CN113554673A - Method and system for automatically identifying cracks based on while-drilling electrical imaging image - Google Patents
Method and system for automatically identifying cracks based on while-drilling electrical imaging image Download PDFInfo
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Abstract
The invention provides a method and a system for automatically identifying cracks based on while-drilling electrical imaging images, wherein the method comprises the steps of preprocessing measured data, acquiring corresponding while-drilling electrical imaging images based on the preprocessed while-drilling electrical imaging data, and segmenting the while-drilling electrical imaging images by adopting a set method to acquire target crack characteristic images; determining a corresponding fracture characteristic equivalent curve equation based on the determined fracture extraction depth; and finally, outputting the characteristic parameters of the crack characteristic equivalent curve equation meeting the evaluation requirement. The method overcomes the defects that the prior art can not automatically identify the fracture characteristics in the drilling environment and the accuracy of the identification result is insufficient, is based on the preprocessed data and the fracture characteristic extraction strategy, is high in calculation speed and accuracy on the basis of effectively extracting the regular fracture characteristics in the electrical imaging image while drilling, and is beneficial to geological guidance and development guidance in the field of oil reservoir exploration.
Description
Technical Field
The invention relates to the technical field of oil reservoir exploration and development, in particular to a method and a system for automatically identifying cracks based on an electrical imaging image while drilling.
Background
With the great application of highly deviated wells and horizontal wells in oil and gas exploration and development, some logging instruments are difficult to get into the well, such as conventional cable logging instruments, based on the technical problem, the logging while drilling technology can solve the technical problem, realize real-time measurement in the drilling process, and is applied to the measurement process of the highly deviated wells or the horizontal wells. In the real-time measurement means of oil reservoir exploration, because the logging while drilling is carried out at the first time of drilling the stratum, the transformation degree of the stratum by the borehole environment is lower, the stratum parameter information obtained by measurement is closer to the original stratum information, more reliable logging information is provided for reservoir evaluation, real-time geosteering can be carried out on the basis of realizing the real-time reservoir evaluation, and important basis is provided for real-time decision-making and well completion optimization design of drilling.
The resistivity logging while drilling technology is one of the earliest developed and applied resistivity logging while drilling technologies, technical personnel in the field also provide a resistivity imaging logging while drilling technology based on the logging technology, realize field application and mainly use the resistivity imaging logging while drilling technology for geological guidance and formation evaluation, but the existing imaging logging while drilling technology has technical barriers in the aspects of instrument manufacturing, data processing technology and the like. In the aspect of data processing, identification and extraction of reservoir fracture characteristics are very important data processing contents in the field of cable electrical imaging and the field of electrical imaging while drilling, while in the field of electrical imaging while drilling, the existing fracture identification method cannot realize automatic identification in a measurement-while-drilling environment, and the accuracy of a fracture characteristic identification result is insufficient, so that the requirement of an oil reservoir exploration technology cannot be met. Compared with the existing fracture extraction means, such as cable electrical imaging fracture extraction, the method for extracting the fracture by electrical imaging while drilling also has different operation requirements, so that a reasonable and systematic processing method capable of automatically identifying the reservoir fracture characteristics in the measurement while drilling environment is needed.
Disclosure of Invention
To solve the above problems, the present invention provides a method for automatically identifying fractures based on while-drilling electrical imaging images, which in one embodiment comprises:
step S1, preprocessing the measured while-drilling electrical imaging data, and acquiring a corresponding while-drilling electrical imaging image according to the preprocessed while-drilling electrical imaging data;
s2, segmenting the while-drilling electrical imaging image by adopting a method of combining an absolute segmentation threshold and a data proportion threshold to obtain a target fracture characteristic image;
s3, determining the fracture extraction depth of the target fracture characteristic image, and determining a fracture characteristic equivalent curve equation of the target fracture characteristic image based on the fracture extraction depth;
and S4, evaluating a crack characteristic equivalent curve equation of the target crack characteristic image, and if the crack characteristic equivalent curve equation meets the evaluation requirement, outputting corresponding crack characteristic parameters.
In one embodiment, in step S1, the preprocessing of the measured while-drilling electrical imaging data includes:
carrying out measurement depth standardization processing on the measured while-drilling electrical imaging data, so that the depth of each row of data of the while-drilling electrical imaging data is unique and the depth sampling intervals between adjacent rows of measurement data are the same; and
detecting the obtained while-drilling electrical imaging data to determine bad data in the data and repairing the bad data; wherein the bad data comprises: a predefined invalid measurement, a measurement of the delta anomaly from an adjacent measurement, a set of measurements of the delta anomaly from an adjacent row measurement, and a set of measurements of the delta anomaly from an adjacent column measurement.
In one embodiment, in the step S1, the method further includes: and eliminating background differences of measurement curves of different directions in the electrical imaging while drilling image by adopting an imaging data equalization processing method.
In one embodiment, in the step S2, the following operations are included:
taking all feature points of which the measured values meet a set absolute segmentation threshold condition in the feature points of the while-drilling electrical imaging image as initial target feature points;
acquiring the number of the initial target feature points, and if the number of the initial target feature points meets a set data proportion threshold condition, taking all the current initial target feature points as target feature points; otherwise, selecting the feature points meeting the condition quantity of the set data proportion threshold value from all the initial target feature points as target feature points according to the measured values;
and taking an image formed by the acquired target characteristic points as a target crack characteristic image.
In one embodiment, in the step S3, the process of determining the fracture extraction depth of the target fracture feature image includes:
selecting a measuring curve with a set azimuth from the target crack characteristic image, and selecting a continuous interval meeting a set absolute segmentation threshold condition from the measuring curve;
and determining the fracture extraction depth of the target fracture characteristic image according to the starting depth and the ending depth of each continuous interval.
In one embodiment, in the step S3, the process of determining the fracture characteristic equivalent curve equation of the target fracture characteristic image based on the fracture extraction depth includes
Calculating a sine curve base number of the crack characteristic equivalent curve equation by adopting a half-cycle point pair coordinate statistical counting method;
and determining the sine curve amplitude value and the initial phase value of the fracture characteristic equivalent curve equation by adopting a traversal cycle method.
In one embodiment, in the step S3, the fracture characteristic equivalence curve equation is as follows:
y＝Asin(x+β)+y0
in the formula, A is a sinusoidal amplitude, x is an azimuth value of a target crack characteristic image, beta is an initial phase value, and y is0Is a sine curve base.
In one embodiment, before determining the sinusoidal amplitude of the fracture characteristic equivalent curve equation, the method further includes:
and screening the sinusoidal amplitude according to the set window length to eliminate interference items and ensure that a sufficiently large relative included angle is reserved.
In one embodiment, in the step S4, the process of evaluating the fracture characteristic equivalent curve equation of the target fracture characteristic image includes:
and substituting the azimuths of all columns of the target crack characteristic image into a crack characteristic curve equation, calculating and obtaining all azimuth-function value point value pairs, determining the number of point value pairs at the target characteristic point, calculating a target number identification value according to the number of the point value pairs and the azimuths of all columns of the target characteristic image, and determining that the corresponding crack characteristic equivalent curve equation meets the evaluation requirement if the target number identification value meets a set number threshold value.
In accordance with other aspects of any one or more of the embodiments described above, the present invention also provides a system for automatically identifying fractures based on electrical while drilling images, the system performing the method described in any one or more of the embodiments described above.
Compared with the closest prior art, the invention also has the following beneficial effects:
according to the method and the system for automatically identifying the cracks based on the electrical imaging while drilling image, provided by the invention, the measured data is preprocessed, the corresponding electrical imaging while drilling image is obtained based on the preprocessed electrical imaging while drilling data, reliable data support is provided for the identification of the crack characteristics, the accuracy of the identification result is ensured to a considerable extent, the crack characteristic equivalent curve equation of the target crack characteristic image is determined based on the determined crack extraction depth, the characteristic parameters meeting the evaluation requirement are used as the crack characteristic identification result, the technical problems of limitation on the measurement environment while drilling and insufficient accuracy of the identification result in the prior art are solved, the calculation speed is high, the accuracy is high, and the method and the system are favorable for geological guidance and development guidance in the field of exploration of oil reservoirs.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic flow chart of a method for automatically identifying fractures based on while-drilling electrical imaging images in an embodiment of the invention;
FIG. 2 is a detailed schematic diagram of the implementation of the method for automatically identifying fractures based on while-drilling electrical imaging images in an embodiment of the invention;
FIG. 3 is a diagram illustrating a statistical method for determining the amplitude A and the initial phase β of the sinusoidal parameters in the automatic crack identification method according to an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating comparison between a target feature image and an original still image and an original moving image according to an embodiment of the present invention;
FIG. 5 is a schematic view of a fracture face intersection model with a wellbore in an embodiment of the invention;
FIG. 6 is a schematic structural diagram of a system for automatically identifying fractures based on while-drilling electrical imaging images in an embodiment of the invention.
Detailed Description
The following detailed description will be provided for the embodiments of the present invention with reference to the accompanying drawings and examples, so that the practitioner of the present invention can fully understand how to apply the technical means to solve the technical problems, achieve the technical effects, and implement the present invention according to the implementation procedures. It should be noted that, unless otherwise conflicting, the embodiments and features of the embodiments of the present invention may be combined with each other, and the technical solutions formed are all within the scope of the present invention.
With the great application of highly deviated wells and horizontal wells in oil and gas exploration and development, some logging instruments are difficult to get into the well, such as conventional cable logging instruments, based on the technical problem, the logging while drilling technology can solve the technical problem, realize real-time measurement in the drilling process, and is applied to the measurement process of the highly deviated wells or the horizontal wells. In the real-time measurement means of oil reservoir exploration, because the logging while drilling is carried out at the first time of drilling the stratum, the transformation degree of the stratum by the borehole environment is lower, the stratum parameter information obtained by measurement is closer to the original stratum information, more reliable logging information is provided for reservoir evaluation, real-time geosteering can be carried out on the basis of realizing the real-time reservoir evaluation, and important basis is provided for real-time decision-making and well completion optimization design of drilling.
The resistivity logging while drilling technology is one of the earliest developed and applied resistivity logging while drilling technologies, technical personnel in the field also provide a resistivity imaging logging while drilling technology based on the logging technology, realize field application and mainly use the resistivity imaging logging while drilling technology for geological guidance and formation evaluation, but the existing imaging logging while drilling technology has technical barriers in the aspects of instrument manufacturing, data processing technology and the like. In the aspect of data processing, identification and extraction of reservoir fracture characteristics are very important data processing contents in the field of cable electrical imaging and the field of electrical imaging while drilling, while in the field of electrical imaging while drilling, the existing fracture identification method cannot realize automatic identification in a measurement-while-drilling environment, and the accuracy of a fracture characteristic identification result is insufficient, so that the requirement of an oil reservoir exploration technology cannot be met.
The logging information of the electrical imaging while drilling directly changes data into a visual image reflecting geological phenomena, can more intuitively reflect geological characteristics, and is more convenient to apply. Compared with other logging-while-drilling instruments, the electrical imaging logging-while-drilling instrument has high measurement resolution, can continuously reveal detailed characteristics of a rock structure on the surface of a shaft, has directionality, and has the capability of accurately analyzing the inclination angle, the direction and the contact relation between strata. The identification and extraction of crack characteristics are very important data processing contents in the field of cable electrical imaging and the field of while-drilling electrical imaging, but in the field of while-drilling electrical imaging, the automatic crack identification method is not abundant, and the application effects of various methods are not ideal. Compared with cable electrical imaging fracture extraction, the method for extracting the fracture by electrical imaging while drilling has different requirements, and an effective fracture treatment method is lacked in the measurement while drilling environment.
In order to overcome the defects in the prior art and meet the requirements, the invention provides a method and a system for automatically identifying cracks based on an electrical imaging while drilling image. The method has the characteristics of high calculation speed, high extraction accuracy and capability of simultaneously calculating a plurality of fracture parameters, and can be applied to the real-time geological steering and the post-drilling well logging interpretation and evaluation process. Various embodiments of the present invention will be described below with reference to the accompanying drawings.
Example one
Fig. 1 is a schematic flow chart illustrating a method for automatically identifying a fracture based on an electrical imaging while drilling image according to an embodiment of the present invention, and referring to fig. 1, the method includes the following steps.
And S110, preprocessing the measured while-drilling electrical imaging data, and acquiring a corresponding while-drilling electrical imaging image based on the preprocessed while-drilling electrical imaging data.
In practical application, the measurement data of the electrical imaging while drilling obtained in the oil reservoir exploration process in the field has the following characteristics: the instrument is used for measuring data in a plurality of directions in the circumferential direction in the actual measuring process, the data in the whole measuring process is two-dimensional array data, different columns represent measured values in different directions, different rows represent measured values in different depths, the two-dimensional array data are displayed according to color codes to form an electrical imaging image while drilling, and the measured image is characterized in that the circumferential direction of a borehole is fully covered by 360 degrees, so that the measured crack characteristics have continuity in the circumferential direction, which is not possessed by the electrical cable imaging image.
In order to accurately and quickly extract fracture characteristics in the electrical imaging while drilling image, the accuracy of the electrical imaging while drilling data needs to be guaranteed, so that a series of preprocessing is performed on the electrical imaging while drilling data before the electrical imaging while drilling data are processed to realize automatic identification of the fracture characteristics, and abnormal interference can be effectively reduced and the fracture extraction rate can be improved in the automatic fracture extraction process. FIG. 2 is a detailed schematic diagram of the implementation of the method for automatically identifying fractures based on while-drilling electrical imaging images in the embodiment of the present invention, as shown in FIG. 2, in the step of preprocessing the measured while-drilling electrical imaging data, including:
and performing measurement depth standardization processing on the measured while-drilling electrical imaging data, so that the depth of each row of data of the while-drilling electrical imaging data is unique, and the depth sampling intervals between adjacent rows of measurement data are the same.
In practical application, the above steps are to perform measurement depth normalization processing on the while-drilling electrical imaging data, the step needs to perform equal-depth data screening processing and measurement data depth resampling processing, the operation is performed to ensure that each row of data of the measured while-drilling electrical imaging two-dimensional array data has uniqueness in depth, and every two adjacent rows of measurement data have the same depth sampling interval,
further, still include: detecting the obtained while-drilling electrical imaging data to determine bad data in the data and repairing the bad data; wherein the bad data includes: a predefined invalid measurement, a measurement of the delta anomaly from an adjacent measurement, a set of measurements of the delta anomaly from an adjacent row measurement, and a set of measurements of the delta anomaly from an adjacent column measurement.
Specifically, in combination with practical application, in the operation, the while-drilling electrical imaging data obtained through deep normalization processing is detected, bad data in the data are obtained, and the obtained bad data are repaired. In the embodiment of the invention, four types of bad data are mainly defined as follows: the measured value is an invalid value defined in the instrument measurement, such as-999.25; the value of a single measuring point is larger or smaller than that of the adjacent measuring point, and the single measuring point is represented as a bright or dark point in the image; thirdly, the plurality of transversely (circumferentially) continuous measuring points are larger or smaller than the abnormal size of the upper and lower rows of measuring data, and are represented as bright or dark transverse stripes in an imaging picture; and fourthly, the plurality of measurement points which are continuous in the longitudinal direction (the depth direction) are larger or smaller than the measurement data of the upper and lower lines, and the measurement points are represented as vertical stripes with light colors or dark colors in the imaging picture. And carrying out detection and repair processing on the bad data of the type. The purpose of executing the above operation is to eliminate bad data in the imaging data as much as possible and reduce the influence on the subsequent image feature extraction.
After the measurement depth normalization processing and the bad data detection and repair of the while-drilling electrical imaging data are completed through the preprocessing, background features except for fracture features in an image corresponding to the while-drilling electrical imaging data need to be filtered, so that the step S120 is provided for segmenting the while-drilling electrical imaging image by adopting a method of combining an absolute segmentation threshold and a data proportion threshold to obtain a target fracture feature image.
By performing the above operation to obtain the target feature object for crack recognition, the method specifically includes the following operations: taking all feature points of which the measured values meet the set absolute segmentation threshold condition in the feature points of the while-drilling electrical imaging image as initial target feature points;
acquiring the number M of initial target feature points, and if the number M meets a set data proportion threshold condition, taking all the current initial target feature points as target feature points; otherwise, selecting the feature points meeting the condition quantity of the set data proportion threshold value from all the initial target feature points as target feature points according to the measured values; and taking an image formed by the acquired target characteristic points as a target crack characteristic image.
In practical application, the implementation process and method of the imaging data segmentation method combining the absolute threshold and the data proportion threshold are as follows: firstly, assuming that the measured value of a certain point is Vi, the given segmentation threshold is Va, the data volume segmentation proportion threshold is Pa, if Vi > Va, the point is regarded as a target feature point, otherwise, the point is regarded as a non-feature point, the position of the feature point is determined to be set to be 1, and if not, the position is set to be 0, and a preliminary target feature image is obtained. Secondly, calculating a final target characteristic image according to a given proportion threshold value Pa, assuming that the number of current target characteristic points is M, if M is larger than Pa x N, sequencing the original measured values of all the target characteristic points in an ascending order, taking the largest Pa x N point as a target characteristic point, setting the rest points as non-target characteristic points, if M is smaller than Pa x N, not processing, and forming the final target crack characteristic image after the processing. The irrelevant background features can be effectively removed in the step, the obtained target crack feature image is used as an object for automatically identifying the crack features, the interference of background data on calculation operation is reduced, the efficiency of identifying the crack features can be guaranteed to a certain extent, and meanwhile the reliability of an identification result is improved.
According to the embodiment of the invention, the automatic identification of the crack characteristics is realized by utilizing the corresponding crack characteristic equivalent curve equation based on the obtained target crack characteristic image. The method comprises the step S130 of determining the fracture extraction depth of the target fracture characteristic image and determining the fracture characteristic equivalent curve equation of the target fracture characteristic image based on the fracture extraction depth.
When one crack surface is obliquely crossed with the well hole and penetrates through the well hole, the crack is expressed as a sine curve in an expansion diagram of the well wall, so that an equivalent curve equation of the designed crack characteristic is shown as the following formula:
y＝Asin(x+β)+y0
in the formula, A is a sinusoidal amplitude, x is an azimuth value of a target fracture characteristic image, beta is an initial phase value, and y0 is a sinusoidal base number.
Before the automatic identification of the crack features is realized, the extraction position of the crack needs to be calculated and determined, so the method comprises the following steps: determining the crack extraction depth of the target crack characteristic image, specifically comprising:
selecting a measuring curve with a set azimuth from a target crack characteristic image, and selecting a continuous interval meeting a set absolute segmentation threshold condition from the measuring curve;
and determining the fracture extraction depth of the target fracture characteristic image according to the starting depth and the ending depth of each continuous interval.
In one embodiment, the method for determining the fracture extraction location is implemented as follows: firstly, taking a 180-azimuth measurement curve, and detecting all continuous intervals with the characteristics of 1, wherein the number of the continuous intervals is assumed to be n. And secondly, determining the initial depth of each continuous interval as Si, determining the termination depth as Ei, and determining the middle point depth Fi of the interval as (Si + Ei)/2, wherein the obtained Fi value is the depth position to be subjected to crack feature extraction.
Further, in one embodiment, in the process of determining the fracture characteristic equivalent curve equation of the target fracture characteristic image based on the fracture extraction depth, the parameters of the fracture characteristic equivalent curve equation are determined according to the following steps:
and solving the sine curve base number of the crack characteristic equivalent curve equation by adopting a half-cycle point pair coordinate statistical counting method.
And determining the sine curve amplitude value and the initial phase value of the fracture characteristic equivalent curve equation by adopting a traversal cycle method.
In combination with practical application, a calculation window length W is given before calculation, W is a longitudinal measurement point number, and fracture characteristics are extracted within the window length W, and the embodiment of the invention adopts a half-cycle point pair coordinate statistical counting method to solve a sine curve base number of a fracture characteristic equivalent curve equation, and specifically comprises the following steps: traversing target feature point pairs with interval of T/2 in all periods in the window length; ② the average value Y of the Y-coordinate values Y1 and Y2 for each set of point pairsmCounting the number of the samples in (y1+ y 2)/2; finding the highest ymPoint is ymaxAnd thus determine the baseline y0＝ymax。
The sine period defaults to 2 pi, namely omega is taken by default1. If the amplitude a and the initial phase β are solved again, the equation of the sinusoid can be uniquely determined. Fig. 3 shows a schematic diagram of a statistical method for determining the amplitude a and the initial phase β of a sinusoidal parameter in an automatic crack identification method in an embodiment of the present invention, and with reference to the analysis of fig. 3, points on the same crack feature all conform to the same sinusoidal equation, that is, both have the same a and β values, or have very close a and β values, and further, in the process of determining the sinusoidal amplitude and the initial phase value of the equivalent curve equation of the crack feature by using a traversal loop method, the following operations are included: since the depth of each target feature point within the window length is known, that is, y is Asin (ω x + β) + y0The value of y in the equation is known, and the expression a for the amplitude a is found as (y-y)0) And/sin (x + beta), wherein x represents the orientation value of the measuring point (the orientation of the measuring point is a known quantity), and the value of A is required to be only the value of beta. ② for each target characteristic point cycle beta value (beta E0 DEG, 360 DEG)]Beta is increased according to a certain step length) to obtain a plurality of point value pairs (A, beta); thirdly, circularly carrying out cycle on all target characteristic points and the initial phase beta in the window length to obtain a plurality of (A, beta) point value pairs, and carrying out statistical counting on the (A, beta) point value pairs; and fourthly, after all target characteristic points within the window length are calculated, counting the point value pair with the maximum counting rate (A, beta), and further determining A and beta of the point as A and beta parameters of the crack characteristic equation.
Further, a curve equation of the crack characteristics can be obtained after the above process is completed, and at this time, whether the obtained crack parameter equation meets the requirements cannot be determined, and error calculation of the crack characteristic curve needs to be performed, so that whether the calculation result meets the error requirements is judged. Therefore, step S140 is performed to evaluate the fracture characteristic equivalent curve equation of the target fracture characteristic image, and if the fracture characteristic equivalent curve equation meets the evaluation requirement, the corresponding characteristic parameter is output.
The process of evaluating the fracture characteristic equivalent curve equation of the target fracture characteristic image comprises the following steps:
substituting the orientations of all columns of the target crack characteristic image into a crack characteristic curve equation, calculating and obtaining all orientation-function value point value pairs, determining the number of target characteristic points in the point value pairs, calculating a target number identification value according to the target characteristic points and the orientation number of all columns of the target characteristic image, and if the target number identification value meets a set number threshold value, determining that the corresponding crack characteristic equivalent curve equation meets the evaluation requirement.
In one embodiment, the crack characteristic curve equation y is introduced according to the orientation of each column of the target characteristic image, Asin (x + beta) + y0In (3), the value of y is calculated and taken into the orientations x of all columnsiA plurality of y is obtainediI.e. groups (x)i，yi) For dot value pairs, all column numbers (number of bits) are assumed to be Q. Check these (x)i，yi) Whether the point value pairs are all located on the feature points of the target feature image or not is assumed to be (x)i，yi) The number of the point value pairs at the target characteristic points is K, and a coincidence percentage threshold value P is givenfCalculating the value of K/Q, i.e., the target number recognition value, the larger the target number recognition value is, the smaller the recognition error is, and therefore, if K/Q is calculated>PfThe target feature image is determined to be satisfactory.
Fig. 4 shows a schematic comparison diagram of a target feature image and an original static and dynamic image corresponding to an identification result in the embodiment of the present invention, and it can be known that, by designing and using a unique data processing strategy and a unique fracture feature extraction strategy in the embodiment of the present invention, a regular fracture feature in an electrical imaging while drilling image can be effectively extracted, and the calculation speed is fast, the accuracy is high, and the processing capability of electrical imaging while drilling data can be effectively improved.
The above embodiment is only one embodiment of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. The specific using method can be correspondingly adjusted according to actual needs. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.
Example two
In this embodiment, a method for automatically identifying a fracture based on an electrical imaging while drilling image is provided, and steps in the method that are the same as those in the above embodiment are not described again, and only the steps that have differences are described.
In this embodiment, the process of preprocessing the measured while-drilling electrical imaging data includes:
and performing measurement depth normalization processing on the measurement-while-drilling electrical imaging data to obtain the electrical imaging-while-drilling data with unique data depth and the same depth sampling interval between adjacent rows of measurement data.
In practical application, the measurement depth normalization processing is carried out on the electrical imaging while drilling data through the step, the step needs to carry out the screening processing of the equal-depth data and the depth resampling processing of the measurement data, and the operation is carried out to ensure that each row of data of the measured electrical imaging while drilling two-dimensional array data has uniqueness in depth, and the same depth sampling interval exists between every two adjacent rows of measurement data.
Detecting bad data in the electrical imaging while drilling data and repairing the bad data; wherein the bad data includes: a predefined invalid measurement, a measurement of the delta anomaly from an adjacent measurement, a set of measurements of the delta anomaly from an adjacent row measurement, and a set of measurements of the delta anomaly from an adjacent column measurement.
Further, this embodiment further includes: and eliminating background differences of measurement curves of different directions in the electrical imaging while drilling image by adopting an imaging data equalization processing method.
Through the operation, imaging data equalization processing is carried out on the electrical imaging while drilling image, background differences of measurement curves in different directions can be effectively eliminated, actual geological features are more continuously represented in the image, and a common imaging data equalization processing method is used for processing in the step. The operation balances the backgrounds of the measurement curves of different directions of the electrical imaging while drilling image, provides reliable image data support for the step of identifying and acquiring the target fracture characteristic image, and can further ensure the accuracy of the identification result.
EXAMPLE III
The embodiment provides a method for automatically identifying cracks based on an electrical imaging while drilling image, and the steps in the method which are the same as those in the embodiment are not repeated, and only the steps with differences are explained.
In step S130, after the sine curve base number of the fracture characteristic equivalent curve equation is obtained by using a method of counting the coordinates by half-cycle points, before determining the sine curve amplitude of the fracture characteristic equivalent curve equation, the method for automatically identifying a fracture further includes:
and screening the sinusoidal curve amplitude according to the set window length to eliminate interference items and ensure that a sufficiently large relative included angle is reserved.
In practical application, the A values are screened by adopting the operation before the calculated A values are counted, so that the memory overhead can be reduced and the calculation speed can be improved. The A value screening strategy designed by the embodiment of the invention is as follows: given minimum and maximum interval ranges [ A ]min，Amax]Amax represents the upper limit of A, AminRepresents the lower value limit of A. A is taken according to experiencemaxBecause the amplitude of the fracture characteristic is not allowed to exceed the length of the calculation window, because the common oblique fracture has a certain relative included angle with the borehole but is not vertical to the borehole, take AminAnd W/12, not only can the crack with a large relative included angle be kept, but also a part of interference items can be removed. Fig. 5 is a schematic diagram of an intersection model of a fracture surface and a borehole in an embodiment of the present invention, and it can be known from the information disclosed in fig. 5 that retaining a fracture with a large relative included angle helps to select a fracture characteristic equivalent curve equation.
Interference items in the sinusoidal curve amplitude are removed through the operation, the sinusoidal curve amplitude of the crack characteristic equivalent curve equation is obtained based on reasonable and reliable item expansion calculation, the reliability of the sinusoidal curve amplitude calculation result is favorably guaranteed, meanwhile, the accuracy of the initial phase value of the correlation calculation result is improved to a certain extent, the reliability of the crack characteristic equivalent curve equation can be effectively improved, and the method is a powerful support for guaranteeing the accuracy of the crack characteristic identification result.
Example four
Based on other technical content of the above embodiments, the invention also provides a system for automatically identifying fractures based on the while-drilling electrical imaging image, and the system performs the method and steps in any one or more of the above embodiments.
Fig. 6 is a schematic structural diagram of a system for automatically identifying a fracture based on an electrical imaging while drilling image according to an embodiment of the present invention, where each module in the system respectively performs the corresponding steps of the first to third embodiments.
As shown in fig. 6, the accelerated test system according to the embodiment of the present invention mainly includes:
the data preprocessing module 61 is used for preprocessing the measured while-drilling electrical imaging data and acquiring a corresponding while-drilling electrical imaging image based on the preprocessed while-drilling electrical imaging data;
the data segmentation module 63 is used for segmenting the while-drilling electrical imaging image by adopting a method of combining an absolute segmentation threshold and a data proportion threshold to obtain a target fracture characteristic image;
the crack feature recognition module 65 is used for determining the crack extraction depth of the target crack feature image and automatically recognizing the target crack according to a crack feature equivalent curve equation based on the crack extraction depth;
and the characteristic evaluation module 67 is used for outputting the characteristic parameters of the equivalent curve equation of the fracture characteristic corresponding to the identified target fracture.
In one embodiment, the data pre-processing module 61 pre-processes the measured while drilling electrical imaging data by:
carrying out measurement depth standardization processing on the measured while-drilling electrical imaging data, so that the depth of each row of data of the while-drilling electrical imaging data is unique and the depth sampling intervals between adjacent rows of measurement data are the same; and
detecting the obtained while-drilling electrical imaging data to determine bad data in the data and repairing the bad data; wherein the bad data comprises: a predefined invalid measurement, a measurement of the delta anomaly from an adjacent measurement, a set of measurements of the delta anomaly from an adjacent row measurement, and a set of measurements of the delta anomaly from an adjacent column measurement.
In one embodiment, the data preprocessing module 61 is further configured to: and eliminating background differences of measurement curves of different directions in the electrical imaging while drilling image by adopting an imaging data equalization processing method.
In one embodiment, the data segmentation module 63 performs the segmentation process on the electrical while drilling image by:
taking all feature points of which the measured values meet a set absolute segmentation threshold condition in the feature points of the while-drilling electrical imaging image as initial target feature points;
acquiring the number of the initial target feature points, and if the number of the initial target feature points meets a set data proportion threshold condition, taking all the current initial target feature points as target feature points; otherwise, selecting the feature points meeting the condition quantity of the set data proportion threshold value from all the initial target feature points as target feature points according to the measured values;
and taking an image formed by the acquired target characteristic points as a target crack characteristic image.
In one embodiment, the fracture feature identification module 65 determines the fracture extraction depth of the target fracture feature image by:
selecting a measuring curve with a set azimuth from the target crack characteristic image, and selecting a continuous interval meeting a set absolute segmentation threshold condition from the measuring curve;
and determining the fracture extraction depth of the target fracture characteristic image according to the starting depth and the ending depth of each continuous interval.
In one embodiment, the fracture feature identification module 65 determines a fracture feature equivalent curve equation of the target fracture feature image based on the fracture extraction depth, including
Calculating a sine curve base number of the crack characteristic equivalent curve equation by adopting a half-cycle point pair coordinate statistical counting method;
and determining the sine curve amplitude value and the initial phase value of the fracture characteristic equivalent curve equation by adopting a traversal cycle method.
In one embodiment, the fracture characteristic equivalence curve equation is as follows:
y＝Asin(x+β)+y0
in the formula, A is a sinusoidal amplitude, x is an azimuth value of a target crack characteristic image, beta is an initial phase value, and y is0Is a sine curve base.
In one embodiment, the fracture characteristic identification module 65, prior to determining the sinusoidal amplitudes of the fracture characteristic equivalent curve equation, is further configured to:
and screening the sinusoidal amplitude according to the set window length to eliminate interference items and ensure that a sufficiently large relative included angle is reserved.
In one embodiment, the feature evaluation module 67 evaluates the fracture feature equivalent curve equation for the target fracture feature image by:
and substituting the azimuths of all columns of the target crack characteristic image into a crack characteristic curve equation, calculating and obtaining all azimuth-function value point value pairs, determining the number of point value pairs at the target characteristic point, calculating a target number identification value according to the number of the point value pairs and the azimuths of all columns of the target characteristic image, and determining that the corresponding crack characteristic equivalent curve equation meets the evaluation requirement if the target number identification value meets a set number threshold value.
In the system for automatically identifying the cracks based on the while-drilling electrical imaging images, provided by the embodiment of the invention, each module or unit structure can be independently operated or operated in a combined mode according to actual requirements so as to realize corresponding technical effects.
It is to be understood that the disclosed embodiments of the invention are not limited to the particular structures, process steps, or materials disclosed herein but are extended to equivalents thereof as would be understood by those ordinarily skilled in the relevant arts. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.
Reference in the specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Thus, appearances of the phrase "an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment.
Although the embodiments of the present invention have been described above, the above descriptions are only for the convenience of understanding the present invention, and are not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A method for automatically identifying fractures based on while drilling electrical imaging images, the method comprising:
step S1, preprocessing the measured while-drilling electrical imaging data, and acquiring a corresponding while-drilling electrical imaging image according to the preprocessed while-drilling electrical imaging data;
s2, segmenting the while-drilling electrical imaging image by adopting a method of combining an absolute segmentation threshold and a data proportion threshold to obtain a target fracture characteristic image;
s3, determining the fracture extraction depth of the target fracture characteristic image, and determining a fracture characteristic equivalent curve equation of the target fracture characteristic image based on the fracture extraction depth;
and S4, evaluating a crack characteristic equivalent curve equation of the target crack characteristic image, and if the crack characteristic equivalent curve equation meets the evaluation requirement, outputting corresponding crack characteristic parameters.
2. The method as claimed in claim 1, wherein in the step S1, the pre-processing of the measured while drilling electrical imaging data comprises:
carrying out measurement depth standardization processing on the measured while-drilling electrical imaging data, so that the depth of each row of data of the while-drilling electrical imaging data is unique and the depth sampling intervals between adjacent rows of measurement data are the same; and
detecting the obtained while-drilling electrical imaging data to determine bad data in the data and repairing the bad data; wherein the bad data comprises: a predefined invalid measurement, a measurement of the delta anomaly from an adjacent measurement, a set of measurements of the delta anomaly from an adjacent row measurement, and a set of measurements of the delta anomaly from an adjacent column measurement.
3. The method according to claim 1 or 2, wherein in the step S1, further comprising: and eliminating background differences of measurement curves of different directions in the electrical imaging while drilling image by adopting an imaging data equalization processing method.
4. The method according to any one of claims 1 to 3, wherein in the step S2, the method comprises the following operations:
taking all feature points of which the measured values meet a set absolute segmentation threshold condition in the feature points of the while-drilling electrical imaging image as initial target feature points;
acquiring the number of the initial target feature points, and if the number of the initial target feature points meets a set data proportion threshold condition, taking all the current initial target feature points as target feature points; otherwise, selecting the feature points meeting the condition quantity of the set data proportion threshold value from all the initial target feature points as target feature points according to the measured values;
and taking an image formed by the acquired target characteristic points as a target crack characteristic image.
5. The method according to any one of claims 1 to 4, wherein in the step S3, the process of determining the crack extraction depth of the target crack feature image comprises:
selecting a measuring curve with a set azimuth from the target crack characteristic image, and selecting a continuous interval meeting a set absolute segmentation threshold condition from the measuring curve;
and determining the fracture extraction depth of the target fracture characteristic image according to the starting depth and the ending depth of each continuous interval.
6. The method of claim 1 or 5, wherein in the step S3, the process of determining the fracture feature equivalent curve equation of the target fracture feature image based on the fracture extraction depth comprises
Calculating a sine curve base number of the crack characteristic equivalent curve equation by adopting a half-cycle point pair coordinate statistical counting method;
and determining the sine curve amplitude value and the initial phase value of the fracture characteristic equivalent curve equation by adopting a traversal cycle method.
7. The method of claim 6, wherein in the step S3, the fracture characteristic equivalence curve equation is expressed as follows:
y＝Asin(x+β)+y0
in the formula, A is a sinusoidal amplitude, x is an azimuth value of a target crack characteristic image, beta is an initial phase value, and y is0Is a sine curve base.
8. The method of claim 6, further comprising, prior to determining the sinusoidal amplitudes of the fracture characteristic equivalence curve equation:
and screening the sinusoidal amplitude according to the set window length to eliminate interference items and ensure that a sufficiently large relative included angle is reserved.
9. The method of claim 1, wherein in the step S4, the process of evaluating the fracture characteristic equivalent curve equation of the target fracture characteristic image comprises:
and substituting the azimuths of all columns of the target crack characteristic image into a crack characteristic curve equation, calculating and obtaining all azimuth-function value point value pairs, determining the number of point value pairs at the target characteristic point, calculating a target number identification value according to the number of the point value pairs and the azimuths of all columns of the target characteristic image, and determining that the corresponding crack characteristic equivalent curve equation meets the evaluation requirement if the target number identification value meets a set number threshold value.
10. A system for automatic fracture identification based on while-drilling electrical imaging images, characterized in that the system performs the method according to any one of claims 1 to 9.
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