CN114458306B - Method, device, equipment and medium for determining fluid flow based on noise logging - Google Patents

Abstract

The present application discloses a method, device, equipment and medium for determining fluid flow based on noise logging, and relates to the field of noise logging. The method comprises: obtaining a first test point data set at n depth positions in the noise logging process, the first test point data set comprising time data and noise spectrum data in the time domain, and n is a positive integer; processing the first test point data set to obtain a second test point data set, the second test point data set comprising depth position data and noise spectrum data in the depth domain; synthesizing a frequency energy spectrum curve according to the second test point data set, the frequency energy spectrum curve being used to indicate the flow of fluid in a geological reservoir; dividing each flow unit according to the frequency energy spectrum curve; and determining the fluid flow in each flow unit according to the flow ratio of each flow unit. The flow amount of each layer in the well is calculated intuitively and efficiently, thereby realizing quantitative analysis of the fluid flow in the flow unit.

Description

Method, device, equipment and medium for determining fluid flow rate based on noise logging

Technical Field

The present application relates to the field of system engineering, and in particular to a method, device, equipment and medium for determining fluid flow based on noise logging.

Background Art

Noise logging refers to an acoustic logging method that directly measures the natural sound field in the well without setting up an artificial sound source underground.

Noise logging can reflect the flow of fluid outside the pipe. During the process of noise logging, the acoustic wave data collected at the test point changes with time. The acoustic wave data is plotted into a spectrum image, and the flow of downhole fluid is qualitatively analyzed by analyzing the image changes of the spectrum image, such as analyzing the type of fluid, determining the flow rate of the fluid, etc. Taking the determination of the flow rate of the fluid as an example, the flow rate of the fluid is usually determined by modeling based on the temperature parameter, and the flow rate of the fluid is determined by using the temperature gradient change of the real-time measurement temperature relative to the original temperature.

In the above technical solution, the determination of the fluid flow rate based on temperature modeling will be affected by various factors such as cross-channeling, and it is impossible to accurately determine the fluid flow rate in each energy reservoir underground.

Summary of the invention

The embodiment of the present application provides a method, device, equipment and medium for determining fluid flow based on noise logging, which can intuitively and efficiently calculate the flow volume of each layer in the well without the need to identify the size of noise based on the naked eye to determine the distribution of downhole fluid, thereby realizing quantitative analysis of the fluid flow in the flow unit. The technical solution includes:

According to one aspect of the present application, a method for determining fluid flow rate based on noise logging is provided, the method comprising:

Acquire a first test point data set at n depth positions in a noise logging process, wherein the first test point data set includes time data and noise spectrum data, and n is a positive integer;

Processing the first test point data set to obtain a second test point data set, wherein the second test point data set includes depth position data and noise spectrum data in a depth domain;

Obtaining a frequency spectrum curve according to the second test point data set, wherein the frequency spectrum curve is used to indicate each flow unit after the geological layer under the well is divided;

Determine the flow ratio corresponding to each of the flow units according to the frequency spectrum curve;

The fluid flow rate in each of the flow units is determined according to the flow rate ratio.

In an optional embodiment, synthesizing a frequency spectrum curve according to the second test point data set includes:

synthesizing a frequency energy spectrum according to the second test point data set, wherein the frequency energy spectrum includes noise spectrum data corresponding to test points at different depth positions in the second test point data set;

The frequency energy spectrum curve is extracted from the frequency energy spectrum diagram.

In an optional embodiment, extracting the frequency energy spectrum curve from the frequency energy spectrum graph includes:

Extracting first noise spectrum data in the same depth domain from the frequency energy spectrum;

Performing geometric mean processing on the first noise spectrum data in each depth domain to obtain the second noise spectrum data in each depth domain;

The frequency energy spectrum curve is obtained according to the second noise spectrum data.

In an optional embodiment, determining the flow ratio corresponding to each of the flow units according to the frequency spectrum curve includes:

According to each of the flow units indicated by the frequency spectrum curve, the noise spectrum data corresponding to each of the flow units are respectively accumulated to obtain a third test point data set;

The third test point data set is normalized to obtain the flow ratio corresponding to each of the flow units.

In an optional embodiment, determining the fluid flow in each of the flow units according to the flow ratio includes:

Obtaining the total flow rate of the first fluid corresponding to the production logging process;

The fluid flow rate in each of the flow units is determined according to the total flow rate of the first fluid and the flow rate ratio corresponding to each of the flow units.

In an optional embodiment, the determining the fluid flow in each of the flow units according to the flow ratio further includes:

In response to receiving the surface flow information, the fluid flow injected into each of the flow units is determined according to the surface flow information and the flow ratio corresponding to each of the flow units, and the surface flow information is used to characterize the total injection amount corresponding to the injection of fluid into each of the flow units.

In an optional embodiment, the processing the first test point data set to obtain the second test point data set includes:

Screening the first test point data set to obtain a screened first test point data set;

The test point data corresponding to m test points at the same depth position in the filtered first test point data set are stacked and averaged to obtain the second test point data set, where m is a positive integer.

According to another aspect of the present application, a device for determining fluid flow rate of noise logging is provided, the device comprising:

An acquisition module is used to acquire a first test point data set at n depth positions in a noise logging process, wherein the first test point data set includes time data and noise spectrum data in the time domain, and n is a positive integer;

A processing module, configured to process the first test point data set to obtain a second test point data set, wherein the second test point data set includes depth position data and noise spectrum data in a depth domain;

The processing module is used to synthesize a frequency spectrum curve according to the second test point data set, wherein the frequency spectrum curve is used to indicate each flow unit after the geological layer downhole is divided;

The processing module is used to determine the flow ratio corresponding to each of the flow units according to the frequency spectrum curve;

The processing module is used to determine the fluid flow in each of the flow units according to the flow ratio.

In an optional embodiment, the processing module is used to synthesize a frequency energy spectrum map based on the second test point data set, and the frequency energy spectrum map includes noise spectrum data corresponding to test points at different depth positions in the second test point data set; and extract the frequency energy spectrum curve from the frequency energy spectrum map.

In an optional embodiment, the processing module is used to extract first noise spectrum data in the same depth domain from the frequency energy spectrum diagram; perform geometric mean processing on the first noise spectrum data in each depth domain to obtain second noise spectrum data in each depth domain; and obtain the frequency energy spectrum curve based on the second noise spectrum data.

In an optional embodiment, the processing module is used to accumulate the noise spectrum data corresponding to each flow unit indicated by the frequency spectrum curve to obtain a third test point data set; and normalize the third test point data set to obtain the flow ratio corresponding to each flow unit.

In an optional embodiment, the acquisition module is used to obtain the total flow rate of the first fluid corresponding to the production logging process; the processing module is used to determine the fluid flow rate in each flow unit based on the total flow rate of the first fluid and the flow rate ratio corresponding to each flow unit.

In an optional embodiment, the processing module is used to determine the fluid flow rate injected into each of the flow units in response to receiving ground flow information, based on the ground flow information and the flow ratio corresponding to each of the flow units, and the ground flow information is used to characterize the total injection amount corresponding to the injection of fluid into each of the flow units.

In an optional embodiment, the processing module is used to filter the first test point data set to obtain a filtered first test point data set; and to perform superposition and averaging processing on the test point data corresponding to m test points at the same depth position in the filtered first test point data set to obtain the second test point data set, where m is a positive integer.

According to another aspect of the present application, a computer device is provided, which includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the method for determining fluid flow based on noise logging as described in the above aspects.

According to another aspect of the present application, a computer-readable storage medium is provided, in which at least one instruction, at least one program, a code set or an instruction set is stored, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by a processor to implement the method for determining fluid flow based on noise logging as described in the above aspects.

According to another aspect of the present application, a computer program product or a computer program is provided, wherein the computer program product or the computer program comprises computer instructions, wherein the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method for determining fluid flow rate based on noise logging as described in the above aspect.

The beneficial effects brought by the technical solution provided by the embodiment of the present application include at least:

The frequency spectrum curve is synthesized by the second test point data set obtained from the first test point data set, and the various geological layers in the well are divided by the frequency spectrum curve, so as to determine the flow ratio corresponding to each flow unit and the fluid flow in the flow unit in combination with the frequency spectrum curve. It allows technicians in this field to intuitively and efficiently calculate the flow volume of each layer in the well without having to identify the size of the noise based on the naked eye, thereby achieving quantitative analysis of the fluid flow in the flow unit.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

FIG1 is a framework diagram of a computer system provided by an exemplary embodiment of the present application;

FIG2 is a flow chart of a method for determining fluid flow based on noise logging provided by an exemplary embodiment of the present application;

FIG3 is a flow chart of a method for determining fluid flow based on noise logging provided by another exemplary embodiment of the present application;

FIG4 is a noise spectrum data diagram provided by an exemplary embodiment of the present application;

FIG5 is a frequency energy spectrum diagram provided by an exemplary embodiment of the present application;

FIG6 is a schematic diagram of the influence of tube waves on the geometric mean provided by an exemplary embodiment of the present application;

FIG7 is a frequency energy spectrum diagram provided by another exemplary embodiment of the present application;

FIG8 is a flow chart of a method for determining fluid flow based on noise logging provided by another exemplary embodiment of the present application;

FIG9 is a structural block diagram of a fluid flow determination device based on noise logging provided by an exemplary embodiment of the present application;

FIG. 10 is a schematic diagram of the device structure of a computer device provided by an exemplary embodiment of the present application.

DETAILED DESCRIPTION

In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

First, the terms involved in the embodiments of the present application are introduced.

Noise logging: refers to the acoustic logging method that directly measures the natural sound field in the well without setting up an artificial sound source underground. When the liquid or gas in the well moves, the friction can produce sound with a characteristic spectrum. Therefore, according to noise logging, the gas production layer and fluid absorption layer can be divided in the open hole well, and the location of the fluid string groove outside the pipe, the fluid type, the flow rate in the pipe and the flow rate of the perforation hole can be detected in the cased well.

Production Logging (PL or Production Logging Tool, PLT): Also known as development logging, it refers to the logging of downhole testing using various testing instruments to obtain corresponding underground information during the entire production process from the start of production to the scrapping of oil wells (including oil production wells, water injection wells, observation wells, etc.). The important task of production logging is to measure the fluid flow profile of production wells and injection wells. The measurement parameters include fluid velocity (flow), density, water holdup, temperature, pressure, etc. By understanding the properties and flow of the fluid produced or sucked in each perforated layer, it is possible to evaluate the occurrence of the oil well and the characteristics of oil layer production.

Ground flow rate: refers to the flow rate of fluid when fluid is injected into the well from the outside. For example, the amount of water injected into the well. In order to distinguish the fluid flowing downhole from the fluid injected from the outside, the embodiment of the present application names the flow rate corresponding to the fluid injected from the outside as the ground flow rate.

Frequency energy spectrum: refers to an image drawn based on the depth position data and noise spectrum data in the collected test point data. The two coordinate axes of the image are the depth position and the amplitude value corresponding to the noise at different frequencies, that is, the size.

Flow unit: refers to the unit corresponding to the fluid after the geological layer downhole is divided according to the frequency spectrum curve. The frequency spectrum curve is calculated based on the test point data in the second test point data set and is used to indicate the flow of the fluid in the geological reservoir.

The method for determining the fluid flow rate based on noise logging provided in the embodiment of the present application can be applied to a computer device with strong data processing capabilities. In a possible implementation, the method for determining the fluid flow rate based on noise logging provided in the embodiment of the present application can be applied to a personal computer, a workstation or a server, that is, a personal computer, a workstation or a server can be used to quantitatively analyze the fluid conditions of each flow unit in the well.

The method for determining flow rate based on noise logging can be implemented as a data processing application program, which is installed and runs in a computer device. The data processing application program can be used to perform quantitative analysis on the collected noise spectrum data.

FIG1 shows a schematic diagram of a computer system provided by an exemplary embodiment of the present application. The computer system 100 includes a noise collection device 13 and a computer device 14, wherein the noise collection device 13 and the computer device 14 perform data communication via a communication network, or the noise collection device 13 is connected to the computer device 14 via a data line. Optionally, the communication network can be a wired network or a wireless network, and the communication network can be at least one of a local area network, a metropolitan area network, and a wide area network.

The computer device 14 is installed with an application program that supports processing the collected noise spectrum data. The detection head 12 in the well 11 is connected to the noise collection device 13, and the collected noise is transmitted to the noise collection device 13 through the detection head 12. The noise collection device 13 stores the collected noise spectrum data. In some embodiments, the noise collection device 13 transmits the noise spectrum data to the computer device 14.

Optionally, the computer device 14 can be a mobile terminal such as a smart phone, a smart watch, a tablet computer, a laptop computer, an intelligent robot, or a desktop computer, a projection computer, or other terminals. The embodiment of the present application does not limit the type of computer device.

Optionally, the noise collection device 13 is a device with a noise collection function. In some embodiments, the noise collection device also has a function of screening or filtering the collected noise spectrum data.

As shown in FIG1 , in this embodiment, a probe 12 is placed in a well 11, and the collected noise spectrum data is transmitted to a noise acquisition device 13 through the probe 12. The noise spectrum data includes the noise spectrum data at the time corresponding to each test point and the noise spectrum data in the depth domain, and the noise spectrum data includes the noise amplitude values at different frequencies. For the same test point, the noise spectrum data changes with time. The noise acquisition device 13 transmits the noise spectrum data to a computer device 14 through a data line, and the noise spectrum data is processed by installing a data processing application on the computer device, thereby obtaining a frequency spectrum curve (Frequency Spectrum Energy, FSE) for analyzing the fluid conditions in the well.

For the convenience of description, the following embodiments are described by taking the method for determining fluid flow based on noise logging executed by a computer device as an example.

FIG2 shows a flow chart of a method for determining fluid flow based on noise logging provided by an exemplary embodiment of the present application. This embodiment is described by taking the method used in the computer device 14 shown in FIG1 as an example, and the method includes the following steps:

Step 201 : obtaining a first test point data set at n depth positions in a noise logging process, wherein the first test point data set includes time data and noise spectrum data in the time domain, and n is a positive integer.

In the process of exploiting oil, natural gas and other energy sources, noise logging is used to detect the type of fluid and the distribution of fluid flow in the well. Noise logging refers to an acoustic logging method that directly measures the natural sound field in the well without setting up an artificial sound source in the well. When the liquid or gas in the well moves, friction can produce sounds with characteristic spectra. According to the noise logging method, information such as the type of fluid and distribution position in the well can be obtained.

As shown in FIG1 , noise spectrum data is collected by a noise collection device 13, and a detection head 12 of the collection device 13 is placed in a target well 11 (a well to be detected). The noise collection device 13 stores the noise spectrum data collected by the detection head 12, and sends the noise spectrum data to a computer device 14. A data processing application is installed and run in the computer device 14, and the data processing application is used to perform quantitative analysis on the collected noise spectrum data.

Schematically, the detection head 12 collects noise spectrum data corresponding to k test points at the same depth in the well and within a period of time, where k is a positive integer. For example, the detection head 12 collects noise spectrum data corresponding to 128 test points at the same depth in the well, and the 128 test points are evenly distributed on the wall of the target well, or the 128 test points are distributed on the wall of the target well at arbitrary intervals. The embodiment of the present application does not limit the distribution state of the test points. In some embodiments, the number of test points can be 256, or more. The embodiment of the present application does not limit the number of test points.

The noise spectrum data corresponding to the test points at multiple depth positions constitute a first test point data set, and the noise spectrum data includes noise amplitude values at different frequencies.

The noise collection device 13 collects the noise spectrum data of each test point according to the time change. The noise spectrum data corresponding to the same test point fluctuates with the time change. For example, the data collected at the test point a is: at 0.0051 seconds, the noise frequency value is 10 Hz, and the noise amplitude is 20 millivolts (mV); at 0.4064 seconds, the noise frequency value is 20 Hz, and the noise amplitude is 20 mV. When collecting the noise spectrum data, the embodiment of the present application divides the noise spectrum data according to the frequency, and determines the divided noise spectrum data as the first test point data set.

Schematically, the first test point data set is represented by Table 1.

Table 1

Among them, serial numbers 1 to 128 refer to 128 test points at the same depth position, and the amplitude refers to the maximum amplitude generated when the noise deviates from the equilibrium state vibration. The embodiment of the present application takes the amplitude unit as millivolt and the time unit as second as an example for explanation, and the amplitude of the 128 test points is divided according to the noise frequency range, such as, the noise amplitude value corresponding to the test point of serial number 1 is included in the noise frequency range of 0 to 10 Hz; the noise amplitude value corresponding to the test point of serial number 3 is included in the noise frequency range of 20 to 30 Hz. Table 1 is only an example of the noise spectrum data of the test point corresponding to a depth position.

Step 202: Process the first test point data set to obtain a second test point data set, where the second test point data set includes depth position data and noise spectrum data in a depth domain.

The data processing application performs superposition and averaging processing on the noise spectrum data in the first test point data set to obtain the noise spectrum data corresponding to the test point at the same depth position. That is, the noise spectrum data associated with time is converted into noise spectrum data associated with the depth position. Schematically, the test point No. 1 in Table 1 is used as an example for explanation. The noise spectrum data "1.767, 1.358...1.347" corresponding to the test point No. 1 is superimposed and averaged, and the result obtained is the noise spectrum data corresponding to the test point No. 1 at a depth position of X meters.

Schematically, Table 2 shows part of the data in the second test point data set.

Table 2

Taking the noise spectrum data of the test point corresponding to the test point No. 1 at a depth of 2985.23 meters as an example, the noise amplitude value at this point is 1.527mV, which is obtained by superimposing and averaging the noise spectrum data corresponding to the test point No. 1 in Table 1.

By analogy, the noise spectrum data of 128 test points at the same depth position can be calculated, and the noise spectrum data corresponding to the test points at multiple depth positions constitute a second test point data set. Since the second test point data set is obtained by processing the first test point data set, the test point data in the second test point data set are noise spectrum data in different depth domains.

In some embodiments, before performing superposition and averaging processing on the first test point data set, the first test point data set needs to be screened to remove abnormal data (data corresponding to abnormally fluctuating noise) in the first test point data set to obtain stable noise spectrum data.

Step 203: obtaining a frequency spectrum curve according to the second test point data set, wherein the frequency spectrum curve is used to indicate each flow unit after the geological layer downhole is divided.

The data processing application draws a frequency energy spectrum through the second test point data set, and extracts a frequency energy spectrum curve from the frequency energy spectrum. A flow unit refers to a unit corresponding to the fluid after the geological layer downhole is divided according to the frequency energy spectrum curve, that is, a flow unit is a unit artificially divided by mathematical operations. There may be gas, liquid and other substances in the flow unit, and different flow units may store the same type of fluid or different types of fluid. The frequency energy spectrum curve represents the distribution of flow units in the downhole geological layer. For example, if a fluid storage layer stores a large amount of fluid, the corresponding frequency energy spectrum curve there will have a peak value. For another example, if a fluid storage layer stores a very small amount of fluid, the corresponding frequency energy spectrum curve there will have a valley value.

Step 204: determine the flow ratio corresponding to each flow unit according to the frequency spectrum curve.

Since the frequency spectrum curve indicates the location where each flow unit appears, the data processing application processes the noise amplitude values at different frequencies corresponding to each test point in the second test point data set to obtain the noise amplitude values corresponding to each flow unit, and the data processing application calculates the flow ratio corresponding to each flow unit based on the noise amplitude values. For example, a curve segment on the frequency spectrum curve indicates flow unit 1, and the flow ratio of flow unit 1 is determined based on the noise amplitude value corresponding to the curve segment.

Step 205, determining the fluid flow in each flow unit according to the flow ratio.

The fluid flow rate includes at least one of the flow rate of the fluid stored in the flow unit and the flow rate of the fluid injected into the flow unit from the outside.

Illustratively, when the data processing application receives the total fluid flow in the well measured by production logging, the fluid flow corresponding to each flow unit is calculated according to the flow ratio corresponding to each flow unit.

Illustratively, when the data processing application receives the total flow rate of the fluid injected into the flow unit from the outside, the fluid flow rate corresponding to each flow unit is calculated according to the flow rate ratio corresponding to each flow unit.

In summary, the method provided in this embodiment synthesizes a frequency spectrum curve through the second test point data set obtained from the first test point data set, and uses the frequency spectrum curve to divide each geological layer in the well, so as to determine the flow ratio corresponding to each flow unit and the fluid flow in the flow unit in combination with the frequency spectrum curve. This allows those skilled in the art to intuitively and efficiently calculate the flow volume of each layer in the well without having to identify the size of the noise based on the naked eye, thereby achieving quantitative analysis of the fluid flow in the flow unit.

FIG3 shows a flow chart of a method for determining fluid flow based on noise logging provided by another exemplary embodiment of the present application. This embodiment is described by taking the method used in the computer device 14 shown in FIG1 as an example, and the method includes the following steps:

Step 301 : obtaining a first test point data set at n depth positions in a noise logging process, wherein the first test point data set includes time data and noise spectrum data in the time domain, and n is a positive integer.

Schematically, multiple test points are collected at the same depth position in the well, and the noise spectrum data corresponding to a certain depth position is represented by multiple test points. In some embodiments, a depth position is regarded as a point position, and a point position is determined as a pass. The collected noise spectrum data is transmitted to the computer device 14 using the noise collection device 13 shown in Figure 1. The computer device 14 loads the first test point data set into the data processing application. The noise spectrum data in the first test point data set changes with the change of time data, that is, the noise amplitude value corresponding to a test point changes continuously with the change of time.

Step 302: Process the first test point data set to obtain a second test point data set, where the second test point data set includes depth position data and noise spectrum data in a depth domain.

Schematically, the computer device displays the first test point data set in the form of Table 1, or, through a drawing application, draws the first test point data set into a noise data image for display. The drawing application is an application capable of drawing the test point data into a noise data image. The data processing application in the embodiment of the present application can also draw the noise data image. Step 302 can be replaced by the following steps:

Step 3021: Screening the first test point data set to obtain a screened first test point data set.

During the process of noise logging, any noise in the well may be recorded, so the image area corresponding to the abnormal fluctuating noise will appear on the noise data image. When the wellbore environment is good, the test point data collected by the noise acquisition equipment is of high quality, and the data processing application selects data with stable amplitude values from the test point data; when the wellbore environment is unstable, the effective information in the flow unit (such as information on fluid type, fluid flow rate, etc.) will be masked to a certain extent, that is, the signal-to-noise ratio (SNR or S/N) is reduced, and it is necessary to give priority to selecting data with stable amplitude values.

Schematically, the data processing application synthesizes the noise data image according to the filtered first test point data set, as shown in FIG4. The data processing application divides the noise spectrum data according to the noise frequency, such as 0 to 200 Hz is divided into the first interval, 200 Hz to 400 Hz is divided into the second interval, 400 Hz to 600 Hz is divided into the third interval, 600 Hz to 1000 Hz is divided into the fourth interval, 1000 Hz to 4000 Hz is divided into the fifth interval, and 4000 Hz to 6000 Hz is divided into the sixth interval. The noise amplitude is drawn into the noise data image according to the above-mentioned noise frequency intervals. For example, the amplitude value of the noise spectrum data corresponding to a noise segment is 200 mV, and the corresponding frequency is 600 Hz to 1000 Hz. The noise spectrum data is classified as the fourth interval to draw the image, thereby drawing the image shown in FIG4. Each noise frequency interval corresponds to a noise amplitude curve, the first interval corresponds to a noise amplitude curve 101, the second interval corresponds to a noise amplitude curve 102, the third interval corresponds to a noise amplitude curve 103, the fourth interval corresponds to a noise amplitude curve 104, the fifth interval corresponds to a noise amplitude curve 105, and the sixth interval corresponds to a noise amplitude curve 106. Among them, area 107 is the image area corresponding to when the noise has abnormal fluctuations, and the data processing application filters out the data corresponding to area 107 to obtain the filtered first test point data set.

Step 3022, performing superposition and averaging processing on the test point data corresponding to the m test points at the same depth position in the filtered first test point data set to obtain a second test point data set, where m is a positive integer.

The noise spectrum data associated with time is converted into noise spectrum data associated with a depth position. Schematically, the test point No. 1 in Table 1 is used as an example for explanation. The noise spectrum data "1.767, 1.358...1.347" corresponding to the test point No. 1 is superimposed and averaged, that is, the noise spectrum data corresponding to the test point No. 1 is added and divided by the number corresponding to the test point No. 1 to obtain the average value of the noise spectrum data corresponding to the test point No. 1, which is the noise spectrum data corresponding to the test point No. 1 when the depth position is X meters.

Step 303: synthesize a frequency energy spectrum diagram according to the second test point data set, wherein the frequency energy spectrum diagram includes noise spectrum data corresponding to the test points at different depth positions in the second test point data set.

The second test point data set obtained through step 3021 and step 3022 is shown in Table 3.

Table 3

Among them, serial numbers 1 to 128 refer to 128 test points at the same depth position, the amplitude refers to the maximum amplitude generated when the noise deviates from the equilibrium state vibration, and the embodiment of the present application is explained using the amplitude unit as millivolt and the depth unit as meter as an example.

It can be seen from Table 3 that the data processing application sorts the test point data in the second test point data set according to the depth domain. Schematically, the test point data in the second test point data set are sorted in order from large to small depth values, that is, the noise amplitude data at each depth position is arranged according to the depth position to form a continuous noise profile in the depth domain, which is a frequency energy spectrum.

Schematically, the data processing application can also synthesize a frequency energy spectrum according to the test point data in Table 3, as shown in FIG5. The frequency energy spectrum includes three parts, namely: the well structure, the frequency energy spectrum of the amplified interval, and the original frequency energy spectrum. Among them, the well structure includes a perforation section 51, a water injection port, and a packer 52. The perforation section 51 refers to an area formed by multiple holes arranged on the wellbore, which is used to assist the fluid to flow from the oil pipe to the well; the water injection port refers to a window arranged on the oil pipe for injecting water into the well; the packer 52 refers to an elastic sealing element, which is used to isolate the annular space between various sizes of tubings and the wellbore and between tubings, and isolate the production layer to control the injected or produced fluid and protect the downhole tools of the casing.

The original frequency energy spectrum 53 is drawn according to the sorted second test point data set, the noise amplitude of the original frequency energy spectrum 53 is in the interval between 0 and 25 mV, and the frequency energy spectrum corresponding to a part of the interval is selected from the interval to obtain the frequency energy spectrum of the amplified interval 54. Schematically, the interval selected in the embodiment of the present application is 0 to 10 mV.

Step 304: extracting a frequency energy spectrum curve from the frequency energy spectrum diagram.

The frequency spectrum curve is obtained as follows:

Step 3041: extract first noise spectrum data in the same depth domain from the frequency energy spectrum.

As shown in Table 3, 128 test points at the same depth position correspond to 128 first noise amplitude values, and the 128 first noise amplitude values are noise amplitude values at different frequencies, that is, first noise spectrum data.

Step 3042: Perform geometric mean processing on the first noise spectrum data in each depth domain to obtain second noise spectrum data in each depth domain.

The first noise spectrum data in each depth domain is subjected to geometric mean processing by the following formula to obtain the second noise spectrum data.

Among them, FSE (Frequency Spectrum Energy) represents the geometric mean, a(i) represents the amplitude value corresponding to different frequencies in the depth domain, and k represents the number of test points at the same depth position.

Instructively, taking the number of test points at the same depth position as 128 and the noise spectrum data corresponding to the test point at a depth position of 1985.23 meters in Table 3 as an example, "1.527, 23.143, 20.324, 13.102, 5.832, 3.109...0.409" are substituted into the above formula to calculate the geometric mean, which is the second noise spectrum data (the second noise amplitude values at different frequencies).

Step 3043: Obtain a frequency energy spectrum curve according to the second noise spectrum data.

The second noise spectrum data corresponding to the test points at the same depth position is calculated in the above manner, and the second noise amplitude data corresponding to the test points at multiple depth positions are connected on the frequency energy spectrum to obtain a frequency energy spectrum curve. That is, the FSE value corresponding to each depth position is calculated according to the above formula to form a frequency energy spectrum curve 55, which represents the change of the frequency energy spectrum in the interval of 0 to 10mV.

It should be noted that the influence of wellbore tube wave noise on the division of flow units can be eliminated in actual processing. After research and comparison, it is generally found that the influence of tube waves is relatively consistent. Whether it is eliminated or not, it has a linear effect on the geometric mean (as shown in Figure 6). Therefore, after the subsequent normalization processing, this influence will be eliminated and will not affect subsequent calculations.

Step 305: determine the flow ratio corresponding to each flow unit according to the frequency spectrum curve.

Since the flow units are divided according to the porosity or saturation according to the conventional logging method, and the depth that can be detected by the conventional logging method is limited. In actual situations, the geological layer itself has a certain degree of connectivity, that is, the noise generated by the fluid flow in the geological layer outside the wellbore is relatively continuous, rather than dividing the reservoir with intervals like the conventional logging method. It should be noted that the flow unit is an artificially divided unit according to the above calculation method.

Schematically, as shown in FIG7 , the data processing application program synthesizes a noise quantitative analysis image with the division result. In order to compare with the result of the actual division of the present application, FIG7 also includes the result after the division in the conventional logging method. Area 61 includes a structural schematic diagram of the perforation section, the water injection port, and the depth, area 62 includes the fluid flow corresponding to the divided flow unit, and area 63 includes an amplified frequency energy spectrum. It can be seen from FIG7 that the result of the division in the conventional logging method is that the flow unit is disconnected and has intervals (such as flow unit 78 and flow unit 79, the dark area is used to represent the fluid flow contained in the flow unit, such as the fluid flow in flow unit 78 does not fill the flow unit, and the fluid flow in flow unit 79 fills the flow unit).

The result of the division of the method provided in the embodiment of the present application is that the flow unit is continuous, which corresponds one to one with the change law of the frequency energy spectrum 53. In area 62, the division marks of the flow units corresponding to the perforation sections are displayed, which are the first division mark 64 and the second division mark 65. The division marks are represented by horizontal lines, and the length of the horizontal line represents the injection volume of the flow unit (the injection volume is between 0 and -2000 BW/D). BW/D (Barrel Water/Day) means that the flow rate of the injected or absorbed fluid is measured by the number of standard barrels based on the capacity of the standard barrel. QWZI (Quantity of Water Zone Intake) represents the water intake area of the injected fluid flow. Step 305 can be replaced by the following steps:

Step 3051: According to each flow unit indicated by the frequency spectrum curve, the noise spectrum data corresponding to each flow unit is accumulated to obtain a third test point data set.

Taking the second test point data set shown in Table 3 as an example, the second test point data set is divided into flow units according to the frequency spectrum curve, and the test point data of sequence number 1 in Table 3 is accumulated in units of each flow unit according to the divided flow units. For example, the frequency spectrum curve indicates that the depth of flow unit 1 is 1985.23 meters to 1987.67 meters, that is, the test point data of sequence number 1 at 1985.23 meters to the test point data of sequence number 1 at 1987.67 meters are accumulated to obtain the first accumulation result. The first accumulation result is the noise spectrum data corresponding to flow unit 1 (as shown in the first column of Table 4).

Step 3052: normalize the third test point data set to obtain the flow ratio of each flow unit.

The first accumulated result corresponding to the test point data of sequence number 1 is normalized. For example, the frequency spectrum curve indicates that the depth of flow unit 1 is 1985.23 meters to 1987.67 meters. That is, each test point data from the test point data of sequence number 1 at 1985.23 meters to the test point data of sequence number 1 at 1987.67 meters is divided by the first accumulated result obtained in step 3051, and then multiplied by 100 to obtain the first normalized result. Similarly, the normalized result corresponding to each test point data of the sequence number corresponding to the same flow unit in Table 1 is calculated, and the calculation results are shown in the second column of Table 4.

Table 4

Step 306, determining the fluid flow in each flow unit according to the flow ratio.

There are two ways to determine the fluid flow rate in each flow unit:

1. Calculate the fluid flow rate corresponding to each flow unit according to the production logging. Step 306 can be replaced by the following steps:

Step 3061a, obtaining the total flow rate of the first fluid corresponding to the production logging process.

Schematically, the total flow rate of the first fluid corresponding to the production logging process is 2818 STB/D (Standard Barrel/Day), wherein the fluid flow rate corresponding to the first flow unit divided by the first division mark 64 is 1866 STB/D, and the fluid flow rate corresponding to the second flow unit corresponding to the second division mark 65 is 953 STB/D. The first division mark 64 and the second division mark 65 are respectively located at the positions shown in FIG. 7 (indicated by the thick horizontal lines).

Step 3062a, determining the fluid flow rate in each flow unit according to the total flow rate of the first fluid and the flow rate ratio corresponding to each flow unit.

As can be seen from Table 4, the normalized results corresponding to each flow unit are obtained by multiplying the normalized results corresponding to each flow unit by the total flow of the first fluid, respectively, to obtain the fluid flow corresponding to each flow unit. According to the fluid flow corresponding to each flow unit, the fluid flow corresponding to the two flow units shown in Figure 7 is calculated, which are 1833STB/D and 985STB/D respectively. It can be seen that the fluid flow corresponding to each flow unit calculated in the embodiment of the present application is similar to the fluid flow flowing from the wellbore to each water nozzle during the production logging process.

2. Calculate the fluid flow rate corresponding to each flow unit according to the surface flow rate information. Step 306 can be replaced by the following steps:

Step 3061b, in response to receiving the surface flow information, determine the fluid flow rate injected into each flow unit according to the surface flow information and the flow ratio corresponding to each fluid information, and the surface flow information is used to characterize the total injection amount corresponding to the injection of fluid into each flow unit.

The surface flow rate information refers to the information corresponding to the flow rate of the fluid injected into the well from the outside. Schematically, the staff performing water injection can determine the flow rate of the fluid injected into the well.

Calculating the fluid flow rate injected into each flow unit using the surface flow information is consistent with the implementation of step 3072a and will not be repeated here.

In summary, the method of this embodiment synthesizes a frequency spectrum curve through the second test point data set obtained from the first test point data set, and uses the frequency spectrum curve to divide each geological layer in the well, so as to determine the flow ratio corresponding to each flow unit and the fluid flow in the flow unit in combination with the frequency spectrum curve. This allows those skilled in the art to intuitively and efficiently calculate the flow volume of each layer in the well without having to identify the size of the noise based on the naked eye, thereby achieving quantitative analysis of the fluid flow in the flow unit.

The relationship between the depth position data and the noise amplitude data in the second test point data set is used to synthesize a frequency energy spectrum, and the frequency energy spectrum curve is used to depict the changes in the noise amplitude data of the test point in the frequency energy spectrum as the depth position changes, so that the flow unit can be divided more accurately.

The first noise amplitude value is processed by means of geometric mean processing, and the processed noise amplitude value is used to synthesize the frequency spectrum curve, so that the synthesized frequency spectrum curve is more accurate, thereby ensuring that the flow unit divided by relying on the frequency spectrum curve is more accurate.

The fluid flow in each flow unit can be calculated through the flow ratio and the total flow of the first fluid obtained by testing during the production logging process, thereby achieving quantitative analysis of the fluid flow of each flow unit.

The second test point data set is obtained by screening and superimposing the first test point data set, thereby converting the corresponding noise amplitude data collected in the time domain into noise amplitude data in the depth domain, ensuring the subsequent accurate synthesis of the frequency energy spectrum curve.

In one example, taking water injection into a well as an example, the method for determining fluid flow rate based on noise logging provided in an embodiment of the present application is described. As shown in FIG8 , the method includes the following steps:

Step 801, loading the original noise point measurement data.

Load the noisy raw point data (first test point data set) into a data processing application.

Step 802, prioritize the stable spectrum amplitude segments in the data.

The data processing application preferentially selects data with stable spectrum amplitude from the first test point data set. In some embodiments, when the noise collected in the well is stable, the data processing application performs a "cut-off" process on the collected noise spectrum data, that is, selects the amplitude data of the middle frequency band. The amplitude data of the middle frequency band is the second test point data set.

Step 803, combining into a frequency energy spectrum according to the depth.

The selected amplitude data (second test point data set) are sorted according to the depth value to obtain a sorted second test point data set, and a frequency energy spectrum is synthesized according to the sorted test point data set.

Step 804, obtain the geometric mean of frequency energy (FSE) and normalize it.

According to the formula in the above embodiment, the geometric mean of the noise amplitude values at the same depth position is calculated to obtain the FSE value corresponding to the test point at the same depth position. The FSE value is normalized to obtain the proportion of spectrum energy.

Step 805, dividing the layers according to the frequency energy spectrum, and cumulatively calculating the proportion of the spectrum energy of each layer.

The frequency energy spectrum diagram displays a frequency energy spectrum curve, which is used to indicate each flow unit after the geological layer is divided, and the frequency spectrum energy proportion of each layer is calculated according to each flow unit (the percentage obtained after normalizing the FSE value). See the implementation method of step 305, which will not be repeated here.

Step 806: Is there any production logging flow data?

The data processing application determines whether the production logging flow data is received, and the production logging flow data includes the total water injection volume. If the data processing application does not receive the production logging flow data, the process proceeds to step 807; if the data processing application receives the production logging flow data, the process proceeds to step 808.

Step 807: If there is no production logging flow data, then receive surface flow information.

The staff who inject water outside the well can determine the total flow rate of water injected into the well, and determine the fluid flow rate of the injection well corresponding to each layer based on the total flow rate and the proportion of spectrum energy of each layer calculated in step 805.

Step 808: If there is production logging flow data, the total flow is distributed according to the total flow and the proportion of spectrum energy of each layer.

If the data processing application receives production logging flow data, the fluid flow in the injection well corresponding to each layer is determined based on the total injection volume in the production logging data and the proportion of spectrum energy of each layer calculated in step 805.

In summary, the method provided in this embodiment can calculate the fluid flow corresponding to each layer based on the proportion of the spectrum energy of each layer, thereby realizing quantitative analysis of the fluid flow.

It should be noted that even without relevant information such as production logging flow data or surface flow information, the profile can be adjusted and the size of the structure such as the oil pipe in the well can be optimized according to the percentage indicated by the frequency spectrum curve to achieve the purpose of flexible flow adjustment.

At present, the quantitative evaluation of the flow of fluid outside the pipe is mainly based on temperature modeling, rather than using the noise spectrum data itself. Therefore, the evaluation of noise data still remains in the qualitative evaluation stage. The method of the embodiment of the present application elevates the noise data from the qualitative essence to the quantitative stage; the data processing application implemented by the method provided by the embodiment of the present application can intuitively and efficiently obtain the flow of each layer outside the pipe and give a specific value; and completely abandon the quantitative evaluation of the water absorption of the layer outside the pipe of the injection well based on temperature modeling. Temperature modeling will be affected by many uncertain factors, and the deviation of quantitative evaluation is difficult to control. Temperature modeling itself has the problem of multiple solutions of temperature being affected by many interference factors, and it will also bring many other problems. For example, it is necessary to shut down the well logging temperature for comparison, and measure it multiple times to see the speed of temperature change relative to the original geothermal gradient. This situation is extremely inefficient. At the same time, the temperature is also affected by unpredictable factors, such as stringing, the development of adjacent wells, etc., and the method provided by the embodiment of the present application can avoid the interference of temperature factors, and the fluid flow of each flow unit can be determined without shutting down the logging, thereby improving the measurement efficiency.

FIG9 shows a structural block diagram of a device for determining fluid flow based on noise logging provided by an exemplary embodiment of the present application, the device comprising the following parts:

An acquisition module 910 is used to acquire a first test point data set at n depth positions in a noise logging process, where the first test point data set includes time data and noise spectrum data in the time domain, and n is a positive integer;

A processing module 920 is used to process the first test point data set to obtain a second test point data set, where the second test point data set includes depth position data and noise spectrum data in a depth domain;

The processing module 920 is used to synthesize a frequency spectrum curve according to the second test point data set, and the frequency spectrum curve is used to indicate each flow unit after the geological layer under the well is divided;

The processing module 920 is used to determine the flow ratio corresponding to each flow unit according to the frequency spectrum curve;

The processing module 920 is used to determine the fluid flow in each flow unit according to the flow ratio.

In an optional embodiment, the processing module 920 is used to synthesize a frequency energy spectrum diagram based on the second test point data set, the frequency energy spectrum diagram including noise spectrum data corresponding to test points at different depth positions in the second test point data set; and extract a frequency energy spectrum curve from the frequency energy spectrum diagram.

In an optional embodiment, the processing module 920 is used to extract first noise spectrum data in the same depth domain from the frequency energy spectrum diagram; perform geometric mean processing on the first noise spectrum data in each depth domain to obtain second noise spectrum data in each depth domain; and obtain a frequency energy spectrum curve based on the second noise spectrum data.

In an optional embodiment, the processing module 920 is used to accumulate the noise spectrum data corresponding to each flow unit according to the flow unit indicated by the frequency spectrum curve to obtain a third test point data set; and normalize the third test point data set to obtain the flow ratio corresponding to each flow unit.

In an optional embodiment, the acquisition module 910 is used to obtain the total flow rate of the first fluid corresponding to the production logging process; the processing module 920 is used to determine the fluid flow rate in each flow unit based on the total flow rate of the first fluid and the flow rate ratio corresponding to each flow unit.

In an optional embodiment, the processing module 920 is used to determine the fluid flow rate injected into each flow unit in response to receiving ground flow information, based on the ground flow information and the flow ratio corresponding to each flow unit, and the ground flow information is used to characterize the total injection amount corresponding to the injection of fluid into each flow unit.

In an optional embodiment, the processing module 920 is used to filter the first test point data set to obtain a filtered first test point data set; and to perform superposition and averaging processing on the test point data corresponding to m test points at the same depth position in the filtered first test point data set to obtain a second test point data set, where m is a positive integer.

In summary, the device provided in this embodiment synthesizes a frequency spectrum curve through the second test point data set obtained from the first test point data, and uses the frequency spectrum curve to divide the various geological layers in the well, so as to determine the flow ratio corresponding to each flow unit and the fluid flow in the flow unit by combining the first test point data set and the frequency spectrum curve. There is no need to determine the distribution of the fluid in the well based on the size of the noise identified by the naked eye, and the flow amount of each layer in the well is calculated intuitively and efficiently, thereby realizing quantitative analysis of the fluid flow in the flow unit.

The relationship between the depth position data and the noise amplitude data in the second test point data set is used to synthesize a frequency energy spectrum, and the frequency energy spectrum curve is used to depict the changes in the noise amplitude data of the test point in the frequency energy spectrum as the depth position changes, so that the flow unit can be divided more accurately.

The first noise amplitude value is processed by means of geometric mean processing, and the processed noise amplitude value is used to synthesize the frequency spectrum curve, so that the synthesized frequency spectrum curve is more accurate, thereby ensuring that the flow unit divided by relying on the frequency spectrum curve is more accurate.

The fluid flow in each flow unit can be calculated through the flow ratio and the total flow of the first fluid obtained by testing during the production logging process, thereby achieving quantitative analysis of the fluid flow of each flow unit.

The second test point data set is obtained by screening and superimposing the first test point data set, so as to convert the corresponding noise amplitude data collected in the time domain into noise amplitude data in the depth domain, thereby ensuring the subsequent accurate synthesis of the frequency energy spectrum curve.

It should be noted that: the device for determining fluid flow based on noise logging provided in the above embodiment is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device for determining fluid flow based on noise logging provided in the above embodiment and the method for determining fluid flow based on noise logging belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

FIG10 shows a block diagram of a computer device 1000 provided by an exemplary embodiment of the present application. The computer device 1000 may be a portable mobile terminal, such as a smart phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 player (Moving Picture Experts Group Audio Layer IV). The computer device 1000 may also be called a user device, a portable terminal, or other names.

Typically, the computer device 1000 includes a processor 1001 and a memory 1002 .

The processor 1001 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 1001 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 1001 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 1001 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 1001 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

The memory 1002 may include one or more computer-readable storage media, which may be tangible and non-transitory. The memory 1002 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 1002 is used to store at least one instruction, which is used to be executed by the processor 1001 to implement the method for determining the fluid flow rate based on noise logging provided in the embodiments of the present application.

In some embodiments, the computer device 1000 may further include: a peripheral device interface 1003 and at least one peripheral device. Specifically, the peripheral device includes: at least one of a radio frequency circuit 1004, a touch display screen 1005, a camera assembly 1006, an audio circuit 1007, a positioning assembly 1008 and a power supply 1009.

The peripheral device interface 1003 may be used to connect at least one peripheral device related to I/O (Input/Output) to the processor 1001 and the memory 1002. In some embodiments, the processor 1001, the memory 1002, and the peripheral device interface 1003 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 1001, the memory 1002, and the peripheral device interface 1003 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

The radio frequency circuit 1004 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 1004 communicates with the communication network and other communication devices through electromagnetic signals. The radio frequency circuit 1004 converts the electrical signal into an electromagnetic signal for transmission, or converts the received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 1004 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The radio frequency circuit 1004 can communicate with other terminals through at least one wireless communication protocol. The wireless communication protocol includes but is not limited to: the World Wide Web, a metropolitan area network, an intranet, various generations of mobile communication networks (2G, 3G, 4G, 5G and combinations thereof), a wireless local area network, and a wireless fidelity network (Wireless Fidelity, WiFi). In some embodiments, the radio frequency circuit 1004 may also include circuits related to NFC (Near Field Communication, short-range wireless communication), which is not limited in this application.

The touch display screen 1005 is used to display a UI (User Interface). The UI may include graphics, text, icons, videos, and any combination thereof. The touch display screen 1005 also has the ability to collect touch signals on the surface or above the surface of the touch display screen 1005. The touch signal may be input to the processor 1001 as a control signal for processing. The touch display screen 1005 is used to provide virtual buttons and/or virtual keyboards, also known as soft buttons and/or soft keyboards. In some embodiments, the touch display screen 1005 may be one, and the front panel of the computer device 1000 is set; in other embodiments, the touch display screen 1005 may be at least two, which are respectively set on different surfaces of the computer device 1000 or are folded; in other embodiments, the touch display screen 1005 may be a flexible display screen, which is set on a curved surface or a folded surface of the computer device 1000. Even, the touch display screen 1005 can also be set to a non-rectangular irregular shape, that is, a special-shaped screen. The touch display screen 1005 can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

The camera assembly 1006 is used to capture images or videos. Optionally, the camera assembly 1006 includes a front camera and a rear camera. Typically, the front camera is used to realize video calls or selfies, and the rear camera is used to realize the shooting of photos or videos. In some embodiments, there are at least two rear cameras, which are any one of a main camera, a depth of field camera, and a wide-angle camera, so as to realize the fusion of the main camera and the depth of field camera to realize the background blur function, and the fusion of the main camera and the wide-angle camera to realize panoramic shooting and VR (Virtual Reality) shooting functions. In some embodiments, the camera assembly 1006 may also include a flash. The flash can be a monochrome temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm light flash and a cold light flash, which can be used for light compensation at different color temperatures.

The audio circuit 1007 is used to provide an audio interface between the user and the computer device 1000. The audio circuit 1007 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, and convert the sound waves into electrical signals and input them into the processor 1001 for processing, or input them into the radio frequency circuit 1004 to achieve voice communication. For the purpose of stereo acquisition or noise reduction, there may be multiple microphones, which are respectively arranged at different parts of the computer device 1000. The microphone may also be an array microphone or an omnidirectional acquisition microphone. The speaker is used to convert the electrical signal from the processor 1001 or the radio frequency circuit 1004 into sound waves. The speaker may be a traditional film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert the electrical signal into sound waves audible to humans, but also convert the electrical signal into sound waves inaudible to humans for purposes such as distance measurement. In some embodiments, the audio circuit 1007 may also include a headphone jack.

The positioning component 1008 is used to locate the current geographical location of the computer device 1000 to implement navigation or LBS (Location Based Service). The positioning component 1008 can be a positioning component based on the US GPS (Global Positioning System), China's Beidou system or Russia's Galileo system.

The power supply 1009 is used to power various components in the computer device 1000. The power supply 1009 can be an alternating current, a direct current, a disposable battery, or a rechargeable battery. When the power supply 1009 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged through a wired line, and a wireless rechargeable battery is a battery that is charged through a wireless coil. The rechargeable battery can also be used to support fast charging technology.

Those skilled in the art will appreciate that the structure shown in FIG. 10 does not limit the computer device 1000 , and may include more or fewer components than shown in the figure, or combine certain components, or adopt a different component arrangement.

An embodiment of the present application also provides a computer device, which includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor to implement the method for determining fluid flow based on noise logging as described above.

An embodiment of the present application also provides a computer-readable storage medium, which stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor to implement the method for determining fluid flow based on noise logging as described above.

The embodiment of the present application also provides a computer program product or a computer program, wherein the computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method for determining fluid flow based on noise logging as described above.

A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

The above description is only an optional embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims (5)

1. A method for determining fluid flow based on noise logging, characterized in that the method comprises: Acquire a first test point data set of multiple test points at each depth position of n depth positions in the noise logging process, wherein the first test point data set includes time data and noise spectrum data in the time domain, and n is a positive integer; The noise spectrum data in the first test point data set is divided according to the noise frequency to obtain various noise frequency intervals; the amplitude of the noise spectrum data is plotted according to each noise frequency interval to draw a noise data image, and the noise spectrum data corresponding to the noise frequency interval when the noise has abnormal fluctuations is filtered out to obtain a filtered first test point data set; Performing superposition and averaging processing on the test point data corresponding to m test points at the same depth position in the filtered first test point data set to obtain a second test point data set, wherein the second test point data set includes noise spectrum data corresponding to the test points at multiple depth positions, m is a positive integer, and the second test point data set includes depth position data and noise spectrum data in a depth domain; synthesizing a frequency energy spectrum according to the second test point data set, wherein the frequency energy spectrum includes noise spectrum data corresponding to test points at different depth positions in the second test point data set; Extracting first noise spectrum data in the same depth domain from the frequency energy spectrum; Performing geometric mean processing on the first noise spectrum data in each depth domain to obtain the second noise spectrum data in each depth domain; Obtaining a frequency energy spectrum curve according to the second noise spectrum data, wherein the frequency energy spectrum curve is used to indicate each flow unit after the geological layer under the well is divided; According to each of the flow units indicated by the frequency spectrum curve, the noise spectrum data corresponding to each of the flow units are respectively accumulated to obtain a third test point data set; the third test point data set is normalized to obtain a flow ratio corresponding to each of the flow units; Determining the fluid flow rate in each of the flow units according to the flow rate ratio, wherein the fluid flow rate includes at least one of the flow rate of the fluid stored in the flow unit and the flow rate of the fluid injected into the flow unit from the outside; The step of determining the fluid flow rate in each of the flow units according to the flow rate ratio includes: In response to receiving the surface flow information, the fluid flow injected into each of the flow units is determined according to the surface flow information and the flow ratio corresponding to each of the flow units, and the surface flow information is used to characterize the total injection amount corresponding to the injection of fluid into each of the flow units. 2. The method according to claim 1, characterized in that the step of determining the fluid flow in each of the flow units according to the flow ratio further comprises: Obtaining the total flow rate of the first fluid corresponding to the production logging process; The fluid flow rate in each of the flow units is determined according to the total flow rate of the first fluid and the flow rate ratio corresponding to each of the flow units. 3. A device for determining fluid flow based on noise logging, characterized in that the device comprises: An acquisition module, used to acquire a first test point data set of multiple test points at each depth position of n depth positions in a noise logging process, wherein the first test point data set includes time data and noise spectrum data in the time domain, and n is a positive integer; A processing module is used to divide the noise spectrum data in the first test point data set according to the noise frequency to obtain various noise frequency intervals; draw noise data images according to the amplitudes of the noise spectrum data in various noise frequency intervals, and filter out the noise spectrum data corresponding to the noise frequency interval when the noise has abnormal fluctuations to obtain a screened first test point data set; perform superposition and averaging processing on the test point data corresponding to m test points at the same depth position in the screened first test point data set to obtain a second test point data set, wherein the second test point data set includes noise spectrum data corresponding to the test points at multiple depth positions, m is a positive integer, and the second test point data set includes depth position data and noise spectrum data in the depth domain; The processing module is used to synthesize a frequency energy spectrum according to the second test point data set, wherein the frequency energy spectrum includes noise spectrum data corresponding to test points at different depth positions in the second test point data set; extract first noise spectrum data in the same depth domain from the frequency energy spectrum; perform geometric mean processing on the first noise spectrum data in each depth domain to obtain second noise spectrum data in each depth domain; obtain a frequency energy spectrum curve according to the second noise spectrum data, wherein the frequency energy spectrum curve is used to indicate each flow unit after the geological layer in the well is divided; The processing module is used to perform accumulation processing on the noise spectrum data corresponding to each of the flow units indicated by the frequency spectrum curve to obtain a third test point data set; perform normalization processing on the third test point data set to obtain a flow ratio corresponding to each of the flow units; The processing module is used to determine the fluid flow rate in each of the flow units according to the flow rate ratio, and the fluid flow rate includes at least one of the flow rate of the fluid stored in the flow unit and the flow rate of the fluid injected into the flow unit from the outside; The processing module is used to: in response to receiving ground flow information, determine the fluid flow rate injected into each flow unit according to the ground flow information and the flow ratio corresponding to each flow unit, and the ground flow information is used to characterize the total injection amount corresponding to the injection of fluid into each flow unit. 4. A computer device, characterized in that the computer device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the method for determining fluid flow based on noise logging as described in any one of claims 1 to 2. 5. A computer-readable storage medium, characterized in that the readable storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by a processor to implement the method for determining fluid flow based on noise logging as described in any one of claims 1 to 2.