Method and device for detecting liquid level position in oil casing annulus

Abstract

The invention relates to a method and device for detecting the liquid level position in an oil casing annulus. The method comprises the following steps: acquiring a temperature distribution data sequence of an oil well acquired by a distributed optical fiber sensing system; feature points caused by the tubing coupling are recognized through a peak detection algorithm, and the number and positions of the feature points are recorded; obtaining a first liquid level according to the number of the feature points; determining a second liquid level based on local temperature characteristics of the temperature distribution data sequence; and calculating the liquid level depth according to the feature point position, the first liquid level and the second liquid level. By the adoption of the method, continuous and real-time working fluid level detection can be achieved through existing distributed optical fiber sensing data, dependence on special hardware is avoided, and system complexity and cost are remarkably reduced on the basis that measurement robustness and accuracy are improved.

Description

Technical Field

[0001] This application relates to the field of oil and gas extraction monitoring technology, and in particular to a method and apparatus for detecting the liquid level position in the annulus of an oil casing. Background Technology

[0002] In oilfield development, dynamic fluid level depth is a key dynamic parameter reflecting formation fluid supply capacity, assessing pumping system efficiency, and optimizing production processes. Accurate and continuous monitoring of fluid level position is crucial for determining bottomhole flowing pressure, calculating submersion, developing appropriate operating procedures, and implementing intelligent extraction strategies.

[0003] Traditional techniques for monitoring the fluid level in the casing annulus primarily employ various methods, including the echo method, the weighing method, and the nitrogen injection method. The echo method involves transmitting acoustic pulses from the wellhead into the casing annulus and receiving the echo signals reflected by the tubing couplings and the fluid surface; the depth of the fluid level is calculated by analyzing the echo time difference. The weighing method measures the weight of the tubing string in the fluid at the wellhead and calculates the fluid level position based on the tubing string parameters. The nitrogen injection method injects nitrogen into the annulus and monitors pressure changes, using the gas law to estimate the fluid level height.

[0004] However, the above measurement methods have the following drawbacks: they rely on specialized hardware, such as sound-generating devices, weighing equipment, or nitrogen cylinders; traditional echo methods are highly susceptible to downhole mechanical vibrations, fluid noise, and casing pressure fluctuations, resulting in low echo signal-to-noise ratios and difficulties in identifying surface waves; and due to limitations in gas supply and operational procedures, they can typically only perform intermittent measurements at minute or even longer intervals. In summary, the above measurement methods suffer from system complexity, high cost, and susceptibility to operating condition interference, making it difficult to achieve low-cost, highly robust, continuous real-time monitoring. Summary of the Invention

[0005] Therefore, it is necessary to provide a method and apparatus for detecting the liquid level position in the annulus of the oil casing and tubing, which can reduce hardware dependence and simplify the system structure, in order to address the above-mentioned technical problems.

[0006] Firstly, this application provides a method for detecting the liquid level position in the annulus of an oil casing, comprising:

[0007] A distributed fiber optic sensing system deployed in the annulus of the oil casing was used to acquire temperature distribution data sequences of the oil well.

[0008] Using a peak detection algorithm, feature points caused by pipe couplings are identified from the temperature distribution data sequence, and the number of these feature points is recorded.

[0009] The number of oil pipe couplings above the liquid surface is obtained based on the number of the feature points, and the first liquid level is obtained based on the length of a single oil pipe.

[0010] By acquiring local temperature features from the temperature distribution data sequence, determining the position of the liquid surface on the optical fiber length coordinate, and obtaining the second liquid surface position;

[0011] The liquid depth is calculated based on the position of the feature point on the fiber length coordinate, the first liquid level, and the second liquid level.

[0012] In one embodiment, the position of the feature point on the fiber length coordinate, the first liquid level, and the second liquid level are used to calculate the liquid level depth, which includes:

[0013] Obtain the position of the feature point corresponding to the oil pipe coupling closest to the liquid surface, and calculate the local distance to the second liquid surface position;

[0014] The liquid level is obtained by adding the first liquid level to the local distance.

[0015] In one embodiment, the step of determining the number of pipe couplings above the liquid surface based on the number of feature points, and determining the first liquid level based on the length of a single pipe, includes:

[0016] Based on the number N of the feature points, the number N of the oil pipe couplings above the liquid surface is obtained;

[0017] The length of the single oil pipe Multiplying the liquid level by the number N of the feature points yields the first liquid level.

[0018] In one embodiment, obtaining local temperature features in the temperature distribution data sequence, determining the position of the liquid surface on the fiber length coordinates, and obtaining the second liquid surface position includes:

[0019] The depth range of the liquid surface is obtained based on the number of feature points and the length of the single oil pipe;

[0020] Obtain the local variance curve of the temperature distribution data sequence within the depth range, and identify the boundary of the transition from high to low value of the local variance curve;

[0021] The position of the change boundary on the fiber length coordinate is taken as the second liquid level.

[0022] In one embodiment, the depth range for obtaining the liquid level includes:

[0023] Based on the number N of the feature points and the length of the single oil pipe The depth range is obtained as follows .

[0024] In one embodiment, the distributed fiber optic sensing system includes a distributed temperature sensing system or a distributed acoustic sensing system.

[0025] In one embodiment, the distributed optical fiber sensing system includes a distributed optical fiber sensing cable, an optical signal acquisition device, an optical signal demodulator, and a real-time signal processing device.

[0026] In one embodiment, the distributed optical fiber sensing cable can be deployed on: the outer wall of the oil pipe, the inner wall of the casing, or the annular space between the oil pipe and the casing.

[0027] In one embodiment, the temperature distribution data sequence includes a temperature distribution curve, a temperature distribution pseudo-color map, a geothermal gradient curve, or a geothermal gradient pseudo-color map.

[0028] In one embodiment, the peak detection algorithm includes:

[0029] On the temperature gradient curve, identify all local extreme points whose amplitude exceeds a preset threshold;

[0030] The values ​​of the local extreme points that conform to the preset spacing rule of the oil pipe coupling are taken as the feature points.

[0031] In one embodiment, after obtaining the temperature distribution data sequence, the method further includes processing the temperature distribution data sequence using a noise reduction algorithm.

[0032] In one embodiment, the noise reduction algorithm includes a digital filtering algorithm or an artificial intelligence noise reduction algorithm.

[0033] Secondly, this application also provides a device for detecting the liquid level position in the annulus of an oil casing, comprising:

[0034] The data acquisition module is used to acquire the temperature distribution data sequence of the oil well using a distributed optical fiber sensing system deployed in the annulus of the casing and tubing.

[0035] The feature recognition module is used to identify feature points caused by the oil pipe coupling from the temperature distribution data sequence using a peak detection algorithm, and to record the number of the feature points;

[0036] The interval positioning module is used to obtain the number of oil pipe couplings above the liquid surface based on the number of feature points, and to obtain the first liquid level based on the length of a single oil pipe.

[0037] A local positioning module is used to acquire local temperature features in the temperature distribution data sequence, determine the position of the liquid surface on the optical fiber length coordinate, and obtain the second liquid surface position.

[0038] The liquid level calculation module is used to calculate the liquid level depth based on the position of the feature point on the fiber length coordinate, the first liquid level, and the second liquid level.

[0039] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in any one of the first aspects.

[0040] The aforementioned detection method and apparatus for the liquid level position in the annulus of the casing and tubing utilizes distributed optical fiber temperature data to sequentially perform a technical solution of identifying coupling features to construct a depth scale, analyzing local temperature features to identify the gas-liquid interface, and fusion data to calculate the liquid level depth. This solution fully reuses the data collected by the optical fiber logging system that is widely deployed in oilfields, eliminating the dependence on dedicated hardware equipment in traditional methods. By leveraging the anti-interference characteristics of optical fiber sensing data, the robustness and accuracy of the measurement are improved, enabling continuous and real-time dynamic liquid level detection. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is an application environment diagram of a method for detecting the liquid level position in the annulus of the oil casing in one embodiment;

[0043] Figure 2 This is a flowchart illustrating a method for detecting the liquid level position in the annulus of the oil casing in one embodiment;

[0044] Figure 3 This is a pseudo-color image of the temperature distribution in one embodiment;

[0045] Figure 4 This is a flowchart illustrating the step of calculating the second liquid level in one embodiment;

[0046] Figure 5 This is a monitoring graph used to calculate the second liquid level in one embodiment;

[0047] Figure 6 This is a flowchart illustrating the steps for identifying feature points caused by tubing couplings in one embodiment;

[0048] Figure 7 This is a monitoring map for identifying feature points caused by tubing couplings in one embodiment;

[0049] Figure 8 This is a block diagram of a device for detecting the liquid level position in the annulus of the oil casing in one embodiment;

[0050] Figure 9 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0052] The method for detecting the liquid level position in the annulus of the casing provided in this application can be applied to, for example... Figure 1 In the application environment shown, the distributed fiber optic sensing system includes a distributed fiber optic sensing cable 101, an optical signal acquisition device 102, an optical signal demodulator 103, and a real-time signal processing device 104. The system communicates with surface equipment outside the wellhead device 105 via the distributed fiber optic sensing cable 101. The distributed fiber optic sensing cable 101 can be installed on the outer wall of the tubing 106, the inner wall of the casing 107, or in the annular space between the tubing 106 and the casing 107 within the oil and gas well. Individual tubing sections are connected into a complete tubing system via tubing couplings 108. The optical signal acquisition device 102 is responsible for emitting probe light and receiving the sensing signals returned from the optical fiber. The optical signal demodulator 103 analyzes the monitoring data using algorithms, and the real-time signal processing device 104 performs real-time analysis of the state of the downhole oil and gas reservoir 109. Distributed fiber optic sensing systems can be independent physical devices, device clusters or distributed systems composed of multiple sensor signal demodulation modules, or intelligent demodulation platforms that integrate data acquisition, analysis and early warning functions.

[0053] In one exemplary embodiment, such as Figure 2 As shown, a method for detecting the liquid level position in the annulus of an oil casing is provided, and this method is applied to... Figure 1 Taking the real-time signal processing device 104 as an example, the process includes the following steps S201 to S205. Wherein:

[0054] Step S201: Use a distributed optical fiber sensing system deployed in the annulus of the oil casing to acquire the temperature distribution data sequence of the oil well.

[0055] It should be noted that the dynamic fluid level is the dynamically changing fluid level within the annulus between the tubing and casing during normal production in an oil well. Its depth is a key dynamic parameter reflecting whether the formation's fluid supply capacity matches the operating regime of the pumping equipment. The annulus refers to the ring-shaped space between the tubing and the outer casing in an oil well. It is the channel for downhole fluids (such as crude oil, natural gas, and water) and gases, and it is also the location of the dynamic fluid level.

[0056] Distributed fiber optic sensing systems are a sensing technology that uses an entire optical fiber as the sensing element and transmission medium to continuously and in real-time acquire information about the spatial distribution of a measured object. A distributed fiber optic sensing system includes a distributed fiber optic sensing cable, an optical signal acquisition device, an optical signal demodulator, and a real-time signal processing device, and is currently widely used in oil and gas wells. However, current applications of this technology in oil and gas wells are mostly focused on monitoring parameters such as temperature, acoustics, and strain. Existing liquid level detection methods have not fully utilized the structural and fluid interface information contained in these high-dimensional sensing data, especially in the critical scenario of dynamic liquid level detection. This invention, by reusing distributed fiber optic sensing equipment in oil and gas wells, fundamentally eliminates the dependence on dedicated hardware in traditional measurement methods. This not only significantly reduces equipment procurement, installation, and maintenance costs but also completely avoids the risk of failure caused by wear, aging, and blockage of mechanical components, resulting in a highly simplified system structure and significantly improved reliability.

[0057] The distributed fiber optic sensing technology used in this invention can include techniques such as Distributed Temperature Sensing (DTS) and Distributed Strain Sensing (DSS). DTS utilizes the Raman scattering effect in optical fibers, emitting laser pulses into the fiber and measuring the intensity of the returned Raman scattered light to determine the temperature at the fiber's location. DSS utilizes the Rayleigh scattering effect in optical fibers, monitoring minute changes in the phase of the Rayleigh scattered light to sense the dynamic strain acting on the fiber, further determining the temperature at the fiber's location.

[0058] It is understandable that traditional acoustic methods are highly susceptible to downhole mechanical vibrations, fluid noise, and casing pressure fluctuations, resulting in low echo signal-to-noise ratios and difficulties in identifying fluid surface waves. In contrast, DTS and DSS are less affected by operating noise, providing more stable and reliable data, and can solve the problems of inaccurate measurements and easy failures under complex well conditions. Furthermore, they can directly process continuously acquired DTS / DSS data streams, enabling continuous output of fluid surface position information.

[0059] A temperature distribution data sequence refers to a series of temperature measurements distributed along well depth, obtained via optical fiber. For this data sequence, subsequent analysis can be performed by plotting temperature curves, which include, but are not limited to, temperature distribution curves, temperature distribution pseudo-color maps, geothermal gradient curves, or geothermal gradient pseudo-color maps. Among these, a temperature distribution pseudo-color map arranged chronologically is shown below. Figure 3 As shown.

[0060] In some embodiments, after obtaining the temperature distribution data sequence, the process further includes processing the raw temperature data. For example, digital filtering algorithms such as moving average filters and low-pass digital filters, as well as artificial intelligence noise reduction algorithms, can be used for processing.

[0061] Step S202: Using a peak detection algorithm, identify feature points caused by the pipe coupling from the temperature distribution data sequence and record the number of feature points.

[0062] The tubing coupling is a short section connecting a single tubing segment. Its wall thickness, material, and thermal resistance to the environment differ from the tubing body. This structural discontinuity causes subtle changes in axial heat flow along the wellbore direction at the coupling, resulting in characteristic temperature anomalies on the continuous temperature profile acquired by sensors, typically appearing as peaks or depressions. Ultra-high resolution fiber optic temperature or strain sensing systems can capture these subtle temperature fluctuations. By identifying these approximately equidistant temperature anomalies, a "scale" corresponding to the location of the downhole tubing coupling can be established on the fiber optic length coordinate system.

[0063] It should be noted that since the deployment path of the optical fiber (such as the presence of a guide section, slack or entanglement) does not correspond strictly one-to-one with the actual length of the tubing, directly reading the coupling spacing (i.e., the fiber length) measured by DTS data to calculate the liquid level depth will result in a systematic error compared to the actual tubing length.

[0064] The peak detection algorithm can be one that identifies local extrema in a one-dimensional data sequence, such as methods based on amplitude thresholds, peak prominence, or comparison with adjacent data points. The peak detection algorithm identifies and records the number of feature points and their positions on the fiber optic length coordinate system. The number of feature points directly reflects the number of tubing couplings above the fluid surface, while the positions on the fiber optic length coordinate system provide a framework for subsequent calculations based on fiber optic measurements. Although there are systematic errors between the fiber optic length and the actual well depth, the coupling sequence can provide a reliable relative position reference.

[0065] Step S203: The number of oil pipe couplings above the liquid surface is obtained based on the number of feature points, and the first liquid level is obtained based on the length of a single oil pipe.

[0066] It is understandable that the number of identified feature points can represent the number of tubing couplings above the liquid surface. For example, the calculation of the first liquid level can be implemented as follows:

[0067] First, based on the number N of the feature points, the number N of the oil pipe couplings above the liquid surface is obtained. That is, if N feature points are identified, it means that the liquid surface is located between the Nth and N+1th couplings.

[0068] Secondly, the length of the single oil pipe Multiplying this by the number N of feature points yields the first liquid level. This is then combined with the known length of a single oil pipe. Then the first liquid level can be determined as The first fluid level is a rough location information indexed by the tubing coupling.

[0069] Step S204: Obtain local temperature features from the temperature distribution data sequence, determine the position of the liquid surface on the fiber length coordinate, and obtain the second liquid surface position.

[0070] Local temperature characteristics are statistical quantities used to quantify local temperature fluctuations or stability. Their core physical basis is the significant difference in thermal conductivity between the gas phase and liquid phase media within the annulus.

[0071] It should be noted that the gaseous medium above the liquid surface in the annulus has a much lower thermal conductivity than the liquid medium below (such as crude oil or water). This difference in physical properties leads to fundamentally different thermal response behaviors. In the liquid segment, due to the high thermal conductivity of the liquid, heat transfer is more rapid, making the temperature sensed by the fiber optic cable less affected by local disturbances (such as fluid micro-convection and wellbore heat loss). Its local temperature characteristics are characterized by a relatively smooth temperature curve and a small calculated local temperature variance. In the gas segment, however, the poor thermal conductivity makes the temperature distribution more prone to local gradients and fluctuations, resulting in a noisier temperature curve and a higher local temperature variance. Therefore, the liquid surface position can be determined based on the local temperature characteristics of the temperature curve.

[0072] By detecting local temperature characteristics, a precise location, namely the second liquid level, can be determined on the fiber optic length coordinate system. This step solves the problem of liquid surface echo signals being easily interfered with by noise and difficult to identify in traditional methods. By utilizing the inherent and stable thermophysical property differences between the gas and liquid phases, it achieves highly robust identification of the interface.

[0073] Step S205: Calculate the liquid depth based on the position of the feature point on the fiber length coordinate, the first liquid level, and the second liquid level.

[0074] For example, the liquid level depth can be calculated as follows.

[0075] First, the position of the feature point corresponding to the nearest pipe coupling to the liquid surface is obtained, and the local distance to the second liquid surface level is calculated. Then, the position of the Nth feature point (i.e., the last identified coupling) recorded in step S202 on the fiber optic coordinate system, and the second liquid surface level determined in step S204 are used. The difference in fiber length between the two can be calculated. .

[0076] Next, the first liquid level is added to the local distance to obtain the liquid level depth. The true depth of the liquid level is determined by the formula... The calculation yielded the result.

[0077] The advantage of the above calculation method is that it avoids the cumulative systematic error between the entire measurement system and the actual well depth caused by the presence of a guide section of the optical fiber at the wellhead, or by slack or entanglement downhole. The calculation relies only on a short distance after the last coupling. This makes the calibration error of this local section much smaller than the error generated by calibrating the entire wellbore, thereby significantly improving the absolute accuracy of the final result.

[0078] The aforementioned method for detecting the liquid level position within the annulus of the casing pipe utilizes the coupling structure features and gas-liquid thermal conductivity differences embedded in the sensor data. Combined with a two-level positioning and coordinate transformation algorithm, and based on software algorithms processing existing distributed fiber optic temperature data, it sequentially achieves coupling feature recognition to construct a depth scale and local temperature feature analysis to identify the gas-liquid interface, ultimately calculating the liquid level depth. This method achieves real-time annular liquid level monitoring without additional hardware, with high robustness and ultra-high resolution, effectively solving the problems of traditional methods such as strong hardware dependence, susceptibility to operating condition interference, and inability to perform continuous measurements.

[0079] In one exemplary embodiment, such as Figure 4 As shown, obtaining the local temperature features in the temperature distribution data sequence, determining the position of the liquid surface on the optical fiber length coordinates, and obtaining the second liquid surface position includes steps S301 to S303. Wherein:

[0080] Step S301: Obtain the depth range of the liquid surface based on the number of feature points and the length of a single oil pipe.

[0081] Specifically, after step S203, the depth range where the liquid surface is located can be determined. The area between these sections is then used as the target region for subsequent precise detection of the liquid level. This step focuses the calculation scope from the entire wellbore to a specific segment, reducing computational load and avoiding interference from signals from irrelevant well sections, thereby improving the efficiency of the algorithm and the accuracy of positioning.

[0082] Step S302: Obtain the local variance curve of the temperature distribution data sequence within the depth range, and identify the variance change boundary of the local variance curve.

[0083] For example, a preferred method for identifying local temperature characteristics is to use local variance. The calculation method is as follows: define a sliding window of fixed length and slide it point-by-point across the temperature distribution data sequence within the depth interval; for each position within the window, calculate the variance value of all temperature data points within that window; connect the variance values ​​of all positions sequentially to form a local variance curve. The level of variance directly reflects the stability of the temperature field at that location; high variance corresponds to temperature fluctuations in the gas phase region, while low variance corresponds to temperature stability in the liquid phase region.

[0084] Step S303: The position of the variance change boundary on the fiber length coordinate is taken as the second liquid level.

[0085] like Figure 5 As shown, in one possible implementation, the identification of the change boundary can be achieved by finding the inflection point of the variance curve or by setting a threshold. For example, the data processing device can scan the local variance curve to locate the starting point where the variance value significantly and continuously decreases. This starting point is the precise location of the gas-liquid interface on the fiber optic length coordinate system, and it is recorded as the second liquid level. .

[0086] In this embodiment, the target area is first determined using the coupling structure of the oil casing, and then precise point positioning is achieved by utilizing the inherent thermophysical property differences between the gas and liquid phases. By combining macroscopic positioning with local feature recognition, the problem of inaccurate liquid surface positioning caused by the low signal-to-noise ratio of the echo signal in the traditional echo method is solved. Based on the continuous and stable temperature field information provided by distributed optical fiber sensing, high robustness and ultra-high resolution measurement can still be maintained under complex working conditions.

[0087] In step S202, the method of identifying feature points caused by the pipe coupling from the temperature distribution data sequence using a peak detection algorithm is not unique; the core lies in effectively amplifying and capturing the subtle temperature anomalies caused by the coupling. In one embodiment, such as... Figure 6 As shown, this is achieved in the following way:

[0088] Step S401: Calculate the first difference of the temperature distribution data sequence to obtain the temperature gradient curve.

[0089] The tubing coupling generates a small disturbance to the axial heat flow around it. Its first-order difference can significantly highlight this local, transient temperature change, making it appear as a sharp pulse signal (positive or negative peak), thereby greatly improving the signal-to-noise ratio and recognizability of the feature point.

[0090] Specifically, for a temperature value sequence sampled at equal intervals according to well depth, its temperature gradient is calculated. On the gradient curve, the position corresponding to the coupling will show a local extreme point with an amplitude significantly higher than the background noise.

[0091] Step S402: On the temperature gradient curve, identify all local extreme points where the amplitude exceeds a preset threshold.

[0092] The preset threshold can be calculated based on the statistical characteristics of the entire gradient curve or the target well section, or it can be set as a fixed value based on historical experience data. All points with amplitudes greater than this threshold are identified and recorded as candidate local extrema.

[0093] Step S403: The values ​​that conform to the preset spacing law of the oil pipe coupling among the local extreme points are taken as feature points.

[0094] In one possible implementation, the preset spacing rule refers to the tubing coupling being positioned downhole at a distance equal to the length of a single tubing section. This is due to the characteristic of a periodically approximately equally spaced distribution. A sequence analysis is performed on all candidate local extrema points to examine the spacing between adjacent points. This will allow for the formation of a spacing within... Extreme points of a periodic sequence within the allowable error range are identified as valid feature points.

[0095] like Figure 7 As shown, the spikes within the dashed boxes represent the 10 feature points identified in this embodiment. Furthermore, it can be understood that by using interval positioning, the actual position of the liquid surface is determined within the depth interval. Within a defined range.

[0096] In this embodiment, the identification of the tubing coupling is analyzed by using a temperature gradient curve, which improves the ability to detect weak structural features.

[0097] To fully illustrate the specific embodiments and technical effects of the present invention, several embodiments and a comparative example are provided below. In specific embodiments, the following aspects may be selected, but they do not affect the effectiveness of the present invention.

[0098] Example 1: Liquid level monitoring based on raw temperature profiles and annular fiber optic deployment. The data source for this example is a production well in an oilfield. Distributed optical fibers are freely deployed (not tightly bound) along the annulus of the casing and tubing. The data type is raw DTS temperature data, with a sampling interval of 0.1 meters and a temperature resolution of 0.1℃. The specific implementation steps are as follows:

[0099] S11, the data processing equipment obtains the raw temperature distribution data sequence of the production well for 24 consecutive hours from the DTS system in order to observe the dynamic stability of the liquid surface.

[0100] S12, In order to improve data quality, the data processing device uses algorithms including digital filtering and artificial intelligence noise reduction to process the temperature distribution data sequence.

[0101] S13, on the denoised temperature curve, a peak detection algorithm based on amplitude thresholds is used to automatically identify the top 10 obvious temperature peaks as feature points of the tubing coupling, and their fiber optic coordinates are recorded. The standard length of a single tubing used in this well is known. rice.

[0102] S14, based on the 10 identified couplings, it is preliminarily determined that the liquid level is below the 10th oil pipe, with a depth range of [96, 105.6] meters.

[0103] S15, calculate the local variance curve of the temperature distribution data sequence.

[0104] S16, on the obtained local variance curve, it was observed that after the 10th splice, the variance value decreased by approximately [missing value] within a range of about 150 meters on the fiber optic coordinates. Continued to decline to approximately .

[0105] S17 reduces the local variance value to the peak variance value of the gas phase region ahead of it. 80% of The position is defined as the starting point of a significant decrease in variance, and the position of this point on the fiber length coordinate system is determined as the precise position of the liquid surface. .

[0106] S18, Measure the fiber optic coordinates of the 10th coupling feature point and... The fiber optic distance between them was measured. rice.

[0107] S19, calculate the true depth of the liquid surface according to the formula: H = 10 × 9.6 + 4.2 = 100.2 meters.

[0108] Example 2: Liquid level monitoring based on temperature gradient curves and fiber optic cables bundled to the outer wall of the tubing. The data source for this example is a water injection well. The fiber optic cable is tightly bundled to the outer wall of the tubing and lowered into the well with the tubing string. The sensing system is a DSS system. By demodulating the static strain data sensed by the DSS system, an equivalent temperature distribution data sequence is obtained through inversion, and its first-order gradient (temperature change rate) is further calculated. The specific implementation steps are as follows:

[0109] S21. Obtain data from the DSS system, calculate the first derivative of the equivalent temperature distribution data sequence, and obtain the temperature gradient curve.

[0110] S22 identified N=12 coupling characteristic signals on the temperature gradient curve. The length of a single tubing in this well is known. =10 meters.

[0111] S23, based on the 12 identified couplings, the data processing equipment initially determined that the liquid level was below the 12th oil pipe, with a depth range of [120, 130] meters.

[0112] S24, within the depth interval [120,130], calculate the local variance curve of the temperature gradient data sequence.

[0113] S25, on the local variance curve, it was observed that in the fiber coordinates after the 12th splice, the variance value changes over a short distance of approximately 5 meters from... sharp drop This indicates that the gas-liquid interface transition zone is very narrow.

[0114] S26, determine the midpoint of the segment where the variance decreases sharply as the precise position of the liquid surface. .

[0115] S27, Measure the fiber optic coordinates of the 12th splice feature point and... The fiber optic distance between them was measured. = 6.8 meters.

[0116] S28. Finally, the true depth of the liquid surface was calculated according to the formula, and H = 12 × 10 + 6.8 = 126.8 meters.

[0117] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0118] Based on the same inventive concept, this application also provides an apparatus for implementing the above-described method for detecting the liquid level position in the annulus of a casing pipe. The solution provided by this apparatus is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the apparatus for detecting the liquid level position in the annulus of a casing pipe provided below can be found in the limitations of the method for detecting the liquid level position in the annulus of a casing pipe described above, and will not be repeated here.

[0119] In one exemplary embodiment, such as Figure 8 As shown, a device 500 for detecting the liquid level position in the annulus of an oil casing is provided, comprising: a data acquisition module 501, a feature recognition module 502, an interval positioning module 503, a local positioning module 504, and a liquid level calculation module 505, wherein:

[0120] The data acquisition module 501 is used to acquire the temperature distribution data sequence of the oil well using a distributed optical fiber sensing system deployed in the annulus of the casing and tubing.

[0121] The feature recognition module 502 is used to identify feature points caused by the oil pipe coupling from the temperature distribution data sequence using a peak detection algorithm, and to record the number of the feature points;

[0122] The interval positioning module 503 is used to obtain the number of oil pipe couplings above the liquid surface based on the number of feature points, and to obtain the first liquid level based on the length of a single oil pipe.

[0123] Local positioning module 504 is used to acquire local temperature features in the temperature distribution data sequence, determine the position of the liquid surface on the optical fiber length coordinate, and obtain the second liquid surface position.

[0124] The liquid level calculation module 505 is used to calculate the liquid level depth based on the position of the feature point on the optical fiber length coordinate, the first liquid level position, and the second liquid level position.

[0125] Each module in the aforementioned detection device for the liquid level position within the annulus of the casing can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0126] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 9As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When executed by the processor, the computer program implements a method for detecting the liquid level position in the annulus of an oil casing. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0127] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0128] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0129] Those skilled in the art will understand that all or part of the processes in the methods of 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, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory 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, artificial intelligence (AI) processors, etc., and are not limited to these.

[0130] 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 are 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 application.

[0131] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. 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 application should be determined by the appended claims.

Claims

1. A method for detecting the liquid level position in the annulus of an oil casing, characterized in that, The method includes: A distributed fiber optic sensing system deployed in the annulus of the oil casing was used to acquire temperature distribution data sequences of the oil well. Using a peak detection algorithm, feature points caused by pipe couplings are identified from the temperature distribution data sequence, and the number of these feature points is recorded. The number of oil pipe couplings above the liquid surface is obtained based on the number of the feature points, and the first liquid level is obtained based on the length of a single oil pipe. By acquiring local temperature features from the temperature distribution data sequence, determining the position of the liquid surface on the fiber length coordinate, and obtaining the second liquid surface position; The liquid depth is calculated based on the position of the feature point on the fiber length coordinate, the first liquid level, and the second liquid level.

2. The method according to claim 1, characterized in that, The location of the feature point on the fiber length coordinate, the first liquid level and the second liquid level, and the calculation of the liquid level depth include: Obtain the position of the feature point corresponding to the oil pipe coupling closest to the liquid surface, and calculate the local distance to the second liquid surface position; The liquid level is obtained by adding the first liquid level to the local distance.

3. The method according to claim 1, characterized in that, The step of determining the number of oil pipe couplings above the liquid surface based on the number of feature points, and determining the first liquid level based on the length of a single oil pipe, includes: Based on the number N of the feature points, the number N of the oil pipe couplings above the liquid surface is obtained; The length of the single oil pipe Multiplying the liquid level by the number N of the feature points yields the first liquid level.

4. The method according to claim 1, characterized in that, The step of acquiring local temperature features in the temperature distribution data sequence and determining the position of the liquid surface on the fiber length coordinate to obtain the second liquid surface position includes: The depth range of the liquid surface is obtained based on the number of feature points and the length of the single oil pipe; Obtain the local variance curve of the temperature distribution data sequence within the depth range, and identify the variance variation boundary of the local variance curve; The position of the change boundary on the fiber length coordinate is taken as the second liquid level.

5. The method according to claim 4, characterized in that, The depth range for obtaining the liquid level includes: Based on the number of feature points and the length of the single oil pipe The depth range is obtained as follows .

6. The method according to claim 1, characterized in that, The distributed optical fiber sensing system includes a distributed temperature sensing system or a distributed strain sensing system.

7. The method according to claim 1, characterized in that, The distributed optical fiber sensing system includes a distributed optical fiber sensing cable, an optical signal acquisition device, an optical signal demodulator, and a real-time signal processing device.

8. The method according to claim 7, characterized in that, The distributed optical fiber sensing cable can be deployed on: the outer wall of the oil pipe, the inner wall of the casing, or the annular space between the oil pipe and the casing.

9. The method according to claim 1, characterized in that, The temperature distribution data sequence includes temperature distribution curves, temperature distribution pseudo-color maps, geothermal gradient curves, or geothermal gradient pseudo-color maps.

10. The method according to claim 9, characterized in that, The peak detection algorithm includes: On the temperature gradient curve, identify all local extreme points whose amplitude exceeds a preset threshold; The values ​​of the local extreme points that conform to the preset spacing rule of the oil pipe coupling are taken as the feature points.

11. The method according to claim 1, characterized in that, After obtaining the temperature distribution data sequence, the method further includes processing the temperature distribution data sequence using a noise reduction algorithm.

12. The method according to claim 11, characterized in that, The noise reduction algorithm includes digital filtering algorithms or artificial intelligence noise reduction algorithms.

13. A device for detecting the liquid level position inside the annulus of an oil casing, characterized in that, The device includes: The data acquisition module is used to acquire the temperature distribution data sequence of the oil well using a distributed optical fiber sensing system deployed in the annulus of the casing and tubing. The feature recognition module is used to identify feature points caused by the oil pipe coupling from the temperature distribution data sequence using a peak detection algorithm, and to record the number of the feature points; The interval positioning module is used to obtain the number of oil pipe couplings above the liquid surface based on the number of feature points, and to obtain the first liquid level based on the length of a single oil pipe. A local positioning module is used to acquire local temperature features in the temperature distribution data sequence, determine the position of the liquid surface on the optical fiber length coordinate, and obtain the second liquid surface position. The liquid level calculation module is used to calculate the liquid level depth based on the position of the feature point on the fiber length coordinate, the first liquid level, and the second liquid level.

14. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 12.

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