CN117111157A

CN117111157A - Well seismic combined with alluvial fan reservoir distribution determination method and device

Info

Publication number: CN117111157A
Application number: CN202311116130.7A
Authority: CN
Inventors: 李庆; 何羽; 吴胜和; 岳大力; 周源; 李江山; 安可钦; 李隆禹; 刘慎思
Original assignee: China University of Petroleum Beijing
Current assignee: China University of Petroleum Beijing
Priority date: 2023-08-31
Filing date: 2023-08-31
Publication date: 2023-11-24
Anticipated expiration: 2043-08-31
Also published as: CN117111157B

Abstract

This specification relates to the technical field of oil and gas exploration and development, and specifically discloses a method and device for determining the distribution of alluvial fan reservoirs based on well seismic and seismic combinations. The method includes: based on the well logging data and seismic data of the alluvial fan reservoir in the target area, Establish a fine isochronous stratigraphic framework of the alluvial fan reservoir in the target area; use the matching pursuit strong reflection stripping method to preprocess the seismic data of the alluvial fan reservoir in the target area to obtain preprocessed seismic data; Combined with the well logging data, perform seismic frequency division inversion on the seismic data volume in the preprocessed seismic data to obtain seismic frequency division inversion results; use the seismic frequency division inversion results to determine the target Sand body distribution data of regional alluvial fan reservoirs. The above scheme can accurately predict the sand body distribution characteristics of alluvial fan reservoirs, has reference significance for similar reservoir prediction and reservoir architecture research, and can enrich the theoretical research on alluvial fan architecture.

Description

Well-shock combined alluvial fan reservoir distribution determining method and device

Technical Field

The specification relates to the technical field of oil and gas exploration and development, in particular to a method and a device for determining well-seismic combined alluvial fan reservoir distribution.

Background

With the continuous advancement of oil and gas exploration and development work, the quality of seismic data acquisition is continuously improved, and the exploration of geological researchers on well-seismic combined reservoir prediction technology is promoted. Reservoir prediction technology is a comprehensive crossing technology set, and how to combine seismic technology with geological information and find the optimal solution of geological laws under crossing has become the research direction focused at present. The most widely used reservoir earthquake prediction technology at present mainly comprises two types, namely analysis of sand bodies by an earthquake attribute technology and simulation of sand body distribution by an earthquake inversion technology, wherein the two types combine the superiority of earthquake data in the transverse direction and the superiority and interaction of logging data in the longitudinal direction, and the accuracy of sand body prediction is greatly improved.

Bohai Bay basin is a typical land phase fracture basin, the protrusion of the beach county and the periphery of the protrusion are located in the northwest of the depression, and the pottery group is the dominant development layer of the beach county. The target layer of the pottery groups Ng1 to Ng4 mainly develop alluvial fan-river phase deposition, and the stratum is strong in heterogeneity and transverse contrast and polynosicity. The former people have conducted some researches on the protrusion of the county, mainly focused on the formation and storage rules, the reservoir characteristics and the like of the protrusion of the county, and the results exert better guiding effects on the early exploration of the county area, but are difficult to meet the requirements of development work such as fine prediction, characterization and the like of the subsequent sand body. The bottom of the liberal pottery set is in contact with the non-integrated stratum due to the existence of the protrusions, so that a large-scale strong reflection shielding layer penetrating through the whole area is formed, and the difficulty in sand prediction is high. The influence of special topography in the county region can lead the knowledge of reservoir prediction and alluvial fan configuration of the county protrusion in the study of the former to be almost blank, and restrict the follow-up finer exploration and development of the cursive pottery group of the county protrusion.

In view of the above problems, no effective solution has been proposed at present.

Disclosure of Invention

The embodiment of the specification provides a well-seism combined alluvial fan reservoir distribution determining method and device, which are used for solving the problem that sand prediction and characterization are difficult due to strong heterogeneity of a fractured basin stratum in the prior art.

The embodiment of the specification provides a well-seism combined alluvial fan reservoir distribution determining method, which comprises the following steps: establishing a fine isochronous stratigraphic framework of the alluvial fan reservoir of the target area based on the logging data and the seismic data of the alluvial fan reservoir of the target area; preprocessing the seismic data of the alluvial fan reservoir of the target area by using a matching pursuit strong reflection stripping method to obtain preprocessed seismic data; combining the logging data, and performing seismic frequency division inversion on a seismic data body in the preprocessed seismic data to obtain a seismic frequency division inversion result; and determining sand body distribution data of the alluvial fan reservoir of the target area by using the seismic frequency division inversion result.

The embodiment of the specification also provides a well-shock combined alluvial fan reservoir distribution determining device, which comprises: the building module is used for building a fine isochronous stratum grid of the alluvial fan reservoir of the target area based on the logging data and the seismic data of the alluvial fan reservoir of the target area; the preprocessing module is used for preprocessing the seismic data of the alluvial fan reservoir of the target area by utilizing a matching pursuit strong reflection stripping method to obtain preprocessed seismic data; the inversion module is used for carrying out seismic frequency division inversion on the seismic data body in the preprocessed seismic data by combining the logging data to obtain a seismic frequency division inversion result; and the determining module is used for determining sand body distribution data of the alluvial fan reservoir of the target area by utilizing the seismic frequency division inversion result.

The embodiments of the present disclosure also provide a computer device, including a processor and a memory for storing instructions executable by the processor, where the processor executes the instructions to implement the steps of the method for determining a reservoir distribution of a well-seismic combined alluvial fan described in any of the embodiments above.

The present description embodiments also provide a computer-readable storage medium having stored thereon computer instructions that, when executed, implement the steps of the well-shock combined alluvial fan reservoir distribution determination method described in any of the embodiments above.

In the embodiment of the specification, a well-seismic combined alluvial fan reservoir distribution determining method is provided, and a fine isochronous stratum grid of the alluvial fan reservoir of a target area can be established based on logging data and seismic data of the alluvial fan reservoir of the target area; preprocessing the seismic data of the alluvial fan reservoir of the target area by using a matching pursuit strong reflection stripping method to obtain preprocessed seismic data; combining the logging data, and performing seismic frequency division inversion on a seismic data body in the preprocessed seismic data to obtain a seismic frequency division inversion result; and determining sand body distribution data of the alluvial fan reservoir of the target area by using the seismic frequency division inversion result. According to the scheme, the well logging seismic data can be fully mined, the seismic data resolution is improved through a seismic data processing means, well-seismic combination reservoir prediction and research area alluvial fan reservoir configuration research are carried out on the basis, sand body distribution characteristics of a research area are defined through comparison of a plurality of reservoir prediction methods, alluvial fan configuration distribution characteristics of a county region are dissected, reference significance is brought to later similar reservoir prediction and reservoir configuration research, and alluvial fan configuration theoretical research can be enriched.

Drawings

The accompanying drawings are included to provide a further understanding of the specification, and are incorporated in and constitute a part of this specification. In the drawings:

FIG. 1 illustrates a flow chart of a method of determining a well-shock combined alluvial fan reservoir distribution in an embodiment of the present description;

FIG. 2 illustrates a single well rotation demarcation diagram in an embodiment of the present disclosure;

FIG. 3 shows a schematic diagram of wavelets for synthesizing a record in one embodiment of the present disclosure;

FIG. 4 shows a destination layer spectrum analysis diagram in an embodiment of the present specification;

FIG. 5 shows a plot of resultant recorded calibration results in an embodiment of the present disclosure;

FIG. 6 illustrates a three-dimensional stratigraphic model schematic in accordance with one embodiment of the present disclosure;

FIG. 7 shows a schematic diagram of a matching pursuit algorithm in one embodiment of the present disclosure;

FIG. 8 shows a comparison of the match pursuit strong reflection separation before and after an embodiment of the present disclosure;

FIG. 9 is a graph showing a comparison of local effects before and after separation of matching pursuit strong reflection in one embodiment of the present disclosure;

FIG. 10 illustrates a frequency division attribute fusion workflow for support vector regression in one embodiment of the present disclosure;

FIG. 11 shows a schematic diagram of AVF relationship in one embodiment of the present specification;

FIG. 12 shows a frequency division inversion flow chart in an embodiment of the present disclosure;

FIG. 13 shows a comparison of normalized GR curves before and after normalization in one embodiment of the present disclosure;

FIG. 14 is a graph showing the results of a curve fitting based on the SVR method in an embodiment of the present disclosure;

FIG. 15 shows a frequency division inversion effect diagram in an embodiment of the present disclosure;

FIG. 16 is a graph showing the comparison of effects before and after the de-emphasis axis in one embodiment of the present disclosure;

FIG. 17 illustrates an inversion property value versus gamma curve intersection plot in an embodiment of the present disclosure;

FIG. 18 shows a diagram of a sand-mud probability relationship analysis in an embodiment of the present disclosure;

FIG. 19 is a graph showing the thickness of an Ng4-1 small layer sand body in one embodiment of the present disclosure;

FIG. 20 shows a chart of Ng4-2 small layer sand thickness in an embodiment of the present disclosure;

FIG. 21 shows a chart of Ng4-3 small layer sand thickness in an embodiment of the present disclosure;

FIG. 22 shows a schematic diagram of a well-shock combined alluvial fan reservoir distribution determination apparatus in an embodiment of the present disclosure;

fig. 23 shows a schematic diagram of a computer device in an embodiment of the present description.

Detailed Description

The principles and spirit of the present specification will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are presented merely to enable one skilled in the art to better understand and practice the present description, and are not intended to limit the scope of the present description in any way. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

Those skilled in the art will appreciate that the embodiments of the present description may be implemented as a system, apparatus, method, or computer program product. Accordingly, the present disclosure may be embodied in the form of: complete hardware, complete software (including firmware, resident software, micro-code, etc.), or a combination of hardware and software.

In order to solve the above problems, a method for determining a reservoir distribution of a well-shock combined alluvial fan is provided in this embodiment. In the scheme, the well logging seismic data are fully excavated, the seismic data resolution is improved through a seismic data processing means, well-seismic combination reservoir prediction and research area alluvial fan reservoir configuration research are carried out on the basis, sand body distribution characteristics of a research area are defined by comparing and optimizing various reservoir prediction methods, alluvial fan configuration distribution characteristics of a Bin county area are dissected, reference significance is provided for similar reservoir prediction and reservoir configuration research in the future, and alluvial fan configuration theoretical research is enriched.

FIG. 1 illustrates a flow chart of a method of determining well-shock combined alluvial fan reservoir distribution in an embodiment of the present description. Although the present description provides methods and apparatus structures as shown in the following examples or figures, more or fewer steps or modular units may be included in the methods or apparatus based on conventional or non-inventive labor. In the steps or the structures of the apparatuses, which logically do not have the necessary cause and effect relationship, the execution order or the structure of the modules of the apparatuses are not limited to the execution order or the structure of the modules shown in the drawings and described in the embodiments of the present specification. The described methods or module structures may be implemented sequentially or in parallel (e.g., in a parallel processor or multithreaded environment, or even in a distributed processing environment) in accordance with the embodiments or the method or module structure connection illustrated in the figures when implemented in a practical device or end product application.

Specifically, as shown in fig. 1, the method for determining the well-shock combined alluvial fan reservoir distribution according to an embodiment of the present disclosure may include the following steps:

step S101, establishing a fine isochronous stratigraphic framework of the alluvial fan reservoir of the target area based on the logging data and the seismic data of the alluvial fan reservoir of the target area.

The method in the present embodiment can be applied to a computer device. Near-side data and seismic data of a alluvial fan reservoir of a target region may be acquired. The target area is the area to be studied and can be a land fracture basin. For example, the target layer of land-phase subsidence basin, the liberal pottery groups Ng1 to Ng4, mainly develop alluvial fan-river phase deposition, and the formation heterogeneity is strong.

In some embodiments of the present description, establishing a fine isochronous stratigraphic grid of a alluvial fan reservoir of a target zone based on log data and seismic data of the alluvial fan reservoir of the target zone may include: constraining formation contrast using a marker layer of a alluvial fan reservoir of the target region; dividing a deposition rotation by a logging curve and wavelet decomposition in the logging data, and comparing stratum by the deposition rotation; based on the seismic synthesis record conversion seismic horizon calibration to the seismic section of the corresponding position, determining the position of a seismic reflection phase axis corresponding to the uphole interface, and performing seismic time-depth conversion to develop full-area contrast; based on logging curve characteristics, sedimentary gyratory characteristics and seismic event reflection characteristics, and combining time depth calibration results, multidimensional interactive comparison is carried out, and a fine isochronous stratum grid is established. In the embodiment, based on the principle of sedimentology and layer sequence stratigraphy, a well-earthquake combination method is adopted, and the principle of marking layer constraint, well-earthquake combination, gyratory contrast and hierarchical control is followed, so that a fine isochronous stratum grid can be established, and the requirement of fine characterization of an oil reservoir is met.

Step S102, preprocessing the seismic data of the alluvial fan reservoir of the target area by using a matching pursuit strong reflection stripping method to obtain preprocessed seismic data.

The quality of seismic data is largely related to the fineness of reservoir predictions, and strongly shielding the reflective layer is one of the important factors affecting the quality of seismic data. The bottom boundary T1 of the librarian ceramic group is close to Ng4, has obvious strong reflection homophase shafts, almost continuously penetrates through the whole region, and a plurality of strong waveform amplitudes are mutually overlapped when the waveforms are displayed, so that a single waveform cannot be clearly displayed, the effective reflection of a sand body is covered, and the difficulty of reservoir prediction is aggravated. Thus, the seismic data may be preprocessed to obtain new seismic volumes after strong shielding reflections have been attenuated. Specifically, the seismic data of the alluvial fan reservoir of the target area can be preprocessed by using a matching pursuit strong reflection stripping method, so that the preprocessed seismic data can be obtained.

In some embodiments of the present disclosure, preprocessing the seismic data of the alluvial fan reservoir of the target area by using a matching pursuit strong reflection stripping method to obtain preprocessed seismic data may include: constructing a wavelet atomic dictionary according to the seismic section of the seismic data to obtain atomic parameters; projecting the strong reflection signals to be decomposed into the atomic dictionary, and stripping the strong reflection signals within a range limited by a signal residual error through adaptive iteration; matching is carried out in the stripping process, the in-phase axis display after the strong reflection is stripped is obtained, and the preprocessed seismic data volume is output.

Specifically, a wavelet atomic dictionary with reasonable structure and good performance is constructed according to an original seismic section to obtain complete atomic parameters; projecting the strong reflection signals to be decomposed into a complete atomic dictionary, and stripping the strong reflection signals within a range defined by signal residual errors through adaptive iteration; and matching in the stripping process to obtain the best matching atoms, obtaining the best in-phase axis display after stripping strong reflection, and outputting a new data body, namely the preprocessed seismic data. The seismic data body obtained by applying the matching pursuit strong reflection stripping method has good effect on strong shielding treatment of the librarian group, and the original librarian group seismic channel signals are better displayed.

Step S103, combining the logging data, and performing seismic frequency division inversion on the seismic data body in the preprocessed seismic data to obtain a seismic frequency division inversion result.

The frequency division inversion is nonlinear attribute inversion, and the nonlinear inversion can be higher in accuracy than conventional inversion by establishing a nonlinear mapping relation between amplitude and frequency, and can directly invert lithologic physical data.

In some embodiments of the present disclosure, in combination with the log data, performing a frequency-division inversion on the seismic data volume in the preprocessed seismic data to obtain a frequency-division inversion result may include: selecting a target curve according to the logging data, and carrying out standardized correction on the target curve to obtain a corrected target curve; performing spectrum analysis according to the seismic data, selecting an effective frequency band, and setting a frequency division interval according to actual data to obtain frequency division data volumes of a plurality of different frequency bands; determining a relationship graph of amplitude versus frequency at different time thicknesses; based on a support vector machine, establishing a nonlinear mapping relation between the seismic waveform and the corrected target curve; inversion calculation is carried out on the frequency division data volumes with different frequency bands, and an earthquake frequency division inversion result is obtained.

Specifically, a target curve is selected according to a logging data base, and standardized correction is carried out on the target curve. And carrying out frequency spectrum analysis according to the seismic data, selecting an effective frequency band, and setting a frequency division interval according to the actual data to obtain a plurality of frequency division data volumes with different frequency bands. And obtaining a relation map of the amplitude and the frequency under different time thicknesses by analyzing the AVF relation. By the implementation method of the SVR support vector machine, a nonlinear mapping relation between the seismic waveform and the target curve is established, and inversion calculation is performed. By adopting the seismic frequency division inversion to carry out inversion calculation, the sand body identification in the transverse direction and the longitudinal direction is improved, the coincidence rate of well logging interpretation is higher, and the resolution ratio of the transverse direction and the longitudinal direction is superior to that of the first two methods.

And step S104, determining sand body distribution data of the alluvial fan reservoir of the target area by using the seismic frequency division inversion result.

In some embodiments of the present disclosure, determining sand distribution data for a alluvial fan reservoir of the target region using the seismic crossover inversion results may include: and on the seismic data body of the preprocessed seismic data, on the basis of the seismic frequency division inversion result, defining a sand body boundary by extracting the probability relation between inversion attribute values and sand mud, and obtaining sand body distribution data. On a new seismic data body obtained by suppressing a strong axis by a matching pursuit strong reflection separation method, a sand body boundary is defined by extracting a probability relation between inversion attribute values and sand mud on the basis of a frequency division inversion result which is selected optimally. By the method, sand body distribution data of the alluvial fan reservoir of the target area can be accurately predicted.

In the embodiment, the well logging seismic data are fully excavated, the seismic data resolution is improved through a seismic data processing means, well-seismic combination reservoir prediction and research area alluvial fan reservoir configuration research are carried out on the basis, sand body distribution characteristics of a research area are defined by comparing and optimizing various reservoir prediction methods, alluvial fan configuration distribution characteristics of a Bin county area are dissected, reference significance is provided for similar reservoir prediction and reservoir configuration research in the future, and alluvial fan configuration theoretical research can be enriched.

In some embodiments of the present description, the method may further comprise: and carrying out seismic frequency division fusion on the seismic data volume in the preprocessed seismic data to obtain sand distribution data of the alluvial fan reservoir of the target area.

By way of example, the seismic data of the liberal pottery group in the Bin county area has a main frequency of 30Hz and an effective frequency band ranging from 10Hz to 60Hz, and thus the frequency division of the original data volume is performed at frequencies of 10Hz,15Hz,20Hz,25Hz,30Hz,35Hz,40Hz,45Hz,50Hz,55Hz and 60Hz every 5 Hz. The optimized 15Hz+30Hz data body is the data body with the highest relativity with the sand body, and the frequency division fusion is carried out by analyzing the optimized RMS characteristic attribute of the original data body, so as to predict the sand body of the ceramic group in the library.

In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. Specific reference may be made to the foregoing description of related embodiments of the related process, which is not described herein in detail.

The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

The above method is described below in connection with a specific embodiment, however, it should be noted that this specific embodiment is only for better illustrating the present specification and should not be construed as unduly limiting the present specification.

The embodiment provides a well-shock combined alluvial fan reservoir distribution determining method. In this embodiment, the method for determining the well-shock combined alluvial fan reservoir distribution may include the following steps:

Step 1, well-seismic combination establishes a fine isochronous stratigraphic framework.

The development of reservoir prediction and configuration characterization studies has been kept away from the creation of fine-grained isochronic stratigraphic grids. Due to the influence of multi-stage construction movement of the subsidence basin, the formation heterogeneity in the county area is strong, and the transverse contrast multi-solution is strong, so that the difficulty of isochronous formation contrast is increased. As the target dimensions of hydrocarbon research become smaller, finer demands are also placed on the accuracy of the stratigraphic framework. The original stratum division scheme can not meet the requirement of oil reservoir fine characterization, restricts further exploration and development of important purpose layers in the county area, and needs to be compared again for demonstration, so that a more accurate isochronous stratum grid is established. The study is based on the principle of sedimentology and layer sequence stratigraphy, adopts a well-earthquake combination method, and follows the principles of 'marking layer constraint, well-earthquake combination, gyratory contrast and hierarchical control', so as to develop the fine isochronous stratum grid study.

Step 1.1, constraining formation contrast by a more widely distributed marking layer with stable isochrony.

The marking layer is a lithology or rock stratum interface which is widely distributed, is easy to identify on the stratum section and is formed in a certain range and the same time period. In the case of isochronous layer comparison, since the marker layers are generally stable in isochrony and widely distributed, the stable and continuously developed marker layers can provide a powerful support for the establishment of a fine isochronous layer lattice to assist in identifying and dividing the layers. The bottom interface T1 and the top interface T0 of the librarian group in the work area are obvious mark layers.

There are two contact relations between the T1 interface and the underlying strata in the Bin county region, one is in contact with the lower sand river street group (Es) and the other is in contact with the lower Taigu stratum. In the range of contact with the formations of the sand river street group (e.g., S63 well), the SP value above the T1 interface is relatively low, and the SP value below the T1 interface is relatively high. The resistivity curve starts to increase from below the interface, and response foldback is obvious. The CAL and AC curves are more jagged above T1, gradually returning to low values from below T1, smoother than at the top. Because of lithology differences, the resistivity curve above the T1 interface has a continuous low value that is flat throughout the range of contact with the formation of the kingdom of the pacific (e.g., S87 well), and has an increasing trend from below the T1 interface. The AC and CAL curves are in a combination of a toothed box shape and a bell shape at the T1 interface, and have serious jaggies, and have a descending trend from the lower part of the T1. The SP curve overall assumes a medium and low value. The differences in log morphology and baseline for different wells indicate the differential distribution of the combination of depositions at the upper and lower interfaces.

From the logging feature, the GR and SP curves are mainly medium and high values, the resistivity curves are mainly medium and low values, and the CAL and AC curves are medium and high values below the T0 interface.

The T0 top interface shows medium-strong amplitude, medium-low continuity interface characteristics on the seismic section, and the whole area can be continuously tracked. From the seismic section, the T0 interface is the boundary between the explicit town group (Nm) and the liberty ceramic group (Ng) in the Binxian county region, and reflects the degradation caused by the rapid drop of the lake plane in the primary lake withdrawal period.

The T1 bottom interface becomes a medium-intensity amplitude, medium-high continuous interface characteristic on the seismic section, and the whole area can be continuously tracked. The lower part of the T1 interface can identify obvious cut-off reflection termination phenomenon, and the cut-off reflection termination phenomenon and the underlying stratum are in an angle-unconformity contact relation. The upper part of the T1 interface can see the super seismic reflection structure, and the obvious demarcation phenomenon between the upper and lower sedimentary bodies reflects the occurrence of lifting and degrading of the structure and indicates that the ceramic group is in non-integrated contact relation with the lower stratum. Wherein a alluvial fan deposition system develops at the T1 interface above the intermediate bump location.

And 1.2, dividing a deposition rotation by a logging curve and wavelet decomposition, and comparing stratum by the deposition rotation.

A sedimentary gyratory is a set of rock formations that are repeated in succession in a regular fashion in a longitudinal section. The formation is partitioned from large to small hierarchical control by comparing the depositional curl characteristics. The three-level sequence is correspondingly arranged between the marking layers T0 and T1 by integrating the previous investigation and the well logging seismic information, and can be divided into a long-term rotation. On the basis of which. And identifying each corresponding long-term rotation inside the librarian pottery group.

The characteristic of the logging curve reflects the comprehensive signal response of the sediment under the control of the different levels of the reference surface rollbacks, and the different levels of the deposition rollbacks are difficult to effectively distinguish by only using the characteristic of the logging curve, so that a wavelet change method is introduced to assist in dividing the internal rollbacks. The wavelet transformation technology can adjust the local shape corresponding relation between time and frequency, extract the layer sequence stratum development characteristics hidden in the original logging data, solve the problem of contradiction between resolution scales between time and frequency, and highlight the deposition rotation change trend in the target horizon.

And selecting a gamma curve as a wavelet dominance curve, and performing one-dimensional discrete change on the wavelet dominance curve, wherein S represents an original curve, the other 11 kinds of the gamma curves are change curves with different levels, and the curves with different levels in the first 11 columns are added to obtain an original S curve in the last column. The comparison shows that the cycle characteristic of the d 8-level curve is high in matching degree with the mid-period gyre. Thus, the liberal pottery group is divided into four middle-period gyrations from bottom to top, which are named MNG1, MNG2, MNG3 and MNG4 in sequence. MNG1, MNG2, MNG3 and MNG4 correspond to Ng1, ng2, ng3 and Ng4 sand groups, respectively.

Since Ng1 and Ng2 do not develop alluvial fans for deposition, only a few alluvial fans develop at individual positions in the Ng3 sand group, and the dominant horizon for exploration and development of the zone is Ng4, ng1 to Ng4 sand groups of more than 100 wells in the zone are divided in this embodiment. In combination with the core data and the change rule of the logging curve, the Ng4 sand group of the developing alluvial fan is subdivided into Ng4-1, ng4-2 and Ng4-3 small layers (shown in the following table 1), and the short-term convolutions (SNG 1 to SNG 3) respectively correspond to each other, and are named as SNG1 to SNG11 in sequence from bottom to top, and a single-well convolutions division diagram is shown in fig. 2.

TABLE 1

And 1.3, calibrating to the seismic section of the corresponding position through seismic synthesis record conversion, and determining the position of a seismic reflection phase axis corresponding to the uphole interface, and performing time-depth conversion, thereby carrying out full-zone comparison.

The whole western well pattern in the county area is sparse, the eastern side and the southern side are locally dense, the specific value of the average well spacing is 200m to 2km, the uncertainty of the formation is large only by means of log data contrast, and the multi-solution of log division is reduced by combining seismic data. The seismic data is like the X-ray film of the earth, can irradiate the trend of the transverse change of the underground stratum, reflects the change of stratum interfaces between wells, can reduce the multi-resolution caused by interpolation of the logging data, is beneficial to closing the whole area, and improves the stratum contrast precision. Therefore, a stratum contrast method based on well-seismic combination is adopted to develop research, and the characteristics of the earthquake marks and the logging curve marks are combined from the characteristics of unconformity, break points, layer sequence interfaces and the like displayed on the earthquake section in the county area. Starting from a single well, the interfaces divided on the well are calibrated to the seismic sections at the corresponding positions through seismic synthesis record conversion, the positions of the seismic reflection phase axes corresponding to the interfaces on the well are determined, and time-depth conversion is carried out, so that comparison is carried out in the whole region of the county.

The seismic horizon calibration and interpretation are the most indispensable basic links in the well seismic calibration process, and determine the scale and precision of the subsequent reservoir prediction result. The calibration result is not only the basis for performing seismic-logging joint inversion, but also a powerful basis for performing inter-well stratum contrast, and the initial steps of horizon calibration and interpretation are the manufacture of synthetic seismic records. The synthetic seismic record refers to a seismic channel result generated by artificial transformation, has wide application in the seismic model technology, is a medium bridge for converting a geological information model and a seismic information model, and has the accuracy directly influencing the accurate calibration of the horizon. The basic principle can be simply understood as that the reflection coefficient and the seismic wavelet are convolved to obtain a calculation result capable of reflecting the underground, and the manufacturing process is equivalent to a simplified version of one-dimensional forward modeling process.

The time domain on the seismic section is converted into the depth domain on the logging by making the synthetic record, so that the seismic reflection phase axis corresponds to the logging geologic layering, meaning on the time domain is given to the well, the time domain corresponds to the depth domain, and the stratum is transversely calibrated.

The synthetic record calculation formula is as follows:

S(t)＝R(t)W(t)

As can be seen from the formula of the composite record, the composite record S (t) is created by paying attention to a plurality of variable points related to the reflection coefficient sequence R (t) and the wavelet change W (t). Such as acoustic curve values and density curve values (the product of the acoustic curve values and the density curve values is a reflection coefficient), seismic data signal-to-noise ratio, wavelet type selection, correspondence of well bypass information, stretching compression rationality and the like. As shown in FIG. 3, a schematic diagram of wavelets for synthesizing a record is shown. In fig. 3, the horizontal axis represents Time (Time), and the vertical axis represents relative amplitude (Relative amplitude). All the points can have different degrees of influence and restriction on the synthesized recording result, the obtained seismic record after convolution is not completely consistent with the actual seismic section generally, and different sizes can be generated, so that the phenomenon of energy inconsistency or layer inconsistency can occur during transverse comparison, namely, the width of the same phase axis is not high or the matching length of the same phase axis time window is limited.

The geophysical data are rich and various, most of the seismic data are measured point by point on the ground by an observation instrument and are obtained by arrangement of corresponding analysis tools, various measurement errors and interference factors can be generated in the process, and the obtained seismic data body is a mixture of effective information and random abnormal information, so that comprehensive statistical processing is carried out on the seismic basic data before seismic calibration is accurately carried out.

Because the value of the impedance of the seismic wave is the product of the acoustic wave and the density, before the time-depth conversion calibration is carried out, the acoustic wave curve (AC) and the density curve (DEN) are subjected to standardized treatment, the field value caused by the error of a logging instrument is removed, and after the standardization, the value of the impedance and the value of the reflection coefficient of the seismic wave obtained by multiplying the acoustic wave and the density are more accurate.

As can be seen from the basic data, the time domain depth of the ceramic group stratum in the Bin county region ranges from 950ms to 1150ms, so that the amplitude spectrum analysis is performed on the target layer region by taking 950ms to 1150ms as an amplitude spectrum analysis time window. According to spectrum analysis, the main frequency of the target layer of the ceramic group is about 33hz, the effective frequency bandwidth is 10hz to 65hz, part of the ceramic groups are branched off in phase, and the reflection information of the side channels of the seismic well is disordered. Therefore, 30hz Rake wavelets are selected to combine with the parawell seismic trace information to assist in calibration, the sampling interval is set to be 1ms, and a target layer spectrum analysis chart is shown in FIG. 4. In fig. 4, the horizontal axis represents Frequency (Frequency), and the vertical axis represents power (power).

Aiming at the specific characteristics of seismic data in the county area, in the implementation, a Well bypass wavelet in the county area is extracted by using a Well-gateway process module under a GeoInvitation system, and then a Synthetic module under the Geog system performs Synthetic record calibration work on more than 100 heavy-point vertical wells in the work area.

By combining with the literature research foundation, through analyzing logging and seismic data in the Bin county area, the bottom of the librarian group is respectively developed with a set of strong reflection shafts with larger amplitude, and the bottom strong reflection shaft is named as a strong reflection layer A. The reasons for this formation are mainly two: the lower basal stratum is mainly composed of granite gneiss in the Taigu world from the lithology, lithology difference exists between the lower basal stratum and sand shale of the upper basal stratum, the lithology change causes different propagation speeds of seismic waves, the speed difference is overlapped to form wave impedance difference, and a stronger and more continuous homophase shaft is formed at the lithology junction; and secondly, from the view of the non-integration relation of stratum contact, the strong reflection axis generated by non-integration generates shielding effect on the phase axis with weaker surrounding, so that the phase axis shows weak reflection or blank reflection on the section.

Because the strong axis mark layer A is positioned at the bottom of the Ng4-3 small layer and is stable in development in the whole area, the set of stable strong reflection layer A is used as the mark layer, and the selected well group is calibrated and converted by combining the profile spread characteristics of a single waveform and a reflection wave group, so that the final synthetic seismic record is obtained.

Taking the synthetic record calibration result of the S105 well as an example, as shown in fig. 5, a synthetic record calibration result diagram is shown. The calibration result of a single well shows that the well side channel seismic information at the bottom of Ng4-3 and the top of Ng1 have better correspondence with the record synthesized by the Rake wavelet, the calibration effect is stronger, and other small-layer interfaces in the well can also have better correspondence.

Combining the calibration result of a single well, carrying out seismic horizon tracking interpretation on each small layer of a pottery group in a Bai county area by taking a strong axis near the bottom Ng4-3 as a basis of horizon tracking interpretation, and realizing three-dimensional interpretation closure of the whole area in a gridding manner by using 10 times 10 survey line interpretation precision.

And 1.4, multidimensional interaction, full-area closure and establishment of a fine isochronous stratigraphic framework.

According to the logging curve characteristics of the Bin county region, the sedimentary gyratory characteristics and the seismic event reflection characteristics are combined, and the time depth calibration results are combined to develop and explain the horizon tracking of 10 times 10 line precision on the small layers of Ng1, ng2 and Ng3 sand groups, ng4-1, ng4-2 and Ng4-3 of the ceramic group in the Bin county region. On the basis of closing the whole area, well-seismic joint comparison is carried out through more than 30 well-connecting sections.

As can be seen from the comparison of the continuous well profile along the material source, the closer the well point is to the raised position, the thinner the overall deposition thickness of the stratum is, the farther the stratum is away from the raised position, and the thickness of the stratum deposition is gradually increased. Meanwhile, the position of the well connecting section passes through the east-west co-deposition positive fault southward fault of the research area, and the stratum division comparison result shows that the stratum deposition thickness at the lower disc of the fault is obviously thicker. Under the influence of the formation movement, a large amount of sediment builds up on the fault floor, making the formation sediment thicker.

As can be seen from comparison of the well-tie profile of the cut source, the overall deposition trend of the formation thickness in this direction is not greatly changed, and the overall thickness is uniform.

Based on high-resolution layer sequence stratigraphy, a method of 'flat-profile interaction and three-dimensional closure' is applied to obtain two-dimensional and three-dimensional double closure, and the isochrony of the fine isochronal stratigraphic framework is ensured.

The method is characterized in that a stratum skeleton profile in the direction of a material source (east-west direction) and a stratum skeleton profile in the direction of a material source (north-south direction) are sequentially established by combining a deposition background of a research area and an ancient water flow direction, and on the basis of comparison of two typical skeleton profiles, more than 100 wells in a work area are connected with 30 auxiliary profiles to form a two-dimensional plane distribution map of a well connecting profile. The method is characterized in that the stratum interface is finely compared according to the principle of well-seismic combination and multidimensional interaction, and the section result is converted into visual three-dimensional display through the reservoir digital characterization software Direct, so that the stratum comparison result can be conveniently checked, and a three-dimensional stratum model schematic diagram is shown as shown in fig. 6.

As can be seen from the formation comparison result of the whole region of the Binxian county, the formation thickness of the whole research region greatly changes. The formation thickness of the Ng1, ng2 and Ng3 sand groups ranges primarily from 30 meters to 50 meters; the Ng4-1 small layer, the Ng4-2 small layer and the Ng4-3 small layer have a thickness mainly ranging from 10 meters to 25 meters. The high-value distribution of the stratum thickness is mainly distributed at the southeast and middle-south parts of the research area, and is also a position with dense distribution of key wells. The stratum thickness near the protruding position is thinner, the stratum thickness near the depressed position of the southeast part is thicker, the stratum thickness of each sand group and the small layer has obvious trend of protruding thinning towards the middle part, and the thickness thinning trend of each layer has continuity. It can be seen that during the Ng3 deposition period, the middle bump position is still a low thickness region, which proves that the bump is exposed at this time, and has some influence on the stratum at the bump edge. Until the Ng2 deposition period, the average formation thickness value at the mid-region position of the work area begins to rise, and the mid-region protrusion is substantially no longer exposed as the deposition evolves, indicating that the formation activity during this period is no longer as intense as during the Ng4-3 to Ng4-2 deposition periods.

The evolution trend of the stratum thickness contour line of the liberal pottery group is analyzed from bottom to top, and the stratum thickness is thickened from Ng4-1 to Ng 1. The local stratum thickness changes faster under the influence of the protruding topography of the beach county. The overall formation thickness of Ng3 through Ng1 is more evenly distributed than Ng4-3 through Ng 4-1.

And 2, well earthquake combined reservoir prediction.

By establishing a fine isochronous stratigraphic framework in the county area, stratigraphic distribution characteristics are defined. Based on the original seismic data body, the seismic data is developed and preprocessed by wavelet decomposition reconstruction and matching tracking strong reflection stripping means, and a strong reflection shielding layer of a librarian group is suppressed to obtain a new seismic body after weakening strong reflection shielding. Reservoir prediction work is carried out on a new seismic body after strong reflection shielding is suppressed, the prediction effect of a seismic inversion method and a seismic attribute method on the county area is compared, and analysis finds that the attribute fusion method cannot well guide the distribution of the sand bodies of the librarian groups. And then a frequency division inversion method suitable for the region of the Bin county is optimized, and the sand body prediction of the target layer librarian ceramic group is developed. In addition, the earthquake prediction result is verified by combining well data, the accuracy of the sand prediction result is ensured by well earthquake combination, and finally the sand distribution characteristics which accord with the deposition characteristics of the librarian groups in the county area are obtained, so that a foundation is laid for the next configuration analysis.

In order to obtain a better reservoir prediction effect, the data quality of the original seismic data volume of the county region should be analyzed first. Two sets of seismic data bodies are shared in the county area, wherein the track interval of a newer set of data body Block A is 25, the resolution is higher, and the main frequency is about 33Hz; the other set of data volumes Block B had a track pitch of 12.5 and had poor resolution due to the long acquisition period, with a dominant frequency of about 29Hz. Therefore, a first set of seismic data volume Block A is selected when reservoir prediction work is performed in the county region.

The quality of seismic data is largely related to the fineness of reservoir predictions, and strongly shielding the reflective layer is one of the important factors affecting the quality of seismic data. The bottom boundary T1 of the librarian ceramic group is close to Ng4, has obvious strong reflection homophase shafts, almost continuously penetrates through the whole region, and a plurality of strong waveform amplitudes are mutually overlapped when the waveforms are displayed, so that a single waveform cannot be clearly displayed, the effective reflection of a sand body is covered, and the difficulty of reservoir prediction is aggravated.

The reason for its strong shielding formation is mainly two: the lower basal stratum is mainly composed of granite gneiss of the Taigu kingdom from the lithology, and the sand shale of the upper basal stratum has larger wave impedance difference, so that the intersection is stronger in section, and the seismic reflection waveform shows redundant connected-piece distribution; and secondly, from the view of the non-integration relation of stratum contact, the strong axis generated by non-integration generates shielding effect on the phase axis with weaker surrounding, so that the phase axis is characterized by weak reflection or blank reflection on the section.

As can be seen from the seismic facies near the strongly shielding reflective layer, the alluvial fan developed at the Ng4 position is generally in a rhombus shape or a hillock shape on the seismic section, and most of the hillock interior is blank and irregularly reflected. The deposition main body of the alluvial fan is the sand stones which are randomly piled up, and after long-term weathering, when the deposition main body is contacted with granite gneiss at the bottom of a boss in a county, the boundary between the alluvial fan and bedrock is generally unclear, and the factors also influence the quality of seismic data in the area.

Therefore, a strong shielding is stripped by a seismic data processing means, the resolution and the signal to noise ratio of a seismic section are improved, and the identification of effective information of the earthquake in the sand body prediction process is enhanced.

And 2.1, weakening a strong shielding shaft of the earthquake.

The subsurface stratum of the alluvial fan reservoir is often a basal stratum, and the lithology, rock density and the like of the subsurface stratum are obviously different from those of an overlying stratum, so that a strong seismic amplitude reflection axis is often formed, and further the prediction of seismic data on the reservoir is affected. The method and the device compare wavelet decomposition reconstruction methods with the matching pursuit strong reflection stripping methods, and further optimize the matching pursuit strong reflection stripping methods to preprocess seismic data of a research area, weaken strong shielding and further improve reservoir prediction accuracy.

The complex domain matching pursuit algorithm can effectively extract useful information in the seismic signals, and is an emerging intelligent algorithm in signal sparse decomposition. The basic principle is that the information carried by each atom in the original seismic section is regarded as a complete function set, as shown in fig. 7, which shows a schematic diagram of a matching pursuit algorithm in this embodiment. And tracking to a strong reflection position through processing of a matching algorithm. The strong seismic reflection signals to be decomposed are projected in a function set and adaptively searched in a complete atom library. And continuously iterating in the range that the residual signal does not exceed the tolerance of the error, matching to the optimal atomic information, and finally obtaining the optimal display of the processed seismic signal.

In addition, the algorithm can adaptively adjust parameters of the atomic dictionary according to specific characteristics of each seismic component signal, so that matching accuracy is improved. The method has the advantages that the effective information in the redundant seismic signals is extracted efficiently, the ineffective information is removed, and valuable references are provided for seismic research and prediction. After the self-matching pursuit algorithm is raised in recent years, a plurality of scholars apply the method to research and solve the problem of the strong reflection shielding layer, for example, when the scholars Yin Xingyao and the like research the strong reflection layer of the A block which is also positioned in the depression of the ataxia, the strong reflection separation technology of the self-matching pursuit algorithm is added, so that the strong reflection interference is weakened, and a better effect is obtained.

The matching pursuit strong reflection stripping method comprises the following specific steps:

(1) firstly, constructing a wavelet atomic dictionary with reasonable structure and good performance according to an original seismic section to obtain complete atomic parameters; (2) projecting the strong reflection signals to be decomposed into a complete atomic dictionary, and stripping the strong reflection signals within a range defined by signal residual errors through adaptive iteration; (3) and matching in the stripping process to obtain the best matching atoms, obtaining the best in-phase axis display after stripping strong reflection, and outputting a new data body.

As shown in FIG. 8, the upper graph in FIG. 8 is the original seismic profile before the strong reflection event at the Ng4-3 position is stripped using matching pursuits, and the lower graph in FIG. 8 may be the new data volume profile after the strong reflection event at Ng4-3 is stripped.

The integral section display can clearly show that the strong axis formed by the variation of sandstone layer and granite layer rock and the strong axis generated by the non-integrated contact of the bottom stratum are effectively pressed, the amplitude intensity energy of the seismic reflection phase axis which can be continuously tracked in the whole region of the Binxian county is greatly reduced, and the original librarian group seismic channel signal is better displayed under the condition that the residual signal is in the error allowable range through the self-adaptive decomposition of matching atoms. The original data body before the matching pursuit strong reflection stripping and the data body obtained by the matching pursuit strong reflection separation method are compared in an enlarged mode, and the seismic data body obtained by the matching pursuit strong reflection stripping method has a good effect on the strong shielding treatment of the librarian groups. Referring to fig. 9, as shown in the left graph of fig. 9, the waveform at the position a before processing has a larger amplitude, and the energy is overlapped and covered, so that the waveform cannot be clearly identified; as shown in the right hand graph of fig. 9, after matching pursuit strong reflection stripping, not only is the specific waveform of each trace shown, but the upper one of the effective reflection patterns is enhanced, rather than over-removing the reflection pattern as in the wavelet decomposition reconstruction method.

Analysis considers that the matching pursuit strong shielding separation method has better effect of weakening the strong shielding, so that the prediction of the pottery group reservoir in the Bin county is carried out by selecting a new data body after the matching pursuit strong reflection separation, and a foundation is laid for the subsequent sand body prediction result. In order to verify the effectiveness of the method, frequency division inversion operation can be respectively carried out on two sets of seismic data bodies before and after matching pursuit stripping, as shown in step 2.3.

And 2.2, frequency division fusion of the seismic data volume.

The frequency division attribute fusion method can be adopted to predict the ceramic group sand bodies in the librarian. As shown in fig. 10, a frequency division attribute fusion workflow based on support vector regression is shown. The main principle is that the original data body is divided into three parts of a low-frequency data body, a medium-frequency data body and a high-frequency data body, and different attributes are respectively extracted from the seismic data bodies in the three parts. By contrast, the attribute with better correlation is preferably selected as a feature vector (the preferred attribute is RMS attribute at this time), three-fifth wells in the Bin county region are randomly selected as a training data set, the other two-fifth wells are selected as a test data set, a nonlinear mapping relation between feature attribute values at well points and sand thickness at the well points is established under the participation of a support vector machine algorithm, and the sand distribution rule of the whole region is popularized and predicted.

The main frequency of the seismic data of the pottery group in the beach county area is 30Hz, the effective frequency band ranges from 10Hz to 60Hz, and then the frequency division of the original data body is realized at the frequencies of 10Hz,15Hz,20Hz,25Hz,30Hz,35Hz,40Hz,45Hz,50Hz,55Hz and 60Hz at intervals of 5 Hz. The optimized 15Hz+30Hz data body is the data body with the highest relativity with the sand body, and the frequency division fusion is carried out by combining with the RMS characteristic attribute optimized by analyzing the original data body. The analysis of the result of the fusion of the frequency division attributes of each layer in the Bin county region finds that the obtained fusion attribute effects are mostly distributed in disorder, no obvious regularity exists, the distribution mode of the alluvial fan-river phase sand body is not met, and the correlation between the training set and the test set is low. Taking Ng 4-hour layer by layer as an example, the correlation between the fusion attribute of the optimized 15Hz+30Hz frequency division data body and the thickness of the sand body is only 0.37, the distribution diverges and changes rapidly, and the sand body of the area cannot be predicted through the fusion attribute.

Thus, the main reason why it is considered that the seismic attributes are difficult to guide the sand distribution in the county region may be related to the granite base at the bottom. Previous studies have shown that seismic wave impedance is greatly affected by formation lithology, the base lithology of the beach county is granite, and the wave impedance value of granite is higher than that of sandstone and mudstone formations. When the seismic attribute of each layer at the position is extracted by taking the well point as the center of a circle, the attribute value in the radius range is easily influenced by the high-value wave impedance of granite, so that the extracted attribute has insufficient accuracy, and the effective information is covered by a large piece. In view of the fact that the attribute method cannot guide the prediction of the liberal pottery group sand body well, the seismic attribute method is not used for the research of the county area in the embodiment, and the seismic inversion method which is more visual and has higher precision is selected.

And 2.3, seismic frequency division inversion.

Seismic inversion methods are very diverse and rapidly evolving, with more widely used constrained sparse pulse inversion, colored inversion, and the like. Inversion calculation is a process of directly converting a stratum interface into a lithology interface through a mathematical method, through the process, physical imaging can be carried out on underground lithology distribution to obtain a wave impedance data body, and then analysis and explanation are carried out on the obtained underground inversion section by combining with geological thinking to comprehensively predict a target reservoir.

Three inversion methods, namely colored inversion, constraint sparse pulse inversion and frequency division inversion, are selected in the study, and the sand bodies of the librarian groups are predicted. By trying three inversion methods, the inversion method which can most represent the librarian pottery group in the Bin county area is expected to be optimized, and sand bodies in a research area are delineated.

The color inversion belongs to spectrum inversion and is originally proposed by a scholars Lancaster in 2000. The inversion is achieved through a colored filtering process, inversion is carried out in a frequency domain, convolution is carried out by relying on a matching operator in the filtering process, the operator matches the uphole wave impedance with the seismic spectrum, and finally the inversion is completed. The uncertainty factor of extracting the well bypass wavelet from the seismic data volume is more, and the uncertainty factor is influenced by the well seismic calibration result, the time-space change of the wavelet and the wavelet calculation method. The factors can have different degrees of influence on inversion results, so that the colored inversion avoids the process, the process is automatically optimized, the wavelet extraction process is omitted, and objective inversion is simply and rapidly carried out on the original geological phenomenon. The concrete flow of the colored inversion is divided into four parts: (1) analyzing the wave impedance spectrum beside the well; (2) performing spectrum analysis on the earthquake; (3) designing a matching operator to match the spectrum of the earthquake with the wave impedance spectrum of the well; (4) and (5) applying a matching operator to the seismic body to finish inversion. According to the colored inversion effect in the county area, the inverted sand body form has a certain similar prediction effect worse than the precision of the original seismic reflection phase axis, and large sand bodies developed on Ng4 small layers are not predicted, and other methods are required to be tried for optimal comparison.

The Bayes constraint sparse pulse inversion is different from the conventional sparse pulse inversion which only considers maximum likelihood estimation, and the prior condition which considers the reflection coefficient is added. The target result is constrained by the uphole wave impedance, the low-frequency part of the inversion result is compensated, and meanwhile, under the comprehensive consideration of Bayes, the optimal solution under the constraint conditions of all aspects is obtained in the probability density model. According to the Bayesian inversion effect in the region of the beach county, the Bayesian constraint sparse pulse inversion has a relatively improved resolution in the longitudinal direction compared with the colored inversion, but the resolution in the transverse direction is reduced, and the overall effect is improved compared with the colored inversion. It has resolved some of the sand at the Ng4 destination layer, but still does not achieve the expected predictive effect, so the frequency division inversion approach has been continued.

The frequency division inversion is a nonlinear attribute inversion method, and the nonlinear inversion method can be used for directly inverting lithologic physical data by establishing a nonlinear mapping relation between amplitude and frequency, and has higher accuracy than conventional inversion.

In performing the frequency division inversion, the known parameter is amplitude data. Then, in order to obtain the seismic waveform profile, two data parameters, namely, the time thickness and the wave impedance, related to the seismic waveform need to be obtained from the data parameter, namely, the amplitude. The process of obtaining two data from one data is full of polynomials, and the corresponding geological meaning is that the amplitude characteristics of different strata under the same main frequency wavelet under the same time thickness are different, even if the amplitude characteristics of the same strata under the same main frequency wavelet under the same time thickness are also different, as shown in fig. 11. Fig. 11 shows an AVF relationship diagram. The left plot of fig. 11 shows Amplitude (Amplitude) versus time Thickness (Thickness) as a function of frequency. The right plot of fig. 11 shows Amplitude (Amplitude) versus Frequency (Frequency).

The frequency division inversion introduces an AVF relationship in its principle for establishing a relationship between frequency and amplitude to reduce uncertainty and multi-resolution. The principle of AVF is that in a defined wedge model, the rake wavelets of different frequencies are calculated by convolution to obtain a tuning curve (i.e., a tuning curve relationship diagram as shown in the left diagram of fig. 11) of amplitude versus thickness as a function of frequency. Then, the relation between the amplitude and the frequency variation (as shown in the right graph of fig. 11) is obtained through conversion, so that the multi-resolution is reduced. However, relying on AVF relationships alone is not sufficient because its complexity is difficult to express accurately with a certain function. Therefore, a SVR support vector machine method is introduced to assist in establishing the nonlinear mapping relation required by us.

Under the control of three parameters, SVR overcomes the network instability problem and the local optimization problem of the neural network, and is a statistical algorithm added with artificial intelligence intervention. By means of SVR, a nonlinear mapping relation of well vibration is established, and then AVF relation is used as a contrast to carry out inversion, and the two methods are combined, so that the degree of uncertainty of inversion freedom is reduced, and inversion results with higher precision are obtained.

Referring to fig. 12, a frequency division inversion flow in the present embodiment is shown. As shown in fig. 12, a specific procedure for developing the frequency division inversion may include the following steps.

(1) Firstly, selecting a target curve according to a logging data base, and carrying out standardized correction on the target curve. (2) And carrying out frequency spectrum analysis according to the seismic data, selecting an effective frequency band, and setting a frequency division interval according to the actual data to obtain a plurality of frequency division data volumes with different frequency bands. (3) And obtaining a relation map of the amplitude and the frequency under different time thicknesses by analyzing the AVF relation. (4) By the implementation method of the SVR support vector machine, a nonlinear mapping relation between the seismic waveform and the target curve is established, and inversion calculation is performed.

Because the GR curve can better distinguish the sand and mud rock information, the GR curve is selected as the data tag. In order to take the instrument error of the logging curve during acquisition into consideration, before the GR curve is applied to perform full-area inversion, the mean variance correction is adopted on the GR curve to remove individual abnormal values, the correction is performed within an effective range by taking the coring well S63 as a standard, and a comparison graph of the GR curve before and after normalization in the embodiment is shown in FIG. 13.

According to the frequency division inversion flow, the effective frequency band range of the seismic data of the librarian group in the Bin county area needs to be mastered first. Therefore, the spectrum analysis is firstly performed on the target layer interval librarian group, the proper frequency segmentation is selected from 10Hz to 60Hz according to the frequency band distribution range of the librarian group, and a plurality of frequency division data bodies with different frequency bands are obtained at intervals of 5Hz, as shown in the following table 2. And selecting a 15Hz low-frequency division body, a 30Hz intermediate-frequency division body and a 50Hz high-frequency data body from the obtained low-medium-high frequency division body to participate in inversion.

TABLE 2

A natural Gamma curve (GR) of a fifth well in Bin county is selected as a test checking set, other wells are used as training sets, iteration training is carried out for a plurality of times under the support of an AVF (automatic video frequency) relation and an SVR (singular value decomposition) algorithm by debugging Epsilon, gamma and Segments parameters, and rich well information is combined with seismic information to establish a non-mapping relation between natural Gamma (GR) of the region and the seismic information.

Finally, the average correlation coefficient of the test set for the sand body and the learning value on the well is 0.9076, the correlation coefficient of the test set and the training set is 0.8911, the complex coefficient is 0.2658, the average fitting value of the target curve and the learning curve is mainly distributed about 0.85, and as shown in fig. 14, a curve fitting result schematic diagram based on the SVR method is shown. In fig. 14, the blue curve is the original curve, the red curve is the target curve of the frequency division inversion of this time, and the fitting degree of the two curves is high, which indicates that the learning result is faithful to the well earthquake information in the county area. On the basis, a nonlinear mapping relation between waveforms conforming to the geological law of the librarian groups in the Bin county region and the GR curve is established, and a final inversion effect is obtained.

Referring to fig. 15, a frequency division inversion effect diagram in the present embodiment is shown. As shown in fig. 15, the inversion section obtained based on the GR standardization curve can be seen that the gravel rock body is distributed in a continuous piece shape on the inversion section, the continuity is better, and the obvious boundaries of most of the sand bodies can be identified, so that the effect is better than the colored inversion and the bayesian constraint sparse pulse inversion used in the former two methods.

And after the inversion result is obtained, extracting the attribute value of the GR inversion body of the well bypass, comparing with the well interpretation sand body, and verifying the accuracy of prediction. The frequency division inversion has good sand identification effect on the liberal pottery group from 7 meters to 15 meters and more.

Through summarizing and comparing, the colored inversion has simple operation flow compared with other inversion, can avoid the problem of extracting wavelets, but has relatively high degree of freedom, lacks wavelet definition control, and has low coincidence degree with the obtained result and the sand on the well; the Bayes constraint sparse pulse inversion has the advantages that the integral prediction effect is improved compared with the colored inversion, the prediction precision is close to the seismic reflection phase axis, the transverse precision is improved, but the resolution of the Bayes constraint sparse pulse inversion on sand bodies in the longitudinal direction is reduced, and the sand bodies are difficult to divide into periods in the longitudinal direction; compared with the former two inversion methods, the frequency division inversion has the advantages that the sand body identification in the transverse direction and the longitudinal direction is improved, the coincidence rate in well logging interpretation is higher, and the resolution ratio in the transverse direction and the longitudinal direction is better than that in the former two methods.

In summary, the frequency division inversion method is considered to be higher in reliability of the target horizon of the research area through comparison and optimization, so that the frequency division inversion is selected for the research to describe the distribution of the ceramic group gravel rock mass in the Binxian county area.

In addition, in order to bidirectionally verify the influence of the matching pursuit intensity-removed reflection method selected in the step 2.1 on the inversion effect, the original seismic data body before intensity-removed shielding is used for frequency division inversion, the divided frequency division segments are kept consistent, other parameters are kept consistent, and the specific comparison effect is as follows. Referring to fig. 16, a comparison graph of effects before and after de-emphasis axis is shown. Taking this particular section shown in fig. 16 as an example, the a-plot in fig. 16 is the inversion result obtained by applying the original data volume, and the b-plot in fig. 16 is the inversion result obtained after applying the peeled strong reflection.

Taking the sand body at the Ng4 position of the key layer as an example, the response of the sand body at the Ng4 position on the inversion section is weak and can not be identified almost before strong reflection shielding is removed; after weakening the strong reflection shield, the response of the sand body at Ng4 appears obviously, and the fact that the reservoir prediction effect in the county area is improved by stripping the strong reflection is verified.

And 2.4, reservoir distribution prediction.

On a new seismic data body obtained by suppressing a strong axis by a matching pursuit strong reflection separation technology, a sand body boundary is defined by extracting a probability relation between inversion attribute values and sand mud on the basis of a frequency division inversion result which is selected optimally.

Firstly, the GR inversion data body is subjected to time-depth conversion and converted into a depth domain data body, then the response value of the GR inversion body in a well bypass is extracted, and is analyzed with corresponding lithology data, as shown in FIG. 17, an inversion attribute value and gamma curve intersection diagram in the embodiment is shown. Referring to fig. 18, a diagram of a sand-mud probability relationship analysis is shown. As shown in fig. 18, as can be seen from the probability relationship between the two, when the inversion attribute value is greater than 115API, the probability of sandstone development is 0; when the inversion attribute value is less than 85API, the probability of sandstone development is 100%; between 85API and 115API, the probability of sand mud is continually decreasing. Wherein, the attribute value of the juncture of the sand and the mud is about 100API, the probability ratio of the sand and the mud at the moment is about 50 percent, and the sand envelope is drawn by taking the probability relation as a boundary. Under the guidance of a deposition mode, correcting the thickness interpolation of the sand body on the well by referring to the inversion attribute trend, macroscopically controlling the distribution range of the sand body, and obtaining the sand body thickness distribution map of each small sand group layer.

Referring to fig. 19-21, a Ng4-1 small layer sand thickness map, a Ng4-2 small layer sand thickness map, and a Ng4-3 small layer sand thickness map are shown, respectively. The direction of the ceramic composition source in the research area mainly comes from the middle bulge and the north part. The strip sand bodies at two sides are influenced by north material sources, the spreading direction is approximately the north-south direction and the north-east-south-west direction, the transverse width is approximately 3km to 4.5km throughout the whole work area; the material source of the fan-shaped connected sheet-shaped sand bodies in the middle is provided by the middle bulge, the spreading direction of the gravel rock bodies extends towards the south eastern direction along the bulge edge, the transverse width is approximately 6km to 12km, and the river sand bodies can be seen to be contacted with the fan boundaries at local positions, as shown in fig. 19, 20 and 21.

Analysis of the distribution plots of sand thickness for each layer of the liberal ceramic group shown in FIGS. 19, 20 and 21 shows that there is a tendency for the overall sand thickness values to decrease during the Ng4-3 to Ng4-1 layer deposition period. In combination with the deposition mode in the region of the county, the deposition of the fan mainly develops from the Ng4-3 small layer to the Ng4-1 small layer, the thickness of the sand body is thicker when the sand body is close to the raised edge, and the granite gneiss at the raised substrate of the county is weathered to provide a rich source for the formation of the fan at the raised edge. From the evolution of the Ng4-3 deposition period to the Ng4-1 deposition period, the trend of the thickness change of the sand body is compared, and as the protrusion of the county is gradually reduced in exposure, the fan-shaped connected sheet-shaped sand bodies distributed around the edge of the protrusion are also gradually thinned, so that the sand body meets the evolution rule of the alluvial fan. Analysis of the local features shows that the sand distribution is controlled by the construction factors besides the influence of the bulges, and the sand thickness value at the position of the lower-middle part is relatively large.

Based on the same inventive concept, a well-shock combined alluvial fan reservoir distribution determining device is also provided in the embodiments of the present specification, as described in the following embodiments. Because the principle of solving the problem of the well-shock combined alluvial fan reservoir distribution determining device is similar to that of the well-shock combined alluvial fan reservoir distribution determining method, the implementation of the well-shock combined alluvial fan reservoir distribution determining device can be referred to the implementation of the well-shock combined alluvial fan reservoir distribution determining method, and repeated parts are not repeated. As used below, the term "unit" or "module" may be a combination of software and/or hardware that implements the intended function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated. FIG. 22 is a block diagram of a well-shock combined alluvial fan reservoir distribution determination apparatus according to an embodiment of the present disclosure, as shown in FIG. 22, comprising: the construction is described below with respect to the setup module 221, the preprocessing module 222, the inversion module 223, and the determination module 224.

A building module 221 for building a fine isochronous stratigraphic grid of alluvial fan reservoirs of the target area based on the log data and seismic data of the alluvial fan reservoirs of the target area.

And the preprocessing module 222 is configured to preprocess the seismic data of the alluvial fan reservoir in the target area by using a matching pursuit strong reflection stripping method, so as to obtain preprocessed seismic data.

And the inversion module 223 is used for carrying out seismic frequency division inversion on the seismic data body in the preprocessed seismic data by combining the logging data to obtain a seismic frequency division inversion result.

And the determining module 224 is configured to determine sand distribution data of a alluvial fan reservoir of the target area according to the seismic frequency division inversion result.

From the above description, it can be seen that the following technical effects are achieved in the embodiments of the present specification: well logging seismic data are fully excavated, the resolution of the seismic data is improved through a seismic data processing means, well-seismic combined reservoir prediction and research area alluvial fan reservoir configuration research are carried out on the basis, sand body distribution characteristics of a research area are defined through comparison and optimization of various reservoir prediction methods, alluvial fan configuration spreading characteristics of a Bin county area are dissected, reference significance is provided for later similar reservoir prediction and reservoir configuration research, and alluvial fan configuration theoretical research can be enriched.

The embodiment of the present disclosure further provides a computer device, specifically may refer to a schematic structural diagram of a computer device of the method for determining a reservoir distribution of a well-seismic combined alluvial fan provided based on the embodiment of the present disclosure shown in fig. 23, where the computer device may specifically include an input device 231, a memory 232, and a processor 233. Wherein the memory 232 is configured to store processor-executable instructions. The processor 233, when executing the instructions, implements the steps of the borehole seismic combination alluvial fan reservoir distribution determination method described in any of the embodiments above.

In this embodiment, the input device may specifically be one of the main apparatuses for exchanging information between the user and the computer system. The input device may include a keyboard, mouse, camera, scanner, light pen, handwriting input board, voice input device, etc.; the input device is used to input raw data and a program for processing these numbers into the computer. The input device may also acquire and receive data transmitted from other modules, units, and devices. The processor may be implemented in any suitable manner. For example, the processor may take the form of, for example, a microprocessor or processor, and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a programmable logic controller, and an embedded microcontroller, among others. The memory may in particular be a memory device for storing information in modern information technology. The memory may comprise a plurality of levels, and in a digital system, may be memory as long as binary data can be stored; in an integrated circuit, a circuit with a memory function without a physical form is also called a memory, such as a RAM, a FIFO, etc.; in the system, the storage device in physical form is also called a memory, such as a memory bank, a TF card, and the like. In this embodiment, the specific functions and effects of the computer device may be explained in comparison with other embodiments, and will not be described herein.

There is further provided in an embodiment of the present specification a computer storage medium based on a method for determining a reservoir distribution of a well-shock combined alluvial fan, the computer storage medium storing computer program instructions which, when executed, implement the steps of the method for determining a reservoir distribution of a well-shock combined alluvial fan in any of the embodiments described above.

In the present embodiment, the storage medium includes, but is not limited to, a random access Memory (Random Access Memory, RAM), a Read-Only Memory (ROM), a Cache (Cache), a Hard Disk (HDD), or a Memory Card (Memory Card). The memory may be used to store computer program instructions. The network communication unit may be an interface for performing network connection communication, which is set in accordance with a standard prescribed by a communication protocol.

In this embodiment, the functions and effects of the program instructions stored in the computer storage medium may be explained in comparison with other embodiments, and are not described herein.

It will be apparent to those skilled in the art that the modules or steps of the embodiments described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, they may alternatively be implemented in program code executable by computing devices, so that they may be stored in a storage device for execution by computing devices, and in some cases, the steps shown or described may be performed in a different order than herein, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps within them may be fabricated into a single integrated circuit module. Thus, embodiments of the present specification are not limited to any specific combination of hardware and software.

It is to be understood that the above description is intended to be illustrative, and not restrictive. Many embodiments and many applications other than the examples provided will be apparent to those of skill in the art upon reading the above description. The scope of the disclosure should, therefore, be determined not with reference to the above description, but instead should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the embodiments of the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification should be included in the protection scope of the present specification.

Claims

1. A method for determining well-shock combined alluvial fan reservoir distribution, comprising:

establishing a fine isochronous stratigraphic framework of the alluvial fan reservoir of the target area based on the logging data and the seismic data of the alluvial fan reservoir of the target area;

preprocessing the seismic data of the alluvial fan reservoir of the target area by using a matching pursuit strong reflection stripping method to obtain preprocessed seismic data;

Combining the logging data, and performing seismic frequency division inversion on a seismic data body in the preprocessed seismic data to obtain a seismic frequency division inversion result;

and determining sand body distribution data of the alluvial fan reservoir of the target area by using the seismic frequency division inversion result.

2. The method of well-to-seismic combined alluvial fan reservoir distribution determination of claim 1, wherein establishing a fine isochronous stratigraphic grid of alluvial fan reservoirs for a target zone based on log data and seismic data for the alluvial fan reservoirs for the target zone comprises:

constraining formation contrast using a marker layer of a alluvial fan reservoir of the target region;

dividing a deposition rotation by a logging curve and wavelet decomposition in the logging data, and comparing stratum by the deposition rotation;

based on the seismic synthesis record conversion seismic horizon calibration to the seismic section of the corresponding position, determining the position of a seismic reflection phase axis corresponding to the uphole interface, and performing seismic time-depth conversion to develop full-area contrast;

based on logging curve characteristics, sedimentary gyratory characteristics and seismic event reflection characteristics, and combining time depth calibration results, multidimensional interactive comparison is carried out, and a fine isochronous stratum grid is established.

3. The method for determining the well-to-seismic combined alluvial fan reservoir distribution according to claim 1, wherein preprocessing the seismic data of the alluvial fan reservoir of the target area by using a matching pursuit strong reflection stripping method to obtain preprocessed seismic data comprises:

constructing a wavelet atomic dictionary according to the seismic section of the seismic data to obtain atomic parameters;

projecting the strong reflection signals to be decomposed into the atomic dictionary, and stripping the strong reflection signals within a range limited by a signal residual error through adaptive iteration;

matching is carried out in the stripping process, the in-phase axis display after the strong reflection is stripped is obtained, and the preprocessed seismic data volume is output.

4. The method for determining the well-to-seismic combined alluvial fan reservoir distribution according to claim 1, wherein performing a seismic frequency division inversion on a seismic data volume in the preprocessed seismic data in combination with the well-logging data to obtain a seismic frequency division inversion result comprises:

selecting a target curve according to the logging data, and carrying out standardized correction on the target curve to obtain a corrected target curve;

performing spectrum analysis according to the seismic data, selecting an effective frequency band, and setting a frequency division interval according to actual data to obtain frequency division data volumes of a plurality of different frequency bands;

Determining a relationship graph of amplitude versus frequency at different time thicknesses; based on a support vector machine, establishing a nonlinear mapping relation between the seismic waveform and the corrected target curve;

inversion calculation is carried out on the frequency division data volumes with different frequency bands, and an earthquake frequency division inversion result is obtained.

5. The method of well-to-seismic joint alluvial fan reservoir distribution determination of claim 1, wherein determining sand distribution data of alluvial fan reservoirs of the target region using the seismic crossover inversion results comprises:

and on the seismic data body of the preprocessed seismic data, on the basis of the seismic frequency division inversion result, defining a sand body boundary by extracting the probability relation between inversion attribute values and sand mud, and obtaining sand body distribution data.

6. The method of well-shock bonded alluvial fan reservoir distribution determination of claim 1, further comprising:

and carrying out seismic frequency division fusion on the seismic data volume in the preprocessed seismic data to obtain sand distribution data of the alluvial fan reservoir of the target area.

7. A well-shock combined alluvial fan reservoir distribution determination device, comprising:

the building module is used for building a fine isochronous stratum grid of the alluvial fan reservoir of the target area based on the logging data and the seismic data of the alluvial fan reservoir of the target area;

The preprocessing module is used for preprocessing the seismic data of the alluvial fan reservoir of the target area by utilizing a matching pursuit strong reflection stripping method to obtain preprocessed seismic data;

the inversion module is used for carrying out seismic frequency division inversion on the seismic data body in the preprocessed seismic data by combining the logging data to obtain a seismic frequency division inversion result;

and the determining module is used for determining sand body distribution data of the alluvial fan reservoir of the target area by utilizing the seismic frequency division inversion result.

8. The well-shock combined alluvial fan reservoir distribution determination device of claim 7, wherein the establishment module is specifically configured to: constraining formation contrast using a marker layer of a alluvial fan reservoir of the target region; dividing a deposition rotation by a logging curve and wavelet decomposition in the logging data, and comparing stratum by the deposition rotation; based on the seismic synthesis record conversion seismic horizon calibration to the seismic section of the corresponding position, determining the position of a seismic reflection phase axis corresponding to the uphole interface, and performing seismic time-depth conversion to develop full-area contrast; based on logging curve characteristics, sedimentary gyratory characteristics and seismic event reflection characteristics, and combining time depth calibration results, multidimensional interactive comparison is carried out, and a fine isochronous stratum grid is established.

9. A computer device comprising a processor and a memory for storing processor-executable instructions which when executed by the processor implement the steps of the method of any one of claims 1 to 6.

10. A computer readable storage medium having stored thereon computer instructions, which when executed by a processor, implement the steps of the method of any of claims 1 to 6.