Method for acquiring strain field of key area of shaft wall

Abstract

A method for obtaining a strain field of a key area of a shaft wall comprises the steps that sensing optical fibers are buried in concrete of a key well section of the inner wall of the shaft in the circumferential direction of the shaft; carrying out data acquisition on the sensing optical fiber of the key well section; interference of temperature on strain measurement is eliminated through regional temperature compensation; using a covariance function to represent the correlation of the spatial data; performing interpolation through known data points; and after the predicted values of all interpolation points are calculated, all interpolation results are drawn into a strain thermodynamic diagram of the key horizon. According to the invention, the high-density sensing optical fibers are arranged in the concrete of the inner wall of the 360-degree whole area of the key well section of the shaft, so that the strain field change data can be economically and feasibly obtained in real time, and the high-density space coverage of the shaft strain monitoring sensor at the key part under the condition of low cost is met; therefore, the precision of the well wall strain field of the local well section is improved, and the precise monitoring of the strain evolution of the potential fracture risk area is realized.

Description

Technical Field

[0001] This invention relates to a method for obtaining the strain field of a key area of ​​the wellbore wall, belonging to the field of wellbore safety monitoring technology for key sections of deep vertical wells. Background Technology

[0002] There are currently a large number of deep vertical shafts in operation. In these shafts, the interaction between groundwater movement, strata movement caused by mining and the well wall has led to high-risk fracture sections in the well section at the junction of thick aquifer and aquitard, the junction of topsoil and bedrock, and the section where the well wall cross-section changes diameter. Moreover, with the complex development and changes in strata movement, these key sections are generally exposed to the risk of fracture or even multiple fractures.

[0003] Existing wellbore monitoring technologies mainly fall into two categories. The first category is single-point sensors, which typically involve setting up 5 to 8 monitoring layers along the wellbore depth, with 3 to 4 measuring points arranged circumferentially in each layer. The sensors are usually embedded in the inner wall concrete of the wellbore. Their advantages include tight coupling between the sensor and the wellbore concrete, accurate measurements, and good long-term stability due to their location within the wellbore. However, they also suffer from poor spatial continuity and cannot reflect the important objective fact of uneven stress distribution within the wellbore. The second category involves locally deployed strain fiber optic monitoring on the wellbore surface, typically using distributed optical fibers. Its purpose is to obtain strain changes on the wellbore surface. The advantage of this method is relatively continuous local measuring points, but it suffers from relatively high investment costs, insufficient reliability of measurement results, and poor long-term stability. Summary of the Invention

[0004] This invention provides a method for obtaining the strain field of a key area of ​​a wellbore. This method can obtain the 360° full-domain strain field of the wellbore of a key section of a deep vertical well, meet the high-density spatial coverage of wellbore strain monitoring sensors in key parts under low-cost conditions, improve the accuracy of the strain field of the wellbore in local sections, and realize accurate monitoring of strain evolution in areas with potential rupture risks. To achieve the above objectives, the present invention provides a method for obtaining the strain field of a key region of a wellbore, comprising the following steps: S1. Sensing optical fibers are embedded circumferentially inside the concrete of key well sections on the inner wall of the well shaft. The key well sections include the well wall diameter change section, the water-rich sand / clay layer boundary section, and the topsoil-bedrock boundary section. The sensing optical fibers in each key well section include multiple vertical sensing optical fibers and arc-shaped sensing optical fibers. The upper and lower ends of any vertical sensing optical fiber in the same buried area are respectively connected to one end of the arc-shaped sensing optical fiber on its adjacent two sides, forming a "square wave" shape that is ringed on the inner wall of the well section in that buried area. S2. Data is acquired from the sensing fiber optic cables in key well sections. The acquired data is represented as follows: ,in Represents the x-coordinate of the data point. Represents the y-coordinate of the data point. This represents the collected values ​​of the data points, and the collected data is organized into a dataset containing the spatial location of each data point and its corresponding strain value; S3. By using regional temperature compensation, the interference of temperature on strain measurement is eliminated, ensuring that the measured strain reflects the actual deformation of the wellbore inner wall.

[0005] S4. Use the covariance function to represent the correlation of spatial data; S5. Interpolate using known data points; S6. After calculating the predicted values ​​for all interpolation points, plot all interpolation results as a strain thermogram of the key layers.

[0006] Furthermore, in S1, the height of each vertical sensing fiber is 3~5m, and the angle formed by the extension line of each arc sensing fiber's two ends and the central axis of the wellbore is 30 to 45 degrees; both the vertical sensing fiber and the arc sensing fiber are high-density strain sensing fibers, and the interval between the measuring points set on them is no more than 0.5m; the vertical sensing fiber and the arc sensing fiber are installed through grooves opened on the inner wall of the wellbore, and the groove width is selected according to the actual fiber thickness, and the groove depth is 26~30mm according to the actual wellbore curvature and the thickness of the wellbore protective layer.

[0007] Furthermore, the data acquisition in S2 is automated using a cabinet-type dense distributed demodulator.

[0008] Furthermore, the regional temperature compensation model in S3 is expressed as follows: ; in, To respond realistically, For the strain obtained by measurement, The coefficient of thermal expansion is 1 / 3. This refers to localized temperature differences.

[0009] Furthermore, the specific process of S4 is as follows: After constructing the covariance matrix, calculate the covariance between all known data points; for any two data points and Covariance The covariance function is as follows: ; in, There are two data points. The distance between them It is a variation function. It is the variance of the variation model. It is a scale parameter, that is, a parameter that controls spatial correlation.

[0010] Furthermore, the specific process of S5 is as follows: The Kriging equation is established based on the covariance function, for a given interpolation point. Assume the predicted value for this point is obtained by a weighted average of known data points: ; in, These are weighting coefficients. These are the observations of known data points; Weighting coefficient Solve using the following system of linear equations: ; in, Indicates the first Covariance between points represents the Lagrange multipliers, used to ensure the unbiasedness of interpolation and data (their sum is 0). The weighting coefficients are obtained through calculation. Substituting into the formula yields the given interpolation point. interpolation results .

[0011] This invention achieves economical and feasible real-time acquisition of strain field change data by deploying high-density sensing optical fibers in the 360° inner wall concrete of key well sections. By nearly non-destructively embedding densely distributed optical fiber sensing technology within the inner wall concrete of deep vertical wells, while simultaneously ensuring near-continuous monitoring of vertical and circumferential strain within key well sections, and constructing a continuous annular strain field using Kriging interpolation based on discrete optical fiber measurement point data, this invention achieves high-density spatial coverage of wellbore strain monitoring sensors in key areas under low-cost conditions. This improves the accuracy of the wellbore wall strain field in local sections, enabling precise monitoring of strain evolution in areas with potential fracture risks. Attached Figure Description

[0012] Figure 1 This is a schematic diagram of the installation of sensing optical fibers in the key well section of this invention; Figure 2 This is a schematic diagram of the arrangement of the sensing optical fibers in this invention. Detailed Implementation

[0013] The invention will now be further described with reference to the accompanying drawings.

[0014] A method for obtaining the strain field in a key area of ​​a wellbore wall includes the following steps: S1, such as Figure 1As shown, sensing optical fibers are embedded circumferentially within the concrete of key sections of the wellbore wall; these key sections include the wellbore diameter variation section, the water-rich sand / clay layer interface section, and the topsoil-bedrock boundary section; as shown... Figure 2 As shown, the sensing optical fibers in each key well section buried area include multiple vertical sensing optical fibers and arc-shaped sensing optical fibers. The upper and lower ends of any vertical sensing optical fiber in the same buried area are respectively connected to one end of the arc-shaped sensing optical fiber on its adjacent sides, forming a "square wave" ring on the inner wall of the well section in that buried area. S2. Data is acquired from the sensing fiber optic cables in key well sections. The acquired data is represented as follows: ,in Represents the x-coordinate of the data point. Represents the y-coordinate of the data point. This represents the collected values ​​of the data points, and the collected data is organized into a dataset containing the spatial location of each data point and its corresponding strain value; S3. By using regional temperature compensation, the interference of temperature on strain measurement is eliminated, ensuring that the measured strain reflects the actual deformation of the wellbore inner wall.

[0015] S4. Use the covariance function to represent the correlation of spatial data; S5. Interpolate using known data points; S6. After calculating the predicted values ​​for all interpolation points, plot all interpolation results as a strain thermogram of the key layers.

[0016] In a preferred embodiment, the height of each vertical sensing fiber in S1 is 3-5m, and the angle formed by the extension line of each arc sensing fiber's two ends and the central axis of the wellbore is 30-45 degrees. Both the vertical and arc sensing fibers are high-density strain sensing fibers, and the interval between the measuring points set on them is no more than 0.5m. The vertical and arc sensing fibers are installed through grooves opened on the inner wall of the wellbore. The width of the groove is selected according to the actual fiber thickness, which is 3-5mm in this invention. The groove depth is 26-30mm according to the actual wellbore curvature and the thickness of the wellbore protective layer.

[0017] In a preferred embodiment, the data acquisition in S2 is automated using a cabinet-type dense distributed demodulator.

[0018] As a preferred embodiment, the regional temperature compensation model in S3 is expressed as follows: ; in, To respond realistically, For the strain obtained by measurement, The coefficient of thermal expansion is 1 / 3. This refers to localized temperature differences.

[0019] As a preferred embodiment, the specific process of S4 is as follows: After constructing the covariance matrix, calculate the covariance between all known data points; for any two data points and Covariance The covariance function is as follows: ; in, There are two data points. The distance between them It is a variation function. It is the variance of the variation model. It is a scale parameter, that is, a parameter that controls spatial correlation.

[0020] As a preferred embodiment, the specific process of S5 is as follows: The Kriging equation is established based on the covariance function, for a given interpolation point. Assume the predicted value for this point is obtained by a weighted average of known data points: ; in, These are weighting coefficients. These are the observations of known data points; Weighting coefficient Solve using the following system of linear equations: ; in, Indicates the first Covariance between points represents the Lagrange multipliers, used to ensure the unbiasedness of interpolation and data (their sum is 0). The weighting coefficients are obtained through calculation. Substituting into the formula yields the given interpolation point. interpolation results .

Claims

1. A method for obtaining the strain field in a key region of a wellbore, characterized in that, Includes the following steps: S1. Sensing optical fibers are embedded circumferentially inside the concrete of key well sections on the inner wall of the well shaft. The key well sections include the well wall diameter change section, the water-rich sand / clay layer boundary section, and the topsoil-bedrock boundary section. The sensing optical fibers in each key well section include multiple vertical sensing optical fibers and arc-shaped sensing optical fibers. The upper and lower ends of any vertical sensing optical fiber in the same buried area are respectively connected to one end of the arc-shaped sensing optical fiber on its adjacent two sides, forming a "square wave" shape that is ringed on the inner wall of the well section in that buried area. S2. Data is acquired from the sensing fiber optic cables in key well sections. The acquired data is represented as follows: ,in Represents the x-coordinate of the data point. Represents the y-coordinate of the data point. This represents the collected values ​​of the data points, and the collected data is organized into a dataset containing the spatial location of each data point and its corresponding strain value; S3. By using regional temperature compensation, the interference of temperature on strain measurement is eliminated, ensuring that the measured strain reflects the actual deformation of the inner wall of the wellbore. S4. Use the covariance function to represent the correlation of spatial data; S5. Interpolate using known data points; S6. After calculating the predicted values ​​for all interpolation points, plot all interpolation results as a strain thermogram of the key layers.

2. The method for obtaining the strain field of a key region of a wellbore according to claim 1, characterized in that, The height of each vertical sensing fiber in S1 is 3~5m, and the angle formed by the extension line of each arc sensing fiber's two ends and the central axis of the wellbore is 30 to 45 degrees. Both the vertical sensing fiber and the arc sensing fiber are high-density strain sensing fibers, and the interval between the measuring points set on them is no more than 0.5m. The vertical sensing fiber and the arc sensing fiber are installed through grooves opened on the inner wall of the well. The width of the groove is selected according to the actual thickness of the fiber, and the groove depth is 26~30mm according to the actual curvature of the wellbore and the thickness of the wellbore protective layer.

3. The method for obtaining the strain field of a key region of the wellbore wall according to claim 1 or 2, characterized in that, The data acquisition in S2 is automated using a cabinet-type dense distributed demodulator.

4. The method for obtaining the strain field of a key region of the wellbore wall according to claim 1, characterized in that, The regional temperature compensation model in S3 is expressed as follows: ； in, To respond realistically, For the strain obtained by measurement, The coefficient of thermal expansion is 1 / 3. This refers to localized temperature differences.

5. The method for obtaining the strain field of a key region of a wellbore according to claim 1, characterized in that, The specific process of S4 is as follows: After constructing the covariance matrix, calculate the covariance between all known data points; for any two data points and Covariance The covariance function is as follows: ； in, There are two data points. The distance between them It is a variation function. It is the variance of the variation model. It is a scale parameter, that is, a parameter that controls spatial correlation.

6. The method for obtaining the strain field of a key region of a wellbore according to claim 1, characterized in that, The specific process of S5 is as follows: The Kriging equation is established based on the covariance function, for a given interpolation point. Assume the predicted value for this point is obtained by a weighted average of known data points: ； in, These are weighting coefficients. These are the observations of known data points; Weighting coefficient Solve using the following system of linear equations: ； in, Indicates the first Covariance between points This represents the Lagrange multiplier, used to ensure the unbiasedness of interpolation and data; The weighting coefficients are obtained through calculation. Substituting into the formula yields the given interpolation point. interpolation results .

Images