A method for fracture identification and evaluation

Abstract

A method for fault identification and evaluation, belonging to the field of oil and gas exploration, comprises: 1. setting window length and step length parameters, reading the azimuth angle of the No. 1 plate and the conductivity data of all plates of the multi-arm formation dip logging instrument; 2. taking the azimuth angle of the No. 1 plate as a reference, regularizing the azimuth angles of the other plates within 0° to 360°; 3. constructing a two-dimensional matrix M (x i ,y i ), azimuth and conductivity are respectively used as the x of the matrix i 、y i Coordinates; 4. Perform two-dimensional matrix color mapping to generate a simulated image; 5. Image inspection and optimization to determine whether the image meets expectations; 6. Identify fracture types through image visualization and quantitatively calculate six parameters. This invention solves the problems of poor visualization and quantification in conventional well logging interpretation methods, as well as the low data acquisition rate and blind spots in imaging logging interpretation methods.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of oil and gas exploration, and particularly relates to a fracture identification and evaluation method. BACKGROUND

[0002] Fracture refers to a break or discontinuous interface in rock, including two types of faults and fractures. China has a large number of landmasses with small scales, and has been subjected to the action of three major plates of Siberia, Pacific and India for a long time, and has experienced multiple tectonic movements, strong collision and extrusion, resulting in the development of fractures in most sedimentary basins in China. With the improvement of oil and gas exploration and development, the control of fractures on oil and gas migration and accumulation has attracted more and more attention. However, the fracture forms are various, the genetic mechanism and distribution law are complex, and the fracture identification and evaluation are industry problems. The existing method has low precision and high cost, and it is difficult to support the systematic research on the influence of fractures on oil and gas migration, accumulation and storage, resulting in insufficient production of oil and gas layers and waste of oil and gas resources.

[0003] At present, the commonly used fracture identification and evaluation methods mainly include geological outcrop and core sample observation method, dynamic test method, geophysical data interpretation method, etc. The three methods have different application characteristics due to the constraints of data acquisition rate, oil and gas well type and timeliness. Geological outcrop and core sample observation method is a relatively traditional fracture research method. Through systematic sampling observation and quantitative testing of field geological outcrop profile, drilling core, rock thin section, etc., the length, height, density, dip angle, strike, tendency, etc. of the fracture are analyzed, which has the typical advantages of intuitive observation, quantitative testing and high reliability. However, geological outcrops are mainly distributed in the tectonic denudation area of the basin edge, drilling coring is only implemented in a few exploration wells in the key target interval, and rock thin sections are mainly prepared by sampling the local core, which leads to low data acquisition rate, weak comparability and certain limitations in scale application. Dynamic test method is to inject water, gas or other tracers in oil and gas wells, and to monitor the sampling in surrounding wells. The produced samples are analyzed to obtain the production conditions of the tracers, and the advancing direction, displacement speed and swept area of the tracers are determined to reflect the connectivity of the formation between the oil and water wells in the water injection development process. This method is mainly applied to the research of oil well water injection effect evaluation, remaining oil distribution, development policy adjustment and fracturing effect monitoring, which can indirectly reflect the fracture size, fracture strike and fracture length, but cannot directly identify and quantitatively evaluate the fracture length, height and density. At the same time, it has the limitations of high cost and poor timeliness. Geophysical data interpretation method is to process and interpret geophysical logging and seismic data, calibrate with geological outcrop or core sample, summarize the fracture response characteristics of geophysical data, and then quantitatively identify and evaluate the fracture. Using seismic data, the methods of ant tracking to predict fracture, seismic attribute to identify fracture, multi-scale sweet spot to predict fracture and BP neural network to identify fracture are formed. However, the longitudinal resolution of seismic data is mainly 8-15 m, and there are limitations of sporadic acquisition and local measurement.

[0004] Geophysical logging data has unique advantages of universal measurement, strong comparability and high precision in oil and gas wells, and it is of great theoretical and practical significance to deepen the research on logging identification and evaluation of fractures.

[0005] The existing conventional logging data identification method of fracture mainly includes three kinds of methods, such as artificial experience identification method of conventional logging curve, quantitative identification method of conventional logging curve and formation dip angle logging interpretation method. The traditional method of identifying fracture by conventional logging is to manually play back the logging curve, analyze the change characteristics of each conventional curve to infer the development zone of fracture, such as different types of fractures developed in strata ( Figure 1 ), drilling mud invades the stratum along the fracture, which will cause frequent changes of high and low frequency of resistivity logging curve ( Figure 2 ), large amplitude jump of acoustic time difference curve ( Figure 3response characteristics. But this method has poor visualization effect, is difficult to quantify, is greatly influenced by human subjective judgment, has strong multi-solution, and has poor repeatability. The conventional logging data quantitative identification method includes probability index identification fracture method, information fusion identification fracture method, wavelet transform identification fracture method, neural network prediction fracture method, and the like. But these methods have certain randomness when selecting modeling samples, and the modeling samples directly determine the calculation results, but it is difficult to select the generally representative data samples, or there are few of them, which leads to that the above-mentioned methods have good application effect in the laboratory, but have uneven effect in the oil and gas field promotion practice, and it is still difficult to quantitatively evaluate the fracture parameters. The formation dip logging interpretation method is to use a multi-arm formation dip logging instrument (commonly used are four-arm, six-arm, eight-arm, sixteen-arm, twenty-four-arm, and the like, taking the six-arm as an example) to measure the 1# plate azimuth angle (the angle between the 1# plate and the north direction) and the 1#-6# plate conductivity (or resistivity) data under different depth conditions. The instrument can realize accurate measurement of the formation data information under the complex conditions of irregular wellbore and instrument not centered, through the six-arm plate centralization. At present, the conductivity anomaly detection, hole diameter comparison, and conductivity comparison fracture identification methods are established based on the formation dip logging data. The method can be used for identification and evaluation of single large opening and high angle fracture, and is difficult to identify and evaluate low angle fracture and induced fracture, and the visualization effect is still poor. Therefore, the conventional logging method has poor visualization effect and is difficult to quantitatively evaluate the fracture length, height, density, dip angle, strike, and tendency parameters.

[0006] The imaging logging interpretation method is a kind of fracture identification and evaluation method with high reliability at present, but still has two aspects of defects, one is that the data acquisition rate is extremely low, and the method cannot be implemented in the cased hole, and the application is limited; the other is that the imaging coverage rate is mainly 60%-80% due to the influence of the gap between the measurement plates, and the imaging image has a white strip blind area, which is difficult to support the system to carry out the fracture identification and evaluation research. SUMMARY

[0007] The purpose of the present application is to provide a kind of fracture identification and evaluation method, which solves the problems of poor visualization effect of conventional logging interpretation method, difficult to quantify and low data acquisition rate of imaging logging interpretation method, and image blind area.

[0008] The technical scheme adopted by the present application is:

[0009] A kind of fracture identification and evaluation method, comprising the following steps:

[0010] Step one: set window length parameter, step length parameter, read in the 1# plate azimuth angle of multi-arm formation dip logging instrument and the conductivity data of all plates;

[0011] Step two: the multi-arm formation dipmeter is every adjacent polar plate azimuth angle apart 360° / n, wherein, n represents the polar plate quantity; with the No. 1 polar plate azimuth angle as the benchmark, the other polar plate azimuth angles are regularly processed in 0°-360°, namely when any polar plate azimuth angle is greater than 360°, the polar plate azimuth angle is reduced by 360°, so that the polar plate azimuth angles are between 0-360°;

[0012] Step three: the well bore azimuthal conductivity two-dimensional matrix M(x i ,y i ) is constructed in the window length, first the 360° azimuth of a depth point in the window length is evenly divided into m*n equal parts, wherein, m represents the equal parts between adjacent two polar plates, and m≥n; the azimuth of each equal part point is calculated by using the polar plate azimuth angle as the x i coordinate of the matrix M(x i ,y i ), and simultaneously, the conductivity of each equal part point is calculated by using the polar plate azimuth angle and the conductivity data as the interpolation sample points as the y i coordinate of the matrix M(x i ,y i );

[0013] whether the data in the window length depth range is processed, if not, the depth is increased by a step, and the next sampling depth is jumped to step two to step three, until the azimuthal conductivity matrix in the window length depth range is completely constructed;

[0014] Step four: the two-dimensional matrix color scale mapping is carried out to generate the pseudo-imaging graph, first, the color spectrum of any 2-5 different RGB color modes is set, and simultaneously, the well bore azimuthal conductivity two-dimensional matrix M(x i ,y i ) in the window length depth range is arranged in order from small to large according to the conductivity value y i , when the conductivity values are equal, arranged in order from small to large according to the azimuth value x i , and when the conductivity value and the azimuth value are equal, arranged in order according to the same order; then the color spectrum partition is carried out to generate the well bore azimuthal conductivity pseudo-imaging graph; the image coverage rate can reach 100%, which is significantly higher than the traditional electrical imaging logging image coverage rate.

[0015] Step five: image inspection and optimization, whether the image effect meets the expectation, if not, whether the parameter setting is reasonable is checked, the parameters are re-set, step one to step five are executed, the image is optimized, until the effect meets the expectation, the parameters including the window length, the step, the azimuth equal parts and the color spectrum need to be checked; then, whether the whole well section data processing is completed is judged, if not, the depth is increased by a step, the next window length is jumped to step one to step five, until the whole well section data processing is completed;

[0016] Step six: identify the fracture type by image visualization, quantitatively calculate six parameters, respectively, fracture density, length, height, dip, strike, and tendency, and then save the data and image.

[0017] Further, in step one, the multi-arm formation dip logging instrument selects one of four-arm, six-arm, eight-arm, sixteen-arm, and twenty-four-arm formation dip logging instruments.

[0018] Further, in step one, the window length parameter is 1.0-10.0 m, and the step length parameter is 0.00254-0.001 m.

[0019] Further, in step three, the interpolation method used in the interpolation calculation is selected from one of the four interpolation methods of linear interpolation, cubic spline interpolation, cubic B-spline interpolation, and Akima interpolation.

[0020] Further, in step four, the color spectrum is set to white, yellow, red, and black, and is divided into three zones of white-yellow, yellow-red, and red-black.

[0021] Further, in step four, the color spectrum zoning process is: the first sorted matrix element corresponds to white RGB(255:255:255), the one-third sorted matrix element (if the position is not an integer, the rounding method is used to determine the position) corresponds to yellow RGB(255:255:0), the two-thirds sorted matrix element (if the position is not an integer, the rounding method is used to determine the position) corresponds to red RGB(255:0:0), and the last sorted matrix corresponds to black RGB(0:0:0), thereby dividing the two-dimensional matrix into three color spectrum zones.

[0022] The RGB color value corresponding to the remaining sorted matrix elements is obtained by a hybrid operation mapping method to achieve one-to-one mapping of all elements in the borehole azimuth conductivity two-dimensional matrix M(x i ,y i ) and the RGB mode color scale, as follows:

[0023] ① Determine the multiplication factor K and the addition factor A of the hybrid operation mapping method: let the minimum conductivity in each color spectrum zone be E min , the maximum conductivity be E max , the left color scale value be C lvalue , and the right color scale value be C rvalue , when E min =E max , the multiplication factor K=0, and the addition factor A=C lvalue ; when E min ≠E maxWhen, the multiplication factor K and the addition factor A are calculated by formula (1) and formula (2) respectively:

[0024]

[0025] A=C lvalue -K×E min (2)

[0026] ②The mixed operation mapping method is adopted to realize one-to-one correspondence between the two-dimensional matrix and the color scale: assuming that the color scale corresponding value of the two-dimensional matrix M(x i ,y i ) is represented by M RGB (x i ,y i ), formula (3) is obtained:

[0027] M RGB (x i ,y i )=M(x i ,y i )×K+A (3)

[0028] ③After the color scale mapping of the two-dimensional matrix of the first color spectrum partition is completed, the second color spectrum partition is entered, and steps ① and ② are executed until the color scale mapping of the two-dimensional matrix in the range of all color spectrum partitions is completed.

[0029] The beneficial effects of the present application are:

[0030] The present application realizes the visual identification of fractures by using the No. 1 polar plate azimuth angle and all polar plate conductivity (or resistivity) data of the multi-arm formation dip angle logging instrument, through five processing steps of parameter setting and reading data, polar plate azimuth angle regularization, construction of azimuthal conductivity two-dimensional matrix, color spectrum setting and two-dimensional matrix color scale mapping, image inspection and optimization, and fracture identification and parameter evaluation, and six parameters of fracture density, length, height, dip angle, strike and tendency are quantitatively evaluated.

[0031] The method combines the advantages of high data acquisition rate of conventional logging and strong visualization of imaging logging, excavates the implicit information of conventional dip angle logging conductivity and polar plate azimuth data, establishes the azimuthal conductivity imaging-imitating method for identifying and evaluating fractures, and performs digital image processing on the conventional logging data, so that high-angle, medium-angle, low-angle fractures and induced fractures can be directly identified, and the method has the advantages of strong visualization, high reliability and quantification compared with the conventional logging interpretation method, and has the advantages of general data measurement, low cost and high imaging coverage rate compared with the imaging logging interpretation method. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 is a schematic diagram of low-angle fracture logging response;

[0033] Figure 2 is a schematic diagram of a medium-angle fracture logging response;

[0034] Figure 3 is a schematic diagram of a high-angle fracture logging response;

[0035] Figure 4 is a flow chart of a processing and interpretation process of a directional conductivity pseudo-imaging method;

[0036] Figure 5 is a top view of a six-arm formation dip logging instrument;

[0037] Figure 6 is a diagram of a two-dimensional matrix method of directional conductivity around a structure well;

[0038] Figure 7 is a comparison diagram of processing results of common interpolation algorithms;

[0039] Figure 8 is a diagram of a color scale mapping method of directional conductivity two-dimensional matrix;

[0040] Figure 9 is a comparison diagram of high-angle fracture response characteristics (TZ1 well);

[0041] Figure 10 is a comparison diagram of medium-angle fracture response characteristics (TZ1 well);

[0042] Figure 11 is a comparison diagram of low-angle fracture response characteristics (TZ2 well);

[0043] Figure 12 is a comparison diagram of induced fracture response characteristics (TZ3 well). DETAILED DESCRIPTION

[0044] A fracture identification and evaluation method is disclosed, as shown in the accompanying drawings, comprising the following steps: Figure 4

[0045] Step 1: Set window length and step length parameters, and read in the azimuth angle of No. 1 polar plate and the conductivity data of No. 1 to No. 6 polar plates of a six-arm formation dip logging instrument; the window length parameter is set to 1.0-10.0 m, based on the fact that the fracture height in a work area is mostly below 1.0 m and the maximum fracture height rarely exceeds 10.0 m, and in this embodiment, it is set to 1.0 m; the step length parameter is set to 0.00254-0.001 m, based on the fact that the sampling interval of the dip logging instrument is 0.00254 m and the longitudinal resolution of the dip logging conductivity is 0.001 m, and in this embodiment, it is set to 0.00254 m.

[0046] Step 2: The azimuth angle of each adjacent polar plate of the six-arm formation dip logging instrument is separated by 60° Figure 5 ​), taking the azimuth angle of plate No. 1 as the reference, regularize the azimuth angles of other plates within the range of 0° to 360°, i.e., when the azimuth angle of any plate is greater than 360°, subtract 360° from the azimuth angle of the plate to make the azimuth angles of all plates between 0° and 360°;

[0047] Step 3: Construct the two-dimensional matrix M(x i ,y i ), first divide the 360° azimuth of a certain depth point within the window length into 6×m equal parts (requirement: m is a factor of 60 and m≥6). In this embodiment, m=6, that is, it is divided into 36 equal parts. Use the azimuth angle of each plate to calculate the azimuth angle of each divided point as the matrix M(x i ,y i ) of x i Coordinates, at the same time, the conductivity of each equally divided point is calculated by using the azimuth angle and conductivity data of each plate as interpolation sample points, as the matrix M(x i ,y i ) of y i coordinate( Figure 6 );

[0048] The interpolation method used in the interpolation calculation is selected from one of four interpolation methods: linear interpolation, cubic spline interpolation, cubic B-spline interpolation and Akima interpolation. In this embodiment, Akima interpolation is preferred.

[0049] The azimuth angle and conductivity data of plates 1 to 6 are used as sample points M(x i ,y i ), and compared the interpolation results of four commonly used interpolation algorithms, including linear interpolation, cubic spline interpolation, cubic B-spline interpolation, and Akima interpolation, on the conductivity data of equally divided points around the well ( Figure 7 ), the four interpolation methods can all meet the requirements of logging data interpolation and resampling, but the effects are different. Among them, the linear interpolation method has a simple calculation principle and a small amount of calculation, but the calculation result is only determined by the values ​​of adjacent interpolation sample points, and there are defects such as poor smoothness and unnatural curve transition; the cubic spline interpolation method has a smooth calculation result, but it is easy to produce a large offset to the local extreme point, and the calculation result is unstable; the cubic B-spline interpolation method is a global smooth fitting interpolation method, which avoids the problem of curve oscillation at local extreme points and is mainly suitable for steady-state data interpolation operations. Any data error in the interpolation sample will affect all calculation results, and the error transmission is strong; the Akima interpolation method is a local interpolation method with rigorous mathematical principles, simple calculation process, strong continuity of the calculation curve, high fitting accuracy and not prone to oscillation. Through comparative analysis, the Akima interpolation method is selected to interpolate the conductivity data of equally divided points around the well to obtain the conductivity data around the well and construct the two-dimensional azimuthal conductivity matrix M(x iy i )。

[0050] determine whether the data in the window length depth range is processed, if not, increase a step, jump to the next sampling depth, execute steps two to three, until the azimuthal conductivity matrix in the window length depth range is constructed completely;

[0051] Step four: perform two-dimensional matrix color mapping to generate a pseudo-imaging graph, first set an arbitrary 2-5 different RGB color mode color spectrum, and arrange the wellbore azimuthal conductivity two-dimensional matrix M(x i ,y i ) in the window length depth range in ascending order of conductivity value y i , in ascending order of azimuth value x i when the conductivity value and the azimuth value are equal, then sort by the same order (the meaning of sorting by the same order is to treat these equal matrix elements as the same partition position, and correspond to the same color when performing color spectrum mapping); then perform color spectrum partitioning to generate a wellbore azimuthal conductivity pseudo-imaging graph; the image coverage can reach 100%, which is significantly higher than the image coverage of traditional electrical imaging logging.

[0052] The color spectrum described in the embodiment is set to four colors: white, yellow, red, and black, which are divided into three partitions: white-yellow, yellow-red, and red-black. The color spectrum partitioning process is as follows: the first-order matrix element corresponds to white RGB (255:255:255), the one-third order matrix element (if the order is not an integer, the rounding method is used to determine the order) corresponds to yellow RGB (255:255:0), the two-thirds order matrix element (if the order is not an integer, the rounding method is used to determine the order) corresponds to red RGB (255:0:0), and the last-order matrix corresponds to black RGB (0:0:0), thereby dividing the two-dimensional matrix into three color spectrum partitions;

[0053] The RGB color scale values corresponding to the remaining matrix elements in the sorting position are obtained by a hybrid operation mapping method to achieve one-to-one mapping of all elements in the wellbore azimuthal conductivity two-dimensional matrix M(x i ,y i ) and the RGB mode color scale ( Figure 8 ), which is as follows:

[0054] ① Determine the multiplication factor K and the addition factor A of the hybrid operation mapping method: let the minimum conductivity value in each color spectrum partition range be E min , the maximum conductivity value be E max , the left color scale value of each color spectrum partition be C lvalue , and the right color scale value be C rvalue , when E min=E max When the multiplication factor K = 0, the addition factor A = C lvalue ; when E min ≠E max When , the multiplication factor K and the addition factor A are calculated using formula (1) and formula (2) respectively:

[0055]

[0056] A=C lvalue -K×E min (2)

[0057] ②Use the mixed operation mapping method to achieve one-to-one correspondence between the two-dimensional matrix and the color label: Let the two-dimensional matrix M(x i ,y i ) color scale corresponding value is M RGB (x i ,y i ), then we have formula (3):

[0058] M RGB (x i ,y i )=M(x i ,y i )×K+A (3)

[0059] ③ After completing the two-dimensional matrix color code mapping of the first chromatographic partition, go to the second chromatographic partition and execute steps ① and ② until the two-dimensional matrix color code mapping within the range of all chromatographic partitions is completed.

[0060] Step 5: Image inspection and optimization: determine whether the image effect meets expectations. If not, check whether the parameter settings are reasonable, reset the parameters, and execute steps 1 to 5 to optimize the image until the effect meets expectations. The parameters that need to be checked include window length, step length, number of azimuths, and color spectrum. After that, determine whether the data of the entire well section has been processed. If not, increase the step length by depth, jump to the next window length, and execute steps 1 to 5 until the data of the entire well section has been processed.

[0061] Step 6: Identify the fault type through image visualization and quantitatively calculate six parameters, namely fault density, length, height, dip, strike, and inclination. Save the data and image and end the program.

[0062] The fractures are divided into four types according to fracture occurrence and genesis, i.e. high-angle fracture, medium-angle fracture, low-angle fracture and induced fracture. In the embodiment, the azimuthal conductivity pseudo-imaging graph is compared with the electrical imaging logging graph, and the response characteristics of the azimuthal conductivity pseudo-imaging graph of different types of fractures are summarized. According to the comparison and analysis of the well data processing results in the working area, it is further verified that the identification and evaluation results of the method are reliable, and the accuracy of the identification and evaluation of fractures by the conventional logging data is effectively improved.

[0063] 1. Application example comparison

[0064] (1) Identification and evaluation of high-angle fractures

[0065] The high-angle fracture refers to a fracture with an inclination angle greater than 70°. Such fractures are mostly generated by tectonic action, have a wide opening and a large fracture height, are easy to communicate with the medium-angle fractures and the low-angle fractures, and thus form a fracture network, which can greatly improve the reservoir permeability and has a significant destructive effect on the tight barrier layer and the cap rock sealing property, and is an important channel for long-distance oil and gas migration. The high-angle fracture is commonly displayed as a black "double track" with an interval of 180° and approximately parallel to the well axis on the electrical imaging logging graph. Compared with the 192-group button measuring electrode of the imaging logging, the six-arm measuring electrode of the inclination logging has a larger "blind area" between the electrodes, and the high-angle fracture is commonly displayed as a black "double track" with an interval of 180° and approximately parallel to the well axis on the azimuthal conductivity pseudo-imaging graph, however, the "continuity" of the double track is weaker than that of the electrical imaging graph, and the width of the double track is greater than that of the electrical imaging graph.

[0066] Comparison of the three methods for interpreting the high-angle fracture in the 3949-3953m interval of TZ1 well Figure 9 The high-angle fracture is displayed as a black "double track" with an interval of 180° on the electrical imaging graph. The conventional logging methods such as the conductivity anomaly detection, the caliper comparison and the conductivity comparison have no obvious response characteristics. The high-angle fracture is displayed as a black "double track" with an interval of 180° on the azimuthal conductivity pseudo-imaging graph, the "continuity" of the double track is weaker than that of the electrical imaging graph, the width of the double track is greater than that of the electrical imaging graph, the fracture density of the pseudo-imaging graph is the same as that of the electrical imaging graph, the fracture length, height and inclination of the pseudo-imaging graph are close to those of the electrical imaging graph, the relative error is not more than 20%, and the fracture trend and inclination of the pseudo-imaging graph are basically the same as those of the electrical imaging graph (Table 1).

[0067] Table 1 Comparison of the interpretation results of the high-angle fracture

[0068]

[0069] (2) Identification and evaluation of medium-angle fractures

[0070] The medium angle fracture refers to the fracture with the dip angle of 30°-70°. The fracture is usually produced by tectonic action and appears in groups. The fracture opening degree increases with the increase of the dip angle under the influence of the overlying strata pressure. The fracture can improve the reservoir permeability to a large extent and has a certain destructive effect on the tight barrier condition. The medium angle fracture is an important space for oil and gas migration and accumulation. The medium angle fracture is usually displayed as the black trajectory line with the sine or cosine shape on the electrical imaging logging chart. The black trajectory line with the sine or cosine shape is also displayed on the azimuthal conductivity pseudo-imaging chart. The continuity of the black trajectory line on the pseudo-imaging chart is weaker than that on the electrical imaging chart due to the limitation of the "blind area" between the electrodes. The width of the black trajectory line on the pseudo-imaging chart is greater than that on the electrical imaging chart.

[0071] The three method interpretation results of the medium angle fracture in the TZ1 well 3880-3883m section are compared. Figure 10 The medium angle fracture is displayed as the black trajectory line with the cosine shape on the electrical imaging chart. The conventional methods such as the electrical conductivity anomaly detection, the caliper comparison and the electrical conductivity comparison have no obvious response characteristics. The medium angle fracture is displayed as the black trajectory line with the cosine shape on the azimuthal conductivity pseudo-imaging chart. The continuity of the black trajectory line on the pseudo-imaging chart is weaker than that on the electrical imaging chart. The width of the black trajectory line on the pseudo-imaging chart is greater than that on the electrical imaging chart. The fracture density, the fracture length, the fracture height and the fracture dip angle on the pseudo-imaging chart are basically the same as those on the electrical imaging chart. The relative error is not more than 5.0%. The fracture strike and the fracture trend on the pseudo-imaging chart are basically the same as those on the electrical imaging chart (Table 2).

[0072] Table 2 Comparison of the medium angle fracture interpretation results

[0073]

[0074]

[0075] (3) Low angle fracture identification and evaluation

[0076] The low angle fracture refers to the fracture with the dip angle less than 30°. The fracture is usually produced by tectonic action or differential diagenesis. The fracture height is usually low and the fracture opening degree is usually small. The fracture has strong lateral continuity and is an important channel for controlling the lateral migration of oil and gas. The low angle fracture is usually displayed as the black trajectory line with the low amplitude sine, cosine or horizontal shape on the electrical imaging logging chart. The low angle fracture is also displayed as the black trajectory line with the sine, cosine or horizontal shape on the azimuthal conductivity pseudo-imaging chart. The continuity of the black trajectory line on the pseudo-imaging chart is weaker than that on the electrical imaging chart due to the limitation of the "blind area" between the electrodes. The width of the black trajectory line on the pseudo-imaging chart is greater than that on the electrical imaging chart.

[0077] The three method interpretation results of the two low angle fractures in the TZ2 well 3894-3896m section are compared. Figure 11) The two low-angle fractures on the electrical imaging map are shown as black trace lines in sinusoidal shape. The conventional conductivity anomaly detection method has a certain conductivity anomaly display for the first low-angle fracture with relatively large opening (0.0354 mm), but the directivity is poor, and has no obvious response to the second low-angle fracture with relatively small opening (0.0211 mm). The two low-angle fractures on the azimuthal conductivity pseudo-imaging map are shown as black trace lines in sinusoidal shape, and the continuity of the trace lines is weaker than that on the electrical imaging map. The width of the trace lines is larger than that on the electrical imaging map. The fracture density on the pseudo-imaging map is the same as that on the electrical imaging map. The fracture length, height, and dip angle on the pseudo-imaging map are relatively close to those on the electrical imaging map, and the relative error is not more than 20.0%. The fracture strike and dip on the pseudo-imaging map are relatively close to those on the electrical imaging map (Table 3).

[0078] Table 3 Comparison of low-angle fracture interpretation results

[0079]

[0080] (4) Induced fracture pseudo-imaging identification and evaluation

[0081] Induced fracture refers to micro-fracture formed in the process of drilling due to the change of original formation pressure or ground stress balance, resulting in pressure difference or stress difference between wellbore and formation. The fracture is usually low in height, small in length, and narrow in opening, and although the fracture density is large, it is usually formed in the process of drilling after oil and gas accumulation, and has little effect on oil and gas migration and accumulation. The strike of induced fracture is parallel to the direction of maximum principal stress of the formation, and the parameter information of induced fracture can play a certain guiding role in optimizing the parameters of reservoir fracturing operation. Due to the small size of induced fracture, it is displayed in non-standard sine and cosine pattern on the electrical imaging map, and is usually a series of black trace lines in the shape of “wild goose”. Due to the existence of “blind area” between the electrodes, the “wild goose” feature of induced fracture on the azimuthal conductivity pseudo-imaging map is weaker than that on the electrical imaging map, and there is also no obvious response feature on the azimuthal conductivity pseudo-imaging map for some induced fractures with too narrow opening (statistical fracture opening ≤0.0160 mm).

[0082] Comparison of induced fracture interpretation results by three methods in the 3945-3949 m interval of TZ3 well Figure 12 ) The induced fractures on the electrical imaging map are usually shown as black trace lines in the shape of “wild goose”. The conductivity anomaly detection, caliper comparison, and conductivity comparison are usually displayed in disorder, and have strong multiple solutions. The induced fractures on the azimuthal conductivity pseudo-imaging map are shown as black trace lines in the shape of “wild goose”. Due to the existence of “blind area” between the electrodes, the regularity of the image is weaker than that on the electrical imaging map. The density, length, height, and dip angle of the induced fractures calculated by the pseudo-imaging map are smaller than those on the electrical imaging map, and the relative error is between 20% and 50.0%. The strike and dip of the induced fractures on the pseudo-imaging map are relatively close to those on the electrical imaging map (Table 4).

[0083] Table 4 Comparison of induced fracture interpretation results

[0084]

[0085] 2. Application effect analysis

[0086] The processing and interpretation practice of a large number of logging data in Ordos Basin shows that the identification and evaluation method of azimuthal conductivity pseudo-imaging can directly identify four types of fractures, i.e. high-angle fracture, medium-angle fracture, low-angle fracture and induced fracture, and quantitatively evaluate six parameters, i.e. fracture density, length, height, strike, tendency and dip angle, especially for the accurate judgment of strike and tendency of various types of fractures. Due to the limitation of the "blind area" of the polar plate measurement, the accuracy of the identification and evaluation of the method for medium-angle fractures is the highest, and the average relative error of the calculation of fracture length, height, density and dip angle is not higher than 5.0%; the accuracy of the identification and evaluation of high-angle and low-angle fractures is the second, and the average relative error of the calculation of fracture length, height, density and dip angle is not higher than 20.0%; the accuracy of the identification and evaluation of induced fractures is relatively the lowest, and the average relative error of the calculation of fracture length, height, density and dip angle is not higher than 50.0% (Table 5). By increasing the number of polar plate acquisition and improving the instrument measurement speed, the range of the "blind area" of the polar plate measurement can be reduced to a certain extent, which helps to improve the accuracy of the azimuthal conductivity pseudo-imaging method. In summary, the azimuthal conductivity pseudo-imaging method combines the high acquisition rate of conventional logging data and the strong visualization of imaging logging, which greatly improves the accuracy of visual identification and quantitative evaluation of fractures using conventional logging data.

[0087] Table 5 Effect table of identification and evaluation of fractures by azimuthal conductivity pseudo-imaging method

[0088]

[0089] In summary, due to the complexity of the fracture cause, the diversity of the fracture occurrence and the popularity of the data, the existing fracture identification and evaluation methods such as the geological outcrop and core sample observation method, the tracer dynamic test method and the seismic data interpretation method have different degrees of application limitations. Scholars generally pay attention to the conventional logging machine learning algorithm and the imaging logging visual identification and evaluation of fractures, and often ignore the method research of visual fracture identification and evaluation using conventional logging data. In view of the poor visualization effect of the conventional logging fracture interpretation method, the difficulty in quantification, the low imaging logging acquisition rate and the existence of white strip blind area in the imaging diagram, the conventional stratum dip angle logging conductivity (or resistivity) and the polar plate azimuth data are applied to establish the azimuthal conductivity imaging fracture identification and evaluation method, which has the advantages of visualization, quantification, reliability and the like compared with the conventional method, and has the advantages of oil and gas field data, high imaging diagram coverage and low cost compared with the imaging logging interpretation method (Table 6), and has important popularization and application value.

[0090] Table 6 Comparison table of application effect of fracture identification and evaluation method

[0091]

[0092] The principle of the application is that the six-arm stratum dip angle logging instrument is rotated from the well bottom and is pulled up, and the azimuth angle of the No. 1 polar plate (the angle between the No. 1 polar plate and the north direction) and the conductivity (or resistivity) data of the No. 1-6 polar plates at different depths are measured. When the stratum develops a fracture, the drilling mud invades the fracture, which will cause the conductivity at the fracture to present abnormal changes, and there is a significant difference from the non-fracture development layer. When a low-angle fracture develops, the abnormal response connection line of the conductivity (or resistivity) curves of the No. 1-6 polar plates presents a nearly horizontal curve feature; when a medium-angle fracture develops, the abnormal response connection line of the conductivity (or resistivity) curves of the No. 1-6 polar plates presents a nearly sine or cosine curve feature; when a high-angle fracture or induced fracture develops, due to the diversity of the fracture trend and occurrence, there will be various response features such as no polar plate detection, single polar plate detection, adjacent double polar plate detection and interval double polar plate detection. Therefore, the fracture can be visually identified and the fracture parameters can be quantitatively evaluated according to the conductivity (or resistivity) curve response features.

Claims

1. A fracture identification and evaluation method, characterized in that: The following steps are involved: Step 1: Set the window length and step length parameters, and read the azimuth of plate 1 and the conductivity data of all plates of the multi-arm formation dip logging tool; Step 2: The azimuth angles of adjacent plates of the multi-arm formation dip logging tool are separated by 360° / n, where n represents the number of plates; taking the azimuth angle of plate No. 1 as a reference, regularizing the azimuth angles of the other plates within the range of 0° to 360°, i.e., when the azimuth angle of any plate is greater than 360°, subtracting 360° from the azimuth angle of the plate to ensure that the azimuth angles of the plates are between 0° and 360°; Step 3: Construct the two-dimensional matrix M(x i ,y i ), first divide the 360° azimuth of a certain depth point within the window length into m×n equal parts, where m represents the number of equal parts between two adjacent plates, and m≥n; use the azimuth angle of each plate to calculate the azimuth angle of each divided point as the matrix M(x i ,y i ) of x i Coordinates, at the same time, the conductivity of each equally divided point is calculated by using the azimuth angle and conductivity data of each plate as interpolation sample points, as the matrix M(x i ,y i ) of y i coordinate; Determine whether the data within the window depth range has been processed. If not, increase the depth by one step and jump to the next sampling depth. Execute steps 2 to 3 until the azimuthal conductivity matrix within the window depth range is fully constructed. Step 4: Perform two-dimensional matrix color mapping to generate a simulated image. First, set any 2 to 5 different RGB color modes of the color spectrum, and at the same time map the wellbore azimuth conductivity two-dimensional matrix M (x i ,y i ) According to the conductivity value y i Arrange in order from small to large, when the conductivity values ​​are equal, according to the orientation value x i Arrange them in order from small to large. When the conductivity value and the azimuth value are equal, they are sorted in the same order. Then, perform chromatographic partitioning to generate a simulated image of the azimuth conductivity around the well. Step 5: Image inspection and optimization: determine whether the image effect meets expectations. If not, check whether the parameter settings are reasonable, reset the parameters, and execute steps 1 to 5 to optimize the image until the effect meets expectations. The parameters that need to be checked include window length, step length, number of azimuths, and color spectrum. After that, determine whether the data of the entire well section has been processed. If not, increase the step length by depth, jump to the next window length, and execute steps 1 to 5 until the data of the entire well section has been processed. Step 6: Identify the fault type through image visualization and quantitatively calculate six parameters: fault density, length, height, dip, strike, and inclination. Then save the data and image.

2. A fracture identification and evaluation method according to claim 1, characterized in that: In step 1, the multi-arm formation dip logging tool is selected from one of four-arm, six-arm, eight-arm, sixteen-arm and twenty-four-arm formation dip logging tools.

3. A fracture identification and evaluation method according to claim 1, characterized in that: In step 1, the window length parameter is 1.0 to 10.0 m; the step length parameter is 0.00254 to 0.001 m.

4. A fracture identification and evaluation method according to claim 1, characterized in that: In step three, the interpolation method used in the interpolation calculation is selected from one of four interpolation methods: linear interpolation, cubic spline interpolation, cubic B-spline interpolation and Akima interpolation.

5. A fracture identification and evaluation method according to claim 1, characterized in that: In step 4, the color spectrum is set to four colors: white, yellow, red, and black, and divided into three partitions: white-yellow, yellow-red, and red-black.

6. A fracture identification and evaluation method according to claim 5, characterized in that: In step 4, the chromatographic partitioning process is as follows: the first-order matrix element corresponds to white RGB (255:255:255), the third-order matrix element corresponds to yellow RGB (255:255:0), the second-third-order matrix element corresponds to red RGB (255:0:0), and the last-order matrix element corresponds to black RGB (0:0:0), thereby dividing the two-dimensional matrix into three chromatographic partitions; The RGB color scale values ​​corresponding to the matrix elements of the remaining sorting positions are obtained by the mixed operation mapping method to realize the well circumferential azimuthal conductivity two-dimensional matrix M(x i ,y i ) is mapped one-to-one to the RGB color scale, as follows: ① Determine the multiplication factor K and addition factor A of the mixed operation mapping method: Assume that the minimum conductivity value within each chromatographic partition is E min , the maximum conductivity is E max , the left color value of each chromatographic partition is C lvalue , the right color value is C rvalue , when E min =E max When the multiplication factor K = 0, the addition factor A = C lvalue ; when E min ≠E max When , the multiplication factor K and the addition factor A are calculated using formula (1) and formula (2) respectively: A=C lvalue -K×E min (2) ②Use the mixed operation mapping method to achieve one-to-one correspondence between the two-dimensional matrix and the color label: Let the two-dimensional matrix M(x i ,y i ) color scale corresponding value is M RGB (x i ,y i ), then we have formula (3): M RGB (x i ,y i )=M(x i ,y i )×K+A (3) ③ After completing the two-dimensional matrix color code mapping of the first chromatographic partition, go to the second chromatographic partition and execute steps ① and ② until the two-dimensional matrix color code mapping within the range of all chromatographic partitions is completed.

Images