A system for evaluating pressure relief in a fractured zone of a horizontal well

Abstract

The present application relates to oil and gas exploitation technical field, disclose the present application is put forward the pressure relief evaluation system, including evaluation module, the evaluation module includes several assignment subunit, assignment subunit is used for according to the expert in the field is based on individual experience and professional knowledge for each element is assigned weight, and the different numerical value of element is assigned, and the assignment is according to the different risk value of element numerical value size corresponds; The element is the consideration factor of pressure relief evaluation;Data input module, data input module is used for collecting the numerical value of each element in the pressure relief area;Data processing module, the numerical value of each element collected is input into different expert assignment subunit.The present application is based on the experience of experts to assign values to each consideration factor, which can make the risk assessment more accurate and more practical, and with more and more expert assignment in data, the risk assessment can be more and more accurate.

Description

Technical Field

[0001] This invention relates to the field of oil and gas extraction technology, and in particular to a surface horizontal well fracturing zone pressure relief assessment system. Background Technology

[0002] Surface horizontal well fracturing zones refer to the areas in oil and gas exploration and development where horizontal wells are drilled underground and fracturing technology is used to release oil and gas resources from rock reservoirs. During fracturing, a fluid mixture is injected into the rock formation under high pressure, causing the rock to fracture and creating fissures, thereby increasing the rock's permeability.

[0003] A surface horizontal well fracturing zone typically includes the following key components: Wellbore: The drilling process for horizontal wells requires bending the wellbore underground to extend horizontally through the target reservoir. This design maximizes the contact area between the wellbore and the reservoir, increasing production. Fracturing Fluid: Fracturing fluid is a liquid mixture injected into the wellbore to apply high pressure to the target reservoir and create fractures. Fracturing fluid typically consists of water, sand, and chemical additives. Water is used to transmit pressure and propel sand particles into the rock fractures, while the sand helps to open the fractures.

[0004] Fracturing pump: A fracturing pump is a device that injects fracturing fluid into the wellbore. It provides high pressure and flow rate to deliver the fracturing fluid into the rock formation, thereby creating fractures.

[0005] Fracturing and cementing: Cementing operations begin once fracturing fluid is injected into the rock formation and fractures are formed. Cementing is the process of injecting cement or other solidifying materials into the fractures to stabilize them and prevent oil and gas from escaping. The establishment and operation of fracturing zones in horizontal wells on the surface can increase the efficiency and production of oil and gas exploration and development, improve reservoir permeability, and unlock more energy resources.

[0006] Therefore, monitoring and early warning in the fracturing zone of a surface horizontal well are particularly important: installing a monitoring and early warning system can continuously monitor parameters such as pressure and temperature, and issue alarms when they exceed set thresholds. This helps to detect anomalies in advance and take corresponding measures. Depressurization in the fracturing zone of a surface horizontal well is a crucial control measure to ensure operational safety and reduce risks. Currently, there is no system specifically designed to assess the depressurization process in the fracturing zone of a surface horizontal well. Therefore, this paper proposes a depressurization assessment system for the fracturing zone of a surface horizontal well to monitor the potential risks during depressurization, providing further assurance for safe production. Summary of the Invention

[0007] To address the technical problems mentioned in the background section, this invention provides a surface horizontal well fracturing zone pressure relief assessment system.

[0008] This invention is achieved using the following technical solution: a surface horizontal well fracturing zone pressure relief assessment system, comprising...

[0009] The evaluation module includes several assignment sub-units. These sub-units are used to assign weights to each element based on the personal experience and professional knowledge of experts in the field, and to assign values ​​to different numerical values ​​of the elements. The assignment values ​​correspond to different risk values ​​based on the numerical values ​​of the elements. The elements are the factors to be considered in the stress relief assessment.

[0010] The data input module is used to collect values ​​of various elements within the pressure relief area.

[0011] The data processing module inputs the collected values ​​of various elements into different expert assignment sub-units to obtain the risk values ​​output by different sub-units, and calculates the average risk value of all the risk values ​​output by the expert assignment sub-units.

[0012] Furthermore, in the evaluation module, the weighting of each element is set as w1, w2, ..., w n The corresponding data values ​​are x1, x2, ..., x n ;

[0013] In the data processing module, the corresponding risk value S for each sub-unit is... i = (w1*x1+w2*x2+...+w n *x n ) / n, i = 1, 2, 3...n

[0014] Then S sum =S1+S2+S3+...+S n

[0015] The average risk value is F = S sum / n.

[0016] Furthermore, factors to consider in pressure relief assessment include fracturing fluid properties, fracturing fracture properties, pressure relief rate, and stress redistribution.

[0017] Furthermore, the fracturing fluid properties include viscosity, density, and permeability; the weight of fracturing fluid properties in the pressure relief assessment is calculated using the following formula.

[0018] P 压裂液性质 = (K1*Y1+K2*Y2+K3*Y3) / 3

[0019] Where K1 is the viscosity of the fracturing fluid, K2 is the density of the fracturing fluid, and K3 is the permeability of the fracturing fluid.

[0020] Furthermore, the fracturing fracture properties include the fracturing fracture geometry, length, width, and connectivity. The weight of fracturing fracture properties in the pressure relief assessment is calculated using the following formula.

[0021] P 压裂裂缝性质 =(Q1*Z1+Q2*Z2+Q3*Z3+Q4*Z4) / 4

[0022] Where Q1 is the geometry of the fracturing fracture, Q2 is the length of the fracturing fracture, Q3 is the width of the fracturing fracture, and Q4 is the connectivity of the fracturing fracture.

[0023] Furthermore, it also includes a primary data correction module, which includes...

[0024] The data input unit is used to store historical data;

[0025] The data processing unit is used to extract the value information of each consideration factor, as well as the information on whether a risk occurs when a certain consideration factor has different values; and to calculate the correlation between each consideration factor and whether a risk occurs, and obtain the weight value of each consideration factor.

[0026] The data correction unit is used to combine the weight values ​​of each consideration factor obtained by the data processing unit and the weight values ​​of the consideration factor in all assignment sub-units into a data group, take the median of each data group as the corrected weight 1 of each consideration factor, and calculate the average value of all weight 1; then, based on the average value of the corrected weight 1 and the information of each element in the area to be tested, the comprehensive score of the risk occurrence is calculated.

[0027] Furthermore, it also includes a secondary correction module;

[0028] The secondary correction module is used to extract data from each group of all risk accidents, and to form data groups for the same element in different accidents. This way, several data groups with the same number of factors as the factors being considered are obtained. Then, the median of each data group is obtained. Finally, the weights are corrected according to the following principles.

[0029] Scenario 1: When the factors considered and the probability of the risk occurring are positively correlated:

[0030] If the feature value of the area to be tested is lower than the corresponding median, the weight of this consideration factor is adjusted to X% of the average weight in the assigned sub-unit;

[0031] If the feature value of the area to be tested is greater than the corresponding median, the weight of that feature is adjusted to Y% of the average weight in the assigned sub-unit;

[0032] Scenario 2: When the factors considered and the probability of the risk occurring are negatively correlated:

[0033] If the number of factors considered in the area to be tested is lower than the corresponding median, the weight of that factor is adjusted to Y% of the average weight in the assigned sub-unit;

[0034] If the number of factors considered in the area to be tested is greater than the corresponding median, the weight of that factor is adjusted to X% of the average weight in the assigned sub-unit.

[0035] Where X is less than 100 and Y is greater than 100.

[0036] Furthermore, a linear regression model is used to fit the relationship between the elements and the results. In the trained linear regression model, the weights can represent the degree of contribution of each element to the results. A larger regression coefficient indicates that the element contributes more to the results. The weight values ​​of each factor are obtained according to the above method.

[0037] Furthermore, X = 100 - (L / N*0.5), where the total length of the data is N, which is also the total number of data in the dataset, and L is the distance of the data from the median of the dataset, that is, the number of data in the dataset that are between the median and the median.

[0038] Furthermore, Y = 100 + (L / N * 0.5), where the total length of the data is N, which is also the total number of data in the dataset, and L is the distance of the data from the median of the dataset, that is, the number of data in the dataset that are between the median and the median.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0040] The pressure relief assessment system proposed in this invention includes an assessment module, which comprises several assignment sub-units. These assignment sub-units assign weights to each element based on the experience and expertise of experts in the field, and assign values ​​corresponding to different numerical values ​​of the elements. The assigned values ​​correspond to different risk values ​​based on the numerical values ​​of the elements. The elements are the factors considered in the pressure relief assessment. A data input module collects the values ​​of each element within the pressure relief area. A data processing module inputs the collected values ​​of each element into different expert assignment sub-units to obtain the risk values ​​output by each sub-unit, and calculates the average risk value of all the risk values ​​output by the expert assignment sub-units.

[0041] This invention assigns values ​​to various consideration factors based on expert experience and judgment, which makes risk assessment more accurate and more in line with reality. Furthermore, as more and more expert values ​​are assigned to the data, the risk assessment becomes increasingly accurate.

[0042] The pressure relief assessment system proposed in this invention also includes a primary data correction module and a secondary data correction module, which can make multiple corrections to the values ​​given by experts based on historical data, and finally obtain a more accurate weight of the factors considered, thereby further optimizing the data accuracy of the conclusions made by this assessment system. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the pressure relief assessment system proposed in this invention.

[0044] Figure 2 This is a tree diagram of the factors considered in the pressure relief assessment system proposed in this invention;

[0045] Figure 3 This is a flowchart of the pressure relief assessment system proposed in this invention. Detailed Implementation

[0046] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments. It should be noted that, without conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments.

[0047] Example 1:

[0048] Reference Figure 1-3 This solution proposes a surface horizontal well fracturing zone pressure relief assessment system, including...

[0049] The evaluation module includes several assignment sub-units. These sub-units are used to assign weights to each element based on the personal experience and professional knowledge of experts in the field, and to assign values ​​to different numerical values ​​of the elements. The assignment values ​​correspond to different risk values ​​based on the numerical values ​​of the elements. The elements are the factors to be considered in the stress relief assessment.

[0050] The data input module is used to collect values ​​of various elements within the pressure relief area.

[0051] The data processing module inputs the collected values ​​of various elements into different expert assignment sub-units to obtain the risk values ​​output by different sub-units, and calculates the average risk value of all the risk values ​​output by the expert assignment sub-units.

[0052] In the specific calculation, the weight of each element is set as w1, w2, ..., w in the evaluation module. n The corresponding data values ​​are x1, x2, ..., x n ;

[0053] In the data processing module, the corresponding risk value S for each sub-unit is... i= (w1*x1+w2*x2+...+w n *x n ) / n, i = 1, 2, 3...n

[0054] Then S sum =S1+S2+S3+...+S n

[0055] The average risk value is F = S sum / n.

[0056] It should be noted that, at present, the main factors to be considered in pressure relief assessment include fracturing fluid properties, fracturing fracture properties, pressure relief rate, and stress redistribution.

[0057] The properties of the fracturing fluid include viscosity, density, and permeability; the proportion of fracturing fluid properties in the factors considered in the pressure relief assessment is calculated using the following formula.

[0058] P 压裂液性质 = (K1*Y1+K2*Y2+K3*Y3) / 3

[0059] Where K1 is the viscosity of the fracturing fluid, K2 is the density of the fracturing fluid, and K3 is the permeability of the fracturing fluid.

[0060] Furthermore, the fracturing fracture properties include the fracturing fracture geometry, length, width, and connectivity. The weight of fracturing fracture properties in the pressure relief assessment is calculated using the following formula.

[0061] P 压裂裂缝性质 =(Q1*Z1+Q2*Z2+Q3*Z3+Q4*Z4) / 4

[0062] Where Q1 is the geometry of the fracturing fracture, Q2 is the length of the fracturing fracture, Q3 is the width of the fracturing fracture, and Q4 is the connectivity of the fracturing fracture.

[0063] It also includes a primary data correction module, which includes...

[0064] The data input unit is used to store historical data;

[0065] The data processing unit is used to extract the value information of each consideration factor, as well as the information on whether a risk occurs when a certain consideration factor has different values; and to calculate the correlation between each consideration factor and whether a risk occurs, and obtain the weight value of each consideration factor.

[0066] The data correction unit is used to combine the weight values ​​of each consideration factor obtained by the data processing unit and the weight values ​​of the consideration factor in all assignment sub-units into a data group, take the median of each data group as the corrected weight 1 of each consideration factor, and calculate the average value of all weight 1; then, based on the average value of the corrected weight 1 and the information of each element in the area to be tested, the comprehensive score of the risk occurrence is calculated.

[0067] It also includes a secondary correction module;

[0068] The secondary correction module is used to extract data from each group of all risk accidents, and to form data groups for the same element in different accidents. This way, several data groups with the same number of factors as the factors being considered are obtained. Then, the median of each data group is obtained. Finally, the weights are corrected according to the following principles.

[0069] Scenario 1: When the factors considered and the probability of the risk occurring are positively correlated:

[0070] If the feature value of the area to be tested is lower than the corresponding median, the weight of this consideration factor is adjusted to X% of the average weight in the assigned sub-unit;

[0071] If the feature value of the area to be tested is greater than the corresponding median, the weight of that feature is adjusted to Y% of the average weight in the assigned sub-unit;

[0072] Scenario 2: When the factors considered and the probability of the risk occurring are negatively correlated:

[0073] If the number of factors considered in the area to be tested is lower than the corresponding median, the weight of that factor is adjusted to Y% of the average weight in the assigned sub-unit;

[0074] If the number of factors considered in the area to be tested is greater than the corresponding median, the weight of that factor is adjusted to X% of the average weight in the assigned sub-unit.

[0075] Where X is less than 100 and Y is greater than 100.

[0076] A linear regression model is used to fit the relationship between the factors and the results. In a well-trained linear regression model, the weights can represent the degree of contribution of each factor to the results. A larger regression coefficient indicates that the factor contributes more to the results. The weight values ​​of each factor are obtained according to the above method.

[0077] X = 100 - (L / N*0.5), where the total length of the data is N, which is the total number of data in the dataset, and L is the distance of the data from the median of the dataset, that is, the number of data in the dataset that are between the median and the median.

[0078] The formula Y = 100 + (L / N * 0.5) is given, where N is the total length of the data, which is also the total number of data in the dataset, and L is the distance of the data from the median of the dataset, which is the number of data in the dataset that are between the median and the median.

[0079] The above embodiments are merely preferred embodiments of the present invention and should not be construed as limiting the scope of protection of the present invention. Any non-substantial changes and substitutions made by those skilled in the art based on the present invention shall fall within the scope of protection claimed by the present invention.

Claims

1. A surface horizontal well fracturing zone pressure relief assessment system, characterized in that, include: The evaluation module includes several assignment sub-units. These sub-units are used to assign weights to each element based on the personal experience and professional knowledge of experts in the field, and to assign values ​​to different numerical values ​​of the elements. The assignment values ​​correspond to different risk values ​​based on the numerical values ​​of the elements. The elements are the factors to be considered in the stress relief assessment. The data input module is used to collect the values ​​of various elements within the depressurization area. The data processing module inputs the collected values ​​of various elements into different expert assignment sub-units to obtain the risk values ​​output by different sub-units, and calculates the average risk value of all the risk values ​​output by the expert assignment sub-units. In the evaluation module, the weighting of each element is set as w1, w2, ..., w n The corresponding data values ​​are x1, x2, ..., x n ; In the data processing module, the corresponding risk value S for each sub-unit is... i = (w1 * x1 + w2 * x2 + ... + w n * x n ) / n, i=1,2,3...m; Then S sum =S1+S2+S3+...+S m ; The average risk value is F=S sum / m; Factors to consider in pressure relief assessment include fracturing fluid properties, fracturing fracture properties, and pressure relief rate; The properties of the fracturing fluid include viscosity, density, and permeability; the proportion of fracturing fluid properties in the factors considered in the pressure relief assessment is calculated using the following formula; P 压裂液性质 =(K1*Y1+K2*Y2+K3*Y3) / 3; Where K1 is the viscosity of the fracturing fluid, K2 is the density of the fracturing fluid, and K3 is the permeability of the fracturing fluid; The properties of the fracturing fractures include their geometry, length, width, and connectivity. The weight of the properties of the fracturing fractures in the pressure relief assessment is calculated using the following formula. P 压裂裂缝性质 =(Q1*Z1+Q2*Z2+Q3*Z3+Q4*Z4) / 4; Where Q1 is the geometry of the fracturing fracture, Q2 is the length of the fracturing fracture, Q3 is the width of the fracturing fracture, and Q4 is the connectivity of the fracturing fracture.

2. The surface horizontal well fracturing zone pressure relief assessment system as described in claim 1, characterized in that, It also includes a primary data correction module, which includes: The data input unit is used to store historical data; The data processing unit is used to extract the value information of each consideration factor, as well as the information on whether a risk occurs when a certain consideration factor has different values; and to calculate the correlation between each consideration factor and whether a risk occurs, and obtain the weight value of each consideration factor. The data correction unit is used to combine the weight values ​​of each consideration factor obtained by the data processing unit with the weight values ​​of the consideration factor in all assignment sub-units to form a data group, take the median of each data group as the corrected weight of each consideration factor, and calculate the average of all weights; then, based on the average of the corrected weights and the information of each element in the area to be tested, the comprehensive risk score is calculated.

3. The surface horizontal well fracturing zone pressure relief assessment system as described in claim 1, characterized in that, It also includes a secondary correction module; The secondary correction module is used to extract data from each group of all risk accidents, and to form data groups for the same element in different accidents. This way, several data groups with the same number of factors as the factors being considered are obtained. Then, the median of each data group is obtained. Finally, the weights are corrected according to the following principles. Scenario 1: When the factors considered and the probability of the risk occurring are positively correlated: If the feature value of the area to be tested is lower than the corresponding median, the weight of this factor is adjusted to X% of the average weight in the assigned sub-unit. If the feature value of the area to be tested is greater than the corresponding median, the weight of that feature is adjusted to Y% of the average weight of the assigned sub-cells. Scenario 2: When the factors considered and the probability of the risk occurring are negatively correlated: If the number of factors considered in the area to be tested is lower than the corresponding median, the weight of that factor is adjusted to Y% of the average weight in the assigned sub-unit. If the number of factors considered in the area to be tested is greater than the corresponding median, the weight of that factor is adjusted to X% of the average weight in the assigned sub-unit; where X is less than 100 and Y is greater than 100.

4. The surface horizontal well fracturing zone pressure relief assessment system as described in claim 3, characterized in that, A linear regression model is used to fit the relationship between the factors and the results. In a well-trained linear regression model, the weights can represent the degree of contribution of each factor to the results. A larger regression coefficient indicates that the factor contributes more to the results. The weight values ​​of each factor are obtained according to the above method.

5. The surface horizontal well fracturing zone pressure relief assessment system as described in claim 1, characterized in that, The formula X = 100 - (L / N * 0.5) is used, where N is the total length of the data, which is also the total number of data in the dataset, and L is the distance of the data from the median of the dataset, that is, the number of data in the dataset that are between the median and the median.

6. The surface horizontal well fracturing zone pressure relief assessment system as described in claim 1, characterized in that, The formula Y = 100 + (L / N * 0.5) is given, where N is the total length of the data, which is also the total number of data points in the dataset, and L is the distance of the data point from the median of the dataset, i.e., the number of data points in the dataset that are between the median and the median.

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