volume flow rates of 9 perforation clusters are output in the form of a bar chart according to the cluster numbers. The horizontal axis in the bar chart represents the perforation cluster numbers, and the vertical axis represents the normalized flow rate values, forming a flow rate distribution diagram of the perforation clusters, as Figure 2 shown.

[0104] The beneficial effects of the present invention are as follows: By constructing a method for inverting the volume flow rate based on the distributed fiber optic acoustic vibration response, establishing a response model between the acoustic energy index and the unit flow velocity, and combining the structural parameters such as the aperture and the number of holes of the perforation clusters, the present invention realizes the estimation of the volume flow rates in multiple perforation cluster areas in the wellbore of a fractured well. Based on the acquired acoustic response data, through experimental modeling, parameter substitution, and formula calculation, the present invention completes the normalization processing of the volume flow rates of each perforation cluster and expresses the results in a graphical manner. This method has the characteristics of clear structural parameters, a closed-loop modeling logic, and traceable data sources, and is applicable to the scenario of fractured wells with clear wellbore structures and stable fluid conditions for the analysis and identification of the flow rate distribution of perforation clusters.

[0105] Figure 3 shown is the structural diagram of the flow rate inversion system for perforation clusters in a fractured well based on distributed fiber optic acoustic vibration.

[0106] Referring to Figure 3 , the present invention also proposes a flow rate inversion system 30 for perforation clusters in a fractured well based on distributed fiber optic acoustic vibration. The flow rate inversion system 30 for perforation clusters in a fractured well based on distributed fiber optic acoustic vibration includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, which is used to implement the steps in a method for inverting the flow rate of perforation clusters in a fractured well based on distributed fiber optic acoustic vibration. The flow rate inversion system 30 for perforation clusters in a fractured well based on distributed fiber optic acoustic vibration runs on computing devices such as desktop computers, laptops, and palm computers.

[0107] The system includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. The processor executes the computer program and runs in the following units of the inversion system:

[0108] An experimental modeling unit 301, which is used to collect the response data of the perforation model under multiple flow velocity conditions under laboratory conditions and construct a response relationship model between the unit flow velocity and the acoustic energy index;

[0109] A data acquisition unit 302, which is used to collect on-site distributed fiber optic acoustic vibration data during the fracturing construction stage;

[0110] The feature extraction unit 303 is configured to perform time-window segmentation, frequency-domain filtering, and index calculation on the acoustic data to extract the acoustic energy index of each perforation cluster.

[0111] The structural parameter identification unit 304 is configured to determine the structural parameters of each perforation cluster, including the aperture and the number of holes, by combining information such as the perforation construction design drawing and the well depth measurement data.

[0112] The flow velocity prediction unit 305 is configured to substitute the acoustic indexes of each cluster into the power-law fitting model established by the experimental modeling unit 301 to predict the unit flow velocity.

[0113] The volume flow rate calculation unit 306 is configured to calculate the volume flow rate of each perforation cluster by combining the structural parameters.

[0114] The normalization analysis unit 307 is configured to perform normalization processing on the volume flow rates of each perforation cluster to generate a normalization index.

[0115] The graphic output unit 308 is configured to output the normalized volume flow rate in the form of a bar chart to assist the user in discriminating the response differences of each production interval.

[0116] The perforation cluster flow rate inversion system 30 based on distributed fiber optic acoustic vibration can operate in computing devices such as desktop computers, laptops, and palm computers. Those skilled in the art should understand that the above schematic description of the system structure is only one embodiment of the present invention and does not constitute a limitation on the system structure. The inversion system may include more or fewer components or implement its corresponding functions in other forms. For example, the system may further include a visualization interface, an acoustic signal acquisition front end, a network communication module, etc.

[0117] By executing the perforation cluster flow rate inversion method based on distributed fiber optic acoustic vibration proposed by the present invention through the perforation cluster flow rate inversion system 30 based on distributed fiber optic acoustic vibration, the distributed fiber optic acoustic vibration data can be converted into the volume flow rate indexes of each perforation cluster in the wellbore, and the quantitative estimation and visualization output of the volume flow rate of each cluster can be realized by combining the structural parameters. This system helps on-site technicians identify the differences between clusters and assist in subsequent production adjustments.

[0118] It should be understood that each component of the present invention can be implemented by hardware, software, firmware, or any combination thereof. In the above embodiments, multiple steps or methods can be executed by computer program instructions on a processor or completed by dedicated logic circuits. The above implementation should not be construed as the only limitation on the implementation.

[0119] The term "one embodiment" used in this specification is only used to illustrate the implementation form of the technical solution of the present invention and should not be construed as the only limitation on the embodiment unless otherwise clearly stated.

[0120] The terms "first", "second", etc. used in the specification are for distinguishing purposes only, to indicate different elements or steps, and do not represent limitations in structure or order, nor do they indicate any technical priority.

[0121] The directional terms such as "up", "down", "front", "back", "inside", "outside", etc. in this specification are only for structural description and can be adjusted according to different scenarios in actual applications, and should not be regarded as limitations on spatial directions.

[0122] Unless otherwise specified, the structures, functions or parameters described in this specification can be replaced, combined or omitted according to specific needs. Without departing from the basic concept of the present invention, those skilled in the art can make equivalent deformations or adjustments to them, and these deformations and adjustments should fall within the protection scope of the present invention.

Claims

1. A method for inverse flow rate of perforation clusters in a fractured well based on distributed fiber optic acoustic vibration, characterized in that The method includes the following steps: S100. Establish a response calibration model between the unit flow rate and the acoustic energy index: The calibration model constructs a wellbore simulation device under a controlled experimental environment, sets different combinations of experimental parameters to form multiple modeling conditions; among them, the combinations of experimental parameters include different perforation aperture sizes, different numbers of perforations, and single-phase liquid types. Vibration signals are collected from the outer wall of the wellbore tubing through a distributed optical fiber. The signals are processed by a band-pass filter of 20 Hz to 2000 Hz, and the acoustic energy index E of each group of signals is extracted using a fixed time window. a , and the calculation formula is as follows: Among them, E a is the acoustic energy index, with the unit of g; x i is the vibration signal amplitude value of the i-th sampling point within the time window, and n is the total number of sampling points within this window; the sampling frequency is not less than 1000 Hz to ensure that at least 1000 effective sample points are collected within each 1-second time window, meeting the time resolution requirements; the window length is set between 1 second and 5 seconds, and the sliding step is 1 second; After the above treatment, the extracted E a is nonlinearly fitted with the unit flow velocity v, pore diameter d, and perforation number N under this set of parameters to construct a power-law model: v = a·(E a ) b ·(d) c ·(N) e Wherein, a, b, c, and e are model fitting parameters; N is the number of perforations; v is the unit flow velocity, with the unit of m / s; d is the aperture, with the unit of mm; The non-linear power-law model is non-linearly regression-fitted by the least squares method. Through the input of multiple sets of combined data, parameter regression of the model is performed to obtain the parameter values of a, b, c, and e; S200, Deploy a distributed fiber optic acoustic vibration system in the fracturing wellbore: The distributed fiber optic acoustic vibration system arranges a fiber optic sensing cable on the outer wall of the tubing. The spatial interval between each sampling point of the fiber optic system is not greater than 1 meter, and the sampling frequency of the demodulation device is set to 1000 Hz or above to meet the requirements of the time resolution for the calculation of the acoustic energy index and ensure the stable coupling between the cable and the pipe string, and collect the acoustic vibration signals in the wellbore; S300, Obtain and process the acoustic vibration signals: Using the distributed fiber optic deployed in the wellbore, acoustic signal acquisition is performed on multiple perforation cluster regions in the wellbore. The acoustic signals are the vibration response time series data of the corresponding well sections of each perforation cluster, and the recording form is the sequence of the vibration amplitude changing with time of the fiber optic sampling points arranged along the well depth direction; The signal is processed by band-pass filtering from 20 Hz to 2000 Hz, and the acoustic energy index of each group of signals is extracted using a fixed time window, denoted as the on-site acoustic energy index E'. a , and the calculation formula is as follows: where x i is the amplitude value of the vibration signal at the i-th sampling point within the time window, and n is the total number of sampling points within the window; the sampling frequency is not less than 1000 Hz to ensure that at least 1000 effective sample points are collected within each 1-second time window, meeting the time resolution requirements; the window length is set between 1 second and 5 seconds, and the sliding step size is 1 second; S400, Input the on-site acoustic energy index into the calibration model to calculate the volume flow rate: Combining the structural parameters of the perforation clusters, input them together with the on-site acoustic energy index into the power-law model to calculate the unit velocity. The calculation formula is as follows: Among them, a, b, c, and e are coefficients obtained by fitting in step S100; N i is the number of perforations in the i-th perforation cluster; v i is the unit flow velocity of the i-th perforation cluster, with the unit of m / s; d i is the aperture of the i-th perforation cluster, with the unit of mm; E′ a,i is the on-site acoustic energy index of the i-th perforation cluster, with the unit of g; Subsequently, calculate the volumetric flow rate Q in combination with the structural parameters of the perforation cluster i , and its calculation method is as follows: Among them, Q i is the volumetric flow rate of the i-th perforation cluster, with the unit of m 3 / s; v i is the flow velocity of the i-th perforation cluster, with the unit of m / s; N i is the number of perforations of the i-th perforation cluster, d i is the aperture of the i-th perforation cluster, with the unit of mm; S500, Output the perforation cluster flow distribution diagram: For the volumetric flow rate Q of each perforation cluster calculated in step S400 i perform normalization to obtain the normalized volumetric flow rate index Q' i , where the normalized volumetric flow rate Q' i is calculated by normalizing with the total flow rate of the entire well section, and the formula is: Where: Q i is the volumetric flow rate of the i-th perforation cluster, with the unit of m 3 / s; Q′ i is the normalized flow rate value of the i-th perforation cluster, used for subsequent graphical display, with the unit of dimensionless ratio; j represents the number variable of each cluster participating in the summation; the normalized result Q′ i can be output in the form of a bar chart or a sectional view.

2. A fracture well perforation cluster flow rate inversion system based on distributed fiber optic acoustic vibration, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it is used to implement each method step described in claim 1.

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