A distributed optical fiber acoustic wave sensing production profile logging data quality control method

Abstract

The present application belongs to the field of oil and gas and natural gas hydrate exploitation engineering, and particularly relates to a distributed optical fiber acoustic sensing production fluid profile logging data quality control method. The present application quantitatively evaluates the rationality of the spatial distribution of the distributed optical fiber acoustic sensing production fluid profile logging data by calculating the ratio of the RMS variation amplitude of different channels (depths) in the same period to the current RMS; at the same time, the rationality of the time distribution of the distributed optical fiber acoustic sensing production fluid profile logging data is quantitatively evaluated by calculating the ratio of the standard deviation variation amplitude of each channel to the current standard deviation. Based on the above evaluation results, high-quality data that meets the corresponding requirements of spatial distribution and time distribution can be effectively screened from the extracted distributed optical fiber acoustic sensing data under a certain working system, which is used for subsequent data interpretation, and the accuracy of the data is ensured.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas and natural gas hydrate extraction engineering, specifically relating to a distributed fiber optic acoustic wave sensing method for quality control of production profile logging data. Background Technology

[0002] In oil and gas field development, distributed fiber optic acoustic sensing technology is being widely used in monitoring fields such as injection-production profiles, hydraulic fracturing, wellbore integrity, and carbon dioxide storage to obtain acoustic response parameters along the wellbore in real time, providing field engineers with a means to understand the production dynamics of the entire wellbore.

[0003] Due to the complex wellbore structure, varied production processes, and the massive volume of data generated by distributed fiber optic acoustic sensing well logging (typically exceeding 1 GB / s), analysis is extremely challenging. Accurate interpretation of distributed fiber optic acoustic sensing well logging data requires high-quality data support. However, current data interpretation largely relies on human experience for data selection, lacking a standardized metric to evaluate the data and ensure its rationality and accuracy. Summary of the Invention

[0004] To overcome the above-mentioned shortcomings, this invention proposes a quality control method for distributed fiber optic acoustic wave sensing production profile logging data. By utilizing the rationality of the spatial and temporal distribution of the data, the distributed fiber optic acoustic wave sensing logging data is analyzed and quality controlled. This method is used to compensate for the lack of understanding in data interpretation and meet the needs of oil and gas production dynamic analysis for the interpretation of distributed fiber optic acoustic wave sensing production profile logging data.

[0005] The technical problem to be solved by this invention is achieved by the following technical solution: a method for quality control of well logging data of fluid production profile using distributed fiber optic acoustic sensing, comprising the following steps: S1. Establish a working system for distributed fiber optic acoustic wave sensing production profile logging. Based on the production history data and reservoir properties of the target well, design a production profile logging system. S2. On-site acquisition of distributed fiber optic acoustic wave sensor data The optical cable is lowered into the target well, and the status of the optical cable is continuously monitored in real time using the distributed optical fiber acoustic wave sensing equipment on the ground. After the instrument is lowered into the target area, the production profile logging operation system set in step S1 is executed, the start and end times of each operation system are recorded, the acoustic wave signals along the wellbore under each operation system are collected, and the data is stored in the ground storage device. S3. Extract distributed fiber optic acoustic wave sensing data under a specific operating system. Based on the time of each working system recorded during the data acquisition process in step S2, and combined with the working system that needs to be analyzed on site, the acoustic data within a spatial and temporal range under the corresponding working system is extracted from the ground storage device, and the data depth correction work is completed. S4. Calculate the rationality of the spatial distribution of the data. The rate of change of sound wave intensity within the same spatial range is calculated, and the rationality of the spatial distribution of data is determined based on the rate of change of sound wave intensity. The data with a reasonable spatial distribution is determined by setting a threshold range. S5. Calculate the rationality of the time distribution of the data. The rationality of the time distribution of data is determined by the amplitude changes of the production layer signal and data, and the data with a reasonable time distribution is determined by setting a threshold range; S6. Data Quality Evaluation Results Based on the evaluation results of steps S4 and S5, high-quality data that simultaneously meet the threshold requirements of steps S4 and S5 are selected from the distributed fiber optic acoustic wave sensing data extracted under a certain working state for subsequent data interpretation.

[0006] Preferably, in step S1 of this invention, the designed logging operation system should include at least a long shut-in system, a stable production system, a temporary shut-in system, and a temporary open system.

[0007] In a preferred embodiment of the present invention, the method for evaluating the rationality of data spatial distribution in step S4 is: 1) First, calculate the root mean square (RMS) value of DAS data for each channel in the target layer during the same time period, and draw a waterfall plot; 2) Then, calculate the ratio R of the RMS change amplitude of different channels in the same period to the current RMS. The calculation formula is shown in equation (1): (1) In the formula, Represents the root mean square of the i-th channel; This represents the root mean square of the (i+1)th channel; 3) Based on the following evaluation criteria, provide the evaluation results on the rationality of the spatial distribution of the data: The data is reasonable when 0 ≤ R ≤ 0.5; When 0.5 < R < 1, the data is questionable; When R ≥ 1, the data is abnormal; 4) Calculate the R-value and plot a waterfall diagram of space versus time; 5) Identify the data range where anomalies exist, and select the data corresponding to 0≤R≤0.5 for use.

[0008] In a preferred embodiment of the present invention, the method for evaluating the rationality of data spatial distribution in step S5 is as follows: 1) First, for the DAS data of each channel, calculate the standard deviation (STD) within each unit time period and draw a waterfall plot; 2) Then, calculate the ratio S of the standard deviation change of each channel to the current standard deviation. The calculation formula is shown in equation (2): (2) In the formula, This represents the standard deviation of the i-th channel; This represents the standard deviation of the (i+1)th channel; 3) Based on the following evaluation criteria, provide the evaluation results regarding the reasonableness of the data time distribution: D. 0 ≤ S ≤ 0.5, accepted; E, 0.5 < S < 1, warning; F and S≥1, serious; 4) Calculate the S-value and plot a waterfall diagram of space versus time; 5) Identify the data range where anomalies exist, and select the data corresponding to 0≤S≤0.5 for use.

[0009] Compared with the prior art, the beneficial effects of the present invention are: This invention quantitatively evaluates the spatial distribution rationality of distributed fiber optic acoustic wave sensing production profile logging data by calculating the ratio of the RMS variation amplitude of different channels (depths) within the same time period to the current RMS. Simultaneously, it quantitatively evaluates the temporal distribution rationality of the distributed fiber optic acoustic wave sensing production profile logging data by calculating the ratio of the standard deviation variation amplitude of each channel to the current standard deviation. Based on these evaluation results, high-quality data that simultaneously meets the spatial and temporal distribution requirements can be effectively screened from the distributed fiber optic acoustic wave sensing data extracted under a specific operating condition for subsequent data interpretation, ensuring data accuracy. Attached Figure Description

[0010] Figure 1 This is a flowchart of the distributed fiber optic acoustic wave sensing well logging data quality control method for fluid production profiles according to the present invention. Figure 2 A waterfall plot of RMS data for evaluating the spatial distribution rationality of this invention; Figure 3 This is a diagram showing the evaluation results of the spatial distribution rationality of the present invention; Figure 4 A waterfall plot of STD data for evaluating the rationality of the time distribution in this invention; Figure 5 This is a graph showing the evaluation results of the time distribution rationality of the present invention. Detailed Implementation

[0011] The invention described above will be further explained in conjunction with the accompanying drawings, but is not limited thereto.

[0012] To facilitate understanding of the technical solution, the term "channel" will be explained. Since the optical fiber itself is a continuous sensing line, the system does not treat the entire optical fiber as a single signal, but rather cuts it into numerous small segments according to a fixed spatial length: (1) Each small segment of optical fiber corresponds to one channel; (2) Each channel continuously outputs the vibration waveform (DAS original time domain signal) at that segment location; (3) Channel number = spatial location on the optical fiber (distance from the host in kilometers / meters). For example: If the total length of the optical fiber is 10km and the spatial resolution is 10m, then the total number of channels = 10000 ÷ 10 = 1000 channels, channel 1: 0~10m, channel 2: 10~20m, ... channel 1000: 9990~10000m.

[0013] This embodiment provides a quality control method for distributed fiber optic acoustic wave sensing production profile logging data. It utilizes distributed fiber optic acoustic wave sensing data acquired under different operating conditions, and evaluates the rationality and accuracy of the distributed fiber optic acoustic wave sensing data used for analysis by calculating the rationality of its spatial and temporal distribution. Figure 1 As shown, the process includes the following steps: S1. Establish a working system for distributed fiber optic acoustic wave sensing production profile logging. Based on the production history data and reservoir properties of the target well, a production profile logging system is designed. This system refers to designing multiple production rates and corresponding testing times for a specific target well, which is a well-known and standard practice in the field.

[0014] The designed logging system should include at least a long-term shut-in system, a stable production system, a temporary shut-in system, and a temporary open system.

[0015] S2. On-site acquisition of distributed fiber optic acoustic wave sensor data, specifically including the following: 1) Upon arrival at the target well, confirm whether the target well meets the logging conditions. For target wells that meet the conditions, check the integrity of the distributed fiber optic acoustic sensing equipment and optical cable to ensure that the equipment can meet the normal measurement requirements.

[0016] 2) During testing and construction, the speed of lowering the optical cable should not exceed 3000 m / h. When the optical cable passes through locations such as the wellbore reducer, bottom ball (reverse circulation valve), bell mouth, cross, and tailpipe (the structure of each well is different; some wells have reducers, some do not; this example is just a general description), the lowering speed should be reduced. During the lowering process, the construction personnel should constantly observe the tension indicator, depth indicator, and wellhead conditions. If obstruction is encountered, the operation should be stopped immediately. The optical cable is required to pass through the lowest production layer.

[0017] In this embodiment, one end (starting end) of the optical cable is connected to a distributed fiber optic acoustic wave sensor on the ground, while the other end (ending end) of the optical cable is lowered into the well from the wellhead. During the lowering process, the distributed fiber optic acoustic wave sensor on the ground needs to remain on to continuously monitor the status of the optical cable in real time. The main function during the lowering process is to measure the status of the optical cable, and when the working cycle is executed, the fluid production response of each layer is measured.

[0018] 3) After the end of the optical cable is lowered into the target area, the production profile logging operation system formulated in step S1 is executed, the start and end times of each operation system are recorded, the acoustic signals along the wellbore under each operation system are collected, and the data is stored in the ground storage device.

[0019] S3. Extract distributed fiber optic acoustic wave sensing data under a specific operating system. Based on the time of each working system recorded during the data acquisition process in step S2, and combined with the working system that needs to be analyzed on site, the acoustic data of a spatial (depth) and time range under the corresponding working system is extracted from the ground storage device (the time range is the time limit of the corresponding working system mentioned above, and the depth range is the depth range covered by all producing layers). The data depth correction work is then completed (there are many methods for depth correction, such as tapping the optical cable at the wellhead. At this time, there is a response in the DAS signal. The number of channels corresponding to this response is recorded (for example, the 100th channel). That is, the 100th channel in the DAS signal corresponds to the wellhead. If 1 channel interval represents 1m, then the 1100th channel represents 1000m downhole).

[0020] S4. Calculate the rationality of the spatial distribution of the data. The basic principle of evaluating the rationality of data spatial distribution is that when sound waves propagate in a continuous medium, their intensity will not change abruptly. Distributed fiber optic acoustic wave sensing technology measures the changes in sound waves within a certain spatial (depth) range. Therefore, the data measured in adjacent channels (depths) should be similar in magnitude and will not change abruptly.

[0021] The method for evaluating the rationality of data spatial distribution is: 1) First, calculate the root mean square (RMS) value of DAS data for each channel (depth) of the target layer within the same time period (RMS calculation is a common mathematical method), and plot a waterfall chart, such as... Figure 2 As shown; 2) Then, calculate the ratio R of the RMS change amplitude of the i-th and (i+1)-th adjacent channels (depth) in the same time period to the RMS of the i-th channel (to evaluate the abrupt change of the sound wave; the root mean square RMS of each channel in the target layer has been calculated in step S1). The calculation formula is shown in equation (1): (1) In the formula, ; 3) Based on the following evaluation criteria, provide the evaluation results on the rationality of the spatial distribution of the data (this standard was obtained through evaluation and analysis of a large amount of measured data in this invention): A. 0 ≤ R ≤ 0.5, reasonable data; B. 0.5 < R < 1, the data is questionable (the range of this invention is equivalent to a confidence interval. If the evaluated R value is in this range, the reliability is about 50-80%, which is not very reliable. Data in this range cannot be selected when interpreting the data later. If R > 1, it means that the reliability of the data is about less than 50%, which is extremely unreliable. This data cannot be used for subsequent data interpretation). C and R ≥ 1, abnormal data; 4) Calculate the R-value and plot a waterfall chart of space (depth) versus time, such as... Figure 3 As shown.

[0022] 5) Identify the data regions containing anomalies (R≥1), such as... Figure 3 As indicated by the red arrow in the middle.

[0023] S5. Calculate the rationality of the time distribution of the data. The basic principle of evaluating the rationality of data temporal distribution is that the distribution characteristics of data over time should be consistent with the operating conditions at that time. During normal production, the amplitude of the production zone signal fluctuation should remain basically constant. When the operating system changes from a shut-in / shutdown state to an open-loop production state, the amplitude of the production zone signal and data should generally change from small to large. Conversely, when the operating system changes from an open-loop production state to a shut-in / shutdown state, the amplitude of the production zone signal and data should generally change from large to small.

[0024] The method for evaluating the rationality of data spatial distribution is: 1) First, for each channel's DAS data, calculate the standard deviation within each unit time period. Draw a waterfall diagram, such as Figure 4 As shown.

[0025] 2) Then, calculate the ratio S of the standard deviation variation of the adjacent i-th and (i+1)-th channels to the standard deviation of the i-th channel. The calculation formula is shown in equation (2): (2) In the formula,

[0026] 3) Based on the following evaluation criteria, provide the evaluation results on the rationality of the data time distribution (this invention is obtained through evaluation and analysis of a large amount of measured data): D. 0≤S≤0.5, reasonable data.

[0027] E, 0.5 < S < 1, data is questionable (the range of this invention is only to provide a judgment standard to tell the user that the data is unreliable and that the data cannot be selected when interpreting the data in the future, and no other measures are needed).

[0028] F, S≥1, abnormal data.

[0029] 4) Calculate the S-value and plot a waterfall chart of space (depth) versus time, such as... Figure 5 As shown.

[0030] 5) Identify the data region containing anomalies (S≥1), such as... Figure 5 As indicated by the red arrow in the middle.

[0031] S6. Data Quality Evaluation Results Based on the evaluation results of steps S4 and S5, high-quality data that simultaneously meets the threshold requirements of steps S4 and S5 is selected from the extracted distributed fiber optic acoustic wave sensing data under a certain operating regime for subsequent data interpretation. The high-quality data refers to... Figure 3 and Figure 5 Continuous data with a median value below 0.5.

[0032] Example 1 like Figure 1 As shown, the method of the present invention is applied to the quality control of production profile logging data of a distributed fiber optic acoustic sensing system in an offshore oil well. The specific steps are as follows: S1. Establish a working system for distributed fiber optic acoustic wave sensing production profile logging.

[0033] S2. On-site acquisition of distributed fiber optic acoustic wave sensor data 1) Upon arrival at the target well, confirm that the target well meets the logging conditions, check the integrity of the relevant equipment and optical cables, and ensure that the equipment can meet the normal measurement requirements.

[0034] 2) During the test construction, the fiber optic cable was lowered at a speed of 2000 m / h. During the lowering process, the construction personnel constantly observed the tension indicator (a tension gauge was installed to measure the cable tension as it entered the well, allowing the personnel to know the force exerted on the cable), the depth indicator, and whether fluid was gushing out of the wellhead. No obstruction was observed. The fiber optic cable passed through the bottommost production layer.

[0035] 3) During measurement, the distributed fiber optic acoustic wave sensing equipment on the ground needs to be kept on to continuously detect the status of the optical cable in real time. After the instrument is lowered into the target area, the production profile logging operation system formulated in step S1 is executed, the start and end times of each operation system are recorded, the acoustic wave signals along the wellbore under each operation system are collected, and the data is saved in the ground storage device.

[0036] S3. Extract distributed fiber optic acoustic wave sensing data under a specific operating system. Based on the time of each working system recorded during the data acquisition process in step S2, and combined with the working system that needs to be analyzed on site, the acoustic data of the well opening working system with a depth range of 3601m-4406m and a time range of 7 hours 31 minutes to 7 hours 35 minutes are extracted from the ground storage device, and the data depth correction work is completed.

[0037] S4. Calculate the rationality of the spatial distribution of the data. 1) First, calculate the root mean square (RMS) of the DAS data within the depth range of 3601m-4406m and the time range of 7 hours 31 minutes to 7 hours 35 minutes, and plot the waterfall chart, as shown below. Figure 2 As shown; 2) Then, calculate the ratio of the RMS change amplitude of different channels (depths) in the same time period to the current RMS. The calculation formula is shown in equation (1): (1) 3) Based on the following evaluation criteria, provide the evaluation results on the rationality of the spatial distribution of the data: A. 0 ≤ R ≤ 0.5, reasonable data B. 0.5 < R < 1, the data is questionable. C, R≥1, outlier data 4) Calculate the R-value and plot a waterfall chart of space (depth) versus time, such as... Figure 3 As shown.

[0038] 5) As can be seen from the graph, there are a small number of localized, scattered data mutations, such as... Figure 3 As indicated by the red arrow in the middle.

[0039] S5. Calculate the rationality of the time distribution of the data. 1) First, for the DAS data of each channel within the depth range of 3601m-4406m, calculate the standard deviation within each unit time period. Draw a waterfall diagram, such as Figure 4 As shown.

[0040] 2) Then, calculate the ratio of the standard deviation change of each channel to the current standard deviation. The calculation formula is shown in equation (2): (2) 3) Based on the following evaluation criteria, provide the evaluation results regarding the reasonableness of the data time distribution: D. 0 ≤ S ≤ 0.5, accepted.

[0041] E, 0.5 < S < 1, warning.

[0042] F and S≥1, serious.

[0043] 4) Calculate the S-value and plot a waterfall chart of space (depth) versus time, such as... Figure 5 As shown.

[0044] 5) As can be clearly seen from the graph, there are a very small number of localized, scattered data mutations, such as... Figure 5 As indicated by the red arrow in the middle.

[0045] S6. Data Quality Evaluation Results Based on the evaluation results of steps S4 and S5, the data within the range of 40 seconds to 70 seconds is determined to be high-quality data, and this part of the data is selected for subsequent interpretation work.

[0046] After determining the data range using the method of this invention, the frequency band energy is calculated. If the data is reasonable, the calculated frequency band energy is, for example, 100; if the data is unreasonable and contains some abrupt changes, the calculated frequency band energy may be 150.

Claims

1. A method for quality control of well logging data from distributed fiber optic acoustic sensing fluid production profiles, characterized in that, Includes the following steps: S1. Establish a working system for distributed fiber optic acoustic wave sensing production profile logging. Based on the production history data and reservoir properties of the target well, design a production profile logging system. S2. On-site acquisition of distributed fiber optic acoustic wave sensor data The optical cable is lowered into the target well, and the status of the optical cable is continuously monitored in real time using the distributed optical fiber acoustic wave sensing equipment on the ground. After the instrument is lowered into the target area, the production profile logging operation system set in step S1 is executed, the start and end times of each operation system are recorded, the acoustic wave signals along the wellbore under each operation system are collected, and the data is stored in the ground storage device. S3. Extract distributed fiber optic acoustic wave sensing data under a specific operating system. Based on the time of each working system recorded during the data acquisition process in step S2, and combined with the working system that needs to be analyzed on site, the acoustic data within a spatial and temporal range under the corresponding working system is extracted from the ground storage device, and the data depth correction work is completed. S4. Calculate the rationality of the spatial distribution of the data. The rate of change of sound wave intensity within the same spatial range is calculated, and the rationality of the spatial distribution of data is determined based on the rate of change of sound wave intensity. The data with a reasonable spatial distribution is determined by setting a threshold range. In step S4, the method for evaluating the rationality of data spatial distribution is: 1) First, calculate the root mean square (RMS) value of DAS data for each channel in the target layer during the same time period and draw a waterfall plot; 2) Then, calculate the ratio R of the RMS change amplitude of different channels in the same period to the current RMS. The calculation formula is shown in equation (1): （1） In the formula, This represents the root mean square of the i-th channel; This represents the root mean square of the (i+1)th channel; 3) Based on the following evaluation criteria, provide the evaluation results on the rationality of the spatial distribution of the data: The data is reasonable when 0 ≤ R ≤ 0.5; When 0.5 < R < 1, the data is questionable; When R ≥ 1, the data is abnormal; 4) Calculate the R-value and plot a waterfall diagram of space versus time; 5) Identify the data regions where anomalies exist; S5. Calculate the rationality of the time distribution of the data. The rationality of the time distribution of data is determined by the amplitude changes of the production layer signal and data, and the data with a reasonable time distribution is determined by setting a threshold range; In step S5, the method for evaluating the rationality of data spatial distribution is as follows: 1) First, for the DAS data of each channel, calculate the standard deviation (STD) within each unit time period and draw a waterfall plot; 2) Then, calculate the ratio S of the standard deviation change of each channel to the current standard deviation. The calculation formula is shown in equation (2): （2） In the formula, This represents the standard deviation of the i-th channel; This represents the standard deviation of the (i+1)th channel; 3) Based on the following evaluation criteria, provide the evaluation results regarding the reasonableness of the data time distribution: D. 0 ≤ S ≤ 0.5, accepted; E, 0.5 < S < 1, warning; F and S≥1, serious; 4) Calculate the S-value and plot a waterfall diagram of space versus time; 5) Identify the data regions where anomalies exist; S6. Data Quality Evaluation Results Based on the evaluation results of steps S4 and S5, high-quality data that simultaneously meet the threshold requirements of steps S4 and S5 are selected from the distributed fiber optic acoustic wave sensing data extracted under a certain working state for subsequent data interpretation.

2. The method for quality control of well logging data from distributed fiber optic acoustic sensing fluid production profiles according to claim 1, characterized in that: In step S1, the designed logging system should include at least a long shut-in system, a stable production system, a temporary shut-in system, and a temporary open system.

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