Low-yield well distributed optical fiber sound wave wellbore flow interpretation system and method based on thermal excitation

Abstract

The invention belongs to the technical field of oil and gas exploitation, and particularly relates to a low-yield well distributed optical fiber sound wave wellbore flow interpretation system and method based on thermal excitation. Downhole fluid is heated through a downhole heating device, and a thermal fluid slug with the temperature higher than that of unheated fluid is formed; the moving distance of the thermal fluid slug in the shaft in unit time is tracked by drawing an optical fiber strain response diagram, and the downhole fluid flow speed can be obtained. The shaft flow interpretation method is small in underground flow interference and obvious in optical fiber signal response, the technology is simple and easy to implement, and the problem that signals of the DAS in a low-yield well are not obvious can be effectively solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of oil and gas production, and in particular relates to a distributed optical fiber acoustic wave wellbore flow interpretation system and method for low-yield wells based on thermal excitation. Background Art

[0002] In the oil and gas production process, flow testing is an important means of obtaining downhole reservoir production dynamics. Understanding the production dynamics of downhole reservoirs is of great significance for understanding reservoir properties and optimizing mining plans. At present, field flow testing mainly relies on different traditional flow meters to cope with different well conditions, such as turbine flow meters, tracer flow meters, ultrasonic flow meters, electromagnetic flow meters, etc. However, these traditional flow testing technologies have technical limitations. For example, the test results of turbine flow meters in wells with high fluid density and viscosity are poor in accuracy; the resolution of tracer flow meters is significantly reduced when the fluid flow pattern changes; the measurement results of ultrasonic flow meters are poor in accuracy under complex flow conditions; and electromagnetic flow meters are easily interfered with by strong external electromagnetic fields. In addition, the traditional flow testing method is complicated to operate in the well, and it is impossible to continuously collect flow information at multiple points at the same time, making it difficult to obtain downhole production dynamics in real time and make corresponding adjustments.

[0003] With the advancement and development of fiber optic technology, distributed acoustic sensing (DAS) is being used in the oil and gas sector and is playing an increasingly important role in downhole flow dynamic testing. By monitoring the strain on the fiber caused by changes in various physical fields downhole, this technology enables real-time, continuous, high-resolution, and high-precision downhole flow monitoring during production. Existing DAS data processing relies on strong acoustic signals to induce strain on the fiber. However, in low-yield wells, the flow rate is too low to generate a strong acoustic signal.

[0004] Chinese patent CN118149913A (202211555872.5) discloses a fiber-optic acoustic wave sensing flow rate choke test system and interpretation method for low-yield wells. The system includes a test optical cable, a surface demodulator, a test string, multiple chokes, and a casing. The test optical cable is attached to the outer wall of the test string and connected to the surface demodulator. The test string is located within the casing, and the multiple chokes are connected to the test string. The casing passes through multiple production intervals. Fluid produced from the multiple production intervals flows upward through the test string, sequentially passing through the multiple chokes, and then out to the surface. The surface demodulator transmits pulsed laser light through the single-mode optical fiber of the test optical cable to collect acoustic wave data of the fluid flowing through the multiple chokes and calculates the flow rate based on the acoustic wave response frequency and flow model. However, this device changes the flow of fluid in the wellbore through throttling, and the calculated flow rate is often greater than the actual flow rate.

[0005] Chinese patent CN118094186A (202410204106.7) discloses a wellbore flow interpretation method based on fiber-optic monitoring data. This method uses the flow signals of multiphase fluids collected by distributed fiber-optic sensors to decompose the acoustic signals into periodic frequency signals. This model then establishes a correlation model between flow and frequency. This model is then used as an adaptation function and trained using a machine learning algorithm with the root mean square energy in the frequency domain as a characteristic parameter to determine the flow rate. This method uses machine learning to calculate flow, so it requires sufficient sample data, which may not be sufficient for field DAS data. Furthermore, due to differences in acoustic models due to varying well conditions and wellbore structures, model calibration training is required for each well, which is time-consuming and costly, and not suitable for low-yield wells.

[0006] Chinese patent CN118774767A (202410731156.0) discloses a system and method for interpreting oil well production profiles based on distributed fiber optic acoustic monitoring. The system includes a fiber optic cable for downhole monitoring and a surface DAS data acquisition device. By setting an initial water cut, the FBE value for each producing layer is calculated. Based on a plotted FBE and flow rate chart, or by linearly interpolating the intermediate values ​​between two assumed water cuts, the flow rate for each layer under the assumed water cut is obtained. Finally, corrections are made using the wellhead flow rate and the flow rate corresponding to the assumed water cut. When the error meets the required accuracy, the water cut and flow rate for each producing layer are output. This method assumes an initial water cut and corrects it by comparing actual production with the assumed production calculated using the FBE chart. However, this method assumes a large deviation from the initial value, resulting in a long iterative processing time. Furthermore, the flow rate determined using the chart method is subject to certain errors, and this method is not suitable for low-yield wells.

[0007] Aiming at the problem that DAS production monitoring of low-yield wells has unclear acoustic wave response, the present invention provides a simple and reliable method to meet the needs of dynamic production monitoring of low-yield wells. Summary of the Invention

[0008] The primary objective of this invention is to provide a distributed fiber-optic acoustic wellbore flow interpretation system and method for low-yield wells based on thermal excitation. This system utilizes a downhole heating device to heat the downhole fluid, forming a hot fluid slug with a higher temperature than the unheated fluid. By plotting the fiber-optic strain response and tracking the distance the hot fluid slug moves per unit time in the wellbore, the downhole fluid velocity can be determined. This wellbore flow interpretation method minimizes downhole flow interference, provides a clear fiber-optic signal response, and is simple and easy to implement. It effectively addresses the issue of unclear DAS signals in low-yield wells.

[0009] The technical problem to be solved by the present invention is achieved by adopting the following technical solution: a distributed fiber optic acoustic wellbore flow interpretation system for low-yield wells based on thermal excitation, comprising an oil pipe, a downhole optical cable, a downhole thermal excitation device and a surface DAS monitoring system; The downhole optical cable is arranged in the oil pipe or on the inner wall of the oil pipe, and the downhole thermal excitation device is located in the oil pipe and connected to the downhole optical cable; The surface DAS monitoring system is connected to the downhole optical cable, and the DAS monitoring system includes a real-time data acquisition system and a DAS low-yield wellbore flow interpretation module; The downhole thermal excitation device is used to heat the downhole fluid to form a hot fluid slug; The real-time data acquisition system is used to collect DAS data of downhole optical cables in real time; The DAS low-yield wellbore flow interpretation module processes DAS data acquired by the real-time data acquisition system. By plotting the optical fiber strain response, it tracks the movement of the thermal fluid slug and determines the real-time flow rate of the low-yield wellbore. For single-shot measurements, the downhole optical cable and downhole thermal excitation device are lowered into the tubing. For longer-term measurements, the downhole optical cable can be lowered to the tubing's inner wall to ensure its sensitivity to thermal fluids.

[0010] Preferably, the DAS low-yield wellbore flow interpretation module comprises a data preprocessing module, an image drawing module and a wellbore flow calculation module; The data preprocessing module is used to filter and downsample the acquired data to obtain the slow strain characteristic response frequency; The image drawing module is used to draw a slow strain response diagram using the pre-processed DAS data, and to draw a straight line for the fluid motion trajectory in the slow strain response diagram; The wellbore flow calculation module is used to calculate the slope of the drawn straight line to obtain the wellbore flow rate at the target point.

[0011] Preferably, the data preprocessing module includes a Butterworth filter and a downsampling processing module; The Butterworth filter is used to perform low-pass filtering on the data; The downsampling processing module is used to reduce the data to the frequency domain where the temperature data signal is located.

[0012] Preferably, the Butterworth filter is a Gaussian anti-aliasing filter. The Butterworth filter is used to remove aliasing between high-frequency and low-frequency signals, thereby reducing the impact of aliasing on the accuracy of downsampled data, and the Gaussian anti-aliasing filter is used to achieve a higher anti-aliasing function.

[0013] Preferably, the image drawing module includes a DAS slow strain drawing module and a straight line detection module; The DAS slow strain drawing module is used to draw an image of the pre-processed data to obtain a DAS acoustic wave slow strain response diagram related to temperature; The straight line detection module is used to perform normalized grayscale processing, Canny algorithm edge detection and Hough transform on the drawn DAS acoustic wave slow strain response map in sequence, detect the straight lines in the DAS acoustic wave slow strain response map and draw the corresponding straight lines.

[0014] Preferably, in the present invention, the downhole thermal excitation device includes a resistive heater connected to the downhole optical cable.

[0015] Preferably, the present invention further comprises a temperature sensor and a ground temperature control system; The temperature sensor is arranged on the downhole optical cable and is used to measure the fluid temperature at the downhole thermal excitation device; The surface temperature control system is connected to the downhole thermal actuator and is used to control the downhole thermal actuator's start and stop and set its target temperature. A temperature sensor is integrated with the downhole thermal actuator; the surface temperature control system has a built-in switch that can be used to stop the resistor from operating.

[0016] A downhole thermal excitation device is lowered into the well, and the temperature of the downhole fluid is measured using a temperature sensor. The temperature of the downhole hot fluid segment is set through the surface temperature control system. The downhole thermal excitation device starts working. When the temperature measured by the temperature sensor reaches the preset heating temperature, the surface temperature control system cuts off the working circuit of the downhole thermal excitation device, and the heating is completed.

[0017] The present invention also discloses a DAS wellbore fluid velocity interpretation method for a low-yield well based on active thermal stimulation, comprising the following steps: S1. Data collection: By installing a downhole optical cable in the oil pipe or on the inner wall of the oil pipe of a low-yield well, setting a downhole thermal excitation device on the downhole optical cable, and connecting the downhole thermal excitation device to the surface DAS monitoring system; Using a downhole thermal excitation device to heat the downhole fluid to form a hot fluid slug; Real-time collection of DAS data of downhole optical cables; S2. Data preprocessing: The collected DAS data is pre-processed by using a Butterworth filter to perform low-pass filtering on the data; after filtering, the data is downsampled to the frequency domain where the temperature data signal is located; S3. Image drawing: The pre-processed data is imaged to obtain the DAS acoustic wave slow strain response diagram with respect to temperature; S4. Detect straight lines in the image: The DAS acoustic slow strain response image is processed with normalized grayscale, edge detection with the Canny algorithm, and Hough transform in turn to detect the straight lines in the DAS slow strain image and draw the corresponding straight lines. S5. Calculate the wellbore flow rate: The slope of the straight line is obtained using the fitting function to obtain the wellbore flow velocity at the target point.

[0018] Preferably, in the present invention, in step S4, the flow rate at the target point is obtained according to the size of the oil pipe.

[0019] Preferably, in step S4, the Canny algorithm edge detection uses a dual-threshold Canny detection algorithm to make edge recognition of the detected image more accurate.

[0020] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention uses a downhole heating device to actively introduce a hot fluid slug, enhancing the optical fiber's response. The DAS slow strain diagram is then used to determine the wellbore flow rate. This method leverages the temperature sensitivity of the optical fiber to artificially introduce a hot fluid slug. The movement of the hot fluid slug is then tracked by plotting the optical fiber's strain response, thereby determining the wellbore fluid flow rate.

[0021] 2. The method of the present invention is simple, low-cost, and has little environmental pollution. Heat, as a clean energy source, does not pollute the environment. At the same time, heating the downhole fluid only requires lowering a heating source along with the optical fiber, which is simple to operate. In addition, compared with other flow measurement methods, it does not require expensive chemical reagents or equipment, and the operating cost is low.

[0022] 3. Fiber optics are sensitive to temperature changes. Experimental verification shows that in low-yield wells, significant effects can be achieved when the temperature difference between hot and cold fluids is 5°C. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of a specific embodiment of the distributed fiber optic acoustic wellbore flow interpretation method for low-yield wells based on thermal excitation according to the present invention; Figure 2 This is a schematic diagram of the experimental structure of the distributed fiber-optic acoustic wellbore flow interpretation system for low-yield wells based on thermal excitation according to the present invention; Figure 3 This is a schematic diagram of the actual production structure of the distributed fiber optic acoustic wellbore flow interpretation system for low-yield wells based on thermal excitation of the present invention; Figure 4 This is the slow strain response effect diagram of the thermal fluid slug DAS; Figure 5 This is the effect diagram of the fitted straight line; In the figure, 1 is the optical fiber inside the pipe, 2 is the optical fiber outside the pipe, 3 is the production string, 4 is the heating device, 5 is the screen tube, 6 is the pipeline, 7 is the water reservoir, 8 is the centrifugal pump, 9 is the temperature control system, and 10 is the DAS data acquisition system. 11. Composite cable, 12. Oil pipe, 13. Casing, 14. Ground optical fiber. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings.

[0025] A distributed fiber optic acoustic wellbore flow interpretation system for low-yield wells based on thermal excitation includes an oil pipe, a downhole optical cable, a downhole thermal excitation device and a surface DAS monitoring system.

[0026] The downhole optical cable is arranged in the oil pipe or on the inner wall of the oil pipe, and the downhole thermal excitation device is located in the oil pipe and connected to the downhole optical cable. The installation method of the downhole optical cable is existing technology and will not be described here.

[0027] The surface DAS monitoring system is connected to the downhole optical cable, and the DAS monitoring system includes a real-time data acquisition system and a DAS low-yield wellbore flow interpretation module.

[0028] The downhole thermal excitation device is used to heat the downhole fluid to form a hot fluid slug.

[0029] The real-time data acquisition system is used to acquire DAS data of downhole optical cables in real time.

[0030] The DAS low-yield wellbore flow interpretation module is used to process the DAS data acquired by the real-time data acquisition system, track the movement of the thermal fluid slug by drawing the optical fiber strain response diagram, and obtain the real-time wellbore flow rate of the low-yield well.

[0031] The DAS low-yield wellbore flow interpretation module includes a data preprocessing module, an image rendering module and a wellbore flow calculation module.

[0032] The data preprocessing module is used to filter and downsample the acquired data to obtain the slow strain characteristic response frequency.

[0033] The image drawing module is used to draw a slow strain response diagram using the pre-processed DAS data and to draw a straight line for the fluid motion trajectory in the slow strain response diagram. The image drawing module is implemented using existing drawing software or drawing functions, which will not be described in detail here.

[0034] The wellbore flow calculation module is used to calculate the slope of the drawn straight line to obtain the wellbore flow rate at the target point.

[0035] The data preprocessing module includes a Butterworth filter and a downsampling processing module.

[0036] The Butterworth filter is used to perform low-pass filtering on the data.

[0037] The downsampling processing module is used to reduce the data to the frequency domain where the temperature data signal is located.

[0038] The Butterworth filter is a Gaussian anti-aliasing filter.

[0039] The image drawing module includes a DAS slow strain drawing module and a straight line detection module.

[0040] The DAS slow strain drawing module is used to draw an image of the preprocessed data to obtain a DAS acoustic wave slow strain response diagram related to temperature.

[0041] The straight line detection module is used to perform normalized grayscale processing, Canny algorithm edge detection and Hough transform on the drawn DAS acoustic wave slow strain response map in sequence, detect the straight lines in the DAS acoustic wave slow strain response map and draw the corresponding straight lines.

[0042] The data preprocessing module processes the acquired raw data. The data acquired by DAS is raw data. After performing time differentiation on the raw data, the data matrix is ​​reconstructed to obtain preliminarily processed data. In order to reduce the interference of cluttered signals, the above data needs to be further processed. Here, filtering and downsampling methods are used to process the data. Among them, filtering is to remove the aliasing phenomenon between high-frequency signals and low-frequency signals, and reduce the impact of aliasing on the accuracy of downsampled data. Downsampling is to obtain DAS slow strain data and draw slow strain diagrams. The filtering technology mainly uses the low-pass filter in the Butterworth low-pass filter, with a cutoff frequency of 1Hz. The Butterworth filter is a signal processing technology proposed by British engineer Stephen Butterworth in 1930. The frequency response curve within the passband range is flat in the largest range. It is widely used in signal processing and will not be described in detail here.

[0043] Downsampling technology can be viewed as a process of extracting sample data in the time domain, that is, taking a sample every M samples from a continuous signal sample. For preprocessed DAS data, the downsampling process is as follows: Assume that the initial DAS data matrix is ​​X, with a size of a*b, a downsampling factor of M*M, and the data in row i and column j of the original matrix is Expressed as:

[0044] In order to achieve higher anti-aliasing function, a Gaussian anti-aliasing filter is introduced. It is necessary to set a (a-1)*(b-1) Gaussian kernel G. For the mth row and nth column, the Gaussian kernel Its expression is:

[0045] Where: is the standard deviation, which is used to control the filtering strength. Generally speaking, , for the above formula Expressed as the center coordinates of the Gaussian kernel, its expression is as follows:

[0046] In order to ensure that the Gaussian kernel filter does not change the characteristics of the global signal, that is, to ensure that the signal is not distorted, the Gaussian kernel needs to be normalized and the final Gaussian kernel weight sum is 1. The normalized Gaussian kernel of the mth row and nth column is The expression is as follows:

[0047] Where: represents the sum of all Gaussian kernels.

[0048] Using the designed Gaussian kernel, the DAS original data matrix X is downsampled and divided into M*M non-overlapping sub-matrices. Then, Gaussian weighted average is performed on each sub-matrix to obtain the downsampled matrix Z. The downsampled matrix size is (a / M, b / M). The kth row in Z is l DAS downsampled data for the column The mathematical expression is as follows:

[0049] The wellbore flow calculation module is a processing flow of the DAS slow strain map drawn after preprocessing the DAS raw data. In the present invention, Matlab software is used to process the drawn image, which mainly includes image reading, image recognition, feature line detection and parameter calculation.

[0050] The image reading is to read the drawn DAS slow strain map and convert it into a corresponding matrix Z'. In the present invention, the converted matrix Z' is the matrix Z after downsampling.

[0051] Image recognition is the process of processing the DAS slow strain response map to obtain the flow rate. The core is to identify the migration process of the hot fluid slug, which requires the DAS slow strain response map to process. Figure 4 The DAS slow strain response diagram shown above uses the Canny operator plus Hough transform line detection method to extract the line. The specific steps are as follows: The first step is to normalize and grayscale the image to improve the contrast of the image and enhance the visual effect. The present invention uses the maximum and minimum value normalization method to normalize the matrix Z obtained after the downsampling process. The matrix after normalization is The expression is as follows:

[0052] Where, Represent the maximum and minimum values ​​in the matrix Z respectively, Represents the element in row k and column l of Z.

[0053] Step 2: Use the Canny algorithm for edge detection. The Canny algorithm can be used to extract useful structural information from images. To reduce the impact of image noise on edge detection, a Gaussian filter is first used for smoothing. The core of the algorithm is to use the Gaussian function for convolution. The mathematical principle is as follows:

[0054]

[0055] Where: , are the images before and after convolution, respectively. is a two-dimensional Gaussian function, is the standard deviation, which is used to control the smoothness of the Gaussian filter. x and y represent the spatial directions.

[0056] Like other operators, the Canny edge detection algorithm obtains the gradient and gradient direction through differential operation. Here, the Sobel operator is used to perform horizontal and vertical convolution on the image, and its convolution kernel uses the convolution kernel of the Sobel operator. From this, the gradient direction can be calculated. and gradient strength , its expression is as follows.

[0057] Gradient direction:

[0058] Gradient Strength:

[0059] Where: 、 Represents the gradient difference in the x and y directions respectively.

[0060] In order to make the edge recognition of the detected image more accurate, the dual-threshold Canny detection algorithm is usually used. Its implementation principle is as follows: The core of dual-threshold detection is to set two thresholds, high and low. That is, if the gradient value is greater than the high threshold, it is a strong edge; if the gradient is between the high and low thresholds, it is a weak edge; when the gradient value is less than the low threshold, it is not considered an edge. Using a dual-threshold method can effectively solve the problems of edge discontinuity and edge misjudgment that may occur with a single threshold.

[0061] Step 3: Feature detection using the Hough transform. The Hough transform, proposed by Hough in 1962, can be used to identify straight line structures in images. It detects lines by detecting the intersection of lines in parameter space and fitting points that correspond to collinear features in image space. The mathematical principle is as follows: For a pixel in the image , the straight lines passing through it have the following form

[0062] In the xy parameter space, the line passes through the point ; Correspondingly, there is a straight line 1 in the ab parameter space, which must pass through the point ; If there is another point and If a straight line is determined, then in the ab space, there will be a straight line 2 that intersects with the straight line 1 at point When the point and Each additional point on the determined straight line intersects at point There will be one more straight line, which will intersect the ab space at The number of straight lines is recorded as the number of cells, and a threshold for the number of cells is set. When the number of cells exceeds the threshold, it is considered a straight line. The points on the line are then saved in a new matrix. In this method, the original matrix X is filtered and downsampled to obtain the matrix Z. Z is processed using the Canny edge detection algorithm and the Hough transform algorithm to obtain the matrix vector C of the points on the same line. If there are different lines in the image, the matrix vector C will contain multiple matrices.

[0063] Step 4: Linear fitting. Draw data points through each point in matrix C, use the fitting function to fit the drawn data points to get the slope of the line With the intercept The slope of the straight line is the wellbore fluid velocity, as shown in the following formula:

[0064] Where: v represents the wellbore fluid velocity, x represents the movement distance of the hot fluid slug, and t represents the unit time. Based on the well structure size A, the wellbore flow rate can be determined. .

[0065] The downhole thermal excitation device includes a resistive heater connected to a downhole optical cable.

[0066] The distributed optical fiber acoustic wave wellbore flow interpretation system for low-yield wells based on thermal excitation also includes a temperature sensor and a ground temperature control system.

[0067] The temperature sensor is arranged on the downhole optical cable and is used to measure the fluid temperature at the downhole thermal excitation device.

[0068] The surface temperature control system is connected to the downhole thermal actuator, controlling its on / off function and setting its target temperature. The downhole heating device is a resistive heater with a heating power of 1 kW. The surface temperature control system controls the heating time for varying downhole temperatures, creating a hot fluid slug approximately 5°C higher than the unheated fluid.

[0069] like Figure 1 As shown, a DAS wellbore fluid velocity interpretation method for a low-yield well based on active thermal stimulation includes the following steps: S1. Data collection: 1-1) After arriving at the target well, confirm whether the target well meets the logging conditions. For target wells that meet the conditions, check the integrity of related equipment and optical cables to ensure that the equipment can meet normal measurement requirements.

[0070] 1-2) During testing, lower the optical cable and heating device at a speed not exceeding 3000 m / h. The lowering speed should be reduced near unusual points such as the downhole reducer, bottom ball (reverse circulation valve), bellmouth, cross, and tailpiece. During the lowering process, operators should constantly monitor the tension and depth indicators, as well as the wellhead conditions, and stop the operation immediately if any obstruction is encountered.

[0071] 1-3) During measurement, the distributed acoustic sensing equipment on the ground needs to remain on to continuously monitor the status of the optical cable in real time. After the instrument is lowered into the target area, the heating device is turned on to heat the fluid and obtain the fluid thermal signal distributed along the oil pipe or wellbore trajectory. At the same time, the data is stored in the ground storage device and monitored in real time on the ground monitoring screen to observe any abnormalities. If any abnormalities are found, they should be handled immediately. 1-4) During the measurement process, check the quality of the logging data. If it does not meet the on-site quality control requirements, perform additional measurements.

[0072] A downhole optical cable is installed in the oil pipe or on the inner wall of the oil pipe of a low-yield well, a downhole thermal excitation device is set on the downhole optical cable, and the downhole thermal excitation device is connected to the ground DAS monitoring system.

[0073] A downhole thermal excitation device is used to heat the downhole fluid to form a hot fluid slug.

[0074] Real-time DAS data collection of downhole optical cables. Installing an active thermally activated DAS system through indoor and field experiments to collect DAS data under different operating conditions (different operating temperatures, different flow rates, etc.).

[0075] S2. Data preprocessing: The collected DAS data is preprocessed and low-pass filtered using a Butterworth filter; after filtering, the data is downsampled to the frequency domain where the temperature data signal is located.

[0076] The collected raw DAS signal is filtered to remove the influence of noise, which is done by applying a low-pass filter and selecting a special frequency band including slow strain signals.

[0077] The filtered DAS data is downsampled to obtain the slow strain characteristic response frequency. Related research shows that the DAS signal can be downsampled to 1Hz, which can better observe the slow strain signal.

[0078] S3. Image drawing: The preprocessed data is imaged to obtain a DAS acoustic wave slow strain response diagram with respect to temperature. Figure 4 The figure shows the slow strain response effect of the hot fluid slug DAS. The figure shows the response diagram when the heating power is 1000W and the heating time is 120s.

[0079] The pre-processed DAS data were used to draw a slow strain map to determine the approximate range of flow velocity.

[0080] S4. Detect straight lines in the image: The DAS acoustic wave slow strain response image is subjected to normalized grayscale processing, Canny algorithm edge detection, and Hough transform in sequence to detect straight lines in the DAS slow strain image and draw the corresponding straight lines. In step S4, the Canny algorithm edge detection uses a dual-threshold Canny detection algorithm.

[0081] Perform grayscale transformation on the drawn slow strain map to enhance the contrast of the signal. Figure 5 As shown, the flow velocity is obtained by fitting the straight line obtained by using the Canny detection algorithm + hough transform (i.e., Hough transform) and fitting the slope.

[0082] The gradient difference algorithm is used to differentiate the points in the image to obtain the extreme values ​​in the slow strain map, and the double threshold detection method is used to determine the edge area of ​​the image.

[0083] Using Hough transform, edge regions on the same straight line are detected and the straight line is drawn.

[0084] S5. Calculate the wellbore flow rate: The Fit function is used to fit a straight line, and the slope detection algorithm can be used to detect the slope of the straight line. The slope obtained is the fluid flow rate in the wellbore.

[0085] In step S4, the flow rate at the target point is calculated according to the size of the oil pipe.

[0086] Application Example 1 like Figure 2 As shown, based on the above principle, an indoor horizontal well thermal excitation experiment was first carried out to simulate flow detection under long-term production conditions. The specific steps are as follows: Step 1: String assembly, according to Figure 2 As shown, the in-tube optical fiber 1 and the external optical fiber 2 are fixed to a production string 3 designed according to the actual dimensions of the site. The production string 3 simulates the oil pipeline, while the in-tube optical fiber 1 serves as the downhole optical cable. A heating device 4, or downhole thermal excitation device, is fixed to the end of the production string, and then the production string is encapsulated. The encapsulated production string is connected to a water reservoir 7 and a centrifugal pump 8 via a screen 5 and a pipeline 6, enabling fluid circulation within the pipeline. Simultaneously, an external temperature control system 9 is connected to the heating device 4 to achieve variable temperature control.

[0087] Step 2: Connect the optical fiber 2 outside the pipe near the front end of the production string to the DAS data acquisition system 10, i.e., the surface DAS monitoring system, and process the optical fiber inside the pipe near the front end of the production string to obtain a stable signal.

[0088] Step 3: Debug the experimental equipment, start the DAS equipment and open the production string fluid circulation, check the equipment working condition, and adjust the DAS system parameters to complete the preparation for the experiment.

[0089] Step 4: Open the DAS data acquisition system 10 and use the acquisition software to collect the DAS data during the experiment.

[0090] Step 5: Use the above-mentioned distributed fiber optic acoustic wellbore flow interpretation method for low-yield wells to process the DAS data and obtain the real-time flow rate of the wellbore.

[0091] Step 6: Change the fluid flow and heat input conditions, repeat steps 4 and 5, and evaluate the effectiveness of the distributed fiber optic acoustic wellbore flow interpretation method for low-yield wells under different flow conditions.

[0092] Step 7: Turn off the device and complete the experiment.

[0093] Table 1

[0094] Table 1 compares the calculated and actual flow rates using the DAS wellbore flow rate interpretation method for low-yield wells based on thermal stimulation (this table shows the flow rate comparison when the heating power is 1000W and the heating time is 120s). In indoor horizontal well thermal stimulation experiments, different circulation flow rates were set, and theoretical flow rates were calculated based on flow data and wellbore structural dimensions. Simultaneously, DAS data was collected, and the flow rate calculation method described in this paper was used to calculate the flow rate. A comparison of the two flow rates revealed that the error between the two was small at low flow rates, meeting actual engineering needs. This method is an effective and feasible DAS wellbore flow rate interpretation method for low-yield wells based on thermal stimulation.

[0095] Application Example 2 like Figure 3 As shown, when the method of the present invention is applied to the flow velocity measurement of the production wellbore of a low-yield well, the flow rate detection under a single production state is carried out on site. The specific steps are as follows: Step 1: Determine the fiber optic cable length based on the target well on site. Ensure the fiber optic cable and heating device can be lowered into the target formation. Integrate the downhole optical fiber and heating cable into a composite cable 11. The heating cable is insulated to prevent significant heat loss and potential damage to the optical fiber. Connect the heating device 4 to the heating cable at the end of the composite cable 11. Lower the cable directly into the target producing formation through the oil pipe 12. The heating cable is connected to the surface temperature control system 9 (not shown). The oil pipe 12 is placed within the casing 13.

[0096] Step 2: Connect the ground optical fiber 14 to the DAS data acquisition system 10 placed on the ground.

[0097] Step 3: Debug and monitor the installed DAS wellbore flow monitoring system equipment for low-yield wells.

[0098] Step 4: Start production and start the DAS data acquisition system 10.

[0099] Step 5: Turn on the temperature control system 9 and set appropriate heating power and heating time according to the on-site well conditions.

[0100] Step 6: Observe the DAS data displayed in the DAS data acquisition system 10 to see if there are any abnormalities, and collect the data.

[0101] Step 7: Process the DAS data using the low-yield well DAS wellbore flow rate interpretation method of the present invention to obtain the fluid flow rate of the target layer.

[0102] Step 8: Change the working system and repeat steps 4-7 to obtain the wellbore flow rate of the same low-yield well under different working systems to facilitate production system optimization.

[0103] Step 9: After the test is completed, the DAS data acquisition system 10 is turned off, and the composite cable 11 and the heating device 4 are removed from the oil pipe 12 to complete the temporary monitoring of the wellbore flow rate of the low-yield well.

Claims

1. A distributed fiber-optic acoustic wellbore flow interpretation system for low-yield wells based on thermal excitation, characterized by: Including oil pipes, downhole optical cables, downhole thermal excitation devices and surface DAS monitoring system; The downhole optical cable is arranged in the oil pipe or on the inner wall of the oil pipe, and the downhole thermal excitation device is located in the oil pipe and connected to the downhole optical cable; The surface DAS monitoring system is connected to the downhole optical cable, and the DAS monitoring system includes a real-time data acquisition system and a DAS low-yield wellbore flow interpretation module; The downhole thermal excitation device is used to heat the downhole fluid to form a hot fluid slug; The real-time data acquisition system is used to collect DAS data of downhole optical cables in real time; The DAS low-yield wellbore flow interpretation module is used to process the DAS data acquired by the real-time data acquisition system, track the movement of the thermal fluid slug by drawing the optical fiber strain response diagram, and obtain the real-time wellbore flow rate of the low-yield well.

2. The distributed fiber optic acoustic wellbore flow interpretation system for low-yield wells based on thermal excitation according to claim 1 is characterized by: The DAS low-yield wellbore flow interpretation module includes a data preprocessing module, an image rendering module and a wellbore flow calculation module; The data preprocessing module is used to filter and downsample the acquired data to obtain the slow strain characteristic response frequency; The image drawing module is used to draw a slow strain response diagram using the pre-processed DAS data, and to draw a straight line for the fluid motion trajectory in the slow strain response diagram; The wellbore flow calculation module is used to calculate the slope of the drawn straight line to obtain the wellbore flow rate at the target point.

3. The distributed fiber optic acoustic wellbore flow interpretation system for low-yield wells based on thermal excitation according to claim 2, characterized in that: The data preprocessing module includes a Butterworth filter and a downsampling processing module; The Butterworth filter is used to perform low-pass filtering on the data; The downsampling processing module is used to reduce the data to the frequency domain where the temperature data signal is located.

4. The distributed fiber optic acoustic wellbore flow interpretation system for low-yield wells based on thermal excitation according to claim 3, characterized in that: The Butterworth filter is a Gaussian anti-aliasing filter.

5. The distributed fiber optic acoustic wellbore flow interpretation system for low-yield wells based on thermal excitation according to claim 2, characterized in that: The image drawing module includes a DAS slow strain drawing module and a straight line detection module; The DAS slow strain drawing module is used to draw an image of the pre-processed data to obtain a DAS acoustic wave slow strain response diagram related to temperature; The straight line detection module is used to perform normalized grayscale processing, Canny algorithm edge detection and Hough transform on the drawn DAS acoustic wave slow strain response map in sequence, detect the straight lines in the DAS acoustic wave slow strain response map and draw the corresponding straight lines.

6. The distributed fiber optic acoustic wellbore flow interpretation system for low-yield wells based on thermal excitation according to claim 1, characterized in that: The downhole thermal excitation device includes a resistive heater connected to a downhole optical cable.

7. The distributed fiber optic acoustic wellbore flow interpretation system for low-yield wells based on thermal excitation according to claim 6, characterized in that: It also includes temperature sensors and a ground temperature control system; The temperature sensor is arranged on the downhole optical cable and is used to measure the fluid temperature at the downhole thermal excitation device; The surface temperature control system is connected to the downhole thermal excitation device and is used to control the start and stop of the downhole thermal excitation device and to set the target temperature of the downhole thermal excitation device.

8. A DAS wellbore fluid velocity interpretation method for low-yield wells based on active thermal stimulation, characterized in that: The following steps are involved: S1. Data collection: By installing a downhole optical cable in the oil pipe or on the inner wall of the oil pipe of a low-yield well, setting a downhole thermal excitation device on the downhole optical cable, and connecting the downhole thermal excitation device to the surface DAS monitoring system; Using a downhole thermal excitation device to heat the downhole fluid to form a hot fluid slug; Real-time collection of DAS data of downhole optical cables; S2. Data preprocessing: The collected DAS data is pre-processed by using a Butterworth filter to perform low-pass filtering on the data; after filtering, the data is downsampled to the frequency domain where the temperature data signal is located; S3. Image drawing: The pre-processed data is imaged to obtain the DAS acoustic wave slow strain response diagram with respect to temperature; S4. Detect straight lines in the image: The DAS acoustic slow strain response image is processed with normalized grayscale, edge detection with the Canny algorithm, and Hough transform in turn to detect the straight lines in the DAS slow strain image and draw the corresponding straight lines. S5. Calculate the wellbore flow rate: The slope of the straight line is obtained using the fitting function to obtain the wellbore flow velocity at the target point.

9. The distributed fiber optic acoustic wellbore flow interpretation method for low-yield wells based on thermal excitation according to claim 8, characterized in that: In step S4, the flow rate at the target point is calculated according to the size of the oil pipe.

10. The distributed fiber optic acoustic wellbore flow interpretation method for low-yield wells based on thermal excitation according to claim 8, characterized in that: In step S4, the Canny algorithm edge detection uses a dual-threshold Canny detection algorithm.

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