Elastic parameter inversion method, device and computer readable storage medium

Abstract

This disclosure relates to the field of oil and gas geophysical exploration technology, and particularly to a method, apparatus, and computer-readable storage medium for inverting elastic parameters. The method involves acquiring seismic data volumes and sedimentary facies maps of the study area, as well as drilling data from adjacent study areas. The drilling data includes: well logging data from the wells; determining, based on the sedimentary facies map, well logging data matching the characteristics of each sedimentary facies zone in the study area as virtual well logging data; determining the optimal location of virtual wells within each sedimentary facies zone; determining the elastic parameter curves of each virtual well based on the logging data of each virtual well; establishing a facies-controlled low-frequency model of elastic parameters under both longitudinal and transverse constraints based on the optimal location of each virtual well and the corresponding elastic parameter curves; and performing pre-stack facies-controlled inversion based on the seismic data volume and the facies-controlled low-frequency model to obtain the inversion results of the elastic parameters. This method can obtain elastic parameter inversion results in low-exploration areas that better conform to actual geological sedimentary patterns, thereby more accurately characterizing the distribution features of sand bodies.

Description

Technical Field

[0001] This disclosure relates to the field of oil and gas geophysical exploration technology, and in particular to a method, apparatus and computer-readable storage medium for inverting elastic parameters. Background Technology

[0002] In the "three new" fields of oil and gas, namely new regions, new strata, and new types, the low level of exploration and lack of corresponding drilling data often result in less prior information when making reservoir predictions, and the seismic inversion lacks well information constraints, thus affecting the accuracy of reservoir predictions. Summary of the Invention

[0003] This disclosure provides a method, apparatus, and computer-readable storage medium for inverting elastic parameters, which can obtain elastic parameter inversion results in low-exploration areas that are more consistent with actual geological sedimentary patterns, thereby enabling a more accurate characterization of sand body distribution features.

[0004] Firstly, this disclosure provides a method for inverting elastic parameters, including:

[0005] Acquire partial angle-stacked seismic data volumes and sedimentary facies maps of the study area that meet the inversion requirements, as well as drilling data from adjacent study areas, wherein the drilling data includes well logging data from drilling.

[0006] Based on the sedimentary facies diagram, well logging data that matches the characteristics of each sedimentary facies zone in the study area is determined from the well logging data and used as the well logging data for virtual wells;

[0007] Determine the optimal location of virtual wells within each sedimentary facies zone;

[0008] The elastic parameter curves of each virtual well are determined based on the logging data of each virtual well.

[0009] Based on the optimal location of each virtual well and the corresponding elastic parameter curves of each virtual well, a phase-controlled low-frequency model of elastic parameters is established under both longitudinal and transverse constraints.

[0010] Based on the seismic data volume and the phased low-frequency model, pre-stack phased inversion is performed to obtain the inversion results of elastic parameters.

[0011] In some embodiments, determining the logging data that matches the characteristics of each sedimentary facies zone in the study area from the logging data based on the sedimentary facies map as the logging data for a virtual well includes:

[0012] Based on the drilling data of the adjacent study areas, wells matching each sedimentary facies zone in the study area are selected. Among them, the geological background similarity between each sedimentary facies zone and the corresponding well is greater than the similarity threshold, the wells encounter the target layer and the difference in burial depth is less than the difference threshold, and the difference in formation thickness is less than the thickness difference threshold.

[0013] The logging data of wells matched to each sedimentary facies zone are determined as the logging data of virtual wells in each sedimentary facies zone in the study area.

[0014] In some embodiments, determining the optimal location of the virtual well within each sedimentary facies zone includes:

[0015] The synthetic seismic record of each virtual well is determined based on the logging data of each virtual well;

[0016] Determine the correlation between the synthetic seismic records of each virtual well and the seismic traces at each location within each sedimentary facies zone;

[0017] The location with the highest correlation is determined as the optimal location of the virtual well within each sedimentary facies zone.

[0018] In some embodiments, determining the elastic parameter curves of each virtual well based on the logging data of each virtual well includes:

[0019] Compaction trend analysis was performed on the logging data of each virtual well to obtain the original compaction trend curve;

[0020] By performing cross-fitting of elastic parameters with logging time and burial depth, compaction trend curves of each elastic parameter and burial depth are established.

[0021] The elastic parameter curves of each virtual well are determined based on the compaction trend curve and the original compaction trend curve.

[0022] In some embodiments, determining the elastic parameter curves of each virtual well based on the compaction trend curve and the original compaction trend curve includes:

[0023] Subtract the original compaction trend curve from the elastic parameters of the virtual well to obtain the relative value curve of the elastic parameters;

[0024] The compaction trend curve is added to the relative value curve of the elastic parameter to obtain the elastic parameter curve of each virtual well.

[0025] In some embodiments, establishing a phase-controlled low-frequency model of elastic parameters based on the optimal location of each virtual well and the corresponding elastic parameter curves of each virtual well, under dual constraints of longitudinal and transverse directions, includes:

[0026] Under longitudinal and lateral constraints, elastic interpolation is performed using the Kriging algorithm based on the optimal location of each virtual well and the corresponding elastic parameter curves of each virtual well to obtain a phase-controlled low-frequency model of the elastic parameters.

[0027] In some embodiments, the elastic parameters include: longitudinal wave velocity, transverse wave velocity, and density.

[0028] Secondly, this disclosure provides an apparatus for inverting elastic parameters, comprising:

[0029] The acquisition module is used to acquire partial angle-stacked seismic data volumes and sedimentary facies maps of the study area that meet the inversion requirements, as well as drilling data of adjacent study areas. The drilling data includes well logging data from drilling.

[0030] The first determining module is used to determine, based on the sedimentary facies diagram, the logging data that matches the characteristics of each sedimentary facies zone in the study area as the logging data of the virtual well.

[0031] The second determining module is used to determine the optimal location of virtual wells within each sedimentary facies zone;

[0032] The third determination module is used to determine the elastic parameter curves of each virtual well based on the logging data of each virtual well.

[0033] A module is established to build a phase-controlled low-frequency model of elastic parameters based on the optimal location of each virtual well and the corresponding elastic parameter curves of each virtual well, under dual constraints of longitudinal and transverse directions.

[0034] The inversion module is used to perform pre-stack phased inversion based on the seismic data volume and the phased low-frequency model to obtain the inversion results of elastic parameters.

[0035] Thirdly, this disclosure 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 the method described in the foregoing aspects.

[0036] Fourthly, this disclosure provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the methods described in the above aspects.

[0037] Fifthly, this disclosure provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the methods described in the foregoing aspects.

[0038] This disclosure provides a method for inverting elastic parameters. It involves acquiring seismic data volumes and sedimentary facies maps of the study area, as well as drilling data from adjacent study areas. The drilling data includes well logging data. Based on the sedimentary facies map, the method determines the logging data of virtual wells in each sedimentary facies zone within the study area from the logging data. It then determines the optimal location of each virtual well within each sedimentary facies zone. Based on the logging data of each virtual well, it determines the elastic parameter curves of each virtual well. Based on the optimal location of each virtual well and the corresponding elastic parameter curves, it establishes a phasing-controlled low-frequency model of the elastic parameters. Pre-stack phasing-controlled inversion is performed based on the seismic data volume and the phasing-controlled low-frequency model to obtain the inversion results of the elastic parameters. This method can obtain elastic parameter inversion results in low-exploration areas that better conform to actual geological sedimentary patterns, thereby more accurately characterizing the sand body distribution features. Attached Figure Description

[0039] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings:

[0040] Figure 1 This is a flowchart illustrating a method for inverting elastic parameters provided in an embodiment of the present disclosure.

[0041] Figure 2 A schematic diagram illustrating the implementation process of an elastic parameter inversion method provided for the implementation of this application;

[0042] Figure 3 A schematic diagram of an elastic parameter inversion device provided in an embodiment of this application;

[0043] Figure 4 The relative value curve of the elastic parameter provided in the embodiments of this application;

[0044] Figure 5 A cross-sectional view of the favorable sand body distribution in the target layer provided in this application embodiment;

[0045] Figure 6 This is a schematic diagram of an elastic parameter inversion device provided in an embodiment of this application.

[0046] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0047] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort should fall within the protection scope of this disclosure.

[0048] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0049] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0050] Example 1

[0051] To address the problems in related technologies, this application provides a method for inverting elastic parameters. This method can be applied to electronic devices such as mobile phones, tablets, wearable devices, in-vehicle devices, augmented reality (AR) / virtual reality (VR) devices, laptops, ultra-mobile personal computers (UMPCs), netbooks, personal digital assistants (PDAs), and data transmission devices. This application does not limit the specific type of electronic device. The electronic device can be a processor of a data transmission device.

[0052] Figure 1 This is a flowchart illustrating a method for inverting elastic parameters provided in an embodiment of this disclosure. Figure 1 As shown, the inversion method for elastic parameters includes:

[0053] Step S101: Obtain partial angle-stacked seismic data volumes and sedimentary facies maps of the study area that meet the inversion requirements, as well as drilling data from adjacent study areas. The drilling data includes well logging data from drilling operations.

[0054] In this application, the seismic data volume is a three-dimensional data set obtained through seismic exploration technology, containing information such as the structure, lithology, and fluid properties of subsurface strata. Sedimentary facies maps are maps drawn based on geological sedimentary characteristics (such as sediment type, sedimentary environment, and sedimentary structures) to show the distribution and variation of sedimentary facies. Drilling data refers to various data and information obtained during the drilling process, including drill cores, logging data, and drilling fluid properties. Logging data is formation information obtained through logging technology, including lithology, porosity, permeability, and saturation of the formations. Drilling data may include: well coordinates, well curves, and well strata data. Well curves include elastic parameter curves such as P-wave velocity (VP), S-wave velocity (VS), and density (RHOB). Adjacent study areas are areas geographically adjacent to the study area and with similar or comparable geological characteristics.

[0055] In this embodiment, seismic exploration operations can be conducted within the study area, including laying out seismic lines, generating seismic waves, and receiving seismic waves to obtain seismic data. The sedimentary environment and processes of the study area can be understood by studying geological outcrops, well cores, and other data. Based on the geological sedimentary characteristics, the study area is divided into different sedimentary facies zones. Based on the division of sedimentary facies zones, sedimentary facies maps are drawn to show the distribution and changes of sedimentary facies.

[0056] Step S102: Based on the sedimentary facies map, determine the logging data from the logging data that matches the characteristics of each sedimentary facies zone in the study area as the logging data for the virtual well.

[0057] In this embodiment, the virtual well is a well virtually constructed within a sedimentary facies zone based on geological sedimentary characteristics and logging data, without the presence of an actual well. A suitable location for the virtual well can be selected within the sedimentary facies zone based on a sedimentary facies map, and then logging data for the virtual well can be obtained using interpolation or fitting methods based on logging data from the actual well.

[0058] In this embodiment of the application, step S102 can be implemented through the following steps: based on the drilling data of the adjacent study area, select wells that match each sedimentary facies zone in the study area, wherein the geological background similarity between each sedimentary facies zone and the corresponding well is greater than the similarity threshold, the wells encounter the target layer and the difference in burial depth is less than the difference threshold, and the difference in formation thickness is less than the thickness difference threshold; and determine the logging data of the wells that match each sedimentary facies zone as the logging data of virtual wells in each sedimentary facies zone in the study area.

[0059] In this embodiment, the geological background describes the geological environment and conditions for the formation and evolution of strata, including sedimentary environment and tectonic setting. The similarity threshold is used as a standard or boundary to determine the degree of similarity between two geological backgrounds, ensuring that the selected wells and sedimentary facies zones in the study area have sufficient similarity in geological background. The target layer is a specific stratum or rock layer of interest in oil and gas exploration, typically possessing the potential for oil and gas accumulation or migration. Depth difference refers to the difference in vertical distance between different wells or strata. Difference threshold: a standard or boundary used to determine whether the depth difference is acceptable. Formation thickness difference: the difference in thickness in the horizontal direction between different wells or strata. Thickness difference threshold: a standard or boundary used to determine whether the formation thickness difference is acceptable.

[0060] In this embodiment, similarity thresholds, difference thresholds, and thickness difference thresholds are set based on the geological background of each sedimentary facies zone within the study area. Wells meeting these criteria are selected from drilling data in adjacent study areas. The geological background information, such as sedimentary environment and structural features, of the wells in adjacent study areas is compared with that of the sedimentary facies zones within the study area. The geological background similarity is calculated, and it is determined whether it exceeds the set similarity threshold. It is checked whether the selected wells have encountered the target layer within the study area. The difference in burial depth between the wells and the sedimentary facies zones within the study area is calculated, and it is determined whether it is less than the set difference threshold. The formation thickness of the selected wells is compared with that of the sedimentary facies zones within the study area. The formation thickness difference is calculated, and it is determined whether it is less than the set thickness difference threshold. For wells meeting all the above criteria, their logging data is used as logging data for virtual wells in the corresponding sedimentary facies zones within the study area. This logging data will be used for subsequent geological interpretation and oil and gas exploration work.

[0061] In some embodiments, the collected well data from adjacent study areas can be classified and organized according to well point structural location, encountered strata, drilling depth, sedimentary facies, formation thickness, curve quality, etc. Taking into account geological background, burial depth compaction, and logging data quality, wells (S1, S2, etc.) that have similar geological background to each sedimentary facies zone in the study area, encountered the target layer with small differences in burial depth, have similar formation thickness, and have high curve quality are selected as virtual wells for this study to carry out subsequent work.

[0062] Step S103: Determine the optimal location of the virtual well within each sedimentary facies zone.

[0063] In this embodiment of the application, the optimal location of the virtual well within the sedimentary facies zone can be determined through comprehensive analysis of geological sedimentary characteristics and well logging data.

[0064] In this embodiment of the application, step S103 can be achieved through the following steps: determining the synthetic seismic record of each virtual well based on the logging data of each virtual well; determining the correlation between the synthetic seismic record of each virtual well and the seismic traces at each location within each sedimentary facies zone; and determining the location with the highest correlation as the optimal location of the virtual well within each sedimentary facies zone.

[0065] In this embodiment, the synthetic seismic record is obtained by mathematically simulating the seismic data based on well logging data and the propagation characteristics of seismic waves. This converts well logging data into seismic data, facilitating comparison and interpretation with seismic data. A seismic trace is a record of seismic wave signals received by a seismic detector during seismic exploration, typically represented as a time series.

[0066] In this embodiment, synthetic seismic records for each virtual well can be simulated using synthetic seismic recording technology based on logging data from each virtual well. Synthetic seismic recording technology typically includes steps such as wavelet extraction, wavelet matching, and synthesis to ensure consistency between the synthetic seismic records and actual seismic data in terms of frequency, phase, and amplitude. Then, well-seismic calibration is performed by repeatedly moving the well locations and comparing them with 3D seismic data. Since the virtual wells are introduced from adjacent areas, their burial depths are similar but also differ. In addition to vertical translation, slight stretching and compression processing may be performed during well-seismic calibration. Therefore, the synthetic seismic records of each virtual well are compared with seismic traces at various locations within the sedimentary facies zone. The correlation between the synthetic seismic records and seismic traces is calculated, typically using correlation coefficients or cross-correlation functions. The correlation reflects the degree of similarity and consistency between the synthetic seismic records and seismic traces. Within the sedimentary facies zone, the seismic trace locations with the highest correlation to the synthetic seismic records of each virtual well are identified. These locations are determined as the optimal locations of the virtual wells within each sedimentary facies zone. The optimal location represents the best position of the virtual well within the sedimentary facies zone, which can more accurately reflect the structure and lithological information of the subsurface strata.

[0067] Step S104: Determine the elastic parameter curves of each virtual well based on the logging data of each virtual well.

[0068] In this embodiment, elastic parameters are physical parameters describing the elastic properties of rocks or formations, such as P-wave velocity, S-wave velocity, Poisson's ratio, Lamé constant, etc. Elastic parameters are used to evaluate the rock mechanical properties, fluid content, and distribution of formations. Elastic parameter curves represent graphs showing how formation elastic parameters change with depth.

[0069] In this embodiment of the application, step S104 can be achieved through the following steps: performing compaction trend analysis on the logging data of each virtual well to obtain the original compaction trend curve; performing cross-fitting of elastic parameters with logging time and burial depth to establish compaction trend curves of each elastic parameter and burial depth; and determining the elastic parameter curve of each virtual well based on the compaction trend curve and the original compaction trend curve.

[0070] In this embodiment, compaction trend analysis studies the process by which rock particles become more compacted due to gravity as burial depth increases, leading to changes in physical properties such as decreased porosity and increased density. The original compaction trend curve reflects the changing trend of the physical properties (such as porosity and density) of the strata as burial depth increases. Cross-plotting is the process of plotting two or more parameters in the same coordinate system and finding the correlation or regularity between them.

[0071] In this embodiment, since the virtual well undergoes recalibration at the new well point, its time depth will change. Different depths are affected by compaction differently. To ensure prediction accuracy, it is necessary to correct the compaction trend at the logging depth. Logging data from each virtual well is collected, especially data related to compaction, such as sonic transit time and density. The trends of these data with burial depth are analyzed, and original compaction trend curves are plotted. These curves reflect the degree of compaction and changes in physical properties of the formation at different burial depths. Elastic parameters (such as P-wave velocity and S-wave velocity) are cross-fitted with the logging time depth. This is typically done by plotting a scatter plot in a two-dimensional coordinate system, with burial depth as the abscissa and elastic parameters as the ordinate. By observing the distribution trend of the scatter plot, the correlation or regularity between the elastic parameters and the burial depth is sought. This may require applying mathematical methods (such as linear regression, polynomial fitting, etc.) to fit these points, thereby obtaining a smoother compaction trend curve. Based on the cross-fit results, a compaction trend curve corresponding to the burial depth is established for each elastic parameter. These curves reflect the changing trends of elastic parameters with burial depth, as well as the compaction differences between different formations. By combining the original compaction trend curves and the compaction trend curves of each elastic parameter, the elastic parameters of each virtual well are corrected and adjusted. This may require considering the influence of factors such as the sedimentary environment and lithological variations on compaction. Finally, based on these corrections and adjustments, the elastic parameter curves of each virtual well at different burial depths are determined. These curves can provide important basis for subsequent geological interpretation, oil and gas exploration, and reservoir prediction.

[0072] In some embodiments, determining the elastic parameter curve of each virtual well based on the compaction trend curve and the original compaction trend curve includes: subtracting the original compaction trend curve from the elastic parameter of the virtual well to obtain the relative value curve of the elastic parameter; adding the compaction trend curve to the relative value curve of the elastic parameter to obtain the elastic parameter curve of each virtual well.

[0073] In this embodiment, for each depth point in the virtual well, the corresponding value on the original compaction trend curve can be found. The elastic parameter value of the virtual well is subtracted from the corresponding value on the original compaction trend curve to obtain the relative value of the elastic parameter. These relative values ​​are plotted as a curve representing the change in depth, i.e., the elastic parameter relative value curve. Then, the original compaction trend curve (which may have been appropriately scaled or shifted to be displayed in the same coordinate system as the elastic parameter relative value curve) is added to the elastic parameter relative value curve. This can be achieved by plotting two curves in the same coordinate system: one is the elastic parameter relative value curve, and the other is the original compaction trend curve (possibly represented by different colors or line types). Alternatively, both curves are displayed in the same coordinate system for comprehensive evaluation, but we can consider this to constitute the final elastic parameter curve analysis. By observing the relative position and shape of these two curves, we can analyze how the elastic properties of the formation change with burial depth and identify possible anomalous areas or features. The final elastic parameter curve can be used for geological interpretation, oil and gas exploration, reservoir prediction, and many other applications.

[0074] Step S105: Based on the optimal location of each virtual well and the corresponding elastic parameter curves of each virtual well, a phase-controlled low-frequency model of elastic parameters is established under the dual constraints of longitudinal and transverse directions.

[0075] In this embodiment, the facies-controlled low-frequency model is a geophysical model that combines information from sedimentary facies zones with low-frequency seismic data (or seismic data with low-frequency components) to describe and predict the petrophysical properties and reservoir characteristics of strata. "Facies-controlled" refers to the model being controlled or constrained by sedimentary facies zones.

[0076] In this embodiment of the application, step S105 can be achieved through the following steps: under the constraints of the construction interpretation layer (vertical constraint) and the boundary constraints of each sedimentary facies zone (lateral constraint), elastic interpolation is carried out using the Kriging algorithm based on the optimal location of each virtual well and the elastic parameter curves corresponding to each virtual well to obtain the phase-controlled low-frequency model of the elastic parameters.

[0077] In this embodiment, the structural interpretation stratigraphic constraint refers to the location of stratigraphic interfaces determined based on seismic interpretation or other geological data. This constraint is used to constrain the geological modeling or interpolation process, ensuring that the model conforms to the known geological structure. The sedimentary facies boundary constraint refers to the boundaries of sedimentary facies zones determined based on sedimentological principles and geological data. This constraint is used to constrain geological modeling, ensuring that the model reflects the sedimentary environment and lithological variations of the strata. The Kriging algorithm is a geostatistical method used to interpolate and predict unknown regions based on the spatial distribution and correlation of known data points (such as elastic parameters of virtual wells). The Kriging algorithm considers the spatial location, distance, and correlation of data points, generating smooth and continuous interpolation results. Elastic interpolation uses known elastic parameter data (such as elastic parameter curves of virtual wells) to interpolate and predict unknown regions, obtaining the elastic parameter distribution of the entire study area.

[0078] In this embodiment, the Kriging algorithm is used to interpolate elastic parameters in an unknown region based on the elastic parameter curves of a virtual well and the set interpolation parameters. During the interpolation process, constraints of structural interpretation horizons and sedimentary facies boundaries are considered to ensure that the interpolation results conform to the geological structure. The interpolated elastic parameter distribution is combined with sedimentary facies information and low-frequency seismic data (if available) to construct a facies-controlled low-frequency model.

[0079] Step S106: Perform pre-stack phased inversion based on the seismic data volume and the phased low-frequency model to obtain the inversion results of elastic parameters.

[0080] In this embodiment, pre-stack facies inversion involves combining sedimentary facies information (i.e., facies control) with pre-stack seismic data (i.e., seismic data at different incident angles) on the seismic data volume to obtain the distribution of elastic parameters of the subsurface medium (such as P-wave impedance, S-wave impedance, Poisson's ratio, etc.). Pre-stack inversion can provide richer rock physical information, which helps to identify fluid types and reservoir characteristics.

[0081] In this embodiment of the application, under the premise of ensuring inversion accuracy and efficiency, pre-stack phased inversion is carried out, an inversion objective function is established, and the three-dimensional pre-stack phased inversion results of P-wave velocity, S-wave velocity, and density are obtained by optimizing the objective function.

[0082] The method provided in this application involves acquiring seismic data volumes and sedimentary facies maps of the study area, as well as drilling data from adjacent study areas. The drilling data includes: well logging data from drilling operations; determining the logging data of virtual wells in each sedimentary facies zone within the study area based on the sedimentary facies map; determining the optimal location of virtual wells within each sedimentary facies zone; determining the elastic parameter curves of each virtual well based on the logging data of each virtual well; establishing a phasing-controlled low-frequency model of elastic parameters based on the optimal location of each virtual well and the corresponding elastic parameter curves; and performing pre-stack phasing-controlled inversion based on the seismic data volume and the phasing-controlled low-frequency model to obtain the inversion results of the elastic parameters. This method can obtain elastic parameter inversion results in low-exploration areas that better conform to actual geological sedimentary patterns, thereby more accurately characterizing the sand body distribution features.

[0083] Example 2

[0084] Based on the above embodiments, this application further provides a method for inverting elastic parameters. Figure 2 This is a schematic diagram illustrating the implementation process of the elasticity parameter inversion method provided in the embodiments of this application, as shown below. Figure 2 As shown, it includes:

[0085] Data preparation:

[0086] The study area can meet the inversion requirements for partial angle-stacked seismic data volumes, sedimentary facies diagrams, well coordinates, well curves, and well stratification data of wells drilled in adjacent study areas. The well curves include elastic parameter curves such as P-wave velocity (VP), S-wave velocity (VS), and density (RHOB).

[0087] Step 1: Virtual Well Optimization

[0088] To achieve facies control constraints, a virtual well needs to be introduced in each facies zone based on the geological sedimentary facies zone division results. The collected well data from adjacent study areas were categorized and organized according to well location, encountered strata, drilling depth, sedimentary facies, formation thickness, and logging quality. Taking into account geological background, depth compaction, and logging data quality, wells (S1, S2…) with similar geological backgrounds to each sedimentary facies zone in the study area, encountered the target layer with minimal depth differences, had similar formation thicknesses, and exhibited high logging quality were selected as virtual wells for this study to conduct subsequent work.

[0089] Step 2: Establishing the optimal virtual well

[0090] Based on the optimal virtual wells selected in step 1 for each sedimentary facies, virtual wells need to be established by selecting optimal well point locations within the corresponding sedimentary facies zones. Within each facies zone, based on the principles of similar geological background, structural depth, and stratigraphic thickness, and the highest possible well-seismic matching degree, the synthetic seismic records of each virtual well are calculated. Well point locations are moved multiple times to perform well-seismic calibration with 3D seismic data. Since the virtual wells are introduced from adjacent areas, their depths are similar but also have differences. In addition to vertical translation, slight stretching and compression processing may be performed during well-seismic calibration. Therefore, the location with the highest correlation between the virtual well synthetic record and the seismic trace is taken as the optimal well point location coordinates for the virtual well, and virtual wells (X1, X2, ...) are established at this location.

[0091] Step 3: Compaction Trend Correction

[0092] Since the virtual well has undergone recalibration at the new well point, its time depth will change. Different depths are affected by compaction differently. To ensure prediction accuracy, it is necessary to correct the compaction trend of the logging depth. Compaction trend analysis is performed on the original S1, S2… elastic parameters such as P-wave velocity (VP), S-wave velocity (VS), and density (RHOB) are cross-fitted with the logging time depth to establish a first or second order relationship (compaction trend curve) between each elastic parameter and the depth. The original elastic parameters are subtracted from the original compaction trend curve to obtain the relative value curves of the elastic parameters (VP_rel, VS_rel, RHOB_rel). After recalibration, the new compaction trend is added to the relative value curves of the elastic parameters to obtain the elastic parameter curves (VP_x, VS_x, RHOB_x) of the virtual well under the new time-depth relationship.

[0093] Step 4: Establishment of a phasor-controlled low-frequency model based on sedimentary characteristics

[0094] During lacustrine fan deposition, the same strata can be divided into multiple sequences due to the alternating ingress and regression of water. Therefore, even within the same strata, sedimentary facies change continuously both laterally and vertically with sedimentation. To obtain more accurate reservoir prediction results, it is necessary to incorporate both the structural interpretation horizons (vertical constraints) and the boundaries of sedimentary facies zones at each stage (lateral constraints) into the low-frequency model. Using the virtual wells and their elastic parameter curves (VP_x, VS_x, RHOB_x) established in step 3, under dual phasing constraints in both vertical and horizontal directions, the classic Kriging algorithm is used to interpolate the model for the three elastic parameters: P-wave velocity, S-wave velocity, and density. This achieves phasing constraints on the low-frequency model, resulting in a phasing-controlled low-frequency model with three elastic parameters that conforms to sedimentary patterns.

[0095] Step 5: Pre-stack phased inversion

[0096] Based on the phased low-frequency model with three elastic parameters obtained in step 4, and the partial angle-stacked seismic data volume that meets the requirements, pre-stack phased inversion is carried out while ensuring inversion accuracy and efficiency. An inversion objective function is established, and the three-dimensional pre-stack phased inversion results of P-wave velocity, S-wave velocity, and density are obtained by optimizing the objective function.

[0097] The method provided in this application offers a pre-stack facies inversion method based on virtual wells in low-exploration areas. For deep, tight clastic reservoirs in low-exploration backgrounds, it prioritizes wells drilled in adjacent areas that best match the sedimentary facies patterns of the region and incorporates them into the local area. Based on principles of similar sedimentary background, similar burial depth and thickness, and high well-seismic calibration consistency, virtual well locations are determined. The established virtual well curves are corrected for compaction trends to obtain virtual well elastic parameter curves that meet reservoir prediction requirements. Then, under the dual constraints of seismic interpretation horizons and phased sedimentary facies zones (vertical and horizontal), a facies-controlled low-frequency model is established. Based on this model, pre-stack facies inversion is performed under the constraints of virtual well information, ultimately obtaining elastic parameter inversion results that better conform to actual geological sedimentary patterns and more accurately characterize sand body distribution features. Simultaneously, it provides a high-precision prediction method for deep, tight clastic reservoirs in low-exploration areas.

[0098] Example 3

[0099] Based on the above embodiments, this application provides a specific example. The embodiment selects a deep, dual-source, steep-slope submarine fan in a depression zone of the Dongpu Depression as the target reservoir. According to the analysis of previous sedimentary characteristics, the main target layer exhibits multiple fan formations vertically and dual-source superposition laterally. Currently, no wells have been drilled to the target layer within the depression zone, affecting the petrophysical analysis and reservoir prediction in this area. Drilling in adjacent depression zones has revealed deep tight gas development in the target layer. The two depressions have similar geological backgrounds; therefore, this invention is used to introduce virtual wells from adjacent depressions to conduct facies-controlled reservoir prediction for the dual-source, steep-slope submarine fan in this work area.

[0100] Based on preliminary data surveys and forward modeling, it was determined that this area is a double-source steep slope underwater fan deposit. Seismic attributes sensitive to facies zones were selected to characterize three sedimentary subfacies: western source fan root, western source + eastern source fan middle, and eastern source fan root.

[0101] Based on the above geological sedimentary facies zone division results, the collected well data from adjacent depressions were classified and organized according to well location, drilling depth, sedimentary facies, formation thickness, and curve quality. Taking into account geological background and logging data quality, wells (W1, W2, and W3) with similar geological backgrounds to the three sedimentary subfacies in the study area, which encountered the target layer with small differences in burial depth, had similar formation thickness, and high curve quality were selected as virtual wells for this study. Figure 3This is a schematic diagram of the original curve of well W1 provided in an embodiment of this application, as shown below. Figure 3 As shown.

[0102] Based on the three adjacent wells selected above, virtual wells are established in this work area. First, well locations need to be optimized for the three different sedimentary facies zones in this work area. The composite seismic records of the three wells are calculated using the convolution algorithm. Within the corresponding three sedimentary facies zones, based on the principles of similar geological background, structural depth, and stratum thickness, and the highest possible well-seismic matching degree, the well locations are moved multiple times to conduct well-seismic calibration with the 3D seismic data. Since the virtual wells are introduced from adjacent areas, their depths are similar but also have differences. In addition to vertical translation, stretching and compression processing may also be performed during well-seismic calibration. The location with the highest correlation between the composite record of the virtual well and the seismic trace is taken as the optimal well location coordinate of the virtual well, and virtual wells (X1, X2, X3) are established at this location.

[0103] The compaction trend of the well logging depth is corrected. Compaction trend analysis is performed on the original S1, S2, and S3 values. Figure 4 This application provides a schematic diagram illustrating the compaction trend analysis performed in steps S1, S2, and S3, as shown in the embodiment. Figure 4 As shown, elastic parameters such as P-wave velocity (VP), S-wave velocity (VS), and density (RHOB) are cross-fitted with logging time depth to establish a quadratic relationship between each elastic parameter and time depth. For example, the compaction trend expression for P-wave velocity is: VP = -3.2 × 10⁻⁶. -4 ×Time 2 +2.24×Time+713.4. Subtract the original compaction trend curve from the original elastic parameters to obtain the relative value curves of the elastic parameters (VP_rel, VS_rel, RHOB_rel). After recalibration, based on the compaction trend relationship between the elastic parameters and the time depth established earlier, add the compaction trends of virtual wells X1, X2, and X3 under the new time-depth relationship to the relative value curves of the elastic parameters to obtain the elastic parameter curves of the virtual wells under the new time-depth relationship (VP_x, VS_x, RHOB_x).

[0104] Under the dual constraints of structural interpretation horizon (vertical constraint) and the boundaries of sedimentary facies zones of each phase (lateral constraint), the established virtual wells and their elastic parameter curves (VP_x, VS_x, RHOB_x) are interpolated using the classical Kriging algorithm within the facies zone where each virtual well is located. This achieves facies-controlled constraints on the low-frequency model, resulting in a facies-controlled low-frequency model with three elastic parameters that conforms to the sedimentary laws.

[0105] Since the target layer is located at a depth of more than 5000 meters, and the maximum incident angle of the pre-stack angle gather is below 30°, considering both inversion accuracy and computational efficiency, based on the phased low-frequency model with the three elastic parameters obtained above, and a partial angle-stacked seismic data volume that meets the requirements, pre-stack inversion is carried out using the Aki-Richard approximation. By optimizing the objective function, the three-dimensional pre-stack phased inversion results of P-wave velocity, S-wave velocity, and density are obtained. Figure 5 A cross-sectional view of the favorable sand body distribution in the target layer provided for an embodiment of this application, such as... Figure 5 As shown in the figure, the invention can predict the lateral distribution characteristics of favorable sand bodies well, and the sand body boundaries are clear, which can provide a strong basis for the characterization of lithological traps.

[0106] Example 4

[0107] This application provides an apparatus for inverting elastic parameters. Figure 6 A schematic diagram of an elastic parameter inversion device provided in an embodiment of this application is shown below. Figure 6 As shown, it includes:

[0108] The acquisition module is used to acquire partial angle-stacked seismic data volumes and sedimentary facies maps of the study area that meet the inversion requirements, as well as drilling data of adjacent study areas. The drilling data includes well logging data from drilling.

[0109] The first determining module is used to determine, based on the sedimentary facies diagram, the logging data that matches the characteristics of each sedimentary facies zone in the study area as the logging data of the virtual well.

[0110] The second determining module is used to determine the optimal location of virtual wells within each sedimentary facies zone;

[0111] The third determination module is used to determine the elastic parameter curves of each virtual well based on the logging data of each virtual well.

[0112] A module is established to build a phase-controlled low-frequency model of elastic parameters based on the optimal location of each virtual well and the corresponding elastic parameter curves of each virtual well, under dual constraints of longitudinal and transverse directions.

[0113] The inversion module is used to perform pre-stack phased inversion based on the seismic data volume and the phased low-frequency model to obtain the inversion results of elastic parameters.

[0114] Example 5

[0115] Based on the above embodiments, this embodiment 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 the method described in the above embodiments.

[0116] In some embodiments of this example, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the method described in the above embodiments.

[0117] In some embodiments of this example, a computer program product is provided, including a computer program / instructions, which, when executed by a processor, implements the steps of the method described in the above embodiments.

[0118] 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.

[0119] 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 can include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, computer storage media (e.g., hard disks, floppy disks, solid-state drives, removable disks), etc. Blu-ray discs, etc.

[0120] 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.

[0121] 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.).

[0122] The processor can communicate with external devices via the I / O bus through wired or wireless networks.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

Claims

1. A method for inverting elastic parameters, characterized in that, include: Acquire partial angle-stacked seismic data volumes and sedimentary facies maps of the study area that meet the inversion requirements, as well as drilling data from adjacent study areas, wherein the drilling data includes well logging data from drilling. Based on the sedimentary facies diagram, well logging data that matches the characteristics of each sedimentary facies zone in the study area is determined from the well logging data and used as the well logging data for virtual wells; Determine the optimal location of virtual wells within each sedimentary facies zone; The elastic parameter curves of each virtual well are determined based on the logging data of each virtual well. Based on the optimal location of each virtual well and the corresponding elastic parameter curves of each virtual well, a phase-controlled low-frequency model of elastic parameters is established under both longitudinal and transverse constraints. Based on the seismic data volume and the phased low-frequency model, pre-stack phased inversion is performed to obtain the inversion results of elastic parameters.

2. The method according to claim 1, characterized in that, The step of determining the logging data that matches the characteristics of each sedimentary facies zone in the study area from the logging data based on the sedimentary facies map as the logging data for the virtual well includes: Based on the drilling data of the adjacent study areas, wells matching each sedimentary facies zone in the study area are selected. Among them, the geological background similarity between each sedimentary facies zone and the corresponding well is greater than the similarity threshold, the wells encounter the target layer and the difference in burial depth is less than the difference threshold, and the difference in formation thickness is less than the thickness difference threshold. The logging data of wells matched to each sedimentary facies zone are determined as the logging data of virtual wells in each sedimentary facies zone of the study area.

3. The method according to claim 1, characterized in that, Determining the optimal location of virtual wells within each sedimentary facies zone includes: The synthetic seismic record of each virtual well is determined based on the logging data of each virtual well; Determine the correlation between the synthetic seismic records of each virtual well and the seismic traces at each location within each sedimentary facies zone; The location with the highest correlation is determined as the optimal location of the virtual well within each sedimentary facies zone.

4. The method according to claim 1, characterized in that, The determination of the elastic parameter curves for each virtual well based on logging data from each virtual well includes: Compaction trend analysis was performed on the logging data of each virtual well to obtain the original compaction trend curve; By performing cross-fitting of elastic parameters with logging time and burial depth, compaction trend curves of each elastic parameter and burial depth are established. The elastic parameter curves of each virtual well are determined based on the compaction trend curve and the original compaction trend curve.

5. The method according to claim 4, characterized in that, The process of determining the elastic parameter curves for each virtual well based on the compaction trend curve and the original compaction trend curve includes: Subtract the original compaction trend curve from the elastic parameters of the virtual well to obtain the relative value curve of the elastic parameters; The compaction trend curve is added to the relative value curve of the elastic parameter to obtain the elastic parameter curve of each virtual well.

6. The method according to claim 1, characterized in that, The phase-controlled low-frequency model of elastic parameters, established under dual constraints of longitudinal and transverse directions, is based on the optimal location of each virtual well and the corresponding elastic parameter curves of each virtual well. This model includes: Under longitudinal and lateral constraints, elastic interpolation is performed using the Kriging algorithm based on the optimal location of each virtual well and the corresponding elastic parameter curves of each virtual well to obtain a phase-controlled low-frequency model of the elastic parameters.

7. The method according to any one of claims 1 to 6, characterized in that, The elastic parameters include: longitudinal wave velocity, transverse wave velocity, and density.

8. 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 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 7.

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