Underground DAS signal processing and production profile interpretation method

Abstract

The invention provides an underground DAS signal processing and production profile interpretation method, and belongs to the field of oil and gas field development engineering. The method mainly comprises two parts of signal processing and output profile interpretation, wherein the signal processing comprises signal noise reduction, signal windowing segmentation, Fourier transform, filtering, feature extraction, analysis of different water contents and statistics of feature frequency bands along a shaft under different flows; the method comprises the following steps: performing f-k transformation on underground DAS signal data, calculating a transformed image by using Rawinter integral, calculating an uplink sound velocity and a downlink sound velocity, thereby obtaining a sound velocity and a liquid production velocity in a shaft, calculating the water content of produced liquid, performing frequency division on underground DAS signals according to the water content of the produced liquid, calculating FBE of a corresponding water content frequency band along the shaft direction, accumulating the FBE, and calculating the water content of the underground DAS signals according to the accumulated FBE. And calculating the proportion of the FBE accumulated sum of each production section, and calculating the flow of each production section according to the total flow of the wellhead.

Description

Technical Field

[0001] The invention relates to the field of oil and gas field development engineering, and in particular to a downhole DAS signal processing and production profile interpretation method. Background Art

[0002] Fiber optic sensing technology is an advanced monitoring technology, among which Distributed Acoustic Sensing (DAS) technology can be applied to underground production profile monitoring. Traditional underground point electrical detectors have bottlenecks such as difficulty in deployment, short life, limited network scale, and little information acquisition in high-temperature and high-pressure underground environments. Distributed DAS sensing has the advantages of low cost, anti-electromagnetic interference, distributed, high sensitivity, and insulation corrosion resistance compared to traditional electronic sensors. Several kilometers of armored optical cables are permanently, semi-permanently, and inserted into oil and gas wells, which can monitor underground acoustic vibration information in real time online to realize the detection and interpretation of underground acoustic vibration.

[0003] In the DAS monitoring of downhole production profiles, when the oil-water two-phase production fluid flows in the wellbore and perforations, the characteristic frequency bands it generates are related to the water content and flow rate of the production fluid. Therefore, in order to more accurately obtain the vibration characteristics of fluids with different water contents flowing along the axial direction of the wellbore, and thus accurately qualitatively and quantitatively analyze the production distribution of the production profile, it is necessary to systematically study and analyze the DAS signals of oil-water two-phase fluids with different water contents flowing in the wellbore. Summary of the invention

[0004] In view of this, the present invention aims to propose a downhole DAS signal processing and production profile interpretation method, which aims to realize production profile status monitoring, quantitatively / qualitatively interprets the production of each production layer, and innovatively establishes a characteristic frequency band library of different water cuts and different flow rates along the wellbore direction in the algorithm. When analyzing the production of each layer of the downhole production profile, the FBE waterfall diagram of the corresponding water cut and the calculation time latitude accumulation result are more accurately established according to the correspondence between the water cut and the characteristic frequency band, so as to obtain a more accurate calculation model and production profile analysis result.

[0005] To achieve the above object, the technical solution of the present invention is implemented as follows: a method for downhole DAS signal processing and production profile interpretation, comprising the following steps:

[0006] S1, performs noise reduction processing on the collected signal to improve the signal-to-noise ratio;

[0007] S2, performing windowing, Fourier transform and spectrum filtering on the signal with high signal-to-noise ratio to obtain frequency domain information of DAS signal;

[0008] S3, analyzing the characteristic frequency bands of DAS signals along the wellbore direction at different water cuts and flow rates, and statistically analyzing the characteristic frequency band spectra related to water cuts and flow rates;

[0009] S4, fk transform is performed on the two-dimensional matrix of the DAS signal, and the sound velocity and the production velocity in the wellbore are calculated using the transformed image, and finally the water content of the production fluid is calculated;

[0010] S5, dividing the signal according to the water content and the water content counted in step S3 and the corresponding signal characteristic frequency band, establishing the FBE wellbore waterfall diagram along the wellbore direction of the corresponding frequency band, and calculating the production proportion and production of each production section.

[0011] Further, step S1 includes the following steps:

[0012] S1.1, use Butterworth filter to low-pass filter the signal, and set the low-pass filter cutoff frequency according to the acquisition frequency;

[0013] S1.2, perform cumulative averaging on the filtered signal to reduce random noise and improve the signal-to-noise ratio;

[0014] S1.3, performs moving average noise reduction on the signal to further improve the signal-to-noise ratio.

[0015] Further, step S2 includes the following steps:

[0016] S2.1, windowing the signal, selecting a fixed-length DAS time signal, windowing the signal using a Hanning window function, and framing the DAS signal;

[0017] S2.2, performing short-time Fourier transform on the framed signal in time sequence to obtain a frequency domain spectrum of the short-time signal.

[0018] Further, step S3 includes the following steps:

[0019] S3.1, analyzing the DAS signals of the produced liquid at different water cuts and different flow rates, and counting the characteristic frequency bands of the DAS signals along the wellbore direction at different water cuts and different flow rates;

[0020] S3.2, analyze the characteristic frequency bands of fluid production along the wellbore direction.

[0021] Further, step S4 includes the following steps:

[0022] S4.1, intercepting the time and position matrix data of DAS signals at different positions along the wellbore, performing a two-dimensional Fourier transform on the matrix, and obtaining the fk image of the DAS signal;

[0023] S4.2, integrating the fk image in different directions according to the Radon integral algorithm, obtaining the directions of the two maximum integral values, calculating the slopes of the straight lines in the two directions, thereby obtaining the upward sound velocity and the downward sound velocity at the spatial position, and obtaining the sound velocity in the produced liquid and the flow velocity of the produced liquid according to the two sound velocities;

[0024] S4.3, calculate the water content at each liquid-producing layer in the wellbore based on the sound velocity.

[0025] Further, step S5 includes the following steps:

[0026] S5.1, dividing the signal according to the calculated water content and the characteristic spectrum statistics established in step S3, and establishing an FBE waterfall diagram of the corresponding frequency band along the wellbore direction;

[0027] S5.2, segment the production section along the wellbore direction to obtain the main production layer section;

[0028] S5.3, cumulatively integrate the FBE of each main production layer to obtain the cumulative FBE energy within a period of time, and calculate the FBE proportion of each layer. According to the total production monitored at the wellhead, calculate the production of each liquid-producing layer.

[0029] Compared with the prior art, the downhole DAS signal processing and production profile interpretation method described in the present invention has the following advantages:

[0030] (1) By adding a detailed study of the distribution of characteristic frequency bands of two-phase flow with different water contents to the algorithm flow of analyzing the production profile using distributed DAS technology, the algorithm flow of calculating the distribution of downhole production layers and production using characteristic frequency bands is improved, thereby more accurately and qualitatively or quantitatively calculating the distribution characteristics of the downhole production profile;

[0031] (2) By counting the characteristic frequency bands of two-phase flow with different water contents flowing along the wellbore direction, a statistical library of characteristic frequency bands of two-phase flow with different water contents is formed. When the water content distribution of each layer in the well is obtained through DAS data, the experiment can more accurately and quantitatively calculate the production of each production layer and establish a more accurate production profile algorithm in the wellbore direction, which can guide the wellbore production more accurately and effectively.

[0032] (3) The present invention effectively combines DAS monitoring technology, signal processing technology, feature extraction technology and image integration technology to establish a real-time monitoring profile of downhole production, which significantly improves the real-time control of the actual downhole production status and helps to improve the safety, stability and efficiency of oil and gas production. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0034] Figure 1 A flow chart of a method for downhole DAS signal processing and production profile interpretation is provided for one embodiment of the present invention;

[0035] Figure 2 It is the characteristic frequency band of DAS signal along the wellbore direction under different water cuts;

[0036] Figure 3 The FBE waterfall diagram along the wellbore direction is obtained after the DAS signal is divided by frequency in the present invention;

[0037] Figure 4 The relative production of the main production layers in channels 100-300;

[0038] Figure 5 Relative production of the main production layers in channels 700-800. DETAILED DESCRIPTION

[0039] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0040] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the examples described are only part of the present invention, not all of the examples.

[0041] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0042] The present invention discloses a method for processing oil-water two-phase flow signals, and an algorithm flow for qualitatively / quantitatively interpreting the production profile according to the signal processing results, which belongs to the field of oil and gas field development engineering. The algorithm flow mainly includes two parts: signal processing and production profile interpretation, wherein the signal processing includes signal noise reduction, signal windowing and segmentation, Fourier transform, filtering, characteristic frequency band extraction, analysis of characteristic frequency bands with different water contents, different flow rates, and along the wellbore, and statistics of characteristic frequency band libraries flowing along the wellbore direction under different water contents; fk transformation is performed on the DAS signal of the production liquid profile, and the transformed image is calculated using Radon integral, and the upward sound velocity and downward sound velocity are calculated, so as to obtain the sound velocity in the wellbore and the fluid velocity flowing along the wellbore, and the water content of the production liquid is calculated. According to the established characteristic frequency bands corresponding to different water contents, an FBE waterfall chart along the wellbore direction is established, the FBE of each production section within a period of time is accumulated, and a production proportion chart of each production section is established, and finally the flow of each production section is calculated through the total flow at the wellhead.

[0043] In terms of signal processing, the focus is on establishing and statistically analyzing the characteristic frequency band library of DAS signals of fluids with different water cuts, different flow rates, and flowing along the wellbore direction. The different flow rates when the water cut is 0, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, and 90% are statistically analyzed, and the corresponding FBE is calculated.

[0044] In the actual production profile production analysis, according to the water content of the produced fluid, the corresponding characteristic frequency band along the well direction is selected and the FBE waterfall chart along the wellbore direction is established. The FBE accumulation of the main production section over a period of time is performed, and the proportion of the FBE cumulative energy of each section is calculated. The production of each main production section over a period of time is calculated through the total production at the wellhead, and the state of the industrial profile is qualitatively and quantitatively analyzed. By combining the water content with the characteristic frequency band, the production status of the production profile can be analyzed more accurately.

[0045] Specifically, the present invention proposes a method for downhole DAS signal processing and production profile interpretation, such as Figure 1 As shown, in the signal processing frequency division processing part of this embodiment, statistics are performed on the characteristic frequency bands of the DAS signal of the oil-water two-phase with different water content and different flow rates along the wellbore, and the statistical law of the characteristic frequency bands of the two-phase flow with different production liquid concentrations along the wellbore of the horizontal well is obtained, and a characteristic frequency band database is established for quantitative / qualitative research on the production distribution along the wellbore of the production profile, so as to more accurately describe the downhole production status. It includes the following steps:

[0046] S1, perform noise reduction processing on the collected signal to improve the signal-to-noise ratio. The specific method is as follows;

[0047] S1.1, use Butterworth filter to low-pass filter the signal, and set the low-pass filter cutoff frequency according to the acquisition frequency;

[0048] The Butterworth filter is also known as the "flattest" amplitude-frequency response filter. The filter is characterized by having the largest flat amplitude in the passband and decreasing monotonically with increasing frequency. The characteristic function (amplitude square function) of the Nth-order low-pass Butterworth filter is:

[0049]

[0050] Where D(u,v) is the distance from the midpoint (u,v) to the center in the frequency domain, D0 is the cutoff frequency, and n is the order of the filter;

[0051] S1.2, perform cumulative averaging on the filtered signal to reduce random noise and improve the signal-to-noise ratio;

[0052] Cumulative averaging refers to the summing and averaging of time points at different locations in space. The cumulative coefficient is k, and the signal matrix is ​​as follows:

[0053]

[0054] After cumulative averaging, the matrix becomes a one-bit vector, and the formula is as follows:

[0055] S k,1＝( a n,1 +a n+1,1 + ... +a n+k,1) / k ........................

[0057] S km, =(a n,m +a n+1,m + ... +a n+k,m );

[0058] S k,1 S k,1 .........S n,m

[0059] S1.3, performs moving average noise reduction on the signal to further improve the signal-to-noise ratio.

[0060] Moving average is a commonly used signal processing technique for smoothing time series data, reducing noise, and extracting trend information from data. The principle of moving average is to use a fixed-length window to move on the data sequence, calculate the average value of the data in the window, and use this average value as the output. By continuously moving the window and updating the average value, a series of smoothed data points can be obtained, thereby achieving smoothing. The formula for moving average can be expressed as:

[0061]

[0062] N is the length of the moving average window, that is, the number of data points considered when calculating each data point. x[n] represents the (n)th data point in the time series.

[0063] S2, the signal with high signal-to-noise ratio is windowed, Fourier transformed and spectrum filtered to obtain the frequency domain information of DAS signal; the time signal is segmented by windowing and framing, and then short-time Fourier transform is performed to obtain the short-time signal spectrum characteristics, which specifically include the following contents:

[0064] S2.1, add window to the signal, select a fixed length DAS time signal, use the Hanning window function to add window to the signal, and frame the DAS signal; by adding a window of a certain length to the signal, the signal is segmented in time to obtain short-time signal fragments, thereby reflecting the signal characteristics in a short time, which can be expressed by the Hanning window formula:

[0065]

[0066] Where a0 is the basic coefficient of the window function, which indicates the initial value of the window function at each sample point; n is the sample point index in the window, starting from 0; and N is the length of the window.

[0067] S2.2, performing short-time Fourier transform on the framed signal in time sequence to obtain a frequency domain spectrum of the short-time signal;

[0068] Short-time Fourier transform is achieved by dividing the signal into shorter time segments and performing Fourier transform on each time segment. The mathematical expression is:

[0069]

[0070] Among them, N is the length of each frame, n is the frame number, m is the number of data in a frame, and k is the spectral line number.

[0071] The short-time Fourier transform refers to the principle: the long-time non-stationary signal is windowed and segmented into short time. In the short time, the signal can be regarded as a stationary signal and can be transformed by FFT. The Fourier transform result of the signal in each window is obtained by sliding the window function. These results are stacked and arranged to obtain a two-dimensional time-frequency domain feature map, which represents the distribution of the entire signal in time and frequency.

[0072] S3, analyzing the characteristic frequency bands of the DAS signal under different water cuts and different flow rates along the wellbore direction, and statistically analyzing the characteristic frequency band spectra related to the water cut and flow rate; Step S3 obtains the DAS signal spectrum under different water cuts and flow rates through Step S2, thereby forming a statistical characteristic spectrum library, which provides a basis for analyzing the calculation of the wellbore liquid production flow rate.

[0073] S3.1, analyzing the DAS signals of the produced liquid at different water cuts and different flow rates, and counting the characteristic frequency bands of the DAS signals along the wellbore direction at different water cuts and different flow rates;

[0074] In order to quantitatively calculate the liquid production under different water content conditions in the well, it is necessary to analyze the DAS signals of the liquid production under different water content and different flow rates, count the corresponding characteristic spectrum bands, and then calculate the FBE of each sub-band. In this patent, the characteristic spectrum bands along the wellbore direction at different flow rates when the water content is 0, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, and 90% are counted, and the corresponding FBE is calculated. Figure 2 Characteristic frequency bands for moisture contents of 20%, 60%, and 80%.

[0075] S3.2, analyze the characteristic frequency bands of fluid production along the wellbore direction.

[0076] The following conclusions were drawn:

[0077] 1) For the same flow rate and different water content, the frequency bands along the wellbore direction are inconsistent. The higher the water content, the more it looks like low-frequency transfer;

[0078] 2) At different flow rates, with the same water content along the wellbore direction, the flow rate increases, the corresponding frequency band energy (FBE) increases, and the frequency band changes are not obvious.

[0079] S4, fk transform (two-dimensional Fourier transform) is performed on the two-dimensional matrix of the DAS signal, and the sound velocity and the production velocity in the wellbore are calculated using the transformed image, and finally the water content of the production fluid is calculated;

[0080] S4.1, DAS equipment receives the vibration signal of the optical cable laid along the wellbore direction. Through signal demodulation and processing, the vibration matrix of each position along the wellbore direction and the duration of each position can be obtained. The position and time matrix data of the DAS signal at different positions along the wellbore are intercepted, and the matrix is ​​subjected to a two-dimensional Fourier transform to obtain the fk image of the DAS signal. In the fk image, the horizontal axis and the vertical axis represent the wave number k and the frequency f of the vibration sound wave, respectively. The slope of the image connecting the origin to a point in the image is:

[0081] c=f / k=λ×f=v

[0082] In the fk image, there are two obvious bright lines in a V-shaped line, which represent the upward and downward speeds of the speed of sound respectively.

[0083] S4.2, using the Radon integral algorithm, the slopes of the two V-shaped oblique lines can be calculated, thereby obtaining the upward sound speed and the downward sound speed at the spatial position, and the actual sound speed and liquid production speed in the production liquid can be obtained based on the two sound speeds;

[0084] S4.3, calculate the water content of each liquid-producing layer in the wellbore according to the sound velocity;

[0085] S5, calculate the liquid production of the liquid production layer. According to the water content and the water content counted in step S3 and the corresponding signal characteristic frequency band, the signal is divided into frequencies, and the FBE wellbore waterfall diagram along the wellbore direction of the corresponding frequency band is established, and the FBE cumulative curve at different time points is established, and the production proportion and production of each production section are calculated.

[0086] S5.1, based on the water content of the produced liquid of each production layer calculated in step S4, combined with the statistical results of the characteristic spectrum of the water content established in step S3, the signal is divided into frequencies, a waterfall diagram of the corresponding characteristic frequency band FBE along the wellbore direction is established, and the distribution of the produced liquid layer is obtained through the waterfall diagram;

[0087] S5.2, according to step S5.1, establish the FBE waterfall diagram along the wellbore direction, so as to obtain the distribution of each production layer section in the wellbore, the distribution of the intensity of each production section over time, accumulate the FBE within a period of time, calculate the relative production of each production section in the corresponding time period, calculate the FBE proportion of each production layer, and calculate the production of each production layer according to the total production monitored at the wellhead, such as Figure 3-Figure 5 As shown, Figure 3 It is the FBE waterfall diagram of each production section flowing along the wellbore. Figure 4 The output percentage of the main liquid production section of 100-300 calculated based on FBE, Figure 5 The production ratio of the main production layer of 700-800 is obtained according to FBE. Then the production ratio between the main production layers is calculated. According to the total production of the wellhead over a period of time, the production of each production layer over a period of time can be calculated.

[0088] The description of the present invention is intended to present a method for qualitative / quantitative analysis of industrial profiles, in which the statistical characteristic spectrum corresponding to different water cuts is highlighted, so as to more accurately calculate the qualitative and quantitative calculation of the production status of the industrial profile under different water cuts in the well, but it is by no means limited to this. It should be emphasized that the present invention has wide applicability and flexibility for downhole oil-water two-phase production. Any person with professional knowledge in this field can make various reasonable adjustments, improvements and derivatives on the basis of it after reading and understanding this specification in depth. These changes may include but are not limited to equivalent replacement of technology, subtle changes in design, and even comprehensive innovation under the premise of keeping the core technical ideas unchanged. All these innovations and changes, as long as they can reflect the core concepts and values ​​pursued by the present invention, should be regarded as effective extensions of the present invention and protected accordingly. Therefore, we encourage various forms of exploration and innovation on the basis of understanding and respecting the basic ideas of the present invention, and jointly promote technological progress and development in related fields.

Claims

1. A method for downhole DAS signal processing and production profile interpretation, characterized in that: The steps include: S1, performs noise reduction processing on the collected signal to improve the signal-to-noise ratio; S2, performing windowing, Fourier transform and spectrum filtering on the signal with high signal-to-noise ratio to obtain frequency domain information of DAS signal; S3, analyzing the characteristic frequency bands of DAS signals along the wellbore direction at different water cuts and flow rates, and statistically analyzing the characteristic frequency band spectra related to water cuts and flow rates; S4, fk transform is performed on the two-dimensional matrix of the DAS signal, and the sound velocity and the production velocity in the wellbore are calculated using the transformed image, and finally the water content of the production fluid is calculated; S5, dividing the signal according to the water content and the water content counted in step S3 and the corresponding signal characteristic frequency band, establishing the FBE wellbore waterfall diagram along the wellbore direction of the corresponding frequency band, and calculating the production proportion and production of each production section.

2. A method for downhole DAS signal processing and production profile interpretation according to claim 1, characterized in that: Step S1 includes the following steps: S1.1, use Butterworth filter to low-pass filter the signal, and set the low-pass filter cutoff frequency according to the acquisition frequency; S1.2, perform cumulative averaging on the filtered signal to reduce random noise and improve the signal-to-noise ratio; S1.3, performs moving average noise reduction on the signal to further improve the signal-to-noise ratio.

3. A method for downhole DAS signal processing and production profile interpretation according to claim 1, characterized in that: Step S2 includes the following steps: S2.1, windowing the signal, selecting a fixed-length DAS time signal, windowing the signal using a Hanning window function, and framing the DAS signal; S2.2, performing short-time Fourier transform on the framed signal in time sequence to obtain a frequency domain spectrum of the short-time signal.

4. A method for downhole DAS signal processing and production profile interpretation according to claim 1, characterized in that: Step S3 includes the following steps: S3.1, analyzing the DAS signals of the produced liquid at different water cuts and different flow rates, and counting the characteristic frequency bands of the DAS signals along the wellbore direction at different water cuts and different flow rates; S3.2, analyze the characteristic frequency bands of fluid production along the wellbore direction.

5. A method for downhole DAS signal processing and production profile interpretation according to claim 1, characterized in that: Step S4 includes the following steps: S4.1, intercepting the time and position matrix data of DAS signals at different positions along the wellbore, performing a two-dimensional Fourier transform on the matrix, and obtaining the fk image of the DAS signal; S4.2, integrating the fk image in different directions according to the Radon integral algorithm, obtaining the directions of the two maximum integral values, calculating the slopes of the straight lines in the two directions, thereby obtaining the upward sound velocity and the downward sound velocity at the spatial position, and obtaining the sound velocity in the produced liquid and the flow velocity of the produced liquid according to the two sound velocities; S4.3, calculate the water content at each liquid-producing layer in the wellbore based on the sound velocity.

6. A method for downhole DAS signal processing and production profile interpretation according to claim 4, characterized in that: Step S5 includes the following steps: S5.1, dividing the signal according to the calculated water content and the characteristic spectrum statistics established in step S3, and establishing an FBE waterfall diagram of the corresponding frequency band along the wellbore direction; S5.2, segment the production section along the wellbore direction to obtain the main production layer section; S5.3, cumulatively integrate the FBE of each main production layer to obtain the cumulative FBE energy within a period of time, and calculate the FBE proportion of each layer. According to the total production monitored at the wellhead, calculate the production of each liquid-producing layer.

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