CN116796231A - Method and device for automatically dividing lithology and comparing lithology based on logging curve - Google Patents
Method and device for automatically dividing lithology and comparing lithology based on logging curve Download PDFInfo
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Abstract
The application provides a method and a device for automatically dividing lithology and comparing based on a logging curve, wherein the method comprises the following steps: selecting a plurality of logging curves meeting preset conditions; normalizing each log data to between 0 and 1; square wave formation shaping treatment is carried out on each normalized logging curve; sequentially carrying out squaring and shaping treatment on all logging curves to obtain new logging curve values; comparing the layering number of each well logging curve, and reclassifying the lithology of the stratum by taking the thickness of the narrowest layering in the well logging curve as a boundary; and classifying the lithology by utilizing cluster analysis according to the re-classification result of the stratum lithology. According to the method, the actual lithology change and classification of the stratum are not considered, and the lithology of the stratum is classified rapidly only according to the change of the logging curve; the lithology classification and identification can be completed without logging interpretation experience of lithology, making a large number of intersection plates, logging interpretation in advance and making a label training sample.
Description
Technical Field
The application belongs to the field of petroleum exploration and development, and is used for lithology division and comparison of well logging curves, in particular to a method and a device for automatically dividing lithology and comparison based on well logging curves.
Background
Lithology identification by log is a common task in oilfield development geology. The conventional lithology recognition method comprises a log intersection plate method, a neural network recognition method and the like.
For the logging curve intersection graph method, a large number of graph plates need to be manufactured, and for the conditions of large lithology change among wells, overlapping ranges of different lithology parameters and the like, the graph plate identification method has poor effect. The application of the neural network to lithology recognition mainly comprises: (1) Performing feature extraction processing on the original logging curve to obtain logging information with lithology features; (2) Inputting the characteristic logging curve as standard training data into an initial neural network structure for training, wherein a large amount of data is required in the training process, adjusting the weight in the network, and determining the nonlinear relation between the logging curve and the lithology of the rock; (3) And inputting the logging curve of unknown lithology into the trained network model to perform lithology recognition. It can be seen that for neural network identification, a large number of lithology tags need to be made for training, and the workload is huge.
Therefore, a lithology classification method with strong adaptability, high speed and high efficiency is required to be provided.
Disclosure of Invention
In order to solve the problems of poor identification effect and huge workload in the prior lithology identification process through a logging curve, the embodiment of the application provides a method and a device for automatically dividing lithology and comparing lithology based on the logging curve, which can quickly classify the lithology of the stratum only according to the change of the logging curve without considering the actual lithology change and classification of the stratum; the lithology classification and identification can be completed without logging interpretation experience of lithology, making a large number of intersection plates, logging interpretation in advance and making a label training sample.
In a first aspect, an embodiment of the present application provides a method for automatically dividing lithology and comparing based on a log, including:
s1, selecting a plurality of logging curves meeting preset conditions;
s2, normalizing the selected data of each logging curve to be between 0 and 1;
s3, square wave shaping treatment is carried out on each normalized logging curve;
s4, sequentially carrying out squaring and shaping treatment on all the logging curves to obtain new logging curve values;
s5, comparing the layering number of each well logging curve, and re-dividing the lithology of the stratum by taking the smallest layering thickness in the well logging curve as a boundary;
s6, dividing and classifying lithology by utilizing cluster analysis according to the re-dividing result of the stratum lithology.
Step S1, selecting a plurality of logging curves meeting preset conditions, including:
and selecting various logging curves with sensitivity degree meeting preset conditions on lithology change response.
The well logging curves comprise gamma well logging curves and acoustic well logging curves.
Step S2, normalizing the selected log data to be between 0 and 1, including:
carrying out normalization processing on each logging curve, setting the maximum value in each logging curve as 1 and the minimum value as 0; the calculation formula is as follows, assuming that the maximum value of the A logging curve is Amax, the minimum value is Amin, the point value to be calculated is Ai, i represents the ith sampling point of the A logging curve, and then the calculation formula comprises:
Ai＝(Ai-Amin)/(Amax-Amin)。
step S3, performing square wave shaping processing on each normalized log, including:
d is the minimum stratum thickness, d is used as a sliding window to calculate the arithmetic mean value and variance of a logging curve, if the variance is within an allowable range, the logging value of the window section is replaced by the arithmetic mean value, and then the window is moved to calculate the next position; if the variance is out of range, determining that the formation lithology has mutation, finding a mutation point, and calculating by taking the logging depth of the lithology mutation point position as a new window starting point;
after square wave treatment of the logging curve, the straight part of the curve shows that the lithology of the section is unchanged, and the section is the same lithology; the place where the curve has mutation represents that lithology is changed, and the upper part and the lower part of the mutation point are two sets of different lithology.
Step S4, after sequentially squaring and shaping all log curves, obtaining new log curve values, including:
for each well-logging curve, the abrupt point of the shaped curve is considered as the demarcation point of the formation lithology, the layering number of each square-wave shaped curve is not necessarily identical, and the layering number of each curve is assumed to be Am1, bm2, … and Nmn respectively, wherein Am1 represents well-logging curve A divided into m1 layers, bm2 represents well-logging curve B divided into m2 layers, and Nmn represents well-logging curve N divided into mn layers.
Step S5, comparing the number of layers of each log, and reclassifying the formation lithology by using the thickness of the smallest layer in the log as a boundary, including:
if the logging curve A is judged to be of one lithology in a certain well section, the logging curve B is judged to be more than two lithologies in the well section, the lithology judgment of the logging curve B is used as the criterion, the lithology layering position and number of the whole stratum are updated, and the like until the lithology classification of all the logging curves and the well section is updated.
Wherein, step S5 further comprises:
if the lithology discrimination layering is carried out, t sets of lithology strata are divided from top to bottom, the shaped log values corresponding to each lithology layering section are taken to form vectors Zi (A, B, …, N), wherein A, B and N respectively represent A, B, N log values after squaring, and Zi represents vectors of N log value combinations of the ith lithology layering.
Step S6, according to the result of the repartitioning of the formation lithology, utilizes cluster analysis to classify and classify the lithology, including:
carrying out cluster analysis on each lithology layering vector Zi, and sequentially calculating the space distance of each vector, wherein a cluster analysis calculation formula is as follows:
in the formula, i and j respectively represent stratum lithology of Zi section and Zj section, K is a logging curve A, B and … N in sequence, and D is a vector space distance; when the space distance D is larger than the threshold value, the lithology of the stratum of the ith section and the stratum of the jth section is two different lithology; when the spatial distance D is less than the threshold, the lithology of the formation of the ith and jth sections is the same lithology.
In a second aspect, the present application provides a device for automatically dividing lithology and comparing based on a log, comprising:
the selecting unit is used for selecting various logging curves meeting preset conditions;
the normalization unit is used for normalizing the selected logging curve data to be between 0 and 1;
the shaping unit is used for carrying out square wave shaping treatment on each normalized logging curve;
the obtaining unit is used for obtaining new logging curve values after carrying out square wave shaping treatment on all logging curves in sequence;
the repartitioning unit is used for comparing the layering number of each well logging curve, and repartitioning the lithology of the stratum by taking the smallest layering thickness in the well logging curve as a boundary;
and the cluster analysis unit is used for classifying and classifying the lithology by utilizing cluster analysis according to the re-classification result of the stratum lithology.
The method and the device for automatically dividing lithology and comparing based on the logging curve have the following beneficial effects:
according to the method, on one hand, the actual lithology change and classification of the stratum can be not considered, and the lithology of the stratum can be rapidly classified only according to the change of the logging curve; on the other hand, the lithology classification and identification can be completed without logging interpretation experience, making a large number of intersection plates and making a label training sample in advance.
Drawings
FIG. 1 is a schematic flow chart of a method for automatically dividing lithology and comparing based on a log in an embodiment of the application;
FIG. 2 is a technical flow chart of automatic lithology division of a log according to an embodiment of the present application;
FIG. 3 shows normalized log curves (ranging from 0 to 1);
FIG. 4 is a schematic representation of squaring normalized log curves in accordance with the present application;
FIG. 5 shows the result of the shaped log and lithology clustering analysis, with the same color corresponding to the same lithology;
FIG. 6 is a schematic diagram of an apparatus for automatically dividing lithology and comparing based on log curves according to an embodiment of the present application.
Detailed Description
The application is further described below with reference to the drawings and examples.
The following description provides various embodiments of the application that may be substituted or combined between different embodiments, and thus the application is also to be considered as embracing all possible combinations of the same and/or different embodiments described. Thus, if one embodiment includes feature A, B, C and another embodiment includes feature B, D, then the present application should also be considered to include embodiments that include one or more of all other possible combinations of features A, B, C, D, although such an embodiment may not be explicitly recited in the following.
Example 1
As shown in fig. 1, the method for automatically dividing lithology and contrast based on a logging curve of the present application comprises: s1, selecting a plurality of logging curves meeting preset conditions; s2, normalizing the selected data of each logging curve to be between 0 and 1; s3, square wave shaping treatment is carried out on each normalized logging curve; s4, sequentially carrying out squaring and shaping treatment on all the logging curves to obtain new logging curve values; s5, comparing the layering number of each well logging curve, and re-dividing the lithology of the stratum by taking the smallest layering thickness in the well logging curve as a boundary; s6, dividing and classifying lithology by utilizing cluster analysis according to the re-dividing result of the stratum lithology.
The method and the device for classifying and dividing the formation lithology or physical properties based on the logging curve accelerate logging interpretation progress and provide a convenient formation lithology recognition scheme for geology personnel.
Example two
As shown in fig. 2-5, the present application proposes a method for automatically dividing and comparing lithology by using multiple logging curves, and in general, the change of underground lithology will cause the change of the amplitude of the logging curves. It can be considered that the place where the log amplitude is abrupt is the interface of formation lithology; where the amplitude of the log is stable, the development of the subsurface lithology can be considered to be stable.
By selecting a proper logging curve combination and analyzing the change of the amplitude of the logging curve, the rapid classification and comparison of the underground lithology can be realized.
The log data participating in the classification calculation is normalized to between 0 and 1 (i.e., minimum value is 0 and maximum value is 1). And then carrying out squaring and shaping processing on each logging curve, wherein the squared values of different logging curves with the same depth can be combined into a group of vectors, and the vector values reflect the type information of the underground lithology. The magnitude of the difference between different lithologies can be described by the distance between the vectors. The closer the vector distance, the more similar the lithology is represented; the farther the vector distance, the greater the lithology difference. Therefore, clustering analysis can be performed by calculating the distance of the vectors, and the underground lithology can be divided and classified.
The method is suitable for well logging classification and division of underground lithology.
Specifically, the method for automatically dividing lithology and comparing based on the logging curve comprises the following steps:
step one, selecting a plurality of logging curves which are sensitive to lithology change response, such as gamma logging curves, acoustic logging curves and the like, as a lithology discrimination combination mode. Because the sensitivity degree of different logging curves to different lithology changes is inconsistent, the lithology classification can be more accurate by selecting proper logging curve combinations.
And step two, carrying out normalization processing on each logging curve, wherein the maximum value in each logging curve is set to be 1, and the minimum value is set to be 0. The calculation formula is as follows, assuming that the maximum value of the A logging curve is Amax, the minimum value is Amin, and the point value to be calculated is Ai (i represents the ith sampling point of the A logging curve), the calculation formula comprises:
Ai＝(Ai-Amin)/(Amax-Amin)；
after normalization of the logging curves, the value ranges of the curves are kept consistent and are all between 0 and 1. The weight of each curve for lithology classification and discrimination is equal.
And thirdly, square wave shaping treatment is carried out on each normalized logging curve. Assuming a minimum formation thickness d, the arithmetic mean and variance of the log is calculated with d as the sliding window. If the variance is within the allowable range, the formation lithology is considered to be not greatly changed, the log value of the window segment is replaced by an arithmetic average value, and then the window is moved for the next position calculation. If the variance is out of range, the formation lithology is considered to have mutation, a mutation point is found, and the depth (the logging depth value of the lithology mutation point position) is taken as a new window starting point for calculation. After square wave treatment of the logging curve, the straight part of the curve shows that the lithology of the section is not changed, the section is a set of identical lithology, the place where the curve has mutation shows that the lithology is changed, and the upper part and the lower part of the mutation point can be regarded as two sets of different lithology.
And step four, sequentially carrying out square wave shaping treatment on all the logging curves, and obtaining new logging curve values. For each log, the abrupt point of the shaped curve is considered as the demarcation point of the lithology of the formation. Therefore, the number of layers of each square wave shaped curve is not necessarily identical. Let the number of layers of each curve be Am1, bm2, …, nmn, respectively. Wherein Am1 represents the division of the log A into m1 layers, bm2 represents the division of the log B into m2 layers, and Nmn represents the division of the log N into mn layers.
And fifthly, comparing the layering number of each well logging curve, and reclassifying the formation lithology by taking the minimum layering thickness in the well logging curve as a boundary. For example, if the logging curve a is determined to be 1 lithology at a certain well section and the logging curve B is determined to be more than 2 lithologies at the well section, the lithology determination of the logging curve B is determined to be accurate, and the lithology layering position and number of the whole stratum are updated. And the like, until the lithology classification of all the logging curves and well sections is updated. Assume that the lithology discrimination layering is divided into t sets of lithology strata from top to bottom, and the shaped log values corresponding to each lithology layering section are taken to form a vector, such as Zi (A, B, …, N), wherein A, B and N respectively represent A, B, N log values after squaring, and Zi represents a vector of N log value combinations of the ith lithology layering.
Step six, carrying out cluster analysis on each lithology layering vector Zi, sequentially calculating the space distance of each vector, wherein the smaller the value is, the closer the representative lithology is, and the lithology can be regarded as the same lithology; the larger the value, the larger the representative lithology difference. The cluster analysis calculation formula is as follows:
in the formula, i and j respectively represent stratum lithology of Zi section and Zj section, K is A, B and … N logging curves in sequence, and D is vector space distance. The larger D, the greater the lithology difference representing the ith and jth formations, which may be considered two different lithologies; conversely, the smaller D represents the smaller the lithology difference between the formation at the ith and jth stages, and it is assumed that both are considered to be of the same lithology within a certain range. The vector space distance D can be used as a lithology classification threshold value and can be obtained by testing according to experience knowledge of geology personnel. Thus, according to a plurality of logging curves of the same logging depth, the lithology of the well section is automatically divided into types and depths through a program.
The application has the following advantages:
1. the actual lithology change and classification of the stratum can be ignored, and the stratum lithology can be rapidly classified only according to the logging curve change.
2. The lithology classification and identification can be completed without logging interpretation experience of lithology, making a large number of intersection plates, logging interpretation in advance and making a label training sample.
The method of the application analyzes the change of the logging curve by selecting a proper logging curve combination, realizes the rapid classification and comparison of the formation lithology, and can be widely applied to the physical properties of reservoirs, the classification of reservoirs, the division of flow units and the like.
Example III
As shown in fig. 6, the device for automatically dividing lithology and contrast based on logging curves of the present application comprises: a selecting unit 201, configured to select a plurality of logging curves that meet a preset condition; a normalizing unit 202, configured to normalize each piece of selected log data to between 0 and 1; the shaping unit 203 is configured to perform square wave shaping processing on each normalized log; an obtaining unit 204, configured to obtain new log values after sequentially performing squaring and shaping processing on all the logs; a repartitioning unit 205, configured to compare the number of layers of each log, and repartition the lithology of the stratum with the smallest thickness of layers in the log as a boundary; and a cluster analysis unit 206 for classifying and classifying lithology by cluster analysis according to the re-classification result of formation lithology.
In the present application, the embodiment of the device for automatically dividing lithology and comparing based on the well-logging curve is basically similar to the embodiment of the method for automatically dividing lithology and comparing based on the well-logging curve, and for the relevant points, reference is made to the description of the embodiment of the method for automatically dividing lithology and comparing based on the well-logging curve.
The embodiment of the application also provides a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and the program is executed by a processor to realize the method steps for automatically dividing lithology and contrast based on the logging curve. The computer readable storage medium may include, among other things, any type of disk including floppy disks, optical disks, DVDs, CD-ROMs, micro-drives, and magneto-optical disks, ROM, RAM, EPROM, EEPROM, DRAM, VRAM, flash memory devices, magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of media or device suitable for storing instructions and/or data.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described embodiment of the apparatus is merely illustrative, and for example, the division of the units is merely a logic function division, and there may be other division manners in actual implementation, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (10)
1. A method for automatically dividing lithology and contrast based on a log, comprising:
s1, selecting a plurality of logging curves meeting preset conditions;
s2, normalizing the selected data of each logging curve to be between 0 and 1;
s3, square wave shaping treatment is carried out on each normalized logging curve;
s4, sequentially carrying out squaring and shaping treatment on all the logging curves to obtain new logging curve values;
s5, comparing the layering number of each well logging curve, and re-dividing the lithology of the stratum by taking the smallest layering thickness in the well logging curve as a boundary;
s6, dividing and classifying lithology by utilizing cluster analysis according to the re-dividing result of the stratum lithology.
2. The method for automatically dividing lithology and comparing based on log curves according to claim 1, wherein step S1, selecting a plurality of log curves meeting preset conditions comprises:
and selecting various logging curves with sensitivity degree meeting preset conditions on lithology change response.
3. The method for automatically classifying lithology and contrast based on log curves according to claim 2, wherein the log curves comprise gamma log curves, sonic log curves.
4. A method for automatically classifying lithology and contrast based on log according to any one of claims 1-3, characterized in that step S2, normalizing the selected log data to between 0 and 1, comprises:
carrying out normalization processing on each logging curve, setting the maximum value in each logging curve as 1 and the minimum value as 0; the calculation formula is as follows, assuming that the maximum value of the A logging curve is Amax, the minimum value is Amin, the point value to be calculated is Ai, i represents the ith sampling point of the A logging curve, and then the calculation formula comprises:
Ai＝(Ai-Amin)/(Amax-Amin)。
5. a method for automatically dividing lithology and comparing based on log curves according to any one of claims 1-3, wherein step S3 comprises square wave shaping each normalized log curve, including:
d is the minimum stratum thickness, d is used as a sliding window to calculate the arithmetic mean value and variance of a logging curve, if the variance is within an allowable range, the logging value of the window section is replaced by the arithmetic mean value, and then the window is moved to calculate the next position; if the variance is out of range, determining that the formation lithology has mutation, finding a mutation point, and calculating by taking the logging depth of the lithology mutation point position as a new window starting point;
after square wave treatment of the logging curve, the straight part of the curve shows that the lithology of the section is unchanged, and the section is the same lithology; the place where the curve has mutation represents that lithology is changed, and the upper part and the lower part of the mutation point are two sets of different lithology.
6. A method for automatically dividing lithology and comparing based on log curves according to any one of claims 1-3, wherein step S4, after sequentially squaring all log curves, obtains new log curve values, comprises:
for each well-logging curve, the abrupt point of the shaped curve is considered as the demarcation point of the formation lithology, the layering number of each square-wave shaped curve is not necessarily identical, and the layering number of each curve is assumed to be Am1, bm2, … and Nmn respectively, wherein Am1 represents well-logging curve A divided into m1 layers, bm2 represents well-logging curve B divided into m2 layers, and Nmn represents well-logging curve N divided into mn layers.
7. A method for automatically dividing and comparing lithology based on well log according to any one of claims 1-3, wherein step S5, comparing the number of layers of each well log, re-dividing the lithology of the formation by the minimum layer thickness in the well log, comprises:
if the logging curve A is judged to be of one lithology in a certain well section, the logging curve B is judged to be more than two lithologies in the well section, the lithology judgment of the logging curve B is used as the criterion, the lithology layering position and number of the whole stratum are updated, and the like until the lithology classification of all the logging curves and the well section is updated.
8. The method of automatically classifying lithology and contrast based on log curves of claim 7, wherein step S5 further comprises:
if the lithology discrimination layering is carried out, t sets of lithology strata are divided from top to bottom, the shaped log values corresponding to each lithology layering section are taken to form vectors Zi (A, B, …, N), wherein A, B and N respectively represent A, B, N log values after squaring, and Zi represents vectors of N log value combinations of the ith lithology layering.
9. The method for automatically classifying and comparing lithology based on log curves according to claim 8, wherein the step S6 of classifying and classifying lithology by cluster analysis according to the re-classification result of formation lithology comprises:
carrying out cluster analysis on each lithology layering vector Zi, and sequentially calculating the space distance of each vector, wherein a cluster analysis calculation formula is as follows:
in the formula, i and j respectively represent stratum lithology of Zi section and Zj section, K is a logging curve A, B and … N in sequence, and D is a vector space distance; when the space distance D is larger than the threshold value, the lithology of the stratum of the ith section and the stratum of the jth section is two different lithology; when the spatial distance D is less than the threshold, the lithology of the formation of the ith and jth sections is the same lithology.
10. An apparatus for automatically dividing lithology and contrast based on a log, comprising:
the selecting unit is used for selecting various logging curves meeting preset conditions;
the normalization unit is used for normalizing the selected logging curve data to be between 0 and 1;
the shaping unit is used for carrying out square wave shaping treatment on each normalized logging curve;
the obtaining unit is used for obtaining new logging curve values after carrying out square wave shaping treatment on all logging curves in sequence;
the repartitioning unit is used for comparing the layering number of each well logging curve, and repartitioning the lithology of the stratum by taking the smallest layering thickness in the well logging curve as a boundary;
and the cluster analysis unit is used for classifying and classifying the lithology by utilizing cluster analysis according to the re-classification result of the stratum lithology.
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| CN117407841B (en) * | 2023-12-15 | 2024-03-22 | 东北石油大学三亚海洋油气研究院 | Shale layer seam prediction method based on optimization integration algorithm |
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