Liquid production layer determination method and device, electronic device, and storage medium
By continuously acquiring and analyzing VSP data through a distributed fiber optic acoustic sensing system, the conical or hyperbolic waveform characteristics of the producing fluid layer are identified, solving the problem of low efficiency in producing fluid layer determination and realizing efficient and economical producing fluid layer location.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HANGZHOU YISHU INTELLIGENT TECHNOLOGY CO LTD
- Filing Date
- 2026-01-22
- Publication Date
- 2026-06-05
Smart Images
Figure CN121556844B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of oil well logging technology, and in particular to methods, apparatus, electronic devices and storage media for determining producing fluid layers. Background Technology
[0002] In oil and gas exploration and development, accurate determination of producing layers is crucial for optimizing production efficiency and reducing costs. Its core objective is to identify which downhole layers are effective producing layers (such as oil, gas, and water layers) and the proportion of production contribution from each layer, providing a technical basis for subsequent operations such as stratified production, water shut-off, and profile control. Current technologies rely on multi-device joint data acquisition and complex function inversion to determine the location of sound / wave sources associated with producing layers, resulting in low efficiency in producing layer determination.
[0003] Currently, there is no effective solution to the low efficiency of liquid layer determination in related technologies. Summary of the Invention
[0004] This application provides a method, apparatus, electronic device, and storage medium for determining the product fluid layer, in order to at least solve the problem of low efficiency in product fluid layer determination in related technologies.
[0005] In a first aspect, embodiments of this application provide a method for determining the product fluid layer, comprising:
[0006] Using a pre-set distributed fiber optic acoustic wave sensing system, VSP data is continuously collected; the VSP data includes the location of the optical fiber receiving the acoustic wave signal, as well as the continuous acquisition time and acoustic wave signal intensity corresponding to each optical fiber location.
[0007] From the VSP data, abnormal acoustic signals excited by the production fluid activity are filtered out;
[0008] Based on the abnormal acoustic signal, a distribution feature between the optical fiber position and time is generated; the distribution feature is used to characterize the difference in signal arrival time at different optical fiber positions when the acoustic signal excited by the liquid production point propagates to the optical fiber, and the distribution feature is conical or hyperbolic.
[0009] Identify conical or hyperbolic waveform features in the distribution characteristics and determine the vertex position of the conical or hyperbolic waveform;
[0010] The depth of the producing fluid layer is obtained based on the vertex position.
[0011] In some embodiments, identifying conical or hyperbolic waveform features in the distribution characteristics and determining the vertex position of the conical or hyperbolic waveform includes:
[0012] Based on a preset conical or hyperbolic model, the distribution characteristics are fitted to obtain the vertex position of the conical or hyperbolic waveform.
[0013] In some embodiments, the step of filtering abnormal acoustic signals excited by the production fluid activity from the VSP data includes:
[0014] From the VSP data, abnormal acoustic signals excited by the production fluid activity are filtered out by a preset acoustic data detector.
[0015] In some embodiments, the acoustic data detector is a filter used to identify abnormal acoustic signal waveform characteristics, the filter including a time-domain filter and a frequency-domain filter; the step of filtering abnormal acoustic signals excited by fluid production activity from the VSP data using a preset acoustic data detector includes:
[0016] Filtered acoustic signals at different fiber locations are obtained from the VSP data through the time-domain filter and the frequency-domain filter.
[0017] By detecting the degree of matching between the filtered acoustic signal and a preset conical or hyperbolic model, it can be determined whether the filtered acoustic signal is an abnormal acoustic signal excited by the fluid production activity.
[0018] In some embodiments, the continuous acquisition of VSP data using a pre-defined distributed fiber optic acoustic wave sensing system includes:
[0019] The distributed fiber optic acoustic wave sensing system is used to continuously acquire raw VSP data;
[0020] The original VSP data is subjected to noise reduction and filtering to obtain the VSP data.
[0021] In some embodiments, generating the distribution characteristics of the fiber location over time based on the anomalous acoustic signal includes:
[0022] Based on the abnormal acoustic signal, the abnormal VSP data segment is obtained;
[0023] Based on the abnormal VSP data segment, the distribution characteristics between fiber location and time are generated.
[0024] In some embodiments, after obtaining the depth of the producing fluid layer, the method further includes:
[0025] The depth of the fluid-producing layer was compared with known geological data and traditional logging data to obtain the effectiveness comparison results;
[0026] The method for determining the producing fluid layer was repeated for the same wellbore at different time periods to obtain reliability comparison results.
[0027] Secondly, embodiments of this application provide a device for determining the product fluid layer, the device comprising:
[0028] The data acquisition module is used to continuously acquire VSP data using a preset distributed fiber optic acoustic wave sensing system; the VSP data includes the position of the optical fiber receiving the acoustic wave signal, as well as the continuous acquisition time and acoustic wave signal intensity corresponding to each optical fiber position.
[0029] An abnormal acoustic signal detection module is used to filter abnormal acoustic signals excited by the production fluid activity from the VSP data.
[0030] The distribution feature generation module is used to generate a distribution feature between the optical fiber position and time based on the abnormal acoustic signal; the distribution feature is used to characterize the difference in signal arrival time at different optical fiber positions when the acoustic signal excited by the liquid production point propagates to the optical fiber, and the distribution feature is conical or hyperbolic.
[0031] A vertex position determination module is used to identify conical or hyperbolic waveform features in the distribution features and determine the vertex position of the conical or hyperbolic waveform.
[0032] The fluid production layer depth calculation module is used to obtain the fluid production layer depth based on the vertex position.
[0033] Thirdly, embodiments of this application provide an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for determining the production layer as described in the first aspect above.
[0034] Fourthly, embodiments of this application provide a storage medium storing a computer program that, when executed by a processor, implements the method for determining the product fluid layer as described in the first aspect above.
[0035] Compared to related technologies, the production layer determination method, apparatus, electronic device, and storage medium provided in this application continuously acquire VSP data using a pre-set distributed fiber optic acoustic wave sensing system. The VSP data includes the fiber position receiving the acoustic wave signal, as well as the continuous acquisition time and acoustic wave signal intensity corresponding to each fiber position. From the VSP data, abnormal acoustic wave signals excited by production activity are filtered out. Based on the abnormal acoustic wave signals, a distribution feature between fiber position and time is generated. The distribution feature is used to characterize the signal arrival time difference of different fiber positions when the acoustic wave signal excited by the production point propagates to the fiber, and the distribution feature is conical or hyperbolic. Conical or hyperbolic waveform features are identified in the distribution feature, and the vertex position of the conical or hyperbolic waveform is determined. Based on the vertex position, the production layer depth is obtained, which solves the problem of low efficiency in production layer determination.
[0036] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects and advantages of this application more readily apparent. Attached Figure Description
[0037] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:
[0038] Figure 1 This is a hardware structure block diagram of a terminal for a method for determining the product fluid layer according to an embodiment of this application;
[0039] Figure 2 This is a flowchart of a method for determining the product fluid layer according to an embodiment of this application;
[0040] Figure 3 This is a distribution feature diagram of the optical fiber position and time according to an embodiment of this application;
[0041] Figure 4 This is a two-dimensional cross-sectional view of a vertical well in an oilfield according to an embodiment of this application;
[0042] Figure 5 This is a two-dimensional VSP profile and hyperbolic fitting curve of a vertical well in an oilfield according to an embodiment of this application;
[0043] Figure 6 This is a two-dimensional VSP profile of a highly deviated horizontal well in a shale gas field according to an embodiment of this application;
[0044] Figure 7 This is a structural block diagram of the product fluid layer determination device according to an embodiment of this application. Detailed Implementation
[0045] To make the objectives, technical solutions, and advantages of this application clearer, the application is described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the application. All other embodiments obtained by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application. Furthermore, it is understood that although the efforts made in this development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, modifications to design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as insufficient disclosure of the content of this application.
[0046] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.
[0047] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application means two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The terms “first,” “second,” “third,” etc., used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.
[0048] The method embodiments provided in this example can be executed on a terminal, computer, or similar computing device. Taking running on a terminal as an example, Figure 1 This is a hardware structure block diagram of a terminal for a method for determining the product fluid layer according to an embodiment of this application. For example... Figure 1 As shown, a terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. Optionally, the terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the terminal described above. For example, the terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.
[0049] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the fluid layer determination method in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thus implementing the above-described method. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.
[0050] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the terminal's communication provider. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module used for wireless communication with the Internet.
[0051] This embodiment provides a method for determining the product fluid layer. Figure 2 This is a flowchart of the method for determining the product fluid layer according to an embodiment of this application, such as... Figure 2 As shown, the process includes the following steps:
[0052] Step S201: Continuously acquire VSP data using a pre-set distributed optical fiber acoustic wave sensing system; VSP data includes the location of the optical fiber receiving the acoustic wave signal, as well as the continuous acquisition time and acoustic wave signal intensity corresponding to each optical fiber location.
[0053] The process involves continuously acquiring VSP (Voice over Polymer) data using a pre-designed distributed fiber optic acoustic sensing system deployed along the downhole wellbore. This fiber optic cable must maintain good coupling with the wellbore wall to ensure stable signal transmission. The acquired VSP data contains three key pieces of information: the specific fiber optic position reached by the acoustic signal during its propagation along the pre-designated fiber optic cable; the continuous time-series acquisition data corresponding to each fiber optic position; and the acoustic signal intensity parameters received at each fiber optic position at different acquisition time points. It should be understood that the fiber optic position must accurately correspond to the actual depth coordinates of the downhole wellbore, the acquisition time must be continuous and uninterrupted to capture the complete acoustic propagation process, and the acoustic signal intensity directly reflects the energy characteristics of the acoustic waves excited by the production fluid activity.
[0054] Step S202: Filter the abnormal acoustic signals excited by the production fluid activity from the VSP data.
[0055] Specifically, by combining the unique patterns of propagation characteristics and energy distribution of acoustic signals excited by fluid production activity from VSP data (which includes continuous acquisition time and acoustic signal intensity information corresponding to different fiber locations), abnormal acoustic signals that exceed the range of conventional signals are automatically detected. At the same time, it is ensured that the extracted VSP data segments completely contain the cone head and part of the cone tail features of the acoustic event, providing comprehensive data support for subsequent cone or hyperbolic waveform identification, and finally accurately screening out the effective abnormal acoustic signals excited by fluid production activity.
[0056] Step S203: Based on the abnormal acoustic signal, generate the distribution characteristics between the optical fiber position and time; the distribution characteristics are used to characterize the difference in signal arrival time at different optical fiber positions when the acoustic signal excited by the liquid production point propagates to the optical fiber, and the distribution characteristics are conical or hyperbolic.
[0057] In this step, VSP data segments containing the complete propagation process of the abnormal acoustic wave signal excited by the fluid production activity are extracted from the screened abnormal acoustic wave signal. (The time range of this data segment needs to cover the moment when the abnormal acoustic wave signal is first detected to the moment when the signal intensity decays to the preset attenuation threshold, and the spatial range needs to cover the fiber position of the abnormal acoustic wave signal propagating along the optical fiber, so as to fully retain the full-dimensional information from the cone head to the cone tail required for subsequent identification of conical or hyperbolic waveform features.) The distribution characteristics between the fiber position and time are further generated. These distribution characteristics intuitively present the spatiotemporal propagation law of the acoustic wave signal excited by the fluid production point as it propagates along the preset optical fiber to both ends. The signal arrival time varies significantly depending on the fiber location. Fibers closer to the production point arrive earlier, while those farther away arrive later. This time difference, corresponding to the fiber location, reflects the propagation path and duration of the sound wave from the production point, ultimately resulting in a visually appealing conical or hyperbolic distribution. This provides crucial data support for subsequent vertex localization and production layer calculation, as well as for identifying the conical or hyperbolic waveform characteristics and locating the production layer. The conical shape is a special form of the hyperbola.
[0058] Step S204: Identify conical or hyperbolic waveform features in the distribution characteristics and determine the vertex position of the conical or hyperbolic waveform.
[0059] Specifically, by combining the physical laws governing the propagation of the produced fluid acoustic wave along both ends of the fiber with the distribution characteristics of the fiber's position and time, a conical or hyperbolic waveform feature exhibiting a hyperbolic shape is accurately identified. This feature, with the produced fluid point as the vertex, shows a typical characteristic of a regular delay in the arrival time of the acoustic wave as the fiber position increases. Subsequently, the vertex position of the conical or hyperbolic waveform is determined through automatic identification algorithms or manual interpretation. Automatic identification can employ image processing technology or fit based on a preset hyperbolic model, while manual interpretation is performed by technicians directly calibrating the waveform through an interactive software interface. The finally locked vertex position is the fiber position corresponding to the acoustic wave excitation source, providing a core basis for subsequent calculation of the produced fluid layer depth.
[0060] It is understandable that when fluid (water or oil) is produced downhole, the fluid will generate sound waves at the production point. These sound waves are continuously recorded by a distributed fiber optic acoustic sensing system. Since the sound waves take different amounts of time to travel from the production point to sensors at different locations on the optical fiber, they will form a cone or hyperbola shape with the production point as the vertex. The vertex is the location where the sound waves are first detected on the entire optical fiber.
[0061] Step S205: Based on the vertex position, obtain the depth of the producing fluid layer.
[0062] Specifically, based on the determined vertex position of the conical or hyperbolic waveform, the fiber optic position scale corresponding to the vertex is converted into the actual downhole fluid-producing layer depth through coordinate transformation logic, and finally the actual depth value of the fluid-producing layer in the geodetic coordinate system is accurately obtained.
[0063] Figure 3 This is a distribution characteristic diagram of the fiber position versus time according to an embodiment of this application, i.e., a two-dimensional cross-sectional view, where the horizontal axis represents time and the vertical axis represents the fiber scale position. As can be observed from the figure, the waveform exhibits a typical conical or hyperbolic waveform, and the fiber scale position corresponding to the inflection point of the broken line is the position of the liquid-generating acoustic wave excitation source.
[0064] Through steps S201 to S205, the existing distributed fiber optic acoustic sensing system utilizes the sensing fibers, eliminating the need for additional detector arrays and directly avoiding the high equipment dependence and cost of traditional methods. Simultaneously, the continuously acquired fiber position, time, and signal strength data provide a continuous data source for the entire well section, solving the problem of insufficient spatial sampling by traditional point sensors. By using dual threshold filtering of intensity and time, the system effectively distinguishes between production signals and instantaneous noise, reducing reliance on the accuracy of single wave arrival time acquisition and overcoming the technical bottleneck of large positioning errors in low signal-to-noise ratio environments. By identifying conical or hyperbolic waveforms and locking their vertices, the complex problem of sound source localization is transformed into intuitive waveform feature recognition, eliminating complex calculation steps such as velocity modeling and nonlinear inversion in traditional methods, significantly improving the efficiency of production layer localization.
[0065] In some embodiments, identifying conical or hyperbolic waveform features in the distribution characteristics and determining the vertex position of the conical or hyperbolic waveform includes:
[0066] Based on a preset conical or hyperbolic model, the distribution characteristics are fitted to obtain the vertex position of the conical or hyperbolic waveform.
[0067] It should be understood that the conical or hyperbolic waveform formed by the propagation of the acoustic wave excited at the liquid production point along both ends of the optical fiber is essentially a hyperbolic shape that conforms to the physical laws of acoustic wave propagation. Therefore, based on a pre-defined hyperbolic model, the sequence of strong energy center positions corresponding to each optical fiber location can be extracted from the distribution characteristics. This sequence can then be fitted, and the optimal parameters of the hyperbolic model can be solved through iterative optimization. This allows for the precise determination of the vertex position of the conical or hyperbolic waveform, which is the optical fiber coordinate corresponding to the excitation source of the liquid production acoustic wave. The conical or hyperbolic waveform formed by acoustic wave propagation can be represented as:
[0068] ;
[0069] Where t(x) represents the arrival time of the sound wave at position x; x is the position coordinate on the optical fiber; x0 is the vertex position of the hyperbola (the liquid production layer to be determined); v is the propagation speed of the sound wave in the optical fiber medium; and t0 is the minimum propagation time of the sound wave from the liquid production point to the nearest sensor.
[0070] Through the above steps, the complex sound source localization problem is transformed into a standardized mathematical fitting process. This avoids the technical bottlenecks of traditional methods that rely on complex inversion algorithms and high-precision wave arrival time picking. Furthermore, the accuracy of vertex position identification is improved through high-energy center sequence extraction and iterative optimization, ensuring the stability and reliability of vertex localization of conical or hyperbolic waveforms under different well conditions. This provides core technical support for the accurate conversion of subsequent fluid production layer depths.
[0071] In some embodiments, the step of filtering abnormal acoustic signals excited by the production fluid activity from the VSP data includes:
[0072] From the VSP data, abnormal acoustic signals excited by the production fluid activity are filtered out by a preset acoustic data detector.
[0073] Specifically, from VSP data containing continuous acquisition time and acoustic signal intensity corresponding to different fiber optic locations, dual signal processing is performed using a preset acoustic data detector (integrating time-domain and frequency-domain filters). First, the time-domain filter removes low-frequency noise signals such as downhole environmental vibration and equipment operation interference. Then, the frequency-domain filter filters out the characteristic frequency band data corresponding to the acoustic signals excited by the fluid production activity. At the same time, combined with the unique energy distribution pattern and duration characteristics of the fluid production acoustic events, reasonable energy thresholds and duration ranges are set to automatically identify and mark abnormal acoustic segments that exceed the range of normal signals. Further verification is made to verify whether the segment completely contains the cone head and part of the cone tail features of the acoustic wave propagation, ensuring that the selected signals can meet the requirements of subsequent waveform recognition. Finally, the effective abnormal acoustic signals excited by the fluid production activity are accurately extracted.
[0074] Through the above steps, irrelevant noise signals such as downhole environmental vibration and equipment operation interference are accurately eliminated by the preset acoustic data detector, effectively improving the signal-to-noise ratio of the target signal and avoiding interference from non-productive acoustic waves to subsequent waveform recognition. At the same time, by matching the unique energy distribution pattern, duration characteristics and frequency range of productive acoustic waves, effective acoustic events containing complete cone heads and tails can be quickly identified without relying on complex manual interpretation, greatly improving the efficiency and accuracy of signal screening. Meanwhile, this screening method is adapted to the continuous and high-density data flow characteristics of distributed fiber optic acoustic sensing technology, fully exploring the value of effective sensing data, laying a reliable data foundation for subsequent cone or hyperbola waveform feature recognition, precise apex location and product layer calculation, thereby overcoming the shortcomings of traditional methods in terms of high data acquisition accuracy and weak anti-interference ability, and ensuring the stability and efficiency of product layer determination under complex well conditions.
[0075] In some embodiments, the acoustic data detector is a filter used to identify abnormal acoustic signal waveform characteristics, the filter including a time-domain filter and a frequency-domain filter; the step of filtering abnormal acoustic signals excited by fluid production activity from the VSP data using a preset acoustic data detector includes:
[0076] Filtered acoustic signals at different fiber locations are obtained from the VSP data through the time-domain filter and the frequency-domain filter.
[0077] By detecting the degree of matching between the filtered acoustic signal and a preset conical or hyperbolic model, it can be determined whether the filtered acoustic signal is an abnormal acoustic signal excited by the fluid production activity.
[0078] The acoustic data detector is a filter used to identify the waveform characteristics of abnormal acoustic signals, and this filter includes a time-domain filter and a frequency-domain filter. Specifically, firstly, from VSP data containing continuous acquisition time and acoustic signal intensity corresponding to different fiber locations, the original signals of each fiber channel are denoised using a time-domain filter to effectively remove low-frequency irrelevant noise such as downhole environmental vibration and equipment operation interference. Then, the characteristic frequency band data corresponding to the acoustic signals excited by the production fluid activity (such as the mid-to-high frequency band unique to production fluid acoustic waves) are screened out using a frequency-domain filter, thereby obtaining pure filtered acoustic signals at different fiber locations.
[0079] Subsequently, based on the conical or hyperbolic propagation pattern of the acoustic waves excited by the production point along the optical fiber, a corresponding waveform model is constructed. By calculating the similarity between the filtered acoustic signal and the preset model (such as the overlap of feature points, curve fitting degree, etc.), the matching degree between the two is detected. When the matching degree is higher than the preset threshold (such as 90%), the filtered acoustic signal can be determined to be an abnormal acoustic signal excited by the production activity. At the same time, it is ensured that the selected signal completely contains the cone head and part of the cone tail features of the acoustic wave propagation, providing reliable data support for subsequent accurate identification of waveform apex and calculation of production layer depth.
[0080] In the above steps, the time-domain filter can effectively remove low-frequency noise such as downhole environmental vibration and equipment operation interference, while the frequency-domain filter can accurately lock the characteristic frequency band of the producing acoustic wave. The synergistic effect of the dual filtering significantly improves the signal-to-noise ratio, clearing away interference for subsequent waveform identification. At the same time, by detecting the degree of matching between the filtered acoustic signal and the preset conical or hyperbolic model, it effectively avoids misjudging non-producing acoustic waves as target signals, significantly improving the accuracy and specificity of abnormal acoustic wave identification. This screening method does not rely on complex manual interpretation and is adapted to the continuous, high-density data flow characteristics of distributed fiber optic acoustic wave sensing technology. It can quickly and efficiently extract effective information from massive VSP data and fully match the propagation law of producing acoustic waves, laying a solid data foundation for the accurate positioning of the apex of the conical or hyperbolic waveform and the accurate calculation of the producing layer depth. This overcomes the shortcomings of traditional methods, such as high requirements for data acquisition accuracy and weak anti-interference ability, ensuring the stability and reliability of producing layer determination under complex well conditions.
[0081] In some embodiments, the continuous acquisition of VSP data using a pre-defined distributed fiber optic acoustic wave sensing system includes:
[0082] The distributed fiber optic acoustic wave sensing system is used to continuously acquire raw VSP data;
[0083] The original VSP data is subjected to noise reduction and filtering to obtain the VSP data.
[0084] Specifically, a distributed fiber optic acoustic wave sensing system is first used to continuously collect raw VSP data covering the propagation information of acoustic waves throughout the entire well section along a preset sensing fiber that is well coupled to the downhole wellbore. This data includes the fiber position along which the acoustic wave signal propagates, the corresponding acquisition time, and the intensity of the raw acoustic wave signal.
[0085] It should be understood that, since the raw data is easily affected by factors such as downhole environmental noise, fiber optic transmission loss, and differences in the response of various sensor channels, it needs to be further processed in a targeted manner. Through noise reduction algorithms and filtering, standardized and reliable VSP data is finally obtained, laying a solid data foundation for subsequent abnormal acoustic signal detection and cone or hyperbola waveform feature recognition.
[0086] Through the above steps, the advantages of continuous monitoring throughout the well section using the Distributed Fiber Optic Acoustic Sensing System (DAS system) can be leveraged to acquire high-density raw data containing continuous time and acoustic signal intensity corresponding to different fiber locations. This fully captures the acoustic characteristics excited by fluid production activity, avoiding the problem of insufficient sampling by traditional point sensors. Furthermore, noise reduction processing effectively eliminates irrelevant noise such as downhole environmental vibration and equipment operation interference. Filtering process selects the characteristic frequency bands of fluid production acoustic waves, significantly improving the data signal-to-noise ratio and eliminating amplitude deviation and time synchronization errors in the raw data. Ultimately, standardized and clean VSP data is obtained, laying a high-quality data foundation for subsequent abnormal acoustic signal screening, accurate identification of conical or hyperbolic waveform characteristics, and vertex location. At the same time, it is adaptable to the monitoring needs of different well types such as vertical and horizontal wells, ensuring the accuracy and stability of fluid production layer determination under complex well conditions, and overcoming the shortcomings of traditional methods such as strong dependence on data quality and weak anti-interference ability.
[0087] In some embodiments, generating the distribution characteristics of the fiber location over time based on the anomalous acoustic signal includes:
[0088] Based on the abnormal acoustic signal, the abnormal VSP data segment is obtained;
[0089] Based on the abnormal VSP data segment, the distribution characteristics between fiber location and time are generated.
[0090] Specifically, based on the abnormal acoustic signal, an abnormal VSP data segment containing the complete propagation process of the abnormal acoustic signal is extracted from the continuously acquired VSP data. This data segment needs to cover the full time-domain information of the signal from excitation to attenuation and the response data of the corresponding fiber position throughout the well section to ensure that no key features of acoustic wave propagation are missed. Then, the abnormal VSP data segment is visualized in two dimensions with the fiber position as the vertical axis and time as the horizontal axis, generating the distribution characteristics between fiber position and time. This characteristic clearly depicts the difference in signal arrival time at different fiber positions when the acoustic wave excited by the production point propagates along both ends of the fiber, laying the foundation for the accurate identification of subsequent conical or hyperbolic waveform characteristics.
[0091] Through the above steps, accurate extraction and visualization of acoustic wave propagation information related to the production fluid were achieved. By extracting data segments containing the entire propagation process of abnormal acoustic wave signals, the time-domain information from excitation to attenuation and the fiber optic response data of the entire well section were completely preserved, avoiding the omission of key propagation features. Furthermore, the distribution features were generated in a two-dimensional visualization form of fiber optic position and time, transforming the abstract acoustic wave propagation law into an intuitive and identifiable spatiotemporal correlation map, clearly depicting the differences in signal arrival time at different fiber optic positions. This not only reduced the difficulty of subsequent conical or hyperbolic waveform feature identification but also provided highly focused and complete data support for feature identification, while avoiding interference from irrelevant data, thus improving the efficiency and accuracy of the entire production fluid layer determination process.
[0092] In some embodiments, after obtaining the depth of the producing fluid layer, the method further includes:
[0093] The depth of the fluid-producing layer was compared with known geological data and traditional logging data to obtain the effectiveness comparison results;
[0094] The method for determining the producing fluid layer was repeated for the same wellbore at different time periods to obtain reliability comparison results.
[0095] Specifically, after obtaining the depth of the producing fluid layer, the calculated depth is cross-compared with known geological data (such as stratigraphic lithology data and reservoir distribution patterns) and traditional logging data (such as resistivity logging and sonic logging results) in multiple dimensions to verify its consistency with existing geological knowledge and measured data, thereby obtaining the validity comparison results. At the same time, the above-mentioned method for determining the producing fluid layer is repeated for the same wellbore at different production stages or different monitoring periods. By analyzing the overlap and fluctuation range of the multiple positioning results, the reliability comparison results are obtained. Finally, by comprehensively considering the degree of agreement of the validity comparison results, the stability of the reliability comparison results, and influencing factors such as the complexity of downhole geological conditions and the data signal-to-noise ratio, a credibility assessment report is generated, which includes a positioning accuracy assessment, result confidence level, and applicable scenario description, providing a more comprehensive decision-making basis for subsequent production strategy formulation.
[0096] Through the above steps, the production layer location results were dually verified and quantitatively evaluated for reliability. Firstly, multi-dimensional cross-comparison with known geological data and traditional logging data verified the consistency between the location results and existing geological knowledge and measured data, ensuring the validity and scientific nature of the results. Secondly, repeated measurements at different time points within the same wellbore analyzed the overlap and fluctuation range of multiple location results, enhancing the stability and repeatability of the results. Finally, considering key influencing factors such as downhole geological complexity and data signal-to-noise ratio, an evaluation report was generated, including location accuracy, confidence level, and applicable scenarios. This not only compensated for the lack of reliability support in a single measurement result but also provided a comprehensive and reliable decision-making basis for subsequent production strategy formulation and stratified mining optimization, further ensuring the rigor and practicality of the entire production layer determination process.
[0097] In some embodiments, a vertical production well in an oilfield was used as the application object to conduct a production layer location experiment, verifying the practicality and accuracy of the method in the vertical well scenario. The experimental well is 2850 meters deep, a typical vertical production well. Single-mode sensing optical fibers are laid along the entire wellbore, with a total fiber length of 2000 meters, ensuring good coupling with the well wall to guarantee signal transmission stability. The distributed optical fiber acoustic wave sensing system used is set to a sampling frequency of 2000Hz, with a spatial resolution of 1 meter, which can meet the requirements of continuous, high-density acoustic wave signal acquisition throughout the well section.
[0098] The experimental procedure strictly followed the steps of the production layer determination method described in this application: First, during normal production of the well, the distributed fiber optic acoustic sensing system was activated to continuously acquire raw VSP data. The acquisition process covered the entire fiber optic sensing channel of the well section, and the fiber position, corresponding acquisition time, and acoustic signal intensity of the acoustic signal propagating along the fiber were recorded simultaneously. Subsequently, the acquired raw data was preprocessed by noise reduction, signal compensation, and time domain alignment to form standardized VSP data. Based on the preset acoustic signal intensity threshold and time threshold, the production acoustic event was detected from the continuous data stream, and the VSP data segment containing the complete propagation process of the event was accurately extracted.
[0099] The extracted VSP data segments are used to generate a two-dimensional profile with fiber optic scale position as the ordinate and time as the abscissa. Please refer to [link / reference]. Figure 4In this profile, the typical conical or hyperbolic waveform characteristics formed by the acoustic waves excited by the fluid production activity can be clearly observed. This waveform, with the production point as its vertex, exhibits a hyperbolic shape as the arrival time of the acoustic waves increases with the fiber position, showing a regular delay. Through an interactive software interface, technicians directly marked the vertex position of this conical or hyperbolic waveform, and the corresponding fiber position was determined to be 908.6 meters. Since this is a vertical well, the optical fiber is laid vertically along the wellbore, and the fiber length corresponds one-to-one with the actual depth of the wellbore. No additional coordinate conversion is required; the fiber length of 908.6 meters corresponding to the vertex is directly determined as the depth of the production layer.
[0100] Experimental results show that the method proposed in this application does not require complex inversion calculations or additional detectors in vertical well scenarios. It can quickly locate the producing layer simply by identifying the conical or hyperbolic waveform features in the VSP data. The location results are in high agreement with the known geological data of the well and traditional logging data, which verifies the accuracy and reliability of the method. It provides an efficient and economical technical solution for real-time monitoring of producing layers in vertical wells.
[0101] In some embodiments, a vertical production well in an oilfield is used as the research object. A hyperbolic fitting method is employed to accurately locate the producing layer, verifying the reliability and accuracy of the proposed technical solution in quantitative fitting scenarios. The experimental well is 2850 meters deep, with single-mode sensing optical fibers deployed along the entire wellbore for a total length of 2000 meters, ensuring tight coupling with the wellbore to guarantee the integrity of the acoustic signal transmission. The distributed optical fiber acoustic sensing system used has a sampling frequency of 2000Hz, a spatial resolution of 1 meter, and 100 sensing elements, enabling high-density, high-fidelity acoustic signal acquisition throughout the well, providing sufficient data support for subsequent mathematical fitting.
[0102] The experimental procedure strictly followed the core process of the fluid production layer determination method described in this application: First, under normal production conditions of the well, a distributed fiber optic acoustic wave sensing system was activated to continuously acquire raw VSP data, simultaneously recording key information such as the fiber position along the propagation of the acoustic signal, the corresponding acquisition time, and the acoustic signal intensity. Subsequently, the acquired raw data underwent noise reduction, signal compensation, and time-domain alignment preprocessing to effectively filter out interference factors such as downhole environmental noise and fiber optic transmission loss, forming standardized VSP data with a high signal-to-noise ratio. Based on preset acoustic signal intensity and time thresholds, fluid production acoustic events were accurately monitored from the continuous data stream, and a complete VSP data segment containing the event from excitation, propagation, to attenuation was extracted, ensuring that no key characteristic information of acoustic wave propagation was missed.
[0103] The extracted VSP data segments are plotted in a two-dimensional profile with time on the x-axis and fiber optic scale position on the y-axis. Please refer to [link to relevant documentation]. Figure 5In this profile, the typical conical or hyperbolic waveform characteristics formed by the acoustic waves excited by the fluid production activity can be clearly observed. This waveform is essentially a hyperbolic shape conforming to the physical laws of acoustic wave propagation. To achieve precise quantification of the vertex position, the peak position corresponding to each depth was extracted from the VSP data, resulting in 100 sets of strong energy center position sequences (ti, Li) (where i ranges from 1 to 100, ti is the acoustic wave arrival time at the corresponding position, and Li is the fiber optic coordinate). Based on a pre-defined hyperbolic model, the 100 sets of strong energy center position sequences were fitted, and the optimal parameters of the model were solved through iterative optimization, ultimately obtaining the fitted curve (red curve), and determining the vertex position of the hyperbola to be 908.0 meters. Since this well is a vertical well, the optical fiber is vertically deployed along the wellbore, and the fiber length has a strict one-to-one correspondence with the actual depth of the wellbore. No additional coordinate conversion is required; the fiber length of 908.0 meters corresponding to the vertex is directly determined as the depth of the fluid production layer.
[0104] Experimental results show that the hyperbolic fitting method used in this application can achieve high-precision positioning of the producing layers. The positioning results are in high agreement with the known geological data and traditional logging data of the well, and the deviation from the positioning results of the direct reading method is only 0.6 meters, which fully verifies the stability and accuracy of the method. This method, through mathematical modeling and fitting analysis, further reduces the subjective error of manual interpretation and is suitable for scenarios with higher positioning accuracy requirements, providing a more reliable technical option for the accurate monitoring of producing layers in vertical wells.
[0105] In some embodiments, a horizontal well with a high inclination in a shale gas field is used as an application scenario to verify the ability of the method in this application to locate producing layers and identify multiple producing points in complex well types (high-inclination horizontal wells). The experimental well has a complex structure, with a horizontal section of 1000 meters and an inclined section of 500 meters, and a maximum inclination angle of 89.5°, which is a typical high-difficulty monitoring well type. In order to adapt to the harsh downhole environment of the horizontal section and ensure the quality of signal transmission, reinforced sensing optical fibers are deployed along the entire horizontal section. These optical fibers have tensile strength, wear resistance and good acoustic coupling performance. The distributed optical fiber acoustic wave sensing system used adopts a high-frequency acquisition mode with a sampling frequency set at 5000Hz, which can accurately capture the high-frequency acoustic wave signals excited by fluid flow in the horizontal section and meet the requirements of high-density and high-fidelity data acquisition under complex well conditions.
[0106] The experimental process strictly followed the core logic of the fluid production layer determination method described in this application, and optimized the process based on the characteristics of horizontal well trajectory: First, during the fracturing and flowback of the well, a distributed fiber optic acoustic sensing system was started to carry out continuous data acquisition throughout the entire period, and key parameters such as the fiber position of the acoustic signal propagating along the reinforced sensing fiber, the corresponding acquisition time, and the acoustic signal intensity were recorded synchronously; then, the acquired raw data was preprocessed in a targeted manner, including adaptive noise reduction, signal compensation, and time domain alignment, to form a standardized VSP dataset with a high signal-to-noise ratio.
[0107] Based on preset acoustic signal intensity and time thresholds, multiple valid acoustic events excited by fluid production activity are accurately identified from the continuous data stream. Each event corresponds to one fluid production process. For each acoustic event, a VSP data segment containing the complete propagation process is extracted, and a two-dimensional profile is generated with time as the x-axis and fiber optic scale position as the y-axis. Please refer to [link to relevant documentation]. Figure 6 In each profile, typical conical or hyperbolic waveform features were clearly identified. These features, with the production point as the apex, extend towards both ends of the optical fiber, exhibiting a hyperbolic shape where the acoustic arrival time is regularly delayed as the optical fiber position increases. Furthermore, the conical or hyperbolic waveforms corresponding to different production events are independent of each other, with no significant superposition interference. The optical fiber coordinates corresponding to the two core production layers were ultimately obtained as 499.7 meters and 896.3 meters, respectively. Since this is a highly deviated horizontal well, the optical fiber length and the actual wellbore depth do not have a linear correspondence. Therefore, it is necessary to combine pre-acquired wellbore trajectory parameters (including inclination angle and azimuth angle at each depth) and optical fiber deployment parameters (the mapping relationship between optical fiber length and actual wellbore depth) with coordinate transformation formulas to eliminate the deviation caused by wellbore inclination and accurately convert the optical fiber position coordinates to the actual location of the production layer in the geodetic coordinate system.
[0108] Experimental results show that the proposed method successfully achieved precise location of multiple production layers in complex horizontal well scenarios with a maximum inclination angle of 89.5°. The location results of the two production points showed high agreement with subsequent verification data of the well. Compared with traditional production logging methods, this method not only enables real-time monitoring without well shutdown operations, but also clearly reveals the spatial distribution characteristics of multi-point production in the horizontal section. It solves the technical pain point of traditional methods in capturing the segmented production patterns of horizontal wells, providing accurate and comprehensive technical basis for the optimization of stratified production, water shut-off and profile control operations, and production system adjustments in this shale gas field. This fully verifies the adaptability and reliability of the proposed method in complex well types.
[0109] This embodiment also provides a device for determining the product fluid layer, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the terms "module," "unit," "subunit," etc., can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.
[0110] Figure 7 This is a structural block diagram of the product fluid layer determination device according to an embodiment of this application, as shown below. Figure 7 As shown, the device includes:
[0111] The data acquisition module 71 is used to continuously acquire VSP data using a preset distributed optical fiber acoustic wave sensing system; the VSP data includes the position of the optical fiber receiving the acoustic wave signal, as well as the continuous acquisition time and acoustic wave signal intensity corresponding to each optical fiber position.
[0112] The abnormal acoustic signal detection module 72 is used to filter abnormal acoustic signals excited by the production fluid activity from the VSP data;
[0113] The distribution feature generation module 73 is used to generate a distribution feature between the optical fiber position and time based on the abnormal acoustic signal; the distribution feature is used to characterize the difference in signal arrival time at different optical fiber positions when the acoustic signal excited by the liquid production point propagates to the optical fiber, and the distribution feature is conical or hyperbolic.
[0114] Vertex position determination module 74 is used to identify conical or hyperbolic waveform features in the distribution features and determine the vertex position of the conical or hyperbolic waveform;
[0115] The fluid production layer depth calculation module 75 is used to obtain the fluid production layer depth based on the vertex position.
[0116] It should be noted that the above modules can be functional modules or program modules, and can be implemented by software or hardware. For modules implemented by hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination. Specific examples in this embodiment can be found in the examples described in the above embodiments and optional implementations, and will not be repeated in this embodiment.
[0117] This embodiment also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.
[0118] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.
[0119] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:
[0120] S1 uses a pre-set distributed fiber optic acoustic wave sensing system to continuously collect VSP data; the VSP data includes the location of the optical fiber receiving the acoustic wave signal, as well as the continuous acquisition time and acoustic wave signal intensity corresponding to each optical fiber location.
[0121] S2, from the VSP data, filter out abnormal acoustic signals excited by the production fluid activity.
[0122] S3, based on the abnormal acoustic signal, generates the distribution characteristics between the position and time of the optical fiber; the distribution characteristics are used to characterize the difference in signal arrival time at different optical fiber positions when the acoustic signal excited by the liquid production point propagates to the optical fiber, and the distribution characteristics are conical or hyperbolic.
[0123] S4 identifies conical or hyperbolic waveform features in the distribution characteristics and determines the vertex position of the conical or hyperbolic waveform.
[0124] S5, based on the vertex position, obtains the depth of the producing fluid layer.
[0125] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.
[0126] Furthermore, in conjunction with the product fluid layer determination method in the above embodiments, this application embodiment can provide a storage medium for implementation. This storage medium stores a computer program; when executed by a processor, the computer program implements any of the product fluid layer determination methods in the above embodiments.
[0127] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.
[0128] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.
[0129] Those skilled in the art should understand that the technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments have been described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0130] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.
Claims
1. A method for determining the product fluid layer, characterized in that, include: Using a pre-set distributed fiber optic acoustic wave sensing system, VSP data is continuously collected; the VSP data includes the location of the optical fiber receiving the acoustic wave signal, as well as the continuous acquisition time and acoustic wave signal intensity corresponding to each optical fiber location. Filtering anomalous acoustic signals excited by fluid production activity from the VSP data includes: filtering anomalous acoustic signals excited by fluid production activity from the VSP data using a preset acoustic data detector; the acoustic data detector is a filter that can be used to identify the waveform characteristics of anomalous acoustic signals, the filter including a time-domain filter and a frequency-domain filter; the filtering of anomalous acoustic signals excited by fluid production activity from the VSP data using the preset acoustic data detector includes: obtaining filtered acoustic signals at different fiber positions from the VSP data using the time-domain filter and the frequency-domain filter; determining whether the filtered acoustic signal is an anomalous acoustic signal excited by fluid production activity by detecting the degree of matching between the filtered acoustic signal and a preset conical or hyperbolic model; Based on the abnormal acoustic signal, a distribution feature between the optical fiber position and time is generated; the distribution feature is used to characterize the difference in signal arrival time at different optical fiber positions when the acoustic signal excited by the liquid production point propagates to the optical fiber, and the distribution feature is expressed as a conical or hyperbolic waveform. Identify conical or hyperbolic waveform features in the distribution characteristics and determine the vertex position of the conical or hyperbolic waveform; The depth of the producing fluid layer is obtained based on the vertex position.
2. The method for determining the product fluid layer according to claim 1, characterized in that, The step of identifying conical or hyperbolic waveform features in the distribution characteristics and determining the vertex position of the conical or hyperbolic waveform includes: Based on a preset conical or hyperbolic model, the distribution characteristics are fitted to obtain the vertex position of the conical or hyperbolic waveform.
3. The method for determining the product fluid layer according to claim 1, characterized in that, The method of continuously acquiring VSP data using a pre-set distributed fiber optic acoustic wave sensing system includes: The distributed fiber optic acoustic wave sensing system is used to continuously acquire raw VSP data; The original VSP data is subjected to noise reduction and filtering to obtain the VSP data.
4. The method for determining the product fluid layer according to claim 1, characterized in that, The step of generating the distribution characteristics of the fiber location over time based on the anomalous acoustic signal includes: Based on the abnormal acoustic signal, the abnormal VSP data segment is obtained; Based on the abnormal VSP data segment, the distribution characteristics between fiber location and time are generated.
5. The method for determining the product fluid layer according to any one of claims 1 to 4, characterized in that, After obtaining the depth of the producing fluid layer, the process further includes: The depth of the fluid-producing layer was compared with known geological data and traditional logging data to obtain the effectiveness comparison results; The method for determining the producing fluid layer was repeated for the same wellbore at different time periods to obtain reliability comparison results.
6. A device for determining the product fluid layer, characterized in that, The device includes: The data acquisition module is used to continuously acquire VSP data using a preset distributed fiber optic acoustic wave sensing system; the VSP data includes the position of the optical fiber receiving the acoustic wave signal, as well as the continuous acquisition time and acoustic wave signal intensity corresponding to each optical fiber position. An abnormal acoustic signal detection module is used to filter abnormal acoustic signals excited by the production fluid activity from the VSP data. The abnormal acoustic signal detection module is also used to filter abnormal acoustic signals excited by the production fluid activity from the VSP data through a preset acoustic data detector; the acoustic data detector is a filter that can be used to identify the waveform characteristics of abnormal acoustic signals, and the filter includes a time-domain filter and a frequency-domain filter. The abnormal acoustic signal detection module is further configured to obtain filtered acoustic signals at different fiber positions from the VSP data through the time-domain filter and the frequency-domain filter; and determine whether the filtered acoustic signal is an abnormal acoustic signal excited by the fluid production activity by detecting the degree of matching between the filtered acoustic signal and the preset conical or hyperbolic model. The distribution feature generation module is used to generate distribution features between fiber position and time based on the abnormal acoustic signal; the distribution features are used to characterize the difference in signal arrival time at different fiber positions when the acoustic signal excited by the liquid production point propagates to the optical fiber, and the distribution features are expressed as conical or hyperbolic waveforms. A vertex position determination module is used to identify conical or hyperbolic waveform features in the distribution features and determine the vertex position of the conical or hyperbolic waveform. The fluid production layer depth calculation module is used to obtain the fluid production layer depth based on the vertex position.
7. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the method for determining the producing fluid layer as described in any one of claims 1 to 5.
8. A storage medium, characterized in that, The storage medium stores a computer program, wherein the computer program is configured to execute the method for determining the product fluid layer as described in any one of claims 1 to 5 when it is run.