Formation velocity determination method and device based on micro-logging, equipment and medium

Abstract

The invention relates to a formation velocity determination method and device based on micro-logging, equipment and a medium, and the method comprises the steps: obtaining micro-logging observation data which comprises the observation depth and vertical time corresponding to each offset pair; performing grouping processing on the micro-logging observation data according to the distribution state or the fitting effect of the micro-logging observation data to obtain one or more groups of data sets; respectively performing polynomial fitting and derivation processing on the one or more groups of data sets to obtain a speed slowness polynomial; and determining stratum velocities corresponding to different observation depths according to the polynomial of the velocity slowness. Through fitting based on the polynomial, the data points in each data set realize point-by-point accurate fitting instead of linear layered segment-by-segment fitting, so that formation velocities corresponding to different observation depths determined according to the polynomial of the velocity slowness are relatively accurate, and improvement of velocity interpretation precision of micro-logging is facilitated.

Description

Technical Field

[0001] The present disclosure relates to the field of geological exploration technology, and in particular to a method, device, equipment and medium for determining formation velocity based on micro-well logging. Background Art

[0002] In the process of geological exploration or oil and gas exploration, mechanical waves are generally emitted through shot points (which can be various types of seismic sources, such as controllable seismic sources), and the geological structure is analyzed through the echoes detected by the detectors.

[0003] In the process of realizing the concept disclosed in the present invention, the inventors found that there are at least the following technical problems in the related technology: micro-logging is currently a major method used to investigate surface velocity, and its interpretation has always been based on the assumption that the formation is a layered medium and the linear fitting method is used to calculate the formation velocity; however, in fact, in many areas corresponding to complex geological structures, the formation is basically not a layered medium, but a continuous medium, and the accuracy of the formation velocity and velocity change law obtained by micro-logging velocity interpretation and analysis based on linear fitting is relatively low. Summary of the invention

[0004] In order to solve the above technical problems or at least partially solve the above technical problems, the embodiments of the present disclosure provide a method, device, equipment and medium for determining formation velocity based on micro-well logging.

[0005] In the first aspect, an embodiment of the present disclosure provides a method for determining formation velocity based on micro-well logging. The above-mentioned method for determining formation velocity includes: obtaining micro-well logging observation data, wherein the above-mentioned micro-well logging observation data includes the observation depth and vertical time corresponding to each shot detection pair; according to the distribution state or fitting effect of the above-mentioned micro-well logging observation data, the above-mentioned micro-well logging observation data is grouped and processed to obtain one or more groups of data sets; for the above-mentioned one or more groups of data sets, polynomial fitting and derivation processing are respectively performed to obtain a polynomial of velocity slowness; according to the above-mentioned velocity slowness polynomial, the formation velocity corresponding to different observation depths is determined.

[0006] In some embodiments, according to the distribution state of the micro-logging observation data, the micro-logging observation data is grouped and processed to obtain one or more data sets, including: drawing a time-depth curve of micro-logging according to the micro-logging observation data; estimating the apparent velocity according to the data points in the time-depth curve to obtain the apparent velocity estimation value corresponding to each data point; determining whether there is a grouping interval point according to the distribution difference of the apparent velocity estimation value corresponding to each data point; if there is no grouping interval point, the micro-logging observation data is divided into a group of data sets; if there is a grouping interval point and it is one grouping interval point, the data points before the grouping interval point are divided into a group of data sets, and the subsequent data points including the grouping interval point are divided into a group of data sets; if there is a grouping interval point and there are at least two grouping interval points, the data points between the surface and the first grouping interval point are divided into a group of data sets; the data points between every two adjacent grouping interval points are divided into a group of data sets, wherein the grouping interval point is the left endpoint of the corresponding group of data sets; the subsequent data points including the last grouping interval point to the bottom of the well are divided into a group of data sets. The above left endpoint is the first number of the data interval.

[0007] In some embodiments, based on the fitting effect of the above-mentioned micro-logging observation data, the above-mentioned micro-logging observation data are grouped and processed to obtain one or more groups of data sets, including: drawing a time-depth curve of micro-logging based on the above-mentioned micro-logging observation data; performing polynomial fitting on the data points in the above-mentioned time-depth curve to obtain a fitting curve; determining candidate data points with differences between the above-mentioned fitting curve and the above-mentioned time-depth curve; determining whether there are at least 3 target data points among the above-mentioned candidate data points whose differences are greater than a set threshold and are continuous; in the absence of the above-mentioned target data points, the above-mentioned micro-logging observation data are divided into a group of data sets; in the case where the above-mentioned target data points exist and are a group of target data points, the above-mentioned target data points are divided into a group of data sets, and the other data points except the above-mentioned target data points are divided into a group of data sets; in the case where the above-mentioned target data points exist and are at least two groups of target data points, each group of target data points is divided into a corresponding group of data sets, and the other data points except the above-mentioned target data points are divided into a group of data sets.

[0008] In some embodiments, polynomial fitting and derivation processing are performed on the one or more sets of data sets mentioned above to obtain a polynomial of speed slowness, including: performing polynomial fitting on the one or more sets of data sets mentioned above to obtain a polynomial fitting result corresponding to each set of data sets; the independent variable of the polynomial fitting result is the observation depth, and the dependent variable is the vertical time; and derivation processing is performed on the polynomial fitting result corresponding to each set of data sets to obtain a polynomial of speed slowness corresponding to each set of data sets.

[0009] In some embodiments, the formation velocities corresponding to different observation depths are determined based on the above-mentioned velocity slowness polynomial, including: performing a reciprocal operation on the velocity slowness polynomial corresponding to each data set in the above-mentioned one or more data sets to obtain a relationship between the formation velocity and the observation depth corresponding to each data set; and determining the formation velocities corresponding to different observation depths using the relationship between the formation velocity and the observation depth under one or more data sets.

[0010] In some embodiments, the shot-detection pair is one of the following: the shot source is located in the well logging, and the geophone is located on the ground surface within a set range near the wellhead; the geophone is located in the well, and the shot source is located on the ground surface within a set range near the wellhead. The observation depth represents the depth of the shot source and the geophone in each shot-detection pair along the extension direction of the well logging; the vertical time is generated by converting the first arrival time at the offset of the shot source and the geophone into the time corresponding to the zero offset.

[0011] In some embodiments, the formation velocity determination method further includes: comparing the formation velocity with the tomographic inversion velocity and outputting the comparison result. Alternatively, in some embodiments, the formation velocity determination method further includes: analyzing the control relationship between the tomographic inversion velocity and the micro-logging formation velocity; and based on the control relationship, controlling the velocity in the tomographic inversion model so that the imaging quality of the pre-stack depth migration meets the set requirements.

[0012] In a second aspect, an embodiment of the present disclosure provides a formation velocity determination device based on micro-logging. The above-mentioned formation velocity determination device includes: a data acquisition module, a data grouping module, a processing module and a velocity determination module. The above-mentioned data acquisition module is used to acquire micro-logging observation data, and the above-mentioned micro-logging observation data includes the observation depth and vertical time corresponding to each gun inspection pair. The above-mentioned data grouping module is used to group the above-mentioned micro-logging observation data according to the distribution state or fitting effect of the above-mentioned micro-logging observation data to obtain one or more groups of data sets. The above-mentioned processing module is used to perform polynomial fitting and derivation processing on the above-mentioned one or more groups of data sets, respectively, to obtain a polynomial of velocity slowness. The above-mentioned velocity determination module is used to determine the formation velocity corresponding to different observation depths according to the above-mentioned velocity slowness polynomial.

[0013] In a third aspect, an embodiment of the present disclosure provides an electronic device. The electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus; the memory is used to store computer programs; and the processor is used to implement the above-mentioned method for determining formation velocity based on micro-logging when executing the program stored in the memory.

[0014] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for determining formation velocity based on micro-logging as described above is implemented.

[0015] The above technical solution provided by the embodiments of the present disclosure has at least some or all of the following advantages:

[0016] By acquiring micro-logging observation data, the micro-logging observation data includes the observation depth and vertical time corresponding to each shot inspection pair; according to the distribution state or fitting effect of the micro-logging observation data, the micro-logging observation data are grouped and processed to obtain one or more data sets; through grouping, data with relatively similar distribution patterns or relatively similar fitting effects can be divided into the same data set, and data with large differences in distribution patterns or large differences in fitting effects can be divided into different data sets, so that in the subsequent process of polynomial fitting and derivation processing for one or more data sets, a relatively accurate polynomial of velocity slowness can be obtained; at the same time, by fitting based on the polynomial, the data points in each data set can be accurately fitted point by point rather than based on linear layered and segmented fitting, so as to ensure that the formation velocity corresponding to different observation depths determined according to the above-mentioned velocity slowness polynomial is relatively accurate, which helps to improve the velocity interpretation accuracy of micro-logging. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0019] Figure 1 The flowchart of the method for determining formation velocity based on micro-well logging according to an embodiment of the present disclosure is schematically shown;

[0020] Figure 2 The detailed implementation flow chart of step S120a according to an embodiment of the present disclosure is schematically shown;

[0021] Figure 3 The detailed implementation flow chart of step S120b according to an embodiment of the present disclosure is schematically shown;

[0022] Figure 4AA schematic diagram of a time-depth curve of micro-well logging obtained by plotting micro-well logging observation data according to an embodiment of the present disclosure is schematically shown;

[0023] Figure 4B Schematically showing a time-depth curve and a schematic diagram of a fitting curve for performing polynomial fitting based on micro-well logging time-depth data according to an embodiment of the present disclosure;

[0024] Figure 4C A schematic diagram schematically shows a fitting error curve of time-depth data and a schematic diagram for determining grouping points according to an embodiment of the present disclosure;

[0025] Figure 5 The figure schematically shows a curve diagram after polynomial fitting is performed on the first set of data sets according to an embodiment of the present disclosure;

[0026] Figure 6 The following schematically shows a curve diagram of formation velocity obtained by performing polynomial fitting and derivative processing on the first set of data sets and converting the formation velocity according to an embodiment of the present disclosure;

[0027] Figure 7 The figure schematically shows a curve diagram after polynomial fitting is performed on the second set of data sets according to an embodiment of the present disclosure;

[0028] Figure 8 The following schematically shows a curve diagram of formation velocity obtained by performing polynomial fitting and derivative processing on the second set of data sets and converting the formation velocity according to an embodiment of the present disclosure;

[0029] Fig. 9 A schematic diagram of a relationship curve between formation velocity and depth obtained based on micro-logging observation data processing according to an embodiment of the present disclosure is schematically shown;

[0030] Fig.10 A schematic diagram schematically shows a comparison between the above-mentioned formation velocity and the tomographic inversion velocity and outputting the comparison result according to an embodiment of the present disclosure;

[0031] Fig.11 The structure block diagram of the formation velocity determination device based on micro-well logging according to an embodiment of the present disclosure is schematically shown;

[0032] Fig.12 The structural block diagram of the electronic device provided by the embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0034] The first exemplary embodiment of the present disclosure provides a formation velocity determination method based on micro-well logging. The formation velocity determination method of this embodiment can be executed by an electronic device with computing capability.

[0035] Figure 1 The flowchart of a method for determining formation velocity based on micro-logging according to an embodiment of the present disclosure is schematically shown.

[0036] Reference Figure 1 As shown, the formation velocity determination method based on micro-well logging includes the following steps: S110, S120, S130 and S140.

[0037] In step S110, micro-logging observation data is obtained, where the micro-logging observation data includes the observation depth and vertical time corresponding to each shot detection pair.

[0038] Micro-logging is one of the main means of surface structure investigation. Because it directly receives upgoing wave information excited from different depths underground on the ground and is less affected by the terrain, the interpretation accuracy is relatively high.

[0039] In some embodiments, the above-mentioned shot-check pair in the micro-well logging observation data is one of the following situations:

[0040] The shot source is located in the wellbore and the geophone is located on the ground surface within a set range near the wellhead; or,

[0041] The detector is located in the well, and the shot point source is located on the ground surface within a set range near the wellhead.

[0042] The above observation depth H represents the depth of the shot point source and the detector in each shot detection pair along the extension direction of the well logging.

[0043] The vertical time T is generated by converting the first arrival time at the offset between the shot source and the detector into the time corresponding to the zero offset.

[0044] The above-mentioned micro-logging observation data includes a data set consisting of multiple observation depths and vertical times, such as {(H 1 , T 1 ), (H 2 , T 2 ), ..., (HN , T N )}, N represents the total number of micro-well logging observation data.

[0045] In step S120, the micro-logging observation data are grouped and processed according to the distribution state or fitting effect of the micro-logging observation data to obtain one or more data sets.

[0046] Figure 2 The detailed implementation flow chart of step S120a according to an embodiment of the present disclosure is schematically shown.

[0047] In some embodiments, in step S120a, the micro-logging observation data are grouped and processed according to the fitting effect of the micro-logging observation data to obtain one or more data sets.

[0048] Reference Figure 2 As shown, the above step S120a includes the following steps: S210, S220, S230, S240, S250a, S250b and S250c.

[0049] In step S210, a time-depth curve of micro-logging is drawn based on the micro-logging observation data.

[0050] Figure 4A A schematic diagram of a time-depth curve of micro-well logging obtained by plotting micro-well logging observation data according to an embodiment of the present disclosure is schematically shown; Figure 4B Schematically showing a time-depth curve and a schematic diagram of a fitting curve for performing polynomial fitting based on micro-well logging time-depth data according to an embodiment of the present disclosure; Figure 4C The figure schematically shows a fitting error curve of time-depth data and a schematic diagram for determining grouping points according to an embodiment of the present disclosure.

[0051] Reference Figure 4A As shown, the observation depth in the micro-logging observation data is in meters (m), the vertical time is in milliseconds (ms), the observation depth is 1m to 346m, and the vertical time is distributed in the range of about 1ms to 170ms. The observation depth corresponding to the data points of each observation data can be evenly distributed or non-evenly distributed, depending on the actual layout status.

[0052] In step S220, a polynomial fit is performed on the data points in the above time-depth curve to obtain a fitting curve.

[0053] Reference Figure 4B As shown in the figure, after polynomial fitting is performed on the data points in the time-depth curve, the formula corresponding to the fitting curve is:

[0054] T (h)=8.2026+0.83417*h-1.68616*h 2 +1.73174*h 3 (1)

[0055] In step S230, candidate data points where there are differences between the fitting curve and the time-depth curve are determined.

[0056] Reference Figure 4C As shown, the points where the values ​​of the fitting error curve are not zero correspond to candidate data points.

[0057] In step S240, it is determined whether there are at least three target data points whose differences are greater than a set threshold and are continuous among the candidate data points. The set threshold can be adjusted or optimized according to the actual situation. Considering that the original data itself also has errors, in order to eliminate the influence of singular values, the candidate data points are required to have not only differences but also continuity. The target data points with real differences among these candidate data points are determined by distributing the difference trend, so as to avoid a single abnormal point generated by the error. If it is regarded as a target data point, the accuracy of abnormality determination will be reduced. In some embodiments, for example (H 4 , T 4 )、(H 5 , T 5 )、(H 6 , T 6 )、(H 7 , T 7 ) and (H 8 , T 8 ) corresponds to a fitting curve and a time-depth curve greater than the set threshold, and these points are continuous, then (H 4 , T 4 )、(H 5 , T 5 )、(H 6 , T 6 )、(H 7 , T 7 ) and (H 8 , T 8 ) These target data points are divided into a set of data sets J0. Other data points are divided into a set of data sets J1.

[0058] Reference Figure 4C As shown, for example, if the threshold is set to 1.5 ms, at least three consecutive candidate data points with a difference greater than 1.5 ms can be determined as target data points.

[0059] In step S250a, if the target data point exists and is a group of target data points, the target data point is divided into a group of data sets, and the other data points except the target data point are divided into another group of data sets. Figure 4C As shown by the dotted line in the middle straight line, the data points in the time-depth curve are divided into two groups, wherein the data points with an observation depth of 1m to 6m (for example, actually including 6 data points) are divided into the first data set, and the data points with an observation depth of 7m to 346m are divided into the second data set, for example, the second data set actually includes 225 data points.

[0060] In step S250b, when there are at least two groups of target data points, each group of target data points is divided into a corresponding group of data sets, and other data points except the target data points are divided into a group of data sets. 4 , T 4 )、(H 5 , T 5 ) and (H 6 , T 6 ) The difference between the fitting curve and the time-depth curve corresponding to (H) is greater than the set threshold, and these points are continuous. 4 , T 4 )、(H 5 , T 5 ) and (H 6 , T 6 ) are divided into the same set of data sets J2; (H 10 , T 10 )、(H 11 , T 11 )、(H 12 , T 12 ) and (H 13 , T 13 ) The difference between the fitting curve and the time-depth curve corresponding to (H) is greater than the set threshold, and these points are continuous. 10 , T 10 )、(H 11 , T 11 )、(H 12 , T 12 ) and (H 13 , T 13 ) are divided into another set of data sets J3. The other data points (the difference is lower than the set threshold) are divided into a set of data sets J4.

[0061] In step S250c, in the absence of the target data point, the micro-logging observation data is divided into a group of data sets.

[0062] In an embodiment including steps S210 to S250c, data can be grouped according to the effect of polynomial fitting. According to the relative sizes of candidate data points that are different from the fitting curve and the time-depth curve, target data points with large differences are screened out, and the target data points and other data points are divided into different data sets. This grouping method can improve the accuracy of polynomial fitting for each group of data sets obtained after the final grouping, thereby ensuring that the formation velocities corresponding to different observation depths determined according to the velocity slowness polynomial are relatively accurate, which helps to improve the velocity interpretation accuracy of micro-well logging.

[0063] Figure 3 The detailed implementation flow chart of step S120b according to an embodiment of the present disclosure is schematically shown.

[0064] In some other embodiments, in step S120b, the micro-logging observation data are grouped and processed according to the distribution status of the micro-logging observation data to obtain one or more data sets.

[0065] Reference Figure 3 As shown, the above step S120b includes the following steps: S310, S320, S330, S340a, S340b and S340c.

[0066] In step S310, a time-depth curve of micro-logging is drawn based on the micro-logging observation data. The drawn time-depth curve is shown in FIG4 .

[0067] In step S320, apparent velocity estimation is performed based on the data points in the above time-depth curve to obtain an apparent velocity estimation value corresponding to each data point.

[0068] In some embodiments, the slope of the tangent line at each data point in the time-depth curve may be used as an estimate of the apparent velocity.

[0069] In step S330, it is determined whether there is a grouping interval point according to the distribution difference of the apparent velocity estimation values ​​corresponding to each data point.

[0070] In some embodiments, the difference in the estimated apparent velocity values ​​between adjacent data points is relatively large, and the starting data point where the large difference begins to appear in the adjacent data points can be determined as the grouping interval point. For example, the difference in the estimated apparent velocity values ​​between adjacent data points x1 and x2 is δ1, the difference in the estimated apparent velocity values ​​between adjacent data points x2 and x3 is δ2, the difference in the estimated apparent velocity values ​​between adjacent data points x3 and x4 is δ3, the difference in the estimated apparent velocity values ​​between adjacent data points x4 and x5 is δ4, and the difference in the estimated apparent velocity values ​​between adjacent data points x5 and x6 is δ5. δ1, δ2, and δ3 are not much different, δ4 is more different from δ1, δ2, and δ3, and δ5 is not much different from δ4. Then, a large difference begins to appear from x5, and the data point x5 corresponding to δ4 can be determined as the grouping interval point.

[0071] In step S340a, when there is a grouping interval point and it is one grouping interval point, the data points before the grouping interval point are divided into a group of data sets, and the subsequent data points including the grouping interval point are divided into a group of data sets.

[0072] In step S340b, when there are grouping interval points and there are at least two grouping interval points, the data points between the surface and the first grouping interval point are divided into a group of data sets; the data points between every two adjacent grouping interval points are divided into a group of data sets, wherein the grouping interval point serves as the left endpoint of the corresponding group of data sets; and the subsequent data points including the last grouping interval point to the bottom of the well are divided into a group of data sets.

[0073] In step S340c, in the absence of grouping interval points, the micro-logging observation data are divided into a group of data sets.

[0074] In an embodiment including steps S310 to S340c, data can be grouped according to the distribution difference between the apparent velocity estimates of each data point. According to the size of the distribution difference of the apparent velocity estimates corresponding to each data point, it is determined whether there is a grouping interval point and grouping is performed. Such a grouping method can improve the accuracy of polynomial fitting of each group of data sets obtained after the final grouping, thereby ensuring that the formation velocities corresponding to different observation depths determined according to the velocity slowness polynomial are relatively accurate, which helps to improve the velocity interpretation accuracy of micro-well logging.

[0075] In step S130, polynomial fitting and derivation processing are performed on the one or more sets of data sets to obtain a speed polynomial.

[0076] In some embodiments, in the above step S130, polynomial fitting and derivation processing are performed on the above one or more sets of data sets to obtain a polynomial of speed slowness, including: performing polynomial fitting on the above one or more sets of data sets to obtain a polynomial fitting result corresponding to each set of data sets; the independent variable of the above polynomial fitting result is the observation depth h, and the dependent variable is the vertical time T; the polynomial fitting result corresponding to each set of data sets is derivation processing to obtain a polynomial of speed slowness corresponding to each set of data sets.

[0077] For example, for the first set of data sets mentioned above, a polynomial fitting is performed to obtain the following polynomial fitting results:

[0078] T (h) =1+2.804h-0.2048h 2 +0.0056h 3 , (2)

[0079] Figure 5 The following schematically shows a curve diagram after polynomial fitting is performed on the first set of data sets according to an embodiment of the present disclosure. Figure 5 As shown, the data points in the first set of data sets are indicated by rectangular boxes, and the curve after polynomial fitting is indicated by a dotted line. The curve after polynomial fitting is used to represent the relationship between depth (i.e., observation depth) and time (i.e., vertical time).

[0080] For example, for the second set of data sets mentioned above, a polynomial fitting is performed to obtain the following polynomial fitting results:

[0081] T (h) =8.704+0.824h-1.633h 2 +1.648h 3 , (3)

[0082] Figure 7 The following schematically shows a curve diagram after polynomial fitting is performed on the second set of data sets according to an embodiment of the present disclosure. Figure 7 As shown, the data points in the second set of data sets are indicated by rectangular boxes, and the curve after polynomial fitting is indicated by a dotted line. The curve after polynomial fitting is used to represent the relationship between depth and time.

[0083] The polynomial fitting results corresponding to the first set of data sets are derivatized to obtain the following polynomial of the speed slowness corresponding to the first set of data sets:

[0084]

[0085] The polynomial fitting results corresponding to the second set of data sets are derivatized to obtain the speed polynomial corresponding to the second set of data sets as follows:

[0086]

[0087] In step S140, the formation velocities corresponding to different observation depths are determined according to the above-mentioned velocity slowness polynomial.

[0088] In some embodiments, in the above step S140, the formation velocities corresponding to different observation depths are determined according to the above velocity polynomial, including: performing an inverse operation on the velocity polynomial corresponding to each data set in the above one or more data sets to obtain a relationship between the formation velocity and the observation depth corresponding to each data set; and determining the formation velocities corresponding to different observation depths using the relationship between the formation velocity and the observation depth under one or more data sets.

[0089] The velocity slowness is the reciprocal of the formation velocity. Since the time above is expressed in milliseconds, when the formation velocity is expressed in m / s, the relationship between the formation velocity and the observation depth corresponding to the first set of data sets is:

[0090] V h1 =1000 / V s1(h) , (6)

[0091] The value range of h is 0-6m.

[0092] Figure 6 The following schematically shows a curve diagram of formation velocity obtained by performing polynomial fitting and derivative processing on the first set of data sets and converting them according to an embodiment of the present disclosure. Figure 6 As shown, the data points in the first set of data sets are converted after polynomial fitting and derivation to obtain a relationship curve between formation velocity and depth (i.e., observation depth).

[0093] The relationship between the formation velocity and observation depth corresponding to the second set of data sets is:

[0094] V h2 =1000 / V s2(h) , (7)

[0095] Among them, the value range of h is 7m-346m.

[0096] Figure 8 The following schematically shows a curve diagram of formation velocity obtained by performing polynomial fitting and derivative processing on the second set of data sets and converting them according to an embodiment of the present disclosure. Figure 8As shown, the data points in the second set of data sets are converted after polynomial fitting and derivation to obtain a relationship curve between formation velocity and depth (i.e., observation depth).

[0097] According to the above relationship between the bottom velocity and the observation depth, the formation velocity corresponding to different observation depths can be determined, and the velocity file of micro-well logging can be generated. The format of each line of the velocity file is: depth, velocity. One line represents an observation depth and the corresponding formation velocity.

[0098] Fig. 9 The figure schematically shows a curve diagram of the relationship between formation velocity and depth obtained based on micro-logging observation data processing according to an embodiment of the present disclosure.

[0099] For example, Figure 7 and Figure 8 The formation velocity of the two corresponding segments is spliced ​​to obtain the formation velocity corresponding to the entire observation depth. Fig. 9 shown.

[0100] Fig.10 The diagram schematically shows a schematic diagram of comparing the above-mentioned formation velocity with the tomographic inversion velocity and outputting the comparison result according to an embodiment of the present disclosure.

[0101] In some embodiments, in addition to the above steps S110 to S140, the above formation velocity determination method further includes: comparing the above formation velocity with the tomographic inversion velocity and outputting the comparison result. Fig.10 As shown, the dotted line is used to represent the velocity calculated in a conventional manner (the conventional theory of micro-logging is used to calculate the formation velocity by a linear fitting method based on the assumption of layered medium in the formation), the solid curve is used to represent the formation velocity determined in the embodiment of the present disclosure, and the short dashed line is used to represent the tomographic inversion velocity.

[0102] Alternatively, in some embodiments, the above-mentioned formation velocity determination method, in addition to the above-mentioned steps S110 to S140, also includes: analyzing the control relationship between the tomographic inversion velocity and the micro-logging formation velocity; and regulating the velocity in the tomographic inversion model based on the above-mentioned control relationship, so that the imaging quality of the pre-stack depth migration meets the set requirements.

[0103] Since the prestack depth migration method is very sensitive to velocity, if there is a deviation in velocity, it is difficult for the depth migration to obtain an imaging result with accurate position and good focus. The formation velocity determination method based on micro-logging provided in the disclosed embodiment can accurately determine the formation velocity, which can be used to improve the velocity accuracy of micro-logging measurement, and can also be used for velocity interpretation of zero-zero offset (VSP) data to improve the interpretation accuracy of formation velocity; the velocity of this method can better meet the shallow velocity modeling requirements of prestack depth migration, and therefore has wide applicability and versatility.

[0104] The second exemplary embodiment of the present disclosure provides a formation velocity determination device based on micro-logging.

[0105] Fig.11 The structural block diagram of the formation velocity determination device based on micro-logging according to the embodiment of the present disclosure is schematically shown.

[0106] Referring to Fig.11 As shown, the formation velocity determination device 1100 based on micro-logging provided by the embodiment of the present disclosure includes: a data acquisition module 1101, a data grouping module 1102, a processing module 1103, and a velocity determination module 1104.

[0107] The above-mentioned data acquisition module 1101 is used to acquire micro-logging observation data, and the above-mentioned micro-logging observation data includes the observation depth and vertical time corresponding to each shot-receiver pair.

[0108] The above-mentioned data grouping module 1102 is used to group the above-mentioned micro-logging observation data according to the distribution state or fitting effect of the above-mentioned micro-logging observation data, and obtain one or more groups of data sets.

[0109] The above-mentioned processing module 1103 is used to perform polynomial fitting and derivative processing on the above-mentioned one or more groups of data sets respectively, and obtain a polynomial of velocity slowness.

[0110] The above-mentioned velocity determination module 1104 is used to determine the formation velocity corresponding to different observation depths according to the polynomial of the above-mentioned velocity slowness.

[0111] In some embodiments, grouping the above-mentioned micro-logging observation data according to the distribution state of the above-mentioned micro-logging observation data to obtain one or more groups of data sets includes: drawing a time-depth curve of the micro-logging according to the above-mentioned micro-logging observation data; estimating the apparent velocity according to the data points in the above-mentioned time-depth curve to obtain the apparent velocity estimation value corresponding to each data point; determining whether there is a grouping interval point according to the distribution difference size of the apparent velocity estimation values corresponding to each data point; in the case where there is no grouping interval point, dividing the above-mentioned micro-logging observation data into a group of data sets; in the case where there is a grouping interval point and it is a single grouping interval point, dividing the data points before the above-mentioned grouping interval point into a group of data sets, and dividing the subsequent data points including the above-mentioned grouping interval point into a group of data sets; in the case where there is a grouping interval point and there are at least two grouping interval points, dividing the data points between the surface and the first grouping interval point into a group of data sets; dividing the data points between every two adjacent grouping interval points into a group of data sets, where the grouping interval point is used as the left end point of the corresponding group of data sets; dividing the subsequent data points including the last grouping interval point to the bottom of the well into a group of data sets.

[0112] In some embodiments, based on the fitting effect of the above-mentioned micro-logging observation data, the above-mentioned micro-logging observation data are grouped and processed to obtain one or more groups of data sets, including: drawing a time-depth curve of micro-logging based on the above-mentioned micro-logging observation data; performing polynomial fitting on the data points in the above-mentioned time-depth curve to obtain a fitting curve; determining candidate data points with differences between the above-mentioned fitting curve and the above-mentioned time-depth curve; determining whether there are at least 3 target data points among the above-mentioned candidate data points whose differences are greater than a set threshold and are continuous; in the absence of the above-mentioned target data points, the above-mentioned micro-logging observation data are divided into a group of data sets; in the case where the above-mentioned target data points exist and are a group of target data points, the above-mentioned target data points are divided into a group of data sets, and the other data points except the above-mentioned target data points are divided into a group of data sets; in the case where the above-mentioned target data points exist and are at least two groups of target data points, each group of target data points is divided into a corresponding group of data sets, and the other data points except the above-mentioned target data points are divided into a group of data sets.

[0113] In some embodiments, polynomial fitting and derivation processing are performed on the one or more sets of data sets mentioned above to obtain a polynomial of speed slowness, including: performing polynomial fitting on the one or more sets of data sets mentioned above to obtain a polynomial fitting result corresponding to each set of data sets; the independent variable of the polynomial fitting result is the observation depth, and the dependent variable is the vertical time; and derivation processing is performed on the polynomial fitting result corresponding to each set of data sets to obtain a polynomial of speed slowness corresponding to each set of data sets.

[0114] In some embodiments, the formation velocities corresponding to different observation depths are determined based on the above-mentioned velocity slowness polynomial, including: performing a reciprocal operation on the velocity slowness polynomial corresponding to each data set in the above-mentioned one or more data sets to obtain a relationship between the formation velocity and the observation depth corresponding to each data set; and determining the formation velocities corresponding to different observation depths using the relationship between the formation velocity and the observation depth under one or more data sets.

[0115] In some embodiments, the shot-detection pair is one of the following: the shot source is located in the well logging, and the geophone is located on the ground surface within a set range near the wellhead; the geophone is located in the well, and the shot source is located on the ground surface within a set range near the wellhead. The observation depth represents the depth of the shot source and the geophone in each shot-detection pair along the extension direction of the well logging; the vertical time is generated by converting the first arrival time at the offset of the shot source and the geophone into the time corresponding to the zero offset.

[0116] In some embodiments, the above-mentioned formation velocity determination device also includes: an application module.

[0117] The application module is used to compare the formation velocity with the tomography inversion velocity and output the comparison result.

[0118] Alternatively, in some embodiments, the above-mentioned application module is also used to: analyze the control relationship between the tomographic inversion velocity and the micro-logging formation velocity; and control the velocity in the tomographic inversion model based on the above-mentioned control relationship so that the imaging quality of the pre-stack depth migration meets the set requirements.

[0119] Any number of the functional modules included in the above-mentioned device 1100 can be combined in one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. At least one of the functional modules included in the device 1100 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in any appropriate combination of any of them. Alternatively, at least one of the functional modules included in the device 1100 can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be performed.

[0120] A third exemplary embodiment of the present disclosure provides an electronic device.

[0121] Fig.12 The structural block diagram of the electronic device provided by the embodiment of the present disclosure is schematically shown.

[0122] Reference Fig.12 As shown, the electronic device 1200 provided in the embodiment of the present disclosure includes a processor 1201, a communication interface 1202, a memory 1203 and a communication bus 1204, wherein the processor 1201, the communication interface 1202 and the memory 1203 communicate with each other through the communication bus 1204; the memory 1203 is used to store computer programs; the processor 1201 is used to implement the formation velocity determination method based on micro-logging as described above when executing the program stored in the memory.

[0123] The fourth exemplary embodiment of the present disclosure further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the formation velocity determination method based on micro-logging as described above is implemented.

[0124] The computer-readable storage medium may be included in the device or apparatus described in the above embodiment; or it may exist independently without being assembled into the device or apparatus. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0125] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0126] It should be noted that the collection, collection, update, analysis, processing, use, transmission, storage and other aspects of user personal information involved in the technical solution provided by the embodiments of the present disclosure are in compliance with the provisions of relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. Necessary measures are taken for user personal information to prevent illegal access to user personal information data and maintain the security of user personal information, network security and national security.

[0127] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0128] The foregoing is merely a specific embodiment of the present disclosure, which enables those skilled in the art to understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features applied herein.

Claims

1. A method for determining formation velocity based on micro-well logging, It is characterized in that include: Acquire micro-well logging observation data, wherein the micro-well logging observation data includes observation depth and vertical time corresponding to each shot inspection pair; According to the distribution state or fitting effect of the micro-well logging observation data, the micro-well logging observation data are grouped and processed to obtain one or more sets of data sets; For the one or more sets of data sets, polynomial fitting and derivation processing are performed respectively to obtain a speed slowness polynomial; The formation velocities corresponding to different observation depths are determined according to the velocity slowness polynomial.

2. The formation velocity determination method according to claim 1, It is characterized in that According to the distribution state of the micro-well logging observation data, the micro-well logging observation data is grouped and processed to obtain one or more sets of data sets, including: Drawing a time-depth curve of micro-well logging based on the micro-well logging observation data; Estimating the apparent velocity according to the data points in the time-depth curve to obtain an estimated apparent velocity value corresponding to each data point; Determine whether there is a grouping interval point according to the distribution difference of the apparent velocity estimation values ​​corresponding to each data point; In the absence of grouping interval points, dividing the micro-logging observation data into a group of data sets; In the case where there is a grouping interval point and it is one grouping interval point, the data points before the grouping interval point are divided into a group of data sets, and the subsequent data points including the grouping interval point are divided into a group of data sets; When there are at least two grouping interval points, the data points from the surface to the first grouping interval point are divided into a group of data sets; the data points between every two adjacent grouping interval points are divided into a group of data sets, where the grouping interval point is the left endpoint of the corresponding group of data sets; and the subsequent data points including the last grouping interval point to the bottom of the well are divided into a group of data sets.

3. The formation velocity determination method according to claim 1, It is characterized in that According to the fitting effect of the micro-well logging observation data, the micro-well logging observation data is grouped and processed to obtain one or more sets of data sets, including: Drawing a time-depth curve of micro-well logging based on the micro-well logging observation data; Performing polynomial fitting on the data points in the time-depth curve to obtain a fitting curve; determining candidate data points at which there are differences between the fitted curve and the time-depth curve; Determine whether there are at least three consecutive target data points whose differences are greater than a set threshold among the candidate data points; In the absence of the target data point, dividing the micro-logging observation data into a set of data sets; In the case where the target data point exists and is a group of target data points, the target data point is divided into a group of data sets, and other data points except the target data point are divided into another group of data sets; In the case that the target data points exist and there are at least two groups of target data points, each group of target data points is divided into a corresponding group of data sets, and other data points except the target data points are divided into one group of data sets.

4. The formation velocity determination method according to claim 1, It is characterized in that For the one or more sets of data sets, polynomial fitting and derivation processing are performed respectively to obtain a speed polynomial, including: For the one or more sets of data sets, polynomial fitting is performed respectively to obtain a polynomial fitting result corresponding to each set of data sets; the independent variable of the polynomial fitting result is the observation depth, and the dependent variable is the vertical time; The polynomial fitting results corresponding to each set of data sets are derivatized to obtain the speed polynomial corresponding to each set of data sets.

5. The formation velocity determination method according to claim 1, It is characterized in that Determining the formation velocities corresponding to different observation depths according to the velocity slowness polynomial includes: Performing a reciprocal operation on a velocity slowness polynomial corresponding to each of the one or more data sets to obtain a relationship between the formation velocity and the observation depth corresponding to each data set; The relationship between the formation velocity and the observation depth under one or more data sets is used to determine the formation velocity corresponding to different observation depths.

6. The formation velocity determination method according to claim 1, It is characterized in that The gun inspection pair is one of the following situations: The shot point source is located in the wellbore, and the geophone is located on the ground surface within a set range near the wellhead; The geophone is located in the well, and the shot point source is located on the ground surface within a set range near the wellhead; The observation depth represents the depth of the shot point source and the detector in each shot detection pair along the logging extension direction; the vertical time is generated by converting the first arrival time at the offset distance of the shot point source and the detector to the time corresponding to the zero offset distance.

7. The formation velocity determination method according to claim 1, It is characterized in that Also includes: Comparing the formation velocity with the tomographic inversion velocity and outputting the comparison result; or, Analyze the regulatory relationship between tomographic inversion velocity and micro-well logging formation velocity; Based on the control relationship, the velocity in the tomographic inversion model is controlled so that the imaging quality of the prestack depth migration meets the set requirements.

8. A formation velocity determination device based on micro-well logging, It is characterized in that include: A data acquisition module, used to acquire micro-logging observation data, wherein the micro-logging observation data includes the observation depth and vertical time corresponding to each shot inspection pair; A data grouping module, used for grouping the micro-well logging observation data according to the distribution state or fitting effect of the micro-well logging observation data to obtain one or more data sets; A processing module, used for performing polynomial fitting and derivation processing on the one or more sets of data sets to obtain a speed polynomial; The velocity determination module is used to determine the formation velocity corresponding to different observation depths according to the velocity slowness polynomial.

9. An electronic device, It is characterized in that It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory, used to store computer programs; The processor is used to implement the formation velocity determination method described in any one of claims 1-7 when executing the program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the formation velocity determination method according to any one of claims 1 to 7 is implemented.

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