Reservoir prediction methods, devices, storage media and processors

Abstract

This invention relates to the field of oil and gas exploration and development, and discloses a reservoir prediction method, device, storage medium, and processor. The prediction method includes: performing multiple rounds of well-seismic calibration on development wells in a 3D seismic testing area; selecting development wells with a well-seismic correlation coefficient greater than a preset value from the development wells corresponding to the last round of well-seismic calibration as target wells; determining target comprehensive wavelet based on each target well; performing seismic inversion on the reservoir based on the target comprehensive wavelet and preset quality control requirements; predicting the planar distribution of the main sand zone in the reservoir using the seismic inversion results; determining the local thickness zone of the sand body based on the planar distribution of the main sand zone; and characterizing individual sand bodies in the reservoir based on the inversion attributes and the local thickness zone of the sand body. By increasing the constraint of dense well network of development wells, performing multiple rounds of well-seismic calibration, and conducting strict quality control during the seismic inversion process, the accuracy of reservoir prediction can be improved, thereby improving the prediction accuracy of individual sand bodies and facilitating the identification of effective individual sand bodies.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration and development technology, specifically to a reservoir prediction method, a reservoir prediction device, a machine-readable storage medium, and a processor. Background Technology

[0002] Reservoirs dominated by large fluvial deposits feature well-developed channel sand bodies, with individual sand layers typically 3-10 meters thick and mudstone interlayers typically 1-20 meters thick. The complex superposition of sand and mudstone layers further complicates reservoir prediction and characterization. For example, in the Fuyu area of ​​the southern Songliao Basin, the sand-to-soil ratio ranges from 30% to 70%, resulting in dense and highly heterogeneous reservoirs. Remaining oil reserves are primarily found in fault-lithological and lithologic oil and gas reservoirs, with significant differences in reservoir formation characteristics across different fault blocks in the lower regions. The outward expansion zone is controlled by local structural and lithological variations, making precise reservoir prediction particularly challenging.

[0003] In existing technologies, conventional impedance retrieval methods are often used to predict reservoirs. However, this method does not provide ideal accuracy in delineating reservoir boundaries and cannot accurately describe the sand body situation within the reservoir. Consequently, the prediction accuracy of the reservoir does not meet production requirements, making it difficult to find effective individual sand bodies and hindering well placement. Summary of the Invention

[0004] The purpose of this invention is to overcome the problems of unsatisfactory reservoir prediction accuracy and difficulty in finding effective single sand bodies in the prior art, and to provide a reservoir prediction method, a reservoir prediction device, a machine-readable storage medium, and a processor.

[0005] To achieve the above objectives, the present invention provides a reservoir prediction method, the prediction method comprising: Multiple rounds of well-seismic calibration were performed on development wells in the 3D seismic survey area; development wells with well-seismic correlation coefficients greater than preset values ​​were selected as target wells from the development wells corresponding to the last round of well-seismic calibration. Target composite wavelet is determined based on each target well, and seismic inversion of the reservoir is performed based on the target composite wavelet and preset quality control requirements. Predicting the planar distribution of the main sand belt in the reservoir using seismic inversion results; Based on the planar distribution of the main sand belt, the local thickness zone of the sand body is determined, and the individual sand bodies in the reservoir are characterized according to the inversion properties and the local thickness zone of the sand body.

[0006] In this embodiment of the application, the multi-round well-seismic calibration includes three rounds of well-seismic calibration.

[0007] In this embodiment of the application, the multi-round well seismic calibration for development wells in a 3D seismic work area includes: The first round of well-seismic calibration was carried out on all development wells in the 3D seismic work area using the Rick wavelet, which is consistent with the dominant frequency of the earthquake. Based on the results of the first round of well seismic calibration, the first comprehensive wavelet is determined, and the first comprehensive wavelet is used to perform the second round of well seismic calibration on the development wells in the 3D seismic work area. Based on the results of the second round of well seismic calibration, a second comprehensive wavelet is determined, and the second comprehensive wavelet is used to conduct a third round of well seismic calibration on development wells in the 3D seismic work area.

[0008] In this embodiment of the application, the step of determining a first composite wavelet based on the first round of well seismic calibration results, and using the first composite wavelet to perform a second round of well seismic calibration on development wells in the 3D seismic work area, includes: Based on the results of the first round of well seismic calibration, wavelet extraction is performed on each development well. Qualified wavelets are determined from the wavelets of each development well, and the first comprehensive wavelet is obtained based on each qualified wavelet. Based on the first comprehensive wavelet, a second round of well vibration calibration is performed on the development wells corresponding to each qualified wavelet.

[0009] In this embodiment of the application, the step of determining the second synthetic wavelet based on the second round of well seismic calibration results, and using the second synthetic wavelet to perform a third round of well seismic calibration on development wells in the 3D seismic work area, includes: Based on the results of the second round of well seismic calibration, wavelet extraction is performed on the development wells corresponding to the second round of well seismic calibration. Qualified wavelets are determined from the wavelets of each development well, and the second comprehensive wavelet is obtained based on each qualified wavelet. The third round of well vibration calibration is performed on the development wells corresponding to each qualified wavelet based on the second comprehensive wavelet.

[0010] In this embodiment of the application, before selecting development wells with well-seismic correlation coefficients greater than a preset value from the development wells corresponding to the last round of well-seismic calibration as target wells, the method further includes: performing quality control on the results of the last round of well-seismic calibration based on the well-connected seismic profile of the three-dimensional seismic work area, and determining whether the results of the last round of well-seismic calibration meet the preset requirements; selecting development wells with well-seismic correlation coefficients greater than a preset value from the development wells corresponding to the last round of well-seismic calibration as target wells includes: If the results of the last round of well-seismic calibration meet the preset requirements, wells with well-seismic correlation coefficients greater than the preset value are selected from the development wells corresponding to the last round of well-seismic calibration as target wells.

[0011] In this embodiment of the application, determining the target composite wavelet based on each target well includes: Wavelet extraction is performed on each target well, qualified wavelets are determined from the wavelets of each target well, and the target composite wavelet is obtained based on each qualified wavelet.

[0012] In this embodiment of the application, the seismic inversion of the reservoir based on the target synthetic wavelet and preset quality control requirements includes: Based on the target synthetic wavelet and preset quality control requirements, sparse pulse wave impedance inversion is performed on the reservoir.

[0013] A second aspect of this application provides a reservoir prediction apparatus, comprising: The well-seismic calibration module is used to perform multiple rounds of well-seismic calibration for development wells in the 3D seismic survey area; the reservoir inversion module is used to select development wells with well-seismic correlation coefficients greater than preset values ​​from the development wells corresponding to the last round of well-seismic calibration as target wells; the target comprehensive wavelet is determined according to each target well, and the reservoir is seismically inverted based on the target comprehensive wavelet and preset quality control requirements; The single sand body characterization module is used to predict the planar distribution of the main sand belt in the reservoir using seismic inversion results; based on the planar distribution of the main sand belt, the local thickness zone of the sand body is determined, and the single sand body in the reservoir is characterized according to the inversion attributes and the local thickness zone of the sand body.

[0014] In this embodiment of the application, the well vibration calibration module is used for: The first round of well-seismic calibration was carried out on all development wells in the 3D seismic work area using the Rick wavelet, which is consistent with the dominant frequency of the earthquake. Based on the results of the first round of well seismic calibration, the first comprehensive wavelet is determined, and the first comprehensive wavelet is used to perform the second round of well seismic calibration on the development wells in the 3D seismic work area. Based on the results of the second round of well seismic calibration, a second comprehensive wavelet is determined, and the second comprehensive wavelet is used to conduct a third round of well seismic calibration on development wells in the 3D seismic work area.

[0015] In this embodiment of the application, the reservoir prediction device further includes a quality control module, which is used to perform quality control on the results of the last round of well seismic calibration in the multi-round well seismic calibration based on the well-connected seismic profile of the three-dimensional seismic work area.

[0016] In this embodiment of the application, the reservoir inversion module is used to extract wavelets from each target well, determine qualified wavelets from the wavelets of each target well, and obtain the target composite wavelet based on each qualified wavelet.

[0017] In this embodiment of the application, the reservoir inversion module is used to perform sparse pulse wave impedance inversion on the reservoir based on the target synthetic wavelet and preset quality control requirements.

[0018] A third aspect of this application provides a processor configured to perform the reservoir prediction method described above.

[0019] A fourth aspect of this application provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the aforementioned reservoir prediction method.

[0020] The above technical solution includes: performing multiple rounds of well-seismic calibration on development wells in a 3D seismic testing area; selecting development wells with a well-seismic correlation coefficient greater than a preset value from the development wells corresponding to the last round of well-seismic calibration as target wells; determining target comprehensive wavelet based on each target well; performing seismic inversion on the reservoir based on the target comprehensive wavelet and preset quality control requirements; predicting the planar distribution of the main sand zone in the reservoir using the seismic inversion results; determining the local thickness zone of the sand body based on the planar distribution of the main sand zone; and characterizing individual sand bodies in the reservoir based on the inversion attributes and the local thickness zone of the sand body. Based on the solution provided in this application, by increasing the density of the development well network, performing multiple rounds of well-seismic calibration, and conducting strict quality control during the seismic inversion process, the reservoir prediction accuracy can be further improved, thereby improving the prediction accuracy of individual sand bodies and facilitating the identification of effective individual sand bodies.

[0021] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0022] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings: Figure 1 The schematic diagram illustrates a flow chart of a reservoir prediction method according to an embodiment of this application; Figure 2 The schematic diagram illustrates a flow chart of another reservoir prediction method according to an embodiment of this application; Figure 3 This illustration schematically shows a process flow diagram of seismic inversion and quality control according to an embodiment of this application; Figure 4 This illustration schematically shows a different wavelet three-round well seismic calibration diagram according to an embodiment of this application; Figure 5 This illustration schematically shows a well-connected seismic calibration profile according to an embodiment of this application; Figure 6 A schematic diagram illustrating a line graph comparing the correlation coefficients of different wavelet calibration wells according to embodiments of this application is shown. Figure 7 A low-frequency trend adjustment comparison chart according to an embodiment of this application is illustrated schematically; Figure 8This illustration schematically shows a correlation diagram between seismic data and synthetic records according to an embodiment of this application; Figure 9 An inversion signal-to-noise ratio planar diagram according to an embodiment of this application is schematically shown; Figure 10 The illustration shows a schematic cross-sectional view of an overlay of a seismic trace and a residual trace according to an embodiment of this application; Figure 11 This illustration schematically shows a comparative cross-sectional view of a bandpass filter and a high cutoff filter according to an embodiment of this application; Figure 12 This illustration schematically shows a comparison diagram of a logging impedance curve and an inverted impedance curve according to an embodiment of this application. Figure 13 This illustration schematically shows a comparison between reservoir prediction results after dense well pattern constraint and conventional reservoir prediction results according to an embodiment of this application; Figure 14 This illustration schematically depicts a single sand body by superimposing wave impedance and seismic profile according to an embodiment of this application. Figure 15 This schematically illustrates a comparison of sandstone maps and wave impedance properties of the Quan 4 Member I Sandstone Group in the study area according to an embodiment of this application. Figure 16 The schematic diagram illustrates the wave impedance properties of the target well area in the study area according to an embodiment of this application, specifically the 3+4 sub-layer wave impedance property map. Figure 17 The illustration shows a predicted thickness map of the 3+4 sub-layers in the target well area within the study area according to an embodiment of this application.

[0023] Figure 18 This schematic diagram illustrates a structural block diagram of a reservoir prediction device according to an embodiment of the present application; Figure 19 The diagram illustrates the internal structure of a computer device according to an embodiment of this application.

[0024] Explanation of reference numerals in the attached figures 210-Well vibration calibration module; 220-Reservoir inversion module; 230-Single sand body characterization module; A01-Processor; A02-Network interface; A03-Internal memory; A04-Display screen; A05-Input device; A06-Non-volatile storage medium; B01-Operating system; B02-Computer program. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0026] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0027] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0028] As described in the background section, reservoirs dominated by large fluvial deposits feature well-developed channel sand bodies. Individual sand layers typically range from 3 to 10 meters in thickness, while mudstone interlayers are usually 1 to 20 meters thick. The complex superposition of sand and mudstone layers further complicates reservoir prediction and characterization. For example, in the Fuyu area of ​​the southern Songliao Basin, the sand-to-soil ratio is 30%-70%, the reservoir is dense and highly heterogeneous, and the remaining oil reserves are mainly fault-lithological and lithologic oil and gas reservoirs. The reservoir formation characteristics vary significantly across different fault blocks in the lower parts of the reservoir, and the outward expansion zone is controlled by local structural and lithological variations. Some wells have low single-well production rates. For this type of reservoir, the current challenge is no longer simply finding channel sand bodies, but rather further refining the reservoir characterization and description of channel sands to reveal their internal information and ultimately locate effective individual sand bodies. Current technologies often employ conventional impedance spectroscopy for reservoir prediction. However, this method does not provide ideal accuracy in delineating reservoir boundaries and cannot accurately describe the sand bodies within the reservoir. Consequently, the accuracy of reservoir prediction does not meet production requirements, making it difficult to find effective single sand bodies and hindering well placement.

[0029] To address this, one embodiment of this application provides a reservoir prediction method, such as... Figure 1 As shown, the reservoir prediction method may include the following steps: Step 101: Conduct multiple rounds of well-seismic calibration for development wells in the 3D seismic work area.

[0030] In practical applications, before conducting multiple rounds of well-seismic calibration, Jason software can be used to establish a three-dimensional seismic work zone for the target area and load seismic data, well data, and interpretation data. Well data may include wellhead coordinates, core elevation, well trajectory, logging curves, etc., while interpretation data may include target layer positions, fault data, etc. The target area can be an area with a dense network of development wells.

[0031] Well data and interpretation data can be collectively referred to as well logging data. After obtaining well logging data, curve correction can also be performed.

[0032] In this embodiment of the application, the multi-round well seismic calibration may include three rounds of well seismic calibration. Therefore, performing multi-round well seismic calibration on development wells in a 3D seismic survey area may specifically include steps one, two, and three: Step 1: Use the Ricker wavelet, which is consistent with the dominant frequency of the earthquake, to perform the first round of well-seismic calibration on all development wells in the 3D seismic work area.

[0033] This first round of well-seismic calibration can align the time interface of the marker layer, that is, align the wave impedance interface of the marker layer above the well with the time axis of the seismic profile.

[0034] Step 2: Based on the results of the first round of well seismic calibration, determine the first comprehensive wavelet, and use the first comprehensive wavelet to perform a second round of well seismic calibration on the development wells in the 3D seismic work area.

[0035] The first synthesized wavelet is a multi-well synthesized wavelet. In specific implementation, data from the first round of well seismic calibration can be used to extract wavelets from each well, extracting the wavelet amplitude and phase information of each well, and then obtaining the multi-well synthesized wavelet based on the wavelets of each well. For example, the average value of the wavelets of all wells can be calculated to generate the multi-well synthesized wavelet.

[0036] The second round of well-seismic calibration is mainly aimed at reservoir location to further improve the well-seismic correlation coefficient. Based on this purpose, in this embodiment of the application, a first comprehensive wavelet is determined based on the results of the first round of well-seismic calibration, and the first comprehensive wavelet is used to perform a second round of well-seismic calibration on development wells in the three-dimensional seismic work area. Preferably, it may include steps (1) and (2), as follows: Step (1): Based on the results of the first round of well vibration calibration, wavelet extraction is performed on each development well. Qualified wavelets are determined from the wavelets of each development well, and the first comprehensive wavelet is obtained based on each qualified wavelet.

[0037] Among them, determining the qualified wavelet from the wavelets of each development well can be done by removing wavelets with severe sidelobe effects and phase anomalies from the wavelets of each development well, thus obtaining the qualified wavelet.

[0038] The first synthesized wavelet is obtained from each qualified wavelet. For example, the average value of each qualified wavelet can be calculated to obtain the first synthesized wavelet.

[0039] Step (2): Based on the first comprehensive wavelet, perform a second round of well vibration calibration on the development wells corresponding to each qualified wavelet.

[0040] The development wells corresponding to each qualified wavelet can also be referred to as the development wells corresponding to the second round of well vibration calibration.

[0041] Step 3: Based on the results of the second round of well seismic calibration, determine the second comprehensive wavelet, and use the second comprehensive wavelet to perform the third round of well seismic calibration on the development wells in the 3D seismic work area.

[0042] In this embodiment of the application, a second composite wavelet is determined based on the results of the second round of well seismic calibration, and the second composite wavelet is used to perform a third round of well seismic calibration on development wells in the three-dimensional seismic work area. Specifically, this may include steps i and ii: Step i: Based on the results of the second round of well seismic calibration, wavelet extraction is performed on the development wells corresponding to the second round of well seismic calibration. Qualified wavelets are determined from the wavelets of each development well, and the second comprehensive wavelet is obtained based on each qualified wavelet.

[0043] The specific implementation details for determining the second synthesized wavelet can be found in the first synthesized wavelet, and will not be repeated here.

[0044] Step ii: Perform a third round of well vibration calibration on the development wells corresponding to each qualified wavelet based on the second integrated wavelet.

[0045] The development wells corresponding to each qualified wavelet can also be referred to as the development wells corresponding to the third round of well seismic calibration.

[0046] The third round of well-seismic calibration can further improve the well-seismic correlation coefficient, especially the well-seismic correlation coefficient at the location of the main formation.

[0047] Step 103: Select development wells with a well-seismic correlation coefficient greater than a preset value from the development wells corresponding to the last round of well-seismic calibration as target wells.

[0048] To ensure quality control of wellbore calibration and accurate selection of target wells; such as Figure 2As shown, before step 103, the reservoir prediction method provided in this application embodiment may further include step 102, which involves quality control of the results of the last round of well seismic calibration in the multi-round well seismic calibration based on the well-connected seismic profile of the three-dimensional seismic work area, and determining whether the results of the last round of well seismic calibration meet preset requirements. Therefore, step 103 may specifically include step 1031, in response to the fact that the results of the last round of well seismic calibration meet the preset requirements, selecting development wells with well-seismic correlation coefficients greater than preset values ​​from the development wells corresponding to the last round of well seismic calibration as target wells.

[0049] It is understandable that, in the case of three rounds of well vibration calibration, the results of the last round of well vibration calibration in the multi-round well vibration calibration are subject to quality control, specifically the quality control of the results of the third round of well vibration calibration, and whether the results of the third round of well vibration calibration meet the preset requirements.

[0050] The preset requirements can be set according to actual needs. For example, the preset requirements may include: all well stratification locations are basically consistent with the interpretation strata, the difference between the upper and lower strata does not exceed 3ms, and the seismic characteristics of the target stratum are continuous.

[0051] It is understandable that, in the case of three rounds of well-seismic calibration, the step of selecting development wells with well-seismic correlation coefficients greater than preset values ​​from the development wells corresponding to the last round of well-seismic calibration as target wells specifically means: selecting development wells with well-seismic correlation coefficients greater than preset values ​​from the development wells corresponding to the third round of well-seismic calibration as target wells.

[0052] In practice, after three rounds of well-seismic calibration, a well-seismic correlation coefficient line graph can be plotted. Then, development wells with a well-seismic correlation coefficient greater than a preset value are selected as target wells. The preset value can be set according to actual needs; for example, it can be 0.7.

[0053] In practical applications, wells with low well-seismic correlation coefficients are generally deviated wells or wells located near faults. Such wells tend not to participate in the subsequent inversion process.

[0054] Step 104: Determine the target integrated wavelet based on each target well, and perform seismic inversion on the reservoir based on the target integrated wavelet and preset quality control requirements.

[0055] The process of determining the target composite wavelet based on each target well may include: extracting wavelets from each target well, determining qualified wavelets from the wavelets of each target well, and obtaining the target composite wavelet based on each qualified wavelet.

[0056] In one implementation, performing seismic inversion of the reservoir based on the target synthetic wavelet and preset quality control requirements may include: performing sparse pulse wave impedance inversion of the reservoir based on the target synthetic wavelet and preset quality control requirements. Specifically, sparse pulse wave impedance inversion may be further performed in conjunction with seismic interpretation horizons.

[0057] In this embodiment of the application, in order to make the inversion results as optimal as possible, the preset quality control requirements may include inversion parameter quality control requirements, merging frequency parameter setting requirements, low-frequency trend optimization requirements, and inversion result quality control requirements.

[0058] Among them, the quality control requirements for inversion parameters are as follows: The more wells involved in the statistical parameters, the better. During the inversion process, the various inversion parameters are statistically derived from the selected wells. If too few wells are involved, the resulting parameter curves will lack regularity. Therefore, in practice, it is essential to select as many wells as possible for inversion parameter statistics to ensure the representativeness of the inversion parameter values. When selecting values, priority should be given to values ​​corresponding to the inflection points of the curves.

[0059] Requirements for setting the merging frequency parameters: The merging frequency usually has the greatest impact on the inversion results, including the selection of low and high frequencies. The optimal value can be obtained through multiple experiments during the inversion process.

[0060] Optimization requirements for low-frequency trends: The selection of low-frequency trends has a significant impact on the strength of the energy of the target layer in the inversion results. By increasing the number of low-frequency trend control points, the low-frequency trend can be made to better match the trend on the well, thereby highlighting the characteristics of the target layer.

[0061] Quality control requirements for inversion results: The final inversion results undergo multiple quality control methods before being used for the next step of attribute extraction. These quality control methods can specifically include five aspects: ① The higher the correlation between the original seismic data and the synthetic record, the better; ② The higher the inversion signal-to-noise ratio, the better; ③ By examining the overlay profile of the seismic trace and the residual trace, the smaller the amplitude of the residual trace and the fewer continuous axes, the better, indicating a more realistic inversion result; ④ Applying bandpass filtering and high-cutoff filtering to the well inversion profile for quality control, the better the remaining mid-frequency and low-frequency information matches the well, the more accurate the lithological lateral variation trend; the lower the seismic dominant frequency, the greater the difference between the bandpass-filtered profile and the high-cutoff-filtered profile; ⑤ Extracting the wave impedance curves at the well points from the inversion results and comparing them with the actual well logging wave impedance curves, the closer the trends, the higher the degree of agreement between the inversion results and the well.

[0062] It is understandable that earthquake inversion based on preset quality control requirements can yield earthquake inversion results that have passed quality control.

[0063] In one specific embodiment, the seismic inversion and quality control process can be as follows: Figure 3 As shown.

[0064] Step 105: Use the seismic inversion results to predict the planar distribution of the main sand belt in the reservoir.

[0065] That is, the planar distribution of the main sand belt in the reservoir is predicted using the seismic inversion results after quality control is qualified.

[0066] In practical implementation, the seismic inversion results after quality control can be used to extract the wave impedance plane properties of the main layer in the target area by opening a time window, and the extracted time window range should be avoided to cross the layer, so as to predict the plane distribution of the main sand belt.

[0067] Step 106: Determine the local thickness zone of the sand body based on the planar distribution of the main sand belt, and characterize the single sand body in the reservoir according to the inversion attributes and the local thickness zone of the sand body.

[0068] When characterizing a single sand body in a reservoir, the boundary delineation principles may include: for cross-faults, the boundary is the fault; for updip directions, the boundary is the pinch-out of the sand body; and for lenses, the boundary is the pinch-out at both ends of the sand body.

[0069] It is understood that the reservoir prediction method provided in this application includes: performing multiple rounds of well-seismic calibration on development wells in a 3D seismic testing area; selecting development wells with a well-seismic correlation coefficient greater than a preset value from the development wells corresponding to the last round of well-seismic calibration as target wells; determining target comprehensive wavelet based on each target well; performing seismic inversion on the reservoir based on the target comprehensive wavelet and preset quality control requirements; predicting the planar distribution of the main sand zone in the reservoir using the seismic inversion results; determining the local thickness zone of the sand body based on the planar distribution of the main sand zone; and characterizing individual sand bodies in the reservoir based on the inversion attributes and the local thickness zone of the sand body. Based on the scheme provided in this application, by increasing the dense well network constraint of development wells, performing multiple rounds of well-seismic calibration, and conducting strict quality control during the seismic inversion process, the reservoir prediction accuracy can be further improved, thereby improving the prediction accuracy of individual sand bodies and facilitating the identification of effective individual sand bodies.

[0070] In fact, as exploration and development continue, the required accuracy of reservoir boundary delineation increases to achieve refined reservoir prediction and enhance reserves. However, the impedance inversion method used in large-scale projects lacks sufficient accuracy in delineating reservoir boundaries, and the sand body situation between wells is not accurately determined, thus limiting expansion potential. The solution provided in this application, through dense well network constraint inversion in the development area, constructs a "one sand, one reservoir" formation model in the anticline flank, and performs refined reservoir prediction by zone and layer, thereby further refining the reservoir prediction results and improving the prediction accuracy of individual sand bodies. This allows for the determination of lithological reservoir boundaries, effectively supporting the expansion of old areas and rapid production establishment.

[0071] The reservoir prediction method provided in the above embodiments of this application will be described below with specific examples. It should be understood that the following examples are only specific implementation methods and do not imply an improper limitation of the solution of this application.

[0072] In this example, local dense well network (development well) well seismic calibration was carried out in the target area. This stratigraphic calibration adopted single-well and multi-well combined well seismic calibration technology. A total of 59 wells in the work area were selected for stratigraphic calibration to produce synthetic records. Two interconnected well seismic profiles were used for multi-well combined well seismic calibration. The reservoir prediction process is as follows: Step I involves applying different wavelets to perform multiple rounds of well-seismic calibration, gradually improving the correlation between well and seismic activity.

[0073] First, using the Ricker wavelet, which matches the dominant frequency of the earthquake, a first round of well-seismic calibration was performed on all development wells within the 3D seismic survey area, aligning the time interface of the marker layer, i.e., aligning the T2 wave impedance interface on the well with the time axis of the seismic profile. Then, wavelet extraction was performed on each well, extracting the wavelet amplitude and phase information for each well to obtain a multi-well composite wavelet. Wells with severe sidelobe effects or phase anomalies were removed for a second round of well-seismic calibration. Next, the multi-well composite wavelet was extracted again for a third round of well-seismic calibration. The three rounds of well-seismic calibration are as follows: Figure 4 As shown, by Figure 4 It can be seen that the correlation coefficient between well and seismic activity gradually increases, especially at the location of the main formation.

[0074] Step II involves quality control of the well seismic calibration results using well-connected seismic profiles.

[0075] A well-connected seismic calibration profile was established, with the T2 reflection layer serving as a marker layer for the area. On the seismic profile, it appears as a reflection wave group composed of one or more relatively strong phases. The lateral variations of each phase correspond well to the development of various sand groups within the Quan 4 Member. The first phase is the T2 reflection layer. Overall, the wave group characteristics are clear, with good continuity along the same axis, facilitating comparison, and showing no obvious cross-axis phenomena. Figure 5 As shown.

[0076] Step III: Select wells with high well-seismic correlation coefficients to participate in the final multi-well integrated wavelet extraction and perform seismic inversion.

[0077] After three rounds of well-seismic calibration, a well-seismic correlation coefficient line graph was generated, as shown below. Figure 6 As shown. Wells with a final well-seismic correlation coefficient > 0.7 are preferred for the final multi-well integrated wavelet extraction for inversion. Some wells with very low well-seismic correlation coefficients are found to be mostly deviated wells or located near faults, and such wells are not included in the inversion.

[0078] Step IV: Quality control of the inversion process.

[0079] Regarding the inversion parameters: The more wells involved in the statistical parameters, the better. During the inversion process, the various inversion parameters are statistically derived from the selected wells. If too few wells are involved, the resulting parameter curves will lack regularity. Therefore, it is necessary to select as many wells as possible for the statistical analysis of inversion parameters to ensure that the values ​​of the inversion parameters are representative. When selecting values, priority should be given to the values ​​corresponding to the inflection points of the curves.

[0080] Regarding the merging frequency parameter settings: Among the parameters selected above, the merging frequency has the greatest impact on the inversion results, including the selection of low and high frequencies. Multiple experiments are needed during the inversion process to obtain the optimal values. When the low frequency is set to 5Hz, the low-frequency compensation is insufficient, resulting in poor lateral continuity of the reservoir in the inversion profile and a short extension distance. When the low frequency is set to 10Hz, the lateral continuity of the reservoir in the inversion profile improves significantly, indicating appropriate low-frequency compensation. When the high frequency is set to 70Hz, the vertical resolution of the reservoir in the inversion profile is low, and the correspondence with wells is poor. When the high frequency is set to 100Hz according to the seismic bandwidth range, the vertical resolution of the inversion results is improved, the correspondence with wells is significantly improved, and the inversion quality is effectively enhanced.

[0081] Optimizations for low-frequency trends: The selection of low-frequency trends significantly impacts the energy intensity of the target segment in the inversion results. For the Quan 4 Member reservoir, the range of wave impedance values ​​does not vary much, and a straight line representing a constant trend is sufficient for most work areas. However, in some work areas, due to large vertical differences in seismic data energy, using a straight line to represent the low-frequency trend can lead to low-energy reservoirs being suppressed by high-energy reservoirs, resulting in indistinct reservoir characteristics in some areas. Increasing the number of low-frequency trend control points can better align the low-frequency trend with the surface trend, ultimately highlighting the characteristics of the target segment. Figure 7 As shown.

[0082] Regarding the inversion results: The final inversion results underwent quality control using multiple methods: ① The higher the correlation between the original seismic data and the synthetic record, the better. The overall correlation in the Fubei area was above 0.8. Figure 8 As shown, this indicates that the inversion results are in good agreement with the earthquake; ② A higher inversion signal-to-noise ratio is better. The overall inversion signal-to-noise ratio in the Fubei area is around 15, such as... Figure 9 As shown, this indicates that there is less noise in the inversion results; ③ By looking at the superimposed profile of the seismic trace and the residual trace, as shown... Figure 10As shown; black represents seismic traces, and red represents the residuals between the synthetic traces and seismic traces. The smaller the amplitude of the residual traces and the fewer continuous axes, the more realistic the inversion results are; ④ Quality control is performed using bandpass and high-cutoff filter profiles. The higher the well matching rate, the better. The remaining intermediate frequency information after bandpass filtering is compared with the well data, and the matching rate is good. The seismic dominant frequency in this work area is 60Hz, and there is little difference between the bandpass and high-cutoff filter profiles. When the seismic dominant frequency is less than 40Hz, the profile differences are significant, such as... Figure 11 As shown; ⑤ The wave impedance curve (red) at the well point is extracted using the inversion results and compared with the actual logging wave impedance curve (blue), as shown. Figure 12 As shown, the closer the trend is, the higher the degree of agreement between the inversion results and the well.

[0083] After passing quality control, the inversion results are used to extract the planar properties of the wave impedance in the main formation during small window openings, predicting the planar distribution of the main sand belt. By increasing the constraint of dense well network in development wells, the prediction results for the main sand belt of the Fuyu oil layer have been continuously improved. Taking the Tan 45 block as an example, the accuracy rate increased from 82.4% to 85.6%. Figure 13 As shown.

[0084] Based on the results of the dense well network constraint inversion, the inversion attributes are used to quantitatively characterize individual sand bodies in the local thickness areas of sand bodies, identify favorable traps, and finally characterize multiple sand groups and different individual sand bodies.

[0085] Wave impedance superimposed with seismic profiles characterizes a single sand body, such as Figure 14 As shown, the boundary delineation principles are: for faults, the boundary is the fault itself; for updip directions, the boundary is the pinch-out of the sand body; and for lenses, the boundary is the pinch-out at both ends of the sand body. The comparison results between the sandstone map and its acoustic impedance properties are as follows: Figure 15 As shown, through dense well network reservoir prediction, the prediction results of the main sand zone continue to improve. The predicted distribution direction of the main sand zone is consistent with the direction of the geological sandstone map. The sandstone thickness gradually decreases towards the northeast. The reservoir prediction results correspond well with the sandstone map and the completed wells. Figure 16 and Figure 17 To characterize individual sand bodies according to the "one sand, one reservoir" accumulation model, inversion properties were used to quantitatively characterize individual sand bodies in local thickness areas, thus identifying favorable traps.

[0086] Then, well placement was carried out, with the following strategy: In the lower parts of the predicted reservoir development, combined with the oil-water interface, exploration and appraisal wells were deployed to locate updip pinch-out lithologic reservoirs. In the lower parts of the anticline flanks, where the predicted sand formations developed and formed updip pinch-out oil and gas reservoirs, well placement was carried out at the highest structural points. Applying seismic prediction results and understanding the oil-water boundary, 14 appraisal wells were deployed in different blocks and fault stages controlled by reverse normal faults in the Fuyu area. Currently, 5 wells have been completed with good exploration results, of which 3 have been tested for oil production and 1 has been put into production.

[0087] This invention, through increased well density constraints and strict quality control, has continuously improved the prediction results of the main sand zone of the main oil-bearing layer in the Jilin Oilfield. The reservoir prediction accuracy rate can be increased to over 85%. The direction of the main sand zone predicted by inversion plane attributes is consistent with the direction of the geological sandstone map, and the reservoir prediction results have a good correspondence with the sandstone map and completed wells. This technology has been well applied in the conventional oil field of the Jilin Oilfield in the Songliao Basin, improving the prediction accuracy of single sand bodies in the aforementioned study area. Combined with the understanding of the oil-water boundary, well locations and development well groups have been deployed, resulting in good exploration and development effects and strongly supporting the expansion and production of old oilfields.

[0088] Based on the same inventive concept, such as Figure 18 As shown, Figure 18 A schematic diagram illustrates a structural block diagram of a reservoir prediction device according to an embodiment of this application. In one embodiment, a reservoir prediction device 200 is provided, including a wellbore calibration module 210, a reservoir inversion module 220, and a single sand body characterization module 230, wherein: Well seismic calibration module 210 is used to perform multiple rounds of well seismic calibration for development wells in a 3D seismic work area; The reservoir inversion module 220 is used to select development wells with a well-seismic correlation coefficient greater than a preset value from the development wells corresponding to the last round of well-seismic calibration as target wells; determine the target comprehensive wavelet according to each target well; and perform seismic inversion on the reservoir based on the target comprehensive wavelet and preset quality control requirements. The single sand body characterization module 230 is used to predict the planar distribution of the main sand belt in the reservoir using seismic inversion results; based on the planar distribution of the main sand belt, the local thickness zone of the sand body is determined, and the single sand body in the reservoir is characterized according to the inversion attributes and the local thickness zone of the sand body.

[0089] In one embodiment, the multi-round well-seismic calibration includes three rounds of well-seismic calibration.

[0090] In one embodiment, the well vibration calibration module 210 is used for: The first round of well-seismic calibration was carried out on all development wells in the 3D seismic work area using the Rick wavelet, which is consistent with the dominant frequency of the earthquake. Based on the results of the first round of well seismic calibration, the first comprehensive wavelet is determined, and the first comprehensive wavelet is used to perform the second round of well seismic calibration on the development wells in the 3D seismic work area. Based on the results of the second round of well seismic calibration, a second comprehensive wavelet is determined, and the second comprehensive wavelet is used to conduct a third round of well seismic calibration on development wells in the 3D seismic work area.

[0091] In one embodiment, the well vibration calibration module 210 is used to: extract wavelets for each development well based on the results of the first round of well vibration calibration, determine qualified wavelets from the wavelets of each development well, and obtain a first comprehensive wavelet based on each qualified wavelet; Based on the first comprehensive wavelet, a second round of well vibration calibration is performed on the development wells corresponding to each qualified wavelet.

[0092] In one embodiment, the well vibration calibration module 210 is used for: Based on the results of the second round of well seismic calibration, wavelet extraction is performed on the development wells corresponding to the second round of well seismic calibration. Qualified wavelets are determined from the wavelets of each development well, and the second comprehensive wavelet is obtained based on each qualified wavelet. The third round of well vibration calibration is performed on the development wells corresponding to each qualified wavelet based on the second comprehensive wavelet.

[0093] In one embodiment, the reservoir prediction device 200 further includes a quality control module, used to perform quality control on the results of the last round of well seismic calibration in the multi-round well seismic calibration based on the well-connected seismic profile of the three-dimensional seismic work area, and to determine whether the results of the last round of well seismic calibration meet preset requirements. Then, the reservoir inversion module 220 is used to select development wells with well-seismic correlation coefficients greater than preset values ​​from the development wells corresponding to the last round of well seismic calibration, in response to the fact that the results of the last round of well seismic calibration meet the preset requirements, as target wells.

[0094] In one embodiment, the reservoir prediction device 200 is used to: extract wavelets from each target well, determine qualified wavelets from the wavelets of each target well, and obtain a target composite wavelet based on each qualified wavelet.

[0095] In one embodiment, the reservoir prediction device 200 is used to perform sparse pulse wave impedance inversion on the reservoir based on the target synthetic wavelet and preset quality control requirements.

[0096] The reservoir prediction device includes a processor and a memory. The well vibration calibration module 210, the reservoir inversion module 220, and the single sand body characterization module 230 are all stored in the memory as program units. The processor executes the program modules stored in the memory to implement the corresponding functions.

[0097] A processor contains a core, which retrieves the corresponding program unit from memory. One or more cores can be configured, and by adjusting the core parameters, fast and efficient computation can be achieved at the entire chip scale.

[0098] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0099] This application provides a machine-readable storage medium storing a program that, when executed by a processor, implements the aforementioned reservoir prediction method.

[0100] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 19 As shown in the figure, the computer device includes a processor A01, a network interface A02, a display screen A04, an input device A05, and a memory (not shown) connected via a system bus. The processor A01 provides computing and control capabilities. The memory includes internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 stored in the non-volatile storage medium A06. The network interface A02 is used for communication with external terminals via a network connection. When the computer program is executed by the processor A01, it implements a reservoir prediction method. The display screen A04 can be a liquid crystal display (LCD) or an e-ink display. The input device A05 can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0101] Those skilled in the art will understand that Figure 19 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0102] In one embodiment, the reservoir prediction device provided in this application can be implemented as a computer program, which can be implemented in the form of, for example, Figure 19 The computer device shown runs on this system. The computer device's memory can store the various program modules that make up the intelligent scheduling device for this construction task, for example... Figure 18 The wellbore calibration module 210, reservoir inversion module 220, and single sand body characterization module 230 are shown. The computer program comprised of these modules causes the processor to execute the steps in the reservoir prediction methods of the various embodiments of this application described in this specification.

[0103] Figure 19 The computer equipment shown can be used as follows Figure 18 The execution method of the well vibration calibration module 210, reservoir inversion module 220 and single sand body characterization module 230 in the reservoir prediction device shown.

[0104] This application provides a device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: Multiple rounds of well-seismic calibration were performed on development wells in the 3D seismic survey area; development wells with well-seismic correlation coefficients greater than preset values ​​were selected as target wells from the development wells corresponding to the last round of well-seismic calibration. Target composite wavelet is determined based on each target well, and seismic inversion of the reservoir is performed based on the target composite wavelet and preset quality control requirements. Predicting the planar distribution of the main sand belt in the reservoir using seismic inversion results; Based on the planar distribution of the main sand belt, the local thickness zone of the sand body is determined, and the individual sand bodies in the reservoir are characterized according to the inversion properties and the local thickness zone of the sand body.

[0105] In one embodiment, the multi-round well-seismic calibration includes three rounds of well-seismic calibration.

[0106] In this embodiment of the application, the multi-round well seismic calibration for development wells in a 3D seismic work area includes: The first round of well-seismic calibration was carried out on all development wells in the 3D seismic work area using the Rick wavelet, which is consistent with the dominant frequency of the earthquake. Based on the results of the first round of well seismic calibration, the first comprehensive wavelet is determined, and the first comprehensive wavelet is used to perform the second round of well seismic calibration on the development wells in the 3D seismic work area. Based on the results of the second round of well seismic calibration, a second comprehensive wavelet is determined, and the second comprehensive wavelet is used to conduct a third round of well seismic calibration on development wells in the 3D seismic work area.

[0107] In one embodiment, determining a first composite wavelet based on the results of the first round of well seismic calibration, and using the first composite wavelet to perform a second round of well seismic calibration on development wells in the 3D seismic survey area, includes: Based on the results of the first round of well seismic calibration, wavelet extraction is performed on each development well. Qualified wavelets are determined from the wavelets of each development well, and the first comprehensive wavelet is obtained based on each qualified wavelet. Based on the first comprehensive wavelet, a second round of well vibration calibration is performed on the development wells corresponding to each qualified wavelet.

[0108] In one embodiment, determining the second composite wavelet based on the second round of well seismic calibration results, and using the second composite wavelet to perform a third round of well seismic calibration on development wells in the 3D seismic survey area, includes: Based on the results of the second round of well seismic calibration, wavelet extraction is performed on the development wells corresponding to the second round of well seismic calibration. Qualified wavelets are determined from the wavelets of each development well, and the second comprehensive wavelet is obtained based on each qualified wavelet. The third round of well vibration calibration is performed on the development wells corresponding to each qualified wavelet based on the second comprehensive wavelet.

[0109] In one embodiment, before selecting development wells with well-seismic correlation coefficients greater than a preset value from the development wells corresponding to the last round of well-seismic calibration as target wells, the method further includes: performing quality control on the results of the last round of well-seismic calibration based on the well-connected seismic profile of the three-dimensional seismic work area, and determining whether the results of the last round of well-seismic calibration meet preset requirements; selecting development wells with well-seismic correlation coefficients greater than a preset value from the development wells corresponding to the last round of well-seismic calibration as target wells includes: If the results of the last round of well-seismic calibration meet the preset requirements, wells with well-seismic correlation coefficients greater than the preset value are selected from the development wells corresponding to the last round of well-seismic calibration as target wells.

[0110] In one embodiment, determining the target synthetic wavelet based on each target well includes: Wavelet extraction is performed on each target well, qualified wavelets are determined from the wavelets of each target well, and the target composite wavelet is obtained based on each qualified wavelet.

[0111] In one embodiment, the seismic inversion of the reservoir based on the target synthetic wavelet and preset quality control requirements includes: Based on the target synthetic wavelet and preset quality control requirements, sparse pulse wave impedance inversion is performed on the reservoir.

[0112] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0113] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0114] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0115] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0116] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0117] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0118] Computer-readable media include both permanent and non-permanent, removable and non-removable media, which can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0119] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0120] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A reservoir prediction method characterized by, The prediction method includes: Multiple rounds of well-seismic calibration were performed on development wells in the 3D seismic survey area; development wells with well-seismic correlation coefficients greater than preset values ​​were selected as target wells from the development wells corresponding to the last round of well-seismic calibration. Target composite wavelet is determined based on each target well, and seismic inversion of the reservoir is performed based on the target composite wavelet and preset quality control requirements. Predicting the planar distribution of the main sand belt in the reservoir using seismic inversion results; Based on the planar distribution of the main sand belt, the local thickness zone of the sand body is determined, and the individual sand bodies in the reservoir are characterized according to the inversion properties and the local thickness zone of the sand body.

2. The reservoir prediction method of claim 1, wherein, The multi-round well-seismic calibration includes three rounds of well-seismic calibration.

3. The reservoir prediction method of claim 2, wherein, The multi-round well seismic calibration for development wells in the 3D seismic work area includes: The first round of well-seismic calibration was carried out on all development wells in the 3D seismic work area using the Rick wavelet, which is consistent with the dominant frequency of the earthquake. Based on the results of the first round of well seismic calibration, the first comprehensive wavelet is determined, and the first comprehensive wavelet is used to perform the second round of well seismic calibration on the development wells in the 3D seismic work area. Based on the results of the second round of well seismic calibration, a second comprehensive wavelet is determined, and the second comprehensive wavelet is used to conduct a third round of well seismic calibration on development wells in the 3D seismic work area.

4. The reservoir prediction method of claim 3, wherein, The process of determining a first composite wavelet based on the first round of well seismic calibration results, and then using the first composite wavelet to perform a second round of well seismic calibration on development wells in the 3D seismic work area, includes: Based on the results of the first round of well seismic calibration, wavelet extraction is performed on each development well. Qualified wavelets are determined from the wavelets of each development well, and the first comprehensive wavelet is obtained based on each qualified wavelet. Based on the first comprehensive wavelet, a second round of well vibration calibration is performed on the development wells corresponding to each qualified wavelet.

5. The reservoir prediction method of claim 3, wherein, The process of determining the second synthetic wavelet based on the second round of well seismic calibration results, and using the second synthetic wavelet to perform a third round of well seismic calibration on development wells in the 3D seismic work area, includes: Based on the results of the second round of well seismic calibration, wavelet extraction is performed on the development wells corresponding to the second round of well seismic calibration. Qualified wavelets are determined from the wavelets of each development well, and the second comprehensive wavelet is obtained based on each qualified wavelet. The third round of well vibration calibration is performed on the development wells corresponding to each qualified wavelet based on the second comprehensive wavelet.

6. The reservoir prediction method of claim 1, wherein, Before selecting development wells with well-seismic correlation coefficients greater than a preset value from the development wells corresponding to the last round of well-seismic calibration as target wells, the method further includes: performing quality control on the results of the last round of well-seismic calibration based on the well-connected seismic profile of the three-dimensional seismic work area, and determining whether the results of the last round of well-seismic calibration meet the preset requirements; selecting development wells with well-seismic correlation coefficients greater than a preset value from the development wells corresponding to the last round of well-seismic calibration as target wells includes: If the results of the last round of well-seismic calibration meet the preset requirements, wells with well-seismic correlation coefficients greater than the preset value are selected from the development wells corresponding to the last round of well-seismic calibration as target wells.

7. The reservoir prediction method of claim 1, wherein, The target composite wavelet is determined based on each target well, including: Wavelet extraction is performed on each target well, qualified wavelets are determined from the wavelets of each target well, and the target composite wavelet is obtained based on each qualified wavelet.

8. The reservoir prediction method of claim 1, wherein, The process of performing seismic inversion on the reservoir based on the target synthetic wavelet and preset quality control requirements includes: Based on the target synthetic wavelet and preset quality control requirements, sparse pulse wave impedance inversion is performed on the reservoir.

9. A reservoir prediction apparatus characterized by comprising: include: The well seismic calibration module is used to perform multiple rounds of well seismic calibration for development wells in 3D seismic survey areas. The reservoir inversion module is used to select development wells with a well-seismic correlation coefficient greater than a preset value from the development wells corresponding to the last round of well-seismic calibration as target wells; determine the target comprehensive wavelet based on each target well; and perform seismic inversion on the reservoir based on the target comprehensive wavelet and preset quality control requirements. The single sand body characterization module is used to predict the planar distribution of the main sand belt in the reservoir using seismic inversion results; based on the planar distribution of the main sand belt, the local thickness zone of the sand body is determined, and the single sand body in the reservoir is characterized according to the inversion attributes and the local thickness zone of the sand body.

10. The reservoir prediction apparatus of claim 9, wherein, The well vibration calibration module is used for: The first round of well-seismic calibration was carried out on all development wells in the 3D seismic work area using the Rick wavelet, which is consistent with the dominant frequency of the earthquake. Based on the results of the first round of well seismic calibration, the first comprehensive wavelet is determined, and the first comprehensive wavelet is used to perform the second round of well seismic calibration on the development wells in the 3D seismic work area. Based on the results of the second round of well seismic calibration, a second comprehensive wavelet is determined, and the second comprehensive wavelet is used to conduct a third round of well seismic calibration on development wells in the 3D seismic work area.

11. The reservoir prediction apparatus of claim 9, wherein, The reservoir prediction device also includes a quality control module, which is used to perform quality control on the results of the last round of well seismic calibration in the multi-round well seismic calibration based on the well-connected seismic profile of the three-dimensional seismic work area.

12. The reservoir prediction apparatus of claim 9, wherein, The reservoir inversion module is used to extract wavelets from each target well, determine qualified wavelets from the wavelets of each target well, and obtain the target composite wavelet based on each qualified wavelet.

13. The reservoir prediction apparatus of claim 9, wherein, The reservoir inversion module is used to perform sparse pulse wave impedance inversion on the reservoir based on the target synthetic wavelet and preset quality control requirements.

14. A processor, comprising: It is configured to perform the reservoir prediction method according to any one of claims 1 to 8.

15. A machine-readable storage medium having stored thereon instructions, the instructions comprising: When executed by a processor, this instruction causes the processor to be configured to perform the reservoir prediction method according to any one of claims 1 to 8.

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