CN113420936A - Stratum comparison method based on dynamic time programming optimization algorithm - Google Patents
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Abstract
The invention relates to the technical field of geophysical logging, in particular to a stratum contrast method based on a dynamic time planning optimization algorithm.
Description
Technical Field
The invention relates to the technical field of geophysical logging, in particular to a stratum comparison method based on a dynamic time programming optimization algorithm.
Background
In the well logging evaluation process of an oil and gas field, multi-well comparison and interpretation becomes an important way for fine evaluation, accurate stratum comparison needs to be carried out to realize the multi-well comparison and interpretation, and the manual stratum comparison mode has the defects of long time consumption and strong subjectivity. Because the logging curve forms have similarity between adjacent wells in the same deposition background in the same oil field, the formation comparison can be realized by adopting a curve similarity calculation algorithm.
The dynamic time warping algorithm can solve the problem of time sequence matching with different lengths, and can effectively process the conditions of layer thickness change, pinch-out and the like when being applied to stratum comparison. However, in the practical application process, the following problems still exist: limited by the algorithm theory, the dynamic time warping algorithm cannot handle the situation of the upwarp section of the horizontal well track; the logging curve data scale is huge, the time complexity of the traditional dynamic time warping algorithm is large, the calculation time is long, although the operation speed of the algorithm can be accelerated by setting the upper and lower search boundaries, the well inclination angle can be changed in the drilling process, and therefore the upper and lower search boundaries cannot be determined to be a certain constant.
Chinese patent CN110852144A discloses an intelligent stratigraphic comparison method and system based on DTW, firstly obtaining original logging data and carrying out normalization preprocessing to form normalized logging data; and then calculating the logging curve form similarity information of the two selected wells based on a dynamic time warping algorithm to realize stratum comparison, however, when the scheme is applied to highly deviated wells and horizontal wells, the dynamic time warping algorithm can only be used for monotonously increasing search, and cannot process the upwarp sections of the horizontal well tracks.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a stratum comparison method based on a dynamic time programming optimization algorithm, and can obtain high-precision stratum comparison results of various real logging wells including highly deviated wells and horizontal wells.
In order to solve the technical problems, the invention adopts the technical scheme that:
a stratum comparison method based on a dynamic time programming optimization algorithm is provided, and comprises the following steps:
s1: acquiring original logging curve sequences and well trajectory curves of a reference well of a divided stratum and a target well of a stratum to be divided, and processing the original logging curve sequences of the reference well of the divided stratum and the target well of the stratum to be divided to obtain a new logging curve sequence containing gradient information;
s2: according to the well track curves of the reference well and the target well, eliminating repeated vertical depth parts caused by upwarping of well tracks in the new well logging curve sequence to obtain a well logging curve sequence of an inclined depth index with monotonically increasing vertical depth;
s3: processing the logging curve sequence of the slant-depth index by using a linear interpolation value according to the well trajectory curves of the reference well and the target well to obtain a logging curve sequence of the vertical-depth index;
s4: giving a search boundary, and performing similarity comparison on the logging curve sequence of the vertical-depth index by using a banded boundary dynamic time programming algorithm to obtain a similar track matrix;
s5: determining a depth upper and lower limit search matrix of the slant depth index according to the similar track matrix;
s6: finding a depth index contained in a depth upper and lower limit search matrix of the slant depth index of the target well to obtain a numerical index matrix when similarity comparison is executed;
s7: and taking the numerical index matrix as a search range, obtaining a final similar track matrix by using a dynamic time programming algorithm, and dividing the target well stratum according to the final similar track matrix.
The stratum comparison method based on the dynamic time programming optimization algorithm comprises the steps of firstly processing original logging curve sequences of a reference well and a target well to obtain a new logging curve sequence containing gradient information, well considering the change trend of the curves, then removing a vertical depth repeated well section in the new logging curve sequences of the reference well and the target well according to well track curves of the reference well and the target well, eliminating well track influence, and firstly converting a logging curve sequence of an inclined depth index into a logging curve sequence of a vertical depth index for similarity comparison; the invention comprehensively utilizes a logging curve sequence and a well trajectory curve, obtains high-precision stratum comparison results of various real logging wells including a highly deviated well and a horizontal well after two times of dynamic time planning algorithm processing, solves the problems that the dynamic time planning algorithm cannot be applied to the highly deviated well and the horizontal well and cannot process the upwarp section of the well trajectory, and simultaneously ensures the precision and the running speed of the algorithm.
Preferably, in step S1, the original logging curve sequence and the well trajectory curve are collected during the drilling process, wherein the original logging curve sequence takes the slant depth as an index and records the formation petrophysical characteristics; the well track curve takes the slant depth as an index, and records the slant depth-vertical depth conversion relation of the whole well section.
Preferably, in step S1, the processing method of the original log sequence is:
constructing a new logging curve sequence F (s (i)) comprehensively considering numerical information and gradient information for an original logging curve sequence s (i) with the length of N and i belonging to [1, N ]:
preferably, in step S2, according to the conversion relationship between slant depth and vertical depth recorded by the well trajectory curve, the depth is interpolated by a linear interpolation method, and the vertical depth corresponding to each depth of the new well logging curve sequence of the slant depth index is calculated for the known matching relationship between slant depth and vertical depth (tmd)1，tvd1)、(tmd2，tvd2) If the new slant depth tmd ∈ [ tmd ∈ >1，tmd2]Then the corresponding vertical depth tvd is:
and traversing the new logging curve sequence from shallow to deep, finding and removing a vertical depth repeated well section caused by the upward warp of the well track, and obtaining a logging curve sequence of an inclined depth index with the vertical depth increasing monotonically.
Preferably, in step S3, a fixed vertical depth index step is given, the vertical depth index sequence is constructed with the maximum and minimum vertical depth values of the logging curve sequence of the slant-depth index obtained in step S2 with the vertical depth monotonically increasing as the boundary, and the logging curve sequence of the slant-depth index is interpolated by using a linear interpolation method to obtain the logging curve sequence of the vertical depth index.
Preferably, in step S4, the similar trajectory matrix is a two-dimensional matrix, and records a similar matching relationship between points in the vertical depth indexed well logging curve sequence of the reference well and the vertical depth indexed well logging curve sequence of the target well, where the first dimension X is the vertical depth indexed well logging curve sequence of the reference well, X is a point in the first dimension X, the second dimension Y is the vertical depth indexed well logging curve sequence of the target well, and Y is a point in the second dimension Y.
Preferably, in step S5, the method for calculating the upper and lower depth limit search matrix of the slant-depth index is as follows:
s51: traversing the similar track matrix in the step S4, and taking the (i + 1) th path point P in the similar track matrixi+1＝[xi+1，yi+1]Comparing the ith path point Pi＝[xi，yi]Obtaining a depth upper and lower limit matrix of the vertical depth index under the change conditions in the first dimension X and the second dimension Y directions;
s52: and converting the vertical depth of the vertical depth index upper and lower limit depth matrix into the vertical depth according to the conversion relation between the vertical depth and the vertical depth recorded by the well track curve, taking the depth of the logging curve sequence of the reference well vertical depth index obtained in the step S2 as an index, interpolating the depth by a linear interpolation method, and calculating the upper and lower limit depth matrix of the target well vertical depth index corresponding to each depth.
Preferably, in step S51, since the time planning algorithm path has monotonicity, the method for calculating the upper and lower limit matrices of the depth of the vertical depth index is as follows:
(1) when x isi+1>xiAnd y isi+1>yiWhen, the upper limit is [ x ]i，yi+1]With a lower limit of [ xi+1，yi]；
(2) When x isi+1>xiAnd y isi+1＝yiThen, the search is continued backward along the first dimension X, and when the path point P is foundi+n＝[xi+n，yi+n]And Pi+n+1＝[xi+n+1，yi+n+1]Having y ofi+n+1>yi+nWhen, the upper limit is [ x ]i，yi]With a lower limit of [ xi+n，yi+n]；
(3) When y isi+1>yiAnd xi+1＝xiWhile continuing to followSecond dimension Y is found backwards when finding path point Pi+n＝[xi+n，yi+n]And Pi+n+1＝[xi+n+1，yi+n+1]Has xi+n+1>xi+nWhen, the upper limit is [ x ]i+n，yi+n]With a lower limit of [ xi，yi]。
Preferably, the method for obtaining the numerical index matrix in step S6 includes: traversing a depth array AD (i) of a logging curve sequence of the slant-depth index of the reference well to obtain a depth upper limit TL (i) and a depth lower limit BL (i) of the slant-depth index of the target well corresponding to each depth; and traversing the depth array BD (j) of the logging curve sequence of the slant-depth index of the target well, finding the values contained in [ TL (i), BL (i) ], recording j, and forming a numerical index matrix.
Preferably, in step S7, similarity comparison is performed on the logging curve sequences of the slant-depth index to obtain a final similar trajectory matrix.
Compared with the background technology, the stratum comparison method based on the dynamic time programming optimization algorithm has the following beneficial effects:
the high-precision stratum contrast results of various real logging wells including highly deviated wells and horizontal wells can be obtained through two times of dynamic time planning algorithm processing, the problem of the upwarp section of the well track can be solved, and meanwhile the operation speed of the algorithm is guaranteed.
Drawings
FIG. 1 is a flow chart of a formation comparison method based on a dynamic time programming optimization algorithm according to the present invention;
FIG. 2 is a logic diagram of the operation of the numerical index matrix according to the present invention.
Detailed Description
The present invention will be further described with reference to the following embodiments.
As shown in fig. 1, a formation comparison method based on a dynamic time programming optimization algorithm includes the following steps:
s1: acquiring original logging curve sequences and well trajectory curves of a reference well of a divided stratum and a target well of a stratum to be divided, and processing the original logging curve sequences of the reference well of the divided stratum and the target well of the stratum to be divided to obtain a new logging curve sequence containing gradient information;
s2: according to the well track curves of the reference well and the target well, eliminating repeated vertical depth parts caused by upwarping of well tracks in the new well logging curve sequence to obtain a well logging curve sequence of an inclined depth index with monotonically increasing vertical depth;
s3: processing the logging curve sequence of the slant-depth index by using a linear interpolation value according to the well trajectory curves of the reference well and the target well to obtain a logging curve sequence of the vertical-depth index;
s4: giving a search boundary, and performing similarity comparison on the logging curve sequence of the vertical-depth index by using a banded boundary dynamic time programming algorithm to obtain a similar track matrix;
s5: determining a depth upper and lower limit search matrix of the slant depth index according to the similar track matrix;
s6: finding a depth index contained in a depth upper and lower limit search matrix of the slant depth index of the target well to obtain a numerical index matrix when similarity comparison is executed;
s7: and taking the numerical index matrix as a search range, obtaining a final similar track matrix by using a dynamic time programming algorithm, and dividing the target well stratum according to the final similar track matrix.
According to the stratum comparison method based on the dynamic time programming optimization algorithm, firstly, original logging curve sequences of a reference well and a target well are processed to obtain a new logging curve sequence containing gradient information, the change trend of the curves is well considered, then, according to well trajectory curves of the reference well and the target well, repeated vertical-depth well sections in the new logging curve sequences of the reference well and the target well are removed, the influence of well trajectories is eliminated, and firstly, a logging curve sequence of an inclined depth index is converted into a logging curve sequence of a vertical depth index for similarity comparison; the invention comprehensively utilizes a logging curve sequence and a well trajectory curve, obtains high-precision stratum comparison results of various real logging wells including a highly deviated well and a horizontal well after two times of dynamic time planning algorithm processing, solves the problems that the dynamic time planning algorithm cannot be applied to the highly deviated well and the horizontal well and cannot process the upwarp section of the well trajectory, and simultaneously ensures the precision and the running speed of the algorithm.
In step S1, collecting an original logging curve sequence and a well trajectory curve in the drilling process, wherein the original logging curve sequence takes the slant depth as an index and records the physical characteristics of stratum rocks; the well track curve takes the slant depth as an index, and records the slant depth-vertical depth conversion relation of the whole well section.
The processing method of the original logging curve sequence comprises the following steps:
constructing a new logging curve sequence F (s (i)) comprehensively considering numerical information and gradient information for an original logging curve sequence s (i) with the length of N and i belonging to [1, N ]:
in step S2, according to the conversion relationship between slant depth and vertical depth recorded by the well trajectory curve, the depth is interpolated by linear interpolation method, and the vertical depth corresponding to each depth of the new logging curve sequence of the slant depth index is calculated, for the known matching relationship between slant depth and vertical depth (tmd)1，tvd1)、(tmd2，tvd2) If the new slant depth tmd ∈ [ tmd ∈ >1，tmd2]Then the corresponding vertical depth tvd is:
and traversing the new logging curve sequence from shallow to deep, finding and removing a vertical depth repeated well section caused by the upward warp of the well track, and obtaining a logging curve sequence of an inclined depth index with the vertical depth increasing monotonically.
In step S3, a fixed vertical depth index step size is given, the vertical depth maximum value and the vertical depth minimum value of the logging curve sequence of the slant depth index with the vertical depth monotonically increasing obtained in step S2 are taken as a boundary to construct a vertical depth index sequence, and the logging curve sequence of the slant depth index is interpolated by using a linear interpolation method to obtain the logging curve sequence of the vertical depth index. When the given vertical depth index step length is 1m, the logging curve sequence indexed according to the vertical depth with the step length of 1m can be obtained by interpolating the logging curve sequence indexed by the inclined depth of the reference well and the logging curve sequence indexed by the inclined depth of the target well.
And giving a vertical floating search range of 5m, and performing similarity comparison on the vertical depth indexed logging curve sequence of the reference well and the vertical depth indexed logging curve sequence of the target well to obtain a similar track matrix.
In step S4, the similar trajectory matrix is a two-dimensional matrix, and records a similar matching relationship between each point of the vertical depth indexed logging curve sequence of the reference well and each point of the vertical depth indexed logging curve set of the target well, where a first dimension X is the vertical depth indexed logging curve set of the reference well, X is a point in the first dimension X, a second dimension Y is the vertical depth indexed logging curve set of the target well, and Y is a point in the second dimension Y.
The calculation method of the depth upper and lower limit search matrix of the slant depth index comprises the following steps:
s51: traversing the similar track matrix in the step S4, and taking the (i + 1) th path point P in the similar track matrixi+1＝[xi+1，yi+1]Comparing the ith path point Pi＝[xi，yi]Obtaining a depth upper and lower limit matrix of the vertical depth index under the change conditions in the first dimension X and the second dimension Y directions;
s52: and converting the vertical depth of the vertical depth index upper and lower limit depth matrix into the vertical depth according to the conversion relation between the vertical depth and the vertical depth recorded by the well track curve, taking the depth of the logging curve sequence of the reference well vertical depth index obtained in the step S2 as an index, interpolating the depth by a linear interpolation method, and calculating the upper and lower limit depth matrix of the target well vertical depth index corresponding to each depth.
In step S51, since the time planning algorithm path has monotonicity, the calculation method of the depth upper and lower limit matrix of the vertical depth index is as follows:
(1) when x isi+1>xiAnd y isi+1>yiWhen, the upper limit is [ x ]i，yi+1]With a lower limit of [ xi+1，yi]；
(2) When x isi+1>xiAnd y isi+1＝yiThen, the search is continued backward along the first dimension X, and when the path point P is foundi+n＝[xi+n，yi+n]And Pi+n+1＝[xi+n+1，yi+n+1]Having y ofi+n+1>yi+nWhen, the upper limit is [ x ]i，yi]With a lower limit of [ xi+n，yi+n]；
(3) When y isi+1>yiAnd xi+1＝xiThen, the search is continued backwards along the second dimension Y, and when the path point P is foundi+n＝[xi+n，yi+n]And Pi+n+1＝[xi+n+1，yi+n+1]Has xi+n+1>xi+nWhen, the upper limit is [ x ]i+n，yi+n]With a lower limit of [ xi，yi]。
As shown in fig. 2, the method for obtaining the numerical index matrix in step S6 includes: traversing a depth array AD (i) of a logging curve sequence of the slant-depth index of the reference well to obtain a depth upper limit TL (i) and a depth lower limit BL (i) of the slant-depth index of the target well corresponding to each depth; and traversing the depth array BD (j) of the logging curve sequence of the slant-depth index of the target well, finding the values contained in [ TL (i), BL (i) ], recording j, and forming a numerical index matrix.
In step S7, similarity comparison is performed on the logging curve sequence of the slant-depth index to obtain a final similar trajectory matrix.
In the detailed description of the embodiments, various technical features may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (10)
1. A stratum comparison method based on a dynamic time programming optimization algorithm is characterized by comprising the following steps:
s1: acquiring original logging curve sequences and well trajectory curves of a reference well of a divided stratum and a target well of a stratum to be divided, and processing the original logging curve sequences of the reference well of the divided stratum and the target well of the stratum to be divided to obtain a new logging curve sequence containing gradient information;
s2: according to the well track curves of the reference well and the target well, eliminating repeated vertical depth parts caused by upwarping of well tracks in the new well logging curve sequence to obtain a well logging curve sequence of an inclined depth index with monotonically increasing vertical depth;
s3: processing the logging curve sequence of the slant-depth index by using a linear interpolation value according to the well trajectory curves of the reference well and the target well to obtain a logging curve sequence of the vertical-depth index;
s4: giving a search boundary, and performing similarity comparison on the logging curve sequence of the vertical-depth index by using a banded boundary dynamic time programming algorithm to obtain a similar track matrix;
s5: determining a depth upper and lower limit search matrix of the slant depth index according to the similar track matrix;
s6: finding a depth index contained in a depth upper and lower limit search matrix of the slant depth index of the target well to obtain a numerical index matrix when similarity comparison is executed;
s7: and taking the numerical index matrix as a search range, obtaining a final similar track matrix by using a dynamic time programming algorithm, and dividing the target well stratum according to the final similar track matrix.
2. The method for stratigraphic comparison based on dynamic time programming optimization algorithm of claim 1, wherein in step S1, the original well log sequence and well trajectory curve are collected during the drilling process, wherein the original well log sequence takes the slant depth as the index and records the petrophysical features of the stratum; the well track curve takes the slant depth as an index, and records the slant depth-vertical depth conversion relation of the whole well section.
3. The formation comparison method based on the dynamic time programming optimization algorithm of claim 2, wherein in step S1, the processing method of the original log sequence is:
constructing a new logging curve sequence F (s (i)) comprehensively considering numerical information and gradient information for an original logging curve sequence s (i) with the length of N and i belonging to [1, N ]:
4. the formation comparison method based on the dynamic time programming optimization algorithm as claimed in claim 3, wherein in step S2, according to the conversion relationship between slant depth and vertical depth recorded by the well trajectory curve, the depth is interpolated by a linear interpolation method, the vertical depth corresponding to each depth of the new well logging curve sequence of the slant depth index is calculated, and for the known matching relationship between slant depth and vertical depth (tmd)1，tvd1)、(tmd2，tvd2) If the new slant depth tmd ∈ [ tmd ∈ >1，tmd2]Then the corresponding vertical depth tvd is:
and traversing the new logging curve sequence from shallow to deep, finding and removing a vertical depth repeated well section caused by the upward warp of the well track, and obtaining a logging curve sequence of an inclined depth index with the vertical depth increasing monotonically.
5. The method for comparing strata according to claim 4, wherein in step S3, a fixed vertical depth index step is given, the vertical depth maximum value and the vertical depth minimum value of the logging curve sequence of the slant depth index with the monotonically increasing vertical depth obtained in step S2 are used as boundaries to construct a vertical depth index sequence, and the logging curve sequence of the slant depth index is interpolated by using a linear interpolation method to obtain the logging curve sequence of the vertical depth index.
6. The method for stratigraphic comparison based on dynamic time programming optimization algorithm of claim 5, wherein in step S4, the similar trajectory matrix is a two-dimensional matrix, and records the similar matching relationship of each point of the vertical depth indexed well log curve sequence of the reference well and the vertical depth indexed well log curve sequence of the target well, the first dimension X is the vertical depth indexed well log curve sequence of the reference well, X is the point in the first dimension X, the second dimension Y is the vertical depth indexed well log curve sequence of the target well, and Y is the point in the second dimension Y.
7. The method for stratigraphic comparison based on dynamic time programming optimization algorithm of claim 6, wherein in step S5, the calculation method of the search matrix of the upper and lower limits of depth of the slant-depth index is as follows:
s51: traversing the similar track matrix in the step S4, and taking the (i + 1) th path point P in the similar track matrixi+1＝[xi+1，yi+1]Comparing the ith path point Pi＝[xi，yi]Obtaining a depth upper and lower limit matrix of the vertical depth index under the change conditions in the first dimension X and the second dimension Y directions;
s52: and converting the vertical depth of the vertical depth index upper and lower limit depth matrix into the vertical depth according to the conversion relation between the vertical depth and the vertical depth recorded by the well track curve, taking the depth of the logging curve sequence of the reference well vertical depth index obtained in the step S2 as an index, interpolating the depth by a linear interpolation method, and calculating the upper and lower limit depth matrix of the target well vertical depth index corresponding to each depth.
8. The method for stratigraphic comparison based on dynamic time programming optimization algorithm of claim 7, wherein in step S51, since the time programming algorithm path has monotonicity, the calculation method of the depth upper and lower limit matrix of the vertical depth index is as follows:
(1) when x isi+1＞xiAnd y isi+1＞yiWhen, the upper limit is [ x ]i，yi+1]With a lower limit of [ xi+1，yi]；
(2) When x isi+1＞xiAnd y isi+1＝yiThen, the search is continued backward along the first dimension X, and when the path point P is foundi+n＝[xi+n，yi+n]And Pi+n+1＝[xi+n+1，yi+n+1]Having y ofi+n+1＞yi+nWhen, the upper limit is [ x ]i，yi]With a lower limit of [ xi+n，yi+n]；
(3) When y isi+1＞yiAnd xi+1＝xiThen, the search is continued backwards along the second dimension Y, and when the path point P is foundi+n＝[xi+n，yi+n]And Pi+n+1＝[xi+n+1，yi+n+1]Has xi+n+1＞xi+nWhen, the upper limit is [ x ]i+n，yi+n]With a lower limit of [ xi，yi]。
9. The method for stratigraphic comparison based on dynamic time programming optimization algorithm of claim 8, wherein the numerical index matrix obtaining method in step S6 is: traversing a depth array AD (i) of a logging curve sequence of the slant-depth index of the reference well to obtain a depth upper limit TL (i) and a depth lower limit BL (i) of the slant-depth index of the target well corresponding to each depth; and traversing the depth array BD (j) of the logging curve sequence of the slant-depth index of the target well, finding the values contained in [ TL (i), BL (i) ], recording j, and forming a numerical index matrix.
10. A formation comparison method based on a dynamic time programming optimization algorithm according to any one of claims 1 to 9, characterized in that in step S7, similarity comparison is performed on the logging curve sequences indexed by slant depth to obtain a final similar trajectory matrix.
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| CN116201535B (en) * | 2023-02-06 | 2024-02-09 | 北京月新时代科技股份有限公司 | Automatic dividing method, device and equipment for oil and gas reservoir target well sign stratum |
| CN116384633A (en) * | 2023-04-13 | 2023-07-04 | 北京蓝海智信能源技术有限公司 | Method, device and equipment for dividing mark layer and readable storage medium |
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