Method and device for determining reservoir distribution of alluvial fan by combining well-seismic analysis

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 in combination with well-seismic data, wherein the method includes: establishing a fine isochronous stratigraphic framework of the alluvial fan reservoirs in the target area based on the well logging data and seismic data of the alluvial fan reservoirs in the target area; preprocessing the seismic data of the alluvial fan reservoirs in the target area using the matching pursuit strong reflection stripping method to obtain preprocessed seismic data; combining the well logging data, performing seismic frequency division inversion on the seismic data body in the preprocessed seismic data to obtain seismic frequency division inversion results; using the seismic frequency division inversion results to determine the sand body distribution data of the alluvial fan reservoirs in the target area. The above scheme can accurately predict the sand body distribution characteristics of the alluvial fan reservoirs, which has reference significance for similar reservoir prediction and reservoir configuration research, and can also enrich the theoretical research on alluvial fan configuration.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration and development, and in particular to a method and device for determining the distribution of alluvial fan reservoirs by combining well seismic analysis. Background Art

[0002] With the continuous advancement of oil and gas exploration and development, the quality of seismic data collection is also continuously improving. These have promoted the exploration of well-seismic combined reservoir prediction technology by geological researchers. Reservoir prediction technology is a comprehensive and cross-cutting technology collection. How to combine seismic technology with geological information and find the optimal solution to geological laws under the intersection has become the current research focus. There are two main types of reservoir seismic prediction technologies that are most widely used. One is to analyze sand bodies through seismic attribute technology, and the other is to simulate sand body distribution through seismic inversion technology. Both of them combine the superiority of seismic data in the lateral direction and the superiority of logging data in the vertical direction, which greatly improves the accuracy of sand body prediction.

[0003] The Jiyang Depression in the Bohai Bay Basin is a typical continental fault basin. The Binxian Uplift and its periphery are located in the northwest of the Jiyang Depression. The Guantao Formation is the main development stratum in the Binxian area. The target layers, Guantao Formation Ng1 to Ng4, mainly develop alluvial fan-fluvial facies deposits, with strong stratum heterogeneity and strong lateral contrast multi-solution. Previous studies have been conducted on the Binxian Uplift, mainly focusing on the accumulation law and reservoir characteristics of the Binxian Uplift. These results have played a good guiding role in the early exploration of the Binxian area, but it is difficult to meet the needs of subsequent development work such as fine prediction and characterization of sand bodies. Due to the existence of the uplift and the unconformity of the formation, a large area of ​​strong reflection shielding layer running through the entire area is formed at the bottom of the Guantao Formation, which makes sand body prediction difficult. Affected by the special topography of the Binxian area, the recognition of reservoir prediction and alluvial fan architecture of the Binxian Uplift in previous studies is almost blank, which restricts the subsequent more detailed exploration and development of the Guantao Formation in the Binxian Uplift of the Jiyang Depression.

[0004] Currently, no effective solution has been proposed to the above problems. Summary of the invention

[0005] The embodiments of this specification provide a method and device for determining the distribution of alluvial fan reservoirs in combination with well-seismic analysis to solve the problem in the prior art that sand body prediction and characterization are difficult due to the strong heterogeneity of the strata in the fault basin.

[0006] The embodiment of the present specification provides a method for determining the distribution of an alluvial fan reservoir by combining well-seismic data and seismic data, comprising: establishing a fine isochronous stratigraphic framework of the alluvial fan reservoir in the target area based on the well logging data and seismic data of the alluvial fan reservoir in the target area; preprocessing the seismic data of the alluvial fan reservoir in the target area by using a matching pursuit strong reflection stripping method to obtain preprocessed seismic data; performing seismic frequency division inversion on the seismic data body in the preprocessed seismic data in combination with the well logging data to obtain a seismic frequency division inversion result; and determining the sand body distribution data of the alluvial fan reservoir in the target area by using the seismic frequency division inversion result.

[0007] The embodiment of the present specification also provides a device for determining the distribution of alluvial fan reservoirs in combination with well-seismic data, including: an establishment module, which is used to establish a fine isochronous stratigraphic framework of the alluvial fan reservoirs in the target area based on the well logging data and seismic data of the alluvial fan reservoirs in the target area; a preprocessing module, which is used to preprocess the seismic data of the alluvial fan reservoirs in the target area using a matching pursuit strong reflection stripping method to obtain preprocessed seismic data; an inversion module, which is used to perform seismic frequency division inversion on the seismic data body in the preprocessed seismic data in combination with the well logging data to obtain a seismic frequency division inversion result; and a determination module, which is used to determine the sand body distribution data of the alluvial fan reservoirs in the target area using the seismic frequency division inversion result.

[0008] An embodiment of the present specification also provides a computer device, including a processor and a memory for storing processor executable instructions, wherein when the processor executes the instructions, the steps of the method for determining the distribution of alluvial fan reservoirs by combining well-seismic analysis described in any of the above embodiments are implemented.

[0009] The embodiments of the present specification also provide a computer-readable storage medium having computer instructions stored thereon, which, when executed, implement the steps of the method for determining the distribution of alluvial fan reservoirs by combining well-seismic analysis as described in any of the above embodiments.

[0010] In the embodiments of this specification, a method for determining the distribution of alluvial fan reservoirs in combination with well-seismic data is provided. Based on the well logging data and seismic data of the alluvial fan reservoirs in the target area, a fine isochronous stratigraphic framework of the alluvial fan reservoirs in the target area can be established; the seismic data of the alluvial fan reservoirs in the target area are preprocessed using the matching pursuit strong reflection stripping method to obtain preprocessed seismic data; combined with the well logging data, seismic frequency division inversion is performed on the seismic data body in the preprocessed seismic data to obtain seismic frequency division inversion results; the sand body distribution data of the alluvial fan reservoirs in the target area is determined using the seismic frequency division inversion results. In the above scheme, well logging seismic data can be fully mined, and the resolution of seismic data can be improved by means of seismic data processing. On this basis, well-seismic combined reservoir prediction and alluvial fan reservoir configuration research in the study area are carried out. By comparing and optimizing a variety of reservoir prediction methods, the sand body distribution characteristics of the study area are clarified, and the alluvial fan configuration distribution characteristics in the Binxian area are dissected, which has reference significance for similar reservoir prediction and reservoir configuration research in the future, and can enrich the theoretical research on alluvial fan configuration. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The drawings described herein are used to provide a further understanding of this specification, constitute a part of this specification, and do not constitute a limitation of this specification. In the drawings:

[0012] Figure 1 A flow chart showing a method for determining reservoir distribution of an alluvial fan by combining well-seismic analysis in an embodiment of this specification;

[0013] Figure 2 A single well cycle division diagram in an embodiment of this specification is shown;

[0014] Figure 3 A schematic diagram of a wavelet for synthetic recording in an embodiment of the present specification is shown;

[0015] Figure 4 A spectrum analysis diagram of a target layer in an embodiment of this specification is shown;

[0016] Figure 5 A graph showing the calibration results of synthetic records in an embodiment of this specification is shown;

[0017] Figure 6 A schematic diagram of a three-dimensional formation model in an embodiment of this specification is shown;

[0018] Figure 7 A schematic diagram of a matching pursuit algorithm in an embodiment of this specification is shown;

[0019] Figure 8 A comparison diagram before and after the matching pursuit strong reflection separation in an embodiment of this specification is shown;

[0020] Fig. 9 A comparison diagram of the local effects before and after the matching pursuit strong reflection separation in an embodiment of this specification is shown;

[0021] Fig.10 A flowchart of the frequency division attribute fusion process of support vector regression in an embodiment of this specification is shown;

[0022] Fig.11 A schematic diagram of the AVF relationship in an embodiment of this specification is shown;

[0023] Fig.12 A frequency division inversion flow chart in an embodiment of this specification is shown;

[0024] Fig.13 A comparison diagram of the GR curves before and after normalization in an embodiment of this specification is shown;

[0025] Fig.14 A schematic diagram of a curve fitting result based on the SVR method in an embodiment of this specification is shown;

[0026] Fig.15 A frequency division inversion effect diagram in an embodiment of this specification is shown;

[0027] Fig.16 A comparison diagram of the effects before and after the strengthening axis is removed in an embodiment of this specification is shown;

[0028] Fig.17 shows an intersection diagram of an inversion property value and a gamma curve in an embodiment of the present specification;

[0029] Fig.18 A sand-mud probability relationship analysis diagram in an embodiment of this specification is shown;

[0030] Fig.19 The thickness diagram of the Ng4-1 small layer sand body in an embodiment of this specification is shown;

[0031] Fig. 20 The thickness diagram of the Ng4-2 small layer sand body in an embodiment of this specification is shown;

[0032] Fig.21 The thickness diagram of the Ng4-3 small layer sand body in an embodiment of this specification is shown;

[0033] Fig. 22 A schematic diagram showing a device for determining reservoir distribution of an alluvial fan by combining well-seismic analysis in an embodiment of this specification;

[0034] Fig.23 A schematic diagram of a computer device in an embodiment of the present specification is shown. DETAILED DESCRIPTION

[0035] The principles and spirit of this specification will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement this specification, and are not intended to limit the scope of this specification in any way. On the contrary, these embodiments are provided to make this specification more thorough and complete, and to fully convey the scope of this disclosure to those skilled in the art.

[0036] Those skilled in the art will appreciate that the embodiments of this specification may be implemented as a system, device, method, or computer program product. Therefore, this specification may be implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0037] The Jiyang Depression in the Bohai Bay Basin is a typical continental fault basin. The Binxian Uplift and its periphery are located in the northwest of the Jiyang Depression. The Guantao Formation is the main development stratum in the Binxian area. The target layers, Guantao Formation Ng1 to Ng4, mainly develop alluvial fan-fluvial facies deposits, with strong stratum heterogeneity and strong lateral contrast multi-solution. Previous studies have been conducted on the Binxian Uplift, mainly focusing on the accumulation law and reservoir characteristics of the Binxian Uplift. These results have played a good guiding role in the early exploration of the Binxian area, but it is difficult to meet the needs of subsequent development work such as fine prediction and characterization of sand bodies. Due to the existence of the uplift and the unconformity of the formation, a large area of ​​strong reflection shielding layer running through the entire area is formed at the bottom of the Guantao Formation, which makes sand body prediction difficult. Affected by the special topography of the Binxian area, the recognition of reservoir prediction and alluvial fan architecture of the Binxian Uplift in previous studies is almost blank, which restricts the subsequent more detailed exploration and development of the Guantao Formation in the Binxian Uplift of the Jiyang Depression.

[0038] In order to solve the above problems, a method for determining the distribution of alluvial fan reservoirs by combining well-logging and seismic data is provided in this embodiment. In this scheme, the well logging seismic data is fully exploited, and the resolution of seismic data is improved by means of seismic data processing. On this basis, well-logging and seismic data reservoir prediction and alluvial fan reservoir configuration research in the study area are carried out. By comparing and optimizing a variety of reservoir prediction methods, the sand body distribution characteristics of the study area are clarified, and the alluvial fan configuration distribution characteristics in the Binxian area are dissected, which has reference significance for similar reservoir prediction and reservoir configuration research in the future, and enriches the theoretical research on alluvial fan configuration.

[0039] Figure 1A flow chart of a method for determining the distribution of alluvial fan reservoirs by combining well seismic analysis in an embodiment of this specification is shown. Although this specification provides method operation steps or device structures as shown in the following embodiments or drawings, more or fewer operation steps or module units may be included in the method or device based on routine or no creative labor. In the steps or structures where there is no necessary causal relationship logically, the execution order of these steps or the module structure of the device is not limited to the execution order or module structure described in the embodiments of this specification and shown in the drawings. When the method or module structure described is applied to an actual device or terminal product, it can be connected according to the method or module structure shown in the embodiments or drawings for sequential execution or parallel execution (for example, a parallel processor or multi-threaded processing environment, or even a distributed processing environment).

[0040] Specifically, Figure 1 As shown, a method for determining the distribution of alluvial fan reservoirs by combining well seismic analysis provided in one embodiment of this specification may include the following steps:

[0041] Step S101, based on the well logging data and seismic data of the alluvial fan reservoir in the target area, a fine isochronous stratigraphic framework of the alluvial fan reservoir in the target area is established.

[0042] The method in this embodiment can be applied to computer equipment. The lateral and seismic data of the alluvial fan reservoir in the target area can be obtained. The target area is the area to be studied, which can be a continental fault basin. For example, the target layer Guantao Formation Ng1 to Ng4 in the continental fault basin mainly develops alluvial fan-river phase deposits, and the formation heterogeneity is strong.

[0043] In some embodiments of the present specification, based on the well logging data and seismic data of the alluvial fan reservoir of the target area, a fine isochronous stratigraphic framework of the alluvial fan reservoir of the target area is established, which may include: using the marker layer of the alluvial fan reservoir of the target area to constrain stratigraphic comparison; dividing the sedimentary cycle by the well logging curve and wavelet decomposition in the well logging data, and performing stratigraphic comparison through the sedimentary cycle; converting the seismic layer calibration to the seismic profile of the corresponding position based on the seismic synthetic record, determining the position of the seismic reflection event axis corresponding to the well interface, and performing seismic time-depth conversion to carry out full-area comparison; based on the well logging curve characteristics, sedimentary cycle characteristics and seismic event axis reflection characteristics, combined with the time-depth calibration results, multi-dimensional interactive comparison is performed to establish a fine isochronous stratigraphic framework. In this embodiment, based on the principles of sedimentology and sequence stratigraphy, a well-seismic combination method is adopted, and the principles of marker layer constraint, well-seismic combination, cycle comparison, and hierarchical control are followed to establish a fine isochronous stratigraphic framework, thereby meeting the requirements of refined characterization of the reservoir.

[0044] Step S102, preprocessing the seismic data of the alluvial fan reservoir in the target area by using a matching pursuit strong reflection stripping method to obtain preprocessed seismic data.

[0045] The quality of seismic data is closely related to the precision of reservoir prediction to a large extent, and the strong shielding reflection layer is one of the important factors affecting the quality of seismic data. There is an obvious strong reflection phase axis near Ng4 at the bottom boundary T1 of the Guantao Formation, which almost continuously runs through the entire area. When the waveform is displayed, multiple strong waveform amplitudes are superimposed on each other, making it impossible to display a single waveform clearly, covering the effective reflection of the sand body and increasing the difficulty of reservoir prediction. Therefore, the seismic data can be preprocessed to obtain a new seismic body after weakening the strong shielding reflection. Specifically, the seismic data of the alluvial fan reservoir in the target area can be preprocessed using the matching pursuit strong reflection stripping method to obtain preprocessed seismic data.

[0046] In some embodiments of the present specification, the seismic data of the alluvial fan reservoir in the target area are preprocessed using the matching pursuit strong reflection stripping method to obtain preprocessed seismic data, which can include: constructing a wavelet atom dictionary based on the seismic profile of the seismic data to obtain atomic parameters; projecting the strong reflection signal to be decomposed into the atom dictionary, and through adaptive iteration, stripping the strong reflection signal within the range limited by the signal residual; matching during the stripping process to obtain the phase axis display after stripping the strong reflection, and outputting the preprocessed seismic data body.

[0047] Specifically, a wavelet atom dictionary with reasonable structure and good performance is first constructed according to the original seismic profile to obtain complete atomic parameters; the strong reflection signal to be decomposed is projected into the complete atom dictionary, and the strong reflection signal is stripped within the range limited by the signal residual through adaptive iteration; the best matching atom is matched during the stripping process, and the best event axis display after stripping the strong reflection is obtained, and a new data body is output, that is, the preprocessed seismic data. The seismic data body obtained by applying the matching pursuit strong reflection stripping method has a good effect on the strong shielding treatment of the Guantao Formation, and the original Guantao Formation seismic trace signal is better displayed.

[0048] Step S103, combining the well logging data, performing seismic frequency division inversion on the seismic data volume in the preprocessed seismic data to obtain a seismic frequency division inversion result.

[0049] Frequency inversion is a nonlinear attribute inversion. Nonlinear inversion establishes a nonlinear mapping relationship between amplitude and frequency, which is more accurate than conventional inversion. It can directly invert lithologic and physical property data.

[0050] In some embodiments of the present specification, in combination with the well logging data, seismic frequency division inversion is performed on the seismic data body in the preprocessed seismic data to obtain the seismic frequency division inversion result, which may include: selecting a target curve according to the well logging data, and standardizing and correcting the target curve to obtain the corrected target curve; conducting spectrum analysis according to the seismic data, selecting an effective frequency band, setting the frequency division interval according to the actual data, and obtaining frequency division data bodies of multiple different frequency bands; determining the relationship graph between amplitude and frequency at different time thicknesses; establishing a nonlinear mapping relationship between the seismic waveform and the corrected target curve based on a support vector machine; and performing inversion calculations on the frequency division data bodies of multiple different frequency bands to obtain the seismic frequency division inversion result.

[0051] Specifically, firstly, based on the well logging data, the target curve is selected and standardized. According to the seismic data, the spectrum analysis is carried out, the effective frequency band is selected, and the frequency division interval is set according to the actual data to obtain the frequency division data bodies of multiple different frequency bands. Relying on the analysis of the AVF relationship, the relationship between the amplitude and the frequency at different time thicknesses is obtained. Through the implementation method of the SVR support vector machine, the nonlinear mapping relationship between the seismic waveform and the target curve is established, and then the inversion calculation is performed. By using seismic frequency division inversion for inversion calculation, the identification of sand bodies in the horizontal and vertical directions is improved, the consistency rate with the well logging interpretation is high, and the horizontal and vertical resolutions are better than the previous two methods.

[0052] Step S104, using the seismic frequency division inversion result, determine the sand body distribution data of the alluvial fan reservoir in the target area.

[0053] In some embodiments of the present specification, using the seismic frequency division inversion results to determine the sand body distribution data of the alluvial fan reservoir in the target area can include: on the seismic data volume of the preprocessed seismic data, based on the seismic frequency division inversion results, the sand body boundary is limited by extracting the probability relationship between the inversion attribute value and the sand and mud to obtain the sand body distribution data. On the new seismic data volume obtained by suppressing the strong axis by the matching pursuit strong reflection separation method, based on the preferred frequency division inversion results, the sand body boundary is limited by extracting the probability relationship between the inversion attribute value and the sand and mud. Through the above method, the sand body distribution data of the alluvial fan reservoir in the target area can be accurately predicted.

[0054] In this embodiment, the logging seismic data are fully exploited, and the resolution of seismic data is improved through seismic data processing. On this basis, well-seismic combined reservoir prediction and alluvial fan reservoir configuration research in the study area are carried out. By comparing and optimizing a variety of reservoir prediction methods, the sand body distribution characteristics of the study area are clarified, and the alluvial fan configuration distribution characteristics in the Binxian area are dissected, which has reference significance for similar reservoir prediction and reservoir configuration research in the future, and can also enrich the theoretical research on alluvial fan configuration.

[0055] In some embodiments of the present specification, the method may further include: performing seismic frequency division fusion on the seismic data volume in the preprocessed seismic data to obtain sand body distribution data of the alluvial fan reservoir in the target area.

[0056] For example, the main frequency of the seismic data of the Guantao Formation in Binxian area is 30Hz, and the effective frequency band range is 10Hz to 60Hz, so the original data volume is divided into 10HZ, 15Hz, 20Hz, 25Hz, 30Hz, 35Hz, 40Hz, 45Hz, 50Hz, 55Hz and 60Hz at intervals of 5Hz. The selected 15Hz+30Hz data volume is the data volume with the highest correlation with the sand body, and then combined with the RMS characteristic attributes selected by analyzing the original data volume, the frequency division fusion is carried out to predict the sand body of the Guantao Formation.

[0057] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. For details, please refer to the description of the above-mentioned related processing related embodiments, and no further description is given here.

[0058] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0059] The above method is described below in conjunction with a specific embodiment. However, it should be noted that the specific embodiment is only for better illustrating the present specification and does not constitute an improper limitation on the present specification.

[0060] This specific embodiment provides a method for determining the distribution of alluvial fan reservoirs by combining well-seismic data. In this specific embodiment, the method for determining the distribution of alluvial fan reservoirs by combining well-seismic data may include the following steps:

[0061] Step 1: Establish a fine isochronous stratigraphic framework by combining well and seismic data.

[0062] The development of reservoir prediction and configuration characterization research is inseparable from the establishment of a fine isochronous stratigraphic framework. Affected by the multi-stage tectonic movement of the fault basin, the strata in the Binxian area are highly heterogeneous and have strong lateral correlation multi-solutions, which increases the difficulty of isochronous stratigraphic correlation. As the target scale of oil and gas research gradually becomes smaller, more detailed requirements are placed on the accuracy of the stratigraphic framework. The original stratigraphic division scheme cannot meet the requirements of refined reservoir characterization, which restricts the further exploration and development of key target layers in the Binxian area. It is urgent to re-compare and verify and establish a more accurate isochronous stratigraphic framework. This study is based on sedimentological principles and sequence stratigraphy, adopts a combination of well and seismic methods, and follows the principles of "marker layer constraints, well and seismic combination, cycle comparison, and hierarchical control" to carry out fine isochronous stratigraphic framework research.

[0063] Step 1.1, constrain stratigraphic correlation by using marker layers that have stable isochronism and are widely distributed.

[0064] Marker layers are widely distributed, easy to identify on stratigraphic sections, and formed in a certain range and at the same time period. When conducting isochronous stratigraphic comparison, because marker layers generally have stable isochronous properties and are widely distributed, stable and continuously developed marker layers can provide strong support for the establishment of a fine isochronous stratigraphic framework and assist in identifying and dividing strata. The bottom interface T1 and top interface T0 of the Guantao Formation in the work area are obvious marker layers.

[0065] There are two contact relationships between the T1 interface and the underlying strata in the Binxian area, one is the contact with the lower Shahejie Formation (Es), and the other is the contact with the lower Archean strata. In the range of contact with the Shahejie Formation (such as Well S63), 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 the response is obvious. The CAL and AC curves are more severely jagged above T1, and gradually return to low values ​​from below T1, which is smoother than the upper part. Due to lithological differences, in the range of contact with the Archean strata (such as Well S87), the resistivity curve above the T1 interface is a straight continuous low value, and it starts to increase from below the T1 interface. The AC and CAL curves are a combination of toothed box and bell shapes at the T1 interface, with severe jaggedness, and a downward trend from the bottom of T1. The SP curve is generally medium-low. The differences in the morphology and baselines of logging curves of different wells indicate the differential distribution of sedimentary facies combinations at the upper and lower interfaces.

[0066] From the logging characteristics, below the T0 interface, the GR and SP curves are mainly medium-high values, the resistivity curve is mainly medium-low values, and the CAL and AC curves are medium-high values.

[0067] The top interface of T0 is characterized by medium-strong amplitude and medium-low continuity on the seismic profile, and can be continuously traced throughout the area. From the seismic profile, the T0 interface is the boundary between the Minghuazhen Formation (Nm) and the Guantao Formation (Ng) in the Binxian area, reflecting the erosion caused by the rapid drop in lake level during a lake retreat period.

[0068] The bottom interface of T1 is manifested as a medium-strong amplitude and medium-high continuous interface feature on the seismic profile, which can be continuously tracked in the whole area. The obvious truncated reflection termination phenomenon can be identified at the bottom of the T1 interface, which is in an angular unconformity contact relationship with the underlying strata. The upper part of the T1 interface shows a downlapping seismic reflection structure. This obvious boundary phenomenon between the upper and lower sedimentary bodies reflects the occurrence of tectonic uplift and erosion, indicating that the Guantao Formation is in an unconformity contact relationship with the underlying strata. Among them, the alluvial fan deposition system is developed at the T1 interface above the middle bulge.

[0069] Step 1.2, divide the sedimentary cycles through logging curves and wavelet decomposition, and perform stratigraphic correlation through sedimentary cycles.

[0070] Sedimentary cycles are a set of rock layers that are repeated alternately in a certain pattern on the vertical section. By comparing the characteristics of sedimentary cycles, the strata are divided from large to small in a graded and controlled manner. Based on previous studies and logging seismic information, the marker layers T0 and T1 correspond to a third-level sequence, which can be divided into a long-term cycle. On this basis, the cycles within the long-term cycle corresponding to the Guantao Formation are identified.

[0071] The logging curve characteristics reflect the comprehensive signal response of the sedimentary body under the control of different levels of base-level cycles. It is difficult to effectively distinguish different levels of sedimentary cycles using only the logging curve characteristics, so the wavelet transformation method is introduced to assist in the division of internal cycles. Wavelet transformation technology can adjust the local shape correspondence between time and frequency, extract the sequence stratigraphic development characteristics hidden in the original logging data, solve the problem of the contradiction between the resolution scale of time and frequency, and thus highlight the sedimentary cycle change trend in the target layer.

[0072] The gamma curve was selected as the wavelet dominant curve and subjected to one-dimensional discrete changes, where S represents the original curve and the other 11 are curves of different levels. The original S curve of the last column is obtained by adding the curves of different levels in the first 11 columns. By comparison, it was found that the periodic characteristics of the d8-order curve were highly consistent with the mid-term cycle. Therefore, the Guantao Formation was divided into four mid-term cycles from bottom to top, named MNG1, MNG2, MNG3 and MNG4. MNG1, MNG2, MNG3 and MNG4 correspond to the Ng1, Ng2, Ng3 and Ng4 sand groups, respectively.

[0073] Since Ng1 and Ng2 do not develop alluvial fan deposits, only a few locations in the Ng3 sand group develop a small amount of alluvial fans, and the main stratum for exploration and development in this area is Ng4, the Ng1 to Ng4 sand groups of more than 100 wells in the area are divided in this embodiment. Combined with the core data and the change law of the logging curve, the Ng4 sand group with alluvial fans is further subdivided into Ng4-1, Ng4-2, and Ng4-3 sublayers (as shown in Table 1 below), each corresponding to a short-term cycle (SNG1 to SNG3), and the short-term cycles are named SNG1 to SNG11 from bottom to top, as shown in Table 1 below. Figure 2 As shown, a single well cycle division diagram is shown.

[0074] Table 1

[0075]

[0076] Step 1.3, by converting and calibrating the seismic synthetic record to the seismic profile at the corresponding position, determine the position of the seismic reflection event axis corresponding to the well interface, perform time-depth conversion, and thus carry out full-area comparison.

[0077] The overall well network in the west of Binxian area is relatively sparse, and the east and south sides are relatively dense. The specific values ​​of the average well spacing range from 200m to 2km. The uncertainty of the division of strata based solely on logging data is large, and it is necessary to combine seismic data to reduce the multi-solution of logging division. Seismic data is like an X-ray of the earth, which can illuminate the trend of lateral changes in underground strata and reflect the changes between the stratigraphic interfaces between wells. At the same time, it can reduce the multi-solution caused by interpolation based on logging data alone, which is conducive to the closure of the entire area and improves the accuracy of stratigraphic comparison. Therefore, a stratigraphic comparison method based on well-seismic combination is used to carry out research. From the unconformity, breakpoint, sequence interface and other features shown on the seismic profile in Binxian area, the seismic mark is combined with the logging curve mark feature. Starting from a single well, the interface divided on the well is converted and calibrated to the seismic profile at the corresponding position through seismic synthetic record conversion, and the position of the seismic reflection event axis corresponding to the well interface is determined, and the time-depth conversion is carried out, so as to carry out comparison in the whole Binxian area.

[0078] Seismic layer calibration and interpretation are the most indispensable basic links in the process of well-seismic calibration, which determine the scale and accuracy of subsequent reservoir prediction results. The calibration results are not only the basis for seismic-logging joint inversion, but also a strong basis for inter-well stratigraphic comparison. The initial step of layer calibration and interpretation is to produce synthetic seismic records. Synthetic seismic records refer to seismic trace results generated by artificial transformation. They are widely used in seismic model technology and are the media bridge for the conversion of geological information models and seismic information models. Their accuracy directly affects the accurate calibration of layers. The basic principle can be simply understood as the convolution of reflection coefficients and seismic wavelets to obtain a calculation result that can reflect the underground. Its production process is equivalent to a simplified version of the one-dimensional forward modeling process.

[0079] By making synthetic records, the time domain on the seismic profile is converted into the depth domain on the well logging, so that the seismic reflection phase axis corresponds to the well logging geological stratification, giving the well a meaning in the time domain, making the time domain correspond to the depth domain, and horizontally calibrating the strata.

[0080] The synthetic record calculation formula is as follows:

[0081] S(t)=R(t)W(t)

[0082] From the formula of synthetic record, we know that when making synthetic record S(t), we should pay attention to several key variables related to reflection coefficient sequence R(t) and wavelet change W(t). Such as acoustic wave curve value and density curve value (the product of the two is reflection coefficient), seismic data signal-to-noise ratio, wavelet type selection, correspondence of well bypass information, and rationality of stretching and compression, etc. Figure 3 , a schematic diagram of the wavelet used for synthetic recording is shown. Figure 3 In the figure, the horizontal axis is time (Time) and the vertical axis is relative amplitude (Relativeamplitude). Each key point will have different degrees of influence and restriction on the synthetic record results. The seismic record obtained by convolution is generally not completely consistent with the actual seismic profile, and there will be differences of varying sizes, resulting in energy mismatch or layer mismatch during horizontal comparison, that is, the consistency of the event axis width is not high or the length of the event axis time window is limited.

[0083] Geophysical data are rich and varied. Most seismic data are measured point by point on the ground by observation instruments and then sorted out by corresponding analysis tools. In this process, various measurement errors and interference factors will be generated. The seismic data obtained is a mixture of effective information and random abnormal information. Therefore, before accurately calibrating the earthquake, comprehensive statistical processing should be carried out on the basic seismic data first.

[0084] Since the value of seismic wave impedance is the product of acoustic wave and density, the acoustic wave curve (AC) and density curve (DEN) are standardized before time-to-depth conversion calibration to remove some wild values ​​caused by logging instrument errors. After standardization, the seismic wave impedance value and reflection coefficient value obtained by multiplying the acoustic wave and density will be more accurate.

[0085] From the basic data, we know that the time domain depth range of the Guantao Formation in Binxian area is 950ms-1150ms, so 950ms-1150ms is used as the amplitude spectrum analysis time window to carry out amplitude spectrum analysis on the target layer area. From the spectrum analysis, we know that the main frequency of the Guantao Formation in the target layer is about 33hz, the effective bandwidth is 10hz to 65hz, some common phase axes are bifurcated, and the reflection information of the seismic well side channel is messy. Therefore, the 30hz Ricker wavelet is selected to assist in the calibration with the well side seismic channel information, and the sampling interval is set to 1ms, such as Figure 4 As shown, a spectrum analysis diagram of the target layer is shown. Figure 4 In the figure, the horizontal axis is frequency and the vertical axis is power.

[0086] According to the specific characteristics of seismic data in Binxian area, this implementation uses the Well-nearby Traces module under the GeoInversion system to extract the well-nearby trace wavelets in Binxian area, and then uses the Synthetic module under the Geolog system to carry out synthetic record calibration for more than 100 key vertical wells in the work area.

[0087] Based on the literature survey, through the analysis of logging and seismic data in Binxian area, it is found that a set of strong reflection axes with large amplitudes are developed at the bottom of Guantao Formation, and the bottom strong reflection axis is named as strong reflection layer A. There are two main reasons for its formation: one is that from the perspective of lithology, the lower basement strata are mainly composed of Archean granite gneiss, which is different from the upper sandstone and mudstone. The lithology change leads to different propagation speeds of seismic waves. The velocity difference is superimposed to form a wave impedance difference, which forms a stronger and more continuous phase axis at the lithology junction; the second is from the perspective of the unconformity relationship of the stratum contact, the strong reflection axis produced by the unconformity has a shielding effect on the surrounding weaker phase axes, resulting in weak reflection or blank reflection on the profile.

[0088] Since the strong axis marker layer A is located at the bottom of the Ng4-3 sublayer and is stably developed in the entire area, this set of stable strong reflection layer A is used as the marker layer. Combined with the profile distribution characteristics of the single waveform and reflection wave group, the selected well group is calibrated and transformed to obtain the final synthetic seismic record.

[0089] Take the synthetic record calibration results of Well S105 as an example. Figure 5 As shown in the figure, the calibration result of synthetic records is shown. From the calibration results of a single well, it can be seen that the seismic information of the well side channel at the bottom of Ng4-3 and the top of Ng1 has a good correspondence with the records synthesized by Ricker wavelet, and the calibration effect is strong. The interfaces of other small layers inside can also correspond well.

[0090] Combined with the calibration results of a single well, the strong axis near the bottom Ng4-3 was used as the basis for stratigraphic tracing interpretation, and seismic stratigraphic tracing interpretation was carried out on each sublayer of the Guantao Formation in Binxian area. With an interpretation accuracy of 10 by 10 survey lines, the three-dimensional interpretation closure of the entire area was achieved in a grid-like manner.

[0091] Step 1.4: multi-dimensional interaction, full-area closure, and establishment of a fine isochronous stratigraphic framework.

[0092] According to the logging curve characteristics, sedimentary cycle characteristics and seismic phase axis reflection characteristics in Binxian area, combined with the time-depth calibration results, the Ng1, Ng2 and Ng3 sand groups, Ng4-1, Ng4-2 and Ng4-3 layers of the Guantao Formation in Binxian area were tracked and interpreted with 10 times 10 survey line accuracy. On the basis of the closure of the whole area, a joint well-seismic comparison was carried out through the establishment of more than 30 well-connected profiles.

[0093] From the comparison of the well sections along the source, it can be seen that the closer the well point is to the uplift, the thinner the overall sedimentary thickness of the stratum is, and the farther away from the uplift, the thicker the stratum sedimentary thickness gradually increases. At the same time, the location of the well section passes through the Binnan Fault, a syn-sedimentary normal fault in the east-west direction of the study area. From the results of stratigraphic division and comparison, it can be seen that the stratum sedimentary thickness at the foot of the fault is significantly thicker. Under the influence of tectonic movement, a large amount of sediments accumulated in the foot of the fault, making the stratum sedimentary thickness thicker.

[0094] From the comparison of the well sections of the cut source, it can be seen that the overall deposition trend of the stratum thickness in this direction has not changed much, and the overall thickness is uniform.

[0095] Based on high-resolution sequence stratigraphy, the method of "interactive horizontal section and three-dimensional closure" is applied to obtain two-dimensional and three-dimensional double closure, ensuring the isochronism of the fine isochronous stratigraphic framework.

[0096] Combined with the sedimentary background and ancient water flow direction of the study area, stratigraphic skeleton sections in the directions of provenance (east-west) and source-crossing (north-south) were established successively. On the basis of the comparison of the two typical skeleton sections, 30 auxiliary sections were connected for more than 100 wells in the work area to form a two-dimensional plane distribution map of the well-connected sections. Following the principle of "combining well and seismic data, multi-dimensional interaction", the stratigraphic interface was finely compared, and the section results were converted into a visual three-dimensional display through the reservoir digital characterization software Direct, which is convenient for checking the stratigraphic comparison results, such as Figure 6 As shown, a schematic diagram of a three-dimensional stratigraphic model is shown.

[0097] From the stratigraphic comparison results of the entire Binxian area, it can be seen that the overall stratigraphic thickness of the study area varies greatly. The stratigraphic thickness of the Ng1, Ng2 and Ng3 sand groups mainly ranges from 30 meters to 50 meters; the stratigraphic thickness of the Ng4-1 sublayer, Ng4-2 sublayer and Ng4-3 sublayer mainly ranges from 10 meters to 25 meters. The high-value distribution of stratigraphic thickness is mainly distributed in the southeast and south-central parts of the study area, which is also the location where key wells are densely distributed. Affected by the central uplift of Binxian County, the stratigraphic thickness near the uplift is thinner, and the stratigraphic thickness near the southeast depression is thicker. The stratigraphic thickness of each sand group and sublayer has a clear trend of thinning toward the central uplift, and the trend of thinning thickness of each layer is continuous. It can be seen that during the Ng3 deposition period, the central uplift position was still a low-thickness area, proving that the uplift was still exposed at this time and still had some impact on the stratigraphic formations at the edge of the uplift. Until the Ng2 deposition period, the average stratigraphic thickness in the central part of the work area began to rise, and the central uplift was basically no longer exposed with the evolution of sedimentation, indicating that the stratigraphic tectonic activity during this period was no longer as intense as that during the Ng4-3 to Ng4-2 deposition period.

[0098] The overall analysis of the evolution trend of the stratigraphic thickness contour lines of the Guantao Formation from bottom to top shows that the stratigraphic thickness increases from Ng4-1 to Ng1. Affected by the Binxian uplift topography, the local stratigraphic thickness changes rapidly. The overall stratigraphic thickness from Ng3 to Ng1 is more evenly distributed than that from Ng4-3 to Ng4-1.

[0099] Step 2: Combine well and seismic data with reservoir prediction.

[0100] By establishing a fine isochronous stratigraphic framework in Binxian area, the stratigraphic distribution characteristics are clarified. On this basis, based on the original seismic data volume, the seismic data is preprocessed by wavelet decomposition and reconstruction and matching pursuit of strong reflection stripping, the strong reflection shielding layer of the Guantao Formation is suppressed, and a new seismic volume with weakened strong shielding reflection is obtained. Reservoir prediction work is carried out on the new seismic volume after suppressing the strong reflection shielding. The prediction effects of seismic inversion method and seismic attribute method for Binxian area are compared. It is found that the attribute fusion method cannot well guide the distribution of sand bodies in the Guantao Formation. Then the frequency division inversion method suitable for Binxian area is selected to carry out the prediction of sand bodies in the target layer Guantao Formation. In addition, the seismic prediction results are verified by combining well data, and the combination of well and seismic ensures the accuracy of the sand body prediction results. Finally, the sand body distribution characteristics that meet the sedimentary characteristics of the Guantao Formation in Binxian area are obtained, laying the foundation for the next configuration analysis.

[0101] In order to obtain better reservoir prediction results, the data quality of the original seismic data in Binxian area should be analyzed first. There are two sets of seismic data in Binxian area, of which the newer set of data, Block A, has a track spacing of 25, a higher resolution, and a main frequency of about 33Hz; the other set of data, Block B, has a track spacing of 12.5, and has a poor resolution due to the long acquisition period, and a main frequency of about 29Hz. Therefore, the first set of seismic data, Block A, is selected for reservoir prediction in Binxian area.

[0102] The quality of seismic data is closely related to the precision of reservoir prediction to a large extent, and the strong shielding reflection layer is one of the important factors affecting the quality of seismic data. There is an obvious strong reflection phase axis near Ng4 at the bottom boundary T1 of the Guantao Formation, which almost continuously runs through the entire area. When the waveform is displayed, multiple strong waveform amplitudes are superimposed on each other, making it impossible to clearly display a single waveform, covering the effective reflection of the sand body and increasing the difficulty of reservoir prediction.

[0103] There are two main reasons for the formation of its strong shielding: one is that from the perspective of lithology, the lower basement strata are mainly composed of Archean granite gneiss, which has a large wave impedance difference with the upper sandstone and mudstone, resulting in a strong display on the section at the junction, and the seismic reflection waveform presents a complicated and continuous distribution; the second is from the perspective of the unconformity relationship of the stratigraphic contact, the strong axis generated by the unconformity has a shielding effect on the surrounding weaker phase axes, resulting in its weak reflection or blank reflection characteristics on the section.

[0104] Combined with the seismic phases near the strong shielding reflection layer, it can be seen that the alluvial fan developed at the Ng4 position is generally rhombic or mound-shaped on the seismic profile, and the interior of the mound is mostly blank and chaotic reflection. In addition, the main body of the alluvial fan sediments is chaotically accumulated sandstone. After long-term weathering, when it contacts the granite gneiss at the bottom of the Binxian uplift, the boundary between the alluvial fan and the bedrock is generally unclear. These factors also affect the quality of seismic data in this area.

[0105] Therefore, it is urgent to use seismic data processing methods to remove strong shielding, improve the resolution and signal-to-noise ratio of seismic profiles, and enhance the recognition of effective seismic information in the process of sand body prediction.

[0106] Step 2.1, weaken the strong earthquake shielding axis.

[0107] The underlying strata of alluvial fan reservoirs are often basement strata, which are significantly different from the overlying strata in lithology and rock density, and often form seismic strong amplitude reflection axes, which in turn affect the prediction of reservoirs by seismic data. This invention compares the wavelet decomposition and reconstruction method and the matching pursuit strong reflection stripping method, and then selects the matching pursuit strong reflection stripping method to pre-process the seismic data in the study area, weaken the strong shielding, and thus improve the reservoir prediction accuracy.

[0108] The complex domain matching pursuit algorithm can effectively extract useful information from seismic signals and is an emerging intelligent algorithm in signal sparse decomposition. Its basic principle is to regard the information carried by each atom in the original seismic profile as a complete set of functions, such as Figure 7 As shown, the schematic diagram of the matching pursuit algorithm in this embodiment is shown. Through the matching algorithm processing, the strong reflection position is tracked. The strong seismic reflection signal to be decomposed is projected into the function collection, and an adaptive search is performed in the complete atom library. It is continuously iterated within the error tolerance range of the residual signal to match the best atomic information, and finally the best display of the processed seismic signal is obtained.

[0109] In addition, the algorithm can also adaptively adjust the parameters of the atomic dictionary according to the specific characteristics of each seismic signal to improve the accuracy of matching. By efficiently extracting effective information from redundant seismic signals and removing invalid information, it provides valuable reference for earthquake research and prediction. Since the emergence of the matching pursuit algorithm in recent years, many scholars have applied this method to study and solve the problem of strong reflection shielding layer. For example, when studying the strong reflection layer of Block A in the Jiyang Depression, scholars Yin Xingyao and others added the strong reflection separation technology of the matching pursuit algorithm to weaken the strong reflection interference and achieved good results.

[0110] The specific steps of the matching pursuit strong reflection stripping method are as follows:

[0111] ①First, construct a wavelet atom dictionary with reasonable structure and good performance according to the original seismic profile to obtain complete atomic parameters; ②Project the strong reflection signal to be decomposed into the complete atom dictionary, and through adaptive iteration, strip the strong reflection signal within the range limited by the signal residual; ③In the stripping process, match the best matching atom to obtain the best event axis display after stripping the strong reflection, and output the new data body.

[0112] like Figure 8 As shown, Figure 8 The upper figure in the figure is the original seismic section before the strong reflection event axis at Ng4-3 is stripped by matching pursuit. Figure 8 The figure below is the new data volume profile after stripping off the strong reflection event axis at Ng4-3.

[0113] It can be clearly seen from the overall profile display that the strong axis formed by the lithology change of the sandstone layer and the granite layer, and the strong axis caused by the unconformity contact of the bottom strata, have been effectively suppressed. The amplitude intensity energy of the seismic reflection phase axis that can be continuously tracked in the entire Bin County area has been greatly reduced. Through the adaptive decomposition of matching atoms, the original Guantao Formation seismic trace signal is better displayed when the residual signal is within the allowable error range. By enlarging and comparing the original data volume before the matching pursuit strong reflection stripping and the data volume obtained by the matching pursuit strong reflection separation method, it can be found that the seismic data volume obtained by the matching pursuit strong reflection stripping method has a better effect on the strong shielding treatment of the Guantao Formation. Please refer to Fig. 9 ,like Fig. 9 As shown in the left figure, before processing, the waveform amplitude at A is large, the energy overlaps and covers each other, and the waveform cannot be clearly identified; Fig. 9 As shown in the right figure, after matching pursuit strong reflection stripping, not only the specific waveform of each seismic trace is displayed, but also the shape of the effective reflection above is strengthened, rather than excessively removing the reflection shape like the wavelet decomposition and reconstruction method.

[0114] The analysis shows that the matching pursuit strong shielding separation method is more effective in weakening strong shielding, so the new data body after matching pursuit strong reflection separation is selected to predict the reservoir of Guantao Formation in Binxian County, laying the foundation for the subsequent sand body prediction results. To verify its effectiveness, the frequency division inversion operation can be carried out on the two sets of seismic data before and after matching pursuit stripping, as shown in step 2.3.

[0115] Step 2.2: frequency division and fusion of seismic data volume.

[0116] The frequency division attribute fusion method can be used to predict the sand bodies of the Guantao Formation. Fig.10 As shown in the figure, the workflow diagram of frequency attribute fusion based on support vector regression is shown. The main principle is to divide the original data body into three parts: low-frequency data body, medium-frequency data body and high-frequency data body, and extract different attributes from each seismic data body in the three parts. After comparison, the attribute with better correlation is selected as the feature vector (RMS attribute is selected this time), and three-fifths of the wells in Binxian area are randomly selected as the training data set, and the remaining two-fifths of the wells are selected as the test data set. With the participation of the support vector machine algorithm, a nonlinear mapping relationship between the characteristic attribute value at the well point and the sand thickness at that location is established, and the sand body distribution law of the entire area is predicted in an extended manner.

[0117] The main frequency of the seismic data of Guantao Formation in Binxian area is 30Hz, and the effective frequency band range is 10Hz to 60Hz. Therefore, the original data volume is divided into 10HZ, 15Hz, 20Hz, 25Hz, 30Hz, 35Hz, 40Hz, 45Hz, 50Hz, 55Hz and 60Hz at intervals of 5Hz. The selected 15Hz+30Hz data volume is the data volume with the highest correlation with the sand body. Combined with the RMS characteristic attributes selected by analyzing the original data volume, the frequency division fusion is carried out. The analysis of the results of the fusion of the frequency division attributes of each layer in Binxian area shows that most of the fusion attribute effects are distributed in a disorderly manner without obvious regularity, which does not conform to the distribution pattern of the alluvial fan-river phase sand body, and the correlation between the training set and the test set is low. Taking the Ng4 sublayer as an example, the correlation between the fusion attribute and the sand body thickness of the selected 15Hz+30Hz frequency division data volume is only 0.37, the distribution is divergent and changes rapidly, and the sand body in this area cannot be predicted by the fusion attribute.

[0118] Therefore, it is believed that the main reason why seismic attributes are difficult to guide the distribution of sand bodies in the Binxian area may be related to the granite basement at the bottom. Previous studies have shown that seismic wave impedance is greatly affected by the lithology of the strata. The basement lithology in the Binxian area is granite, and the wave impedance value of granite is higher than that of sandstone and mudstone strata. When the seismic attributes of each layer are extracted with the well point as the center of the circle, the attribute values ​​within the radius are easily affected by the high-value wave impedance of the granite, resulting in insufficient accuracy of the extracted attributes and large areas of effective information being covered. In view of the fact that the attribute method cannot better guide the prediction of the Guantao Formation sand bodies, the seismic attribute method is no longer used in the research on the Binxian area in this embodiment, and a more intuitive and more accurate seismic inversion method is selected.

[0119] Step 2.3, seismic frequency inversion.

[0120] There are many types of seismic inversion methods and they are developing rapidly. Among them, constrained sparse pulse inversion, colored inversion, etc. are widely used. Inversion calculation is a process of directly converting the formation interface into the lithology interface through mathematical methods. Through this process, the underground lithology distribution can be physically imaged to obtain the wave impedance data body. Combined with geological thinking, the obtained underground inversion profile can be analyzed and interpreted to comprehensively predict the target reservoir.

[0121] This study selected three inversion methods, namely colored inversion, constrained sparse pulse inversion and frequency division inversion, to predict the sand bodies of the Guantao Formation. By trying the three inversion methods, it is hoped that the inversion method that best characterizes the Guantao Formation in Binxian area can be selected to characterize the sand bodies in the study area.

[0122] Colored inversion belongs to spectral inversion and was first proposed by scholar Lancaster in 2000. It achieves the inversion purpose through a colored filtering process, performs inversion in the frequency domain, and relies on matching operators for convolution during the filtering process. The operator matches the wellbore wave impedance with the seismic spectrum to complete the inversion. There are many uncertain factors in extracting wellside channel wavelets from the seismic data body, which will be affected by the well-seismic calibration results, the temporal and spatial changes of the wavelets, and the wavelet calculation method. These factors will have different degrees of influence on the inversion results, so colored inversion avoids this process, automatically optimizes, has no wavelet extraction process, and objectively inverts the original geological phenomena simply and quickly. The specific process of colored inversion is divided into four parts: ① Analyze the wave impedance spectrum beside the well; ② Perform spectrum analysis on the earthquake; ③ Design a matching operator to match the seismic spectrum with the wave impedance spectrum of the well; ④ Apply the matching operator to the seismic body to complete the inversion. According to the colored inversion effect in Binxian area, it can be seen that the inverted sand body morphology has a certain similarity with the accuracy of the original seismic reflection phase axis, and the prediction effect is poor. The large sand body developed in the Ng4 layer is not predicted. It is necessary to try other methods for optimization and comparison.

[0123] Bayesian constrained sparse pulse inversion is different from conventional sparse pulse inversion that only considers maximum likelihood estimation. It adds a priori conditions for reflection coefficients. By constraining the target results with the wellbore wave impedance, the low-frequency part of the inversion results is compensated. At the same time, under the comprehensive consideration of Bayes, the optimal solution under various constraints is obtained in the probability density model. According to the Bayesian inversion effect in Binxian area, it can be seen that the Bayesian constrained sparse pulse inversion has a relatively improved vertical resolution compared with the colored inversion, but the horizontal resolution has decreased. Its overall effect is improved compared with the colored inversion. It has identified some sand bodies located in the Ng4 target layer, but it still did not achieve the expected prediction effect, so the frequency division inversion method was tried again.

[0124] Frequency inversion is a type of nonlinear attribute inversion. Nonlinear inversion establishes a nonlinear mapping relationship between amplitude and frequency, which is more accurate than conventional inversion. It can directly invert lithologic and physical property data.

[0125] When performing frequency inversion, the known parameters are amplitude data. So in order to obtain the seismic waveform profile, it is necessary to use the amplitude data parameter to obtain the time thickness and wave impedance, two data parameters related to the seismic waveform. The process of obtaining two data from one data is full of multiple solutions. The corresponding geological meaning here is that different strata have different amplitude characteristics under the same main frequency sub-wave at the same time thickness. Even the same stratum has different amplitude characteristics under the same main frequency sub-wave at the same time thickness. For example, Fig.11 shown. Fig.11A schematic diagram of the AVF relationship is shown. Fig.11 The left figure shows a schematic diagram of the relationship between amplitude (Amplitude) and time thickness (Thickness) as a function of frequency. Fig.11 The right figure shows a schematic diagram of the relationship between amplitude (Amplitude) and frequency (Frequency).

[0126] Therefore, the frequency division inversion introduces the AVF relationship in its principle to establish the relationship between frequency and amplitude to reduce uncertainty and multiple solutions. The principle of AVF is to calculate the Ricker wavelets of different frequencies through convolution within a limited wedge model to obtain the tuning curve of amplitude and thickness with frequency (such as Fig.11 The left figure is the tuning curve relationship diagram). Then, through conversion, the relationship between amplitude and frequency is obtained (such as Fig.11 As shown in the right figure of ), this is used as a limitation to reduce the multi-solution. However, relying solely on the AVF relationship is not enough to meet the requirements, because its complexity is difficult to be accurately expressed by a certain function. Therefore, the SVR support vector machine method is introduced to assist in establishing the nonlinear mapping relationship we need.

[0127] Under the control of three parameters, SVR overcomes the network instability and local optimization problems of neural networks. It is a statistical algorithm that incorporates artificial intelligence intervention. Through the SVR method, a nonlinear mapping relationship between well and seismic is established, and then the AVF relationship is used as a control to carry out inversion. The combination of the two methods reduces the degree of freedom and uncertainty of the inversion, which is conducive to obtaining more accurate inversion results.

[0128] Please refer to Fig.12 , shows the frequency division inversion process in this embodiment. Fig.12 As shown, the specific process of carrying out frequency division inversion may include the following steps.

[0129] ①First, based on the logging data, select the target curve and standardize and correct the target curve. ②Carry out spectrum analysis based on seismic data, select the effective frequency band, set the frequency division interval according to the actual data, and obtain the frequency division data volume of multiple different frequency bands. ③Relying on the analysis of AVF relationship, obtain the relationship between amplitude and frequency at different time thicknesses. ④Through the implementation method of SVR support vector machine, establish the nonlinear mapping relationship between seismic waveform and target curve, and then perform inversion calculation.

[0130] Since the GR curve can better distinguish sandstone and mudstone information, the GR curve is selected as the data label. In order to take into account the instrument error when the logging curve is collected, before using the GR curve for full-area inversion, the GR curve should be corrected by mean variance to remove individual abnormal values, and corrected within the effective range with the core well S63 as the standard, such as Fig.13 As shown, a comparison diagram of the GR curves before and after normalization in this embodiment is shown.

[0131] According to the frequency division inversion process, we first need to master the effective frequency band range of the Guantao Formation seismic data in Binxian area. Therefore, we first conduct spectrum analysis on the target layer Guantao Formation, and select the appropriate frequency segmentation of 10Hz to 60Hz according to the frequency band distribution range of the Guantao Formation, and obtain multiple frequency division data bodies of different frequency bands at intervals of 5Hz, as shown in Table 2 below. For the obtained low, medium and high frequency division data bodies, the 15Hz low-frequency division body, the 30Hz medium-frequency division body and the 50Hz high-frequency data body are selected to participate in the inversion.

[0132] Table 2

[0133]

[0134] The natural gamma curves (GR) of one-fifth of the wells in Bin County were selected as the test set, and the other wells were used as the training set. By debugging the Epsilon, Gamma and Segments parameters, and with the support of the AVF relationship and SVR algorithm, multiple iterative training was carried out to combine the rich well information with the seismic information, and establish a non-mapping relationship between the natural gamma (GR) and seismic information in this area.

[0135] Finally, it is shown that the average correlation coefficient between the well sand body and the learning value of the test set is 0.9076, the correlation coefficient with the training set is 0.8911, the complexity coefficient is 0.2658, and the fitting average values ​​of the target curve and the learning curve are mainly distributed around 0.85, such as Fig.14 As shown, a schematic diagram of the curve fitting results based on the SVR method is shown. Fig.14 In the figure, the blue curve is the original curve, and the red curve is the target curve of this frequency division inversion. The two have a high degree of fit, indicating that the learning result is faithful to the well seismic information in Binxian area. On this basis, a nonlinear mapping relationship between the waveform that conforms to the geological law of the Guantao Formation in Binxian area and the GR curve is established to obtain the final inversion effect.

[0136] Please refer to Fig.15 , shows the frequency division inversion effect diagram in this embodiment. Fig.15 As shown, it is the inversion section obtained based on the GR standardized curve. It can be seen that in this inversion section, the sandstone body is distributed in a continuous sheet-like manner with good continuity. The obvious boundaries of most sand bodies can be identified, and its effect is better than the previous two methods of colored inversion and Bayesian constrained sparse pulse inversion.

[0137] After obtaining the inversion results, the attribute values ​​of the GR inversion body of the well side channel are extracted and compared with the sand body interpreted on the well to verify the accuracy of the prediction. The frequency division inversion has a good effect on identifying sand bodies of 7 meters to 15 meters and above in the Guantao Formation.

[0138] Through summary and comparison, it can be found that although the colored inversion has a simple operation process compared with other inversions and can avoid the problem of extracting wavelets, its degree of freedom is relatively high, lacks wavelet limitation control, and the results obtained are not consistent with the sand bodies on the well; although the Bayesian constrained sparse pulse inversion has improved the overall prediction effect compared with the colored inversion, the prediction accuracy is close to the seismic reflection phase axis, and the lateral accuracy is improved, but the resolution of the sand body in the vertical direction is reduced, and it is difficult to divide the periods in the vertical direction; compared with the first two inversion methods, the frequency division inversion has improved the recognition of sand bodies in the horizontal and vertical directions, has a higher consistency rate with the logging interpretation, and the horizontal and vertical resolutions are better than the first two methods.

[0139] In summary, after comparison and optimization, it is believed that the reliability of the frequency division inversion method for the target strata in this study area is relatively high, so this study uses the frequency division inversion method to characterize the distribution of the Guantao Formation conglomerate in Binxian area.

[0140] In addition, in order to bidirectionally verify the effect of the matching pursuit de-emphasis method selected in step 2.1 on the inversion effect, the original seismic data volume before de-emphasis shielding was used for frequency division inversion. The divided frequency bands and other parameters were kept consistent. The specific comparison results are described below. Please refer to Fig.16 , showing the effect comparison before and after the strengthening axis is removed. Fig.16 Taking the specific cross section shown as an example, Fig.16 Figure a in the figure is the inversion result obtained by applying the original data volume. Fig.16 Figure b in the figure shows the inversion result obtained after applying the stripping strong reflection.

[0141] Taking the sand body at the key layer Ng4 as an example, before removing the strong reflection shielding, the response of the sand body at Ng4 on the inversion profile was weak and almost unrecognizable; after weakening the strong reflection shielding, the sand body at Ng4 showed an obvious response, verifying that stripping the strong reflection has indeed improved the reservoir prediction effect in Binxian area.

[0142] Step 2.4, reservoir distribution prediction.

[0143] On the new seismic data volume obtained by suppressing the strong axis using the matching pursuit strong reflection separation technology, based on the optimized frequency division inversion results, the sand body boundary is defined by extracting the probability relationship between the inversion attribute value and the sand and mud.

[0144] First, the GR inversion data volume is converted into a depth domain data volume by time-depth conversion. Then, the response value of the GR inversion volume in the well bypass is extracted and analyzed with the corresponding lithology data, such as Fig.17 As shown, the inversion property value and the gamma curve intersection diagram in this embodiment are shown. Please refer to Fig.18 , showing the sand-mud probability relationship analysis diagram. Fig.18 As shown in the figure, it can be seen from the probability relationship between the two that 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 and mud continues to decrease. Among them, the attribute value at the junction of sand and mud is about 100API. At this time, the probability of sand and mud accounts for about 50%. The sand body envelope is drawn based on this probability relationship. Under the guidance of the sedimentary model, the sand body thickness interpolation on the well is corrected with reference to the inversion attribute trend, and the sand body distribution range is macroscopically controlled, so as to obtain the sand body thickness distribution map of each sand group layer.

[0145] Please refer to Figures 19 to 21 , showing the thickness map of the Ng4-1 sublayer sand body, the Ng4-2 sublayer sand body and the Ng4-3 sublayer sand body respectively. The provenance direction of the Guantao Formation in the study area mainly comes from the central uplift and the north. The strip-shaped sand bodies on both sides are affected by the northern provenance, and the distribution direction is roughly north-south and northeast-southwest, running through the entire work area, with a lateral width of approximately 3km to 4.5km; the source of the fan-shaped continuous sand body in the middle is provided by the central uplift, and the distribution direction of the sandstone body is roughly extending to the southeast along the edge of the uplift, with a lateral width of approximately 6km to 12km. In some local locations, it can be seen that the river channel sand body is in contact with the fan body boundary, such as Fig.19 , Fig. 20 and Fig.21 shown.

[0146] analyze Fig.19 , Fig. 20 and Fig.21 The distribution diagram of the sand body thickness of each layer of the Guantao Formation shown in shows that the overall thickness of the sand body has a thinning trend during the deposition period from the Ng4-3 sublayer to the Ng4-1 sublayer. Combined with the sedimentary pattern of the Binxian area, it can be seen that alluvial fan deposition mainly developed during the Ng4-3 sublayer to the Ng4-1 sublayer, and the sand body thickness was thicker near the edge of the uplift. The granite gneiss at the base of the Binxian uplift provided a rich source for the formation of the alluvial fan at the edge of the uplift after weathering. From the Ng4-3 deposition period to the Ng4-1 deposition period, the trend of the change in sand body thickness can be seen from the comparison of the trend of the change in sand body thickness. As the Binxian uplift gradually decreases in exposure, the fan-shaped continuous sheet sand bodies distributed around the edge of the uplift are also gradually thinning, which is in line with the law of alluvial fan evolution. Analysis of local characteristics shows that in addition to being affected by the uplift, its sand body distribution is also controlled by tectonic factors. The sand body thickness value near the downthrown plate of the Binnan fault in the middle and lower parts is relatively large.

[0147] Based on the same inventive concept, a device for determining the distribution of alluvial fan reservoirs by combining well seismic data with well seismic data is also provided in the embodiments of this specification, as described in the following embodiments. Since the principle of solving the problem by the device for determining the distribution of alluvial fan reservoirs by combining well seismic data with well seismic data is similar to that of the method for determining the distribution of alluvial fan reservoirs by combining well seismic data with well seismic data, the implementation of the device for determining the distribution of alluvial fan reservoirs by combining well seismic data with well seismic data can refer to the implementation of the method for determining the distribution of alluvial fan reservoirs by combining well seismic data with well seismic data, and the repeated parts will not be repeated. As used below, the terms "unit" or "module" may be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation in hardware, or a combination of software and hardware, is also possible and conceivable. Fig. 22 is a structural block diagram of a device for determining reservoir distribution of an alluvial fan by combining well-seismic analysis according to an embodiment of this specification. Fig. 22 As shown, it includes: an establishment module 221, a preprocessing module 222, an inversion module 223 and a determination module 224, and the structure is described below.

[0148] The building module 221 is used to build a fine isochronous stratigraphic framework of the alluvial fan reservoir in the target area based on the well logging data and seismic data of the alluvial fan reservoir in the target area.

[0149] The preprocessing module 222 is used to preprocess the seismic data of the alluvial fan reservoir in the target area by using the matching pursuit strong reflection stripping method to obtain preprocessed seismic data.

[0150] The inversion module 223 is used to perform seismic frequency division inversion on the seismic data volume in the pre-processed seismic data in combination with the well logging data to obtain a seismic frequency division inversion result.

[0151] The determination module 224 is used to determine the sand body distribution data of the alluvial fan reservoir in the target area by using the seismic frequency division inversion result.

[0152] From the above description, it can be seen that the embodiments of this specification have achieved the following technical effects: fully exploit the logging seismic data, improve the resolution of seismic data through seismic data processing methods, and on this basis carry out well-seismic combined reservoir prediction and alluvial fan reservoir configuration research in the study area, by comparing and optimizing a variety of reservoir prediction methods, clarify the sand body distribution characteristics of the study area, and dissect the alluvial fan configuration distribution characteristics in the Binxian area, which has reference significance for similar reservoir prediction and reservoir configuration research in the future, and can enrich the theoretical research on alluvial fan configuration.

[0153] This specification also provides a computer device, which can be found in Fig.23The computer device structure diagram of the method for determining the distribution of alluvial fan reservoirs by combining well-seismic analysis with that provided in the embodiment of this specification is shown, and the computer device may specifically include an input device 231, a memory 232, and a processor 233. The memory 232 is used to store processor executable instructions. When the processor 233 executes the instructions, the steps of the method for determining the distribution of alluvial fan reservoirs by combining well-seismic analysis with that provided in any of the above embodiments are implemented.

[0154] In this embodiment, the input device may specifically be one of the main devices for information exchange between the user and the computer system. The input device may include a keyboard, a mouse, a camera, a scanner, a light pen, a handwriting input board, a voice input device, etc.; the input device is used to input the original data and the program for processing these numbers into the computer. The input device can also obtain and receive data transmitted from other modules, units, and devices. The processor can be implemented in any appropriate manner. For example, the processor can take the form of a computer-readable medium, a logic gate, a switch, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), a programmable logic controller, and an embedded microcontroller, etc., such as a microprocessor or a processor and a computer-readable program code (such as software or firmware) that can be executed by the (micro) processor. The memory may specifically be a memory device used to store information in modern information technology. The memory may include multiple levels. In a digital system, anything that can store binary data can be a memory; in an integrated circuit, a circuit with a storage function without a physical form is also called a memory, such as a RAM, a FIFO, etc.; in a system, a storage device with a physical form is also called a memory, such as a memory stick, a TF card, etc. In this embodiment, the functions and effects specifically realized by the computer device can be explained in comparison with other embodiments and will not be described in detail here.

[0155] The present specification also provides a computer storage medium based on a method for determining the distribution of alluvial fan reservoirs in combination with well seismic data. The computer storage medium stores computer program instructions, which, when executed, implement the steps of the method for determining the distribution of alluvial fan reservoirs in combination with well seismic data in any of the above embodiments.

[0156] In this embodiment, the storage medium includes, but is not limited to, a random access memory (RAM), a read-only memory (ROM), a cache, a hard disk (HDD) or a memory card. The memory may be used to store computer program instructions. The network communication unit may be an interface for network connection communication set in accordance with the standard specified by the communication protocol.

[0157] In this embodiment, the functions and effects specifically implemented by the program instructions stored in the computer storage medium can be explained in comparison with other embodiments and will not be repeated here.

[0158] Obviously, those skilled in the art should understand that the modules or steps of the above-mentioned embodiments of this specification can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. In this way, the embodiments of this specification are not limited to any specific combination of hardware and software.

[0159] It should be understood that the above description is for illustration and not for limitation. Many embodiments and many applications beyond the examples provided will be apparent to those skilled in the art upon reading the above description. Therefore, the scope of this specification should not be determined with reference to the above description, but should be determined with reference to the preceding claims and the full scope of equivalents to which these claims belong.

[0160] The above description is only the preferred embodiment of this specification and is not intended to limit this specification. For those skilled in the art, the embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this specification shall be included in the protection scope of this specification.

Claims

1. A method for determining the distribution of alluvial fan reservoirs by combining well-seismic data, characterized in that: include: Based on the well logging data and seismic data of the alluvial fan reservoir in the target area, the sedimentary cycles are divided by wavelet decomposition, and multi-dimensional interactive comparison is performed in combination with the seismic phase axis reflection characteristics to establish a fine isochronous stratigraphic framework of the alluvial fan reservoir in the target area; Based on the seismic reflection shielding characteristics of the alluvial fan reservoir, an atomic dictionary matching the alluvial fan strong reflection phase axis is constructed, and the seismic data of the alluvial fan reservoir in the target area are preprocessed by using the matching pursuit strong reflection stripping method to obtain preprocessed seismic data; Combined with the logging data, seismic frequency division inversion is performed on the seismic data volume in the preprocessed seismic data to obtain seismic frequency division inversion results; the frequency division inversion includes: selecting an effective frequency band and dividing the frequency at intervals of 5 Hz, combining an amplitude and frequency tuning relationship map and a support vector machine to establish a nonlinear mapping between a seismic waveform and a logging curve; Using the seismic frequency division inversion results, the sand body boundary is defined through the sand-mud probability relationship and the deposition mode, and the sand body distribution data of the alluvial fan reservoir in the target area is determined; The method comprises: extracting the inversion attribute value of the GR inversion body of the well bypass from the seismic frequency division inversion result; determining the probability relationship between the inversion attribute value of the GR inversion body and sandstone and mudstone; defining the sand body boundary based on the probability relationship to obtain sand body distribution data; wherein, 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%; when the inversion attribute value is between 85API and 115API, the probability of sand and mud decreases continuously, wherein, when the attribute value at the junction of sand and mud is 100API, the probability of sand and mud accounts for 50%, and drawing the sand body envelope based on this probability relationship.

2. The method for determining the distribution of alluvial fan reservoirs by combining well-seismic data according to claim 1, characterized in that: Based on the well logging data and seismic data of the alluvial fan reservoir in the target area, the sedimentary cycles are divided by wavelet decomposition, and multi-dimensional interactive comparison is performed in combination with the seismic phase axis reflection characteristics to establish a fine isochronous stratigraphic framework of the alluvial fan reservoir in the target area, including: Using marker layers of the alluvial fan reservoir in the target area to constrain stratigraphic correlation; Divide the sedimentary cycles by using the logging curves in the logging data and wavelet decomposition, and perform stratigraphic correlation by using the sedimentary cycles; Based on the conversion of seismic synthetic records, the seismic layer is calibrated to the seismic profile at the corresponding position, the position of the seismic reflection event axis corresponding to the well interface is determined, and the seismic time-depth conversion is performed to carry out the whole area comparison; Based on the characteristics of well logging curves, sedimentary cycles and seismic event reflection characteristics, combined with the time-depth calibration results, multi-dimensional interactive comparison is carried out to establish a fine isochronous stratigraphic framework.

3. The method for determining the distribution of alluvial fan reservoirs by combining well-seismic data according to claim 1, characterized in that: The seismic data of the alluvial fan reservoir in the target area are preprocessed by using the matching pursuit strong reflection stripping method to obtain preprocessed seismic data, including: Projecting the strong reflection signal to be decomposed into the atomic dictionary, and stripping the strong reflection signal within the range limited by the signal residual through adaptive iteration; Matching is performed during the stripping process to obtain the event axis display after stripping the strong reflection, and output the preprocessed seismic data volume.

4. The method for determining the distribution of alluvial fan reservoirs by combining well-seismic data according to claim 1, characterized in that: Combined with the well logging data, seismic frequency division inversion is performed on the seismic data volume in the preprocessed seismic data to obtain seismic frequency division inversion results, including: Selecting a target curve according to the logging data, and performing standardization correction on the target curve to obtain a corrected target curve; Conducting spectrum analysis based on the seismic data, selecting effective frequency bands, setting frequency division intervals based on actual data, and obtaining frequency division data bodies of multiple different frequency bands; Determine the relationship between amplitude and frequency at different time thicknesses; establish a nonlinear mapping relationship between the seismic waveform and the corrected target curve based on a support vector machine; The frequency division data bodies of multiple different frequency bands are inverted and calculated to obtain the seismic frequency division inversion results.

5. The method for determining the distribution of alluvial fan reservoirs by combining well-seismic data according to claim 1, characterized in that: Also includes: Seismic frequency division and fusion are performed on the seismic data volume in the preprocessed seismic data to obtain sand body distribution data of the alluvial fan reservoir in the target area.

6. A device for determining the distribution of alluvial fan reservoirs by combining well-seismic data, characterized in that: include: Establishing a module for establishing a fine isochronous stratigraphic framework of the alluvial fan reservoir in the target area by dividing the sedimentary cycles through wavelet decomposition and combining the seismic phase axis reflection characteristics for multi-dimensional interactive comparison; A preprocessing module is used to construct an atomic dictionary matching the alluvial fan strong reflection event based on the seismic reflection shielding characteristics of the alluvial fan reservoir, and to preprocess the seismic data of the alluvial fan reservoir in the target area by using a matching pursuit strong reflection stripping method to obtain preprocessed seismic data; An inversion module is used to perform seismic frequency division inversion on the seismic data volume in the preprocessed seismic data in combination with the logging data to obtain a seismic frequency division inversion result; the frequency division inversion includes: selecting an effective frequency band and dividing the frequency at intervals of 5 Hz, combining an amplitude and frequency tuning relationship map and a support vector machine to establish a nonlinear mapping between a seismic waveform and a logging curve; A determination module is used to use the seismic frequency division inversion result to define the sand body boundary through the sand-mud probability relationship and the deposition mode, and determine the sand body distribution data of the alluvial fan reservoir in the target area; Among them, the determination module is specifically used to: extract the inversion attribute value of the GR inversion body of the well bypass from the seismic frequency division inversion result; determine the probability relationship between the inversion attribute value of the GR inversion body and sandstone and mudstone; limit the sand body boundary based on the probability relationship to obtain sand body distribution data; wherein, 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%; when the inversion attribute value is between 85API and 115API, the probability of sand and mud continues to decrease, wherein, when the attribute value at the junction of sand and mud is 100API, the probability of sand and mud accounts for 50%, and the sand body envelope is drawn with this probability relationship as the boundary.

7. The device for determining the distribution of alluvial fan reservoirs by combining well-seismic analysis according to claim 6, characterized in that: The establishment module is specifically used for: using the marker layer of the alluvial fan reservoir in the target area to constrain stratigraphic comparison; dividing the sedimentary cycle by the logging curve in the logging data and wavelet decomposition, and performing stratigraphic comparison through the sedimentary cycle; converting the seismic layer calibration to the seismic profile at the corresponding position based on the seismic synthetic record, determining the seismic reflection event position corresponding to the well interface, and performing seismic time-depth conversion to carry out full-area comparison; based on the logging curve characteristics, sedimentary cycle characteristics and seismic event reflection characteristics, combined with the time-depth calibration results, performing multi-dimensional interactive comparison, and establishing a fine isochronous stratigraphic framework.

8. A computer device, characterized in that: The method comprises a processor and a memory for storing processor-executable instructions, wherein the processor implements the steps of the method according to any one of claims 1 to 5 when executing the instructions.

9. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the instructions are executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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