A horizontal well multi-phase flow profile inversion method based on multi-source data fusion

Abstract

The present application relates to oil and gas field development technical field, it discloses a kind of horizontal well multiphase flow profile inversion method based on multi-source data fusion, comprising S1: optical fiber layout and data acquisition;S2: data preprocessing;S3: obtain experience parameter and multi-source database;S4: fluid scene discrimination and solver model construction;S5: iterative solution and phase fraction inversion, according to the experience parameter and the multi-source database, using the solver model of the construction of step S4, in different phase number scene, solution is carried out, and inversion index result is obtained;S6: visual output and abnormal flow detection, the inversion index result obtained by the above step S5 is visualized in wellbore depth direction and is used for auxiliary judgment abnormal phenomenon.The present application is based on equation of state, constructs multiphase flow thermodynamic model, combines DAS sound velocity data, and dynamically inverts water-liquid ratio and gas-oil ratio by iterative optimization algorithm, to realize the high-precision characterization of multiphase flow characteristics.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field development technology, and in particular to a method for inverting multiphase flow profiles of horizontal wells based on multi-source data fusion. Background Technology

[0002] In oil and gas well development, multiphase fluids such as water, oil, and gas often exist within the wellbore. Failure to accurately identify and monitor their distribution along the wellbore can lead to reduced oil and gas well productivity or abnormal flow risks (such as water channeling or gas channeling). Traditional monitoring methods mostly rely on downhole multiphase flow meters or periodic logging, which have the following shortcomings:

[0003] 1. The measurement locations are discrete, and only local area data can be obtained, making it difficult to achieve continuous monitoring of the entire well section;

[0004] 2. Well logging requires interrupting production, affecting economic benefits, and has poor timeliness;

[0005] 3. Insufficient adaptability and inversion accuracy for complex wellbore conditions (high gas-oil ratio, high water-liquid ratio, etc.).

[0006] In recent years, Distributed Acoustic Sensing (DAS) has emerged as a new method for monitoring multiphase flow due to its advantages such as "full well section," "high resolution," and "real-time performance." However, there are still many challenges in effectively combining the sound velocity information acquired by DAS with accurate fluid thermodynamic models and using optimization algorithms to perform real-time inversion of multiphase flow composition (such as water-liquid ratio and gas-oil ratio).

[0007] In horizontal well oil and gas production, the distribution of multiphase fluids (including water, oil, and gas) within the wellbore is crucial for production management and reservoir development. However, limitations of traditional monitoring technologies make real-time, high-resolution monitoring of multiphase flow composition difficult. Traditional downhole multiphase flow meters and pressure gauges can only provide fluid information at discrete points, failing to cover the continuous monitoring needs of the entire wellbore area. Meanwhile, while logging technology can provide gas-liquid interface tracking capabilities, it is costly and requires production interruptions during logging, directly impacting economic efficiency.

[0008] There is currently no good solution for operating conditions such as high gas-oil ratio and complex wellbore. Summary of the Invention

[0009] To address one or more of the aforementioned technical problems, this invention combines DAS data and pressure-volume-temperature (PCT) equation of state data to provide a multiphase flow inversion method for horizontal wells based on multi-source data fusion. By real-time and precise monitoring of the three-phase flow states of water, oil, and gas within the wellbore, and utilizing the accurate description of fluid thermophysical properties by the PCT model, the multiphase flow composition (including water-liquid ratio and gas-oil ratio) within the wellbore is efficiently inverted. This method achieves dynamic monitoring of the entire wellbore section, exhibiting superior performance in terms of high resolution, real-time capability, and adaptability to complex flow conditions. This method provides reliable support for optimizing wellbore production management, improving reservoir development efficiency, and timely identification of abnormal flows (such as water channeling and gas leakage).

[0010] This invention provides the following technical solution:

[0011] A method for inverting multiphase flow profiles in horizontal wells based on multi-source data fusion includes the following steps:

[0012] S1: Fiber Optic Deployment and Data Acquisition

[0013] Fiber optic cables are laid along the outer edge of the casing of the horizontal well and connected to the ground DAS and DTS demodulation systems to achieve real-time acquisition of acoustic waves throughout the wellbore and obtain raw DAS and DTS data.

[0014] S2: Data Preprocessing

[0015] By performing noise reduction, calibration, segmentation, feature extraction, and quality assessment, the original DAS and DTS data are subjected to bandpass filtering for noise reduction, detrending, DC removal, and acoustic velocity calculation to output a relatively smooth waterfall plot distribution that can represent the local area, thus obtaining the preprocessed measured data.

[0016] S3: Obtaining empirical parameters and multi-source databases

[0017] The measured data are combined with empirical parameters in the existing pressure-volume-temperature database to obtain a multi-source database. The empirical parameters include uniform pressure-volume-temperature data, variable water-oil ratio, and variable gas-oil ratio. The multi-source database includes a pressure-volume-temperature database, measured wellbore pressure and temperature, surface production data, and empirical curves.

[0018] S4: Fluid Scene Differentiation and Solver Model Construction

[0019] After identifying the fluid scenario, corresponding solver models are constructed for the number of fluid phases in different scenarios. The solver model types include single-phase, two-phase, and three-phase.

[0020] S5: Iterative Solution and Phase Fraction Inversion

[0021] Based on the empirical parameters and the multi-source database, the solver model constructed in step S4 is used to solve the problem under different phase number scenarios to obtain inversion index results. The inversion index results include real-time sound velocity profile, flow pattern identification results, and segmented production capacity / injection volume. For two-phase flow, the state equation model results output by the solver model include liquid holdup distribution and phase flow rate. For three-phase flow, the state equation model results output by the solver model include water-liquid ratio, gas-oil ratio, and their distribution along the wellbore.

[0022] S6: Visualization Output and Abnormal Flow Detection

[0023] The inversion index results obtained in step S5 above are visualized in the wellbore depth direction to help identify abnormal phenomena.

[0024] Preferably, step S1, fiber optic cable deployment and data acquisition, includes the following steps:

[0025] S11: Fiber Optic Deployment

[0026] Single-mode fiber is used to measure DAS signals, and multimode fiber is used to measure DTS signals. The two types of fibers are coupled along the casing outside the wellbore, covering the entire horizontal section of the wellbore and close to the production formation. They are linearly deployed to record the sound velocity signals inside the wellbore at high frequency. The two types of fibers are respectively connected to the DTS and DAS demodulation equipment on the ground. The ground equipment sends laser pulses to the optical fibers and analyzes the scattering effect in the echo signal.

[0027] S12: Data Acquisition

[0028] Ground equipment sends laser pulses to optical fibers and analyzes the scattering characteristics in the echo signal to generate real-time sound velocity distribution data of the well fluid;

[0029] Continuous monitoring of sound velocity signals inside the wellbore via optical fiber captures the propagation characteristics of fluid sound waves and the dynamic behavior of the wellbore. The collected data includes sound velocity signals, environmental parameters, and other key production data. The vibration signals recorded by the DAS demodulation equipment are stored in a two-dimensional time-depth distribution, reflecting the flow state of the fluid in the wellbore. Pressure data inside the wellbore is recorded by a pressure sensor, and temperature is recorded by a DTS demodulator, providing environmental parameters for subsequent sound velocity inversion.

[0030] Preferably, step S2 specifically includes the following steps:

[0031] S21: Data Denoising

[0032] In the preprocessing of DAS data, an adaptive filtering algorithm is used to separate the effective signal from background noise in the spatiotemporal domain. The filter parameters and structure are automatically adjusted according to changes in the input signal. The filtering process includes error signal calculation, filter coefficient adjustment, and filter coefficient update. The error signal calculation involves calculating the error between the target signal and the filter output signal, and this error is used to adjust the filter coefficients. The filter coefficient adjustment involves updating the filter coefficients based on the error signal, with the goal of minimizing the energy of the error signal. The filter coefficient update uses the least mean square algorithm. Simultaneously, moving window technology is used to further optimize the noise reduction effect, significantly improving signal quality.

[0033] S22: Data Normalization

[0034] The original sound velocity signal is converted into a standardized format, and the original DAS signal is amplitude normalized to eliminate response inconsistencies between different fiber segments.

[0035] S23: Data Segmentation, Signal Feature Extraction, and Analysis

[0036] To capture dynamic changes along the wellbore, the data is divided into multiple spatial segments for independent processing. For each segment, the average sound velocity is calculated, key signal features are extracted from the DAS data, and a two-dimensional Fourier transform is performed on the DAS data to convert the time-depth domain data to the frequency-wavenumber domain. Then, through frequency-wavenumber analysis, positive and negative wavenumber peak maps of the sound velocity distribution are generated to obtain detailed information on the changes in sound velocity over time and space. The measured wave velocity or vibration characteristic distribution of the well section is obtained through frequency-wavenumber analysis. Combined with the theoretical sound velocity calculated by the state equation, matching / minimization error iteration is performed. The DAS data is processed through a low-pass filter to obtain LF-DAS data, which can track key features in multiphase flow, including transient events and fluid velocity, thereby improving the dynamic characteristic description of the data.

[0037] Preferably, step S4 specifically includes the following steps:

[0038] S41: Definition of Flow Direction

[0039] Based on the wellbore's production operation mode, the flow direction is defined, including the production flow direction and the injection flow direction. The production flow direction refers to the process of fluid flowing from the bottom of the wellbore to the wellhead; the injection flow direction refers to the process of fluid being injected from the wellhead to the bottom of the wellbore.

[0040] S42: Fluid Scene Discrimination

[0041] Based on the collected sound velocity characteristics, pressure and temperature conditions, and flow direction, the flow scenario is determined. The flow scenarios include single-phase, two-phase, and three-phase scenarios. The single-phase flow scenario includes the flow of pure oil, pure water, or pure gas. The two-phase flow scenario includes oil-water or oil-gas flow. The three-phase flow scenario includes the coexistence of oil, gas, and water.

[0042] S43: Solver Model Construction

[0043] The solver model is used to calculate the sound velocity distribution under different fluid scenarios. In the single-phase flow scenario, the solver model assumes that the fluid characteristics are uniform and calculates the sound velocity distribution. In the two-phase flow scenario, the solver model combines the equation of state to describe the sound velocity changes at the oil-water or oil-gas interface. In the three-phase flow scenario, the solver model considers the interaction between the three phases of oil, gas and water and uses a dynamic thermodynamic model to calculate complex sound velocity distributions.

[0044] S44: Input of empirical parameters

[0045] Empirical parameters combined with sound velocity data are input into the solver model for calculation;

[0046] S45: Expected Output

[0047] The solver model generates multiple outputs, including water-liquid ratio distribution, gas-oil ratio distribution, and liquid holdup profile. The water-liquid ratio distribution reflects the ratio of water to oil phases through a dynamic change curve along the wellbore depth, providing an important basis for identifying water channeling. The gas-oil ratio distribution reveals the changes in the ratio of gas to liquid phases, helping to identify high gas-oil ratio sections and abnormal gas flow behavior. The liquid holdup profile assesses the spatial proportion of liquid at different depths in the wellbore, providing data support for optimizing production processes.

[0048] Preferably, step S5 specifically includes the following steps:

[0049] S51: Model the state equations

[0050] The appropriate mathematical expression for the state equation is:

[0051]

[0052] Where: P is pressure; T is temperature; V is molar volume; R is the gas constant under specific temperature and pressure conditions; a, b are parameters related to fluid properties, calculated using the following formulas for the fluid's critical temperature Tc and critical pressure Pc:

[0053] a = 0.45724 (R) 2 Tc 2 / Pc)×α(Tr,ω) (2)

[0054] b=0.07780(R Tc / Pc) (3)

[0055] Where α(Tr,ω) is a function of the fluid eccentricity factor ω(omega), Tr=T / Tc, and Tr is the dimensionless temperature;

[0056] S52: Calculation of the speed of sound

[0057] The formula for calculating the speed of sound, c, is as follows:

[0058]

[0059] Where: c is the speed of sound; ρ is the density; P is the pressure; s is the entropy;

[0060] S53: Calculation of sound velocity in mixed fluids

[0061] The calculation of mixed sound velocity in multiphase flow scenarios is based on the following formula:

[0062]

[0063] In the above formula: α is the velocity of sound in the mixed fluid; α1 is the volume fraction of component 1; α1 represents the speed of sound of component 1; α2 represents the volume fraction of component 2. Let be the velocity of sound for component 2; when it is a three-phase system of oil, gas and water, the gas phase is taken as component 1 and the oil and water as component 2. After solving, the influence of component 1 is eliminated, and then the oil and water are separated, with oil as component 1 and water as component 2.

[0064] S54: Iterative Calculation

[0065] The input data for the iterative calculations include DAS sound velocity data, pressure-volume-temperature experimental data, and initial guesses.

[0066] S55: Dynamic Error Calculation

[0067] In each iteration, the algorithm modulates the speed of sound c measured by the DAS. DAS The speed of sound c predicted by the state equation EoS The error between them is calculated, and the expression for the error is:

[0068] E = c DAS -c EoS (6)

[0069] Error E is the deviation between the water-liquid ratio W and the gas-oil ratio G in the current model parameters and the actual wellbore fluid composition;

[0070] S56: Parameter Optimization

[0071] W and G are optimized using gradient descent, with the goal of minimizing the error E. The optimization formula is:

[0072]

[0073] Where α and β are the learning rates, representing the adjustment magnitude of the parameters in each iteration, and n is the number of iterations. Through multiple iterations, the model gradually converges to the optimal parameters.

[0074] S57: Phase Fraction Tracking Algorithm

[0075] By dynamically optimizing and iteratively converging the phase fraction tracking algorithm, and combining DAS data with the equation of state, the distribution inversion of multiphase fluids can be achieved.

[0076] S58: Using a pressure-volume-temperature database and a state equation model, calculate the theoretical speed of sound c. EoS This process combines the state equation and Formula 5, and then calculates the difference between the DAS sound velocity data and the theoretical sound velocity through an error assessment and feedback mechanism. The estimated values ​​of water-liquid ratio and gas-oil ratio are adjusted through a gradient optimization algorithm to gradually reduce the error.

[0077] S59: Determine whether the current model meets the accuracy requirements through a convergence check. If the error reaches the set threshold, output the final result, including the dynamic distribution of water-liquid ratio and gas-oil ratio. If the error exceeds the threshold, return to adjust the model parameters and iterate again.

[0078] S510: Model Results and Evaluation

[0079] Once the algorithm converges, the final output includes the water-liquid ratio, gas-oil ratio distribution, and sound velocity profile, reflecting the dynamic changes and distribution characteristics of the fluid within the wellbore.

[0080] Preferably, in step S53, the sound velocity data measured by DAS is combined with the pressure-volume-temperature parameters to optimize the model parameters, specifically:

[0081] In the single-phase flow scenario, linear regression analysis using DAS sound velocity data and pressure-volume-temperature database parameters is employed to correct key parameters in the equation of state, including critical temperature and critical pressure.

[0082] In multiphase flow scenarios, the pressure-volume-temperature parameters are dynamically adjusted using an error minimization algorithm; the estimated values ​​of fluid component ratios are optimized by fitting the sound velocity calculated by the model with actual measurement data.

[0083] Compared with the prior art, the present invention has the following beneficial effects:

[0084] (1) Combination of pressure-volume-temperature equation of state and dynamic optimization algorithm

[0085] This invention constructs an accurate multiphase flow thermodynamic model based on the equation of state, and combines it with DAS sound velocity data to dynamically invert the water-liquid ratio and gas-oil ratio through an iterative optimization algorithm, thereby achieving high-precision characterization of multiphase flow properties.

[0086] (2) Adaptability to multiple scenarios and working conditions

[0087] This invention develops a highly adaptable solver model for single-phase, two-phase, and three-phase flows, which can accurately describe the fluid thermophysical properties under complex conditions such as high gas-oil ratio and water channeling. It is highly adaptable and has a wide range of applications.

[0088] (3) Real-time identification of abnormal flow behavior

[0089] This invention, through dynamic analysis of sound velocity distribution, can identify abnormal behaviors such as water channeling and gas leakage in the wellbore in real time, and combined with an early warning mechanism, supports production safety and ensures the stability of wellbore operation.

[0090] (4) Real-time dynamic monitoring and high-resolution coverage

[0091] This invention uses DAS technology to acquire high-resolution sound velocity distribution data accurate to 1m. Combined with state equation modeling, it can track the gas-liquid interface and multiphase flow characteristics in real time, realizing continuous dynamic monitoring of the entire well section and making up for the shortcomings of traditional methods that can only provide discrete point information.

[0092] (5) Optimize production parameters and improve economic efficiency

[0093] This invention provides a scientific basis for optimizing production parameters by accurately inverting the water-liquid ratio, gas-oil ratio, and liquid holdup. For example, by dynamically adjusting pump speed, water injection rate, or gas injection rate, production efficiency can be significantly improved. Simultaneously, it avoids the production losses caused by the need to interrupt production using traditional logging techniques, thus enhancing economic benefits.

[0094] (6) Early identification of abnormal flow behavior

[0095] This invention can accurately identify abnormal flow behaviors within the wellbore, such as water channeling, gas leakage, and dynamic changes in high gas-oil ratio areas. Through real-time monitoring and analysis, these anomalies can be effectively identified in their early stages, reducing the risk of safety accidents (such as blowouts) and ensuring stable production operations. Attached Figure Description

[0096] Figure 1 The flowchart of the horizontal well multiphase flow profile inversion method based on multi-source data fusion provided by the present invention.

[0097] Figure 2 This is a schematic diagram illustrating the principle of constructing the S4 fluid scene discrimination and solver model provided in an embodiment of the present invention.

[0098] Figure 3 The diagram shows the results provided for an embodiment of the present invention. Detailed Implementation

[0099] This invention provides a method for real-time monitoring and inversion of multiphase flows (oil, gas, water, etc.) within horizontal wellbores, combining multi-source data. The multi-source data includes acoustic velocity data extracted from distributed acoustic sensing (DAS) and pressure-volume-temperature equation of state data. By acquiring acoustic velocity data within the wellbore in real time, and combining it with fluid thermodynamic models, dynamic optimization algorithms, and empirical parameters, the distribution ratios of water, oil, and gas within the wellbore are accurately inverted, generating a production profile and providing a scientific basis for dynamic monitoring and optimized production in horizontal wells. This method can assist in oilfield production management and early identification of abnormal wellbore flows, featuring high resolution, real-time performance, and high accuracy.

[0100] The technical route of the present invention is as follows Figure 1 As shown, the complete implementation path runs from defining the flow direction to the final output. By clarifying the relationship between the flow direction, scenario definition, solver model, empirical parameter input, and expected output, a clear technical implementation framework is formed.

[0101] The present invention will now be described in detail with reference to embodiments and accompanying drawings. However, it should be understood that the embodiments and drawings are for illustrative purposes only and do not constitute any limitation on the scope of protection of the present invention. All reasonable modifications and combinations included within the inventive spirit of the present invention fall within the scope of protection of the present invention.

[0102] Example 1

[0103] according to Figure 1 The process shown in this embodiment fully utilizes the characteristic of DAS to obtain the acoustic velocity distribution of the entire wellbore, and combines it with the state equation, which has a good ability to describe the thermodynamic properties of fluids, to dynamically invert the characteristic quantities of multiphase flow. The inversion steps are as follows:

[0104] S1: Fiber Optic Deployment and Data Acquisition

[0105] An optical fiber is laid along the outer edge of the casing of the horizontal well and connected to a ground-based DAS demodulation system to achieve real-time acquisition of acoustic waves throughout the wellbore, obtaining raw DAS and DTS data, which facilitates quality control of the acquired data in the next step.

[0106] S2: Data Preprocessing

[0107] The original DAS and DTS data are subjected to bandpass filtering for noise reduction, detrending, and DC removal to complete the preprocessing steps, and the output is a relatively smooth waterfall plot distribution that can represent the local area.

[0108] S3: Obtaining empirical parameters and multi-source databases

[0109] When embedding the pressure-volume-temperature equation of state into the solver, a complete multi-source database needs to be constructed as support, including uniform pressure-volume-temperature mode; variable water-oil ratio mode; variable gas-oil ratio mode; and bottom hole pressure, temperature and flow measurement points.

[0110] Empirical parameters include uniform pressure-volume-temperature data or variable water-oil ratio, variable gas-oil ratio, etc.

[0111] The multi-source database embodies the fusion of multi-source data, specifically including pressure-volume-temperature database, measured bottom hole pressure and temperature, surface production data, empirical curves, etc.

[0112] S4: Fluid Scene Differentiation and Solver Model Construction

[0113] For different fluid phase numbers in different scenarios, corresponding solvers are built, including single-phase / two-phase / three-phase / four-phase.

[0114] In single-phase flow scenarios, the model is relatively simple; in multiphase flow scenarios, it is necessary to consider the interaction of the three phases of oil, gas, and water, as well as the mixing sound velocity formula, etc.

[0115] S5: Iterative solution and phase fraction (component ratio) inversion

[0116] Based on empirical parameters and multi-source databases, the solver in step S4 is used to solve the problem. The core results of the state equation model output by the solver differ slightly under different phase number scenarios. For example, for two-phase flow: liquid holdup distribution, phase flow rate, etc.

[0117] For three-phase flow: water-liquid ratio, gas-oil ratio, and their distribution along the wellbore;

[0118] For four-phase flow: a solid phase is added to the three-phase flow;

[0119] For each different phase flow, real-time sound velocity profiles, flow pattern identification results, segmented production capacity / injection volume, and other key indicators can be further output, yielding multiple inversion index results. Meanwhile, core indicator parameters such as water-liquid ratio distribution, gas-oil ratio distribution, and liquid holdup provide a basis for wellbore multiphase flow monitoring and production optimization.

[0120] S6: Visualization Output and Abnormal Flow Detection

[0121] The inversion index results obtained in step S5 above are visualized in the wellbore depth direction to help identify certain abnormal phenomena (such as water channeling and gas leakage).

[0122] By combining historical or real-time data, a basis is provided for wellbore production management, water (gas) injection strategy adjustment, threshold alarms, etc.

[0123] More specifically, step S1, fiber optic deployment and data acquisition, includes the following steps:

[0124] S11: Fiber Optic Deployment

[0125] Fiber optic deployment is a fundamental prerequisite for DTS and DAS technologies, and its design directly affects the quality of data acquisition and the long-term stability of the system. In this embodiment, single-mode fiber is used to measure DAS signals, while multimode fiber is used to measure DTS signals to improve signal coherence, ensuring a longer measurement distance and higher resolution. The fiber is coupled along the wellbore outside the tubing, covering the entire horizontal section of the wellbore and as close to the production formation as possible. A linear deployment method ensures that the fiber is uniformly distributed along the wellbore wall, enabling high-frequency sampling and recording of sound velocity signals within the wellbore. The fiber is connected to the surface DTS and DAS demodulation equipment via a high-sealing connector. The surface equipment is responsible for sending laser pulses to the fiber and analyzing the scattering characteristics in the echo signal to generate real-time sound velocity distribution data of the wellbore fluid. The fiber is coated with a high-temperature, high-pressure, and corrosion-resistant coating to cope with the extreme environmental conditions within the wellbore, ensuring the stability and reliability of the system during long-term operation.

[0126] S12: Data Acquisition

[0127] Data acquisition involves continuous monitoring of sound velocity signals inside the wellbore via optical fiber, focusing on capturing the propagation characteristics of fluid sound waves and the dynamic behavior of the wellbore. The main data acquired includes sound velocity signals, environmental parameters, and other key production data. The vibration signals recorded by the DAS system are stored in a two-dimensional time-depth distribution, comprehensively reflecting the fluid flow state within the wellbore. Pressure within the wellbore is recorded by specialized sensors, and temperature is recorded by a DTS demodulator, providing necessary environmental parameters for subsequent sound velocity inversion. To meet the monitoring needs of different scenarios, the system's laser pulse width, frequency, and temporal-spatial sampling resolution can be dynamically adjusted. For example, in complex multiphase flow scenarios, the sampling frequency needs to be increased to capture detailed changes.

[0128] S2: Data Preprocessing

[0129] Data quality is controlled through data preprocessing, as the reliability of DTS and DAS data directly affects the accuracy of subsequent inversion calculations. This embodiment improves signal quality through reasonable hardware design and data processing strategies. The system monitors signal fluctuations in real time during acquisition, automatically removes abnormal data, selects an appropriate gauge length, and uses multi-point averaging to reduce measurement errors.

[0130] Step S2, data preprocessing, is a necessary step to transform the raw DAS and DTS signals into high-quality input data. This embodiment systematically improves signal quality and reliability through processes such as noise reduction, calibration, segmentation, feature extraction, and quality assessment, providing a solid data foundation for subsequent multiphase flow inversion and phase fractional tracking algorithms. Its specific implementation includes the following steps:

[0131] S21: Data Denoising

[0132] Noise reduction is the most critical step in DAS data preprocessing. Due to the complex multiphase flow within the wellbore and the influence of the external environment, the acquired signals often contain interference signals such as mechanical vibration, fluid leakage, and random environmental noise. To improve the signal-to-noise ratio, an adaptive filtering algorithm is used. This algorithm can separate the effective signal from background noise in the spatiotemporal domain and automatically adjust the filter parameters and structure according to changes in the input signal. Its principle is to automatically adjust the filter coefficients based on the statistical characteristics of the input and output signals using a least mean square algorithm to achieve optimal filtering characteristics. This can be used to suppress non-stationary noise in fiber optic data sets.

[0133] The implementation steps are as follows:

[0134] 1. Error signal calculation: Calculate the error between the target signal and the filter output signal. The error signal is used to adjust the filter coefficients.

[0135] 2. Filter coefficient adjustment: The filter coefficients are updated according to the error signal, with the goal of minimizing the energy of the error signal.

[0136] 3. Filter coefficient update: The coefficients are updated based on the least mean square algorithm.

[0137] Meanwhile, the noise reduction effect is further optimized by using moving window technology, thereby significantly improving signal quality.

[0138] S22: Data Normalization

[0139] Signal calibration is a crucial step in data preprocessing, aiming to eliminate systematic errors and nonlinear biases and convert the raw sound velocity signal into a standardized format. Amplitude normalization is then performed on the raw DAS signal to eliminate response inconsistencies between different fiber segments.

[0140] S23: Data Segmentation, Signal Feature Extraction, and Analysis

[0141] To capture the dynamic changes along the wellbore, the data was divided into multiple spatial segments for independent processing. For each segment, its average sound velocity was calculated, and its temporal and spatial variation characteristics were analyzed. Furthermore, characteristic parameters of key events within each segment, such as interface transitions and changes in multiphase flow patterns, were extracted to provide important inputs for subsequent model calculations.

[0142] Key signal features are extracted from DAS data. A two-dimensional Fourier transform is performed on the DAS data to convert the time-depth domain data to the frequency-wavenumber domain. Then, frequency-wavenumber analysis is used to generate positive and negative wavenumber peak maps of the sound velocity distribution to obtain detailed information on the variation of sound velocity with time and space. The measured wave velocity or vibration characteristic distribution of the well section is obtained through frequency-wavenumber analysis. Combined with the theoretical sound velocity calculated by the state equation, iterative matching / minimization of errors is performed. The DAS data is processed through a low-pass filter to obtain LF-DAS data, which can track key features in multiphase flow, including transient events and fluid velocity, thereby further improving the dynamic characteristic description of the data.

[0143] The specific steps for obtaining empirical parameters and constructing a multi-source database in step 3 are to combine the measured data (DAS + bottom hole sensor + surface production, etc.) with the empirical parameters (critical pressure, critical temperature, eccentricity factor, etc.) in the pressure-volume-temperature database to obtain the constructed multi-source database.

[0144] Empirical parameters include uniform pressure-volume-temperature data and variable water-oil ratio, variable gas-oil ratio, etc.

[0145] The multi-source database embodies the fusion of multi-source data, specifically including pressure-volume-temperature database, measured bottom hole pressure and temperature, surface production data, empirical curves, etc.

[0146] S4: Fluid Scene Differentiation and Solver Model Construction

[0147] First, based on the collected information such as sound velocity characteristics, pressure and temperature conditions, and production / injection direction, the single-phase, two-phase, and three-phase flow scenarios are identified. Based on previous production data, the production or injection scenario is obtained, and the flow scenario is determined to be single-phase, two-phase, or three-phase. Appropriate composite thermodynamic equations of state (or "solver models") are then established for different scenarios. For example, the phase distribution in oil-gas flow and the mixing density and sound velocity formulas in oil-water-gas flow are differentiated. Figure 2 As shown. Specifically, it includes the following steps:

[0148] S41: Definition of Flow Direction

[0149] The definition of flow direction is the starting point of the technical logic process, directly affecting subsequent scenario classification and solver model construction. Based on the wellbore's production operation mode, flow direction is mainly divided into production flow direction and injection flow direction. Production flow direction refers to the process of fluid flowing from the bottom of the wellbore to the wellhead, commonly used in oil and gas production operations, focusing primarily on the distribution of multiphase fluids within the wellbore and the impact of dynamic changes on production. Injection flow direction, on the other hand, refers to the process of fluid being injected from the wellhead to the bottom of the wellbore, typically used in water or gas injection operations to assess the diffusion behavior of the injected fluid and the wellbore response. The definition of flow direction provides a directional basis for subsequent scenario classification and data analysis, while also determining the acquisition range and analytical boundaries of the DAS sound velocity signal.

[0150] S42: Fluid Scene Discrimination

[0151] Fluid flow scenarios are classified into three categories based on the complexity of the fluid composition: single-phase flow, two-phase flow, and three-phase flow, each applicable to different production and injection scenarios. Single-phase flow scenarios include flows of pure oil, pure water, or pure gas, with a uniform sound velocity distribution and no significant changes; these typically occur in early extraction stages or water injection operations. Two-phase flow scenarios, such as oil-water or oil-gas flows, exhibit a significant jump in sound velocity at the phase interface, reflecting the physical characteristics of multiphase interfaces. In three-phase flow scenarios, oil, gas, and water coexist, resulting in complex flow characteristics. The dynamics of sound velocity are influenced by various factors, and these scenarios often occur in sections with high gas-to-oil ratios or high water-to-liquid ratios.

[0152] S43: Solver Model Construction

[0153] The solver model is used to calculate the sound velocity distribution under different fluid scenarios. In the single-phase flow scenario, the model assumes uniform fluid characteristics, making the sound velocity distribution calculation relatively simple. In the two-phase flow scenario, the model combines the equation of state and Equation 5 (described later) to describe the sound velocity changes at the oil-water or oil-gas interface. In the three-phase flow scenario, the model considers the interactions between the oil, gas, and water phases, and uses a dynamic thermodynamic model to calculate the complex sound velocity distribution.

[0154] S44: Input of empirical parameters

[0155] The empirical parameter inputs primarily come from experimental data in the pressure-volume-temperature database. This data includes thermodynamic parameters such as density, pressure, and temperature, used to describe the fluid properties at different depths and under different conditions. Furthermore, depending on the specific fluid scenario, the pressure-volume-temperature parameters can be selected using either a uniform or variable distribution mode. In single-phase flow scenarios, assuming the fluid properties remain uniform within the wellbore, a uniform distribution mode is suitable. However, in multiphase flow scenarios, it is necessary to consider the dynamic changes of parameters with depth or time, employing a variable distribution mode. These input parameters, combined with DAS sound velocity data, provide reliable boundary conditions for the state equation model calculations.

[0156] S45: Expected Output

[0157] Through model calculations, this embodiment generates several key outputs, including water-liquid ratio distribution, gas-oil ratio distribution, and liquid holdup profile. The water-liquid ratio distribution, through a dynamic variation curve along the wellbore depth, reflects the proportion of the water and oil phases, providing crucial information for identifying water channeling. The gas-oil ratio distribution reveals changes in the proportion of the gas and liquid phases, helping to identify high gas-oil ratio sections and abnormal gas flow behavior. The liquid holdup profile, by assessing the spatial proportion of liquid at different wellbore depths, provides essential data support for optimizing production processes.

[0158] Through the above technical process, a complete technical chain has been realized, from data acquisition, scenario definition, model solving to result output. The above technical roadmap clearly illustrates the logical relationship of each link and its role in the entire technical system, providing a scientific and comprehensive implementation framework for the accurate inversion of multiphase flow in wellbore.

[0159] Step S5, iterative solution and phase fraction (component ratio) inversion, is used to construct the equation of state model. The equation of state is used to describe the thermodynamic properties of the fluid, primarily for calculating the sound velocity and other physical parameters of multiphase flow in the wellbore. This embodiment achieves dynamic multiphase flow modeling by combining the equation of state, a pressure-volume-temperature database, and real-time DAS data. Specifically, it includes the following steps:

[0160] S51: Modeling of State Equations

[0161] The equation of state used in this embodiment is suitable for thermodynamic modeling of complex oil-gas mixtures, and its mathematical expression is:

[0162]

[0163] Where: P - pressure; T - temperature; V - molar volume; R - gas constant under specific temperature and pressure conditions; a, b - parameters related to fluid properties, calculated from the fluid's critical temperature Tc and critical pressure Pc.

[0164] a = 0.45724 (R) 2 Tc 2 / Pc)×α(Tr,ω) (2)

[0165] b=0.07780(R Tc / Pc) (3)

[0166] Wherein, α(Tr,ω) is a function that takes into account the fluid eccentricity factor ω(omega), Tr=T / Tc, and Tr is the dimensionless temperature;

[0167] The advantage of equations of state is that they can better simulate the behavior of oil and gas fluids under high pressure and high temperature conditions.

[0168] S52: Calculation of the speed of sound

[0169] The calculation of the speed of sound c is based on the isentropic condition of the equation of state, which describes the relationship between the speed of sound in a fluid and its density and pressure, as shown in the following formula:

[0170]

[0171] Where: c - speed of sound; ρ - density; P - pressure; s - entropy.

[0172] In multiphase fluid scenarios, the equation of state, by combining pressure, temperature, and component proportions, calculates the sound velocity of each component and ultimately obtains the mixed sound velocity distribution. This indicates that, under the premise of constant entropy (constant s), the partial derivative of pressure with respect to density determines the sound velocity value for the propagation of small disturbances in the medium.

[0173] S53: Calculation of sound velocity in mixed fluids

[0174] The calculation of mixed sound velocity in multiphase flow scenarios is based on Equation 5, which comprehensively considers the acoustic characteristics of different fluid components. The formula is as follows:

[0175]

[0176] In formula 5: - Velocity of sound in the mixed fluid; α1 - Volume fraction of component 1; - Velocity of sound of component 1; α2 - Volume fraction of component 2; - The speed of sound in component 2.

[0177] When the three phases are oil, gas, and water, the gas phase is considered as component 1, and the oil and water phases are considered as component 2. After solving the problem, the influence of component 1 is eliminated, and then the oil and water are separated, with oil as component 1 and water as component 2.

[0178] The model was chosen to fully consider the interactions between the gas, oil, and water phases, and can accurately describe the changes in sound velocity under different flow modes.

[0179] Step S5 involves data input and optimization of the pressure-volume-temperature database. The pressure-volume-temperature database is a crucial data source for equation-of-state modeling, containing experimentally calibrated fluid thermodynamic parameters such as density, saturation pressure, and gas-oil ratio. This data provides reliable boundary conditions for the equation-of-state calculations and supports the construction of dynamic sound velocity models. To improve the applicability of the equation-of-state, this invention combines sound velocity data measured by DAS with pressure-volume-temperature parameters to optimize the model parameters.

[0180] In the single-phase flow scenario, this embodiment employs linear regression analysis using DAS sound velocity data and pressure-volume-temperature database parameters to correct key parameters in the equation of state, including critical temperature and critical pressure. This step ensures that the equation of state model accurately reflects the thermodynamic behavior of the single-phase fluid.

[0181] In multiphase flow scenarios, an error minimization algorithm is used to dynamically adjust the pressure-volume-temperature parameters. By fitting the sound velocity calculated by the model with actual measurement data, the estimated values ​​of fluid component proportions are optimized. This method can adapt to dynamic changes under complex flow conditions, providing higher accuracy for sound velocity calculations in multiphase flow scenarios.

[0182] S54: Iterative Calculation

[0183] The input data mainly includes DAS sound velocity data, pressure-volume-temperature experimental data, and initial guesses. The sound velocity data calculated by DAS is a dynamic signal; the real-time sound velocity distribution acquired by the DAS system is stored in the form of a depth-time two-dimensional matrix. This data directly reflects the local dynamic characteristics of the fluid within the wellbore, providing an important physical basis for phase fraction inversion. The pressure-volume-temperature experimental data includes pressure, temperature, fluid density, and volume parameters of each component. These parameters are obtained through experimental calibration, ensuring the accuracy and reliability of the state equation model calculations. Furthermore, the initial conditions of the algorithm include initial estimates of the water-liquid ratio and gas-oil ratio; these parameters serve as the starting point for iterative optimization.

[0184] S55: Dynamic Error Calculation

[0185] In each iteration, the algorithm modulates the speed of sound (c) measured by the DAS. DAS ) and the speed of sound predicted by the state equation (c EoS The error between () is calculated. The expression for the error is:

[0186] E = c DAS -c EoS (6)

[0187] Error E reflects the deviation between the water-liquid ratio W and the gas-oil ratio G in the current model parameters and the actual fluid composition in the wellbore, and is the core basis for subsequent optimization.

[0188] S56: Parameter Optimization

[0189] The optimization of W and G is performed using gradient descent, with the goal of minimizing the error E. The optimization formula is:

[0190]

[0191] Here, α and β are the learning rates, representing the adjustment magnitude of the parameters in each iteration, and n is the number of iterations. Through multiple iterations, the model gradually converges to the optimal parameters.

[0192] S57: Phase Fraction Tracking Algorithm

[0193] The phase fractional tracking algorithm, through dynamic optimization and iterative convergence, combines DAS data with state equations to achieve the distribution inversion of multiphase fluids. This process clearly demonstrates the complete path from data input to final output, covering all stages. First, by combining wellbore structure and fluid properties, the flow direction and possible multiphase scenarios are defined. For example, typical flow modes such as high gas-oil ratio scenarios, water channeling, or uniformly distributed two-phase flow can be identified, providing clear physical boundary conditions for model calculations.

[0194] S58: Using a pressure-volume-temperature database and a state equation model, calculate the theoretical speed of sound c. EoS This process combines the equation of state and Formula 5, providing the core calculation basis for inversion by accurately describing the mixing sound velocity characteristics of multiphase fluids. After completing the theoretical sound velocity calculation, the difference between the DAS sound velocity data and the theoretical sound velocity is calculated through an error assessment and feedback mechanism. The estimated values ​​of the water-liquid ratio and gas-oil ratio are then adjusted using a gradient optimization algorithm to gradually reduce the error.

[0195] S59: A convergence check determines whether the current model meets the accuracy requirements. If the error reaches a set threshold, the final result, including the dynamic distribution of the water-liquid ratio and gas-oil ratio, is output; if the error exceeds the threshold, the model parameters are adjusted and the iteration is restarted. The "Stable Solution" module in the optimization process is a key node, used to determine whether the current solution meets the expected accuracy, thus deciding whether the algorithm continues to iterate. Additionally, when abnormal jumps or critical values ​​are detected in the spatial distribution of the water-liquid ratio and gas-oil ratio, an alarm / judgment is triggered, possibly indicating gas or water channeling. Based on this complete process, this algorithm can accurately invert the dynamic characteristics of multiphase flow in the wellbore, providing reliable data support for production optimization.

[0196] S510: Model Results and Evaluation

[0197] Once the algorithm converges, the final output includes the water-liquid ratio, gas-oil ratio distribution, and sound velocity profile. These results intuitively reflect the dynamic changes and distribution characteristics of the fluid within the wellbore.

[0198] The output of the state equation model in step S5, based on the construction and optimization methods described above, provides information on the key characteristic distribution and dynamic changes of multiphase fluids.

[0199] These outputs include:

[0200] 1) The sound velocity distribution is a direct output of the equation of state, reflecting the change in sound velocity along the depth direction in the wellbore. The sound velocity distribution curve provides an important basis for analyzing the flow characteristics of fluids within the wellbore.

[0201] 2) The water-liquid ratio and gas-oil ratio are key parameters describing the composition of multiphase fluids. Equation-of-state models can dynamically predict the distribution and changes of these parameters at different depths in the wellbore, helping to identify anomalies in fluid flow.

[0202] 3) Liquid holdup is an important indicator in fluid profile analysis. The liquid holdup curve output by the model can reflect the proportion of liquid in the wellbore, providing data support for optimizing the production process.

[0203] These outputs not only provide accurate basic data for multiphase flow inversion, but also directly support the decision-making process for production optimization, greatly improving the analytical capabilities for wellbore fluid flow behavior.

[0204] The iteration and phase fraction tracking in step S5 employ a dynamic optimization and phase fraction tracking algorithm.

[0205] Step 6: Visualizing the Output and Detecting Abnormal Flow. Visualize the output results from the previous steps.

[0206] This embodiment uses data collected from an oil well and applies the horizontal well multiphase flow profile inversion method based on multi-source data fusion provided in this embodiment to generate, as shown below. Figure 3 The explanation results are as follows:

[0207] The first column represents the fracturing section, the second column represents the perforation cluster, the third column is the cumulative water and oil production distribution curves for all producing sections (all fracturing sections or all perforation clusters), the fourth column is the histogram of water and oil production distribution for each fracturing section, and the fifth column is the histogram of water and oil production distribution for each perforation cluster. Blue represents water, and green represents oil.

[0208] The above embodiments are merely preferred embodiments of the present invention, and the scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for inverting multiphase flow profiles in horizontal wells based on multi-source data fusion, characterized in that: Includes the following steps: S1: Fiber Optic Deployment and Data Acquisition Fiber optic cables are laid along the outer edge of the casing of the horizontal well and connected to the surface DAS and DTS demodulation systems to achieve real-time acquisition of acoustic waves throughout the wellbore and obtain raw DAS and DTS data. S2: Data Preprocessing By performing noise reduction, calibration, segmentation, feature extraction, and quality assessment, the original DAS and DTS data are subjected to bandpass filtering for noise reduction, detrending, DC removal, and acoustic velocity calculation to output a relatively smooth waterfall plot distribution that can represent the local area, thus obtaining the preprocessed measured data. S3: Obtaining empirical parameters and multi-source databases The measured data are combined with empirical parameters in the existing pressure-volume-temperature database to obtain a multi-source database. The empirical parameters include uniform pressure-volume-temperature data, variable water-oil ratio, and variable gas-oil ratio. The multi-source database includes a pressure-volume-temperature database, measured wellbore pressure and temperature, surface production data, and empirical curves. S4: Fluid Scene Differentiation and Solver Model Construction After identifying the fluid scenario, corresponding solver models are constructed for the number of fluid phases in different scenarios. The solver model types include single-phase, two-phase, and three-phase. S5: Iterative Solution and Phase Fraction Inversion Based on the empirical parameters and the multi-source database, the solver model constructed in step S4 is used to solve the problem under different phase number scenarios to obtain inversion index results. The inversion index results include real-time sound velocity profile, flow pattern identification results, and segmented production capacity / injection volume. For two-phase flow, the state equation model results output by the solver model include liquid holdup distribution and phase flow rate. For three-phase flow, the state equation model results output by the solver model include water-liquid ratio, gas-oil ratio, and their distribution along the wellbore. S6: Visualization Output and Abnormal Flow Detection The inversion index results obtained in step S5 above are visualized in the wellbore depth direction to help identify abnormal phenomena. Step S5 specifically includes the following steps: S51: Model the state equations The appropriate mathematical expression for the state equation is: （1） Where: P is pressure; T is temperature; V is molar volume; R is the gas constant under specific temperature and pressure conditions; a and b are parameters related to fluid properties, calculated using the following formulas for the fluid's critical temperature Tc and critical pressure Pc: a = 0.45724 (R² Tc² / Pc) × α (Tr, ω) (2) b = 0.07780 (R Tc / Pc) (3) Where α(Tr, ω) is a function of the fluid eccentricity factor ω(omega), Tr = T / Tc, and Tr is the dimensionless temperature; S52: Calculation of the speed of sound The formula for calculating the speed of sound, c, is as follows: （4） Where: c is the speed of sound; P is density; s is pressure; s is entropy. S53: Calculation of sound velocity in mixed fluids The calculation of mixed sound velocity in multiphase flow scenarios is based on the following formula: （5） In the above formula: The speed of sound in the mixed fluid; This represents the volume fraction of component 1. The speed of sound for component 1; This represents the volume fraction of component 2. Let be the velocity of sound for component 2; when it is a three-phase system of oil, gas and water, the gas phase is taken as component 1 and the oil and water as component 2. After solving, the influence of component 1 is eliminated, and then the oil and water are separated, with oil as component 1 and water as component 2. S54: Iterative Calculation The input data for the iterative calculations include DAS sound velocity data, pressure-volume-temperature experimental data, and initial guesses. S55: Dynamic Error Calculation In each iteration, the algorithm modifies the speed of sound measured by the DAS. The speed of sound predicted by the equation of state The error between them is calculated, and the expression for the error is: （6） Error E is the deviation between the water-liquid ratio W and the gas-oil ratio G in the current model parameters and the actual wellbore fluid composition; S56: Parameter Optimization W and G are optimized using gradient descent, with the goal of minimizing the error E. The optimization formula is: （7） （8） in, Harmony The learning rate represents the adjustment range of the parameters in each iteration, and n is the number of iterations. Through multiple iterations, the model gradually converges to the optimal parameters. S57: Phase Fraction Tracking Algorithm By dynamically optimizing and iteratively converging the phase fraction tracking algorithm, and combining DAS data with the equation of state, the distribution inversion of multiphase fluids can be achieved. S58: Calculate the theoretical speed of sound using a pressure-volume-temperature database and a state equation model. This process combines the state equation and Formula 5, and then calculates the difference between the DAS sound velocity data and the theoretical sound velocity through error assessment and feedback mechanism. The estimated values ​​of water-liquid ratio and gas-oil ratio are adjusted through gradient optimization algorithm to gradually reduce the error. S59: Determine whether the current model meets the accuracy requirements through a convergence check. If the error reaches the set threshold, output the final result, including the dynamic distribution of the water-liquid ratio and the gas-oil ratio. If the error exceeds the threshold, return to adjust the model parameters and iterate again. S510: Model Results and Evaluation Once the algorithm converges, the final output includes the water-liquid ratio, gas-oil ratio distribution, and sound velocity profile, reflecting the dynamic changes and distribution characteristics of the fluid within the wellbore.

2. The horizontal well multiphase flow profile inversion method based on multi-source data fusion according to claim 1, characterized in that: Step S1, fiber optic cable deployment and data acquisition, includes the following steps: S11: Fiber Optic Deployment Single-mode fiber is used to measure DAS signals and multimode fiber is used to measure DTS signals. The two types of fibers are coupled along the wellbore outside the casing, covering the entire horizontal section of the wellbore and close to the production formation. They are linearly deployed to record the sound velocity signals inside the wellbore using high-frequency sampling. The two types of fibers are respectively connected to the DTS and DAS demodulation equipment on the ground. S12: Data Acquisition Ground equipment sends laser pulses to optical fibers and analyzes the scattering characteristics in the echo signal to generate real-time sound velocity distribution data of the well fluid; Continuous monitoring of sound velocity signals inside the wellbore via optical fiber captures the propagation characteristics of fluid sound waves and the dynamic behavior of the wellbore. The collected data includes sound velocity signals, environmental parameters, and other key production data. The vibration signals recorded by the DAS demodulation equipment are stored in a two-dimensional time-depth distribution, reflecting the flow state of the fluid in the wellbore. Pressure data inside the wellbore is recorded by a pressure sensor, and temperature is recorded by a DTS demodulator, providing environmental parameters for subsequent sound velocity inversion.

3. The method for horizontal well multiphase flow profile inversion based on multi-source data fusion according to claim 2, characterized in that: Step S2 specifically includes the following steps: S21: Data Denoising In the preprocessing of DAS data, an adaptive filtering algorithm is used to separate the effective signal from background noise in the spatiotemporal domain. The filter parameters and structure are automatically adjusted according to changes in the input signal. The filtering process includes error signal calculation, filter coefficient adjustment, and filter coefficient update. The error signal calculation involves calculating the error between the target signal and the filter output signal, and this error is used to adjust the filter coefficients. The filter coefficient adjustment involves updating the filter coefficients based on the error signal, with the goal of minimizing the energy of the error signal. The filter coefficient update uses the least mean square algorithm. Simultaneously, moving window technology is used to further optimize the noise reduction effect, significantly improving signal quality. S22: Data Normalization The original sound velocity signal is converted into a standardized format, and the original DAS signal is amplitude normalized to eliminate response inconsistencies between different fiber segments. S23: Data Segmentation, Signal Feature Extraction, and Analysis To capture the dynamic changes along the wellbore, the data is divided into multiple spatial segments for independent processing. For each segment, its average sound velocity value is calculated, key signal features are extracted from the DAS data, and a two-dimensional Fourier transform is performed on the DAS data to convert the time-depth domain data to the frequency-wavenumber domain. Then, through frequency-wavenumber analysis, positive and negative wavenumber peak maps of the sound velocity distribution are generated to obtain detailed information on the changes in sound velocity over time and space. The measured wave velocity or vibration characteristic distribution of the well section is obtained through frequency-wavenumber analysis. Combined with the theoretical sound velocity calculated by the state equation, matching / minimization error iteration is performed. The DAS data is processed through a low-pass filter to obtain LF-DAS data, tracking key features in multiphase flow, including transient events and fluid velocity, thereby improving the dynamic characteristic description of the data.

4. The horizontal well multiphase flow profile inversion method based on multi-source data fusion according to claim 3, characterized in that: Step S4 specifically includes the following steps: S41: Definition of Flow Direction Based on the wellbore's production operation mode, the flow direction is defined, which includes the production flow direction and the injection flow direction. The production flow direction refers to the process of fluid flowing from the bottom of the wellbore to the wellhead; the injection flow direction refers to the process of fluid being injected from the wellhead to the bottom of the wellbore. S42: Fluid Scene Discrimination Based on the collected sound velocity characteristics, pressure and temperature conditions, and flow direction, the flow scenario is determined. The flow scenarios include single-phase, two-phase, and three-phase scenarios. The single-phase flow scenario includes the flow of pure oil, pure water, or pure gas. The two-phase flow scenario includes oil-water or oil-gas flow. The three-phase flow scenario includes the coexistence of oil, gas, and water. S43: Solver Model Construction The solver model is used to calculate the sound velocity distribution under different fluid scenarios. In the single-phase flow scenario, the solver model assumes that the fluid characteristics are uniform and calculates the sound velocity distribution. In the two-phase flow scenario, the solver model combines the equation of state to describe the sound velocity changes at the oil-water or oil-gas interface. In the three-phase flow scenario, the solver model considers the interaction between the three phases of oil, gas and water and uses a dynamic thermodynamic model to calculate complex sound velocity distributions. S44: Input of empirical parameters Empirical parameters combined with sound velocity data are input into the solver model for calculation; S45: Expected Output The solver model generates multiple outputs, including water-liquid ratio distribution, gas-oil ratio distribution, and liquid holdup profile. The water-liquid ratio distribution reflects the ratio of water to oil phases through a dynamic change curve along the wellbore depth, providing an important basis for identifying water channeling. The gas-oil ratio distribution reveals the changes in the ratio of gas to liquid phases, helping to identify high gas-oil ratio sections and abnormal gas flow behavior. The liquid holdup profile assesses the spatial proportion of liquid at different depths in the wellbore, providing data support for optimizing production processes.

5. The horizontal well multiphase flow profile inversion method based on multi-source data fusion according to claim 1, characterized in that: In step S53, the sound velocity data measured by DAS was combined with the pressure-volume-temperature parameters to optimize the model parameters, specifically as follows: In the single-phase flow scenario, linear regression analysis using DAS sound velocity data and pressure-volume-temperature database parameters is employed to correct key parameters in the equation of state, including critical temperature and critical pressure. In multiphase flow scenarios, the pressure-volume-temperature parameters are dynamically adjusted using an error minimization algorithm. The estimated values ​​of fluid component proportions are optimized by fitting the sound velocity calculated by the model with actual measurement data.

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