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

Abstract

The application discloses a fracturing well perforation cluster flow inversion method and system based on distributed optical fiber acoustic vibration, and comprises the following steps: a response calibration model between unit flow rate and an acoustic energy index is established; a distributed optical fiber acoustic vibration system is arranged in a fracturing well wellbore; acoustic vibration signals are acquired and processed; the acoustic energy index is input into the calibration model to calculate volumetric flow; and a perforation cluster flow distribution map is output. According to the method, inversion processing and result output of volumetric flow of multiple perforation clusters in the fracturing well wellbore can be realized, estimation of the volumetric flow of the multiple perforation clusters in the fracturing well wellbore is realized, 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] This invention relates to a method and system for inverting the flow rate of perforation clusters in fractured wells based on distributed optical fiber acoustic vibration, belonging to the field of oil and gas reservoir development technology. Background Technology

[0002] Currently, distributed fiber optic acoustic vibration technology is being gradually adopted in oil and gas engineering due to its advantages such as full well coverage, large data acquisition volume, and passive monitoring. By deploying optical fibers along the wellbore and combining them with ground demodulation devices, acoustic vibration response sensing of the location of perforation clusters inside the wellbore can be achieved, providing technical support for monitoring fractured wells. With the gradual deployment of distributed fiber optic acoustic vibration systems, their potential for perforation cluster assessment during production is becoming increasingly prominent.

[0003] The widespread application of distributed fiber optic acoustic vibration systems in fracturing well monitoring has also brought new challenges. Due to the weak energy of the wellbore acoustic source and the low signal-to-noise ratio of the acoustic signal, current methods mainly rely on manual experience or qualitative trend analysis, and it is not yet possible to achieve quantitative inversion of perforation cluster flow rate based on acoustic signals.

[0004] Therefore, a method and system for inverting the flow rate of perforation clusters in fractured wells based on distributed optical fiber acoustic vibration is proposed. By combining experimental modeling and system deployment, a mapping relationship between unit flow velocity and acoustic energy is constructed to estimate the flow rate of multi-perforation clusters in the production stage of fractured wells, providing reliable data support for the dynamic management of reservoir development. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for inverting the flow rate of perforation clusters in fractured wells based on distributed optical fiber acoustic vibration. This method is used for distributed optical fiber vibration data processing during the production stage of fractured wells, enabling the identification and quantitative inversion of the volumetric flow rate of multiple perforation clusters within the wellbore. It overcomes the technical limitations of existing distributed optical fiber acoustic vibration systems in terms of signal modeling, inversion methods, and perforation cluster contribution identification.

[0006] To achieve the above objectives, a first aspect of the present invention proposes a method for inverting the flow rate of a perforation cluster in a fracturing well based on distributed optical fiber acoustic vibration, the method comprising the following steps:

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

[0008] S200 deploys a distributed fiber optic acoustic vibration system in the wellbore of a fractured well;

[0009] S300 acquires and processes acoustic vibration signals;

[0010] S400: Input acoustic energy parameters into the calibration model to calculate volumetric flow rate;

[0011] S500, output perforation cluster flow distribution diagram.

[0012] The inversion method according to embodiments of the present invention can reflect the acoustic response characteristics of a fractured well and quantitatively estimate the volumetric flow rate of each perforation cluster.

[0013] Furthermore, in step S100, the establishment of the response calibration model between unit flow velocity and acoustic energy index is as follows:

[0014] A wellbore simulation device was constructed under controlled experimental conditions. Multiple sets of experimental parameters for structural and fluid conditions were set to form multiple modeling samples. The experimental parameters included: different perforation diameters (e.g., 3 mm, 4 mm, 5 mm), different perforation numbers (e.g., 6, 8, 10 per cluster), and single-phase liquid types (e.g., water, 1% polymer liquid, crude oil, etc.). The flow rate setting should be adjustable, and the temperature and pressure conditions should be kept stable.

[0015] Under each set of parameter combinations, vibration signals were acquired from the outer wall of the wellbore tubing via distributed optical fiber. The signals were processed by a 20Hz to 2000Hz bandpass filter to reduce interference from background noise and structural resonance. The acoustic energy index E of each set of signals was extracted using a fixed time window. a The calculation formula is as follows:

[0016]

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

[0018] To ensure signal representativeness, it is recommended that the sampling points for each acoustic index be selected from DAS sampling points within 1 meter above and below the center of the perforation cluster. The perforation cluster and sampling point numbers should be aligned in conjunction 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 A power-law model is constructed by nonlinearly fitting the unit flow velocity v, orifice diameter d, and number of perforations N under this set of parameters:

[0020] v=a·(E a ) b ·(d) c ·(N) e

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

[0022] The nonlinear power-law model is fitted using the least squares method for nonlinear regression. By inputting multiple sets of combined data, the model is subjected to parameter regression to obtain the parameter values ​​of a, b, c, and e.

[0023] The model is applicable under the following conditions: the fluid in the well section is a single-phase liquid, and the combination of structural parameters in the field is within the modeling sample space or its acceptable extrapolation range; the 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, and has flexible structural expansion capabilities.

[0024] The beneficial effects of this step are as follows: by constructing a mathematical mapping relationship between unit flow velocity and vibration response under controlled conditions, and combining it with structural parameters to form a prediction model, the method enhances the adaptability and interpretability of acoustic energy indicators; this step provides a high-precision, deployable, and engineering-promotable core modeling foundation for subsequent flow inversion.

[0025] Furthermore, in step S200, deploying the distributed fiber optic acoustic vibration system in the fracturing wellbore is as follows:

[0026] Where the well completion structure of the fracturing well allows, distributed optical fiber sensing cables are laid on the outer wall of the well tubing. The cables can be mechanically fixed to the tubing using methods such as bonding, grooving, or wrapping to ensure effective coupling between the cables and the metal structure under high temperature and high pressure after well completion, and to avoid signal loss due to gaps, floating, or loosening.

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

[0028] The spatial interval between each sampling point of the fiber optic acoustic vibration system is no more than 1 meter, which is used to ensure that the vibration response of different perforation clusters is independent and distinguishable; the demodulation equipment adopts a distributed acoustic demodulator with a sampling frequency of 1000Hz or higher to meet the time resolution requirements for acoustic energy index calculation.

[0029] To avoid aliasing, excessive bending and uneven attachment should be minimized during optical cable laying. If necessary, sleeve scanning can be performed to assist in confirming the coupling status. The optical fiber laying data output in this step, along with the original vibration signal provided by the demodulation acquisition system, will serve as the original input basis for the acoustic energy index of each cluster in the subsequent steps.

[0030] The beneficial effects of this step are as follows: By precisely deploying distributed optical fibers inside the fracturing well and optimizing the coupling strength between them and the tubing string through bonding or wrapping, combined with a demodulation system featuring high spatial resolution (not exceeding 1 meter) and high sampling frequency (≥1000Hz), stable acquisition of the acoustic response of multiple perforation cluster regions in the wellbore is achieved. This deployment scheme ensures that each perforation cluster location has an independent, continuous, and accurate vibration observation basis, providing a high-fidelity, low-interference data source for subsequent extraction of acoustic energy indicators and perforation cluster volumetric flow rate inversion, significantly improving the response stability and field adaptability of the entire system.

[0031] Furthermore, in step S300, acquiring and processing the acoustic vibration signal involves:

[0032] During the production stage of a fracturing well, distributed optical fibers deployed in the wellbore are used to collect acoustic signals from multiple perforation clusters within the wellbore. The acoustic signals are vibration response time series data of each well section corresponding to each perforation cluster, and are recorded as a sequence of vibration amplitude changes over time at optical fiber sampling points deployed along the well depth direction.

[0033] To extract representative acoustic energy index signals, the raw DAS data should undergo the following processing steps: Vibration signals from the outer wall of the wellbore are acquired via distributed optical fiber. The signals are then processed using a 20Hz to 2000Hz bandpass filter to reduce interference from background noise and structural resonance. The acoustic energy index of each signal group is extracted using a fixed time window and denoted as the on-site acoustic energy index E′. a The calculation formula is as follows:

[0034]

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

[0036] It should be noted that the on-site acoustic energy index E′ extracted in this step a The indicator E used in the modeling phase a The extraction method and window definition remain consistent, with the only difference being in the variable symbols due to different application stages.

[0037] The beneficial effects of this step are as follows: by performing bandpass 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 velocity inversion model is enhanced.

[0038] Furthermore, in step S400, the on-site acoustic energy index is input into the calibration model to calculate the volumetric flow rate as follows:

[0039] The on-site acoustic energy index E′ corresponding to each perforation cluster location obtained in step S300 is... a As an input variable, substitute it into the power-law model between unit flow velocity and acoustic energy index established in step S100 to calculate 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 per cluster N i The structural parameter information can be obtained in the following ways: based on the perforation design and construction drawings, perforation gun model and standard parameter table, obtain the design diameter and number of perforations in each cluster; combine the well completion report and depth segmentation data to match the structural parameters with the well depth coordinates; if further verification is required, casing imaging logging, wellbore diameter analysis and other methods can be used to assist in identification.

[0041] Once the structural parameters are defined, they are input along 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 from modeling and fitting in step S100; N i v represents the number of perforations in the i-th perforation cluster; i d represents the unit flow velocity of the i-th perforation cluster, in m / s; i E′ represents the aperture diameter of the i-th perforation cluster, in mm. a,i Let be the in-situ acoustic energy index of the i-th perforation cluster, in g.

[0044] Subsequently, the volumetric flow rate Q was calculated based on the structural parameters of the perforation cluster. i The calculation method is as follows:

[0045]

[0046] Among them, Q i Let m be the volumetric flow rate of the i-th perforation cluster. 3 / s;v i N represents the flow velocity of the i-th perforation cluster, in m / s. i Let d be the number of perforations in the i-th perforation cluster. i Let be the aperture diameter of the i-th perforation cluster, in mm.

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

[0048] Furthermore, in step S500, the output perforation cluster flow rate distribution diagram is as follows:

[0049] The volumetric flow rate Q of each perforation cluster calculated in step S400 i Normalization is performed to obtain the normalized volumetric flow rate index Q′. i The results are then output graphically to construct a wellbore perforation cluster response distribution map, enabling field operators to intuitively identify and judge the production contribution of different perforation clusters.

[0050] The normalized volumetric flow rate Q′ i It is calculated by normalizing the total flow rate of the entire well section, using the following formula:

[0051]

[0052] Among them: Q i Let m be the volumetric flow rate of the i-th perforation cluster. 3 / s;Q′ i is the normalized flow rate value of the i-th perforation cluster, used for subsequent graphical display, in dimensionless proportion; j represents the number variable of each cluster participating in the summation.

[0053] The normalization result Q′ i Output can be in the form of bar charts or profiles. In a bar chart, the horizontal axis represents the perforation cluster number or well depth, and the vertical axis represents the normalized flow rate value. In a profile, the horizontal axis represents the well depth, and the vertical axis displays the normalized flow rate accumulated from the fingertip, reflecting the degree of contribution to production.

[0054] The beneficial effects of this step are as follows: by normalizing the total volumetric flow rate of each perforation cluster and presenting it in the form of bar charts or profiles, the contribution of each perforation cluster in the wellbore is visualized. The normalized flow rate index enhances the comparability and readability between different clusters, providing data support for post-pressure control and measure deployment.

[0055] To achieve the above objectives, a second aspect of the present invention also proposes a flow inversion system for perforation clusters in fractured wells based on distributed optical fiber acoustic vibration. The system includes a processor, a memory, and a computer program stored in the memory and executable on the processor, for implementing the steps in a flow inversion method for perforation clusters in fractured wells based on distributed optical fiber acoustic vibration. The flow inversion system for perforation clusters in fractured wells based on distributed optical fiber acoustic vibration runs on a desktop computer, laptop, or handheld computer.

[0056] The system can perform inversion processing and structured output of the volumetric flow rate of multiple perforation clusters in a fractured well. It can also process and model distributed fiber acoustic acquisition data to estimate the flow rate of multiple perforation clusters in the wellbore and output a flow rate distribution map of the perforation clusters, providing stable technical support for on-site production monitoring, capacity diagnosis and fine control. Attached Figure Description

[0057] Figure 1 The diagram shows a flowchart of a method for inverting the flow rate of a perforation cluster in a fracturing well based on distributed optical fiber acoustic vibration.

[0058] Figure 2 The diagram shows the normalized flow rate distribution of the perforation cluster.

[0059] Figure 3 The diagram shows the structure of a perforation cluster flow inversion system for fractured wells based on distributed optical fiber acoustic vibration. Detailed Implementation

[0060] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein 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 with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0061] Figure 1 The diagram shows a flowchart of a flow inversion method for perforation clusters in fracturing wells based on distributed optical fiber acoustic vibration.

[0062] Reference Figure 1 This invention proposes a method for inverting the flow rate of perforation clusters in fracturing wells based on distributed optical fiber acoustic vibration. The method includes the following steps:

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

[0064] S200 deploys a distributed fiber optic acoustic vibration system in the wellbore of a fractured well;

[0065] S300 acquires and processes acoustic vibration signals;

[0066] S400: Input acoustic energy parameters into the calibration model to calculate volumetric flow rate;

[0067] S500, output perforation cluster flow distribution diagram.

[0068] The inversion method according to embodiments of the present invention can reflect the flow response characteristics within a fractured well and quantitatively estimate the volumetric flow rate of each perforation cluster.

[0069] Furthermore, in step S100, the establishment of the response calibration model between unit flow velocity and acoustic energy index is as follows:

[0070] A wellbore simulation device was built in a laboratory environment, and multiple combinations of conditions consistent with actual working conditions were set to form modeling samples. The experiment was conducted under constant temperature and pressure conditions, with the temperature controlled at 25℃. The experimental liquid used was the same single-phase crude oil as that used in the field, and three different perforation sizes (3 mm, 4 mm, and 5 mm) and three different perforation numbers (6, 8, and 10 per cluster) were set to correspond to the design of the perforation clusters in the field. To improve the stability and adaptability of the model fitting, nine different flow rate control levels were set for each combination of perforation size and number: 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. Liquid was injected at these levels to construct the flow field. The flow rate of each cluster was controlled by a variable frequency pump, and monitored jointly by a high-precision mass flow meter and a rotor flow meter to ensure that the set flow rate remained constant and stable.

[0071] Under each experimental combination, the distributed optical fiber was fixed tightly to the outer wall of the perforation channel, and the vibration signal outside the channel was acquired using a sampling frequency of 1000 Hz. The acquired signal was processed in the frequency domain using a bandpass filter from 20 Hz to 2000 Hz to suppress background noise and structural resonance interference. Subsequently, the signal was segmented and extracted in a 3-second time window, with a sliding step of 1 second, to extract the acoustic energy index E within that window. a The calculation formula is as follows:

[0072]

[0073] Among them, E a The acoustic energy index, measured in grams (g); x i Let be the vibration signal amplitude value at the i-th sampling point within the time window, and n be the total number of sampling points within the window; in the experiment, it is ensured that each window contains at least 3000 valid sample points.

[0074] To ensure signal representativeness, the sampling points for acoustic indicators are selected from DAS sampling points within 1 meter above and below the center of the perforation cluster. The perforation cluster and sampling point numbers are aligned in conjunction 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 A power-law model is constructed by nonlinearly fitting the unit flow velocity v, orifice diameter d, and number of perforations N under this set of parameters:

[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 in m / s; and d is the orifice diameter in mm.

[0078] The nonlinear power-law model is fitted using the least squares method for nonlinear regression. By inputting multiple sets of combined data, the model is subjected to parameter regression, and the final fitted parameter values ​​are: a = 0.95, b = 1.32, c = 0.85 and e = 0.58.

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

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

[0081] The optical cable uses single-mode communication optical fiber, which has good anti-interference and spatial stability. The optical fiber system is set with a spatial resolution of 1 meter, and the acoustic signals at each point are independently collected and analyzed, which is distinguishable. The signal demodulation system is set with a sampling frequency of 1000Hz to ensure that 1000 sample points are collected per second, which meets the requirements for the extraction resolution of acoustic energy indicators within the time window. Based on the fracturing construction drawings and the design position of the perforation cluster, the well depth is calibrated and the optical fiber is marked to ensure that the center position of the 9 perforation clusters is accurately covered by the optical fiber sampling points.

[0082] To improve the accuracy of the acquired signals, the system is equipped with a bandpass filter to perform frequency domain processing on the signals, with a filtering range of 20Hz to 2000Hz, effectively suppressing background noise and structural resonance interference. The acquired signals will be segmented into fixed time windows and processed in subsequent steps, and extracted as an on-site acoustic energy index.

[0083] Furthermore, in step S300, acquiring and processing the acoustic vibration signal involves:

[0084] During the production stage of a fracturing well, distributed optical fibers deployed in the wellbore are used to collect acoustic signals from multiple perforation clusters within the wellbore. The acoustic signals are vibration response time series data of each well section corresponding to each perforation cluster, and are recorded as a sequence of vibration amplitude changes over time at optical fiber sampling points deployed along the well depth direction.

[0085] To extract representative acoustic energy index signals, the raw DAS data should undergo the following processing steps: Vibration signals are acquired from the outer wall of the wellbore via distributed optical fiber; the signals are then bandpass filtered from 20Hz to 2000Hz to reduce interference from background noise and structural resonance; the sampling frequency is set to 1000Hz, the window length to 3 seconds, and the sliding step to 1 second; the acoustic energy index E′ at the site is then acquired. a The calculation formula is as follows:

[0086]

[0087] Where, x i Let be the vibration signal amplitude value at the i-th sampling point within the time window, and n be the total number of sampling points within the window.

[0088] Taking this embodiment as an example, the well depth information of the nine perforation cluster areas in the wellbore is obtained by using the fracturing construction drawings and the designed locations of the perforation clusters. Combined with the actual deployment of distributed optical fibers, the optical fiber sampling points within 1 meter of the center of each perforation cluster are determined as the target sampling window, thereby obtaining the on-site acoustic energy index E′ corresponding to the locations of the nine perforation clusters. a,1 To E′ a,9 This is used as input for the subsequent unit flow velocity inversion model.

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

[0090] The on-site acoustic energy index E′ corresponding to each perforation cluster location obtained in step S300 is... a,1 To E′ a,9 As input variables, the power-law model between unit flow velocity and acoustic energy index established in step S100 is substituted into the model to calculate the unit flow velocity v1 to v9 for each cluster.

[0091] Based on the perforation design and construction drawings, perforation gun model and standard parameter table, obtain the design diameter d1 to d9 of each perforation cluster and the number of perforations N1 to N9 of each perforation cluster; combine the well completion report and depth segmentation data to match the structural parameters with the well depth coordinates.

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

[0093]

[0094] Where a, b, c, and e are the coefficients obtained from modeling and fitting in step S100; N i v represents the number of perforations in the i-th perforation cluster; i d represents the unit flow velocity of the i-th perforation cluster, in m / s; i E′ represents the aperture diameter of the i-th perforation cluster, in mm. a,i Let be the in-situ acoustic energy index of the i-th perforation cluster, in g.

[0095] Subsequently, the volumetric flow rates Q1 to Q9 of each perforation cluster were calculated based on the structural parameters of the cluster, as follows:

[0096]

[0097] Among them, Q i Let m be the volumetric flow rate of the i-th perforation cluster. 3 / s;v i N represents the flow velocity of the i-th perforation cluster, in m / s. i Let d be the number of perforations in the i-th perforation cluster. i Let be the aperture diameter of the i-th perforation cluster, in mm.

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

[0099] The volumetric flow rate Q of each perforation cluster calculated in step S400 i Normalization is performed to obtain the normalized volumetric flow rate index Q′. i The results are then output graphically to construct a wellbore perforation cluster response distribution map, enabling field operators to intuitively identify and quantitatively assess the production contribution of different perforation clusters.

[0100] The normalized volumetric flow rate Q′ i The normalized volumetric flow rates Q′1 to Q′9 are obtained through normalization calculations based on the total flow rate of the entire well section, using the following formulas:

[0101]

[0102] Among them: Q i Let m be the volumetric flow rate of the i-th perforation cluster. 3 / s;Q′ iis the normalized flow rate value of the i-th perforation cluster, used for subsequent graphical display, in dimensionless proportion; j represents the number variable of each cluster participating in the summation.

[0103] In this embodiment, the normalized volumetric flow rates of the nine perforation clusters are output in the form of a bar chart according to the cluster number. The horizontal axis of the bar chart represents the perforation cluster number, and the vertical axis represents the normalized flow rate value, forming a perforation cluster flow rate distribution map, as shown below. Figure 2 As shown.

[0104] The beneficial effects of this invention are as follows: This invention constructs a volumetric flow rate inversion method based on distributed fiber optic acoustic vibration response, establishes a response model between acoustic energy indicators and unit flow velocity, and combines structural parameters such as the aperture and number of perforation clusters to estimate the volumetric flow rate of multiple perforation cluster regions within a fracturing wellbore. Based on the acquired acoustic response data, the method performs experimental modeling, parameter substitution, and formula calculation to normalize the volumetric flow rate of each perforation cluster, and presents the results graphically. This method features clearly defined structural parameters, a closed-loop modeling logic, and traceable data sources, making it suitable for fracturing well scenarios with clear wellbore structures and stable fluid conditions, for the analysis and identification of perforation cluster flow rate distribution.

[0105] Figure 3 The diagram shows the structure of a perforation cluster flow inversion system for fractured wells based on distributed optical fiber acoustic vibration.

[0106] Reference Figure 3 The present invention also proposes a flow inversion system 30 for perforation clusters in fractured wells based on distributed optical fiber acoustic vibration. The flow inversion system 30 for perforation clusters in fractured wells based on distributed optical fiber acoustic vibration includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, for implementing the steps in a flow inversion method for perforation clusters in fractured wells based on distributed optical fiber acoustic vibration. The flow inversion system 30 for perforation clusters in fractured wells based on distributed optical fiber acoustic vibration runs on a desktop computer, laptop, or handheld computer.

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

[0108] Experimental modeling unit 301 is used to collect perforation model response data under multiple flow rate conditions in the laboratory and to construct a response relationship model between unit flow rate and acoustic energy index.

[0109] Data acquisition unit 302 is used to acquire distributed fiber optic acoustic vibration data on site during the fracturing construction phase;

[0110] The feature extraction unit 303 is used 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 used to determine the structural parameters of each perforation cluster, including the diameter and number of holes, by combining information such as perforation construction design drawings and well depth measurement data.

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

[0113] Volumetric flow rate calculation unit 306 is used to calculate the volumetric flow rate of each perforation cluster in combination with structural parameters;

[0114] The normalization analysis unit 307 is used to normalize the volumetric flow rate of each perforation cluster and generate normalized indices.

[0115] The graphic output unit 308 is used to output the normalized volumetric flow rate in the form of a bar chart to help users identify the response differences of each production layer.

[0116] The fracturing well perforation cluster flow inversion system 30 based on distributed fiber acoustic vibration can run on desktop computers, laptops, and handheld computers. Those skilled in the art should understand that the above schematic description of the system structure is merely 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 also include a visualization interface, an acoustic signal acquisition front-end, a network communication module, etc.

[0117] By implementing the distributed fiber optic acoustic vibration-based perforation cluster flow inversion system 30, the flow inversion method for fractured wells based on distributed fiber optic acoustic vibration proposed in this invention can be executed. Distributed fiber optic acoustic vibration data can be converted into volumetric flow rate indicators for each perforation cluster within the wellbore. Combined with structural parameters, this allows for quantitative estimation and visualization of the volumetric flow rate of each cluster. This system helps field technicians identify differences between clusters and assists in subsequent production adjustments.

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

[0119] The terms "an embodiment" and so on used in this specification are only used to describe the implementation of the technical solution of the present invention and should not be construed as the sole limitation on the described implementation unless otherwise expressly stated.

[0120] The terms “first”, “second”, etc. used in this specification are for distinguishing purposes only and are used to indicate different elements or steps. They do not represent structural or sequential limitations, nor do they indicate any technical priority.

[0121] The directional terms used in this manual, such as "up," "down," "front," "back," "inner," and "outer," are only for structural description and can be adjusted according to different scenarios in actual applications. They should not be regarded as restrictions on spatial direction.

[0122] Unless otherwise specified, the structures, functions or parameters described in this specification may be replaced, combined or omitted as needed. Without departing from the basic concept of this invention, those skilled in the art can make equivalent modifications or adjustments, and such modifications and adjustments should fall within the protection scope of this invention.

Claims

1. A method for flow inversion of perforation clusters in fracturing wells based on distributed optical fiber acoustic vibration, characterized in that, The method includes the following steps: S100, Establish a response calibration model between unit flow velocity and acoustic energy index: The calibration model is constructed under controlled experimental conditions using a wellbore simulation device, setting different combinations of experimental parameters to form multiple sets of modeling conditions; wherein, the experimental parameter combinations include different perforation diameters, different perforation numbers, and single-phase liquid types, vibration signals are acquired from the outer wall of the wellbore tubing via distributed optical fiber, the signals are processed by bandpass filtering from 20Hz to 2000Hz, and the acoustic energy index E of each set of signals is extracted using a fixed time window. a The calculation formula is as follows: Among them, E a The acoustic energy index, measured in grams (g); x i Let n be the vibration signal amplitude value at the i-th sampling point within the time window, and n be the total number of sampling points within the window; the sampling frequency is not less than 1000Hz to ensure that at least 1000 valid 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; After the above processing, the extracted E a A power-law model is constructed by nonlinearly fitting the unit flow velocity v, orifice diameter d, and number of perforations N under this set of parameters: v=a·(E a ) b ·(d) c ·(N) e Where a, b, c, and e are model fitting parameters; N is the number of perforations; v is the unit flow velocity in m / s; and d is the orifice diameter in mm. The nonlinear power law model is fitted by nonlinear regression using the least squares method. By inputting multiple sets of combined data, the model is regressed to obtain the parameter values ​​of a, b, c, and e. S200, Deploy a distributed fiber optic acoustic vibration system in the wellbore of a fractured well: The distributed fiber optic acoustic vibration system lays fiber optic sensing cables on the outer wall of the tubing. The spatial interval between each sampling point of the fiber optic system is no more than 1 meter. The demodulation equipment sampling frequency is set to 1000Hz or higher to meet the time resolution requirements for acoustic energy index calculation, ensure stable coupling between the fiber optic cable and the tubing string, and collect acoustic vibration signals in the wellbore. S300, Acquire and process acoustic vibration signals: Using distributed optical fibers deployed in the wellbore, acoustic signals are acquired from multiple perforation cluster areas within the wellbore. The acoustic signals are vibration response time series data of each perforation cluster corresponding to the well section, and are recorded in the form of a time sequence of vibration amplitude changes at optical fiber sampling points deployed along the well depth direction. The signal is processed by bandpass filtering from 20Hz to 2000Hz, 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 The calculation formula is as follows: Where, x i Let n be the vibration signal amplitude value at the i-th sampling point within the time window, and n be the total number of sampling points within the window; the sampling frequency is not less than 1000Hz to ensure that at least 1000 valid 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; S400: Input the on-site acoustic energy parameters into the calibration model to calculate the volumetric flow rate: Combine the structural parameters of the perforation cluster with the on-site acoustic energy parameters and input them together into the power-law model to calculate the unit velocity. The calculation formula is as follows: Where a, b, c, and e are the coefficients obtained from modeling and fitting in step S100; N i v represents the number of perforations in the i-th perforation cluster; i d represents the unit flow velocity of the i-th perforation cluster, in m / s; i E′ represents the aperture diameter of the i-th perforation cluster, in mm. a,i Let be the in-situ acoustic energy index of the i-th perforation cluster, in g; Subsequently, the volumetric flow rate Q was calculated based on the structural parameters of the perforation cluster. i The calculation method is as follows: Among them, Q i Let m be the volumetric flow rate of the i-th perforation cluster. 3 / s;v i N represents the flow velocity of the i-th perforation cluster, in m / s. i Let d be the number of perforations in the i-th perforation cluster. i Let be the aperture diameter of the i-th perforation cluster, in mm; S500, Output perforation cluster flow distribution map: This maps the volumetric flow rate Q of each perforation cluster calculated in step S400. i Normalization is performed to obtain the normalized volumetric flow rate index Q′. i The normalized volumetric flow rate Q′ i It is calculated by normalizing the total flow rate of the entire well section, using the following formula: Among them: Q i Let m be the volumetric flow rate of the i-th perforation cluster. 3 / s;Q′ i Let be the normalized flow rate value of the i-th perforation cluster, used for subsequent graphical display, in dimensionless scale; j represents the index variable of each cluster involved in the summation; the normalized result Q′ i Output in the form of bar charts or cross-sectional views.

2. A flow rate inversion system for perforation clusters in fracturing wells based on distributed optical fiber 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 implements the various method steps described in claim 1.

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