A quality control method and device for seismic inversion reservoir prediction results

Abstract

This invention provides a quality control method and apparatus for seismic inversion reservoir prediction results, which can effectively control the quality of reservoir prediction results, providing accurate and reliable results to support oil and gas exploration and development research. The quality control method includes: acquiring and correcting well logging curve data from drilled wells; performing well-seismic calibration using the corrected well logging curves to obtain the time-depth matching relationship between the wells and the seismic data; screening the corrected drilled wells to obtain target blind wells; verifying the inversion results using the target blind wells on the seismic elastic parameter inversion results, and determining whether the well logging data and the seismic inversion data are consistent; if so, performing quality control analysis on the prediction results of sand bodies, porosity, and hydrocarbon-bearing properties from the perspective of geological and geophysical exploration combined with the matching relationship of lithology-physical properties-hydrocarbon-bearing properties, and determining whether the prediction results of sand bodies, porosity, and hydrocarbon-bearing properties are consistent.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration and development technology, specifically to a quality control method and apparatus for seismic inversion reservoir prediction results. Background Technology

[0002] Clastic rocks are one of the major reservoir types in oil and gas fields worldwide, playing a vital role in ensuring national energy security. With the increasing difficulty of oil and gas exploration and development, simple post-stack impedance inversion can no longer fully meet the needs of describing complex oil and gas reservoirs. Utilizing pre-stack CRP gather data and employing different approximate inversion methods to obtain various elastic parameters related to lithology, physical properties, and hydrocarbon potential, and further using these parameters to predict reservoir lithology, physical properties, and hydrocarbon potential, has become a trend. The essence of seismic inversion reservoir prediction is to use a limited number of known well points and seismic data to extract information that best matches the geological features revealed by drilling, to predict areas without wells. The ultimate goal is to gain a comprehensive understanding of the regional geological characteristics, clarify the distribution of favorable areas, and thus guide the next steps in exploration and development. However, the fundamental prerequisite for guiding oil and gas exploration and development in practice is obtaining reliable seismic inversion reservoir prediction results. If the reservoir prediction results are inaccurate, they will not only fail to effectively guide practical work but may also mislead research, leading to drilling failures and direct economic losses.

[0003] In the field of clastic rocks, current quality control methods for seismic inversion reservoir prediction results mainly involve determining multiple target quality control parameters based on the attribute information of candidate quality control parameters during the seismic reservoir prediction process. Then, these target quality control parameters are preprocessed according to their numerical attributes. Finally, the evaluation result of the seismic reservoir prediction is determined based on the preprocessed target quality control parameters and their corresponding weights. This method primarily evaluates seismic reservoir prediction results at the well point or within the well perimeter based on candidate quality control parameters. While it can control the quality of reservoir prediction results to some extent, it cannot accurately characterize the vertical trend of reservoir prediction results above the well, the reliability of inter-well reservoir prediction results, or the reasonableness of horizontal reservoir prediction results. It lacks a systematic and effective quality control method specifically for these conditions. The existing technical methods mainly have the following three problems: ① Most of the quality control index parameters are quality control parameters in the seismic inversion process. Generally speaking, the well data used for quality control in the seismic inversion process will participate in the modeling process. That is, the wells in the process quality control are no longer blind wells and cannot objectively represent the reliability of the actual reservoir prediction results; ② All target quality control index parameters are the evaluation basis for a single well or the area around the well. There is a lack of corresponding quality control methods for the inter-well information and planar distribution characteristics reflected in the reservoir prediction inversion profile and planar reservoir prediction results of multiple wells; ③ The quality control of reservoir prediction results cannot rely solely on the target quality control index parameters on the well. It is also necessary to consider the actual geological background and give the reservoir prediction results a clear geological meaning. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a quality control method and apparatus for seismic inversion reservoir prediction results, which can effectively control the quality of reservoir prediction results and provide accurate and reliable reservoir prediction results, thereby effectively supporting oil and gas exploration and development research.

[0005] In a first aspect, an embodiment of the present invention provides a quality control method for seismic inversion reservoir prediction results, comprising:

[0006] Acquire and correct well logging data from drilled wells;

[0007] Well-seismic calibration is carried out using the corrected well logging curves to obtain the time-depth matching relationship between the well and the seismic data;

[0008] The corrected drilled wells are then screened to obtain the target blind wells;

[0009] The inversion results were verified using the seismic elastic parameter inversion results from the target blind well, and it was determined whether the results of the well logging data and the seismic inversion data were consistent.

[0010] If so, quality control analysis should be conducted on the prediction results of sand bodies, porosity, and hydrocarbon properties from the perspective of combining geology and geophysics and the matching relationship between lithology, physical properties, and hydrocarbon properties, and it should be determined whether the prediction results of sand bodies, porosity, and hydrocarbon properties are consistent.

[0011] In one embodiment of the present invention, the step of verifying the inversion results using the target blind well on the seismic elastic parameter inversion results and determining whether the well logging data and the seismic inversion data are consistent includes: verifying the inversion results at the well point using the target blind well on the seismic elastic parameter inversion results; the step of verifying the inversion results at the well point using the target blind well on the seismic elastic parameter inversion results includes:

[0012] The blind well logging curves calibrated by well seismic testing are displayed on the seismic inversion profile;

[0013] The consistency between the well logging data and the seismic inversion data is compared to determine the reliability of the inversion results at the quality control well point. The consistency between the well logging data and the seismic inversion data includes analyzing the degree of agreement between the measured results and the inversion results at different vertical layers at the well point, as well as whether the changing trends of the measured data at different layers on the well are consistent with the changing trends of the inversion results.

[0014] In one embodiment of the present invention, the step of verifying the inversion results using the target blind well on the seismic elastic parameter inversion results and determining whether the well logging data and the seismic inversion data are consistent includes: conducting a well-to-well inversion profile analysis using the target blind well on the seismic elastic parameter inversion results; the step of conducting a well-to-well inversion profile analysis using the target blind well on the seismic elastic parameter inversion results includes:

[0015] All blind well logging curves calibrated by well seismic testing are displayed on the seismic inversion profile;

[0016] The consistency between the filtered logging curves of all wells and the seismic inversion data is compared to ensure the reliability of the inversion results at all connected well inversion profiles. The comparison of the consistency between the filtered logging curves of all wells and the seismic inversion data includes: analyzing the degree of agreement between the measured results and the inversion results at different vertical segments at the well points, and whether the changing trends of the measured data at different segments on the well are consistent with the changing trends of the inversion results.

[0017] In one embodiment of the present invention, the step of verifying the inversion results using the target blind well on the seismic elastic parameter inversion results and determining whether the well logging data and the seismic inversion data are consistent includes: selecting highly deviated blind wells along the well trajectory on the seismic elastic parameter inversion results to conduct quality control analysis of the inversion results; the step of selecting highly deviated blind wells along the well trajectory on the seismic elastic parameter inversion results to conduct quality control analysis of the inversion results includes:

[0018] The logging curves of highly deviated blind wells, calibrated by well seismic testing, are displayed on the seismic inversion profile.

[0019] The consistency between the well logging data after well filtering and the seismic inversion data is used to quality control the reliability of the inversion results along the well trajectory. The consistency between the well logging data after well filtering and the seismic inversion data includes: analyzing the consistency between the measured results and the inversion results of different vertical segments along the well trajectory, whether the variation trend of the measured data of different segments in the well is consistent with the variation trend of the inversion results, and the consistency between the vertical variation law of the measured results of the deviated well segment and the inversion results.

[0020] In one embodiment of the present invention, the acquisition and correction of drilled well logging curve data includes: excluding wells that are missing at least one of the following curves: P-wave velocity curve, S-wave velocity curve, and density curve.

[0021] In one embodiment of the present invention, the verification of inversion results using the target blind well on the seismic elastic parameter inversion results includes: verification of inversion results at the well point, verification of inversion profile results of interconnected wells, and verification of inversion results of highly deviated blind wells.

[0022] In one embodiment of the present invention, the quality control analysis of the prediction results of sand bodies, porosity, and hydrocarbon potential from the perspective of the combination of geology and geophysics and the matching relationship between lithology, physical properties, and hydrocarbon potential includes:

[0023] Based on the combination of P-wave velocity, S-wave velocity and density, we obtain logging sensitive elastic parameter combination curves and seismic inversion sensitive elastic parameter data volumes that can characterize sand bodies, porosity and hydrocarbon-bearing properties.

[0024] Quality control analysis was performed on the prediction results of sand bodies, porosity, and hydrocarbon content based on the combined curves of the well logging sensitive elastic parameters and the seismic inversion sensitive elastic parameter data.

[0025] In one embodiment of the present invention, the prediction results of sand bodies are subjected to quality control analysis from the perspective of the combination of geology and geophysical exploration and the matching relationship between lithology, physical properties and hydrocarbon-bearing properties, and the consistency of the prediction results of sand bodies is determined, including:

[0026] Compare the GR curves after well-connected filtering with the results of seismic inversion of sensitive elastic parameters to determine their consistency.

[0027] Compare the good agreement between the filtered GR curve along the well trajectory and the results of seismic inversion of sensitive elastic parameters;

[0028] By comparing the GR curves after well-connected filtering with the results of seismic inversion sensitive elastic parameters, the number of sandstone bodies developed and the number of sandstone bodies predicted by the sensitive elastic parameters were analyzed.

[0029] The consistency between the inter-well sand body thickness variation, lithological variation, and sand body pinch-out point in the geological profile of the well and the inter-well characteristics characterized by the seismic inversion sensitive elastic parameters was compared.

[0030] A comparison of the distribution characteristics of planar sand bodies represented by preliminary geological understanding maps or seismic sensitivity attributes with the distribution patterns of planar sand bodies represented by seismic inversion sensitive elastic parameters;

[0031] A comparison of the variation patterns of planar sand body distribution characteristics in the longitudinal direction of different layers with the variation patterns of planar sand body distribution characteristics characterized by seismic inversion sensitive elastic parameters in different layers.

[0032] In one embodiment of the present invention, the porosity prediction results are subjected to quality control analysis from the perspective of the combination of geological and geophysical exploration and the matching relationship among lithology, physical properties and hydrocarbon-bearing properties, and the consistency of the porosity prediction results is determined, including:

[0033] Compare the porosity curves after well-connected filtering with the porosity prediction results characterized by seismic inversion sensitive elastic parameters to determine their consistency.

[0034] Compare the porosity curve after filtering along the well trajectory with the porosity prediction results characterized by seismic inversion sensitive elastic parameters to determine the degree of agreement.

[0035] Comparing the vertical variation trend of porosity above ground with the vertical variation characteristics of porosity characterized by sensitive elastic parameters from seismic inversion, if the high porosity area is distributed in the mudstone development layer, then the porosity prediction result is unreliable and further optimization and iteration are needed.

[0036] A comparison of the distribution characteristics of sand bodies between wells in the geological profile and the porosity distribution characteristics characterized by sensitive elastic parameters from seismic inversion shows that if the high porosity areas are distributed in the mudstone development zones between wells, the porosity prediction results are unreliable and require further optimization and iteration.

[0037] Comparing the distribution pattern of planar sand bodies with the characteristics of planar porosity distribution, if the high porosity areas are distributed in the mudstone development areas on the plane, then the porosity prediction results are unreliable and need further optimization and iteration.

[0038] By comparing the variation patterns of planar sand body distribution characteristics in different vertical segments with the planar porosity distribution characteristics, if the high porosity areas are distributed in the mudstone development areas on the plane, then the porosity prediction results are unreliable and need further optimization and iteration.

[0039] In one embodiment of the present invention, the prediction results of hydrocarbon potential are subjected to quality control analysis from the perspective of the combination of geology and geophysical exploration and the matching relationship among lithology, physical properties and hydrocarbon potential, and the consistency of the prediction results of hydrocarbon potential is determined, including:

[0040] The consistency between the well logging interpretation results after well-connected filtering and the hydrocarbon prediction results characterized by seismic inversion sensitive elastic parameters was compared.

[0041] Compare the well logging interpretation results after well trajectory filtering with the hydrocarbon prediction results characterized by seismic inversion sensitive elastic parameters;

[0042] The vertical variation trend of surface porosity is compared with the vertical variation characteristics of hydrocarbon-bearing properties characterized by seismic inversion sensitive elastic parameters, and the consistency of the vertical variation trends of the two is analyzed.

[0043] The consistency between the regular changes in the inter-well porosity inversion results and the inter-well hydrocarbon prediction results was analyzed.

[0044] Comparison of planar porosity distribution characteristics and planar hydrocarbon prediction results;

[0045] Comparison of planar porosity variation characteristics in different vertical segments with planar hydrocarbon prediction results.

[0046] In one embodiment of the present invention, determining whether the results of well logging data and seismic inversion data are consistent includes: if not, continuously iterating and optimizing the seismic inversion results until the results of well logging data and seismic inversion data are consistent.

[0047] In one embodiment of the present invention, determining whether the predicted results of sand body, porosity, and oil and gas content are consistent includes:

[0048] If not, continuously iterate and optimize the seismic inversion results until the predicted results of judging sand bodies, porosity, and hydrocarbon content are consistent.

[0049] Secondly, an embodiment of the present invention provides a quality control device for seismic inversion reservoir prediction results, comprising:

[0050] The data acquisition and correction module is used to acquire and correct the drilling logging curve data;

[0051] The well-seismic calibration module is used to perform well-seismic calibration using the corrected well logging curves to obtain the time-depth matching relationship between the well and the earthquake.

[0052] The blind well selection module is used to screen the corrected drilled wells to obtain target blind wells;

[0053] The inversion verification module is used to verify the inversion results based on the seismic elastic parameter inversion results using the target blind well.

[0054] The judgment module is used to determine whether the results of well logging data and seismic inversion data are consistent; and to determine whether the predicted results of sand bodies, porosity and hydrocarbon properties are consistent.

[0055] The quality control analysis module is used to perform quality control analysis on the prediction results of sand bodies, porosity, and hydrocarbon properties from the perspective of the combination of geology and geophysics and the matching relationship between lithology, physical properties, and hydrocarbon properties.

[0056] Thirdly, an embodiment of the present invention provides a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0057] Fourthly, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0058] Fifthly, an embodiment of the present invention provides a computer program product comprising a computer program that, when executed by a processor, implements the steps of any of the methods described above.

[0059] This invention provides a quality control method and apparatus for seismic inversion reservoir prediction results. The quality control method includes: acquiring and correcting well logging curve data from drilled wells; performing well-seismic calibration using the corrected well logging curves to obtain the time-depth matching relationship between the wells and the seismic data; screening the corrected drilled wells to obtain target blind wells; verifying the inversion results using the target blind wells on the seismic elastic parameter inversion results, and determining whether the logging data and the seismic inversion data are consistent; if so, performing quality control analysis on the prediction results of sand bodies, porosity, and hydrocarbon potential from the perspective of geological and geophysical exploration combined with the matching relationship of lithology-physical properties-oil and gas potential, and determining whether the prediction results of sand bodies, porosity, and hydrocarbon potential are consistent. The comparative analysis of well logging data and seismic inversion data involved in this invention uses blind wells that have not participated in seismic inversion modeling, have complete logging curve data, and have good logging curve quality, which can be used more objectively for the quality control analysis of reservoir prediction results. In addition, special emphasis is placed on quality control analysis from the perspective of combining geology and geophysics, as well as the matching relationship between lithology, physical properties, and hydrocarbon-bearing properties. This not only involves comprehensive quality control analysis from a data perspective, but also assigns geological meaning to the target quality control index parameters that characterize different geological meanings. This enables a more accurate evaluation of seismic inversion reservoir prediction results and effectively avoids erroneous results that contradict basic geological theories (such as obviously wrong conclusions like the development of reservoirs with good physical properties in mudstone sections in sandstone reservoirs). It has great innovation and practicality. Attached Figure Description

[0060] Figure 1 The diagram shown is a flowchart illustrating a quality control method for seismic inversion reservoir prediction results according to an embodiment of the present invention.

[0061] Figure 2 The diagram shown is a flowchart illustrating a quality control method for seismic inversion reservoir prediction results provided in another embodiment of the present invention.

[0062] Figure 3 The diagram shown is a structural schematic of a quality control device for seismic inversion reservoir prediction results provided in another embodiment of the present invention. Detailed Implementation

[0063] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] Seismic inversion reservoir prediction results have a certain degree of ambiguity. Well logging data from drilled wells (such as P-wave, S-wave, density, and porosity data) are often the most direct evidence to verify the inversion results. Connecting well profiles from multiple wells is the basis for analyzing the inter-well patterns in the inverted reservoir prediction. Combining this with a holistic understanding of the geological background allows for qualitative analysis of the reservoir prediction results. The spatial continuity of seismic data is the basis for analyzing the planar distribution patterns of reservoirs. Comparing and analyzing the planar results of seismic inversion reservoir prediction with the characteristics of sedimentary facies distribution allows for quality control analysis of the reliability of the seismic inversion data volume. Specific implementation methods are described in the following examples.

[0065] Example 1:

[0066] This embodiment provides a quality control method for seismic inversion reservoir prediction results, such as... Figure 1 As shown, the quality control methods for the seismic inversion reservoir prediction results include:

[0067] Step 01: Acquire and correct the well logging curve data of the drilled well.

[0068] After acquiring the well logging data of the drilled wells, the system sorts out the well logging data, and it is necessary to focus on analyzing the P-wave velocity (Vp, AC or DT), S-wave velocity (DTS or VS), density curve (DEN) and porosity curve (Por) which are closely related to the seismic inversion results.

[0069] Correcting drilled well logging data is necessary because the original logging data often contains invalid values ​​and obvious outliers. It's understandable that invalid values ​​have little impact on the verification and inversion results (invalid values ​​are easily identified in conventional software); only outliers need to be removed.

[0070] Step 02: Use the corrected well logging curves to perform well-seismic calibration to obtain the time-depth matching relationship between the well and the seismic data.

[0071] Repeat the above steps until the logging curve correction and well-seismic calibration of all drilled wells in the study area are completed. It should be noted that not all well logging curves are complete; some may be missing P-wave velocity, S-wave velocity, or density curves. In such cases, the wells with missing curves must be excluded. The reason is that the well data used for quality control analysis of seismic inversion reservoir prediction results are the most direct "hard data." It is generally believed that only logging data obtained from actual testing is primary data and has the highest reliability. Although missing curves can be obtained through empirical formulas or rock physics modeling, these methods are often inaccurate and directly affect the evaluation of seismic inversion results.

[0072] Step 03: Screen the corrected drilled wells to obtain the target blind wells.

[0073] Wells with relatively complete logging curves and after correction were screened, and these screened wells were used as blind wells to verify the inversion results. The selection principles included the following aspects:

[0074] 1. This well was not involved in seismic inversion modeling, but it has complete logging curve data and the logging curve quality is good.

[0075] 2. The selected verification wells should be distributed as evenly as possible within the work area to lay the foundation for subsequent well-connected seismic inversion profile analysis;

[0076] 3. Select wells with complete data on high-angle wells whenever possible, because high-angle wells can not only verify the inversion results at the well point, but also verify the lateral range of the prediction results within the high-angle trajectory range.

[0077] Step 04: Validate the inversion results using the target blind well based on the seismic elastic parameter inversion results, and determine whether the well logging data and seismic inversion data are consistent. If yes, proceed to Step 05; otherwise, iteratively optimize the seismic inversion results until the well logging data and seismic inversion data are consistent.

[0078] Step 05: If yes, conduct quality control analysis on the prediction results of sand bodies, porosity, and hydrocarbon potential from the perspective of combining geological and geophysical exploration and the matching relationship between lithology, physical properties, and hydrocarbon potential, and determine whether the prediction results of sand bodies, porosity, and hydrocarbon potential are consistent. If yes, complete the quality control of the seismic inversion reservoir prediction results. Through the quality control analysis of the sand body, porosity, and hydrocarbon potential results in the above steps, clarify the accuracy and reliability of the sand body prediction, porosity prediction, and hydrocarbon potential prediction results. Sand bodies, porosity, and hydrocarbon potential are a unified whole, that is, reservoirs with good hydrocarbon potential are mainly distributed in sandstone reservoirs with good physical properties. If the prediction results of the three are contradictory or cannot maintain the consistency of the distribution pattern, iterative optimization of the inversion results is required until a seismic inversion data body that can better meet all the above quality control conditions is obtained.

[0079] This method not only conducts comprehensive data quality control analysis on the seismic inversion reservoir prediction results, but also performs quality control analysis from the perspective of the combination of geology and geophysics and the matching relationship between lithology, physical properties and hydrocarbon-bearing properties. It has the characteristics of high reliability and strong practicality, providing a basis for systematically evaluating the seismic inversion reservoir prediction results and helping to reduce exploration and development risks.

[0080] Example 2:

[0081] like Figure 2 As shown in the above embodiments, the verification of inversion results using the target blind well on the seismic elastic parameter inversion results includes: verification of inversion results at the well point, verification of inversion profile results across interconnected wells, and verification of inversion results from highly deviated blind wells. The specific methods for these three verifications are described below.

[0082] For the seismic elastic parameter inversion results (such as P-wave velocity, S-wave velocity, and density), the inversion results at the wellpoints are verified using selected vertical blind wells. The well logging curves of the blind wells, calibrated by well-seismic analysis, are displayed on the seismic inversion profile. The reliability of the wellpoint inversion results is assessed by comparing the consistency between the well logging data and the seismic inversion data. The focus is on analyzing the degree of agreement between the measured results and the inversion results at different vertical segments at the wellpoints. Simultaneously, it is necessary to analyze whether the variation trends of the measured data at different segments in the well are consistent with the variation trends of the inversion results. It should be noted that: ① Because seismic data is band-limited, there are frequency band differences between the well logging data and the seismic inversion results. Filtering of the well logging curves is necessary to unify them within the same frequency band, ensuring comparability. ② When comparing well-seismic data, it is essential to ensure that the color scale ranges of both are within the same scale, using the same color scale display and value range. This facilitates comparison of the degree of agreement and increases the reliability of the results.

[0083] Based on the seismic elastic parameter inversion results (such as P-wave velocity, S-wave velocity, and density), a series of well inversion profiles were analyzed using selected vertical blind wells. The logging curves of all blind wells calibrated by well seismic testing were displayed on the seismic inversion profile. Quality control of the inversion results at all well points was achieved by comparing the consistency between the filtered logging curves and the seismic inversion data. The focus was on analyzing the agreement between the measured results and the inversion results at different vertical layers at each well point, as well as whether the variation trends of the measured data at different layers were consistent with the variation trends of the inversion results. Furthermore, it is crucial to pay attention to the rationality of the inter-well regularity information contained in the series inversion profiles, such as velocity and density variation trends. These require further analysis in conjunction with the geological background, such as lateral lithological variations in drilled wells and fluid properties.

[0084] For the seismic elastic parameter inversion results (such as P-wave velocity, S-wave velocity, and density), a rigorous quality control analysis of the inversion results is conducted by selecting highly deviated blind wells along the well trajectory. The logging curves of highly deviated blind wells calibrated by well seismic testing are displayed on the seismic inversion profile. The reliability of the inversion results along the well trajectory is controlled by comparing the well logging curves after well filtering with the seismic inversion data results. The analysis focuses on the consistency between the measured results and the inversion results in different vertical segments along the well trajectory, whether the variation trend of the measured data in different segments of the well is consistent with the variation trend of the inversion results, and the consistency between the vertical variation law of the measured results in the deviated well segment and the inversion results.

[0085] Example 3:

[0086] like Figure 2 As shown, based on the above embodiments, the quality control analysis of the prediction results of sand bodies, porosity, and hydrocarbon potential from the perspective of the combination of geology and geophysics and the matching relationship between lithology, physical properties, and hydrocarbon potential includes: obtaining a combination curve of logging sensitive elastic parameters and a seismic inversion sensitive elastic parameter data body that can characterize sand bodies, porosity, and hydrocarbon potential based on the combination of P-wave velocity, S-wave velocity, and density; and performing quality control analysis on the prediction results of sand bodies, porosity, and hydrocarbon potential based on the combination curve of logging sensitive elastic parameters and the seismic inversion sensitive elastic parameter data body. Through the above steps of quality control analysis, the accuracy and reliability of the seismic elastic parameter inversion results (such as P-wave velocity, S-wave velocity, and density) are clarified. If the quality control results of single vertical wells, vertical well-connected well profiles, and highly deviated wells are not ideal, it is necessary to further iterate and optimize the inversion results until they can better meet all quality control conditions before further quality control analysis of the prediction results for sand bodies, porosity, and hydrocarbon potential is carried out.

[0087] In seismic inversion reservoir prediction, the elastic parameters used to characterize sand bodies, porosity, and hydrocarbon bearing capacity are all combinations of P-wave velocity, S-wave velocity, and density. The actual well logging data from drilled wells and the seismic inversion elastic parameter results are calculated according to the sensitive elastic parameter combinations that characterize sand bodies, porosity, and hydrocarbon bearing capacity, resulting in well logging sensitive elastic parameter combination curves and seismic inversion sensitive elastic parameter data volumes that characterize sand bodies, porosity, and hydrocarbon bearing capacity. Since the above elastic parameter combinations are all calculated based on P-wave velocity, S-wave velocity, and density, if the quality control results of single vertical wells, vertical well-connected well profiles, and highly deviated wells are good, then the drilled curves obtained using the same calculation formula will also have good agreement with the seismic inversion elastic parameter results. Therefore, subsequent quality control analysis of sand body, porosity, and hydrocarbon bearing capacity results mainly focuses on the combination of geological and geophysical exploration and the matching relationship between lithology, physical properties, and hydrocarbon bearing capacity.

[0088] The quality control analysis for sand body prediction results mainly includes the following: ① Verification of well-connected inversion profile results (comparing the GR curves after well-connected filtering with the seismic inversion sensitive elastic parameter results); ② Verification of inversion profile results for wells with excessively high deflection along the well trajectory (comparing the GR curves after well-connected filtering with the seismic inversion sensitive elastic parameter results); ③ Verification of the actual sand body development quantity in the well-connected profile and the predicted sand body quantity in the inversion profile (comparing the GR curves after well-connected filtering with the seismic inversion sensitive elastic parameter results; low GR represents sandstone, high GR represents mudstone; analyzing the development quantity of low GR sand bodies and the predicted sand body quantity characterized by sensitive elastic parameters); ④ Typical well-connected geology. Comparison of inter-well sand body distribution characteristics with those represented by seismic inversion sensitive elastic parameters (consistency between inter-well sand body thickness variations, lithological variations, and sand body pinch-out points in the geological profiles of interconnected wells and the inter-well characteristics represented by seismic inversion sensitive elastic parameters); ⑤ Comparison of planar sand body distribution characteristics represented by previous geological understanding planar maps or seismic sensitive attributes with those represented by seismic inversion sensitive elastic parameters (consistency analysis of planar sand body distribution characteristics); ⑥ Comparison of the variation patterns of planar sand body distribution characteristics in different vertical segments with those represented by seismic inversion sensitive elastic parameters in different segments (consistency analysis of longitudinal variation trends of planar sand bodies).

[0089] The quality control analysis for porosity prediction results mainly includes the following: ① Verification of well-connected inversion profile results (comparing the consistency between the well-connected filtered porosity curves and the porosity prediction results characterized by seismic inversion sensitive elastic parameters); ② Verification of inversion profile results for wells with excessively deviated well trajectories (comparing the consistency between the well-connected filtered porosity curves and the porosity prediction results characterized by seismic inversion sensitive elastic parameters); ③ Comparison of the vertical porosity variation trend above ground with the vertical porosity variation characteristics characterized by seismic inversion sensitive elastic parameters (analyzing the consistency of the two vertical porosity variation trends requires...). This indicates that the vertical variation trend of porosity should have good consistency with the vertical development quantity of sand bodies. That is, the difference in porosity only exists where the sand body develops. If high porosity areas are distributed in mudstone development zones, it indicates that the porosity prediction results are unreliable and require further optimization and iteration. ④ Comparison of the distribution characteristics of sand bodies between wells in the geological profile and the porosity distribution characteristics characterized by sensitive elastic parameters from seismic inversion (analyzing the consistency of the patterns of sand body and porosity prediction results between wells; that is, the difference in porosity only exists where the sand body develops. If high porosity areas are distributed in mudstone development zones, it indicates that the porosity prediction results are unreliable and require further optimization and iteration). If the sand body development interval is not specified, it indicates that the porosity prediction result is unreliable and further optimization and iteration are needed; ⑤ Comparison of the planar sand body distribution pattern and the planar porosity distribution characteristics (compare the consistency of the two planar distribution characteristics, that is, the planar porosity variation characteristics and the planar sand body distribution pattern should have good consistency, that is, the location of sand body development on the plane should have differences in porosity variation. If the high porosity value area is distributed in the mudstone development area on the plane, it indicates that the porosity prediction result is unreliable and further optimization and iteration are needed); ⑥ The variation pattern of the planar sand body distribution characteristics and the planar porosity distribution characteristics of different vertical intervals. The comparison (sequentially comparing the consistency of the planar distribution characteristics of the two in different vertical segments, i.e., the planar porosity variation characteristics and the planar sand body distribution pattern should have good consistency; that is, the difference in porosity variation should only exist where the sand body is developed in the plane. If the high porosity area is distributed in the mudstone development area in the plane, it indicates that the porosity prediction results are unreliable and further optimization and iteration are needed. It should be added that the vertical sand body and porosity variation trends should not only have good consistency between the two, but also conform to the overall sedimentary geological understanding, such as the direction of sediment supply and the planar distribution characteristics of sedimentary facies, etc.)

[0090] The quality control analysis for hydrocarbon prediction results mainly includes the following: ① Verification of well-connected inversion profile results (comparing the consistency between the well logging interpretation results after well-connected filtering and the hydrocarbon prediction results characterized by seismic inversion sensitive elastic parameters); ② Verification of inversion profile results for wells with excessively deviated well trajectories (comparing the consistency between the well logging interpretation results after well-connected filtering and the hydrocarbon prediction results characterized by seismic inversion sensitive elastic parameters); ③ Comparison between the vertical variation trend of porosity on the well and the vertical variation characteristics of hydrocarbons characterized by seismic inversion sensitive elastic parameters (analyzing the consistency of the two vertical variation trends; it should be noted that the vertical variation trend of porosity should have good consistency with the hydrocarbon prediction results). In other words, only layers with better physical properties are likely to show variations in hydrocarbon potential, while layers with poorer physical properties generally do not contain hydrocarbons. Therefore, if layers with better physical properties do not show hydrocarbon predictions, but layers with poorer physical properties do, then the hydrocarbon predictions are unreliable and require further optimization and iteration. ④ Comparison of well-to-well porosity inversion profiles with well-to-well hydrocarbon prediction profiles (analyzing the consistency between the patterns of porosity inversion results and hydrocarbon prediction results between wells; that is, if areas with better physical properties between wells do not show hydrocarbon predictions, but areas with poorer physical properties do, then...) This indicates that the hydrocarbon prediction results are unreliable and require further optimization and iteration; ⑤ Comparison of planar porosity distribution characteristics with planar hydrocarbon prediction results (compare the consistency of the two planar distribution characteristics, that is, the planar porosity variation characteristics and the planar hydrocarbon prediction results should have good consistency, that is, only locations with better physical properties on the plane show differences in hydrocarbon content. If there are no hydrocarbon prediction results in areas with high porosity on the plane, but there are obvious hydrocarbon prediction results in areas with poor physical properties, then the hydrocarbon prediction results are unreliable and require further optimization and iteration); ⑥ Comparison of planar porosity variation characteristics of different vertical layers with planar hydrocarbon prediction results (in order) The consistency of the planar distribution characteristics of porosity and hydrocarbon-bearing potential across different vertical stratigraphic segments should be compared. Specifically, the planar porosity variation characteristics and the planar hydrocarbon-bearing potential predictions should show good consistency. That is, hydrocarbon-bearing potential variations should only occur in areas with better physical properties. If areas with high porosity do not show hydrocarbon-bearing potential predictions, but areas with poor physical properties do, then the hydrocarbon-bearing potential predictions are unreliable and require further optimization and iteration. It should be further noted that the vertical trends in porosity and hydrocarbon-bearing potential should not only show good consistency between the two but also conform to overall sedimentary geological understanding (such as the lower limit of hydrocarbon charging physical properties and regional hydrocarbon accumulation conditions).

[0091] Example 4:

[0092] Existing technologies determine multiple target quality control parameters based on the attribute information of candidate quality control parameters during the seismic reservoir prediction process. Then, these target quality control parameters are preprocessed according to their numerical attributes. Finally, the evaluation result of the seismic reservoir prediction is determined based on the preprocessed target quality control parameters and their corresponding weights. These techniques primarily evaluate seismic reservoir prediction results at or around the well point based on candidate quality control parameters (such as well-seismic calibration correlation coefficient, AVO correlation coefficient, bullseye count, multi-wavelet correlation score, seismic stratigraphic error, rock physics prediction curve correlation coefficient, inversion pseudo-well curve correlation coefficient, elastic parameter sensitivity, prediction result error, and Mahalanobis distance ratio). While this approach can control the quality of reservoir prediction results to some extent, it cannot accurately characterize the vertical trend of reservoir prediction results above the well, the reliability of inter-well reservoir prediction results, or the reasonableness of horizontal reservoir prediction results. The reason is that most of the aforementioned candidate quality control parameters are quality control parameters used in the seismic inversion process. Generally, well data used for quality control during seismic inversion participates in the modeling process; that is, the wells used for process quality control are no longer blind wells and cannot objectively represent the reliability of the actual reservoir prediction results. Furthermore, all target quality control parameters are evaluation criteria for single wells or within the well perimeter. There is a lack of corresponding quality control methods for the inter-well information and planar distribution characteristics reflected in the reservoir prediction inversion profiles and planar reservoir prediction results of multiple wells. It is particularly important to note that the quality control of reservoir prediction results cannot rely solely on the target quality control parameters on the well surface; the actual geological background must also be considered to give the reservoir prediction results a clear geological meaning. The comparative analysis of well logging data and seismic inversion data involved in this invention uses blind wells that have not participated in seismic inversion modeling, have complete logging curve data, and have good logging curve quality. These can be used more objectively for the quality control analysis of reservoir prediction results. In addition, special emphasis is placed on quality control analysis from the perspective of combining geology and geophysics, as well as the matching relationship between lithology, physical properties, and hydrocarbon-bearing properties. This not only involves comprehensive quality control analysis from a data perspective, but also assigns geological meaning to the target quality control index parameters that characterize different geological meanings. This enables a more accurate evaluation of seismic inversion reservoir prediction results and effectively avoids erroneous results that contradict basic geological theories (such as obviously wrong conclusions like the development of reservoirs with good physical properties in mudstone sections in sandstone reservoirs). It has great innovation and practicality.

[0093] To verify the effectiveness of the technical method of this invention, the quality control method for seismic inversion reservoir prediction results in the clastic rock field was applied to a research area in the eastern sea area. This study area mainly consists of tight sandstone reservoirs with strong heterogeneity. The seismic inversion reservoir prediction results are of great guiding significance for the evaluation of favorable areas and the selection of targets. Therefore, reliable quality control analysis of the seismic inversion reservoir prediction results is required. Using the technical method of this invention, based on blind wells with relatively complete logging curves and after correction processing, and based on typical single-well inversion profiles, inversion profiles of wells with high deviation along the well trajectory, inversion profiles of interconnected wells, and inversion plane results, combined with an understanding of the overall geological background, a quality control analysis of the seismic inversion reservoir prediction results was carried out. This method not only enables comprehensive quality control analysis from a data perspective, but also assigns geological meaning to the target quality control index parameters that characterize different geological meanings. Furthermore, it effectively avoids erroneous results that contradict basic geological theories, and the mutual verification reduces the uncertainty and ambiguity of the reservoir prediction results, thereby improving the reliability of the reservoir prediction results.

[0094] First, the well logging data of drilled wells were systematically reviewed, with a focus on analyzing P-wave velocity (Vp, AC, or DT), S-wave velocity (DTS or VS), density curve (DEN), and porosity curve (Por), which are closely related to the seismic inversion results. Well logging curves with obvious outliers were corrected, and wells lacking P-wave velocity, S-wave velocity, or density curves were excluded. Well-seismic calibration was then performed using the corrected well logging data to obtain the time-depth matching relationship between the wells and the seismic data. This process was repeated until the well logging data correction and well-seismic calibration of all drilled wells in the study area were completed. Wells with relatively complete logging curves and after correction were selected and used as blind wells to verify the inversion results. Based on the seismic elastic parameter inversion results (such as P-wave velocity, S-wave velocity, and density), the consistency between the filtered logging data and the seismic inversion data was compared through typical single wells, wells with high deviation along the well trajectory, and interconnected wells. The analysis focused on the consistency between the measured results and the inversion results at different vertical segments at the well points, the consistency between the variation trend of the measured data at different segments and the variation trend of the inversion results, and the rationality of the well-connected inversion profile reflecting the inter-well regularity. Then, further quality control analysis was conducted on the sand body, porosity, and hydrocarbon prediction results from the perspective of combining geological and geophysical exploration and the matching relationship among lithology, physical properties, and hydrocarbon potential. Sand body prediction profiles, porosity prediction profiles, and hydrocarbon potential prediction profiles were selected from typical wells. The consistency between filtered logging data and seismic inversion data at well points, the consistency between measured results and inversion results in different vertical segments, the consistency of reservoir prediction patterns between wells, and the consistency of the planar distribution characteristics of the three profiles were analyzed and compared. The results show that the sand body prediction profile, porosity prediction profile, and hydrocarbon potential prediction profile of typical wells show good consistency between filtered logging data and seismic inversion data at well points. The variation trends of measured data in different segments are consistent with the variation trends of inversion results. The patterns of reservoir prediction results between wells are also well maintained, and the planar distribution characteristics of the three profiles are basically consistent. Through the above quality control methods, not only was a comprehensive data quality control analysis conducted on the seismic inversion reservoir prediction results, but also the target quality control index parameters that characterize different geological meanings were given geological significance, thereby improving the reliability of the reservoir prediction results.

[0095] Example 5:

[0096] This embodiment provides a quality control device 100 for seismic inversion reservoir prediction results, such as... Figure 3 As shown, the quality control device 100 for the seismic inversion reservoir prediction results includes: a data acquisition and correction module 10, a well-seismic calibration module 20, a blind well selection module 30, an inversion verification module 40, a judgment module 50, and a quality control analysis module 60.

[0097] in,

[0098] The data acquisition and correction module 10 is used to acquire and correct the drilled well logging data. Further, after acquiring the drilled well logging data, the system analyzes the data, focusing on parameters closely related to the seismic inversion results, such as P-wave velocity (Vp, AC, or DT), S-wave velocity (DTS or VS), density curve (DEN), and porosity curve (Por). Correcting the drilled well logging data is necessary because the original logging data often contains invalid values ​​and obvious outliers. It is understood that invalid values ​​have little impact on verifying the inversion results (invalid values ​​are easily identified in conventional software), and only outliers need to be removed.

[0099] The well-seismic calibration module 20 is used to perform well-seismic calibration using the corrected logging curves of drilled wells to obtain the time-depth matching relationship between the well and the seismic event. Furthermore, it completes the logging curve correction and well-seismic calibration work for all drilled wells in the study area. It should be noted that not all well logging curves are complete; for example, some may be missing P-wave velocity curves, S-wave velocity curves, or density curves. In such cases, wells with missing curves need to be excluded. The reason is that the well data used for quality control analysis of seismic inversion reservoir prediction results is the most direct "hard data." It is generally believed that only logging data obtained from actual testing is first-hand data with the highest reliability. Although missing curves can be obtained through empirical formulas or rock physics modeling, these methods are often inaccurate and directly affect the evaluation of seismic inversion results.

[0100] The blind well selection module 30 is used to screen the calibrated drilled wells to obtain target blind wells. Further, drilled wells with relatively complete logging curves and after calibration are screened, and these screened wells are used as blind wells to verify the inversion results. The selection principles include the following aspects:

[0101] 1. This well was not involved in seismic inversion modeling, but it has complete logging curve data and the logging curve quality is good.

[0102] 2. The selected verification wells should be distributed as evenly as possible within the work area to lay the foundation for subsequent well-connected seismic inversion profile analysis;

[0103] 3. Select wells with complete data on high-angle wells whenever possible, because high-angle wells can not only verify the inversion results at the well point, but also verify the lateral range of the prediction results within the high-angle trajectory range.

[0104] The inversion verification module 40 is used to verify the inversion results based on the seismic elastic parameter inversion results using the target blind well.

[0105] The judgment module 50 is used to determine whether the results of well logging data and seismic inversion data are consistent; and to determine whether the prediction results of sand bodies, porosity and hydrocarbon content are consistent.

[0106] The quality control analysis module 60 is used to perform quality control analysis on the prediction results of sand bodies, porosity and hydrocarbon properties from the perspective of the combination of geology and geophysics and the matching relationship between lithology, physical properties and hydrocarbon properties.

[0107] The inversion verification module 40 is also used to verify the inversion results at selected vertical blind wells on the seismic elastic parameter inversion results (such as P-wave velocity, S-wave velocity, and density). The well logging curves of the blind wells, calibrated by well-seismic analysis, are displayed on the seismic inversion profile. The reliability of the inversion results at the well points is controlled by comparing the consistency between the well logging data and the seismic inversion data. The focus is on analyzing the degree of agreement between the measured results and the inversion results at different vertical segments at the well points, and also analyzing whether the changing trends of the measured data at different segments in the well are consistent with the changing trends of the inversion results. It should be noted that: ① Because seismic data is band-limited, there are frequency band differences between the well logging data and the seismic inversion results. Filtering of the well logging curves is necessary to unify them within the same frequency band, ensuring comparability; ② When comparing well and seismic data, it is essential to ensure that the color scale ranges of both are within the same scale, using the same color scale display and value range. This makes it easier to compare the degree of agreement and increases the reliability of the results.

[0108] Based on the seismic elastic parameter inversion results (such as P-wave velocity, S-wave velocity, and density), a series of well inversion profiles were analyzed using selected vertical blind wells. The logging curves of all blind wells calibrated by well seismic testing were displayed on the seismic inversion profile. Quality control of the inversion results at all well points was achieved by comparing the consistency between the filtered logging curves and the seismic inversion data. The focus was on analyzing the agreement between the measured results and the inversion results at different vertical layers at each well point, as well as whether the variation trends of the measured data at different layers were consistent with the variation trends of the inversion results. Furthermore, it is crucial to pay attention to the rationality of the inter-well regularity information contained in the series inversion profiles, such as velocity and density variation trends. These require further analysis in conjunction with the geological background, such as lateral lithological variations in drilled wells and fluid properties.

[0109] For the seismic elastic parameter inversion results (such as P-wave velocity, S-wave velocity, and density), a rigorous quality control analysis of the inversion results is conducted by selecting highly deviated blind wells along the well trajectory. The logging curves of highly deviated blind wells calibrated by well seismic testing are displayed on the seismic inversion profile. The reliability of the inversion results along the well trajectory is controlled by comparing the well logging curves after well filtering with the seismic inversion data results. The analysis focuses on the consistency between the measured results and the inversion results in different vertical segments along the well trajectory, whether the variation trend of the measured data in different segments of the well is consistent with the variation trend of the inversion results, and the consistency between the vertical variation law of the measured results in the deviated well segment and the inversion results.

[0110] The quality control analysis module 60 is also used for quality control analysis of sand body prediction results, mainly including the following: ① Verification of well-connected inversion profile results (comparing the consistency between the well-connected filtered GR curve and the seismic inversion sensitive elastic parameter results); ② Verification of inversion profile results for wells with excessively high deviation along the well trajectory (comparing the consistency between the well-connected filtered GR curve and the seismic inversion sensitive elastic parameter results); ③ Verification of the actual number of sand bodies developed in the well-connected profile and the number of sand bodies predicted in the inversion profile (comparing the consistency between the well-connected filtered GR curve and the seismic inversion sensitive elastic parameter results, where low GR represents sandstone and high GR represents mudstone, analyzing the number of low GR sand bodies developed and the number of sand bodies predicted by the sensitive elastic parameters); ④ Typical Comparison of the distribution characteristics of sand bodies between wells in the geological profile of the series of wells with the distribution characteristics of sand bodies represented by the seismic inversion sensitive elastic parameters (compare the consistency of the thickness variation, lithological variation and pinch-out point of sand bodies between wells in the geological profile of the series of wells with the inter-well characteristics represented by the seismic inversion sensitive elastic parameters); ⑤ Comparison of the planar sand body distribution characteristics represented by the previous geological understanding planar results map or seismic sensitive attributes with the planar sand body distribution patterns represented by the seismic inversion sensitive elastic parameters (consistency analysis of planar sand body distribution characteristics); ⑥ Comparison of the variation patterns of planar sand body distribution characteristics in different vertical segments with the variation patterns of planar sand body distribution characteristics represented by the seismic inversion sensitive elastic parameters in different segments (consistency analysis of the longitudinal variation trend of planar sand bodies).

[0111] The quality control analysis for porosity prediction results mainly includes the following: ① Verification of well-connected inversion profile results (comparing the consistency between the well-connected filtered porosity curves and the porosity prediction results characterized by seismic inversion sensitive elastic parameters); ② Verification of inversion profile results for wells with excessively deviated well trajectories (comparing the consistency between the well-connected filtered porosity curves and the porosity prediction results characterized by seismic inversion sensitive elastic parameters); ③ Comparison of the vertical porosity variation trend above ground with the vertical porosity variation characteristics characterized by seismic inversion sensitive elastic parameters (analyzing the consistency of the two vertical porosity variation trends requires...). This indicates that the vertical variation trend of porosity should have good consistency with the vertical development quantity of sand bodies. That is, the difference in porosity only exists where the sand body develops. If high porosity areas are distributed in mudstone development zones, it indicates that the porosity prediction results are unreliable and require further optimization and iteration. ④ Comparison of the distribution characteristics of sand bodies between wells in the geological profile and the porosity distribution characteristics characterized by sensitive elastic parameters from seismic inversion (analyzing the consistency of the patterns of sand body and porosity prediction results between wells; that is, the difference in porosity only exists where the sand body develops. If high porosity areas are distributed in mudstone development zones, it indicates that the porosity prediction results are unreliable and require further optimization and iteration). If the sand body development interval is not specified, it indicates that the porosity prediction result is unreliable and further optimization and iteration are needed; ⑤ Comparison of the planar sand body distribution pattern and the planar porosity distribution characteristics (compare the consistency of the two planar distribution characteristics, that is, the planar porosity variation characteristics and the planar sand body distribution pattern should have good consistency, that is, the location of sand body development on the plane should have differences in porosity variation. If the high porosity value area is distributed in the mudstone development area on the plane, it indicates that the porosity prediction result is unreliable and further optimization and iteration are needed); ⑥ The variation pattern of the planar sand body distribution characteristics and the planar porosity distribution characteristics of different vertical intervals. The comparison (sequentially comparing the consistency of the planar distribution characteristics of the two in different vertical segments, i.e., the planar porosity variation characteristics and the planar sand body distribution pattern should have good consistency; that is, the difference in porosity variation should only exist where the sand body is developed in the plane. If the high porosity area is distributed in the mudstone development area in the plane, it indicates that the porosity prediction results are unreliable and further optimization and iteration are needed. It should be added that the vertical sand body and porosity variation trends should not only have good consistency between the two, but also conform to the overall sedimentary geological understanding, such as the direction of sediment supply and the planar distribution characteristics of sedimentary facies, etc.)

[0112] The quality control analysis for hydrocarbon prediction results mainly includes the following: ① Verification of well-connected inversion profile results (comparing the consistency between the well logging interpretation results after well-connected filtering and the hydrocarbon prediction results characterized by seismic inversion sensitive elastic parameters); ② Verification of inversion profile results for wells with excessively deviated well trajectories (comparing the consistency between the well logging interpretation results after well-connected filtering and the hydrocarbon prediction results characterized by seismic inversion sensitive elastic parameters); ③ Comparison between the vertical variation trend of porosity on the well and the vertical variation characteristics of hydrocarbons characterized by seismic inversion sensitive elastic parameters (analyzing the consistency of the two vertical variation trends; it should be noted that the vertical variation trend of porosity should have good consistency with the hydrocarbon prediction results). In other words, only layers with better physical properties are likely to show variations in hydrocarbon potential, while layers with poorer physical properties generally do not contain hydrocarbons. Therefore, if layers with better physical properties do not show hydrocarbon predictions, but layers with poorer physical properties do, then the hydrocarbon predictions are unreliable and require further optimization and iteration. ④ Comparison of well-to-well porosity inversion profiles with well-to-well hydrocarbon prediction profiles (analyzing the consistency between the patterns of porosity inversion results and hydrocarbon prediction results between wells; that is, if areas with better physical properties between wells do not show hydrocarbon predictions, but areas with poorer physical properties do, then...) This indicates that the hydrocarbon prediction results are unreliable and require further optimization and iteration; ⑤ Comparison of planar porosity distribution characteristics with planar hydrocarbon prediction results (compare the consistency of the two planar distribution characteristics, that is, the planar porosity variation characteristics and the planar hydrocarbon prediction results should have good consistency, that is, only locations with better physical properties on the plane show differences in hydrocarbon content. If there are no hydrocarbon prediction results in areas with high porosity on the plane, but there are obvious hydrocarbon prediction results in areas with poor physical properties, then the hydrocarbon prediction results are unreliable and require further optimization and iteration); ⑥ Comparison of planar porosity variation characteristics of different vertical layers with planar hydrocarbon prediction results (in order) The consistency of the planar distribution characteristics of porosity and hydrocarbon-bearing potential across different vertical stratigraphic segments should be compared. Specifically, the planar porosity variation characteristics and the planar hydrocarbon-bearing potential predictions should show good consistency. That is, hydrocarbon-bearing potential variations should only occur in areas with better physical properties. If areas with high porosity do not show hydrocarbon-bearing potential predictions, but areas with poor physical properties do, then the hydrocarbon-bearing potential predictions are unreliable and require further optimization and iteration. It should be further noted that the vertical trends in porosity and hydrocarbon-bearing potential should not only show good consistency between the two but also conform to overall sedimentary geological understanding (such as the lower limit of hydrocarbon charging physical properties and regional hydrocarbon accumulation conditions).

[0113] Example 6:

[0114] Based on the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps of the method described in the above embodiments, including:

[0115] Step 01: Acquire and correct the well logging curve data of the drilled well.

[0116] After acquiring the well logging data of the drilled wells, the system sorts out the well logging data, and it is necessary to focus on analyzing the P-wave velocity (Vp, AC or DT), S-wave velocity (DTS or VS), density curve (DEN) and porosity curve (Por) which are closely related to the seismic inversion results.

[0117] Correcting drilled well logging data is necessary because the original logging data often contains invalid values ​​and obvious outliers. It's understandable that invalid values ​​have little impact on the verification and inversion results (invalid values ​​are easily identified in conventional software); only outliers need to be removed.

[0118] Step 02: Use the corrected well logging curves to perform well-seismic calibration to obtain the time-depth matching relationship between the well and the seismic data.

[0119] Repeat the above steps until the logging curve correction and well-seismic calibration of all drilled wells in the study area are completed. It should be noted that not all well logging curves are complete; some may be missing P-wave velocity, S-wave velocity, or density curves. In such cases, the wells with missing curves must be excluded. The reason is that the well data used for quality control analysis of seismic inversion reservoir prediction results are the most direct "hard data." It is generally believed that only logging data obtained from actual testing is primary data and has the highest reliability. Although missing curves can be obtained through empirical formulas or rock physics modeling, these methods are often inaccurate and directly affect the evaluation of seismic inversion results.

[0120] Step 03: Screen the corrected drilled wells to obtain the target blind wells.

[0121] Wells with relatively complete logging curves and after correction were screened, and these screened wells were used as blind wells to verify the inversion results. The selection principles included the following aspects:

[0122] 1. This well was not involved in seismic inversion modeling, but it has complete logging curve data and the logging curve quality is good.

[0123] 2. The selected verification wells should be distributed as evenly as possible within the work area to lay the foundation for subsequent well-connected seismic inversion profile analysis;

[0124] 3. Select wells with complete data on high-angle wells whenever possible, because high-angle wells can not only verify the inversion results at the well point, but also verify the lateral range of the prediction results within the high-angle trajectory range.

[0125] Step 04: Verify the inversion results using the target blind well based on the seismic elastic parameter inversion results, and determine whether the well logging data and seismic inversion data are consistent. If yes, proceed to Step 05; otherwise, iteratively optimize the seismic inversion results until the well logging data and seismic inversion data are consistent. See the following embodiment for details.

[0126] For the seismic elastic parameter inversion results (such as P-wave velocity, S-wave velocity, and density), the inversion results at the wellpoints are verified using selected vertical blind wells. The well logging curves of the blind wells, calibrated by well-seismic analysis, are displayed on the seismic inversion profile. The reliability of the wellpoint inversion results is assessed by comparing the consistency between the well logging data and the seismic inversion data. The focus is on analyzing the degree of agreement between the measured results and the inversion results at different vertical segments at the wellpoints. Simultaneously, it is necessary to analyze whether the variation trends of the measured data at different segments in the well are consistent with the variation trends of the inversion results. It should be noted that: ① Because seismic data is band-limited, there are frequency band differences between the well logging data and the seismic inversion results. Filtering of the well logging curves is necessary to unify them within the same frequency band, ensuring comparability. ② When comparing well-seismic data, it is essential to ensure that the color scale ranges of both are within the same scale, using the same color scale display and value range. This facilitates comparison of the degree of agreement and increases the reliability of the results.

[0127] Based on the seismic elastic parameter inversion results (such as P-wave velocity, S-wave velocity, and density), a series of well inversion profiles were analyzed using selected vertical blind wells. The logging curves of all blind wells calibrated by well seismic testing were displayed on the seismic inversion profile. Quality control of the inversion results at all well points was achieved by comparing the consistency between the filtered logging curves and the seismic inversion data. The focus was on analyzing the agreement between the measured results and the inversion results at different vertical layers at each well point, as well as whether the variation trends of the measured data at different layers were consistent with the variation trends of the inversion results. Furthermore, it is crucial to pay attention to the rationality of the inter-well regularity information contained in the series inversion profiles, such as velocity and density variation trends. These require further analysis in conjunction with the geological background, such as lateral lithological variations in drilled wells and fluid properties.

[0128] For the seismic elastic parameter inversion results (such as P-wave velocity, S-wave velocity, and density), a rigorous quality control analysis of the inversion results is conducted by selecting highly deviated blind wells along the well trajectory. The logging curves of highly deviated blind wells calibrated by well seismic testing are displayed on the seismic inversion profile. The reliability of the inversion results along the well trajectory is controlled by comparing the well logging curves after well filtering with the seismic inversion data results. The analysis focuses on the consistency between the measured results and the inversion results in different vertical segments along the well trajectory, whether the variation trend of the measured data in different segments of the well is consistent with the variation trend of the inversion results, and the consistency between the vertical variation law of the measured results in the deviated well segment and the inversion results.

[0129] Step 05: If yes, conduct quality control analysis on the prediction results of sand bodies, porosity, and hydrocarbon potential from the perspective of combining geological and geophysical exploration and the matching relationship between lithology, physical properties, and hydrocarbon potential, and determine whether the prediction results of sand bodies, porosity, and hydrocarbon potential are consistent. If yes, complete the quality control of the seismic inversion reservoir prediction results. Through the quality control analysis of the sand body, porosity, and hydrocarbon potential results in the above steps, clarify the accuracy and reliability of the sand body prediction, porosity prediction, and hydrocarbon potential prediction results. Sand bodies, porosity, and hydrocarbon potential are a unified whole, that is, reservoirs with good hydrocarbon potential are mainly distributed in sandstone reservoirs with good physical properties. If the prediction results of the three are contradictory or cannot maintain the consistency of the distribution pattern, iterative optimization of the inversion results is required until a seismic inversion data body that can better meet all the above quality control conditions is obtained. See the following example for details.

[0130] In seismic inversion reservoir prediction, the elastic parameters used to characterize sand bodies, porosity, and hydrocarbon bearing capacity are all combinations of P-wave velocity, S-wave velocity, and density. The actual well logging data from drilled wells and the seismic inversion elastic parameter results are calculated according to the sensitive elastic parameter combinations that characterize sand bodies, porosity, and hydrocarbon bearing capacity, resulting in well logging sensitive elastic parameter combination curves and seismic inversion sensitive elastic parameter data volumes that characterize sand bodies, porosity, and hydrocarbon bearing capacity. Since the above elastic parameter combinations are all calculated based on P-wave velocity, S-wave velocity, and density, if the quality control results of single vertical wells, vertical well-connected well profiles, and highly deviated wells are good, then the drilled curves obtained using the same calculation formula will also have good agreement with the seismic inversion elastic parameter results. Therefore, subsequent quality control analysis of sand body, porosity, and hydrocarbon bearing capacity results mainly focuses on the combination of geological and geophysical exploration and the matching relationship between lithology, physical properties, and hydrocarbon bearing capacity.

[0131] The quality control analysis for sand body prediction results mainly includes the following: ① Verification of well-connected inversion profile results (comparing the GR curves after well-connected filtering with the seismic inversion sensitive elastic parameter results); ② Verification of inversion profile results for wells with excessively high deflection along the well trajectory (comparing the GR curves after well-connected filtering with the seismic inversion sensitive elastic parameter results); ③ Verification of the actual sand body development quantity in the well-connected profile and the predicted sand body quantity in the inversion profile (comparing the GR curves after well-connected filtering with the seismic inversion sensitive elastic parameter results; low GR represents sandstone, high GR represents mudstone; analyzing the development quantity of low GR sand bodies and the predicted sand body quantity characterized by sensitive elastic parameters); ④ Typical well-connected geology. Comparison of inter-well sand body distribution characteristics with those represented by seismic inversion sensitive elastic parameters (consistency between inter-well sand body thickness variations, lithological variations, and sand body pinch-out points in the geological profiles of interconnected wells and the inter-well characteristics represented by seismic inversion sensitive elastic parameters); ⑤ Comparison of planar sand body distribution characteristics represented by previous geological understanding planar maps or seismic sensitive attributes with those represented by seismic inversion sensitive elastic parameters (consistency analysis of planar sand body distribution characteristics); ⑥ Comparison of the variation patterns of planar sand body distribution characteristics in different vertical segments with those represented by seismic inversion sensitive elastic parameters in different segments (consistency analysis of longitudinal variation trends of planar sand bodies).

[0132] The quality control analysis for porosity prediction results mainly includes the following: ① Verification of well-connected inversion profile results (comparing the consistency between the well-connected filtered porosity curves and the porosity prediction results characterized by seismic inversion sensitive elastic parameters); ② Verification of inversion profile results for wells with excessively deviated well trajectories (comparing the consistency between the well-connected filtered porosity curves and the porosity prediction results characterized by seismic inversion sensitive elastic parameters); ③ Comparison of the vertical porosity variation trend above ground with the vertical porosity variation characteristics characterized by seismic inversion sensitive elastic parameters (analyzing the consistency of the two vertical porosity variation trends requires...). This indicates that the vertical variation trend of porosity should have good consistency with the vertical development quantity of sand bodies. That is, the difference in porosity only exists where the sand body develops. If high porosity areas are distributed in mudstone development zones, it indicates that the porosity prediction results are unreliable and require further optimization and iteration. ④ Comparison of the distribution characteristics of sand bodies between wells in the geological profile and the porosity distribution characteristics characterized by sensitive elastic parameters from seismic inversion (analyzing the consistency of the patterns of sand body and porosity prediction results between wells; that is, the difference in porosity only exists where the sand body develops. If high porosity areas are distributed in mudstone development zones, it indicates that the porosity prediction results are unreliable and require further optimization and iteration). If the sand body development interval is not specified, it indicates that the porosity prediction result is unreliable and further optimization and iteration are needed; ⑤ Comparison of the planar sand body distribution pattern and the planar porosity distribution characteristics (compare the consistency of the two planar distribution characteristics, that is, the planar porosity variation characteristics and the planar sand body distribution pattern should have good consistency, that is, the location of sand body development on the plane should have differences in porosity variation. If the high porosity value area is distributed in the mudstone development area on the plane, it indicates that the porosity prediction result is unreliable and further optimization and iteration are needed); ⑥ The variation pattern of the planar sand body distribution characteristics and the planar porosity distribution characteristics of different vertical intervals. The comparison (sequentially comparing the consistency of the planar distribution characteristics of the two in different vertical segments, i.e., the planar porosity variation characteristics and the planar sand body distribution pattern should have good consistency; that is, the difference in porosity variation should only exist where the sand body is developed in the plane. If the high porosity area is distributed in the mudstone development area in the plane, it indicates that the porosity prediction results are unreliable and further optimization and iteration are needed. It should be added that the vertical sand body and porosity variation trends should not only have good consistency between the two, but also conform to the overall sedimentary geological understanding, such as the direction of sediment supply and the planar distribution characteristics of sedimentary facies, etc.)

[0133] The quality control analysis for hydrocarbon prediction results mainly includes the following: ① Verification of well-connected inversion profile results (comparing the consistency between the well logging interpretation results after well-connected filtering and the hydrocarbon prediction results characterized by seismic inversion sensitive elastic parameters); ② Verification of inversion profile results for wells with excessively deviated well trajectories (comparing the consistency between the well logging interpretation results after well-connected filtering and the hydrocarbon prediction results characterized by seismic inversion sensitive elastic parameters); ③ Comparison between the vertical variation trend of porosity on the well and the vertical variation characteristics of hydrocarbons characterized by seismic inversion sensitive elastic parameters (analyzing the consistency of the two vertical variation trends; it should be noted that the vertical variation trend of porosity should have good consistency with the hydrocarbon prediction results). In other words, only layers with better physical properties are likely to show variations in hydrocarbon potential, while layers with poorer physical properties generally do not contain hydrocarbons. Therefore, if layers with better physical properties do not show hydrocarbon predictions, but layers with poorer physical properties do, then the hydrocarbon predictions are unreliable and require further optimization and iteration. ④ Comparison of well-to-well porosity inversion profiles with well-to-well hydrocarbon prediction profiles (analyzing the consistency between the patterns of porosity inversion results and hydrocarbon prediction results between wells; that is, if areas with better physical properties between wells do not show hydrocarbon predictions, but areas with poorer physical properties do, then...) This indicates that the hydrocarbon prediction results are unreliable and require further optimization and iteration; ⑤ Comparison of planar porosity distribution characteristics with planar hydrocarbon prediction results (compare the consistency of the two planar distribution characteristics, that is, the planar porosity variation characteristics and the planar hydrocarbon prediction results should have good consistency, that is, only locations with better physical properties on the plane show differences in hydrocarbon content. If there are no hydrocarbon prediction results in areas with high porosity on the plane, but there are obvious hydrocarbon prediction results in areas with poor physical properties, then the hydrocarbon prediction results are unreliable and require further optimization and iteration); ⑥ Comparison of planar porosity variation characteristics of different vertical layers with planar hydrocarbon prediction results (in order) The consistency of the planar distribution characteristics of porosity and hydrocarbon-bearing potential across different vertical stratigraphic segments should be compared. Specifically, the planar porosity variation characteristics and the planar hydrocarbon-bearing potential predictions should show good consistency. That is, hydrocarbon-bearing potential variations should only occur in areas with better physical properties. If areas with high porosity do not show hydrocarbon-bearing potential predictions, but areas with poor physical properties do, then the hydrocarbon-bearing potential predictions are unreliable and require further optimization and iteration. It should be further noted that the vertical trends in porosity and hydrocarbon-bearing potential should not only show good consistency between the two but also conform to overall sedimentary geological understanding (such as the lower limit of hydrocarbon charging physical properties and regional hydrocarbon accumulation conditions).

[0134] In some embodiments of this example, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the steps of the method described in the above embodiments.

[0135] In some embodiments of this example, a computer program product is provided, including a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the method described in the above embodiments.

[0136] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component, for executing the methods in the above embodiments.

[0137] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof. Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (e.g., hard disks, floppy disks, solid-state drives, removable disks, Blu-ray discs, etc.).

[0138] Computer-readable storage media may also store at least one computer-executable program / instruction, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0139] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).

[0140] The processor can communicate with external devices via the I / O bus through wired or wireless networks.

[0141] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0142] In the embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0143] It should be noted that, in this disclosure, 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 a process, method, article, or apparatus. Without further limitation, an element limited 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.

[0144] While the embodiments disclosed herein are as described above, the foregoing content is merely for the purpose of facilitating understanding of this disclosure and is not intended to limit this disclosure. Any person skilled in the art to which this disclosure pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope of this disclosure; however, the scope of patent protection of this disclosure shall still be determined by the scope defined in the appended claims.

[0145] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A quality control method for seismic inversion reservoir prediction results, characterized in that, include: Acquire and correct well logging data from drilled wells; Well-seismic calibration is carried out using the corrected well logging curves to obtain the time-depth matching relationship between the well and the seismic data; The corrected drilled wells are then screened to obtain the target blind wells; The inversion results were verified using the seismic elastic parameter inversion results from the target blind well, and it was determined whether the results of the well logging data and the seismic inversion data were consistent. If so, quality control analysis should be conducted on the prediction results of sand bodies, porosity, and hydrocarbon properties from the perspective of combining geology and geophysics and the matching relationship between lithology, physical properties, and hydrocarbon properties, and it should be determined whether the prediction results of sand bodies, porosity, and hydrocarbon properties are consistent.

2. The quality control method for seismic inversion reservoir prediction results according to claim 1, characterized in that, The process of verifying the inversion results using the target blind well on the seismic elastic parameter inversion results, and determining whether the well logging data and the seismic inversion data are consistent, includes: verifying the inversion results at the well point using the target blind well on the seismic elastic parameter inversion results; the process of verifying the inversion results at the well point using the target blind well on the seismic elastic parameter inversion results includes: The blind well logging curves calibrated by well seismic testing are displayed on the seismic inversion profile; The consistency between the well logging data and the seismic inversion data is compared to determine the reliability of the inversion results at the quality control well point. The consistency between the well logging data and the seismic inversion data includes analyzing the degree of agreement between the measured results and the inversion results at different vertical layers at the well point, as well as whether the changing trends of the measured data at different layers on the well are consistent with the changing trends of the inversion results.

3. The quality control method for seismic inversion reservoir prediction results according to claim 1, characterized in that, The process of verifying the inversion results using the target blind wells on the seismic elastic parameter inversion results, and determining whether the well logging data and seismic inversion data are consistent, includes: analyzing the well-connected inversion profile results using the target blind wells on the seismic elastic parameter inversion results; the analysis of the well-connected inversion profile results using the target blind wells on the seismic elastic parameter inversion results includes: All blind well logging curves calibrated by well seismic testing are displayed on the seismic inversion profile; The consistency between the filtered logging curves of all wells and the seismic inversion data is compared to ensure the reliability of the inversion results at all connected well inversion profiles. The comparison of the consistency between the filtered logging curves of all wells and the seismic inversion data includes: analyzing the degree of agreement between the measured results and the inversion results at different vertical segments at the well points, and whether the changing trends of the measured data at different segments on the well are consistent with the changing trends of the inversion results.

4. The quality control method for seismic inversion reservoir prediction results according to claim 1, characterized in that, The process of verifying the inversion results using the target blind well on the seismic elastic parameter inversion results and determining whether the well logging data and the seismic inversion data are consistent includes: selecting highly deviated blind wells along the well trajectory on the seismic elastic parameter inversion results to conduct quality control analysis of the inversion results; the process of selecting highly deviated blind wells along the well trajectory on the seismic elastic parameter inversion results to conduct quality control analysis of the inversion results includes: The logging curves of highly deviated blind wells, calibrated by well seismic testing, are displayed on the seismic inversion profile. The consistency between the well logging data after well filtering and the seismic inversion data is used to quality control the reliability of the inversion results along the well trajectory. The consistency between the well logging data after well filtering and the seismic inversion data includes: analyzing the consistency between the measured results and the inversion results of different vertical segments along the well trajectory, whether the variation trend of the measured data of different segments in the well is consistent with the variation trend of the inversion results, and the consistency between the vertical variation law of the measured results of the deviated well segment and the inversion results.

5. The quality control method for seismic inversion reservoir prediction results according to claim 1, characterized in that, The acquisition and correction of drilled well logging data includes excluding wells with missing P-wave velocity curves, S-wave velocity curves, and density curves.

6. The quality control method for seismic inversion reservoir prediction results according to claim 1, characterized in that, The quality control analysis of the prediction results of sand bodies, porosity, and hydrocarbon potential from the perspective of combining geological and geophysical exploration and matching the relationship between lithology, physical properties, and hydrocarbon potential includes: Based on the combination of P-wave velocity, S-wave velocity and density, we obtain logging sensitive elastic parameter combination curves and seismic inversion sensitive elastic parameter data volumes that can characterize sand bodies, porosity and hydrocarbon-bearing properties. Quality control analysis was performed on the prediction results of sand bodies, porosity, and hydrocarbon content based on the combined curves of the well logging sensitive elastic parameters and the seismic inversion sensitive elastic parameter data.

7. The quality control method for seismic inversion reservoir prediction results according to claim 1, characterized in that, The quality control analysis of sand body prediction results was conducted from the perspective of combining geological and geophysical exploration, as well as the matching relationship between lithology, physical properties, and hydrocarbon-bearing properties, and the consistency of sand body prediction results was determined, including: Compare the GR curves after well-connected filtering with the results of seismic inversion of sensitive elastic parameters to determine their consistency. Compare the good agreement between the filtered GR curve along the well trajectory and the results of seismic inversion of sensitive elastic parameters; By comparing the GR curves after well-connected filtering with the results of seismic inversion sensitive elastic parameters, the number of sandstone bodies developed and the number of sandstone bodies predicted by the sensitive elastic parameters were analyzed. The consistency between the inter-well sand body thickness variation, lithological variation, and sand body pinch-out point in the geological profile of the well and the inter-well characteristics characterized by the seismic inversion sensitive elastic parameters was compared. A comparison of the distribution characteristics of planar sand bodies represented by preliminary geological understanding maps or seismic sensitivity attributes with the distribution patterns of planar sand bodies represented by seismic inversion sensitive elastic parameters; A comparison of the variation patterns of planar sand body distribution characteristics in the longitudinal direction of different layers with the variation patterns of planar sand body distribution characteristics characterized by seismic inversion sensitive elastic parameters in different layers.

8. The quality control method for seismic inversion reservoir prediction results according to claim 1, characterized in that, The porosity prediction results were analyzed for quality control from the perspective of combining geological and geophysical exploration, as well as the matching relationship between lithology, physical properties, and hydrocarbon-bearing properties. The consistency of the predicted porosity results was assessed, including: Compare the porosity curves after well-connected filtering with the porosity prediction results characterized by seismic inversion sensitive elastic parameters to determine their consistency. Compare the porosity curve after filtering along the well trajectory with the porosity prediction results characterized by seismic inversion sensitive elastic parameters to determine the degree of agreement. Comparing the vertical variation trend of porosity above ground with the vertical variation characteristics of porosity characterized by sensitive elastic parameters from seismic inversion, if the high porosity area is distributed in the mudstone development layer, then the porosity prediction result is unreliable and further optimization and iteration are needed. A comparison of the distribution characteristics of sand bodies between wells in the geological profile and the porosity distribution characteristics characterized by sensitive elastic parameters from seismic inversion shows that if the high porosity areas are distributed in the mudstone development zones between wells, the porosity prediction results are unreliable and require further optimization and iteration. Comparing the distribution pattern of planar sand bodies with the characteristics of planar porosity distribution, if the high porosity areas are distributed in the mudstone development areas on the plane, then the porosity prediction results are unreliable and need further optimization and iteration. By comparing the variation patterns of planar sand body distribution characteristics in different vertical segments with the planar porosity distribution characteristics, if the high porosity areas are distributed in the mudstone development areas on the plane, then the porosity prediction results are unreliable and need further optimization and iteration.

9. The quality control method for seismic inversion reservoir prediction results according to claim 1, characterized in that, The quality control analysis of hydrocarbon prediction results was conducted from the perspective of combining geological and geophysical exploration and the matching relationship among lithology, physical properties, and hydrocarbon potential, and the consistency of hydrocarbon prediction results was determined, including: The consistency between the well logging interpretation results after well-connected filtering and the hydrocarbon prediction results characterized by seismic inversion sensitive elastic parameters was compared. Compare the well logging interpretation results after well trajectory filtering with the hydrocarbon prediction results characterized by seismic inversion sensitive elastic parameters; The vertical variation trend of surface porosity is compared with the vertical variation characteristics of hydrocarbon-bearing properties characterized by seismic inversion sensitive elastic parameters, and the consistency of the vertical variation trends of the two is analyzed. The consistency between the regular changes in the inter-well porosity inversion results and the inter-well hydrocarbon prediction results was analyzed. Comparison of planar porosity distribution characteristics and planar hydrocarbon prediction results; Comparison of planar porosity variation characteristics in different vertical segments with planar hydrocarbon prediction results.

10. The quality control method for seismic inversion reservoir prediction results according to claim 1, characterized in that, The determination of whether the well logging data and the seismic inversion data are consistent includes: if not, continuously iterating and optimizing the seismic inversion results until the well logging data and the seismic inversion data are consistent.

11. The quality control method for seismic inversion reservoir prediction results according to claim 1, characterized in that, The determination of whether the predicted results of sand body, porosity, and hydrocarbon content are consistent includes: If not, continuously iterate and optimize the seismic inversion results until the predicted results of judging sand bodies, porosity, and hydrocarbon content are consistent.

12. A quality control device for seismic inversion reservoir prediction results, characterized in that, include: The data acquisition and correction module is used to acquire and correct the drilling logging curve data; The well-seismic calibration module is used to perform well-seismic calibration using the corrected well logging curves to obtain the time-depth matching relationship between the well and the earthquake. The blind well selection module is used to screen the corrected drilled wells to obtain target blind wells; The inversion verification module is used to verify the inversion results based on the seismic elastic parameter inversion results using the target blind well. The judgment module is used to determine whether the results of well logging data and seismic inversion data are consistent; and to determine whether the prediction results of sand bodies, porosity, and hydrocarbon properties are consistent. The quality control analysis module is used to perform quality control analysis on the prediction results of sand bodies, porosity, and hydrocarbon properties from the perspective of the combination of geology and geophysics and the matching relationship between lithology, physical properties, and hydrocarbon properties.

13. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 11.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program performs the steps of the method described in any one of claims 1 to 11.

15. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program performs the steps of the method described in any one of claims 1 to 11.

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