CN115113281B - Method, device, equipment and storage medium for determining marker well and marker layer thereof - Google Patents

Method, device, equipment and storage medium for determining marker well and marker layer thereof Download PDF

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CN115113281B
CN115113281B CN202210540165.2A CN202210540165A CN115113281B CN 115113281 B CN115113281 B CN 115113281B CN 202210540165 A CN202210540165 A CN 202210540165A CN 115113281 B CN115113281 B CN 115113281B
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刘晓庆
任钰
敬再阳
武鑫
杨双豪
徐东兴
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Beijing Jincheng Qiuyuan Technology Co ltd
Jiang Qingqing
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Abstract

The method comprises the steps of obtaining well tracks, well logging curves and formation data of all wells, and obtaining first similarity of the same well logging curves of the formations corresponding to every two wells according to the well tracks, the formation data and the well logging curves; selecting a marked well from each well by adopting a greedy search idea according to the first similarity; and selecting a marking layer of the marking well according to the first similarity. This application has the effect that accurate efficient confirmed sign well and mark layer thereof.

Description

Method, device, equipment and storage medium for determining marker well and marker layer thereof
Technical Field
The present application relates to the technical field of formation data processing, and in particular, to a method, an apparatus, a device, and a storage medium for determining a marker well and a marker layer thereof.
Background
The determination of the marker well and the marker layer thereof is used as a core technology for fine oil and gas reservoir description, is the basis for developing geological research of the oil and gas reservoir, and is also the key premise for subsequently making scientific and efficient oil and gas reservoir development schemes. In the face of a large number of scattered oil and gas reservoirs, and thousands of wells in each block of the oil and gas reservoir, the conventional method is to manually observe the data of each well in the block to realize the selection of the marker wells and the marker layers thereof.
The traditional manual method seriously depends on experience recognition of service personnel on a specific block, consumes much time and energy of the service personnel, has the problems of strong subjectivity, long time consumption, inconsistent results and the like, and brings huge working pressure to oil reservoir evaluation personnel, oil reservoir research personnel and well logging interpreters. Therefore, an intelligent and automatic method is urgently needed to replace the traditional manual method, so that the workload of personnel is reduced, and the efficiency of determining the marking well and the marking layer thereof is improved.
Disclosure of Invention
In order to accurately and efficiently determine a marker well and a marker layer thereof, the application provides a method, a device, equipment and a storage medium for determining the marker well and the marker layer thereof.
In a first aspect, the present application provides a marking well and a method for determining a marking layer thereof, which adopts the following technical scheme:
a method of determining a marker well and its marker formation, comprising:
acquiring a well track, a logging curve and formation data of each well, and acquiring first similarity of the logging curves of the same kind of the corresponding formation of every two wells according to the well track, the formation data and the logging curve;
selecting a marker well from each well by adopting a greedy search idea according to the first similarity;
and selecting the marker layer of the marker well according to the first similarity.
By adopting the technical scheme, the greedy algorithm is adopted to process the first similarity, so that the marker wells can be intelligently and automatically selected; in addition, the marking layer of the marking well can be automatically selected through the first similarity, and the problems of high working strength, low efficiency, poor repeatability and the like in the prior art adopting a manual determination method are solved.
Preferably, the first similarity includes a mean similarity, a variance similarity and a morphology similarity; the method for acquiring the well track, the logging curve and the stratum data of each well, and acquiring the first similarity of the same logging curve of the stratum corresponding to every two wells according to the well track, the stratum data and the logging curve comprises the following steps:
preprocessing the logging curve, acquiring depths corresponding to all stratums of a target well according to the stratum data, acquiring observation points corresponding to all stratums in a well track according to the depths corresponding to all the stratums of the target well, and acquiring a curve value corresponding to each observation point on the preprocessed logging curve;
obtaining the mean similarity and variance similarity of the same logging curve of the stratum corresponding to each two wells according to the curve values;
and adopting a DTW algorithm to obtain the form similarity of the same logging curves of the stratum corresponding to every two wells.
By adopting the technical scheme, the logging curve is preprocessed, and the preprocessed logging curve data is more complete and accurate; matching the formation data, the well track and the logging curve according to the depth, so as to facilitate the subsequent calculation of the first similarity; the first similarity is obtained according to three different similarities, namely mean similarity, variance similarity and form similarity, so that the obtained first similarity is more representative, the similarity between wells can be more accurately represented, and the foundation technology construction is made for subsequently selecting the marker well and the marker layer thereof.
Preferably, the preprocessing the well log comprises: longitudinal splicing, invalid value deletion, curve attribution, structuralization, logarithm taking processing and normalization processing.
Preferably, the obtaining of the morphological similarity of the same kind of logging curves of the stratum corresponding to each two wells by using the DTW algorithm includes:
acquiring an observation point distance matrix of the stratum corresponding to each two wells by adopting a DTW algorithm;
carrying out boundary constraint on the observation point distance matrix, backtracking a non-boundary constraint part of the observation point distance matrix, and obtaining a shortest path;
and according to the shortest path, acquiring the form similarity of the same logging curves of the stratums corresponding to the two wells.
By adopting the technical scheme, according to experiments, the shortest paths are always on the diagonal part of the matrix, so that the boundary constraint is increased, backtracking is only performed on the non-boundary constraint part, and the boundary constraint part does not participate in backtracking calculation, so that the search space is reduced, and the calculation amount is reduced.
Preferably, the selecting a marked well from each well according to the first similarity by using a greedy search concept comprises:
acquiring a second similarity of the logging curve of each well according to the first similarity;
setting a first threshold value according to a sigma principle, and selecting a selection curve according to the second similarity and the first threshold value;
acquiring a third similarity of the two wells according to the similarity of the selection curves corresponding to the two known wells;
and setting a second threshold value according to a sigma principle, and selecting the marker well by using a greedy thought according to the third similarity and the second threshold value.
By adopting the technical scheme, the marking well selecting method of greedy search is adopted, and the well corresponding to the logging curve with good curve form is selected as the marking well.
Preferably, the selecting the marker layer of the marker well according to the first similarity includes:
and acquiring the stratum similarity of the stratum of the marker well according to the first similarity, and selecting the marker layer of the marker well according to the stratum similarity.
In a second aspect, the present application provides a device for determining a marker well and a marker layer thereof, which adopts the following technical scheme:
a marking well and a device for determining a marking layer thereof comprise,
the acquisition module is used for acquiring well tracks, logging curves and stratum data of all wells, and acquiring first similarity of the same logging curves of the stratum corresponding to every two wells according to the well tracks, the stratum data and the logging curves;
the selecting module is used for selecting a marker well from each well by adopting a greedy search idea according to the first similarity; and (c) a second step of,
and the selecting module is used for selecting the marking layer of the marking well according to the first similarity.
In a third aspect, the present application provides a marking well and a marking layer determination device thereof, which adopts the following technical scheme:
a marker well and its marker layer determination apparatus comprising a memory and a processor, the memory having stored thereon a computer program that can be loaded by the processor and that performs the method of marker well and its marker layer determination of any one of the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solutions:
a computer readable storage medium storing a computer program capable of being loaded by a processor and executing the method of determining a marker well and its marker bed according to any of the first aspect.
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Fig. 1 is a schematic flow chart of the determination of a marker well and its marker layer provided in the embodiments of the present application.
Fig. 2 is a schematic diagram of continuous partial data provided by an embodiment of the present application.
Fig. 3 is a schematic diagram of a first similarity provided in an embodiment of the present application.
Fig. 4 is a schematic diagram of a third mean value, a fourth mean value and a fifth mean value provided in an embodiment of the present application.
Fig. 5 is a schematic diagram of a third similarity provided by an embodiment of the present application.
Fig. 6 is a schematic diagram of a similarity matrix provided in an embodiment of the present application.
Fig. 7 is a block diagram of a marker well and a marker layer determination device thereof according to an embodiment of the present application.
Fig. 8 is a schematic structural diagram of a marker well and a marker layer determination device thereof provided in an embodiment of the present application.
Fig. 9 is a schematic diagram of an observation point distance matrix provided in an embodiment of the present application.
Fig. 10 and fig. 11 are schematic diagrams of a process of finding a shortest path provided by an embodiment of the present application.
FIG. 12 is a schematic diagram of a boundary constraint section provided in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
The present embodiment provides a method for determining a marker well and a marker layer thereof, as shown in fig. 1, the main flow of the method is described as follows (steps S101 to S103):
step S101: and acquiring the well track, the logging curve and the stratum data of each well, and acquiring the first similarity of the logging curves of the same kind of stratum corresponding to every two wells according to the well track, the stratum data and the logging curve.
Where each well may be all of the wells in a block of the reservoir.
The method for acquiring the well track of the well specifically comprises the following steps:
and acquiring the XY offset of the wellhead data and the well deviation data of each well, and calculating to obtain the XY coordinate corresponding to the current vertical depth according to the XY offset of the vertical depth in the well deviation data relative to the wellhead data.
The current vertical depth is Z, and its corresponding coordinates are (X, Y). Wherein, X = X Well head +X Offset of ；Y＝Y Well head +Y Offset of (ii) a Wherein the wellhead data comprises X Well head And Y Well head (ii) a The XY offset comprises X Offset of And Y Offset of 。
The well track comprises a plurality of observation points and coordinates of the observation points, and the observation points are connected in sequence according to the coordinates of the observation points to obtain the well track of the well. Wherein the coordinates of the observation point are (X, Y, Z).
The method comprises the steps of obtaining a logging curve and preprocessing the logging curve, wherein the preprocessing comprises longitudinal splicing, invalid value deletion, curve attribution, structuring, log taking processing (namely log processing) and normalization processing. The preprocessing sequence can be longitudinal splicing, invalid value deletion, curve attribution, structuralization, logarithm processing and normalization processing in sequence.
Wherein, vertically splice: acquiring different series of well logging curves, and if the well logging curves have data files corresponding to different depth sections, longitudinally splicing the well logging curves according to the depth, namely splicing the data files of the different depth sections according to the depth value sequence; if the well logging curve is a data file of a complete depth section, longitudinal splicing is not needed.
Deleting invalid values: and deleting invalid values in each log according to the continuity and the size of the numerical values. Such as-999.25, -9999, and null, etc.
Attribution of a curve: and attributing the logging curves to corresponding wells, and associating the logging curves belonging to the same well. For example, 8 logging curves (in the present application, each logging curve in a plurality of logging curves of a well is of its own type; the type may refer to a name; hereinafter, the types of logging curves may refer to names) are obtained, and after analysis, 3 logging curves are curves of a well a, the 3 logging curves are classified into a well a, and the other 5 logging curves are curves of a well b, the 5 logging curves are classified into a well b; correlating 3 logging curves of the well a one by one according to depths, namely, corresponding values on the 3 logging curves one by one according to the depths; similarly, the 5 well logs of well b are correlated one by one according to depth.
Structuring: and acquiring the stratum data of the well, and correspondingly matching the stratum data, the well track and the logging curve of the well. Specifically, the depth measurement in the well log corresponds to the vertical depth in the well trace, each observation point of the well trace can find a corresponding well log value on the corresponding well log, and the depth value in the formation data (the depth value in the formation data is also the depth measurement) corresponds to the depth measurement in the well log and the vertical depth in the well trace respectively.
Taking logarithm treatment: the resistance is a part of the data in the logging curve data, and the resistance is subjected to logarithmic processing according to the artificial stratum contrast process.
Normalization treatment: in order to calculate the similarity subsequently, the dimensions of the data of the logging curves are consistent, the comparison is convenient, and in order to prevent the influence of non-uniformity of the dimensions on the marking stratum dividing effect of the subsequent target well, the logging curves are subjected to normalization processing.
Referring to fig. 2, in all the data of the log, the corresponding continuous data of the stratum part of the well on the log are intercepted: acquiring the top depth of a first stratum and the bottom depth of a last stratum, and solving the average thickness of each stratum; acquiring an upper limit and a lower limit according to the top depth of the first stratum, the bottom depth of the last stratum and the average thickness, wherein the upper limit is the difference of the top depth of the first stratum minus the average thickness, and the lower limit is the difference of the bottom depth of the last stratum plus the average thickness; first data corresponding to the upper limit on the logging curve and second data corresponding to the lower limit on the logging curve are obtained, and the continuous partial data are all data (including the first data and the second data) between the first data and the second data. Respectively solving a maximum value and a minimum value in continuous partial data, and normalizing the logging curve data according to the maximum value and the minimum value, wherein a specific formula is as follows:
Figure RE-GDA0003788240100000081
wherein x is part For successive partial data, x i The corresponding logging curve value of the depth value on the logging curve is obtained; x is the number of j Is normalized x i 。
By the formula, the normalization processing of the whole logging curve is completed, and the influence of the head and tail non-formation parts on final data is eliminated.
The first similarity comprises mean similarity, variance similarity and form similarity.
Mean similarity: and acquiring the depth corresponding to each stratum according to the stratum data, and acquiring the curve value corresponding to each depth value on the preprocessed logging curve (namely the curve values corresponding to all observation points corresponding to the stratum on the preprocessed logging curve) according to the depth corresponding to the stratum.
And respectively acquiring the mean value similarity of the same logging curves of the stratums corresponding to every two wells (the corresponding stratums refer to two stratums with the same name, and the same logging curves refer to logging curves with the same name) according to the corresponding curve values of the observation points on the logging curves. Specifically, the stratum corresponding to two wells refers to a stratum having the same stratum name as the two wells. Referring to fig. 3, the first column and the second column respectively refer to the names of two wells, the third column refers to the names of the stratums corresponding to the two wells, and the fourth column refers to the names of the log curves of the same kind of the two wells. Respectively defining the stratums corresponding to the two wells as a first corresponding stratum and a second corresponding stratum, calculating the mean value of curve values of all observation points of the first corresponding stratum on the corresponding logging curve, and defining the mean value as a first mean value; similarly, calculating the mean value of curve values of all the observation points of the second corresponding stratum on the corresponding logging curve, and defining the mean value as a second mean value; the mean value similarity of the same kind of logging curves of the stratums corresponding to the two wells is the difference between the first mean value and the second mean value. The smaller the value of the mean similarity is, the higher the similarity of the formations corresponding to the two wells is, and the logging curve segments of the formations corresponding to the two wells are considered to be within the same fluctuation threshold. It should be noted that the two well logs used for calculating the first mean and the second mean are the same kind of well logs.
Variance similarity: the method for calculating the variance similarity is consistent with the method for calculating the mean similarity in principle, the variance of the curve values of all the observation points of the first corresponding stratum on the corresponding logging curve is calculated, and the variance is defined as a first variance; similarly, calculating the variance of the curve values of all the observation points of the second corresponding stratum on the corresponding logging curve, and defining the variance as a second variance; the variance similarity of the same kind of well logs of the corresponding stratum of the two wells is the difference of the first variance and the second variance. The smaller the value of the variance similarity is, the higher the similarity of the corresponding stratum of the two wells is, and the fluctuation degrees of the logging curve segments of the corresponding stratum of the two wells tend to be the same. It is noted that the two logs used to calculate the first variance and the second variance are the same kind of log.
Morphological similarity: and adopting a DTW algorithm to obtain the form similarity of the same logging curves of the stratum corresponding to every two wells. The method comprises the following specific steps:
(1) And calculating the distance between the observation points of the stratums corresponding to the two wells to obtain an observation point distance matrix. The formula for solving the distance between the observation points of the stratums corresponding to the two wells is as follows:
d(i，j)＝|one i -two j |；
wherein d (i, j) is between observation points of strata corresponding to two wellsThe distance of (a); one (C) i Representing a curve value corresponding to an observation point i of a well stratum (namely a first corresponding stratum) on a logging curve of the well; two (two) j And representing the curve value corresponding to the observation point j of the formation corresponding to the other well (namely, the second corresponding formation) on the logging curve of the well.
The observation point distance matrix is shown in fig. 9.
(2) The distance of each possible path is calculated in the observation point distance matrix, noting that the selected path must start from the lower left square and end at the upper right square of the observation point distance matrix, and that each square cannot be traversed repeatedly. When the distance from any square cell in ((i-1, j-1), (i-1, j) or (i, j-1)) to the next square cell (i, j) is d (i, j) if the distance is horizontal or vertical, and is 2d (i, j) if the distance is diagonal.
The calculation formula of the distance g (i, j) between the observation points of the stratums corresponding to the two wells is as follows:
Figure RE-GDA0003788240100000101
(3) And (5) backtracking the observation point distance matrix by adopting a backtracking method to obtain the shortest path.
The process of finding the shortest path is described with reference to fig. 10 and 11.
(4) And according to the shortest path, acquiring the form similarity of the same logging curves of the stratums corresponding to the two wells (the logging curve of one stratum is a section of curve on the whole logging curve of the well).
Redefining the calculation mode of the shortest path, so that under the condition that the lengths of the same type of well logging curves of the stratum of one well and the stratum corresponding to the other well are closer (the closer the lengths are, the closer the number of observation points corresponding to the well logging curve of one stratum and the number of observation points corresponding to the well logging curve of the other stratum are), the smaller the acquired morphological similarity is, and the well logging curves of the stratum of one well and the stratum corresponding to the other well are more similar.
The formula for solving the morphological similarity best _ dis of the logging curves of the same kind of stratum corresponding to the two wells is as follows:
best_dis＝g(m，n)/min(m，n)；
wherein m and n are the lengths of the logging curves of the same type of the well stratum and the stratum corresponding to the other well respectively, and g (m and n) is the distance value of the shortest path obtained in the step (3).
Since the measurement mode of the morphological similarity is measured according to the distance between the observation points of the two wells, the smaller the value of the morphological similarity is, the higher the similarity of the stratum corresponding to the two wells is.
Optionally, an improvement is performed on the basis of the DTW algorithm, specifically, in the step (4), the boundary constraint is performed on the observation point distance matrix, and a backtracking method is adopted to backtrack the non-boundary constraint part of the observation point distance matrix to obtain the shortest path. Selecting a right triangle at the upper left corner of the observation point distance matrix as a shadow triangle, and taking N percent of the abscissa from left to right 1 As a length of a right angle of the shadow triangle, N percent of the ordinate is taken from top to bottom 2 Another right angle length as a shadow triangle; similarly, a right triangle is also selected from the lower right corner of the observation point distance matrix as a shadow triangle, and N percent of the abscissa is taken from right to left 3 Taking N percent of ordinate from bottom to top as a length of a right angle of the shadow triangle 4 As another right angle length of the shaded triangle. In this example, N 1 、N 2 、 N 3 And N 4 Are all twenty.
The area of the two hatched triangles is the boundary constraint part, see fig. 12.
Step S102: and selecting the marker wells from the wells according to the first similarity by adopting a greedy search idea.
(1) And acquiring a second similarity of the logging curve of each well according to the first similarity:
calculating the mean value of the mean value similarity of the same well logging curves of all the stratums, and defining the mean value as a third mean value; calculating the mean value of the variance similarity of the same well logging curves of all the stratums of the well, and defining the mean value as a fourth mean value; and calculating the mean value of the morphological similarity of the same well log of all the stratums of the well, and defining the mean value as a fifth mean value. Referring to fig. 4, the first column of fig. 4 is a fifth mean value, which is the mean value of the morphological similarity of CBL _ cementing quality logs (log names) of all the formations of the two wells in fig. 1, and the second column of fig. 4 is a third mean value, which is the mean value of the similarity of CBL _ cementing quality logs (log names) of all the formations of the two wells in fig. 1; the third column of FIG. 4 is the fourth mean value, which is the mean value of the variance similarity of CBL _ Wellmass logs (log names) of all the formations of the two wells in FIG. 1.
And respectively carrying out weighted summation on the third average value, the fourth average value and the fifth average value of the logging curves of the same kind of the two wells to obtain a second similarity of the logging curves. Referring to fig. 4, the result of weighted summation of the third, fourth and fifth mean values between two wells is the second similarity.
(2) Setting a first threshold value according to a sigma principle, and selecting a selection curve according to the second similarity and the first threshold value:
the first threshold is a difference between a mean of the second similarities of all the well logs and a variance of the second similarities of all the well logs. Judging whether the second similarity is smaller than a first threshold value; if so, taking the logging curve corresponding to the second similarity as the selection curve.
(3) According to the similarity of the selection curves corresponding to the two known wells, acquiring the third similarity of the two wells:
wherein, the similarity of the selected curve is the corresponding second similarity. Referring to fig. 5, in all the same well logging curves of two wells, the average value of the similarity of the corresponding selected curves is selected, and the average value is the final similarity of the two wells, namely the third similarity.
(4) Setting a second threshold value according to a sigma principle, selecting a marker well according to a greedy search idea and according to a third similarity and the second threshold value:
referring to FIG. 6, a similarity matrix between wells is created based on the third similarity.
Wherein the second threshold is a difference between a mean of all the third similarities and a variance of all the third similarities (all the third similarities refer to all the values in the similarity matrix).
The method comprises the following steps: and selecting the minimum third similarity in the similarity matrix.
Step two: judging whether the selected minimum third similarity is smaller than a second threshold value or not; if yes, executing the third step; if not, executing the step six.
Step three: and selecting the two wells corresponding to the minimum third similarity as the marking wells.
Step four: judging whether the number of the marker wells is larger than a first preset value and smaller than a second preset value; if yes, stopping selecting the marking well; if not, executing the fifth step; the first predetermined value is P percent of the number of reference wells, the second predetermined value is Q percent of the number of reference wells, Q is greater than P, P is ten and Q is fifty in this embodiment.
Step five: and (4) taking the row and the column where the minimum third similarity exists (the row and the column where the minimum third similarity belongs in the similarity matrix) as the current row and column, removing the minimum third similarity in the current row and column, continuously selecting the minimum third similarity in the remaining third similarities of the current row and column, and returning to the step two.
Step six: and (4) removing the row and the column where the historical minimum third similarity is located (the historical minimum third similarity is all the minimum third similarities related to the first step to the fifth step), selecting the minimum third similarity from the rest rows and columns, and returning to the second step.
It is noted that the third similarity at the same position in the similarity matrix is not selected repeatedly.
After the marked wells are selected, the remaining wells except the marked wells are non-marked wells in all the wells.
Step S103: and selecting the marker layer of the marker well according to the first similarity.
The method comprises the steps that a marked well is provided with a plurality of stratums, and the mean value similarity, the variance similarity and the form similarity of all logging curve sections of each stratum (all logging curve sections of a stratum are curve sections corresponding to all logging curves of one stratum respectively) are obtained respectively; carrying out weighted summation on the mean similarity, the variance similarity and the form similarity belonging to the same logging curve segment to obtain a sum value; and calculating the average value of the sum values of all the logging curve segments of the stratum, wherein the average value is the stratum similarity of the stratum.
And selecting a marking layer of the marking well according to the stratum similarity. For example, in all the stratums of the marking well, the stratum similarities are sorted from small to large, and the stratums corresponding to the first n stratum similarities are selected as the marking layers of the marking well in the sorting result; for another example, a first preset threshold is set, whether the stratum similarity is smaller than the first preset threshold is judged, and if yes, the stratum corresponding to the stratum similarity is used as a marker layer of the marker well.
Optionally, after dividing the marker well into a plurality of strata according to the stratum data, before obtaining corresponding observation points of the strata in the well trajectory, obtaining the thickness of the strata, and judging whether the thicknesses of a plurality of continuous strata are all smaller than a thickness threshold; if the thickness of the stratum is smaller than the thickness of the stratum, the characteristic of the stratum is not obvious enough, so that the plurality of continuous strata with the smaller thickness can also be called small layers, and the plurality of continuous small layers are combined into one stratum, so that the characteristic of the stratum is obvious enough.
Optionally, if the marker well is deep, combining a plurality of continuous small layers into one stratum; if the marker well is shallow, there is no need to merge multiple successive sub-layers into one formation, since the shallow marker well itself has no several formations and does not need to be combined.
Correspondingly, the method for selecting the marker layer of the marker well can also comprise the following steps: and calculating the similarity of the same logging curves of the two sign wells corresponding to the small layers, wherein the similarity also comprises mean similarity, variance similarity and form similarity, and calculating the stratum similarity of the small layers (the principle of the calculation method of the stratum similarity of the small layers is consistent with that of the calculation method of the stratum similarity of the medium stratum, and the repeated description is omitted here). And selecting a plurality of small layers with smaller stratum similarity from the stratum similarities of all the small layers of the mark layer to be recombined into a new mark layer. For example, in all the small layers of the mark layer, the stratum similarities of the small layers are sorted from small to large, the small layers corresponding to the first m stratum similarities are selected in the sorting result, and the selected small layers are recombined into a new mark layer; for another example, a second preset threshold is set, and the small layers corresponding to the stratum similarity of the small layers smaller than the second preset threshold are recombined into a new mark layer.
In order to better implement the above method, the embodiment of the present application further provides a device for determining a marker well and a marker layer thereof, which may be specifically integrated in a device for determining a marker well and a marker layer thereof, such as a terminal or a server, where the terminal may include, but is not limited to, a mobile phone, a tablet computer, or a desktop computer.
Fig. 7 is a structural block diagram of a device for determining a marker well and a marker layer thereof according to an embodiment of the present application, and as shown in fig. 7, the device mainly includes:
the acquisition module 201 is configured to acquire a well trajectory, a logging curve and formation data of each well, and acquire a first similarity of the same logging curve of the formation corresponding to each two wells according to the well trajectory, the formation data and the logging curve;
a selecting module 202, configured to select a marked well from each well according to the first similarity by using a greedy search idea; and (c) a second step of,
and the selecting module 203 is used for selecting the marking layer of the marking well according to the first similarity.
Various changes and specific examples in the method provided in the above embodiments are also applicable to the marking well and the device for determining the marking layer thereof in this embodiment, and through the foregoing detailed description of the marking well and the method for determining the marking layer thereof, those skilled in the art can clearly know the method for implementing the marking well and the device for determining the marking layer thereof in this embodiment, and for the sake of brevity of the description, details are not described here.
In order to better execute the program of the method, the embodiment of the present application further provides a device for determining a marker well and a marker layer thereof, as shown in fig. 8, a device 300 for determining a marker well and a marker layer thereof includes a memory 301 and a processor 302.
The marker well and its marker layer determination device 300 may be implemented in various forms including devices such as cell phones, tablets, palm top computers, laptop and desktop computers.
The memory 301 may be used to store, among other things, instructions, programs, code sets, or instruction sets. The memory 301 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (for example, obtaining a first similarity, a second similarity, a third similarity, and the like), and instructions for implementing the method for determining the marker wells and the marker layers thereof provided in the foregoing embodiments, and the like; the storage data area may store data and the like involved in the method for determining the marker wells and the marker layers thereof provided in the above embodiments.
Processor 302 may include one or more processing cores. The processor 302 performs various functions of the present application and processes data by executing or executing instructions, programs, sets of code or instruction sets stored in the memory 301 to invoke data stored in the memory 301. The Processor 302 may be at least one of an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a Central Processing Unit (CPU), a controller, a microcontroller, and a microprocessor. It is understood that, for different devices, the electronic devices for implementing the functions of the processor 302 may be other devices, and the embodiments of the present application are not limited in particular.
An embodiment of the present application provides a computer-readable storage medium, including: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk. The computer readable storage medium stores a computer program that can be loaded by a processor and executed to perform the method of determining a marker well and its marker bed of the above embodiments.
The specific embodiments are merely illustrative and not restrictive, and various modifications that do not materially contribute to the embodiments may be made by those skilled in the art after reading this specification as required, but are protected by patent laws within the scope of the claims of this application.

Claims (7)

1. A method for determining a marker well and a marker layer thereof is characterized by comprising the following steps:
acquiring a well track, a logging curve and formation data of each well, and acquiring first similarity of the same logging curve of the formation corresponding to each two wells according to the well track, the formation data and the logging curve; the first similarity comprises mean similarity, variance similarity and form similarity;
wherein, the obtaining of the first similarity of the same kind of well logging curves of the stratum corresponding to every two wells comprises:
preprocessing the logging curve, acquiring depths corresponding to all stratums of a target well according to the stratum data, acquiring observation points corresponding to all stratums in a well track according to the depths corresponding to all the stratums of the target well, and acquiring a curve value corresponding to each observation point on the preprocessed logging curve;
obtaining the mean similarity and the variance similarity of the same logging curve of the stratum corresponding to each two wells according to the curve values;
the DTW algorithm is adopted to obtain the morphological similarity of the logging curves of the same kind of stratum corresponding to every two wells,
according to the first similarity, selecting a marker well from the wells by adopting a greedy search thought, and the method comprises the following steps:
acquiring a second similarity of the logging curve of each well according to the first similarity and a weighted summation algorithm;
setting a first threshold according to a sigma principle, and selecting a selection curve according to the second similarity and the first threshold;
acquiring a third similarity of the two wells according to the similarity of the selection curves corresponding to the two known wells;
setting a second threshold value according to a sigma principle, and selecting a marking well according to the third similarity and the second threshold value;
and selecting the marker layer of the marker well according to the first similarity.
2. The method of claim 1, wherein the pre-processing the well log comprises: longitudinal splicing, invalid value deletion, curve attribution, structurization, logarithm processing and normalization processing.
3. The method of claim 1, wherein the obtaining morphological similarity of the same log of the formation for each two wells using the DTW algorithm comprises:
acquiring an observation point distance matrix of the stratum corresponding to each two wells by adopting a DTW algorithm;
carrying out boundary constraint on the observation point distance matrix, backtracking a non-boundary constraint part of the observation point distance matrix, and obtaining a shortest path;
and according to the shortest path, acquiring the form similarity of the same logging curves of the stratums corresponding to the two wells.
4. The method of claim 1, wherein selecting a marker layer of the marker well based on the first similarity comprises:
and acquiring the stratum similarity of the stratum of the marker well according to the first similarity, and selecting the marker layer of the marker well according to the stratum similarity.
5. A device for determining a marker well and a marker layer thereof is characterized by comprising,
the acquisition module is used for acquiring the well track, the logging curve and the stratum data of each well and acquiring the first similarity of the logging curves of the same kind of stratum corresponding to every two wells according to the well track, the stratum data and the logging curve; the first similarity comprises mean similarity, variance similarity and form similarity;
the acquisition module is further used for preprocessing the logging curve, acquiring depths corresponding to all stratums of a target well according to the stratum data, acquiring observation points corresponding to all stratums in a well track according to the depths corresponding to all stratums of the target well, and acquiring a curve value corresponding to each observation point on the preprocessed logging curve;
obtaining the mean similarity and variance similarity of the same logging curve of the stratum corresponding to each two wells according to the curve values;
the DTW algorithm is adopted to obtain the morphological similarity of the logging curves of the same kind of stratum corresponding to every two wells,
the selecting module is used for selecting a marker well from each well by adopting a greedy search idea according to the first similarity;
acquiring a second similarity of the logging curve of each well according to the first similarity and a weighted sum algorithm;
setting a first threshold according to a sigma principle, and selecting a selection curve according to the second similarity and the first threshold;
acquiring a third similarity of the two wells according to the similarity of the selection curves corresponding to the two known wells;
setting a second threshold value according to a sigma principle, and selecting a marking well according to the third similarity and the second threshold value;
and (c) a second step of,
and the selecting module is used for selecting the marking layer of the marking well according to the first similarity.
6. A marker well and marker layer determination apparatus therefor, comprising a memory and a processor, the memory having stored thereon a computer program which can be loaded by the processor and which performs the method of any one of claims 1 to 4.
7. A computer-readable storage medium, characterized in that a computer program is stored which can be loaded by a processor and which executes a method according to any one of claims 1 to 4.
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