Formation velocity determination method and device based on microlog, equipment and medium

Abstract

The present disclosure relates to a microlog-based formation velocity determination method, device, equipment and medium, the method comprising: obtaining microlog observation data, the microlog observation data containing observation depth and vertical time corresponding to each shot-receiver pair; grouping the microlog observation data according to the distribution state or fitting effect of the microlog observation data to obtain one or more data sets; performing polynomial fitting and derivation processing on the one or more data sets respectively to obtain a velocity-slowness polynomial; and determining the formation velocity corresponding to different observation depths according to the velocity-slowness polynomial. By fitting based on the polynomial, the data points in each data set are accurately fitted point by point rather than linearly fitted layer by layer, so that the formation velocity corresponding to different observation depths determined according to the velocity-slowness polynomial is relatively accurate, which helps to improve the velocity interpretation accuracy of the microlog.

Description

Technical Field

[0001] This disclosure relates to the field of geological exploration technology, and in particular to a method, apparatus, equipment and medium for determining formation velocity based on micrologging. Background Technology

[0002] In geological exploration or oil and gas exploration, mechanical waves are generally emitted from a shot point (which can be various types of seismic sources, such as controlled seismic sources), and the geological structure is analyzed by the echo detected by a geophone.

[0003] In realizing the present invention, the inventors discovered at least the following technical problems in the related technology: Micrologging is currently a major method for investigating surface velocities, and its interpretation has always been based on the assumption of layered media in the formation and the linear fitting method is used to calculate the formation velocity; however, in reality, in many areas corresponding to complex geological structures, the formation is not a layered medium, but a continuous medium. The accuracy of the formation velocity and velocity variation law obtained by interpreting and analyzing micrologging velocity based on the linear fitting method is low. Summary of the Invention

[0004] To solve the above-mentioned technical problems, or at least partially solve them, embodiments of this disclosure provide a method, apparatus, device, and medium for determining formation velocity based on micrologging.

[0005] In a first aspect, embodiments of this disclosure provide a method for determining formation velocity based on micrologging. The method includes: acquiring micrologging observation data, which includes the observation depth and vertical time corresponding to each shot-receiver pair; grouping the micrologging observation data according to its distribution or fitting effect to obtain one or more datasets; performing polynomial fitting and differentiation on each of the datasets to obtain a polynomial for velocity slowness; and determining the formation velocity corresponding to different observation depths based on the polynomial for velocity slowness.

[0006] In some embodiments, based on the distribution of the micrologging observation data, the micrologging observation data is grouped to obtain one or more datasets, including: plotting a time-depth curve of the micrologging based on the micrologging observation data; estimating apparent velocity based on the data points in the time-depth curve to obtain the apparent velocity estimate corresponding to each data point; determining whether there is a grouping interval point based on the distribution difference of the apparent velocity estimate corresponding to each data point; if there is no grouping interval point, dividing the micrologging observation data into one dataset; if there is a grouping interval point and there is only one grouping interval point, dividing the data points before the grouping interval point into one dataset, and dividing the subsequent data points including the grouping interval point into another dataset; if there are at least two grouping interval points, dividing the data points from the surface to the first grouping interval point into one dataset; dividing the data points between every two adjacent grouping interval points into one dataset, wherein the grouping interval point is the left endpoint of the corresponding dataset; and dividing the subsequent data points including the last grouping interval point to the bottom of the well into another dataset. The left endpoint is the first number of the data interval.

[0007] In some embodiments, based on the fitting effect of the micro-logging observation data, the micro-logging observation data is grouped to obtain one or more datasets, including: plotting a time-depth curve of micro-logging based on the micro-logging observation data; performing polynomial fitting on the data points in the time-depth curve to obtain a fitted curve; determining candidate data points where there is a difference between the fitted curve and the time-depth curve; determining whether there are at least three consecutive target data points among the candidate data points whose differences are greater than a set threshold; if there are no target data points, dividing the micro-logging observation data into one dataset; if there are target data points and there is one set of target data points, dividing the target data points into one dataset and dividing the other data points into another dataset; if there are target data points and there are at least two sets of target data points, dividing each set of target data points into its corresponding dataset and dividing the other data points into another dataset.

[0008] In some embodiments, polynomial fitting and differentiation are performed on the one or more sets of datasets to obtain a polynomial for the slowness of velocity, including: performing polynomial fitting on the one or more sets of datasets to obtain a polynomial fitting result corresponding to each set of datasets; the independent variable of the polynomial fitting result is the observation depth and the dependent variable is vertical time; and differentiating the polynomial fitting result corresponding to each set of datasets to obtain a polynomial for the slowness of velocity corresponding to each set of datasets.

[0009] In some embodiments, determining the formation velocity corresponding to different observation depths based on the polynomial of the aforementioned velocity slowness includes: taking the reciprocal of the polynomial of the velocity slowness corresponding to each of the above one or more sets of data to obtain the relationship between the formation velocity and the observation depth for each set of data; and determining the formation velocity corresponding to different observation depths based on the relationship between the formation velocity and the observation depth for one or more sets of data.

[0010] In some embodiments, the above-mentioned shot-detector pair is one of the following: the shot source is located in the well log, and the geophone is located on the surface within a predetermined range near the wellhead; or the geophone is located in the well, and the shot source is located on the surface within a predetermined range near the wellhead. The above-mentioned observation depth represents the depth of the shot source and the geophone in each shot-detector pair along the well log extension direction; the above-mentioned vertical time is generated by converting the initial arrival time at the offset between the shot source and the geophone into the time corresponding to 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 controlling the velocity in the tomographic inversion model based on the control relationship so that the imaging quality of the pre-stack depth migration meets the set requirements.

[0012] Secondly, embodiments of this disclosure provide a formation velocity determination device based on micrologging. The formation velocity determination device includes: a data acquisition module, a data grouping module, a processing module, and a velocity determination module. The data acquisition module acquires micrologging observation data, which includes the observation depth and vertical time corresponding to each shot-receiver pair. The data grouping module groups the micrologging observation data according to its distribution or fitting effect, obtaining one or more datasets. The processing module performs polynomial fitting and differentiation on each of the one or more datasets to obtain a polynomial for velocity slowness. The velocity determination module determines the formation velocity corresponding to different observation depths based on the polynomial for velocity slowness.

[0013] Thirdly, embodiments of this disclosure provide an electronic device. The electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, communication interface, and memory communicate with each other via the communication bus; the memory stores computer programs; and the processor, when executing the program stored in the memory, implements the formation velocity determination method based on micro-logging as described above.

[0014] Fourthly, embodiments of this disclosure provide a computer-readable storage medium. The computer-readable storage medium stores a computer program that, when executed by a processor, implements the formation velocity determination method based on micro-logging as described above.

[0015] The technical solutions provided in the embodiments of this disclosure have at least some or all of the following advantages:

[0016] By acquiring micrologging observation data, which includes the observation depth and vertical time corresponding to each shot-receiver pair, and grouping the data according to its distribution or fitting effect, one or more datasets are obtained. Grouping allows data with similar distribution patterns or fitting effects to be grouped into the same dataset, while data with significantly different distribution patterns or fitting effects are grouped into different datasets. This enables the subsequent polynomial fitting and differentiation of one or more datasets to obtain a relatively accurate polynomial for velocity slowness. Furthermore, by fitting based on the polynomial, each dataset achieves a point-by-point accurate fit rather than a linear, segmented fit. This ensures that the formation velocities at different observation depths determined by the polynomial for velocity slowness are relatively accurate, thus improving the accuracy of micrologging velocity interpretation. Attached Figure Description

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

[0018] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating a formation velocity determination method based on micrologging according to an embodiment of the present disclosure is shown schematically.

[0020] Figure 2 A detailed implementation flowchart of step S120a according to an embodiment of the present disclosure is illustrated schematically;

[0021] Figure 3 A detailed implementation flowchart of step S120b according to an embodiment of the present disclosure is illustrated schematically;

[0022] Figure 4AA schematic diagram of a time-depth curve of a micrologging well obtained from micrologging observation data according to an embodiment of the present disclosure is shown.

[0023] Figure 4B The illustration schematically shows a time-depth curve and a schematic diagram of the fitting curve based on polynomial fitting of micro-well time-depth data according to an embodiment of the present disclosure;

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

[0025] Figure 5 A schematic diagram of a curve obtained by polynomial fitting of a first set of datasets according to an embodiment of the present disclosure is shown.

[0026] Figure 6 The diagram illustrates a curve of formation velocity obtained by polynomial fitting and differentiation of the first dataset according to an embodiment of the present disclosure.

[0027] Figure 7 A schematic diagram of a curve obtained by polynomial fitting of a second dataset according to an embodiment of the present disclosure is shown.

[0028] Figure 8 The diagram illustrates a curve showing the formation velocity obtained by polynomial fitting and differentiation of the second dataset according to an embodiment of the present disclosure.

[0029] Figure 9 A schematic diagram illustrating the relationship between formation velocity and depth obtained from micrologging observation data processing according to an embodiment of the present disclosure is shown.

[0030] Figure 10 This diagram schematically illustrates the comparison of the aforementioned formation velocity with the tomographic inversion velocity according to an embodiment of the present disclosure, and the output of the comparison result.

[0031] Figure 11 A schematic block diagram of a formation velocity determination device based on micrologging according to an embodiment of the present disclosure is shown.

[0032] Figure 12 A schematic block diagram of an electronic device provided in an embodiment of the present disclosure is shown. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0034] The first exemplary embodiment of this disclosure provides a formation velocity determination method based on micrologging. This formation velocity determination method can be executed by an electronic device with computing capabilities.

[0035] Figure 1 A flowchart illustrating a formation velocity determination method based on micrologging according to an embodiment of the present disclosure is shown schematically.

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

[0037] In step S110, micrologging observation data is acquired, which includes the observation depth and vertical time corresponding to each shot-detector pair.

[0038] Micrologging is one of the main methods for investigating surface structures because it directly receives up-flow wave information generated at different depths downhole on the surface, and is less affected by topography, so the interpretation accuracy is relatively high.

[0039] In some embodiments, the aforementioned shot-detector pair in the micrologging observation data falls into one of the following categories:

[0040] The seismic source is located in the well log, and the geophone is located on the surface within a predetermined range near the wellhead; or,

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

[0042] The aforementioned observation depth H represents the depth of the source and detector at each shot-detector alignment along the logging extension direction.

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

[0044] The aforementioned micrologging observation data includes multiple data sets consisting of observation depth and vertical time, such as {(H1, T1), (H2, T2), ..., (H... N T N )}, where N represents the total number of micrologging observation data.

[0045] In step S120, based on the distribution or fitting effect of the micro-logging observation data, the micro-logging observation data is grouped to obtain one or more datasets.

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

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

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

[0049] In step S210, the time-depth curve of the micrologging is plotted based on the micrologging observation data.

[0050] Figure 4A A schematic diagram of a time-depth curve of a micrologging well obtained from micrologging observation data according to an embodiment of the present disclosure is shown. Figure 4B The illustration schematically shows a time-depth curve and a schematic diagram of the fitting curve based on polynomial fitting of micro-well time-depth data according to an embodiment of the present disclosure; Figure 4C The diagram illustrates a fitting error curve of time-depth data and a schematic diagram of determining grouping points according to an embodiment of the present disclosure.

[0051] Reference Figure 4A As shown, the unit of observation depth in micrologging data is meters (m), and the unit of vertical time is milliseconds (ms). The observation depth ranges from 1m to 346m, and the vertical time ranges from approximately 1ms to 170ms. The observation depths corresponding to the data points of each observation data point can be uniformly distributed or non-uniformly distributed, depending on the actual deployment.

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

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

[0054] T (h) =8.2026 + 0.83417 * h - 1.68616 * h 2 +1.73174*h3 (1)

[0055] In step S230, candidate data points that show a difference between the fitted curve and the time-depth curve are identified.

[0056] Reference Figure 4C As shown, the points on the fitting error curve that are not zero correspond to candidate data points.

[0057] In step S240, it is determined whether there are at least three consecutive target data points among the candidate data points whose differences are greater than a set threshold. 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 outliers, the candidate data points are required not only to be different but also to be continuous. The target data points with real differences among these candidate data points are determined by the distribution of the difference trend, so as to avoid a single outlier caused by error. If such an outlier is regarded as a target data point, it will lead to a decrease in the accuracy of anomaly detection. In some embodiments, for example, if the difference between the fitted curves and the time-depth curves corresponding to (H4, T4), (H5, T5), (H6, T6), (H7, T7), and (H8, T8) is greater than the set threshold and these points are continuous, then the target data points (H4, T4), (H5, T5), (H6, T6), (H7, T7), and (H8, T8) can be assigned to a set of datasets J0. Other data points are assigned to a set of datasets J1.

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

[0059] In step S250a, if the aforementioned target data points exist and constitute a set of target data points, the aforementioned target data points are divided into one dataset, and the other data points besides the aforementioned target data points are divided into another dataset. (Refer to...) Figure 4C As shown by the dashed line in the middle, the data points in the time-depth curve are divided into two groups. The data points with an observation depth of 1m to 6m (for example, actually containing 6 data points) are assigned to the first group of data points, and the data points with an observation depth of 7m to 346m are assigned to the second group of data points. For example, the second group of data points actually contains 225 data points.

[0060] In step S250b, if the aforementioned target data points exist and there are at least two sets of target data points, each set of target data points is divided into its corresponding dataset, and other data points besides the aforementioned target data points are divided into a dataset. For example, if the difference between the fitted curves corresponding to (H4, T4), (H5, T5), and (H6, T6) and the time-depth curve is greater than a set threshold, and these points are continuous, (H4, T4), (H5, T5), and (H6, T6) can be assigned to the same dataset J2; (H 10 T 10 ), (H 11 T 11 ), (H 12 T 12 ) and (H 13 T 13 If the difference between the fitted curve and the time-depth curve is greater than a set threshold, and these points are continuous, then (H) can be used to... 10 T 10 ), (H 11 T 11 ), (H 12 T 12 ) and (H 13 T 13 The remaining data points (those with differences below a set threshold) are assigned to another dataset, J3. Other data points (those with differences below a set threshold) are assigned to another dataset, J4.

[0061] In step S250c, if the target data point does not exist, the micro-logging observation data is divided into a dataset.

[0062] In embodiments including steps S210 to S250c, data can be grouped according to the effect of polynomial fitting. Based on the relative size of candidate data points that differ from the fitted curve and the time-depth curve, target data points with large differences are selected, and the target data points and other data points are divided into different datasets. This grouping method can improve the accuracy of polynomial fitting for each group of datasets after grouping, thereby ensuring that the formation velocity corresponding to different observation depths determined by the polynomial of velocity slowness is relatively accurate, which helps to improve the velocity interpretation accuracy of micrologging.

[0063] Figure 3 A detailed implementation flowchart of step S120b according to an embodiment of the present disclosure is illustrated schematically.

[0064] In other embodiments, in step S120b, the micro-logging observation data is grouped according to the distribution of the micro-logging observation data to obtain one or more datasets.

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

[0066] In step S310, the time-depth curve of the micrologging is plotted based on the micrologging observation data described above. The plotted time-depth curve is shown in Figure 4.

[0067] In step S320, visual velocity is estimated based on the data points in the time-depth curve to obtain the visual velocity estimate corresponding to each data point.

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

[0069] In step S330, based on the magnitude of the distribution difference of the apparent velocity estimates corresponding to each data point, it is determined whether there are grouping interval points.

[0070] In some embodiments, the difference in visual velocity estimates between adjacent data points is relatively large. The starting data point where a significant difference begins to appear among adjacent data points can be determined as the grouping interval point. For example, if the difference in visual velocity estimates between adjacent data points x1 and x2 is δ1, between adjacent data points x2 and x3 is δ2, between adjacent data points x3 and x4 is δ3, between adjacent data points x4 and x5 is δ4, and between adjacent data points x5 and x6 is δ5, then δ1, δ2, and δ3 are relatively similar, δ4 is significantly different from δ1, δ2, and δ3, and δ5 is relatively similar to δ4. Therefore, a significant 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, if there is a grouping interval point and there is only one grouping interval point, the data points before the grouping interval point are divided into a group of datasets, and the subsequent data points including the grouping interval point are divided into a group of datasets.

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

[0073] In step S340c, if there are no grouping interval points, the above micro-logging observation data are divided into a set of datasets.

[0074] In the embodiment including steps S310 to S340c, data can be grouped according to the distribution difference between the apparent velocity estimates of each data point. Based on the magnitude of the distribution difference of the apparent velocity estimates corresponding to each data point, it is determined whether there are grouping interval points and grouping is performed. This grouping method can improve the accuracy of polynomial fitting of each group of datasets after grouping, thereby ensuring that the formation velocity corresponding to different observation depths determined by the polynomial of velocity slowness is relatively accurate, which helps to improve the velocity interpretation accuracy of micrologging.

[0075] In step S130, polynomial fitting and differentiation are performed on one or more sets of datasets to obtain the polynomial of the speed.

[0076] In some embodiments, step S130 above, performing polynomial fitting and differentiation on one or more sets of datasets to obtain a polynomial for the slowness of velocity, includes: performing polynomial fitting on one or more sets of datasets to obtain a polynomial fitting result corresponding to each set of datasets; the independent variable of the polynomial fitting result is the observation depth h, and the dependent variable is the vertical time T; and performing differentiation on the polynomial fitting result corresponding to each set of datasets to obtain a polynomial for the slowness of velocity corresponding to each set of datasets.

[0077] For example, for the first dataset mentioned above, a polynomial fitting is performed, and the following polynomial fitting result is obtained:

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

[0079] Figure 5 A schematic diagram illustrating a curve obtained by polynomial fitting of a first dataset according to an embodiment of the present disclosure is shown. (Refer to...) Figure 5 As shown, the data points in the first dataset are represented by rectangular boxes, and the polynomial-fitted curve is represented by dashed lines. The polynomial-fitted curve is used to represent the relationship between depth (i.e., observation depth) and time (i.e., vertical time).

[0080] For example, for the second dataset mentioned above, a polynomial fitting is performed, and the following polynomial fitting result is obtained:

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

[0082] Figure 7A schematic diagram illustrating a curve obtained by polynomial fitting of a second dataset according to an embodiment of the present disclosure is shown. (Refer to...) Figure 7 As shown, the data points in the second dataset are represented by rectangular boxes, and the polynomial-fitted curve is represented by dashed lines. The polynomial-fitted curve is used to represent the relationship between depth and time.

[0083] Taking the derivative of the polynomial fitting results corresponding to the first dataset above, we obtain the following polynomial for the speed of the first dataset:

[0084]

[0085] Taking the derivative of the polynomial fitting results for the second dataset above, we obtain the following polynomial for the slowness of the second dataset:

[0086]

[0087] In step S140, the formation velocity corresponding to different observation depths is determined according to the polynomial of the aforementioned velocity slowness.

[0088] In some embodiments, in step S140 above, determining the formation velocity corresponding to different observation depths based on the polynomial of the velocity slowness includes: taking the reciprocal of the polynomial of the velocity slowness corresponding to each dataset in the set or more datasets to obtain the relationship between the formation velocity and the observation depth for each dataset; and determining the formation velocity corresponding to different observation depths based on the relationship between the formation velocity and the observation depth in the set or more datasets.

[0089] The slowness of the velocity is the reciprocal of the formation velocity. Since the time units mentioned above are milliseconds, when the formation velocity unit is m / s, the relationship between the formation velocity and the observation depth for the first dataset is as follows:

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

[0091] The value of h ranges from 0 to 6m.

[0092] Figure 6 The diagram schematically illustrates a curve showing the formation velocity obtained by polynomial fitting and differentiation of a first set of datasets according to an embodiment of this disclosure. (Refer to...) Figure 6 As shown, the data points in the first dataset are converted after polynomial fitting and differentiation to obtain the relationship curve between formation velocity and depth (i.e., observation depth).

[0093] The relationship between formation velocity and observation depth for the second dataset is as follows:

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

[0095] The value of h ranges from 7m to 346m.

[0096] Figure 8 The diagram schematically illustrates a curve showing the formation velocity obtained by polynomial fitting and differentiation of a second dataset according to an embodiment of this disclosure. (Refer to...) Figure 8 As shown, the data points in the second dataset are converted after polynomial fitting and differentiation to obtain the relationship curve between formation velocity and depth (i.e., observation depth).

[0097] Based on the above formula relating bottom velocity to observation depth, the formation velocity corresponding to different observation depths can be determined, and a micrologging velocity file can be generated. Each line in the velocity file is formatted as: depth, velocity. Each line represents an observation depth and its corresponding formation velocity.

[0098] Figure 9 A schematic diagram illustrating the relationship between formation velocity and depth obtained from micrologging observation data processing according to an embodiment of the present disclosure is shown.

[0099] For example, Figure 7 and Figure 8 The corresponding two segments of formation velocities are spliced ​​together to obtain the formation velocities corresponding to all observation depths, with reference to... Figure 9 As shown.

[0100] Figure 10 The diagram illustrates a comparison of the formation velocity with the tomographic inversion velocity according to an embodiment of the present disclosure, and outputs the comparison result.

[0101] In some embodiments, the formation velocity determination method, in addition to steps S110 to S140, further includes: comparing the formation velocity with the tomographic inversion velocity and outputting the comparison result. For example, refer to... Figure 10 As shown, the dotted lines represent the velocity calculated in the conventional way (using the conventional theory of micrologging, and the linear fitting method based on the assumption of layered medium in the formation to calculate the formation velocity), the solid curves represent the formation velocity determined in the embodiments of this disclosure, and the short dashed lines represent the tomographic inversion velocity.

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

[0103] Because pre-stack depth migration methods are highly sensitive to velocity, any velocity deviation can hinder the acquisition of accurate and well-focused imaging results. The formation velocity determination method based on micrologging provided in this disclosure can accurately measure formation velocities. It can be used to improve the velocity accuracy of micrologging measurements and to interpret zero-zero offset (VSP) data, thereby enhancing the interpretation accuracy of formation velocities. Furthermore, the velocity measured by this method better meets the shallow velocity modeling requirements of pre-stack depth migration, thus exhibiting broad applicability and versatility.

[0104] A second exemplary embodiment of this disclosure provides a formation velocity determination apparatus based on micrologging.

[0105] Figure 11 A schematic block diagram of a formation velocity determination device based on micrologging according to an embodiment of the present disclosure is shown.

[0106] Reference Figure 11 As shown, the formation velocity determination device 1100 based on micro-logging provided in this embodiment includes: a data acquisition module 1101, a data grouping module 1102, a processing module 1103, and a velocity determination module 1104.

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

[0108] The data grouping module 1102 is used to group the micro-logging observation data according to the distribution status or fitting effect of the micro-logging observation data to obtain one or more datasets.

[0109] The aforementioned processing module 1103 is used to perform polynomial fitting and differentiation processing on one or more sets of datasets to obtain a polynomial for the speed.

[0110] The velocity determination module 1104 is used to determine the formation velocity corresponding to different observation depths based on the polynomial of the velocity slowness.

[0111] In some embodiments, the micrologging observation data is grouped according to its distribution to obtain one or more datasets, including: plotting a time-depth curve of the micrologging based on the micrologging observation data; estimating apparent velocity based on the data points in the time-depth curve to obtain the apparent velocity estimate corresponding to each data point; determining whether there is a grouping interval point based on the distribution difference of the apparent velocity estimate corresponding to each data point; dividing the micrologging observation data into one dataset if there is no grouping interval point; dividing the data points before the grouping interval point into one dataset and dividing the subsequent data points including the grouping interval point into another dataset if there is a grouping interval point; dividing the data points from the surface to the first grouping interval point into one dataset if there are at least two grouping interval points; dividing the data points between every two adjacent grouping interval points into one dataset, wherein the grouping interval point is the left endpoint of the corresponding dataset; and dividing the subsequent data points including the last grouping interval point to the bottom of the well into another dataset.

[0112] In some embodiments, based on the fitting effect of the micro-logging observation data, the micro-logging observation data is grouped to obtain one or more datasets, including: plotting a time-depth curve of micro-logging based on the micro-logging observation data; performing polynomial fitting on the data points in the time-depth curve to obtain a fitted curve; determining candidate data points where there is a difference between the fitted curve and the time-depth curve; determining whether there are at least three consecutive target data points among the candidate data points whose differences are greater than a set threshold; if there are no target data points, dividing the micro-logging observation data into one dataset; if there are target data points and there is one set of target data points, dividing the target data points into one dataset and dividing the other data points into another dataset; if there are target data points and there are at least two sets of target data points, dividing each set of target data points into its corresponding dataset and dividing the other data points into another dataset.

[0113] In some embodiments, polynomial fitting and differentiation are performed on the one or more sets of datasets to obtain a polynomial for the slowness of velocity, including: performing polynomial fitting on the one or more sets of datasets to obtain a polynomial fitting result corresponding to each set of datasets; the independent variable of the polynomial fitting result is the observation depth and the dependent variable is vertical time; and differentiating the polynomial fitting result corresponding to each set of datasets to obtain a polynomial for the slowness of velocity corresponding to each set of datasets.

[0114] In some embodiments, determining the formation velocity corresponding to different observation depths based on the polynomial of the aforementioned velocity slowness includes: taking the reciprocal of the polynomial of the velocity slowness corresponding to each of the above one or more sets of data to obtain the relationship between the formation velocity and the observation depth for each set of data; and determining the formation velocity corresponding to different observation depths based on the relationship between the formation velocity and the observation depth for one or more sets of data.

[0115] In some embodiments, the above-mentioned shot-detector pair is one of the following: the shot source is located in the well log, and the geophone is located on the surface within a predetermined range near the wellhead; or the geophone is located in the well, and the shot source is located on the surface within a predetermined range near the wellhead. The above-mentioned observation depth represents the depth of the shot source and the geophone in each shot-detector pair along the well log extension direction; the above-mentioned vertical time is generated by converting the initial arrival time at the offset between the shot source and the geophone into the time corresponding to zero offset.

[0116] In some embodiments, the formation velocity determination device further includes an application module.

[0117] The aforementioned application module is used to compare the formation velocity with the tomographic inversion velocity and output the comparison results.

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

[0119] Any plurality of the functional modules included in the aforementioned device 1100 may be combined into one module, or any one of the modules may be split into multiple modules. Alternatively, at least a portion of the functionality of one or more of these modules may be combined with at least a portion of the functionality of other modules and implemented in one module. At least one of the functional modules included in the device 1100 may be at least partially implemented as hardware circuitry, 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-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any appropriate combination of any of these three implementation methods. Alternatively, at least one of the functional modules included in the device 1100 may be at least partially implemented as a computer program module, which, when run, can perform corresponding functions.

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

[0121] Figure 12 A schematic block diagram of an electronic device provided in an embodiment of the present disclosure is shown.

[0122] Reference Figure 12 As shown, the electronic device 1200 provided in this embodiment includes a processor 1201, a communication interface 1202, a memory 1203, and a communication bus 1204. 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. When the processor 1201 executes the program stored in the memory, it implements the formation velocity determination method based on micro-logging as described above.

[0123] A fourth exemplary embodiment of this disclosure also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program that, when executed by a processor, implements the formation velocity determination method based on micro-logging as described above.

[0124] The computer-readable storage medium may be included in the device or apparatus described in the above embodiments; or it may exist independently and not assembled into the device or apparatus. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.

[0125] According to embodiments of this disclosure, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0126] It should be noted that the collection, gathering, updating, analysis, processing, use, transmission, and storage of user personal information involved in the technical solutions provided in this disclosure comply 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 to prevent unauthorized access to user personal information data and to safeguard user personal information security, network security, and national security.

[0127] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0128] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily 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 this disclosure. Therefore, this disclosure is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for determining formation velocities based on microlog data, characterized by, The method comprises the following steps: acquiring microlog observation data, wherein the microlog observation data comprises observation depth and vertical time corresponding to each shot; grouping the microlog observation data according to the distribution state or fitting effect of the microlog observation data to obtain one or more data sets; performing polynomial fitting and derivation on the one or more data sets to obtain a polynomial of velocity and slowness; determining the formation velocity corresponding to different observation depths according to the polynomial of velocity and slowness; wherein the grouping of the microlog observation data according to the fitting effect of the microlog observation data to obtain one or more data sets comprises: drawing a time-depth curve of the microlog according to the microlog observation data; performing polynomial fitting on the data points in the time-depth curve to obtain a fitting curve; determining candidate data points with differences between the fitting curve and the time-depth curve; determining whether there are at least three target data points with differences greater than a set threshold and being continuous in the candidate data points; in the case where there are no target data points, dividing the microlog observation data into one data set; in the case where there are target data points and one group of target data points, dividing the target data points into one data set and dividing other data points except the target data points into one data set; in the case where there are target data points and at least two groups of target data points, dividing each group of target data points into a corresponding data set and dividing other data points except the target data points into one data set.

2. The formation velocity determination method of claim 1, wherein, The grouping of the microlog observation data according to the distribution state of the microlog observation data to obtain one or more data sets comprises: drawing a time-depth curve of the microlog according to the microlog observation data; estimating the apparent velocity according to the data points in the time-depth curve to obtain an apparent velocity estimation value corresponding to each data point; determining whether there are grouping interval points according to the difference in the distribution of the apparent velocity estimation value corresponding to each data point; in the case where there are no grouping interval points, dividing the microlog observation data into one data set; in the case where there is one grouping interval point, dividing the data points before the grouping interval point into one data set and dividing the subsequent data points including the grouping interval point into one data set; in the case where there are at least two grouping interval points, dividing the data points between the surface and the first grouping interval point into one data set, dividing the data points between each two adjacent grouping interval points into one data set, wherein the grouping interval point is used as the left end point of the corresponding data set, and dividing the subsequent data points including the last grouping interval point to the bottom of the well into one data set.

3. The method of formation velocity determination of claim 1, wherein, performing polynomial fitting and derivation on the one or more data sets to obtain a polynomial of velocity and slowness, comprising: The polynomial fitting is performed on the one or more groups of data sets respectively to obtain a polynomial fitting result corresponding to each group of data sets, wherein the independent variable of the polynomial fitting result is the observation depth, and the dependent variable is the vertical time; Derivation is performed on the polynomial fitting result corresponding to each group of data sets to obtain a polynomial of velocity slowness corresponding to each group of data sets.

4. The method of formation velocity determination of claim 1, wherein, According to the polynomial of velocity slowness, the formation velocity corresponding to different observation depths is determined, including: The reciprocal operation is performed on the polynomial of velocity slowness corresponding to each group of data sets in the one or more groups of data sets to obtain a relationship between the formation velocity and the observation depth corresponding to each group of data sets; The relationship between the formation velocity and the observation depth under the one or more groups of data sets is used to determine the formation velocity corresponding to different observation depths.

5. The method for determining formation velocities as recited in claim 1, wherein, The shot-geophone pair is one of the following cases: The shot source is located in the well, and the geophone is located on the surface within a set range near the well mouth; The geophone is located in the well, and the shot source is located on the surface within a set range near the well mouth; The observation depth represents the depth of the shot source and the geophone in each shot-geophone pair along the extension direction of the well; and the vertical time is generated by converting the first arrival time at the offset distance of the shot source and the geophone into the time corresponding to the zero offset distance.

6. The method of formation velocity determination of claim 1, wherein, Further comprising: Comparing the formation velocity with the tomographic inversion velocity and outputting the comparison result; Or, Analyzing the regulation relationship between the tomographic inversion velocity and the microlog formation velocity; And regulating the velocity in the tomographic inversion model based on the regulation relationship, so that the imaging quality of the pre-stack depth migration meets the set requirement.

7. A microlog-based formation velocity determination apparatus, characterized by, Comprising: A data acquisition module configured to acquire microlog observation data, wherein the microlog observation data includes observation depths and vertical times corresponding to each shot-geophone pair; A data grouping module configured to group the microlog observation data according to the distribution state or fitting effect of the microlog observation data to obtain one or more groups of data sets; A processing module configured to perform polynomial fitting and derivation on the one or more groups of data sets respectively to obtain a polynomial of velocity slowness; A velocity determination module configured to determine the formation velocity corresponding to different observation depths according to the polynomial of velocity slowness; According to the fitting effect of the microlog observation data, the microlog observation data is grouped to obtain one or more groups of data sets, including: Drawing a time-depth curve of the microlog according to the microlog observation data; Performing polynomial fitting on the data points in the time-depth curve to obtain a fitting curve; Determining candidate data points in which differences exist between the fitting curve and the time-depth curve; Determining whether at least three target data points exist in the candidate data points, wherein the target data points are continuously different from each other and have differences greater than a set threshold; In the case where the target data points do not exist, the microlog observation data is divided into one group of data sets; In the case where the target data points exist and are one group of target data points, the target data points are divided into one group of data sets, and other data points except the target data points are divided into one group of data sets. In the presence of the target data points and 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.

8. An electronic device, comprising: The computer device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; The memory is used for storing a computer program; The processor is used for executing the program stored on the memory, and the stratum velocity determination method in any one of claims 1-6 is realized.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor, and the stratum velocity determination method in any one of claims 1-6 is realized.

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