Wellbore deviation risk identification method, system and equipment and medium

Abstract

The invention provides a wellbore skew risk identification method, system and device and a medium, and the method comprises the following steps: estimating the apparent dip angles of a seismic data volume along Inline and Crossline based on a multi-window three-dimensional seismic data vector dip angle estimation method, and obtaining stratigraphic dip angle scanning information; discrete similarity dip angle scanning is carried out based on the scanning information, an apparent dip angle with the maximum similarity coefficient is obtained, and dip angle attributes are obtained based on the maximum apparent dip angle; estimating a vector inclination angle based on inclination angle attribute row three-dimensional extension to obtain an inclination angle in a depth domain; predicting a high-dip-angle stratum of the drilling well based on the dip angle information on the depth domain to obtain a dip angle range and plane distribution, and performing risk identification based on the dip angle range and the plane distribution; according to the method, the high-dip-angle stratum generated by the influence of strike-slip fracture on the inner curtain of the Santaku group can be effectively recognized in the drilling process, the dip angle range can be analyzed in a multi-layer mode, prediction can be conducted on a plane, and early warning can be made in advance.

Description

Technical Field

[0001] The present invention relates to oil and gas exploration and development technologies, belonging to the technical field of seismic while drilling and steering drilling, and specifically relates to a method, system, device and medium for identifying wellbore deviation risks. Background Art

[0002] A certain oilfield is a large-scale ultra-deep marine fault-controlled fracture-vuggy carbonate reservoir with an oil and gas resource volume of 1 billion tons. In the past four years, 9 thousand-ton wells and 150 hundred-ton wells have been harvested in the Fuman Oilfield. The oil and gas in this oilfield are buried in the ultra-deep layer at a depth of 7,500 to 10,000 meters. The formation geology is complex. In the Santamu Formation of the Ordovician System, there is a set of medium-thick gray mudstone and marl with a thickness of about more than 1,300 meters. The clay mineral content reaches 40%, showing weak water sensitivity, and there are a large number of sub-micron cracks on the surface. Affected by the structure and lithology, the formation dip angle of the Santamu Formation in the Ordovician System is large, resulting in difficulties in well inclination control during the drilling process and great difficulty in preventing deviation and drilling straight. During the process of drilling through the fractured zone of the Santamu Formation in the Ordovician System, wellbore instability and collapse are prone to occur due to stress release. Moreover, the sub-micron cracks are highly sensitive to the drilling fluid filtrate, making the safe drilling difficult. The work of correcting deviation and inclination during the drilling process caused by this has cost a large amount of money and time. Taking the Man 3-H6 well as an example, the deviation and inclination of this well were corrected four times in the third open hole section, and the cumulative cycle loss was 9 days. The wellbore of nearly 3,000 meters was in a stepped shape, causing difficulties for the subsequent construction.

[0003] At present, for the positive and negative mutation phenomena of the formation dip angle in some parts of the Santamu Formation interior affected by strike-slip faults, there is no method for early identification in the drilling engineering. Only after drilling through the corresponding high-dip angle formation can re-measurement (after-effect) be carried out, and then re-orientation, deviation correction and inclination correction work can be carried out during the subsequent drilling process, which cannot achieve the expected accurate identification and early warning during the while-drilling work. And the qualitative conclusions obtained during the drilling construction process over the years also show that there is a very close internal relationship between the conventional actual wellbore and the formation attitude. Especially for the directional wellbore constructed without using special downhole tools, the changes in its azimuth and well inclination are often controlled by the variation and magnitude of the formation dip and dip angle.

[0004] In summary, there is an urgent need to propose a new method process to achieve accurate prediction of the high-dip angle formation in the Santamu Formation interior, avoid risks, and provide guidance for increasing reserves and production in the oilfield. Summary of the Invention

[0005] Aiming at the problems existing in the prior art, the present invention provides a method, system, device and medium for identifying wellbore deviation risks to solve the problem of difficult well inclination control during the drilling process caused by the positive and negative mutation phenomena of the formation dip angle in some parts of the oilfield interior affected by strike-slip faults, make early warnings, avoid the engineering complexity caused by drilling through high-angle formations, thereby improving the drilling efficiency, guiding the intermediate completion, and playing a role in prompting the drilling safety of each well.

[0006] The present invention is realized through the following technical solutions:

[0007] A method for identifying the risk of wellbore deviation includes the following steps:

[0008] Estimate the apparent dip along Inline and Crossline of the seismic data volume by using the vector dip estimation method for 3D seismic data based on multiple windows, and obtain the formation dip scanning information;

[0009] Conduct discrete similarity dip scanning based on the scanning information to obtain the apparent dip with the maximum similarity coefficient, and obtain the dip attribute based on the maximum apparent dip;

[0010] Estimate the vector dip by performing 3D extension based on the dip attribute to obtain the dip in the depth domain;

[0011] Predict the high-dip formation of the well being drilled based on the dip information in the depth domain to obtain the dip angle range and planar distribution, and conduct risk identification based on the dip angle range and planar distribution.

[0012] Further, the process of estimating the apparent dip along Inline and Crossline of the seismic data volume by using the vector dip estimation method for 3D seismic data based on multiple windows is as follows:

[0013] Use a quadratic surface to fit the similarity values of a pair of adjacent apparent dips (θx, θy):

[0014]

[0015] Where cs is the similarity; θx is the apparent dip along inline; θy is the apparent dip along crossline; aj is the coefficient, and the coefficient aj is obtained by the least squares method;

[0016] The amplitude variance of the Jth trace in the ith window is:

[0017]

[0018] Where c s is the similarity, K E and K s are respectively the starting point and the ending point in the time samples within the window, x j and y j are respectively the components of the distance from the analysis point to the jth trace on the x-axis and the y-axis;

[0019] Calculate the dip estimate value:

[0020]

[0021] Where, corresponds to the interpolated similar surface c s (θ x , θ y ) maximum apparent dip pair;

[0022] Adopt a multi - analysis window scanning structure to perform scanning and calculate the amplitude variance of the J seismic traces falling within the i - th window:

[0023]

[0024] where var is the amplitude variance, and <u i > represents the average value of u in the i - th analysis window ji .

[0025] Furthermore, in addition to the central window, the multi - analysis window scanning structure also scans a set of non - central, overlapping analysis windows, and all windows contain analysis points of interest inside.

[0026] Furthermore, the process of performing discrete similarity dip scanning based on the scanning information to obtain the apparent dip with the maximum similarity coefficient and obtaining the dip attribute based on the maximum apparent dip is as follows:

[0027] Change the scanning window to a rectangle or a circle, and the scanning parameters change from one dip angle to a pair of apparent dip angles in the x and y directions to obtain the dip attribute;

[0028] The obtained dip attribute includes true dip and azimuth parameters.

[0029] Furthermore, the process of estimating the vector dip angle based on the dip attribute for three - dimensional extension to obtain the dip angle in the depth domain is as follows:

[0030] The instantaneous wave number Kx in the x - direction is:

[0031]

[0032] The instantaneous wave number Ky in the y - direction is:

[0033]

[0034] The frequencies ω in the x and y directions are:

[0035]

[0036] Perform second - order differences on the instantaneous wave number Kx, the instantaneous wave number Ky, and the frequencies ω in the x and y directions respectively to obtain:

[0037]

[0038] The dip angle relationships in the x and y directions, the true time dip angle s and the azimuth relationship, and the corresponding dip angles θx and θy in the depth domain are obtained through analysis.

[0039] Furthermore, the dip angle relationships in the x and y directions are as follows:

[0040]

[0041]

[0042] where p is the apparent dip angle in the x direction; q is the apparent dip angle in the y direction;

[0043] The relationship between the true time dip angle s and the azimuth is as follows:

[0044] s = (p 2 + q 2 ) 1 / 2 (13)

[0045]

[0046] where is the azimuth relationship of the true time dip angle s.

[0047] Furthermore, in the depth domain, the wave number Kz in the z direction is used to replace the frequency ω:

[0048]

[0049] The corresponding dip angles θx and θy are:

[0050]

[0051]

[0052] A wellbore deviation risk identification system includes:

[0053] A preprocessing model, configured to estimate the apparent dip angles along Inline and Crossline of a seismic data volume based on a multi-window three-dimensional seismic data vector dip angle estimation method, and obtain formation dip angle scanning information;

[0054] A scanning model, configured to perform discrete similarity dip angle scanning based on the scanning information, obtain the apparent dip angle with the largest similarity coefficient, and obtain dip angle attributes based on the largest apparent dip angle;

[0055] An estimation model, configured to estimate the vector dip angle based on three-dimensional extension of the dip angle attributes, and obtain the dip angle in the depth domain;

[0056] An output model is configured to predict high-dip formations of a well being drilled based on dip angle information in the depth domain, obtain the dip angle range and planar distribution, and perform risk identification based on the dip angle range and planar distribution.

[0057] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of a well deviation risk identification method.

[0058] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, it implements the steps of a well deviation risk identification method.

[0059] Compared with the prior art, the present invention has the following beneficial technical effects:

[0060] The present invention provides a well deviation risk identification method, system, device, and medium, including the following steps: estimating the apparent dip angles of a seismic data volume along Inline and Crossline based on a multi-window three-dimensional seismic data vector dip angle estimation method to obtain formation dip angle scanning information; performing discrete similarity dip angle scanning based on the scanning information to obtain the apparent dip angle with the maximum similarity coefficient, and obtaining the dip angle attribute based on the maximum apparent dip angle; estimating the vector dip angle by performing three-dimensional extension based on the dip angle attribute to obtain the dip angle in the depth domain; predicting high-dip formations of a well being drilled based on the dip angle information in the depth domain to obtain the dip angle range and planar distribution, and performing risk identification based on the dip angle range and planar distribution; based on the existing seismic data, the present application uses the formation dip angle change rate scanning technology to identify the formation dip angle mutation section, can effectively identify high-dip formations affected by strike-slip faults in the Santamu Formation during the drilling process, can analyze the dip angle range of multiple horizons, and can also make predictions in the plane and give early warnings in advance, innovating and developing the dip angle prediction work in the seismic-while-drilling technology, having incomparable advantages, and providing guarantee for the safe drilling of ultra-deep wells in the Fuman Oilfield. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a flowchart of a well deviation risk identification method in an embodiment of the present application;

[0062] Figure 2 It is a schematic diagram of two-dimensional discrete similarity dip angle scanning in an embodiment of the present application;

[0063] Figure 3 It is a schematic diagram of three-dimensional discrete similarity dip angle scanning in an embodiment of the present application;

[0064] Figure 4 It is a schematic diagram of the spatial result of dip angle calculation in an embodiment of the present application;

[0065] Figure 5 This is a schematic diagram of the dip plane distribution in the embodiments of the present application. Detailed implementation manners

[0066] The following further describes the present invention in detail with specific embodiments, which are explanations rather than limitations of the present invention.

[0067] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0068] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0069] The embodiments of the present application provide a method for identifying the risk of wellbore deviation, as Figure 1 shown, including the following steps:

[0070] Estimate the apparent dips of the seismic data volume along Inline and Crossline based on the multi-window three-dimensional seismic data vector dip estimation method to obtain the formation dip scanning information;

[0071] Carry out discrete similarity dip scanning based on the scanning information to obtain the apparent dip with the largest similarity coefficient, and obtain the dip attribute based on the largest apparent dip;

[0072] Estimate the vector dip by three-dimensional extension based on the dip attribute to obtain the dip in the depth domain;

[0073] Predict the high-dip formation of the well being drilled based on the dip information in the depth domain to obtain the dip angle range and planar distribution, and perform risk identification based on the dip angle range and planar distribution.

[0074] Preferably, the process of estimating the apparent dip angles of the seismic data volume along Inline and Crossline by the multi-window-based 3D seismic data vector dip angle estimation method is as follows:

[0075] A quadratic surface is used to fit the similarity values of a pair of adjacent apparent dip angles (θx, θy):

[0076]

[0077] where cs is the similarity; θx is the apparent dip angle along the inline; θy is the apparent dip angle along the crossline; aj is the coefficient, and the coefficient aj is obtained by the least squares method;

[0078] The amplitude variance of the Jth trace in the ith window is:

[0079]

[0080] where c s is the similarity, K E , K s are respectively the starting point and the ending point in the time samples within the window, x j and y j are respectively the components of the distance from the analysis point to the jth trace on the x-axis and the y-axis. The pair with the maximum coherence is scanned out through many time dip angle pairs (p, q), which is the final result.

[0081] Calculate the dip angle estimation value:

[0082]

[0083] where is the apparent dip angle pair corresponding to the maximum value of the interpolated similarity surface c s (θ x , θ y );

[0084] Adopt a multi-analysis window scanning structure, conduct scanning, and calculate the amplitude variance of the J seismic traces falling within the i window:

[0085]

[0086] where var is the amplitude variance, <u i > represents the average value of u ji in the ith analysis window.

[0087] Preferably, in addition to the central window, the multi-analysis window scanning structure also scans a group of non-central and overlapping analysis windows, and all windows contain the analysis points of interest.

[0088] Preferably, the process of performing discrete similarity dip scanning based on the scanning information to obtain the dip angle with the maximum similarity coefficient and obtaining the dip angle attribute based on the maximum dip angle is as follows Figure 2 and Figure 3 shown, where Figure 2 Figure 236 shows a schematic diagram of two-dimensional discrete similarity dip scanning. It can be seen from the figure that this method can scan the changes in dip angles on both sides of the fault relatively accurately. However, in some areas with small faults, it often ignores these structures. Moreover, the number of calculation points for two-dimensional discrete similarity dip scanning is limited, and for areas with low signal-to-noise ratio or chaotic event axes, the scanning effect is not very satisfactory. The signal-to-noise ratio has a relatively small impact on the central window. Therefore, we introduce three-dimensional similarity dip scanning. The dip angle data volume obtained through three-dimensional similarity dip scanning has achieved good results in identifying faults and other linear structures.

[0089] Specifically:

[0090] The scanning window is changed to a rectangle or a circle, and the scanning parameters change from one dip angle to pairs of apparent dip angles in the x and y directions to obtain the dip angle attribute;

[0091] The obtained dip angle attribute includes the true dip angle and azimuth parameters.

[0092] Preferably, the process of performing three-dimensional extension on the vector dip angle based on the dip angle attribute to estimate the dip angle in the depth domain is as follows:

[0093] The instantaneous wave number Kx in the x direction is:

[0094]

[0095] The instantaneous wave number Ky in the y direction is:

[0096]

[0097] The frequencies ω in the x and y directions are:

[0098]

[0099] Second-order differences are respectively obtained for the instantaneous wave number Kx, the instantaneous wave number Ky, and the frequencies ω in the x and y directions:

[0100]

[0101]

[0102] The relationships of the apparent dip angles in the x and y directions, the relationship between the true time apparent dip angle s and the azimuth, and the corresponding dip angles θx and θy in the depth domain are analyzed.

[0103] Preferably, the relationship between the apparent dip angles in the x and y directions is as follows:

[0104]

[0105]

[0106] where p is the apparent dip angle in the x direction; q is the apparent dip angle in the y direction;

[0107] The relationship between the true time apparent dip angle s and the azimuth is as follows:

[0108] s = (p 2 + q 2 ) 1 / 2 (13)

[0109]

[0110] where is the azimuth relationship of the true time apparent dip angle s.

[0111] Preferably, in the depth domain case, the wave number Kz in the z direction is used to replace the frequency ω:

[0112]

[0113] The corresponding dip angles θx and θy are:

[0114]

[0115]

[0116] Example:

[0117] Taking the well warning work in a certain block of the Fuman Oilfield in the Tarim Basin as an example (the target formation for carbonate rock drilling in this area is O2y, O1-2y, ∈), the risk prediction of the high-dip formation of this well is carried out by means of the method of the present invention. The process and results of this example are as follows:

[0118] 1) Analyze and evaluate the seismic data: preferably select the "two-wide and one-high" high-precision 3D seismic data, start a time window with the Santamu Formation as the main target for spectral analysis. The results show that the main frequency is 21 Hz and the frequency band is in the range of 5 - 40 Hz, which can basically meet the requirements of structural interpretation and subsequent work can be carried out;

[0119] 2) On the selected seismic data, perform a robust formation dip scan: use the 3D seismic data vector dip estimation method based on multiple windows to estimate the apparent dips of the seismic data volume along Inline and Crossline;

[0120] 3) On the basis of the second step, perform discrete similarity dip scanning: change the scanning window to a rectangle or a circle. The scanning parameters change from one dip angle to dip angles in the x and y directions. Obtain the dip angle pair with the largest similarity coefficient to further calculate parameters such as the true dip angle and azimuth angle, improving the accuracy of formation dip angle calculation.

[0121] 4) Composite dip angle calculation: Perform three-dimensional extension based on the dip angle attributes calculated by discrete similarity dip scanning, estimate the vector dip angle, and finally obtain the dip angle in the depth domain.

[0122] 5) According to the dip angle calculation results, effectively predict the high-dip angle formations of the well being drilled, and combine the dip angle calculation results in the pre-drilling design of the well. Make an early warning reminder in the form of a written report, predict the dip angle range and planar distribution, with greatly improved accuracy.

[0123] It should be noted that a wellbore deviation risk identification method provided in this application has been popularized and applied in the Fuman Oilfield in the Tarim Basin. A total of 20 risk warnings for wellbore quality exceeding the standard affected by formation dip angle mutations in carbonate wells have been completed, saving 160 days of drilling cycle for drilling and completion engineering construction, and saving 10.66 million yuan in drilling, cementing, and logging engineering costs. The results obtained in this application can reflect the change range of dip angle degrees in multiple layers spatially. For example, Figure 4 as shown. It can be seen from the figure that according to the dip angle calculation results, the dip angle ranges of the four layers are between 0 - 15°, and the distribution range affected by dip angle changes can also be shown on the plane. For example, Figure 5As shown in the figure, the planar distribution range of dip angle anomalies can be obtained from the figure, and it can be predicted in advance whether the formation dip angle of the formation passed by the drilling trajectory will affect the engineering drilling. For example, the dip angles at the bottom of the Silurian and the bottom of the Terek Awati Formation in the figure exceed 10°, and early warnings need to be given to the drilling team. Before applying this method, for the positive and negative mutation phenomena of the dip angles of some formations in the interior of the Santamu Formation in the study area affected by strike-slip faults, there is no method for early identification in drilling engineering. It can only be re-measured (aftereffect) after encountering the corresponding high-dip formation, and then re-orientation, deviation correction, and inclination correction work are carried out during the subsequent drilling process, which cannot achieve the expected accuracy of accurate identification and early warning during the drilling process. Based on the "two-wide and one-high" high-precision seismic data, using the dip angle results obtained by multiple dip angle calculations and fusion of robust formation dip angle scanning, discrete similarity dip angle scanning, and complex dip angle calculation, the dip angles that could only be measured by aftereffect during the drilling process are innovatively predicted, improving the risk prediction ability during the drilling process. Moreover, the dip angle results calculated by the multi-method fusion have high accuracy, can effectively reflect the occurrence and angle of the high-dip formation, guide the orientation, trajectory, and wellbore optimization work during the well location drilling process, make early engineering preparations for possible encounters with high-dip formations, and improve the engineering quality control process. Finally, this method has no strict requirements on the well location distribution, has a wider applicability, improves the accuracy and reliability of the early warning work during the drilling process, saves the engineering losses caused by the deviation correction and inclination correction processes, and provides strong support for the safe and efficient drilling of the well location.

[0124] The present invention provides a wellbore deviation risk identification system, comprising:

[0125] A preprocessing model, configured to estimate the apparent dip angles along Inline and Crossline of the seismic data volume based on a multi-window three-dimensional seismic data vector dip angle estimation method, and obtain formation dip angle scanning information;

[0126] A scanning model, configured to perform discrete similarity dip angle scanning based on the scanning information to obtain the apparent dip angle with the largest similarity coefficient, and obtain dip angle attributes based on the largest apparent dip angle;

[0127] An estimation model, configured to estimate the vector dip angle based on the three-dimensional extension of the dip angle attributes to obtain the dip angle in the depth domain;

[0128] An output model, configured to predict the high-dip formation of the well being drilled based on the dip angle information in the depth domain, obtain the dip angle range and planar distribution, and perform risk identification based on the dip angle range and planar distribution.

[0129] In another embodiment of the present invention, a computer device is provided. The computer device includes a processor and a memory. The memory is used to store a computer program, and the computer program includes program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function. The processor described in the embodiment of the present invention can be used for the operation of a wellbore deviation risk identification method.

[0130] In another embodiment of the present invention, a storage medium is also provided, specifically a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device in a computer device and is used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and, of course, the extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, and this storage space stores the operating system of the terminal. And, one or more instructions suitable for being loaded and executed by the processor are also stored in this storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. One or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the corresponding steps of a wellbore deviation risk identification method in the above embodiments.

[0131] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0132] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0133] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0134] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0135] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying the risk of wellbore deviation, characterized in that, it includes the following steps: Estimate the apparent dips along Inline and Crossline of the seismic data volume based on the vector dip estimation method of 3D seismic data with multiple windows to obtain formation dip scanning information; Conduct discrete similarity dip scanning based on the scanning information to obtain the apparent dip with the largest similarity coefficient, and obtain the dip attribute based on the largest apparent dip; Estimate the vector dip by three-dimensional extension based on the dip attribute to obtain the dip in the depth domain; Predict the high-dip formation of the well being drilled based on the dip information in the depth domain to obtain the dip angle range and planar distribution, and conduct risk identification based on the dip angle range and planar distribution.

2. The method for identifying the risk of wellbore deviation according to claim 1, characterized in that, the process of estimating the apparent dips along Inline and Crossline of the seismic data volume by the vector dip estimation method of 3D seismic data with multiple windows is as follows: Use a quadratic surface to fit the similarity values of a pair of adjacent apparent dips (θx, θy): where, cs is the similarity; θx is the apparent dip along inline; θy is the apparent dip along crossline; aj is the coefficient, and the coefficient aj is obtained by the least squares method; The amplitude variance of the Jth trace in the ith window is: Among them, c s is the similarity, K E , K s are respectively the starting point and the ending point in the time samples within the window, x j and y j are respectively the components of the distance from the analysis point to the j-th channel on the x-axis and the y-axis; Calculate the dip estimate value: Among them, corresponds to the interpolated similar plane c s (θ x , θ y ) the pair of apparent dip angles of the maximum value; Adopt a multi-analysis window scanning structure, conduct scanning, and calculate the amplitude variance of the J seismic traces falling in the i window: where var is the amplitude variance, and <u i > represents the average value of u in the i-th analysis window. ji ​ 3. The method for identifying the risk of wellbore deviation according to claim 2, characterized in that, in addition to the central window, the multi-analysis window scanning structure also scans a group of non-central, superimposed analysis windows, and all windows contain the analysis points of interest inside.

4. The method for identifying the risk of wellbore deviation according to claim 1, characterized in that, the process of conducting discrete similarity dip scanning based on the scanning information to obtain the apparent dip with the largest similarity coefficient and obtaining the dip attribute based on the largest apparent dip is as follows: Change the scanning window to a rectangle or a circle, and the scanning parameter changes from one dip angle to a pair of apparent dips in the x and y directions to obtain the dip attribute; The obtained dip attribute includes true dip and azimuth parameters.

5. The method for identifying the risk of wellbore deviation according to claim 1, characterized in that, the process of estimating the vector dip by three-dimensional extension based on the dip attribute to obtain the dip in the depth domain is as follows: The instantaneous wave number Kx in the x direction is: The instantaneous wave number Ky in the y direction is: The frequencies ω in the x and y directions are: Perform second-order differences on the instantaneous wave number Kx, the instantaneous wave number Ky, and the frequencies ω in the x and y directions respectively to obtain: Analyze to obtain the relationship of apparent dips in the x and y directions, the relationship of the true time apparent dip s and azimuth, and the corresponding dips θx and θy in the depth domain case.

6. The method for identifying the risk of wellbore deviation according to claim 5, characterized in that, the relationship of apparent dips in the x and y directions is: where, p is the apparent dip in the x direction; q is the apparent dip in the y direction; the relationship of the true time apparent dip s and azimuth is: s = (p 2 + q 2 ) 1 / 2 (13) Among them, The azimuth relationship of the true time visual dip angle s.

7. The method for identifying the risk of wellbore deviation according to claim 6, It is characterized in that in the case of the depth domain, the wave number Kz in the z direction is used to replace the frequency ω: The corresponding dip angles θx and θy are:

8. A wellbore deviation risk identification system It is characterized in that based on the wellbore deviation risk identification method according to any one of claims 1-7, including: A preprocessing model configured to estimate the apparent dip angles along Inline and Crossline of the seismic data volume based on a multi-window three-dimensional seismic data vector dip angle estimation method, and obtain formation dip angle scanning information; A scanning model configured to perform discrete similarity dip angle scanning based on the scanning information, obtain the apparent dip angle with the largest similarity coefficient, and obtain dip angle attributes based on the largest apparent dip angle; An estimation model configured to estimate the vector dip angle by three-dimensional extension based on the dip angle attributes, and obtain the dip angle in the depth domain; An output model configured to predict the high-dip angle formation of the well being drilled based on the dip angle information in the depth domain, obtain the dip angle range and planar distribution, and perform risk identification based on the dip angle range and planar distribution.

9. A computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, It is characterized in that when the processor executes the computer program, the steps of the wellbore deviation risk identification method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, It is characterized in that when the computer program is executed by a processor, the steps of the wellbore deviation risk identification method according to any one of claims 1 to 7 are implemented.

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