A method for distributed monitoring of wellbore three-phase flow based on a radially sensitized optical fiber structure

Abstract

The present application belongs to the field of oil and gas field development, and discloses a kind of wellbore three-phase flow distributed monitoring method based on radial sensitization optical fiber structure.The present application improves the acoustic vibration response intensity of vibration measurement optical fiber through radial sensitization structure, compresses the spatial resolution of vibration measurement optical fiber in well depth direction through spiral path, improves the temperature response speed of temperature measurement optical fiber through heat conduction structure, and realizes the depth continuous monitoring of wellbore oil, gas and water three-phase flow by using the step-by-step collaborative inversion strategy of phase inversion result, oil-water inversion result and wellhead metering constraint.The present application significantly improves the response ability and signal signal-to-noise ratio of vibration measurement optical fiber to weak flow-induced acoustic vibration, compresses the equivalent sampling interval of vibration measurement optical fiber along the well depth direction by means of spiral topology path amplification effect, improves the spatial resolution from meter level to decimeter level, does not need downhole power supply and mechanical instrument, has low maintenance cost, high operation reliability and can be operated for a long time.

Description

Technical Field

[0001] This invention pertains to oil and gas well production profile monitoring technology in the field of oil and gas field development. More specifically, it relates to a distributed monitoring method for three-phase flow in wellbore based on a radially enhanced fiber optic structure. Background Technology

[0002] In oil and gas well production, the spatial distribution of the three-phase flow of oil, gas, and water within the wellbore is crucial for formation identification, productivity evaluation, and production system optimization. Existing wellbore flow monitoring technologies primarily rely on mechanical or electrical instruments such as turbine flow meters, differential pressure gauges, and multiphase flow meters. These sensors not only depend on downhole power supply and complex mechanical structures, but also typically only provide discrete measurements at a limited number of depths, failing to cover long well sections or enable continuous distributed monitoring. In the high-temperature, high-pressure, highly corrosive, and space-constrained wellbore environment, traditional instruments struggle to operate stably for extended periods, resulting in extremely high maintenance costs and significantly limited measurement accuracy and reliability.

[0003] With the development of distributed optical fiber sensing technology, the application of DAS and DTS in wellbore monitoring is gradually increasing, but significant problems still exist when used for multiphase flow measurement. Because traditional optical fibers are laid linearly along the wellbore axis, the contact between the fiber and the tubing is mostly weak, making it difficult to effectively transmit flow-induced vibrations caused by gas-liquid flow to the fiber. This results in low DAS signal amplitude and poor signal-to-noise ratio. Simultaneously, the effective spatial resolution of DAS is limited by the instrument sampling interval and equivalent measurement length, typically ranging from 1 to 10 meters, making it difficult to meet the identification requirements of three-phase flow interfaces, production zone boundaries, and small-scale flow structures. Furthermore, the DAS measurement mechanism dictates its sensitivity to gas content and gas-liquid flow patterns, but it lacks the ability to distinguish between oil and water phases. Conversely, DTS can only distinguish between oil and water based on differences in thermophysical properties and cannot independently invert three-phase flow velocities when a gas phase is present. Existing fiber-based reinforcement structures mainly focus on axial strain or local coupling improvements, lacking systematic solutions to key issues such as improving radial vibration sensitivity, compressing depth resolution, and co-inversion of acoustic-vibration-thermal dual-physics fields. Therefore, there is still a lack of a three-phase flow monitoring method that can achieve high resolution, strong coupling, distributed operation, and long-term operation throughout the entire wellbore. Summary of the Invention

[0004] To address key bottlenecks in existing technologies, such as insufficient coupling between sensing fiber and tubing, low equivalent spatial resolution of DAS (Distributed Acoustic Sensing) technology, and the inability of a single sensing mechanism to support the three-phase separation and inversion of oil, gas, and water, this invention proposes a distributed monitoring method and device for wellbore three-phase flow based on a radially enhanced fiber structure. By constructing a fiber coupling structure with radially enhanced characteristics on the outer wall of the tubing and integrating dual-physics field information from distributed acoustic sensing (DAS) and distributed temperature sensing (DTS), high-resolution, continuous distributed inversion of oil, gas, and water three-phase flow rates throughout the wellbore can be achieved without the need for downhole power supply and mechanical instruments. To achieve the above objectives, this invention provides the following technical solution:

[0005] A distributed monitoring method for three-phase flow in wells based on a radially sensitive fiber structure, the method comprising the following steps:

[0006] S1. A radially sensitive fiber optic column structure is deployed inside the wellbore. The pressure pulsation and heat generated by the fluid flow serve as acoustic and thermal signal sources, respectively, acting on the radially sensitive fiber optic column structure.

[0007] The radially sensitized fiber optic column structure includes a column, a helical radial sensitization groove, a vibration-measuring fiber embedded in the sensitization groove and laid in a helical topology, a heat-conducting medium inside the column, a temperature-measuring fiber disposed in the heat-conducting medium, and a high-modulus encapsulation layer.

[0008] S2. Parallel enhancement and transmission of acoustic and thermal signals: In the acoustic path, fluid pressure pulsations are coupled to the vibration measurement fiber through a high-modulus encapsulation layer. The coupling gain coefficient characterizes the faithful linear transmission of fluid pressure pulsations to the effective strain of the vibration measurement fiber. The spiral topology of the vibration measurement fiber is used to compress the equivalent sampling spacing of the vibration measurement fiber along the well depth direction with a path amplification factor, thereby improving its spatial resolution from the meter level to the decimeter level and matching it with the temperature measurement fiber. In the thermal path, fluid heat is transferred to the temperature measurement fiber through a heat-conducting medium, forming a wellbore temperature field.

[0009] S3. Distributed data acquisition and feature extraction: The DAS demodulator acquires the sensitized strain rate signal and extracts the apparent propagation velocity and characteristic frequency band energy of the acoustic wave through time-frequency analysis. The DTS demodulator acquires the temperature field and analyzes the fluid temperature and temperature attenuation coefficient along the well depth. At the same time, the mapping relationship between fiber coordinates and well depth is established to achieve the alignment of the two types of data in the depth direction.

[0010] S4. Invert gas phase parameters based on acoustic features; Based on the Wood sound velocity mixing model, invert the gas content using the monotonic mapping relationship between the apparent propagation velocity of sound waves and the gas content, and calculate the gas flow rate by combining the characteristic frequency band energy constraints.

[0011] S5. Invert oil and water phase parameters under gas phase constraint; Substitute the determined gas phase information as known conditions into the wellbore heat transfer model based on the one-dimensional convection-heat transfer equation, and construct the objective function based on the temperature decay coefficient to invert the water cut according to the difference in oil and water thermal properties, and then separate the oil phase flow rate and water phase flow rate.

[0012] S6. Perform three-phase joint calculation based on mass conservation and generate production profile; with the total flow rate equal to the sum of the flow rates of each phase as a constraint, perform global optimization correction on the gas phase and liquid phase results obtained in steps S4 and S5 by minimizing the joint objective functional, and output the oil, gas and water three-phase production profile continuously distributed along the well depth.

[0013] This invention utilizes a radially sensitive fiber optic column structure to simultaneously acquire two types of physical information—"flow-induced acoustic vibration" and "wellbore thermal field"—on the same carrier, and achieves three-phase decoupling through acoustic-thermal-flow coordinated inversion. Compared with mechanical and electrical flowmeters that rely on a small number of measuring points and conventional distributed fiber optic interpretation methods, this invention offers the following advantages:

[0014] (1) Strong coupling and high signal-to-noise ratio: The relative slippage of the optical fiber under stress is suppressed by the embedded high-constraint packaging interface, so that the fluid pressure pulsation is linearly transmitted to the optical fiber with a coupling gain coefficient close to 1, which significantly improves the DAS's response to weak flow-induced acoustic vibration and signal-to-noise ratio.

[0015] (2) High spatial resolution: By leveraging the path amplification effect of the spiral topology, the equivalent sampling interval of DAS along the well depth direction is compressed, thereby improving the spatial resolution from the meter level to the decimeter level. It also achieves precise matching with DTS at the depth scale, enabling the identification of small-scale flow structures such as three-phase interfaces and production zone boundaries.

[0016] (3) Three-phase quantitative decoupling: By integrating the acoustic features of DAS and the thermal features of DTS, the gas phase priority inversion and oil-water separation under gas phase constraint are completed in sequence, breaking through the bottleneck that a single sensing mechanism cannot independently invert the three phases of oil, gas and water, and realizing the quantitative characterization of the continuous three-phase production profile of the entire well section.

[0017] (4) Low cost, no need for downhole power supply, and long-term operation: No need for downhole power supply and mechanical instruments. It can realize distributed monitoring of long well sections by relying only on the fiber optic enhancement structure on the outer wall of the tubing and the ground demodulation equipment. It can adapt to the high temperature, high pressure and strong corrosion environment of the well. It has low maintenance cost and high operational reliability, and can replace some conventional multiphase flow meters. Attached Figure Description

[0018] Figure 1 This outlines the overall implementation process and data flow of the present invention.

[0019] Figure 2 This is a schematic diagram of the radially sensitive fiber optic column structure according to an embodiment of the present invention;

[0020] Figure 3 This is a schematic diagram of fluid-structure coupling and the formation of acoustic-thermal dual channels in an embodiment of the present invention;

[0021] Figure 4 This is a design calculation diagram of DAS space compression and DTS thermal response in an embodiment of the present invention;

[0022] Figure 5 This is a diagram showing the DAS time-depth monitoring results of an embodiment of the present invention;

[0023] Figure 6 This is a diagram showing the DTS time-depth temperature monitoring results of an embodiment of the present invention;

[0024] Figure 7 This is an inversion profile of gas content and gas phase flow rate in an embodiment of the present invention;

[0025] Figure 8 This is a cross-section of the oil-water two-phase thermal inversion results in an embodiment of the present invention;

[0026] Figure 9 This is the output profile of the three-phase joint solution in an embodiment of the present invention;

[0027] Figure 10 A comparative diagram of the three-phase flow profiles of oil, water, and gas obtained by acoustic-thermal co-inversion in an embodiment of the present invention.

[0028] Figure labels: Temperature measuring fiber 1; heat-conducting medium 2; vibration measuring fiber 3. Detailed Implementation

[0029] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the following description uses the production profile monitoring of Well A in the X work area as an example, combined with the accompanying drawings, to illustrate the specific implementation of this invention. Well A is a production well with oil, gas, and water coexisting, and the target monitoring section is 2000m to 3000m. A radially sensitive fiber optic string structure is installed inside the wellbore to collect flow-induced acoustic vibration response and temperature profile. Total fluid production, total gas production, and production regime parameters are simultaneously acquired at the wellhead for subsequent three-phase flow conservation correction. This embodiment presents a complete and reproducible distributed monitoring process for three-phase flow in the wellbore, including wellbore structure layout, fluid-structure coupling, parallel acoustic and thermal acquisition, feature extraction, gas phase parameter inversion, gas-phase constrained oil-water two-phase thermal inversion, and three-phase joint solution. The data described is used to illustrate the implementation path of this invention and does not constitute a limitation on the scope of protection. Figure 1 As shown, the specific solution of the present invention is as follows:

[0030] S1. The fluid-structure physical coupling and radial sensitization structure layout aim to establish a physical coupling channel between three-phase fluid disturbances and distributed optical fiber responses downhole. Ordinary axially laid optical fibers mainly rely on the contact between the fiber cable and the pipe string or well wall to transmit vibrations, which is easily affected by uneven contact, encapsulation layer slippage, and local decoupling, resulting in low DAS signal amplitude and poor signal-to-noise ratio. This invention, through a radial sensitization structure, allows fluid pressure pulsations to first act on the pipe wall, and then be transmitted to the vibration measuring optical fiber via an embedded groove, a high-modulus encapsulation layer, and a limiting structure, thereby forming a stable radial mechanical coupling chain.

[0031] Figure 2 As shown, in specific implementation, a radially sensitive fiber optic string structure is installed in the target well section. This structure includes the string body, a helical radially sensitive groove, a vibration-measuring fiber 3 embedded in the sensitive groove, a temperature-measuring fiber 1 placed in the heat-conducting medium 2, and a high-modulus encapsulation layer. During the three-phase flow of oil, gas, and water in the wellbore, bubble coalescence, slug flow impact, liquid film fluctuation, and local turbulence will generate pressure pulsations p'(z,t) and flow-induced vibrations. At the same time, convective heat transfer occurs between the three-phase fluid and the string, forming a temperature disturbance T(z,t). The radially sensitive fiber optic structure can convert the pressure pulsations and temperature disturbances generated by the three-phase flow in the wellbore into demodulated acoustic vibration signals and measurable temperature signals, respectively, forming the basic data channels required for subsequent gas-water-thermal inversion and inversion.

[0032] Figure 3 As shown, this invention simultaneously establishes an acoustic coupling channel and a thermal coupling channel. The former is used to identify gas phase and flow pattern disturbances, while the latter is used to identify differences in oil-water thermal properties. For acoustic coupling, pressure pulsations act on the inner wall of the tubing string, causing minute radial vibrations and circumferential strains. This strain is transmitted to the vibration-measuring fiber 3 through the embedded groove and high-modulus encapsulation layer, resulting in an effective strain in the vibration-measuring fiber 3 that can be identified by the DAS demodulator. The physical transmission chain is as follows: wellbore three-phase flow pressure pulsations → tubing wall radial vibration and circumferential strain → stress transmission through the embedded groove and encapsulation layer → effective strain in the vibration-measuring fiber 3 → DAS phase response signal, and the effective strain α obtained by the vibration-measuring fiber 3. eff The relevant formula for (z,t) is:

[0033]

[0034] This equation illustrates the physical relationship by which a radially sensitized structure converts pressure pulsations into an optical fiber strain response; where G... c p'(z,t) represents the coupling gain coefficient; p'(z,t) represents the fluid pressure pulsation within the wellbore; z represents the well depth coordinate; t represents time; E eq η is the equivalent radial elastic coefficient of the coupled system; η is the loss factor caused by the encapsulation material, interface contact state and structural damping.

[0035] For thermal coupling, the temperature change of the fluid in the wellbore is transmitted to the temperature measuring fiber 1 through the pipe wall and the heat-conducting medium. Due to the different densities, specific heat capacities and thermal conductivity of the three phases of oil, water and gas, their temperature decay laws along the wellbore are different.

[0036] S2. Construction of parallel acoustic enhancement and thermal transfer paths. The purpose of this step is to structurally enhance the acoustic and thermal signals formed in S1. The core of the acoustic path is "radial mechanical sensitization + helical space compression", and the core of the thermal path is "rapid heat transfer through the heat-conducting medium + temperature field stabilization".

[0037] In the acoustic path, the vibration-measuring fiber 3 is spirally arranged along the outer wall of the tubing. This spiral path improves the equivalent spatial resolution of the vibration-measuring fiber 3 in the well depth direction from the meter level to the decimeter level. The heat-conducting medium 2 enables the temperature-measuring fiber 1 to respond to wellbore temperature changes within seconds. Therefore, the two types of fibers can be used for acoustic-thermal co-interpretation at approximately the same well depth scale. Since the actual fiber length per unit well depth is greater than the well depth length, the original spatial sampling interval of the DAS demodulator along the vibration-measuring fiber 3 is compressed after being mapped to the well depth direction. The path amplification factor β determines the well depth resolution compression factor of the vibration-measuring fiber 3, which can be expressed as:

[0038]

[0039] In the formula, β is the path amplification factor; R is the spiral winding radius of the vibration measuring fiber 3, which is used to control the path amplification degree of the vibration measuring fiber 3; p is the pitch, which controls the length of the vibration measuring fiber 3 per unit well depth.

[0040] The equivalent sampling interval Δz of the DAS demodulator in the well depth direction DAS It can be represented as:

[0041]

[0042] In the formula, Δs DAS The original sampling interval of the DAS demodulator along the length of the vibration measuring fiber 3.

[0043] In this embodiment, R=40mm, p=80mm, β is approximately 3.30, and when Δs DAS When the depth is 1m, the equivalent sampling interval in the well depth direction is about 0.3m, which can meet the identification requirements of gas-liquid interface disturbance, production zone inlet disturbance and local flow pattern changes.

[0044] In the thermal path, since the temperature-sensing optical fiber 1 is placed inside the heat-conducting medium 2, to reduce thermal response hysteresis, the heat-conducting medium 2 should have a high thermal diffusivity. This diffusivity is used to evaluate the temperature response speed of the temperature-sensing optical fiber 1, and its calculation formula is:

[0045]

[0046] In the formula, α T k, ρ, and c are the thermal diffusivity, thermal conductivity, density, and specific heat capacity of the thermally conductive medium 2, respectively.

[0047] If the equivalent heat transfer distance from the temperature-sensing fiber 1 to the inner wall of the tube is δ, then the characteristic heat diffusion time t diff for: It is used to determine whether there is a significant lag in the temperature signal.

[0048] Figure 4 As shown, the thermal conductivity of the heat-conducting medium is taken as 3.0 W / (m·K), the density as 2500 kg / m³, the specific heat capacity as 900 J / (kg·K), and the thermal diffusivity as approximately 1.33 × 10⁻⁶. -6 m² / s. When the equivalent heat transfer distance δ is 1 mm, the characteristic heat diffusion time is approximately 0.75 s, which meets the temperature response requirements of the temperature measuring fiber 1 in production profile monitoring.

[0049] S3. Distributed Data Acquisition and Feature Extraction: The purpose of this step is to convert the enhanced optical response after downhole drilling into feature data that can be used for three-dimensional inversion. The DAS demodulator acquires dynamic strain or strain rate signals varying along the well depth position z and time t. The DTS demodulator acquires the temperature signal TDTS(z,t) as it changes along the well depth position z and time t.

[0050] First, establish the mapping relationship between the length coordinate s of the vibration measuring fiber 3 and the well depth coordinate z, so that the two types of fibers in the spiral layout are depth aligned and unified to the same well depth grid:

[0051]

[0052] In the formula, s0 is the length coordinate of the vibration measuring fiber 3 corresponding to the starting point of the target well section; z0 is the starting well depth of the target well section.

[0053] The DAS signal was processed by removing DC components, eliminating abnormal channels, bandpass filtering, time-depth synchronization, and spatial resampling; subsequently, time-frequency analysis and spatial correlation analysis were performed to extract the apparent velocity c. app (z), main frequency fp(z), bandwidth energy E DAS (z), vibration amplitude ADAS(z) and spatial coherence C DAS (z), the apparent speed of sound can be obtained by cross-correlation of the time difference between adjacent spatial channels:

[0054]

[0055]

[0056] In the formula, It is a cross-correlation function; The strain rate signal at well depth z1 at time t; An artificially introduced time offset; For the well depth position z2 in time The strain rate signal; This is the propagation delay corresponding to the maximum value of the cross-correlation function.

[0057] Frequency band energy E DAS (z) can be obtained by integrating the spectrum of the DAS signal within a preset frequency band:

[0058]

[0059] In the formula, f is the DAS signal spectrum at position z; f is the DAS signal frequency; f1 is the lower limit of the DAS signal spectrum; f2 is the upper limit of the DAS signal spectrum.

[0060] The DTS signal was processed by temperature profile smoothing, outlier removal, well depth correction, and time synchronization to obtain the measured temperature profile T of the wellbore fluid. obs (z). Based on this, a local background temperature model is constructed, and the temperature gradient G is calculated. T (z), local temperature attenuation coefficient λ, and local temperature anomaly ΔT(z) are used to identify well sections where the fluid properties, flow contribution, or heat transfer conditions in the wellbore change.

[0061] Within a relatively short local interpretation well section, the equilibrium temperature of the surrounding formation or wellbore environment can be approximated as a constant, and an exponential convective heat transfer model can be used to fit the background temperature variation trend of the wellbore fluid:

[0062]

[0063] In the formula, T fit (z) represents the model fitting temperature; T f The equilibrium temperature of the formation or wellbore environment surrounding the well section is given by z; z0 is the initial reference depth of the analyzed well section; T in The reference depth z0 is the wellbore fluid temperature; obtained through local temperature profile fitting, it is used to characterize the overall heat transfer intensity between the wellbore fluid and the surrounding medium.

[0064] Temperature gradient is defined as the rate of change of measured temperature with well depth:

[0065]

[0066] For the above exponential decay model, its theoretical temperature gradient can be further expressed as:

[0067]

[0068] Local temperature anomalies are defined as deviations of the measured temperature from the background fitted temperature.

[0069]

[0070] Among them, G T The abrupt change in (z) reflects an anomaly in the rate of temperature change with well depth; ΔT(z) is used to characterize the degree of deviation of the measured temperature from the local background temperature trend, with positive and negative values ​​corresponding to local heating and cooling anomalies, respectively. Joint analysis of temperature gradient and local temperature anomalies can weaken the influence of the overall temperature change trend of the wellbore and improve the reliability of identifying abnormal well sections.

[0071] Depend on Figure 5 As shown, the DAS phase response is significantly enhanced near a well depth of 2200m to 2350m, indicating the presence of strong flow-induced vibrations or gas-liquid disturbances in this section; Figure 6 It is evident that the temperature gradient changes more significantly near a well depth of 2300m, indicating anomalies in the fluid thermal properties or flow rate contribution in this section. Based on Table 1, it can be concluded that the area near 2300m is the key interpretation section for subsequent gas and water phase identification.

[0072] Table 1. Examples of DAS and DTS feature extraction results in step S3.

[0073]

[0074] S4. Prioritized Inversion of Gas-Phase Parameters Based on DAS Acoustic Features: This step aims to preferentially utilize DAS acoustic features to invert gas content and gas-phase flow rate. The principle is that gases are far more compressible than oil and water; a small amount of gas can significantly reduce the sound velocity of the mixed fluid and alter frequency band energy, dominant frequency, and spatial coherence. Therefore, determining gas-phase information before the three-phase joint solution can reduce the ill-posedness of oil-water thermal inversion.

[0075] In practical implementation, the apparent speed of sound c obtained from S3 will be... app (z) Substitute the sound velocity mixing model to establish the mapping relationship between the mixed sound velocity and the gas phase volume fraction. The Wood sound velocity mixing model or a sound velocity model corrected through field calibration can be used.

[0076]

[0077] In the formula, c m (z) represents the sound velocity of the mixed fluid, which can be obtained by correcting the apparent sound velocity; ρ m (z) represents the density of the mixed fluid; α g (z) represents the gas phase volume fraction; α l (z) represents the liquid phase volume fraction; ρ g c gThese are the gas phase density and the gas phase sound velocity, respectively; ρ l c l These are the equivalent density and equivalent sound velocity of the liquid phase, respectively. .

[0078] To improve inversion stability, this embodiment also includes the band energy E DAS (z), main frequency f p (z) and spatial coherence C DAS (z) serves as an auxiliary constraint, constructing the gas reversal objective function:

[0079]

[0080] In the formula, J g Let c be the objective function for gas inversion; model (α g E represents the model sound velocity under given gas content conditions. model (α g ) represents the model's frequency band energy; C model (α g The model represents spatial coherence; ω1, ω2, and ω3 are weighting coefficients. After obtaining the gas cut, the gas flow rate is calculated by combining the local mixing velocity or the total gas volume at the wellhead.

[0081]

[0082] In the formula, A is the flow area of ​​the well shaft; v g (z) represents the gas phase flow rate; when gas-liquid slippage exists, a slippage correction coefficient Sg can be introduced to make... .

[0083] Depend on Figure 7 As shown in Table 2, the apparent sound velocity of DAS is lowest and the frequency band energy is highest near a well depth of 2300m, with an inverted gas cut of 17.6% and a gas flow rate of approximately 224 m³ / d, indicating that this well section is the main gas-producing or gas-liquid disturbance section. This gas phase result will be used as a known constraint input for subsequent oil-water two-phase thermal inversion.

[0084] Table 2 Examples of Gas Phase Parameter Inversion Results

[0085]

[0086] S5. Gas-phase constrained oil-water two-phase thermal inversion: Based on the determined gas phase content and flow rate, the water cut, oil flow rate, and water flow rate in the liquid phase are inverted using the DTS temperature profile. The principle is that the oil and water phases have different densities, specific heat capacities, and thermal conductivity, exhibiting different temperature decay characteristics during wellbore flow. When the gas phase contribution is constrained, the main uncertainty in the temperature field is transformed into the oil-water ratio and liquid flow rate.

[0087] The temperature field in the wellbore can be represented by a one-dimensional convective heat transfer equation:

[0088]

[0089] In the formula, T(z) is the wellbore fluid temperature; T f (z) represents the reference temperature outside the formation or wellbore; λ(z) is the temperature decay coefficient, which is related to the thermal properties of fluid mixing and the flow rate, and can be expressed as:

[0090]

[0091] In the formula, h is the overall heat transfer coefficient; P is the wellbore heat transfer perimeter; ṁ(z) is the mass flow rate of the mixed fluid; C p,m (z) represents the isobaric specific heat capacity of the mixed fluid.

[0092] The specific heat capacity of a mixed fluid is determined by the volume fraction of the three phases and their physical properties.

[0093]

[0094] In the formula, α g α o α w These represent the volume fractions of the gas phase, oil phase, and water phase, respectively; ρ g ρ o ρ w The densities of gas, oil, and water are respectively; C pg C po C pw These are the specific heat capacities of gas, oil, and water, respectively.

[0095] Due to α g It has been determined in S4 that the main inversion water content f w (z), oil phase flow rate q o (z) and aqueous phase flow rate q w (z), construct the oil-water-thermal inversion objective function constrained by the gas phase:

[0096]

[0097] In the formula, T DTS (z) represents the actual temperature measured by DTS; T model The model temperature is calculated given the gas content, water content, and liquid flow rate; f w (z) represents the moisture content; q l (z) represents the liquid flow rate; γ represents the spatial smoothing constraint coefficient.

[0098] Depend on Figure 8As shown in Table 3, the temperature decay coefficient is highest near a well depth of 2300m, with an inverted water cut of 52% and a water phase flow rate of approximately 128 m³ / d, indicating that this section may be the main water production contributing section. Since S4 has already taken the gas phase contribution as a known input, this step can avoid misjudging the low heat capacity effect of the gas phase as a change in the oil-water ratio, thereby improving the stability of oil-water separation.

[0099] Table 3 Examples of oil-water two-phase thermal inversion results constrained by the gas phase.

[0100]

[0101] S6, Three-Phase Joint Solution and Distributed Production Profile, unifies and optimizes the gas inversion results of S4, the oil-water inversion results of S5, and wellhead metering constraints, outputting a continuously distributed three-phase production profile of oil, gas, and water along the well depth. The flow rates of each phase are jointly corrected under acoustic, thermal, and mass conservation constraints.

[0102] Gas flow rate q at each well depth g (z), oil phase flow rate q o (z) and aqueous phase flow rate q w (z) represents the variable to be optimized, and a joint objective function is established:

[0103]

[0104] In the formula, J DAS J represents the error term between the DAS acoustic prediction and the measured acoustic characteristics. DTS J represents the error term between the predicted temperature from the DTS and the measured temperature profile. Q J represents the error term in the three-phase flow conservation equation; S For spatial smoothing constraints; ω DAS ω DTS ω Q ω S For the corresponding weighting coefficients, the three-phase local flow rates satisfy:

[0105]

[0106] In the formula, q total (z) represents the total flow rate; if wellhead phase metering or surface separator data exists, then the following conditions must be met:

[0107]

[0108] In the formula, This refers to the oil production at the wellhead. This refers to the water production at the wellhead. This represents the gas production at the wellhead.

[0109] During joint optimization, q obtained from S4 and S5 is used first.g (z), q o (z), q w (z) is used as the initial value; then the flow rates of each phase are iteratively adjusted so that the sound velocity, frequency band energy and temperature profiles calculated by the model simultaneously approximate the measured characteristics of DAS and DTS, and satisfy the total wellhead constraint.

[0110] Depend on Figure 9 , Figure 10 As shown in Table 4, the well depth of 2300m shows an increase in both gas and water phase flow rates, which is judged to be the main gas and water production contributing zone; the well depth of 2500m shows a higher oil phase flow rate and lower water and gas cut, which is judged to be the main oil phase contributing zone.

[0111] Table 4. Examples of Three-Phase Combined Solution Results

[0112]

[0113] To verify the feasibility of the method of the present invention, this embodiment will perform consistency verification between the joint inversion results and the acoustic characteristics of DAS, the temperature characteristics of DTS, and the total wellhead constraints. The verification includes: the consistency between the model predicted sound velocity and the apparent sound velocity of DAS, the consistency between the model predicted temperature profile and the measured temperature profile, and the consistency between the integral value of the three-phase flow rate at each depth and the wellhead metering data.

[0114] This invention improves the acoustic and vibration response intensity of the DAS (Digital Amplifier) ​​through a radially sensitizing structure, compresses the spatial resolution of the vibration-measuring fiber 3 in the well depth direction through a helical path, enhances the temperature response speed of the temperature-measuring fiber 1 through a heat-conducting medium 2, and achieves continuous depth monitoring of the three-phase flow rates of oil, gas, and water in the wellbore by utilizing a step-by-step collaborative inversion strategy based on gas inversion results, oil-water inversion results, and wellhead metering constraints. Table 5 shows that the combined inversion performs better than either the standalone DAS interpretation or the standalone DTS interpretation, demonstrating the feasibility of the technical solution of this invention.

[0115] Table 5 Evaluation of the relative performance of the three-phase joint inversion

[0116]

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A distributed monitoring method for three-phase flow in a wellbore based on a radially enhanced fiber optic structure, characterized in that, The method includes the following steps: S1. A radially sensitive fiber optic column structure is deployed inside the wellbore. The pressure pulsations and heat generated by the fluid flow serve as acoustic and thermal signal sources, respectively, acting on the radially sensitive fiber optic column structure. The radially sensitive fiber optic column structure includes a column, a helical radially sensitive groove, a vibration measuring fiber embedded in the sensitive groove and laid in a helical topology, a heat-conducting medium inside the column, a temperature measuring fiber disposed in the heat-conducting medium, and a high-modulus encapsulation layer. S2. Parallel enhancement and transmission of acoustic and thermal signals: In the acoustic path, fluid pressure pulsations are coupled to the vibration measurement fiber through a high-modulus encapsulation layer. The coupling gain coefficient characterizes the faithful linear transmission of fluid pressure pulsations to the effective strain of the vibration measurement fiber. The spiral topology of the vibration measurement fiber is used to compress the equivalent sampling spacing of the vibration measurement fiber along the well depth direction with a path amplification factor, thereby improving its spatial resolution from the meter level to the decimeter level and matching it with the temperature measurement fiber. In the thermal path, fluid heat is transferred to the temperature measurement fiber through a heat-conducting medium, forming a wellbore temperature field. S3. Distributed data acquisition and feature extraction: The DAS demodulator acquires the sensitized strain rate signal and extracts the apparent sound velocity and characteristic frequency band energy through time-frequency analysis. The DTS demodulator acquires the temperature field and analyzes the fluid temperature and temperature attenuation coefficient along the well depth. At the same time, the mapping relationship between fiber coordinates and well depth is established to achieve the alignment of the two types of data in the depth direction. S4. Invert gas phase parameters based on acoustic features; Based on the Wood sound velocity mixing model, invert the gas content using the monotonic mapping relationship between the apparent propagation velocity of sound waves and the gas content, and calculate the gas flow rate by combining the characteristic frequency band energy constraints. S5. Invert the oil and water phase parameters under gas phase constraint. Substitute the determined gas phase information as known conditions into the wellbore heat transfer model based on the one-dimensional convection-heat transfer equation. Based on the difference in thermal properties of oil and water, construct the objective function from the temperature decay coefficient to invert the water cut, and then separate the oil phase flow rate and water phase flow rate. S6. Perform three-phase joint calculation based on mass conservation and generate production profile; with the total flow rate equal to the sum of the flow rates of each phase as a constraint, perform global optimization correction on the gas phase and liquid phase results obtained in steps S4 and S5 by minimizing the joint objective functional, and output the oil, gas and water three-phase production profile continuously distributed along the well depth.

2. The wellbore three-phase flow distributed monitoring method based on radially enhanced fiber optic structure according to claim 1, characterized in that, The effective strain ɛ of the vibration measuring fiber described in S2 eff The relevant formulas for (z,t) are: In the formula, G c p'(z,t) represents the coupling gain coefficient; p'(z,t) represents the fluid pressure pulsation within the wellbore; z represents the well depth coordinate; t represents time; E eq η is the equivalent radial elastic coefficient of the coupled system; η is the loss factor caused by the encapsulation material, interface contact state and structural damping.

3. The wellbore three-phase flow distributed monitoring method based on radially enhanced fiber optic structure according to claim 1, characterized in that, The path amplification factor mentioned in S2 is expressed as: In the formula, β is the path amplification factor; R is the spiral winding radius of the vibration measuring fiber, which is used to control the path amplification of the vibration measuring fiber; p is the pitch, which controls the length of the vibration measuring fiber per unit well depth. The equivalent sampling interval Δz of the DAS demodulator in the well depth direction DAS It can be represented as: In the formula, Δs DAS The original sampling interval of the DAS demodulator along the length of the vibration measuring fiber is denoted as .

4. The wellbore three-phase flow distributed monitoring method based on radially enhanced fiber optic structure according to claim 1, characterized in that, The formula for calculating the apparent speed of sound described in S3 is: In the formula, It is a cross-correlation function; The strain rate signal at well depth z1 at time t; An artificially introduced time offset; For the well depth position z2 in time The strain rate signal; This is the propagation delay corresponding to the maximum value of the cross-correlation function.

5. The wellbore three-phase flow distributed monitoring method based on radially enhanced fiber optic structure according to claim 1, characterized in that, The specific steps for S4 are as follows: The apparent speed of sound c obtained from S3 app (z) Substitute into the sound velocity mixing model to establish the mapping relationship between the mixed sound velocity and the gas phase volume fraction. Use the Wood sound velocity mixing model or a sound velocity model that has been calibrated and corrected on-site: In the formula, c m (z) represents the sound velocity of the mixed fluid, obtained by correcting for the apparent sound velocity; ρ m (z) represents the density of the mixed fluid; α g (z) represents the gas phase volume fraction; α l (z) represents the liquid phase volume fraction; ρ g c g These are the gas phase density and the gas phase sound velocity, respectively; ρ l c l These are the equivalent density and equivalent sound velocity of the liquid phase, respectively. ; Frequency band energy E DAS (z), main frequency f p (z) and spatial coherence C DAS (z) serves as an auxiliary constraint, constructing the gas reversal objective function: In the formula, J g Let c be the objective function for gas inversion; model (α g E represents the model sound velocity under given gas content conditions. model (α g ) represents the model's frequency band energy; C model (α g The model uses a spatial coherence model; ω1, ω2, and ω3 are weighting coefficients. After obtaining the gas cutoff, the gas flow rate is calculated by combining the local mixing velocity or the total gas volume at the wellhead. In the formula, A is the flow area of ​​the well shaft; v g (z) represents the gas phase flow rate; when gas-liquid slippage exists, a slippage correction coefficient Sg is introduced to make... .

6. The wellbore three-phase flow distributed monitoring method based on radially enhanced fiber optic structure according to claim 1, characterized in that, The objective function for retrieving oil-water two-phase parameters under gas-phase constraint as described in S5 is: In the formula, T DTS (z) represents the actual temperature measured by DTS; T model The model temperature is calculated given the gas content, water content, and liquid flow rate; f w (z) represents the moisture content; q l (z) represents the liquid flow rate; γ represents the spatial smoothing constraint coefficient.

7. The wellbore three-phase flow distributed monitoring method based on radially enhanced fiber optic structure according to claim 1, characterized in that, The specific steps for S6 are as follows: Gas flow rate q at each well depth g (z), oil phase flow rate q o (z) and aqueous phase flow rate q w (z) represents the variable to be optimized, and a joint objective function is established: In the formula, J DAS J represents the error term between the predicted acoustic quantity and the measured acoustic characteristics. DTS J represents the error term between the predicted temperature and the measured temperature profile. Q J represents the error term in the three-phase flow conservation equation; S For spatial smoothing constraints; ω DAS ω DTS ω Q ω S For the corresponding weighting coefficients, the three-phase local flow rates satisfy: In the formula, q total (z) represents the total flow rate; if wellhead phase metering or surface separator data exists, then the following conditions must be met: In the formula, This refers to the oil production at the wellhead. This refers to the water production at the wellhead. This represents the gas production at the wellhead.

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