Wellbore stability value calculation method and system

Abstract

The present invention provides a method for calculating the wellbore stability value, which comprises: determining the associated influencing parameters affecting the wellbore stability value according to historical statistical data, obtaining the data of adjacent wells according to the associated influencing parameters, and bringing the adjacent well data into a multiple regression analysis equation to calculate the regression coefficient; collecting the measured data of the well to be analyzed according to the associated influencing parameters; and calculating the wellbore stability value of the well to be analyzed according to the measured data, the regression coefficient and the multiple regression analysis equation. The present invention combines a variety of associated influencing parameters and obtains the wellbore stability value based on a multiple regression algorithm. The associated influencing parameters can be adjusted accordingly according to the on-site conditions, and the mobility is good. Moreover, the wellbore stability state can be automatically monitored without manual intervention, and the effect of intelligent monitoring of the wellbore stability is achieved. In addition, factors such as formation lithology are also taken into consideration, which is applicable to various formations, and the factors are considered comprehensively, so that the prediction of wellbore stability is more diverse and accurate.

Description

Technical Field

[0001] The present invention relates to the field of drilling engineering, and in particular to a method and system for calculating a wellbore stability value. Background Art

[0002] During the drilling process, optimizing drilling technology can reduce drilling costs and construction accidents, while predicting wellbore stability is the basis for safe drilling. Wellbore stability is a very complex problem often encountered in drilling projects. Before drilling, the rock formation buried deep underground is in a state of equilibrium due to the combined effects of overburden pressure, maximum horizontal geostress, minimum horizontal geostress and pore pressure. After the wellbore is opened, the rock in the well is removed, and the wellbore rock loses its original support, replaced by mud hydrostatic pressure. Under this new condition, the wellbore stress will be redistributed, resulting in high stress concentration near the wellbore wall. If the rock strength is not strong enough, the wellbore wall will be unstable.

[0003] There are many methods for predicting wellbore stability at present, and each of the influencing factors considered has its own emphasis. Most equations have made many simplifications, and the prediction accuracy cannot meet the needs of the site. Even if a few equations are relatively comprehensive, their practicality is limited due to their complex structure and poor operability.

[0004] Therefore, the present invention provides a method and system for calculating wellbore stability value. Summary of the invention

[0005] In order to solve the above problems, the present invention provides a method for calculating the wellbore stability value, which comprises the following steps:

[0006] Determine the associated influencing parameters that affect the wellbore stability value based on historical statistical data, obtain the adjacent well data based on the associated influencing parameters, and bring the adjacent well data into a multiple regression analysis equation to calculate the regression coefficient;

[0007] According to the associated influencing parameters, the measured data of the well to be analyzed are collected;

[0008] The wellbore stability value of the well to be analyzed is calculated based on the measured data, the regression coefficient and the multivariate regression analysis equation.

[0009] According to one embodiment of the present invention, the associated influencing parameters include: rock drillability coefficient x1, geological strength index x2, ground stress coefficient x3, collapse pressure x4, fracture pressure x5, wellbore size x6, bottom hole pressure difference x7 and logging data coefficient x8.

[0010] According to one embodiment of the present invention, the multiple regression analysis equation is represented by the following formula:

[0011] y=β0+β1x1+β2x2+β3x3+β4x4+β5x5+β6x6+β7x7+β8x8+ε

[0012] Wherein, y represents the wellbore stability value, β0, β1, β2, β3, β4, β5, β6, β7 and β8 represent the regression coefficients, and ε represents the random error.

[0013] According to an embodiment of the present invention, the step of acquiring measured data of the well to be analyzed based on the associated influencing parameters further includes the following steps:

[0014] According to a preset time interval, n groups of measured data of the wells to be analyzed are collected, wherein n represents a natural number greater than zero.

[0015] According to one embodiment of the present invention, the step of calculating the wellbore stability value of the well to be analyzed based on the measured data, the regression coefficient and the multivariate regression analysis equation further includes the following steps:

[0016] Calculating the wellbore stability value corresponding to each group of measured data in n groups of measured data according to the multivariate regression analysis equation to obtain n groups of wellbore stability values;

[0017] The wellbore stability value of the well to be analyzed is calculated based on the obtained n groups of wellbore stability values.

[0018] According to one embodiment of the present invention, the wellbore stability value of the well to be analyzed is calculated according to the following formula:

[0019]

[0020] Among them, ε0 represents the wellbore stability value of the well to be analyzed, y i It represents the wellbore stability value corresponding to the i-th group of measured data (i=1, 2, ..., n).

[0021] According to one embodiment of the present invention, the method further comprises:

[0022] After the measured data is collected, average value and false data processing are performed on the measured data.

[0023] According to one embodiment of the present invention, the method further comprises:

[0024] The obtained wellbore stability value of the well to be analyzed is compared with a preset threshold value, and after exceeding the preset threshold value, a wellbore stability warning is issued.

[0025] According to another aspect of the present invention, a wellbore stability value calculation system is also provided, the system comprising:

[0026] A regression coefficient module is used to determine the associated influencing parameters that affect the wellbore stability value based on historical statistical data, obtain the adjacent well data based on the associated influencing parameters, and bring the adjacent well data into the multivariate regression analysis equation to calculate the regression coefficient;

[0027] A collection module, which is used to collect measured data of the well to be analyzed based on the associated influencing parameters;

[0028] A calculation module is used to calculate the wellbore stability value of the well to be analyzed based on the measured data, the regression coefficient and the multivariate regression analysis equation.

[0029] According to one embodiment of the present invention, the acquisition module includes:

[0030] An interval acquisition unit is used to acquire n groups of measured data of the wells to be analyzed according to preset time intervals, wherein n represents a natural number greater than zero.

[0031] The wellbore stability value calculation method proposed in the present invention combines a variety of associated influencing parameters and obtains the wellbore stability value based on a multivariate regression algorithm. The associated influencing parameters can be adjusted accordingly according to the on-site conditions, and the mobility is good. In addition, the present invention can automatically monitor the wellbore stability state without manual intervention, achieving the effect of intelligent monitoring of wellbore stability. In addition, the present invention also takes into account factors such as formation lithology, is applicable to various formations, takes into account comprehensive factors, and makes the prediction of wellbore stability more diverse and accurate.

[0032] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0034] Figure 1 A flow chart of a method for calculating a wellbore stability value according to an embodiment of the present invention is shown;

[0035] Figure 2 A flow chart showing a method for calculating a wellbore stability value according to another embodiment of the present invention; and

[0036] Figure 3 A structural block diagram of a wellbore stability value calculation system according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0037] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings.

[0038] Figure 1 A flow chart of a method for calculating a wellbore stability value according to an embodiment of the present invention is shown.

[0039] At present, wellbore stability is determined by traditional mechanisms or three-pressure profiles. The influencing factors considered are not comprehensive enough, and it is necessary to manually judge the wellbore stability based on the three pressures. Different formations have different calculation methods, and many parameters are difficult to obtain.

[0040] like Figure 1 As shown, in step S101, the associated influencing parameters affecting the wellbore stability value are determined based on historical statistical data, and the adjacent well data are obtained based on the associated influencing parameters, and the adjacent well data are brought into the multivariate regression analysis equation to calculate the regression coefficient.

[0041] According to one embodiment of the present invention, the associated influencing parameters include: rock drillability coefficient x1, geological strength index x2, ground stress coefficient x3, collapse pressure x4, fracture pressure x5, wellbore size x6, bottom hole pressure difference x7 and logging data coefficient x8.

[0042] In one embodiment, the multiple regression analysis equation is as follows:

[0043] y=β0+β1x1+β2x2+β3x3+β4x4+β5x5+β6x6+β7x7+β8x8+ε

[0044] Among them, y represents the wellbore stability value, β0, β1, β2, β3, β4, β5, β6, β7 and β8 represent regression coefficients, and ε represents random error. The regression coefficients can be calculated based on the adjacent well data and the multivariate regression analysis equation. Among them, β1, β2, β3, β4, β5, β6, β7 and β8 represent the correlation coefficients of rock drillability coefficient x1, geological strength index x2, ground stress coefficient x3, collapse pressure x4, fracture pressure x5, wellbore size x6, bottom hole pressure difference x7 and logging data coefficient x8 respectively.

[0045] After the regression coefficient is obtained, in step S102, the measured data of the well to be analyzed is collected based on the associated influencing parameters.

[0046] According to one embodiment of the present invention, the step of collecting measured data may be: collecting n groups of measured data of the wells to be analyzed according to a preset time interval, wherein n represents a natural number greater than zero.

[0047] Finally, in step S103, the wellbore stability value of the well to be analyzed is calculated based on the measured data, the regression coefficient and the multivariate regression analysis equation.

[0048] According to one embodiment of the present invention, the wellbore stability value corresponding to each group of measured data in n groups of measured data is calculated according to the multivariate regression analysis equation to obtain n groups of wellbore stability values. Then, the wellbore stability value of the well to be analyzed is calculated according to the obtained n groups of wellbore stability values.

[0049] In one embodiment, the formula for calculating the wellbore stability value of the well to be analyzed is as follows:

[0050]

[0051] Among them, ε0 represents the wellbore stability value of the well to be analyzed, y i It represents the wellbore stability value corresponding to the i-th group of measured data (i=1, 2, ..., n).

[0052] Preferably, after the measured data is collected, average value and false data processing are performed on the measured data.

[0053] In addition, the obtained wellbore stability value of the well to be analyzed can be compared with a preset threshold value, and when the preset threshold value is exceeded, a wellbore stability warning is issued.

[0054] like Figure 1 The wellbore stability value calculation method shown is based on multivariate regression. The wellbore stability of the entire well section during the drilling process is predicted to obtain a correlation value reflecting the wellbore stability. The wellbore stability can be identified in time during the drilling construction process and countermeasures can be taken to minimize the occurrence of drilling risks.

[0055] The calculation method of the wellbore stability value proposed in the present invention combines a variety of influencing parameters and obtains the wellbore stability value based on a multiple regression algorithm. The eight influencing parameters that can be obtained on site can be used to obtain coefficients through multiple regression. The coefficient of the data that cannot be obtained is 0, which is convenient for on-site application and automatically monitors the wellbore stability state without manual intervention, thereby achieving intelligent monitoring of wellbore stability. Factors such as formation lithology are considered as influencing parameters, which is applicable to various formations, takes comprehensive factors into consideration, and makes the prediction of wellbore stability more diverse and accurate.

[0056] Figure 2 A flow chart of a method for calculating a wellbore stability value according to another embodiment of the present invention is shown.

[0057] like Figure 2As shown, in step S201, the parameters affecting the wellbore stability are determined to include data samples such as logging data, rock drillability coefficient, geological strength index GSI, ground stress coefficient, collapse pressure, fracture pressure and bottom hole pressure difference. In addition to the seven influencing parameters mentioned above, the wellbore size also has an impact on the stability of the wellbore.

[0058] The process of determining the parameters affecting wellbore stability can be: collating and analyzing the data and information of wells that have experienced wellbore instability, and statistically analyzing the changes in all parameters, and obtaining the parameters affecting wellbore stability through statistical analysis.

[0059] Among them, the rock drillability coefficient includes the influence of rock strength and drill bit type and drilling fluid performance related to drillability on wellbore stability. In order to consider the wellbore conditions of different formation lithologies, the rock drillability coefficient of the formation is obtained.

[0060] The geological strength index GSI reflects the comprehensive relationship between mechanical parameters such as elastic modulus, Poisson's ratio, tensile strength, and compressive strength.

[0061] The geostress coefficient is the ratio of the maximum horizontal geostress to the minimum horizontal geostress. The collapse pressure is the collapse pressure value.

[0062] The fracture pressure is the fracture pressure value. The wellbore size can be calculated based on the wellbore diameter. Bottom hole pressure difference: TVD*g p -EMW(PWD) / TVD.

[0063] Equivalent mud weight EMW or pressure while drilling PWD can be calculated in real time based on wellbore structure, wellbore trajectory, drilling tool structure, mud properties, and real-time collected drilling process parameters. TVD represents the burial depth of rock, g p represents the formation pore pressure gradient, g p The unit is g / cm 3 .

[0064] The real-time logging data coefficient includes the total pool volume, inlet flow rate, etc. The coefficient value is only 1 or 2, which is obtained based on the real-time data changes of the total pool volume and inlet flow rate. The change law of the data in the well depth section is 1 for a large amplitude and 2 for a small amplitude.

[0065] After determining the influencing parameters, in step S202, the logging data of the neighboring wells in the oilfield block, the drilling data rock drillability coefficient, the geological strength index GSI, the ground stress coefficient, the collapse pressure, the fracture pressure and the bottom hole pressure difference data samples are collected. In this step, it is necessary to collect the data information related to the influencing parameters of the neighboring wells in the oilfield block to which the well to be analyzed belongs.

[0066] Then, in step S203, the correlation coefficient is determined based on the collected data: the correlation coefficients β1-β8 are determined based on the multivariate regression algorithm in combination with the regional big data.

[0067] In this step, the correlation parameters are confirmed based on the influencing parameters. Generally speaking, multiple regression analysis is to transform the known values ​​of each variable (influencing parameters x1, x2, ..., x m ) into the regression equation to obtain the estimated value of the dependent variable (the predicted value y m ), which can effectively predict the occurrence and development of a certain phenomenon. Assume that the dependent variable y m with independent variables x1, x2, …, x m There is a linear relationship between them, and its mathematical model is:

[0068] y j =β0+β1x 1j +β2x 2j +…+β m x mj +ε j (j=1,2,…n)

[0069] In one embodiment of the present invention, x1, x2, ..., x8 are calculated from a large amount of data such as drilling parameters, geological stratification, pressure gradient and drill bit characteristics in the logging data according to the data of adjacent wells, and a multivariate regression equation is established, and finally the regression coefficients β0~β8 and the correlation coefficients β1-β8 are obtained.

[0070] The multiple regression analysis equation used in the present invention is:

[0071] y=β0+β1x1+β2x2+β3x3+β4x4+β5x5+β6x6+β7x7+β8x8+ε

[0072] Among them, y represents the wellbore stability value, β0, β1, β2, β3, β4, β5, β6, β7 and β8 represent regression coefficients, and ε represents random error. The regression coefficients can be calculated based on the adjacent well data and the multivariate regression analysis equation. Among them, β1, β2, β3, β4, β5, β6, β7 and β8 represent the correlation coefficients of rock drillability coefficient x1, geological strength index x2, ground stress coefficient x3, collapse pressure x4, fracture pressure x5, wellbore size x6, bottom hole pressure difference x7 and logging data coefficient x8 respectively.

[0073] After the regression coefficient is obtained, in step S204, the real-time influencing parameters of the key wells are collected, and the wellbore stability value of the entire well section is analyzed according to the correlation coefficient combined with the regional big data. In actual application, the key well is the well to be analyzed, and the measured data of the well to be analyzed can be obtained. The measured data contains the original data of multiple influencing parameters, including logging data, drilling fluid, drilling tools and drill bit data, etc.

[0074] In one embodiment, the method of obtaining measured data is: starting from the first point of monitoring, collecting relevant parameters (well depth, torque, inlet flow, outlet flow, total pool volume, drilling pressure, hook load, standpipe pressure, drilling time, rotation speed, etc.) from real-time logging data, and using the on-site drilling fluid performance, drilling tool assembly and drill bit data to obtain the most original data. Suppose within a certain time interval △T, a time series is measured as {x(t i )), the input parameters (real-time logging data acquisition related parameters) are processed as average values ​​and false data. According to the time interval △T, a total of 6 groups of data are collected to obtain the measured data.

[0075] Finally, in step S205, the final wellbore stability value is calculated: the calculation results of the above steps are integrated to calculate the final wellbore stability value.

[0076] In one embodiment, for the first set of data, each influencing parameter is calculated to obtain the value of each influencing parameter: x1, x2, ..., x8. Substitute the regression coefficients β0 to β8. Obtain y1. Calculate y2 based on the second set of data, calculate to the fifth point in sequence, and take the average of the first five points. At the same time, remove the values ​​of these five points that are particularly large or particularly small. Finally, calculate to obtain y6, and use the following formula to obtain y0

[0077]

[0078] In addition, the finally obtained wellbore stability value ε0 to be analyzed can be compared with an artificially set wellbore stability threshold value (preset threshold value), and an early warning is issued if the threshold is exceeded.

[0079] According to the calculation method of the wellbore stability value of the present invention, the experiment and application were carried out in the drilling construction monitoring project of the key wells in a certain oil field block. The logging data, drilling fluid, drilling tools, drill bit data, rock drillability coefficient, geological strength index GSI, ground stress coefficient, collapse pressure, fracture pressure and EMW of the adjacent wells in the oil field block were collected. According to the regression correlation coefficient β0~β8 of the multivariate regression algorithm, the wellbore stability coefficient was calculated according to the data of the key well. When the monitoring well X was drilled to 5138 meters, the wellbore stability coefficient exceeded the limit, and a prompt of wellbore stability was given. The on-site engineer conducted an in-depth analysis of the situation and avoided the occurrence of a wellbore collapse. The drilling data of the wells in the same block were used for verification, and it was found that the wellbore stability prediction probability of the present invention was high, and the prediction accuracy reached 90%, indicating that the established equation can meet the drilling construction requirements. The method improves the prediction accuracy of the wellbore, and the calculation process is relatively simple, and the data used are easy to obtain, which is of great significance to the promotion of safe drilling technology.

[0080] Figure 3 A structural block diagram of a wellbore stability value calculation system according to an embodiment of the present invention is shown.

[0081] like Figure 3 As shown, the computing system 300 includes a regression coefficient module 301, a collection module 302 and a computing module 303. The collection module 302 includes an interval collection unit 3021.

[0082] The regression coefficient module 301 is used to determine the associated influencing parameters affecting the wellbore stability value based on historical statistical data, obtain the adjacent well data based on the associated influencing parameters, and bring the adjacent well data into the multivariate regression analysis equation to calculate the regression coefficient.

[0083] The acquisition module 302 is used to acquire measured data of the well to be analyzed according to the associated influencing parameters. The interval acquisition unit 3021 is used to acquire measured data of n groups of wells to be analyzed according to a preset time interval, where n represents a natural number greater than zero.

[0084] The calculation module 303 is used to calculate the wellbore stability value of the well to be analyzed based on the measured data, the regression coefficient and the multivariate regression analysis equation.

[0085] In summary, the wellbore stability value calculation method proposed in the present invention combines a variety of associated influencing parameters and obtains the wellbore stability value based on a multivariate regression algorithm. The associated influencing parameters can be adjusted accordingly according to the on-site conditions, and the maneuverability is good. Moreover, the present invention can automatically monitor the wellbore stability state without manual intervention, achieving the effect of intelligent monitoring of wellbore stability. In addition, the present invention also takes into account factors such as formation lithology, is applicable to various formations, takes into account comprehensive factors, and makes the prediction of wellbore stability more diverse and accurate.

[0086] It should be understood that the embodiments disclosed in the present invention are not limited to the specific structures, processing steps or materials disclosed herein, but should be extended to equivalent substitutions of these features understood by ordinary technicians in the relevant field. It should also be understood that the terms used herein are only used for the purpose of describing specific embodiments and are not meant to be limiting.

[0087] The "one embodiment" or "embodiment" mentioned in the specification means that the specific features, structures or characteristics described in conjunction with the embodiment are included in at least one embodiment of the present invention. Therefore, the phrases "one embodiment" or "embodiment" appearing in various places throughout the specification do not necessarily refer to the same embodiment.

[0088] Although the embodiments disclosed in the present invention are as above, the contents described are only embodiments adopted for facilitating the understanding of the present invention and are not intended to limit the present invention. Any technician in the technical field to which the present invention belongs can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present invention, but the patent protection scope of the present invention shall still be subject to the scope defined in the attached claims.

Claims

1. A method for calculating wellbore stability value, characterized in that: The method comprises the following steps: Determine the associated influencing parameters that affect the wellbore stability value based on historical statistical data, obtain the adjacent well data based on the associated influencing parameters, and bring the adjacent well data into the multivariate regression analysis equation to calculate the regression coefficient, wherein the associated influencing parameters include: rock drillability coefficient x1, geological strength index x2, ground stress coefficient x3, collapse pressure x4, fracture pressure x5, wellbore size x6, bottom hole pressure difference x7 and logging data coefficient x8, wherein the rock drillability coefficient x1 includes the rock strength and the influence of the drill bit type and drilling fluid performance related to drillability on the wellbore stability; According to the associated influencing parameters, the measured data of the well to be analyzed are collected; Calculating the wellbore stability value of the well to be analyzed according to the measured data, the regression coefficient and the multivariate regression analysis equation; The step of acquiring measured data of the well to be analyzed further includes the following steps: acquiring n groups of measured data of the well to be analyzed according to a preset time interval, wherein n represents a natural number greater than zero; The step of calculating the wellbore stability value of the well to be analyzed based on the measured data, the regression coefficient and the multivariate regression analysis equation also includes the following steps: calculating the wellbore stability value corresponding to each group of measured data in n groups of measured data based on the multivariate regression analysis equation to obtain n groups of wellbore stability values; and calculating the wellbore stability value of the well to be analyzed based on the obtained n groups of wellbore stability values.

2. The method according to claim 1, characterized in that The multiple regression analysis equation is expressed by the following formula: y=β0+β1x1+β2x2+β3x3+β4x4+β5x5+β6x6+β7x7+β8x8+ε Wherein, y represents the wellbore stability value, β0, β1, β2, β3, β4, β5, β6, β7 and β8 represent the regression coefficients, and ε represents the random error.

3. The method according to claim 1, characterized in that The wellbore stability value of the well to be analyzed is calculated according to the following formula: Among them, ε0 represents the wellbore stability value of the well to be analyzed, y i It represents the wellbore stability value corresponding to the i-th group of measured data, i=1, 2,…, n.

4. The method according to claim 1, characterized in that The method further comprises: after acquiring the measured data, performing average value and false data processing on the measured data.

5. The method according to any one of claims 1 to 4, characterized in that The method further comprises: The obtained wellbore stability value of the well to be analyzed is compared with a preset threshold value, and after exceeding the preset threshold value, a wellbore stability warning is issued.

6. A wellbore stability value calculation system, characterized in that: The method according to any one of claims 1 to 5 is performed, wherein the system comprises: A regression coefficient module is used to determine the associated influencing parameters that affect the wellbore stability value based on historical statistical data, obtain the adjacent well data based on the associated influencing parameters, and bring the adjacent well data into the multivariate regression analysis equation to calculate the regression coefficient; A collection module, which is used to collect measured data of the well to be analyzed based on the associated influencing parameters; A calculation module is used to calculate the wellbore stability value of the well to be analyzed based on the measured data, the regression coefficient and the multivariate regression analysis equation.

7. The system according to claim 6, characterized in that The acquisition module comprises: An interval acquisition unit is used to acquire n groups of measured data of the wells to be analyzed according to preset time intervals, wherein n represents a natural number greater than zero.

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