CN112346116A - Reservoir stratum prediction method and device - Google Patents
Reservoir stratum prediction method and device Download PDFInfo
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Abstract
The invention discloses a reservoir prediction method and a device, wherein the method comprises the steps of determining a filter operator corresponding to target stratum seismic data according to the amplitude spectrum of the target stratum seismic data, carrying out extreme value identification on the target stratum seismic data to obtain extreme value seismic data, and carrying out convolution on the extreme value seismic data and the filter operator to determine model channel data; determining residual seismic data according to the target layer seismic data and the model channel data; and extracting reservoir prediction parameters of the residual seismic data, and determining reservoir distribution of the target layer. The method obtains extreme value seismic data by carrying out extreme value identification on the target layer seismic data, further carries out convolution on the extreme value seismic data and the filter operator to determine model channel data, determines residual seismic data according to the target layer seismic data and the model channel data, removes the large background of the target layer seismic data from the residual seismic data, carries out reservoir prediction by extracting reservoir prediction parameters of the residual seismic data, and can improve the precision and the efficiency of reservoir prediction.
Description
Technical Field
The invention relates to the technical field of geophysical exploration, in particular to a reservoir prediction method and a reservoir prediction device.
Background
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
With the continuous progress of the three-dimensional seismic exploration technology and the continuous deep application of three-dimensional seismic data in the field of oil field exploration and development, the importance of seismic attribute explanation is increasing day by day. Seismic attributes are primarily time, amplitude, frequency, phase, coherence and attenuation.
In the field of oil field exploration and development, seismic attributes are mainly applied to prediction of parameters such as oil reservoir thickness, porosity and permeability, wherein the application of single seismic attributes such as amplitude and frequency of seismic data is wide. However, at present, the seismic attributes are obtained based on the seismic full data, and then the obtained seismic attributes are used for reservoir prediction, so that the reservoir prediction efficiency is low, and meanwhile, the prediction accuracy is also low.
Therefore, the conventional reservoir prediction has the problems of low prediction efficiency and low prediction accuracy.
Disclosure of Invention
The embodiment of the invention provides a reservoir prediction method for improving the precision and efficiency of reservoir prediction, which comprises the following steps:
determining a filter operator corresponding to the seismic data of the target layer according to the amplitude spectrum of the seismic data of the target layer in a time window range corresponding to the seismic data of the target layer;
carrying out extreme value identification on the seismic data of the target layer in a time window range corresponding to the seismic data of the target layer to obtain extreme value seismic data;
performing convolution on the extreme value seismic data and the filtering operator corresponding to the target layer seismic data to determine model channel data;
determining residual seismic data according to the target layer seismic data and the model channel data;
and extracting reservoir prediction parameters of the residual seismic data in a time window range corresponding to the seismic data of the target layer, and determining the reservoir distribution of the target layer.
The embodiment of the invention also provides a reservoir prediction device for improving the precision and efficiency of reservoir prediction, which comprises:
the filtering operator determining module is used for determining a filtering operator corresponding to the target layer seismic data according to the amplitude spectrum of the target layer seismic data in the time window range corresponding to the target layer seismic data;
the extreme value identification module is used for carrying out extreme value identification on the seismic data of the target layer in a time window range corresponding to the seismic data of the target layer to obtain extreme value seismic data;
the convolution module is used for performing convolution on the filtering operators corresponding to the extreme value seismic data and the target layer seismic data to determine model channel data;
the residual seismic data determining module is used for determining residual seismic data according to the target layer seismic data and the model channel data;
and the reservoir prediction module is used for extracting reservoir prediction parameters of the residual seismic data in a time window range corresponding to the seismic data of the target layer and determining the reservoir distribution of the target layer.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the reservoir prediction method when executing the computer program.
Embodiments of the present invention also provide a computer-readable storage medium, which stores a computer program for executing the reservoir prediction method.
Extreme value seismic data are obtained by carrying out extreme value identification on target layer seismic data, convolution is carried out on the extreme value seismic data and a filter operator corresponding to the target layer seismic data to determine model channel data, then residual seismic data are determined according to the target layer seismic data and the model channel data, the large background of the target layer seismic data is removed from the obtained residual seismic data, then reservoir prediction parameters of the residual seismic data are extracted, reservoir prediction is carried out on the residual seismic data, and the precision and the efficiency of the reservoir prediction can be improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:
FIG. 1 is a flow chart of an implementation of a reservoir prediction method provided by an embodiment of the invention;
fig. 2 is a flowchart illustrating implementation of step 101 in a reservoir prediction method according to an embodiment of the present invention;
FIG. 3 is a flow chart of another implementation of a reservoir prediction method provided by an embodiment of the invention;
fig. 4 is a flowchart illustrating the implementation of step 303 in the reservoir prediction method according to an embodiment of the present invention;
fig. 5 is a flowchart of another implementation of step 303 in the reservoir prediction method according to the embodiment of the present invention;
FIG. 6 is a flowchart illustrating another implementation of step 303 in the reservoir prediction method according to an embodiment of the present invention;
FIG. 7 is a functional block diagram of a reservoir prediction apparatus provided in accordance with an embodiment of the present invention;
fig. 8 is a block diagram illustrating a structure of a filter operator determining module 601 in a reservoir prediction apparatus according to an embodiment of the present invention;
FIG. 9 is another functional block diagram of a reservoir prediction apparatus provided in accordance with an embodiment of the present invention;
fig. 10 is a block diagram illustrating a structure of a normalization module 803 in a reservoir prediction apparatus according to an embodiment of the present invention;
fig. 11 is another structural block diagram of the normalization module 803 in the reservoir prediction apparatus according to the embodiment of the present invention;
fig. 12 is a block diagram of another structure of a normalization module 803 in a reservoir prediction apparatus according to an embodiment of the present invention;
FIG. 13 is a schematic diagram of a synthetic seismic record horizon calibration result for actual reservoir seismic data as provided by an embodiment of the invention;
FIG. 14 is a schematic diagram of an amplitude spectrum of actual reservoir seismic data provided by an embodiment of the invention;
FIG. 15 is a schematic diagram of a filter operator corresponding to actual reservoir seismic data according to an embodiment of the present invention;
FIG. 16 is a schematic diagram of extreme seismic data obtained by identifying an extreme from actual reservoir seismic data according to an embodiment of the present invention;
FIG. 17 is a schematic diagram of extreme value-eliminated final extreme value seismic data according to an embodiment of the present invention;
FIG. 18 is a schematic diagram of model trace data obtained by convolution of actual reservoir seismic data and final extreme seismic data according to an embodiment of the present invention;
FIG. 19 is a diagram illustrating normalized model trace data corresponding to actual reservoir seismic data, in accordance with an embodiment of the present invention;
FIG. 20 is a schematic diagram of residual seismic data from actual reservoir seismic data and standard model trace data, as provided by an embodiment of the present invention;
FIG. 21 is a schematic diagram of an energy plane (root mean square amplitude) of residual seismic data extracted over a time window according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
Fig. 1 illustrates an implementation flow of a reservoir prediction method provided by an embodiment of the present invention, and for convenience of description, only the portions related to the embodiment of the present invention are illustrated, and detailed as follows:
as shown in fig. 1, a reservoir prediction method includes:
102, carrying out extreme value identification on the seismic data of the target layer in a time window range corresponding to the seismic data of the target layer to obtain extreme value seismic data;
103, performing convolution on the extreme value seismic data and the filter operator corresponding to the target layer seismic data to determine model channel data;
104, determining residual seismic data according to the target layer seismic data and the model channel data;
and 105, extracting reservoir prediction parameters of the residual seismic data in a time window range corresponding to the seismic data of the target layer, and determining reservoir distribution of the target layer.
In an embodiment of the invention, the target layer is a target layer of a borehole, i.e. an object or area to be investigated. The time window range corresponding to the target layer seismic data in the time domain can be determined by carrying out well-seismic time depth matching on the target layer seismic data. And determining a filter operator corresponding to the seismic data of the target layer in the time window range according to the amplitude spectrum of the seismic data of the target layer. In one embodiment of the invention, the destination layer seismic data is embodied in the form of a seismic data volume.
And after the target layer seismic data and the time window range corresponding to the target layer seismic data are obtained, carrying out extreme value identification on the target layer seismic data in the time window range corresponding to the target layer seismic data. When extreme value identification is carried out on the seismic data of the target layer, the extreme value of the seismic data of the target layer is reserved, and zero filling processing is carried out on all non-extreme values of the seismic data of the target layer to obtain extreme value seismic data. In one embodiment of the invention, the extremum seismic data is embodied in the form of an extremum seismic data volume.
After the filtering operator corresponding to the target layer seismic data and the extreme value seismic data are respectively determined, convolution operation is carried out on the filtering operator corresponding to the target layer seismic data and the extreme value seismic data, and the model trace data can be obtained after the convolution operation is carried out on the filtering operator corresponding to the target layer seismic data and the extreme value seismic data. In one embodiment of the present invention, the model trace data is embodied in the form of a model trace data volume.
After the model channel data is obtained, residual seismic data can be determined according to the target layer seismic data and the model channel data. Specifically, the residual between the target layer seismic data and the model channel data is obtained within the time window range, and then the residual between the target layer seismic data and the model channel data is used as residual seismic data. In one embodiment of the invention, the residual seismic data is embodied in the form of a residual seismic data volume.
The obtained residual seismic data removes the large background of the seismic data of the target layer, and then the reservoir prediction parameters of the residual seismic data are extracted within the time window range corresponding to the seismic data of the target layer, so that the reservoir distribution of the target layer is determined. The reservoir prediction parameters are parameters capable of achieving the reservoir prediction purpose, and reservoir prediction can be carried out on a target layer according to the reservoir prediction parameters of the extracted residual seismic data.
In the embodiment of the invention, extreme value seismic data are obtained by carrying out extreme value identification on target layer seismic data, convolution is carried out on the extreme value seismic data and a filter operator corresponding to the target layer seismic data to determine model channel data, then residual seismic data are determined according to the target layer seismic data and the model channel data, the large background of the target layer seismic data is removed from the obtained residual seismic data, further reservoir prediction parameters of the residual seismic data are extracted, reservoir prediction is carried out on the residual seismic data, and the precision and the efficiency of the reservoir prediction can be improved.
In one embodiment of the invention, the reservoir prediction parameters of the residual seismic data include at least a root mean square amplitude of the residual seismic data. After the residual seismic data are obtained, extracting the root mean square amplitude of the residual seismic data in a time window range corresponding to the target layer seismic data, and further obtaining the reservoir distribution of the target layer through the root mean square amplitude of the residual seismic data to realize reservoir prediction of the target layer.
Fig. 2 illustrates an implementation flow of step 101 in the reservoir prediction method provided by the embodiment of the present invention, and for convenience of description, only the portions related to the embodiment of the present invention are illustrated, and the details are as follows:
in an embodiment of the present invention, as shown in fig. 2, in step 101, determining a filter operator corresponding to the target layer seismic data according to the amplitude spectrum of the target layer seismic data within a time window range corresponding to the target layer seismic data, includes:
and 204, determining a filter operator corresponding to the seismic data of the target layer according to the frequency band parameter of the amplitude spectrum of the seismic data of the target layer.
The logging data refers to data which is formed in the logging process and can reflect different lithological and horizon characteristics. In the embodiment of the invention, the logging data mainly comprises an acoustic time difference logging curve, a density logging curve and the like. A seismic wavelet is a piece of signal with a defined start time, limited energy and a certain duration, which is the basic unit in a seismic recording. When the synthetic seismic record is determined, the synthetic seismic record is mainly made through well drilling layering of known wells in an exploration area, an acoustic wave time difference well logging curve, a density well logging curve and seismic wavelet excitation.
After the synthetic seismic record is obtained through excitation, the position of the synthetic seismic record is calibrated, and the position calibrated on seismic data of the layering of the well-drilling target layer is determined as the target layer. And determining a time window range corresponding to the seismic data of the target layer by utilizing well seismic time depth matching and using the time range corresponding to the target layer in the seismic section as a time window for extracting the seismic data attribute of the target layer.
After the time window range corresponding to the seismic data of the target layer is determined, the amplitude spectrum of the seismic data of the target layer is extracted in the time window range, and the frequency band parameter of the amplitude spectrum of the seismic data of the target layer is determined according to the amplitude spectrum of the seismic data of the target layer. In the embodiment of the present invention, the band parameters include a low pass band frequency and a high pass band frequency related to a pass band, and the band parameters further include a low cut-off frequency and a high cut-off frequency related to a cut-off frequency.
After the frequency band parameters are obtained according to the amplitude spectrum of the seismic data of the target layer, the filter operators corresponding to the seismic data of the target layer are reasonably designed according to the low frequency of the passband, the high frequency of the passband, the low frequency of the cutoff frequency and the high frequency of the cutoff frequency in the frequency band parameters.
In the embodiment of the invention, the amplitude spectrum of the seismic data of the target layer is extracted in the time window range, the frequency band parameter of the amplitude spectrum of the seismic data of the target layer is determined, and the filter operator corresponding to the seismic data of the target layer is determined according to the frequency band parameter of the amplitude spectrum of the seismic data of the target layer, so that the accuracy of reservoir prediction can be further improved.
Fig. 3 illustrates another implementation flow of the reservoir prediction method provided by the embodiment of the present invention, and for convenience of description, only the relevant portions of the embodiment of the present invention are shown, and the details are as follows:
in an embodiment of the present invention, as shown in fig. 3, on the basis of the above method steps, in step 102, after performing extremum identification on the target layer seismic data within a time window range corresponding to the target layer seismic data to obtain extremum seismic data, the method for layer prediction further includes:
correspondingly, step 103, performing convolution on the extreme value seismic data and the filter operator corresponding to the target layer seismic data to determine model trace data, including:
and 302, performing convolution on the final extreme value seismic data and the filter operator corresponding to the target layer seismic data to determine model trace data.
The applicant has studied and found that extreme values in extreme value seismic data are uneven, and the accuracy of reservoir prediction is influenced by adopting all extreme values of the extreme value seismic data to participate in operation.
Therefore, in order to further improve the precision of reservoir prediction, extreme values with extreme values smaller than a preset extreme value threshold value in extreme value seismic data are removed, the extreme value seismic data with the extreme values which do not meet requirements after being removed are used as final extreme value seismic data to participate in operation, and the precision of reservoir prediction is further improved.
Correspondingly, when the model channel data are determined, the final extreme value seismic data and the filtering operator corresponding to the target layer seismic data are subjected to convolution, and the obtained convolution result is used as the model channel data.
In an embodiment of the present invention, the preset extremum threshold is a preset extremum threshold, and those skilled in the art can understand that the preset extremum threshold can be set according to actual conditions and specific requirements. For example, the preset extremum threshold is set to 1000. One skilled in the art may set the preset extremum threshold to a value other than 1000, for example, 850 or 1200, etc., which is not limited in particular by the embodiment of the present invention.
In the embodiment of the invention, the extreme value of which the extreme value is smaller than the preset extreme value threshold value in the extreme value seismic data is removed, the final extreme value seismic data is determined, and the precision of reservoir prediction can be further improved.
Fig. 3 illustrates another implementation flow of the reservoir prediction method provided by the embodiment of the present invention, and for convenience of description, only the relevant portions of the embodiment of the present invention are shown, and the details are as follows:
in an embodiment of the present invention, as shown in fig. 3, on the basis of the above method steps, before determining residual seismic data according to the target layer seismic data and the model trace data in step 104, the layer prediction method further includes:
correspondingly, step 104, determining residual seismic data according to the target layer seismic data and the model channel data, including:
and step 304, determining residual seismic data according to the standard target layer seismic data and the standard model channel data.
After further research, the applicant finds that the value range distribution ranges of the target layer seismic data and the model trace data may be consistent or inconsistent. Under the condition that the value domain distribution ranges of the target layer seismic data and the model channel data are consistent, residual seismic data can be directly determined according to the target layer seismic data and the model channel data with consistent value domain distribution ranges, and the precision of reservoir prediction can be guaranteed by utilizing the residual seismic data obtained according to the residual seismic data. However, when the value range distribution ranges of the target layer seismic data and the model trace data are not consistent, the reservoir prediction accuracy will still be affected to some extent when reservoir prediction is performed based on the obtained residual seismic data.
Therefore, in order to further improve the accuracy of reservoir prediction, the target layer seismic data and the model trace data are normalized so that the value range distribution ranges of the normalized target layer seismic data and the normalized model trace data are consistent, that is, the value range distribution ranges of the normalized standard target layer seismic data and the normalized model trace data are consistent. And then residual seismic data are determined according to the standardized standard target layer seismic data and the standardized model channel data, and reservoir prediction is carried out based on the obtained residual seismic data, so that the precision of reservoir prediction can be improved.
In the embodiment of the invention, the target layer seismic data and the model channel data are standardized, the standard target layer seismic data and the standard model channel data with consistent value range distribution range are determined, and the reservoir prediction is carried out by using the residual seismic data obtained according to the standard target layer seismic data and the standard model channel data, so that the precision of the reservoir prediction can be further improved.
Fig. 4 illustrates an implementation flow of step 303 in the reservoir prediction method provided by the embodiment of the present invention, and for convenience of description, only the portions related to the embodiment of the present invention are illustrated, and detailed descriptions are as follows:
in an embodiment of the present invention, as shown in fig. 4, step 303 normalizes the target layer seismic data and the model trace data, and determines standard target layer seismic data and standard model trace data with consistent value range distribution ranges, including:
In the case where the target layer seismic data and the model trace data are normalized so that the normalized standard target layer seismic data and the normalized standard model trace data have the same value range distribution, the model trace data is normalized by using the target layer seismic data as a reference, that is, the target layer seismic data as the standard target layer seismic data so that the value range distribution of the target layer seismic data and the normalized model trace data (that is, the standard model trace data) are the same.
When the normalization coefficient of the model channel data is determined, the normalization coefficient of the model channel data can be determined according to the value range distribution of the seismic data of the target layer and the value range distribution of the model channel data. Specifically, the ratio of the value range distribution range of the model trace data to the value range distribution range of the target layer seismic data may be used as a normalization coefficient of the model trace data, and the model trace data may be normalized according to the model trace data and the normalization coefficient of the model trace data to obtain the standard model trace data.
In the embodiment of the invention, the target layer seismic data is taken as a reference, the normalization coefficient of the model channel data is determined based on the value domain distribution of the target layer seismic data and the value domain distribution of the model channel data, and then the model channel data is normalized, so that the accuracy of reservoir prediction can be further improved in view of the consistency of the value domain distribution ranges of the normalized standard target layer seismic data and the normalized standard model channel data.
Fig. 5 illustrates another implementation flow of step 303 in the reservoir prediction method provided by the embodiment of the present invention, and for convenience of description, only the portions related to the embodiment of the present invention are shown, and detailed as follows:
in an embodiment of the present invention, as shown in fig. 5, step 303 normalizes the target layer seismic data and the model trace data, and determines the standard target layer seismic data and the standard model trace data with consistent value range distribution ranges, including:
502, determining a normalization coefficient of the seismic data of the target layer according to the value domain distribution of the seismic data of the target layer and the value domain distribution of the model channel data;
The target layer seismic data and the model channel data are standardized to make the value domain distribution ranges of the standardized standard target layer seismic data and the standardized model channel data consistent, and the model channel data can be used as the reference, namely the model channel data is used as the standard model channel data, so that the target layer seismic data are standardized to make the value domain distribution ranges of the standardized target layer seismic data (namely the standard target layer seismic data) and the model channel data consistent.
When the normalization coefficient of the seismic data of the target layer is determined, the normalization coefficient of the seismic data of the target layer can be determined according to the value range distribution of the seismic data of the target layer and the value range distribution of the model channel data. Specifically, the ratio of the value range distribution range of the seismic data of the target layer to the value range distribution range of the model trace data may be used as a normalization coefficient of the seismic data of the target layer, and then the seismic data of the target layer may be normalized according to the seismic data of the target layer and the normalization coefficient of the seismic data of the target layer, so as to obtain the standard seismic data of the target layer.
In the embodiment of the invention, the model channel data is taken as a reference, the normalization coefficient of the target layer seismic data is determined based on the value domain distribution of the target layer seismic data and the value domain distribution of the model channel data, and then the target layer seismic data is normalized, so that the accuracy of reservoir prediction can be further improved in view of the consistency of the value domain distribution ranges of the normalized standard target layer seismic data and the standard model channel data.
Fig. 6 illustrates another implementation flow of step 303 in the reservoir prediction method provided by the embodiment of the present invention, and for convenience of description, only the portions related to the embodiment of the present invention are shown, and detailed as follows:
in an embodiment of the present invention, as shown in fig. 6, step 303 normalizes the target layer seismic data and the model trace data, and determines standard target layer seismic data and standard model trace data with consistent value range distribution ranges, including:
601, respectively determining a normalization coefficient of the seismic data of the target layer and a normalization coefficient of the model channel data according to the value domain distribution of the seismic data of the target layer and the value domain distribution of the model channel data; the ratio of the value domain distribution range of the seismic data of the target layer to the value domain distribution range of the model channel data is equal to the ratio of the normalization coefficient of the model channel data to the normalization coefficient of the seismic data of the target layer;
The target layer seismic data and the model trace data are standardized to make the value range distribution range of the standardized standard target layer seismic data and the standard model trace data consistent, and the target layer seismic data and the model trace data can be respectively standardized to make the value range distribution range of the standardized target layer seismic data (namely, the standard target layer seismic data) and the value range distribution range of the standardized model trace data (namely, the standard model trace data) consistent.
When the normalization coefficient of the seismic data of the target layer and the normalization coefficient of the model channel data are determined, the normalization coefficient of the seismic data of the target layer and the normalization coefficient of the model channel data can be determined according to the value range distribution of the seismic data of the target layer and the value range distribution of the model channel data.
Specifically, the following needs are satisfied: and the ratio of the value domain distribution range of the seismic data of the target layer to the value domain distribution range of the model channel data is equal to the ratio of the normalization coefficient of the model channel data to the normalization coefficient of the seismic data of the target layer, and accordingly the normalization coefficient of the seismic data of the target layer and the normalization coefficient of the seismic data of the model channel are respectively determined.
Further standardizing the seismic data of the target layer according to the seismic data of the target layer and the standardization coefficient of the seismic data of the target layer to obtain standard seismic data of the target layer; and standardizing the model track data according to the model track data and the standardization coefficient of the model track data to obtain standard model track data.
Step 602 and step 603 have no special sequential execution order, and step 602 may be executed first, or step 603 may be executed first, and then step 602 is executed, or step 602 and step 603 are executed at the same time.
In the embodiment of the invention, the normalization coefficient of the seismic data of the target layer and the normalization coefficient of the model channel data are respectively determined according to the value domain distribution of the seismic data of the target layer and the value domain distribution of the model channel data, and the seismic data of the target layer and the model channel data are respectively normalized.
The embodiment of the invention also provides a reservoir prediction device, which is described in the following embodiment. Because the principle of solving the problems by the devices is similar to that of a reservoir prediction method, the implementation of the devices can be referred to the implementation of the method, and repeated details are not repeated.
Fig. 7 shows functional modules of a reservoir prediction device provided in an embodiment of the present invention, and for convenience of description, only the parts related to the embodiment of the present invention are shown, and detailed as follows:
referring to fig. 7, each module included in the reservoir prediction apparatus is used to execute each step in the embodiment corresponding to fig. 1, and specific reference is made to fig. 1 and the related description in the embodiment corresponding to fig. 1, which are not repeated herein. In the embodiment of the present invention, the reservoir prediction apparatus includes a filter operator determining module 701, an extreme value identifying module 702, a convolution module 703, a residual seismic data determining module 704, and a reservoir prediction module 705.
And the filter operator determining module 701 is configured to determine, within a time window range corresponding to the target layer seismic data, a filter operator corresponding to the target layer seismic data according to the amplitude spectrum of the target layer seismic data.
And the extreme value identification module 702 is configured to perform extreme value identification on the target layer seismic data within a time window range corresponding to the target layer seismic data to obtain extreme value seismic data.
And a convolution module 703, configured to perform convolution on the filtering operators corresponding to the extreme value seismic data and the target layer seismic data to determine model trace data.
And a residual seismic data determination module 704, configured to determine residual seismic data according to the target layer seismic data and the model channel data.
The reservoir prediction module 705 is configured to extract reservoir prediction parameters of residual seismic data in a time window range corresponding to the target layer seismic data, and determine reservoir distribution of the target layer.
In the embodiment of the invention, the extreme value identification module 702 performs extreme value identification on the target layer seismic data to obtain extreme value seismic data, the convolution module 703 performs convolution on the extreme value seismic data and a filter operator corresponding to the target layer seismic data to determine model channel data, then the residual seismic data determination module 704 determines residual seismic data according to the target layer seismic data and the model channel data, the obtained residual seismic data removes a large background of the target layer seismic data, the reservoir prediction module 705 extracts reservoir prediction parameters of the residual seismic data, and reservoir prediction is performed on the residual seismic data, so that the precision and the efficiency of reservoir prediction can be improved.
In one embodiment of the invention, the reservoir prediction parameters of the residual seismic data include at least a root mean square amplitude of the residual seismic data. After the residual seismic data are obtained, extracting the root mean square amplitude of the residual seismic data in a time window range corresponding to the target layer seismic data, and further obtaining the reservoir distribution of the target layer through the root mean square amplitude of the residual seismic data to realize reservoir prediction of the target layer.
Fig. 8 shows a structural schematic diagram of a filter operator determining module 701 in a reservoir prediction apparatus provided by an embodiment of the present invention, and for convenience of description, only the parts related to the embodiment of the present invention are shown, which are detailed as follows:
in an embodiment of the present invention, referring to fig. 8, each unit included in the filter operator determining module 701 is configured to execute each step in the embodiment corresponding to fig. 2, specifically please refer to fig. 2 and the related description in the embodiment corresponding to fig. 2, which is not described herein again. In the embodiment of the present invention, the filter operator determining module 701 includes a synthetic seismic record determining unit 801, a horizon scaling unit 802, a spectrum extracting unit 803, and a filter operator determining unit 804.
And a synthetic seismic record determining unit 801 for determining a synthetic seismic record by using the logging data and the seismic wavelets.
And a horizon calibration unit 802, configured to perform horizon calibration on the synthetic seismic record, and determine a time window range corresponding to the target horizon seismic data.
A spectrum extraction unit 803, configured to extract an amplitude spectrum of the seismic data of the target interval within a time window range, and determine a frequency band parameter of the amplitude spectrum of the seismic data of the target interval; the band parameters include at least a low pass band frequency, a high pass band frequency, a low cut-off frequency, and a high cut-off frequency.
And the filter operator determining unit 804 is configured to determine a filter operator corresponding to the target layer seismic data according to the frequency band parameter of the target layer seismic data amplitude spectrum.
In the embodiment of the invention, the frequency spectrum extraction unit 803 extracts the amplitude frequency spectrum of the seismic data of the target layer in the time window range, determines the frequency band parameter of the amplitude frequency spectrum of the seismic data of the target layer, and then the filter operator determination unit 804 determines the filter operator corresponding to the seismic data of the target layer according to the frequency band parameter of the amplitude frequency spectrum of the seismic data of the target layer, so that the accuracy of reservoir prediction can be further improved.
Fig. 9 shows another module structure of a reservoir prediction device provided by an embodiment of the present invention, and only shows a part related to the embodiment of the present invention for convenience of description, which is detailed as follows:
in an embodiment of the present invention, referring to fig. 9, each module included in the reservoir prediction apparatus is configured to perform each step in the embodiment corresponding to fig. 3, specifically refer to fig. 3 and the related description in the embodiment corresponding to fig. 3, and will not be described again here. In the embodiment of the present invention, as shown in fig. 9, on the basis of the above module structure, the reservoir prediction apparatus further includes an extremum eliminating module 901.
An extreme value eliminating module 901, configured to eliminate extreme values in the extreme value seismic data whose extreme values are smaller than a preset extreme value threshold, and determine final extreme value seismic data.
The convolution module 703 is specifically configured to perform convolution on the final extreme value seismic data and the filter operator corresponding to the target layer seismic data to determine model trace data.
In the embodiment of the present invention, the extreme value eliminating module 901 eliminates the extreme value whose extreme value is smaller than the preset extreme value threshold value from the extreme value seismic data, determines the final extreme value seismic data, and can further improve the accuracy of reservoir prediction.
Fig. 9 shows another module structure of a reservoir prediction device provided by an embodiment of the present invention, and only shows a part related to the embodiment of the present invention for convenience of description, which is detailed as follows:
in an embodiment of the present invention, referring to fig. 9, each module included in the reservoir prediction apparatus is configured to perform each step in the embodiment corresponding to fig. 3, specifically refer to fig. 3 and the related description in the embodiment corresponding to fig. 3, and will not be described again here. In the embodiment of the present invention, as shown in fig. 9, on the basis of the above module structure, the reservoir prediction apparatus further includes a normalization module 903.
And the standardization module 903 is used for standardizing the seismic data of the target layer and the model channel data and determining the standard seismic data of the target layer and the standard model channel data with consistent value range distribution range.
And a residual seismic data determination module 704, configured to determine residual seismic data according to the standard target layer seismic data and the standard model trace data.
In the embodiment of the invention, the standardization module 903 standardizes the target layer seismic data and the model channel data, determines the standard target layer seismic data and the standard model channel data with consistent value range distribution range, and performs reservoir prediction by using the residual seismic data obtained from the standard target layer seismic data and the standard model channel data, so that the precision of reservoir prediction can be further improved.
Fig. 10 shows a structural schematic diagram of the normalization module 903 in the reservoir prediction apparatus provided by the embodiment of the present invention, and for convenience of description, only the part related to the embodiment of the present invention is shown, and the detailed description is as follows:
in an embodiment of the present invention, referring to fig. 10, each unit included in the normalization module 903 is configured to perform each step in the embodiment corresponding to fig. 4, specifically please refer to fig. 4 and the related description in the embodiment corresponding to fig. 4, which is not repeated herein. In the embodiment of the present invention, the normalization module 903 includes a first reference unit 1001, a first normalization coefficient unit 1002, and a standard model track data determination unit 1003.
The first reference unit 1001 is configured to use the target layer seismic data as reference and use the target layer seismic data as standard target layer seismic data.
The first normalization coefficient unit 1002 is configured to determine a normalization coefficient of the model trace data according to the value range distribution of the target layer seismic data and the value range distribution of the model trace data.
The standard model track data determining unit 1003 is configured to determine standard model track data according to the model track data and the normalization coefficient of the model track data.
In the embodiment of the present invention, the first reference unit 1001 uses the target layer seismic data as a reference, the first normalization coefficient unit 1002 determines the normalization coefficient of the model trace data based on the value range distribution of the target layer seismic data and the value range distribution of the model trace data, and the standard model trace data determination unit 1003 normalizes the model trace data, so that the accuracy of reservoir prediction can be further improved in view of the consistency of the value range distribution ranges of the standard target layer seismic data and the standard model trace data after normalization.
Fig. 11 shows another structural schematic diagram of the normalization module 903 in the reservoir prediction apparatus provided in the embodiment of the present invention, and only shows the relevant portions of the embodiment of the present invention for convenience of description, and the detailed description is as follows:
in an embodiment of the present invention, referring to fig. 11, each unit included in the normalization module 903 is configured to perform each step in the embodiment corresponding to fig. 5, specifically please refer to fig. 5 and the related description in the embodiment corresponding to fig. 5, which is not repeated herein. In the embodiment of the present invention, the normalization module 903 includes a second reference unit 1101, a second normalization coefficient unit 1102, and a standard target layer seismic data determination unit 1103.
The second reference unit 1101 is configured to use the model track data as standard model track data with reference to the model track data.
And a second normalization coefficient unit 1102, configured to determine a normalization coefficient of the seismic data of the target layer according to the value range distribution of the seismic data of the target layer and the value range distribution of the model trace data.
The standard target layer seismic data determination unit 1103 determines standard target layer seismic data according to the target layer seismic data and the normalization coefficient of the target layer seismic data.
In the embodiment of the present invention, the second reference unit 1101 uses the model channel data as a reference, the second normalization coefficient unit 1102 determines the normalization coefficient of the target layer seismic data based on the value range distribution of the target layer seismic data and the value range distribution of the model channel data, and the standard target layer seismic data determination unit 1103 normalizes the target layer seismic data, so that the accuracy of reservoir prediction can be further improved in view of the consistency of the value range distribution ranges of the standard target layer seismic data and the standard model channel data after normalization.
Fig. 12 shows a schematic structural diagram of a normalization module 903 in a reservoir prediction apparatus provided in an embodiment of the present invention, and only shows a part related to the embodiment of the present invention for convenience of description, which is detailed as follows:
in an embodiment of the present invention, referring to fig. 12, each unit included in the normalization module 903 is configured to perform each step in the embodiment corresponding to fig. 6, specifically refer to fig. 6 and the related description in the embodiment corresponding to fig. 6, and details are not repeated here. In the embodiment of the present invention, the normalization module 903 includes a normalization coefficient determining unit 1201, a standard target layer seismic data determining unit 1202, and a standard model trace data determining unit 1203.
A normalization coefficient determining unit 1201, configured to determine a normalization coefficient of the seismic data of the target layer and a normalization coefficient of the model channel data according to the value range distribution of the seismic data of the target layer and the value range distribution of the model channel data, respectively; and the ratio of the value domain distribution range of the seismic data of the target layer to the value domain distribution range of the model channel data is equal to the ratio of the normalization coefficient of the model channel data to the normalization coefficient of the seismic data of the target layer.
And the standard target layer seismic data determining unit 1202 is used for determining the standard target layer seismic data according to the target layer seismic data and the normalization coefficient of the target layer seismic data.
The standard model track data determining unit 1203 is configured to determine standard model track data according to the model track data and the normalization coefficient of the model track data.
In the embodiment of the present invention, the normalization coefficient determining unit 1201 determines the normalization coefficient of the target layer seismic data and the normalization coefficient of the model trace data, respectively, based on the value domain distribution of the target layer seismic data and the value domain distribution of the model trace data, and the standard target layer seismic data determining unit 1202 and the standard model trace data determining unit 1203 normalize the target layer seismic data and the model trace data, respectively, and the accuracy of reservoir prediction can be further improved in view of the consistency of the value domain distribution ranges of the standard target layer seismic data and the standard model trace data after normalization.
FIG. 13 is a schematic diagram showing the synthetic seismic record horizon calibration results for actual reservoir seismic data provided by an embodiment of the invention, and for convenience of illustration, only the parts relevant to the embodiment of the invention are shown, and detailed as follows:
as shown in FIG. 13, the horizon calibration for the synthetic seismic record according to FIG. 13 determines a time window range for the destination horizon in the seismic section of about 1970ms to 2060ms, which is about 100 ms.
FIG. 14 shows an amplitude spectrum schematic of actual reservoir seismic data provided by an embodiment of the invention, showing only those portions relevant to an embodiment of the invention for ease of illustration, detailed as follows:
as shown in fig. 14, the amplitude spectrum of actual reservoir seismic data is approximately in the 0 hz to 65 hz interval. Wherein, the low frequency of the passband and the high frequency of the passband in the frequency parameters are respectively about 8 Hz and 32 Hz; the low frequency of the cut-off frequency and the high frequency of the cut-off frequency in the frequency parameters are about 3 Hz and 40 Hz respectively.
FIG. 15 shows a filter operator schematic corresponding to actual reservoir seismic data provided by an embodiment of the invention, and for convenience of illustration, only the parts related to the embodiment of the invention are shown, and the details are as follows:
as shown in fig. 15, the time domain waveform of the filter operator corresponding to the actual reservoir seismic data is shown. As can be seen from fig. 15, the filter operator has an extension length of about 100ms, and the maximum amplitude of the filter operator is 1.
Fig. 16 shows an extreme value seismic data schematic obtained by performing extreme value identification on actual reservoir seismic data according to an embodiment of the present invention, fig. 17 shows a final extreme value seismic data schematic obtained by eliminating extreme values according to an embodiment of the present invention, and fig. 18 shows a model trace data schematic obtained by convolution of actual reservoir seismic data and final extreme value seismic data according to an embodiment of the present invention.
Fig. 19 shows a schematic diagram of normalized model trace data corresponding to actual reservoir seismic data, where the actual reservoir seismic data is used as a reference, a normalization coefficient of the model trace data is determined to be 1.15, and the model trace data is normalized to obtain standard model trace data.
Fig. 20 shows a schematic diagram of residual seismic data obtained from actual reservoir seismic data and standard model trace data according to an embodiment of the present invention, and fig. 21 shows a schematic diagram of an energy plane (root-mean-square amplitude) of residual seismic data extracted within a time window range according to an embodiment of the present invention.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the reservoir prediction method when executing the computer program.
Embodiments of the present invention also provide a computer-readable storage medium, which stores a computer program for executing the reservoir prediction method.
In summary, in the embodiment of the present invention, extreme value identification is performed on target layer seismic data to obtain extreme value seismic data, convolution is performed on the extreme value seismic data and a filter operator corresponding to the target layer seismic data to determine model channel data, residual seismic data is determined according to the target layer seismic data and the model channel data, a large background of the target layer seismic data is removed from the obtained residual seismic data, further, reservoir prediction parameters of the residual seismic data are extracted, and reservoir prediction is performed on the residual seismic data, so that the precision and efficiency of reservoir prediction can be improved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (18)
1. A method of reservoir prediction, comprising:
determining a filter operator corresponding to the seismic data of the target layer according to the amplitude spectrum of the seismic data of the target layer in a time window range corresponding to the seismic data of the target layer;
carrying out extreme value identification on the seismic data of the target layer in a time window range corresponding to the seismic data of the target layer to obtain extreme value seismic data;
performing convolution on the extreme value seismic data and the filtering operator corresponding to the target layer seismic data to determine model channel data;
determining residual seismic data according to the target layer seismic data and the model channel data;
and extracting reservoir prediction parameters of the residual seismic data in a time window range corresponding to the seismic data of the target layer, and determining the reservoir distribution of the target layer.
2. The method of reservoir prediction of claim 1 wherein the reservoir prediction parameters of the residual seismic data comprise at least a root mean square amplitude of the residual seismic data.
3. The reservoir prediction method as set forth in claim 1, wherein determining a filter operator corresponding to the seismic data of the destination layer according to the amplitude spectrum of the seismic data of the destination layer within a time window corresponding to the seismic data of the destination layer comprises:
determining a synthetic seismic record by using the logging data and the seismic wavelets;
carrying out horizon calibration on the synthetic seismic record, and determining a time window range corresponding to the seismic data of a target horizon;
extracting the amplitude spectrum of the seismic data of the target layer in the time window range, and determining the frequency band parameter of the amplitude spectrum of the seismic data of the target layer; the frequency band parameters at least comprise a low pass band frequency, a high pass band frequency, a low cut-off frequency and a high cut-off frequency;
and determining a filter operator corresponding to the seismic data of the target layer according to the frequency band parameter of the amplitude spectrum of the seismic data of the target layer.
4. The method of reservoir prediction according to claim 1, wherein after identifying an extremum for the seismic data of the target interval within a time window corresponding to the seismic data of the target interval and obtaining the extremum seismic data, further comprising:
eliminating extreme values of which the extreme values are smaller than a preset extreme value threshold value in the extreme value seismic data, and determining final extreme value seismic data;
correspondingly, the convolution is carried out on the filtering operators corresponding to the extreme value seismic data and the target layer seismic data to determine model trace data, and the convolution comprises the following steps:
and performing convolution on the final extreme value seismic data and the filtering operator corresponding to the target layer seismic data to determine model trace data.
5. The method of reservoir prediction according to claim 1 or 4, further comprising, prior to determining residual seismic data from the target layer seismic data and the model trace data:
standardizing the seismic data of the target layer and the model channel data, and determining the standard seismic data of the target layer and the standard model channel data with consistent value range distribution range;
correspondingly, determining residual seismic data according to the target layer seismic data and the model channel data comprises the following steps:
and determining residual seismic data according to the standard target layer seismic data and the standard model channel data.
6. The reservoir prediction method of claim 5, wherein the step of normalizing the target layer seismic data and the model trace data to determine standard target layer seismic data and standard model trace data having consistent value range distributions comprises:
taking the seismic data of the target layer as the reference, and taking the seismic data of the target layer as the seismic data of the standard target layer;
determining a normalization coefficient of the model channel data according to the value domain distribution of the seismic data of the target layer and the value domain distribution of the model channel data;
and determining standard model track data according to the model track data and the normalization coefficient of the model track data.
7. The reservoir prediction method of claim 5, wherein the step of normalizing the target layer seismic data and the model trace data to determine standard target layer seismic data and standard model trace data having consistent value range distributions comprises:
taking the model track data as a reference, and taking the model track data as standard model track data;
determining a normalization coefficient of the seismic data of the target layer according to the value domain distribution of the seismic data of the target layer and the value domain distribution of the model channel data;
and determining standard target layer seismic data according to the target layer seismic data and the normalization coefficient of the target layer seismic data.
8. The reservoir prediction method of claim 5, wherein the step of normalizing the target layer seismic data and the model trace data to determine standard target layer seismic data and standard model trace data having consistent value range distributions comprises:
respectively determining a normalization coefficient of the seismic data of the target layer and a normalization coefficient of the seismic data of the model channel according to the value domain distribution of the seismic data of the target layer and the value domain distribution of the seismic data of the model channel; the ratio of the value domain distribution range of the seismic data of the target layer to the value domain distribution range of the model channel data is equal to the ratio of the normalization coefficient of the model channel data to the normalization coefficient of the seismic data of the target layer;
determining standard target layer seismic data according to the target layer seismic data and the normalization coefficient of the target layer seismic data;
and determining standard model track data according to the model track data and the normalization coefficient of the model track data.
9. A reservoir prediction apparatus, comprising:
the filtering operator determining module is used for determining a filtering operator corresponding to the target layer seismic data according to the amplitude spectrum of the target layer seismic data in the time window range corresponding to the target layer seismic data;
the extreme value identification module is used for carrying out extreme value identification on the seismic data of the target layer in a time window range corresponding to the seismic data of the target layer to obtain extreme value seismic data;
the convolution module is used for performing convolution on the filtering operators corresponding to the extreme value seismic data and the target layer seismic data to determine model channel data;
the residual seismic data determining module is used for determining residual seismic data according to the target layer seismic data and the model channel data;
and the reservoir prediction module is used for extracting reservoir prediction parameters of the residual seismic data in a time window range corresponding to the seismic data of the target layer and determining the reservoir distribution of the target layer.
10. The reservoir prediction apparatus as defined in claim 9, wherein the reservoir prediction parameters of the residual seismic data include at least a root mean square amplitude of the residual seismic data.
11. The reservoir prediction apparatus of claim 9, wherein the filter operator determination module comprises:
the synthetic seismic record determining unit is used for determining a synthetic seismic record by utilizing the logging data and the seismic wavelets;
the horizon calibration unit is used for performing horizon calibration on the synthetic seismic record and determining a time window range corresponding to the seismic data of the target horizon;
the frequency spectrum extraction unit is used for extracting the amplitude frequency spectrum of the seismic data of the target layer in the time window range and determining the frequency band parameter of the amplitude frequency spectrum of the seismic data of the target layer; the frequency band parameters at least comprise a low pass band frequency, a high pass band frequency, a low cut-off frequency and a high cut-off frequency;
and the filter operator determining unit is used for determining a filter operator corresponding to the seismic data of the target layer according to the frequency band parameter of the amplitude frequency spectrum of the seismic data of the target layer.
12. The reservoir prediction apparatus of claim 9, further comprising:
the extreme value eliminating module is used for eliminating extreme values of which the extreme values are smaller than a preset extreme value threshold value in the extreme value seismic data and determining final extreme value seismic data;
and the convolution module is specifically used for performing convolution on the final extreme value seismic data and the filtering operator corresponding to the target layer seismic data to determine model channel data.
13. The reservoir prediction apparatus of claim 9 or 12, further comprising:
the standardization module is used for standardizing the seismic data of the target layer and the model channel data and determining the standard seismic data of the target layer and the standard model channel data with consistent value range distribution range;
and the residual seismic data determining module is specifically used for determining residual seismic data according to the standard target layer seismic data and the standard model channel data.
14. The reservoir prediction apparatus of claim 13, wherein the normalization module comprises:
the first reference unit is used for taking the seismic data of the target layer as reference and taking the seismic data of the target layer as standard seismic data of the target layer;
the first normalization coefficient unit is used for determining the normalization coefficient of the model channel data according to the value domain distribution of the seismic data of the target layer and the value domain distribution of the model channel data;
and the standard model track data determining unit is used for determining the standard model track data according to the model track data and the normalization coefficient of the model track data.
15. The reservoir prediction apparatus of claim 13, wherein the normalization module comprises:
the second benchmark unit is used for taking the model track data as benchmark and taking the model track data as standard model track data;
the second normalization coefficient unit is used for determining the normalization coefficient of the seismic data of the target layer according to the value domain distribution of the seismic data of the target layer and the value domain distribution of the model channel data;
and the standard target layer seismic data determining unit is used for determining the standard target layer seismic data according to the target layer seismic data and the normalization coefficient of the target layer seismic data.
16. The reservoir prediction apparatus of claim 13, wherein the normalization module comprises:
the normalization coefficient determining unit is used for respectively determining the normalization coefficient of the seismic data of the target layer and the normalization coefficient of the model channel data according to the value domain distribution of the seismic data of the target layer and the value domain distribution of the model channel data; the ratio of the value domain distribution range of the seismic data of the target layer to the value domain distribution range of the model channel data is equal to the ratio of the normalization coefficient of the model channel data to the normalization coefficient of the seismic data of the target layer;
the standard target layer seismic data determining unit is used for determining standard target layer seismic data according to the target layer seismic data and the normalization coefficient of the target layer seismic data;
and the standard model track data determining unit is used for determining the standard model track data according to the model track data and the normalization coefficient of the model track data.
17. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 8 when executing the computer program.
18. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 8.
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