Local fine velocity modeling method and device and electronic equipment

Abstract

The invention relates to the technical field of geophysical exploration, and discloses a local fine velocity modeling method and device and electronic equipment, and the method comprises the steps: building an initial velocity model according to the velocity information of well data, the velocity information of first arrival inversion and the pre-stack time migration velocity information; performing optimization iteration on the initial velocity model through a network tomography inversion method to obtain an optimized initial velocity model; performing local ray tracing on the unreasonable constructed imaging area, and determining a ray path corresponding to the unreasonable constructed imaging area; and performing local velocity optimization on the optimized initial velocity model according to the ray path to obtain a shallow velocity model. According to the invention, a more accurate shallow velocity model can be obtained, so that the imaging quality of data of double complex regions is improved.

Description

Technical Field

[0001] The present invention relates to the field of geophysical exploration technology, and in particular to a local fine velocity modeling method, device and electronic equipment. Background Art

[0002] Imaging of data in dual-complex areas is one of the key areas that needs to be tackled in the field of geophysical exploration. In dual-complex structural areas, the surface elevation, formation occurrence, lithology, and velocity change dramatically, and the underground structure is complex, with developed faults and superimposed steep-angle structures. Therefore, it is difficult to accurately image dual-complex structural areas and the contradiction between wells and seismic data is prominent.

[0003] In order to achieve accurate imaging of dual complex structural areas, high requirements are placed on depth domain velocity modeling, especially shallow velocity modeling. If the shallow velocity model is not established accurately, the error will accumulate in the underlying strata, affecting the imaging quality and accuracy.

[0004] At present, the depth domain velocity modeling methods for shallow data in double complex areas mainly include:

[0005] (1) Apply the cannon first-arrival tomography technique to establish a near-surface velocity model;

[0006] (2) A model for initial velocity of undulating surface is established by using network tomography inversion conditions under micro-logging constraints;

[0007] (3) For shallow surface velocity modeling, a small smooth surface is used to replace the true surface, and back-reflection wave tomography is used to solve the near-surface velocity model problem.

[0008] Since the shallow velocity model involves many influencing factors, a single modeling method has certain limitations and cannot obtain a more accurate shallow velocity model, which in turn affects the imaging accuracy.

[0009] Therefore, a local fine velocity modeling method is urgently needed to solve the above technical problems. Summary of the invention

[0010] In view of the above problems, the present invention provides a local fine velocity modeling method, device and electronic equipment, which can accurately perform local correction on the shallow velocity model to obtain an accurate shallow velocity model, thereby improving the imaging accuracy of complex underground structures affected by complex surfaces.

[0011] The present invention provides a local fine velocity modeling method, the method comprising:

[0012] An initial velocity model is established based on the velocity information of well data, the velocity information of first arrival inversion and the velocity information of prestack time migration;

[0013] The initial velocity model is optimized and iterated by using the network tomography inversion method to obtain the optimized initial velocity model;

[0014] Perform local ray tracing on the unreasonable structural imaging area to determine the ray path corresponding to the unreasonable structural imaging area;

[0015] The optimized initial velocity model is locally optimized according to the ray path to obtain the shallow velocity model.

[0016] Furthermore, an initial velocity model is established based on the velocity information of the well data, the velocity information of the first arrival inversion, and the velocity information of the prestack time migration, including:

[0017] Determine the velocity range of the formation from shallow to deep based on the velocity information of well data, the velocity information of first arrival inversion and the velocity information of prestack time migration;

[0018] Based on surface information and structural interpretation information, determine the spatial distribution attributes of a large set of stratigraphic layers and faults from shallow to deep;

[0019] Taking the velocity range and spatial distribution attributes of the stratum as structural constraints, the velocity along the layer is preset based on the structural constraints to obtain the initial shallow velocity model;

[0020] The initial shallow velocity model is subjected to velocity scanning and migration processing at different preset scales to obtain shallow imaging results corresponding to each preset scale, and an initial shallow velocity model satisfying preset conditions is determined according to the shallow imaging results, and the initial shallow velocity model satisfying the preset conditions is used as the initial velocity model.

[0021] Furthermore, the preset processing includes:

[0022] Filling, interpolation, smoothing and extrapolation.

[0023] Furthermore, unreasonable structural imaging areas include:

[0024] In the shallow layer, the signal-to-noise ratio is not greater than the preset signal-to-noise ratio threshold and in the inclined strata with underlying fault development, the area where the formation imaging accuracy is not greater than the preset accuracy threshold, and / or, the local imaging anomaly area determined based on geological knowledge and gather characteristics.

[0025] Furthermore, the optimized initial velocity model is locally optimized according to the ray path to obtain a shallow velocity model, including:

[0026] Determine the area range involved in the ray path as the area range to be optimized;

[0027] Determine the speed optimization amount of the area to be optimized according to the preset ideal imaging trend;

[0028] According to the velocity optimization amount, local velocity optimization is performed on the velocity of the area to be optimized in the optimized initial velocity model to obtain a shallow velocity model.

[0029] The present invention also provides a local fine velocity modeling device, the device comprising:

[0030] An initial velocity model building module is used to build an initial velocity model based on the velocity information of well data, the velocity information of first arrival inversion and the velocity information of prestack time migration;

[0031] The first optimization module is used to optimize and iterate the initial velocity model through a network tomography inversion method to obtain an optimized initial velocity model;

[0032] A ray tracing module is used to perform local ray tracing on the unreasonable structural imaging area to determine the ray path corresponding to the unreasonable structural imaging area;

[0033] The second optimization module is used to perform local velocity optimization on the optimized initial velocity model according to the ray path to obtain a shallow velocity model.

[0034] Furthermore, the initial velocity model building module includes:

[0035] The velocity range determination unit is used to determine the velocity range of the formation from shallow to deep according to the velocity information of the well data, the velocity information of the first arrival inversion and the velocity information of the prestack time migration;

[0036] The spatial distribution attribute determination unit is used to determine the spatial distribution attributes of a large set of stratigraphic layers and faults from shallow to deep according to the surface information and structural interpretation information;

[0037] A preprocessing unit is used to use the velocity range and spatial distribution attributes of the stratum as structural constraints, perform preset processing on the velocity along the layer based on the structural constraints, and obtain an initial shallow velocity model;

[0038] The initial velocity model determination unit is used to perform velocity scanning and offset processing on the initial shallow velocity model at different preset scales, obtain shallow imaging results corresponding to each preset scale, determine the initial shallow velocity model that meets the preset conditions according to the shallow imaging results, and use the initial shallow velocity model that meets the preset conditions as the initial velocity model.

[0039] Further, in the preprocessing unit, the preset processing includes:

[0040] Filling, interpolation, smoothing and extrapolation.

[0041] Furthermore, the second optimization module includes:

[0042] A unit for determining the region to be optimized, used for determining the region involved in the ray path as the region to be optimized;

[0043] An optimization value determination unit, used to determine the speed optimization amount of the area to be optimized according to a preset ideal imaging trend;

[0044] The local velocity optimization unit is used to perform local velocity optimization on the velocity of the area to be optimized in the optimized initial velocity model according to the velocity optimization amount, so as to obtain a shallow velocity model.

[0045] The present invention also provides a computer-readable storage medium, wherein the computer program stored in the computer-readable storage medium implements the steps of the above method when executed by one or more processors.

[0046] The present invention also provides an electronic device, comprising a memory and one or more processors, wherein a computer program is stored in the memory, and when the computer program is executed by the one or more processors, the steps of the above method are executed.

[0047] The local fine velocity modeling method, device and electronic device provided by the present invention have at least the following beneficial effects:

[0048] (1) First, an initial velocity model is established based on the velocity information of well data, the velocity information of first arrival inversion, and the velocity information of prestack time migration. Then, the initial velocity model is optimized and iterated through the network tomography inversion method to obtain the optimized initial velocity model. Local ray tracing is performed on the unreasonable structural imaging area to determine the ray path corresponding to the unreasonable structural imaging area. Then, local velocity optimization is performed on the optimized initial velocity model based on the ray path to obtain a more accurate shallow velocity model after local correction, thereby improving the imaging quality of data in the dual complex area and further confirming the structural position.

[0049] (2) The entire modeling process does not completely rely on relevant near-surface information such as first arrival, micro-logging data, and well data, and has a wide range of application areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] It should also be noted that, for the convenience of description, only the parts related to the present invention are shown in the drawings. The drawings constituting a part of the specification of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments and descriptions of the present invention are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0052] Figure 1 A flowchart of the steps of a local fine velocity modeling method provided in the first embodiment of the present invention;

[0053] Figure 2 It is a schematic diagram of the ray path and speed optimization;

[0054] Figure 3 A schematic diagram of the result of prestack depth migration processing based on the shallow velocity model obtained by the prior art;

[0055] Figure 4 A schematic diagram of the result of prestack depth migration processing using the shallow velocity model obtained by the present invention;

[0056] Figure 5 This is a schematic diagram of the structure of a local fine velocity modeling device provided in the second embodiment of the present invention;

[0057] Figure 6 This is a schematic diagram of the structure of an electronic device provided in Embodiment 5 of the present invention;

[0058] Reference numerals:

[0059] Figure 5 In: 501-initial velocity model building module, 502-first optimization module, 503-ray tracing module, 504-second optimization module;

[0060] Figure 6 In it: 600 - electronic device, 601 - processor, 602 - communication bus, 603 - user interface, 604 - communication interface, 605 - memory. DETAILED DESCRIPTION

[0061] The present invention will be further described below in conjunction with the embodiments shown in the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described here.

[0062] It should be noted that the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0063] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more, unless explicitly limited or otherwise indicated in the context.

[0064] In addition, the term "and / or" in the present disclosure is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present disclosure generally indicates that the associated objects before and after are in an "or" relationship.

[0065] It should also be understood that the description of the various embodiments of the present invention focuses on the differences between the various embodiments, and the same or similar aspects thereof can be referenced to each other, and for the sake of brevity, they will not be described one by one.

[0066] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0067] The embodiments of the present invention may not discuss in detail the technologies, methods, and devices known to ordinary technicians in the relevant fields, but where appropriate, the technologies, methods, and devices should be considered as part of the specification.

[0068] Embodiments of the present invention can be applied to electronic devices such as terminal devices, computer systems, servers, etc., which can operate with many other general or special computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, servers, etc. include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, small computer systems, large computer systems, and distributed cloud computing technology environments including any of the above systems, etc.

[0069] Electronic devices such as terminal devices, computer systems, servers, etc. can be described in the general context of computer system executable instructions (such as program modules) executed by computer systems. Generally, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in a distributed cloud computing environment, where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media including storage devices.

[0070] From the background technology, it can be seen that the imaging of data in dual-complex areas is one of the key directions that need to be tackled in the field of geophysical exploration. In dual-complex structural areas, the surface elevation, formation occurrence, lithology, and velocity change dramatically, and the underground structure is complex, with developed faults and superimposed steep-angle structures. Therefore, it is difficult to accurately image dual-complex structural areas and the contradiction between wells and seismic data is prominent.

[0071] In order to achieve accurate imaging of dual complex structural areas, high requirements are placed on depth domain velocity modeling, especially shallow velocity modeling. If the shallow velocity model is not established accurately, the error will accumulate in the underlying strata, affecting the imaging quality and accuracy.

[0072] At present, the depth domain velocity modeling methods for shallow data in double complex areas mainly include:

[0073] (1) Apply the cannon first-arrival tomography technique to establish a near-surface velocity model;

[0074] (2) A model for initial velocity of undulating surface is established by using network tomography inversion conditions under micro-logging constraints;

[0075] (3) For shallow surface velocity modeling, a small smooth surface is used to replace the true surface, and back-reflection wave tomography is used to solve the near-surface velocity model problem.

[0076] Since the shallow velocity model involves many influencing factors, a single modeling method has certain limitations and cannot obtain a more accurate shallow velocity model, which in turn affects the imaging accuracy.

[0077] Therefore, the present invention proposes a local fine velocity modeling method to solve the above technical problems.

[0078] Embodiment 1

[0079] In the first embodiment of the present invention, Figure 1 As shown, a local fine velocity modeling method is provided, and the method specifically includes the following steps:

[0080] Step S101: establishing an initial velocity model according to the velocity information of well data, the velocity information of first arrival inversion and the velocity information of prestack time migration;

[0081] Step S102: optimizing and iterating the initial velocity model by a network tomography inversion method to obtain an optimized initial velocity model;

[0082] Step S103: performing local ray tracing on the unreasonable structural imaging area to determine the ray path corresponding to the unreasonable structural imaging area;

[0083] Step S104: performing local velocity optimization on the optimized initial velocity model according to the ray path to obtain a shallow velocity model.

[0084] Optionally, in step S101, an initial velocity model is established according to velocity information of well data, velocity information of first arrival inversion and velocity information of prestack time migration, including:

[0085] Step S1011: Determine the velocity range of the formation from shallow to deep according to the velocity information of the well data, the velocity information of the first arrival inversion and the velocity information of the prestack time migration;

[0086] Step S1012: Determine the spatial distribution attributes of a large set of stratigraphic layers and faults from shallow to deep according to the surface information and structural interpretation information;

[0087] Step S1013: taking the velocity range and spatial distribution attributes of the stratum as structural constraints, performing preset processing on the layer velocity based on the structural constraints to obtain an initial shallow velocity model;

[0088] Step S1014: Perform velocity scanning and offset processing on the initial shallow velocity model at different preset ratios to obtain shallow imaging results corresponding to each preset ratio, determine an initial shallow velocity model that meets preset conditions based on the shallow imaging results, and use the initial shallow velocity model that meets the preset conditions as the initial velocity model.

[0089] Furthermore, before step S1011, it is necessary to collect velocity information of well data, velocity information of first-arrival inversion, and velocity information of prestack time migration in advance.

[0090] In step S1013 , the preset processing includes filling processing, interpolation processing, smoothing processing and extrapolation processing.

[0091] In step S1014, the preset ratio and preset conditions can be set by the technician according to actual needs, and the present invention does not impose any special restrictions on this.

[0092] For example, in one implementation, determining an initial shallow velocity model that meets preset conditions based on shallow imaging results specifically includes: determining an initial shallow velocity model at a preset ratio corresponding to a shallow imaging result with the best imaging effect in the shallow imaging results as the initial shallow velocity model that meets the preset conditions.

[0093] Optionally, in step S102, the initial velocity model is optimized and iterated by a network tomography inversion method, specifically, multiple rounds of optimization iteration and updating of the anisotropic velocity model are performed to ensure that the existing common imaging point gather information visible in the middle and shallow layers is flattened to reduce the influence of anisotropic parameters other than velocity.

[0094] In step S102, the initial velocity model is optimized and iterated by a network tomography inversion method, which specifically includes the following steps:

[0095] Step S1021: Optimize the common imaging point gathers.

[0096] Specifically, in step S1021, various means in the prior art may be used to significantly improve the signal-to-noise ratio of the CIP gather.

[0097] Step S1022: Multi-parameter joint control to pick up the remaining curvature.

[0098] Step S1023: Multi-attribute constrained reflection wave grid tomographic inversion.

[0099] Step S1024: Scanning at different speed ratios is performed in areas with low signal-to-noise ratio and complex structures.

[0100] Step S1025: Perform velocity update and offset imaging.

[0101] Step S1026: The above steps are repeated for multiple rounds until the gathers are leveled and the imaging cannot be improved any further.

[0102] Optionally, in step S103, constructing an unreasonable imaging area includes:

[0103] In the shallow layer, the signal-to-noise ratio is not greater than the preset signal-to-noise ratio threshold and in the inclined strata with underlying fault development, the area where the formation imaging accuracy is not greater than the preset accuracy threshold, and / or, the local imaging anomaly area determined based on geological knowledge and gather characteristics.

[0104] It should be noted that the preset accuracy threshold and the preset signal-to-noise ratio threshold can be set by technicians according to actual needs, and the present invention does not specifically limit the specific values ​​of the preset accuracy threshold and the preset signal-to-noise ratio threshold.

[0105] Specifically, local ray tracing is performed on the unreasonable structural imaging area, and determining the ray path of the unreasonable structural imaging area can be specifically achieved through the back-illumination technology. The back-illumination technology is based on ray theory, and the ray path passing through a certain imaging position is calculated through ray tracing.

[0106] Optionally, in step S104, local velocity optimization is performed on the optimized initial velocity model according to the ray path to obtain a shallow velocity model, including:

[0107] Step S1041: Determine the region range involved in the ray path as the region range to be optimized.

[0108] Step S1042: Determine the speed optimization amount of the area to be optimized according to the preset ideal imaging trend.

[0109] Step S1043: performing local velocity optimization on the velocity of the area to be optimized in the optimized initial velocity model according to the velocity optimization amount to obtain a shallow velocity model.

[0110] The preset ideal imaging trend in step S1042 is determined based on the imaging accuracy and whether the imaging effect conforms to the geological knowledge. After the preset ideal imaging trend is obtained, the speed increase or decrease value of the area to be optimized, that is, the speed optimization amount, can be determined based on the ideal imaging trend.

[0111] like Figure 2 As shown in the figure, it is a schematic diagram of the ray path and speed optimization corresponding to the unreasonable structural imaging area determined by the back-illumination technology. Figure 2 The triangular area enclosed by the ray path is the area to be optimized, and different colors represent different speed optimization amounts.

[0112] Further, Figure 3 This is the result of prestack depth migration processing based on the shallow velocity model obtained by existing technology. Figure 4 This is the result of prestack depth migration processing based on the shallow velocity model obtained by the present invention. Figure 3 and Figure 4 The circled area is processed by prestack depth migration using the shallow velocity model obtained by the present invention. The obtained profile fault position is re-established, the breakpoint is clear, some messy reflections near the fault are eliminated, the continuity of the event axis is improved, the small fault block wave group characteristics are natural, the fault is clear, and the breakpoint is accurate, which is convenient for interpretation and tracking.

[0113] The local fine velocity modeling method provided in this embodiment first establishes an initial velocity model according to the velocity information of well data, the velocity information of first arrival inversion and the velocity information of pre-stack time migration, and then optimizes and iterates the initial velocity model through the network tomography inversion method to obtain the optimized initial velocity model, and performs local ray tracing on the unreasonable structural imaging area to determine the ray path corresponding to the unreasonable structural imaging area, and then performs local velocity optimization on the optimized initial velocity model according to the ray path to obtain a more accurate shallow velocity model after local correction, thereby improving the imaging quality of data in dual complex areas and further confirming the structural position, and the entire modeling process does not completely rely on relevant near-surface information such as first arrival, micro-logging data, and well data, and has a wide application area and range.

[0114] Embodiment 2

[0115] In the second embodiment of the present invention, Figure 5 As shown, a local fine velocity modeling device is provided, and the device specifically comprises:

[0116] An initial velocity model building module 501 is used to build an initial velocity model based on the velocity information of well data, the velocity information of first arrival inversion and the velocity information of prestack time migration;

[0117] The first optimization module 502 is used to optimize and iterate the initial velocity model by using a network tomography inversion method to obtain an optimized initial velocity model;

[0118] A ray tracing module 503 is used to perform local ray tracing on the unreasonable structural imaging area to determine the ray path corresponding to the unreasonable structural imaging area;

[0119] The second optimization module 504 is used to perform local velocity optimization on the optimized initial velocity model according to the ray path to obtain a shallow velocity model.

[0120] Optionally, the initial velocity model building module 501 includes:

[0121] The velocity range determination unit 5011 is used to determine the velocity range of the formation from shallow to deep according to the velocity information of the well data, the velocity information of the first arrival inversion and the velocity information of the prestack time migration;

[0122] The spatial distribution attribute determination unit 5012 is used to determine the spatial distribution attributes of a large set of stratigraphic layers and faults from shallow to deep according to the surface information and structural interpretation information;

[0123] A preprocessing unit 5013 is used to use the velocity range and spatial distribution attributes of the stratum as structural constraints, perform preset processing on the velocity along the layer based on the structural constraints, and obtain an initial shallow velocity model;

[0124] The initial velocity model determination unit 5014 is used to perform velocity scanning and offset processing on the initial shallow velocity model at different preset scales, obtain shallow imaging results corresponding to each preset scale, determine an initial shallow velocity model that meets preset conditions according to the shallow imaging results, and use the initial shallow velocity model that meets the preset conditions as the initial velocity model.

[0125] Optionally, in the preprocessing unit 5013, the preset processing includes:

[0126] Filling, interpolation, smoothing and extrapolation.

[0127] Optionally, in the first optimization module 502, the initial velocity model is optimized and iterated by a network tomography inversion method, specifically including the following steps:

[0128] Optimize common imaging point gathers;

[0129] Multi-parameter joint control to pick up the residual curvature;

[0130] Reflection wave grid tomographic inversion with multi-attribute constraints;

[0131] Different ratios of velocity scanning are used for complex structural areas with low signal-to-noise ratio;

[0132] Perform velocity updates and migration imaging;

[0133] The above steps are repeated for multiple rounds until the gathers are leveled and the imaging cannot be improved any further.

[0134] Optionally, in the ray tracing module 503, constructing an unreasonable imaging area includes:

[0135] In the shallow layer, the signal-to-noise ratio is not greater than the preset signal-to-noise ratio threshold and in the inclined strata with underlying fault development, the area where the formation imaging accuracy is not greater than the preset accuracy threshold, and / or, the local imaging anomaly area determined based on geological knowledge and gather characteristics.

[0136] Optionally, the second optimization module 504 includes:

[0137] The to-be-optimized region determining unit 5041 is used to determine the region range involved in the ray path as the to-be-optimized region;

[0138] An optimization value determination unit 5042 is used to determine the speed optimization value of the area to be optimized according to a preset ideal imaging trend;

[0139] The local velocity optimization unit 5043 is used to perform local velocity optimization on the velocity of the area to be optimized in the optimized initial velocity model according to the velocity optimization amount, so as to obtain a shallow velocity model.

[0140] The local fine velocity modeling device provided in this embodiment first establishes an initial velocity model according to the velocity information of well data, the velocity information of first arrival inversion and the velocity information of pre-stack time migration, and then optimizes and iterates the initial velocity model through the network tomography inversion method to obtain the optimized initial velocity model, and performs local ray tracing on the unreasonable structural imaging area to determine the ray path corresponding to the unreasonable structural imaging area, and then performs local velocity optimization on the optimized initial velocity model according to the ray path to obtain a more accurate shallow velocity model after local correction, thereby improving the imaging quality of data in dual complex areas and further confirming the structural position, and the entire modeling process does not completely rely on relevant near-surface information such as first arrival, micro-logging data, well data, etc., and has a wide application area and range.

[0141] Embodiment 3

[0142] In the third embodiment of the present invention, a computer program product is also provided. The computer program product includes a computer program or instructions. When the computer program or instructions are executed by a processor, all or part of the steps of the local fine velocity modeling method described in the above embodiment are implemented.

[0143] Among them, the local fine velocity modeling method includes:

[0144] An initial velocity model is established based on the velocity information of well data, the velocity information of first arrival inversion and the velocity information of prestack time migration;

[0145] The initial velocity model is optimized and iterated by using the network tomography inversion method to obtain the optimized initial velocity model;

[0146] Perform local ray tracing on the unreasonable structural imaging area to determine the ray path corresponding to the unreasonable structural imaging area;

[0147] The optimized initial velocity model is locally optimized according to the ray path to obtain the shallow velocity model.

[0148] Optionally, an initial velocity model is established based on the velocity information of the well data, the velocity information of the first arrival inversion, and the velocity information of the prestack time migration, including:

[0149] Determine the velocity range of the formation from shallow to deep based on the velocity information of well data, the velocity information of first arrival inversion and the velocity information of prestack time migration;

[0150] Based on surface information and structural interpretation information, determine the spatial distribution attributes of a large set of stratigraphic layers and faults from shallow to deep;

[0151] Taking the velocity range and spatial distribution attributes of the stratum as structural constraints, the velocity along the layer is preset based on the structural constraints to obtain the initial shallow velocity model;

[0152] The initial shallow velocity model is subjected to velocity scanning and migration processing at different preset scales to obtain shallow imaging results corresponding to each preset scale, and an initial shallow velocity model satisfying preset conditions is determined according to the shallow imaging results, and the initial shallow velocity model satisfying the preset conditions is used as the initial velocity model.

[0153] Optionally, the preset processing includes: filling processing, interpolation processing, smoothing processing and extrapolation processing.

[0154] Optionally, the initial velocity model is optimized iteratively by a network tomographic inversion method, including:

[0155] Optimize common imaging point gathers;

[0156] Multi-parameter joint control to pick up the residual curvature;

[0157] Reflection wave grid tomographic inversion with multi-attribute constraints;

[0158] Different ratios of velocity scanning are used for complex structural areas with low signal-to-noise ratio;

[0159] Perform velocity updates and migration imaging;

[0160] The above steps are repeated for multiple rounds until the gathers are leveled and the imaging cannot be improved any further.

[0161] Optionally, the unreasonable structural imaging area includes:

[0162] In the shallow layer, the signal-to-noise ratio is not greater than the preset signal-to-noise ratio threshold and in the inclined strata with underlying fault development, the area where the formation imaging accuracy is not greater than the preset accuracy threshold, and / or, the local imaging anomaly area determined based on geological knowledge and gather characteristics.

[0163] Optionally, local velocity optimization is performed on the optimized initial velocity model according to the ray path to obtain a shallow velocity model, including:

[0164] The area range involved in the ray path is the area range to be optimized;

[0165] Determine the speed optimization amount of the area to be optimized according to the preset ideal imaging trend;

[0166] According to the velocity optimization amount, local velocity optimization is performed on the velocity of the area to be optimized in the optimized initial velocity model to obtain a shallow velocity model.

[0167] Further, the computer program product may include one or more computer executable components configured to execute the embodiments when the program is run; the computer program product may also include a computer program tangibly contained on a readable medium, the computer program including program code for executing any method in the embodiments of the present invention. In such an embodiment, the computer program may be downloaded and installed from a network through a communication part, and / or installed from a removable medium.

[0168] Embodiment 4

[0169] In the fourth embodiment of the present invention, a computer-readable storage medium is further provided. The computer program stored in the computer-readable storage medium, when executed by one or more processors, implements all or part of the steps of the local fine velocity modeling method described in the above embodiment.

[0170] Among them, the local fine velocity modeling method includes:

[0171] An initial velocity model is established based on the velocity information of well data, the velocity information of first arrival inversion and the velocity information of prestack time migration;

[0172] The initial velocity model is optimized and iterated by using the network tomography inversion method to obtain the optimized initial velocity model;

[0173] Perform local ray tracing on the unreasonable structural imaging area to determine the ray path corresponding to the unreasonable structural imaging area;

[0174] The optimized initial velocity model is locally optimized according to the ray path to obtain the shallow velocity model.

[0175] Optionally, an initial velocity model is established based on the velocity information of the well data, the velocity information of the first arrival inversion, and the velocity information of the prestack time migration, including:

[0176] Determine the velocity range of the formation from shallow to deep based on the velocity information of well data, the velocity information of first arrival inversion and the velocity information of prestack time migration;

[0177] Based on surface information and structural interpretation information, determine the spatial distribution attributes of a large set of stratigraphic layers and faults from shallow to deep;

[0178] Taking the velocity range and spatial distribution attributes of the stratum as structural constraints, the velocity along the layer is preset based on the structural constraints to obtain the initial shallow velocity model;

[0179] The initial shallow velocity model is subjected to velocity scanning and migration processing at different preset scales to obtain shallow imaging results corresponding to each preset scale, and an initial shallow velocity model satisfying preset conditions is determined according to the shallow imaging results, and the initial shallow velocity model satisfying the preset conditions is used as the initial velocity model.

[0180] Optionally, the preset processing includes: filling processing, interpolation processing, smoothing processing and extrapolation processing.

[0181] Optionally, the initial velocity model is optimized iteratively by a network tomographic inversion method, including:

[0182] Optimize common imaging point gathers;

[0183] Multi-parameter joint control to pick up the residual curvature;

[0184] Reflection wave grid tomographic inversion with multi-attribute constraints;

[0185] Different ratios of velocity scanning are used for complex structural areas with low signal-to-noise ratio;

[0186] Perform velocity updates and migration imaging;

[0187] The above steps are repeated for multiple rounds until the gathers are leveled and the imaging cannot be improved any further.

[0188] Optionally, the unreasonable structural imaging area includes:

[0189] In the shallow layer, the signal-to-noise ratio is not greater than the preset signal-to-noise ratio threshold and in the inclined strata with underlying fault development, the area where the formation imaging accuracy is not greater than the preset accuracy threshold, and / or, the local imaging anomaly area determined based on geological knowledge and gather characteristics.

[0190] Optionally, local velocity optimization is performed on the optimized initial velocity model according to the ray path to obtain a shallow velocity model, including:

[0191] The area range involved in the ray path is the area range to be optimized;

[0192] Determine the speed optimization amount of the area to be optimized according to the preset ideal imaging trend;

[0193] According to the velocity optimization amount, local velocity optimization is performed on the velocity of the area to be optimized in the optimized initial velocity model to obtain a shallow velocity model.

[0194] Each functional unit in each embodiment of the present invention may be integrated into a processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. If the functions are implemented in the form of software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium.

[0195] Computer readable storage media can use any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any combination of the above. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (Random Access Memory, referred to as RAM), a read-only memory (Read-Only Memory, referred to as ROM), an erasable programmable read-only memory (Electrically Erasable Programmable read only memory, referred to as EPROM), an optical fiber, a portable compact disc read-only memory (Compact Disc Read-Only Memory, referred to as CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0196] Embodiment 5

[0197] In the fifth embodiment of the present invention, an electronic device 600 is further provided, and the electronic device may be a mobile phone, a computer, or a tablet computer, etc. Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention is shown in FIG. Figure 6 As shown, the electronic device 600 includes: at least one processor 601, at least one communication bus 602, a user interface 603, at least one external communication interface 604, and a memory 605. The communication bus 602 is configured to realize the connection and communication between these components. The user interface 603 may include a display screen, and the external communication interface 604 may include a standard wired interface and a wireless interface. The memory 605 stores a computer program, and the memory 605 and one or more processors 601 are connected to each other in communication. When the computer program is executed by one or more processors, the processor 601 is configured to execute the computer program stored in the memory to implement all or part of the steps of the local fine velocity modeling method in the above-mentioned embodiment.

[0198] Among them, the local fine velocity modeling method includes:

[0199] An initial velocity model is established based on the velocity information of well data, the velocity information of first arrival inversion and the velocity information of prestack time migration;

[0200] The initial velocity model is optimized and iterated by using the network tomography inversion method to obtain the optimized initial velocity model;

[0201] Perform local ray tracing on the unreasonable structural imaging area to determine the ray path corresponding to the unreasonable structural imaging area;

[0202] The optimized initial velocity model is locally optimized according to the ray path to obtain the shallow velocity model.

[0203] Optionally, an initial velocity model is established based on the velocity information of the well data, the velocity information of the first arrival inversion, and the velocity information of the prestack time migration, including:

[0204] Determine the velocity range of the formation from shallow to deep based on the velocity information of well data, the velocity information of first arrival inversion and the velocity information of prestack time migration;

[0205] Based on surface information and structural interpretation information, determine the spatial distribution attributes of a large set of stratigraphic layers and faults from shallow to deep;

[0206] Taking the velocity range and spatial distribution attributes of the stratum as structural constraints, the velocity along the layer is preset based on the structural constraints to obtain the initial shallow velocity model;

[0207] The initial shallow velocity model is subjected to velocity scanning and migration processing at different preset scales to obtain shallow imaging results corresponding to each preset scale, and an initial shallow velocity model satisfying preset conditions is determined according to the shallow imaging results, and the initial shallow velocity model satisfying the preset conditions is used as the initial velocity model.

[0208] Optionally, the preset processing includes: filling processing, interpolation processing, smoothing processing and extrapolation processing.

[0209] Optionally, the initial velocity model is optimized iteratively by a network tomographic inversion method, including:

[0210] Optimize common imaging point gathers;

[0211] Multi-parameter joint control to pick up the residual curvature;

[0212] Reflection wave grid tomographic inversion with multi-attribute constraints;

[0213] Different ratios of velocity scanning are used for complex structural areas with low signal-to-noise ratio;

[0214] Perform velocity updates and migration imaging;

[0215] The above steps are repeated for multiple rounds until the gathers are leveled and the imaging cannot be improved any further.

[0216] Optionally, the unreasonable structural imaging area includes:

[0217] In the shallow layer, the signal-to-noise ratio is not greater than the preset signal-to-noise ratio threshold and in the inclined strata with underlying fault development, the area where the formation imaging accuracy is not greater than the preset accuracy threshold, and / or, the local imaging anomaly area determined based on geological knowledge and gather characteristics.

[0218] Optionally, local velocity optimization is performed on the optimized initial velocity model according to the ray path to obtain a shallow velocity model, including:

[0219] The area range involved in the ray path is the area range to be optimized;

[0220] Determine the speed optimization amount of the area to be optimized according to the preset ideal imaging trend;

[0221] According to the velocity optimization amount, local velocity optimization is performed on the velocity of the area to be optimized in the optimized initial velocity model to obtain a shallow velocity model.

[0222] The processor can be an application specific integrated circuit (ASIC), a digital signal processor (DSP), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components to execute all or part of the steps of the local fine velocity modeling method described in the above embodiment, which will not be repeated in this embodiment.

[0223] The memory can be implemented by any type of volatile or non-volatile storage device or a combination of them, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0224] The local fine velocity modeling method, device and electronic equipment provided by the present invention first establish an initial velocity model according to the velocity information of well data, the velocity information of first arrival inversion and the velocity information of pre-stack time migration, and then optimize and iterate the initial velocity model through a network tomography inversion method to obtain an optimized initial velocity model, and perform local ray tracing on the unreasonable structural imaging area to determine the ray path corresponding to the unreasonable structural imaging area, and then perform local velocity optimization on the optimized initial velocity model according to the ray path to obtain a more accurate shallow velocity model after local correction, thereby improving the imaging quality of data in dual complex areas and further confirming the structural position, and the entire modeling process does not completely rely on relevant near-surface information such as first arrival, micro-logging data, well data, etc., and has a wide application area and range.

[0225] The basic principles of the present invention are described above in conjunction with specific embodiments. However, it should be pointed out that the advantages, strengths, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are necessarily possessed by each embodiment of the present disclosure.

[0226] In addition, the specific details disclosed above are only for the purpose of illustration and facilitating understanding, and are not limiting. The above details do not limit the present invention to being implemented by adopting the above specific details.

[0227] The block diagrams of the devices, apparatuses, equipment, and systems involved in the present invention are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems may be connected, arranged, and configured in any manner. Words such as "including," "comprising," "having," and the like are open-ended words, meaning "including but not limited to," and may be used interchangeably therewith.

[0228] It should also be noted that in the apparatus, device and method disclosed herein, each component or each step can be decomposed and / or reassembled, and such decomposition and / or reassembly should be regarded as an equivalent solution of the present invention.

[0229] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present invention. Therefore, the present invention is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0230] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present invention to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A local fine velocity modeling method, characterized in that: The method comprises: An initial velocity model is established based on the velocity information of well data, the velocity information of first arrival inversion and the velocity information of prestack time migration; The initial velocity model is optimized and iterated by a network tomography inversion method to obtain an optimized initial velocity model; Performing local ray tracing on the unreasonable structural imaging area to determine the ray path corresponding to the unreasonable structural imaging area; The optimized initial velocity model is locally optimized according to the ray path to obtain a shallow velocity model.

2. The local fine velocity modeling method according to claim 1, characterized in that: The initial velocity model is established according to the velocity information of the well data, the velocity information of the first arrival inversion and the velocity information of the prestack time migration, including: Determine the velocity range of the formation from shallow to deep according to the velocity information of the well data, the velocity information of the first arrival inversion and the velocity information of the prestack time migration; Based on surface information and structural interpretation information, determine the spatial distribution attributes of a large set of stratigraphic layers and faults from shallow to deep; Taking the velocity range of the stratum and the spatial distribution attribute as structural constraints, pre-processing the layer velocity based on the structural constraints to obtain an initial shallow velocity model; The initial shallow velocity model is subjected to velocity scanning and migration processing at different preset ratios to obtain shallow imaging results corresponding to each preset ratio, an initial shallow velocity model satisfying preset conditions is determined according to the shallow imaging results, and the initial shallow velocity model satisfying the preset conditions is used as the initial velocity model.

3. The local fine velocity modeling method according to claim 2, characterized in that: The preset processing includes: Filling, interpolation, smoothing and extrapolation.

4. The local fine velocity modeling method according to claim 1, characterized in that: The unreasonable structural imaging areas include: In the shallow layer, the signal-to-noise ratio is not greater than the preset signal-to-noise ratio threshold and in the inclined strata with underlying fault development, the area where the formation imaging accuracy is not greater than the preset accuracy threshold, and / or, the local imaging anomaly area determined based on geological knowledge and gather characteristics.

5. The local fine velocity modeling method according to claim 1, characterized in that: The locally optimizing the optimized initial velocity model according to the ray path to obtain a shallow velocity model includes: Determine the region involved in the ray path as the region to be optimized; Determining the speed optimization amount of the area to be optimized according to a preset ideal imaging trend; The velocity of the area to be optimized in the optimized initial velocity model is locally optimized according to the velocity optimization amount to obtain a shallow velocity model.

6. A local fine velocity modeling device, characterized in that: The device comprises: An initial velocity model building module is used to build an initial velocity model based on the velocity information of well data, the velocity information of first arrival inversion and the velocity information of prestack time migration; A first optimization module is used to optimize and iterate the initial velocity model by a network tomography inversion method to obtain an optimized initial velocity model; A ray tracing module is used to perform local ray tracing on the unreasonable structural imaging area to determine the ray path corresponding to the unreasonable structural imaging area; The second optimization module is used to perform local velocity optimization on the optimized initial velocity model according to the ray path to obtain a shallow velocity model.

7. The local fine velocity modeling device according to claim 6, characterized in that: The initial velocity model building module comprises: A velocity range determination unit, used to determine the velocity range of the formation from shallow to deep according to the velocity information of the well data, the velocity information of the first arrival inversion and the velocity information of the prestack time migration; The spatial distribution attribute determination unit is used to determine the spatial distribution attributes of a large set of stratigraphic layers and faults from shallow to deep according to the surface information and structural interpretation information; A preprocessing unit, configured to use the velocity range of the stratum and the spatial distribution attribute as structural constraints, perform preset processing on the layer velocity based on the structural constraints, and obtain an initial shallow velocity model; an initial velocity model determining unit, for performing velocity scanning and offset processing on the initial shallow velocity model at different preset ratios, obtaining shallow imaging results corresponding to each preset ratio, determining an initial shallow velocity model that meets preset conditions according to the shallow imaging results, and using the initial shallow velocity model that meets the preset conditions as the initial velocity model.

8. The local fine velocity modeling device according to claim 6, characterized in that: The second optimization module comprises: A unit for determining the area range to be optimized, used for determining the area range involved in the ray path as the area range to be optimized; An optimization value determination unit, used to determine the speed optimization value of the area to be optimized according to a preset ideal imaging trend; The local velocity optimization unit is used to perform local velocity optimization on the velocity of the area to be optimized in the optimized initial velocity model according to the velocity optimization amount to obtain a shallow velocity model.

9. A computer-readable storage medium, characterized in that: The computer program stored in the computer-readable storage medium, when executed by one or more processors, implements the steps of the method according to any one of claims 1 to 5.

10. An electronic device, characterized in that: The method comprises a memory and one or more processors, wherein a computer program is stored in the memory, and when the computer program is executed by the one or more processors, the steps of the method according to any one of claims 1 to 5 are performed.

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