A local fine velocity modeling method, device and electronic equipment

Abstract

The present application relates to the technical field of geophysical exploration, and discloses a local fine velocity modeling method, device and electronic equipment, the method comprising: establishing an initial velocity model according to velocity information of well data, velocity information of first arrival inversion and prestack time migration velocity information; optimizing and iterating the initial velocity model through a network tomography inversion method to obtain an optimized initial velocity model; performing local ray tracing on a structure imaging unreasonable area to determine a ray path corresponding to the structure imaging unreasonable area; and performing local velocity optimization on the optimized initial velocity model according to the ray path to obtain a shallow layer velocity model. The present application can obtain a more accurate shallow layer velocity model, thereby improving the imaging quality of data in a double complex area.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geophysical exploration, and in particular to a local fine velocity modeling method and device and electronic equipment. BACKGROUND

[0002] The imaging of data in a double complex area is one of the directions that need to be tackled in the field of geophysical exploration. In a double complex structure area, the surface elevation, the occurrence of strata, the lithology, and the velocity change are drastic, and the underground structure is complex, with developed fractures and superimposed steeply dipping structures, so it is difficult to accurately image the double complex structure area, and the well-seismic contradiction is prominent.

[0003] To realize accurate imaging of the double complex structure area, higher requirements are put forward for depth domain velocity modeling, especially shallow layer velocity modeling. If the shallow layer velocity model is not accurately established, the error will accumulate to the underlying strata, affecting the imaging quality and accuracy.

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

[0005] (1) applying the big cannon first break tomography technology to establish a near-surface velocity model;

[0006] (2) using the micro-logging constrained network tomographic inversion to establish a model for the initial velocity of the undulating surface;

[0007] (3) for shallow surface velocity modeling, using a small smooth surface to replace the true surface, using the turning wave tomography, etc., to solve the near-surface velocity model problem.

[0008] Since the shallow surface velocity model involves many influencing factors, a single modeling method has certain limitations and cannot obtain a relatively accurate shallow layer velocity model, thereby affecting the imaging accuracy.

[0009] Therefore, there is an urgent need for a local fine velocity modeling method to solve the above technical problems. SUMMARY

[0010] To solve the above problems, the present application provides a local fine velocity modeling method, device and electronic equipment, which can accurately correct the shallow layer velocity model and obtain an accurate shallow layer velocity model, thereby improving the imaging accuracy of complex underground structures affected by complex surfaces.

[0011] The present application provides a local fine velocity modeling method, which comprises:

[0012] establishing an initial velocity model according to the velocity information of well data, the velocity information of first break inversion, and the pre-stack time migration velocity information;

[0013] Optimize and iterate the initial velocity model through a network tomography inversion method to obtain an optimized initial velocity model;

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

[0015] Perform local velocity optimization on the optimized initial velocity model according to the ray path to obtain a shallow layer velocity model.

[0016] Further, the initial velocity model is established according to the velocity information of well data, the velocity information of first arrival inversion and the pre-stack time migration velocity information, including:

[0017] According to the velocity information of well data, the velocity information of first arrival inversion and the pre-stack time migration velocity information, determine the velocity range of the stratum from shallow to deep;

[0018] According to the surface information and the structure interpretation information, determine the spatial distribution properties of the large suite of strata and faults from shallow to deep;

[0019] Take the velocity range and spatial distribution properties of the stratum as the structure constraint condition, and perform preset processing on the velocity along the layer based on the structure constraint condition to obtain an initial shallow layer velocity model;

[0020] Perform velocity scanning and migration processing on the initial shallow layer velocity model under different preset proportions to obtain shallow layer imaging results corresponding to each preset proportion, determine the initial shallow layer velocity model meeting the preset condition according to the shallow layer imaging results, and take the initial shallow layer velocity model meeting the preset condition as the initial velocity model.

[0021] Further, the preset processing includes:

[0022] The filling processing, interpolation processing, smoothing processing and extrapolation processing.

[0023] Further, the unreasonable structure imaging area includes:

[0024] In the inclined stratum with a shallow layer signal-to-noise ratio not greater than a preset signal-to-noise ratio threshold and a fracture development existing under the stratum, an area with a stratum imaging precision not greater than a preset precision threshold, and / or a local imaging abnormal area determined according to geological understanding and gather characteristics.

[0025] Further, the local velocity optimization on the optimized initial velocity model according to the ray path to obtain a shallow layer velocity model, including:

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

[0027] Determine the velocity optimization amount of the to-be-optimized area range according to a preset ideal imaging trend;

[0028] According to the velocity optimization quantity, the velocity of the to-be-optimized region range in the optimized initial velocity model is locally optimized to obtain a shallow velocity model.

[0029] The application further provides a local fine velocity modeling device, which comprises:

[0030] An initial velocity model establishing module is configured to establish an initial velocity model according to velocity information of well data, velocity information of first arrival inversion and pre-stack time migration velocity information;

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

[0032] A ray tracing module is configured to perform local ray tracing on the structure imaging unreasonable region to determine a ray path corresponding to the structure imaging unreasonable region;

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

[0034] Further, the initial velocity model establishing module comprises:

[0035] A velocity range determining unit is configured to determine a velocity range of strata from shallow to deep according to velocity information of well data, velocity information of first arrival inversion and pre-stack time migration velocity information;

[0036] A spatial distribution attribute determining unit is configured to determine spatial distribution attributes of a large set of strata and faults from shallow to deep according to surface information and structure interpretation information;

[0037] A preprocessing unit is configured to take the velocity range of strata and the spatial distribution attributes as structure constraint conditions, perform preset processing on the velocity along the layer based on the structure constraint conditions to obtain an initial shallow velocity model;

[0038] An initial velocity model determining unit is configured to perform velocity scanning and migration processing on the initial shallow velocity model under different preset proportions to obtain shallow imaging results corresponding to each preset proportion, determine an initial shallow velocity model meeting preset conditions according to the shallow imaging results and take the initial shallow velocity model meeting the preset conditions as the initial velocity model.

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

[0040] Filling processing, interpolation processing, smoothing processing and extrapolation processing.

[0041] Further, the second optimization module comprises:

[0042] A region range to be optimized determining unit is configured to determine a region range involved by the ray path as the region range to be optimized.

[0043] An optimization value determining unit is configured to determine a velocity optimization amount of the region range to be optimized according to a preset ideal imaging trend.

[0044] A local velocity optimization unit is configured to perform local velocity optimization on the velocity of the region range to be optimized in the optimized initial velocity model according to the velocity optimization amount, and obtain the shallow velocity model.

[0045] The application further provides a computer readable storage medium, which stores a computer program, and when the computer program is executed by one or more processors, the steps of the above method are implemented.

[0046] The application further provides an electronic device, which comprises a memory and one or more processors, and the memory stores a computer program, 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 and device and the electronic device provided by the application have at least the following beneficial effects:

[0048] (1) The initial velocity model is established according to the velocity information of well data, the velocity information of first arrival inversion and the pre-stack time migration velocity information, and then the initial velocity model is iteratively optimized by the network tomography inversion method to obtain the optimized initial velocity model, and the local ray tracing is performed on the unreasonable region of structure imaging to determine the ray path corresponding to the unreasonable region of structure imaging, and then the local velocity optimization is performed on the optimized initial velocity model according to the ray path to obtain the more accurate shallow velocity model after local correction, thereby improving the imaging quality of data in double complex areas and further implementing the structure position.

[0049] (2) The whole modeling process does not completely depend on the first arrival, micro logging data, well data and other related near-surface information, and is applicable to a wide range of areas. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the art, other drawings can be obtained without creative labor based on these drawings.

[0051] It should also be noted that, for ease of description, only the portions relevant to the present invention are shown in the accompanying drawings. The accompanying drawings, which constitute a part of the present invention, are intended to provide a further understanding of the present invention. The exemplary embodiments and descriptions thereof are intended to explain the present invention and do not constitute undue limitations on the present invention. In the accompanying drawings:

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

[0053] Figure 2 Schematic diagram of ray path and speed optimization;

[0054] Figure 3 This is a schematic diagram of the results of pre-stack depth migration processing based on the shallow velocity model obtained based on existing technology;

[0055] Figure 4 This is a schematic diagram of the results of prestack depth migration processing using the shallow velocity model obtained by the present invention;

[0056] Figure 5 This is a schematic structural diagram 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 Middle: 501-initial velocity model building module, 502-first optimization module, 503-ray tracing module, 504-second optimization module;

[0060] Figure 6 In the figure: 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 with reference to the embodiments shown in the accompanying drawings. Obviously, the embodiments described are only a part 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 herein.

[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 one or more of the components, data and structures described in the embodiments of the application can be implemented, individually and / or collectively, by a wide range of hardware, software, firmware, or any combination thereof.

[0064] In addition, the term "and / or" in the present disclosure is merely used to describe an associated relationship, which means that there can be three relationships, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone. In addition, the character " / " in the present disclosure generally represents an "or" relationship between the front and rear associated objects.

[0065] It should also be understood that the description of the application for each embodiment emphasizes the differences between each embodiment, and the same or similar parts can be referred to each other, and for the sake of brevity, will not be repeated.

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

[0067] The technology, methods and devices known to those skilled in the related art may not be discussed in detail in the embodiments of the present application, but in appropriate cases, the technology, methods and devices should be considered as part of the specification.

[0068] The embodiments of the present application can be applied to terminal devices, computer systems, servers and other electronic devices, 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 terminal devices, computer systems, servers and other electronic devices 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, mainframe computer systems and distributed cloud computing technology environments including any of the above systems, and the like.

[0069] The terminal device, computer system, server and other electronic device can be described in the general context of computer system executable instructions, such as program modules, executed by the computer system. Generally, program modules can include routines, programs, objects, components, logic, data structures, and the like, which perform specific tasks or implement specific abstract data types. The computer system / server can be implemented in a distributed cloud computing environment, in which 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 art, the imaging of data in double complex areas is one of the directions that need to be focused on in the field of geophysical exploration. In double complex structural areas, the surface elevation, the occurrence of strata, the lithology and the velocity change greatly, and the underground structure is complex, with developed fractures and superimposed steeply dipping structures, so it is difficult to accurately image double complex structural areas, and the well-to-seismic contradiction is prominent.

[0071] To realize accurate imaging in double complex structural areas, higher requirements are put forward for depth domain velocity modeling, especially shallow layer velocity modeling. If the shallow layer velocity model is not accurately established, the error will accumulate to the underlying strata, affecting the imaging quality and accuracy.

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

[0073] (1) applying the big cannon first break tomography technology to establish a near-surface velocity model;

[0074] (2) establishing a model for the initial velocity of the undulating surface under the condition of micro-logging constrained network tomographic inversion;

[0075] (3) for shallow surface velocity modeling, a small smooth surface is used to replace the true surface, and the near-surface velocity model problem is solved by using the reverberation tomography.

[0076] Since the shallow surface velocity model involves many influencing factors, a single modeling method has certain limitations and cannot obtain a relatively accurate shallow layer velocity model, thereby affecting the imaging accuracy.

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

[0078] Example One

[0079] In the first embodiment of the present application, as shown in Figure 1 , a local fine velocity modeling method is provided, which 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 break inversion and the prestack time migration velocity information;

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

[0082] Step S103: locally ray tracing in the unreasonable area of structural imaging to determine the ray path corresponding to the unreasonable area of structural imaging;

[0083] Step S104: locally optimizing the optimized initial velocity model according to the ray path to obtain a shallow layer 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 pre-stack time migration velocity information, including:

[0085] Step S1011: determining a velocity range of strata from shallow to deep according to velocity information of well data, velocity information of first arrival inversion and pre-stack time migration velocity information;

[0086] Step S1012: determining spatial distribution properties of large suite strata and faults from shallow to deep according to surface information and structure interpretation information;

[0087] Step S1013: taking the velocity range and spatial distribution properties of strata as structure constraint conditions, performing preset processing on the velocity along the layer based on the structure constraint conditions to obtain an initial shallow layer velocity model;

[0088] Step S1014: performing velocity scanning and migration processing on the initial shallow layer velocity model under different preset proportions to obtain shallow layer imaging results corresponding to each preset proportion, determining an initial shallow layer velocity model meeting preset conditions according to the shallow layer imaging results, and taking the initial shallow layer velocity model meeting the preset conditions as the initial velocity model.

[0089] Further, before step S1011, velocity information of well data, velocity information of first arrival inversion and pre-stack time migration velocity information need to be collected 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 proportion and the preset condition can be set by a technician according to actual needs, and the present application does not specially limit this.

[0092] Illustratively, in an implementation manner, the initial shallow layer velocity model meeting the preset conditions is determined according to the shallow layer imaging results specifically includes: taking the initial shallow layer velocity model under a preset proportion corresponding to the shallow layer imaging result with the best imaging effect in the shallow layer imaging results as the initial shallow layer velocity model meeting the preset conditions.

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

[0094] In step S102, the initial velocity model is optimized and iterated through the network tomography inversion method, specifically including the following steps:

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

[0096] Specifically, in this step S1021, various means in the prior art can 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 structure.

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

[0101] Step S1026: Repeat the above steps for multiple rounds of iterations 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 shallow layers with a signal-to-noise ratio not exceeding a preset signal-to-noise ratio threshold and in inclined strata with underlying fault development, areas where the formation imaging accuracy is not exceeding a preset accuracy threshold, and / or areas of local imaging anomaly determined based on geological understanding 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 impose any special restrictions on 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 region with unreasonable structural imaging, and determining the ray path of the region with unreasonable structural imaging can be achieved specifically through back-illumination technology. Back-illumination technology is based on ray theory and calculates the ray path passing through a certain imaging position 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 area range involved in the ray path as the area 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 is consistent with geological knowledge. Once the preset ideal imaging trend is obtained, the speed increase or decrease value of the area to be optimized can be determined based on the ideal imaging trend, that is, the speed optimization amount.

[0111] like Figure 2 The figure shows 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] Furthermore, Figure 3 This is the result of pre-stack 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 pre-stack 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 phase 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 based on the velocity information of well data, the velocity information of first arrival inversion, and the velocity information of pre-stack time migration. The initial velocity model is then iteratively optimized using a network tomography inversion method to obtain an optimized initial velocity model. Local ray tracing is performed on the area with unreasonable structural imaging to determine the ray path corresponding to the area with unreasonable structural imaging. The optimized initial velocity model is then locally optimized based on 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 location. In addition, 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 applications.

[0114] Example Two

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

[0116] The initial velocity model establishing module 501 is configured to establish an initial velocity model according to velocity information of well data, velocity information of first arrival inversion, and pre-stack time migration velocity information;

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

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

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

[0120] Optionally, the initial velocity model establishing module 501 comprises:

[0121] The velocity range determining unit 5011 is configured to determine a velocity range of strata from shallow to deep according to velocity information of well data, velocity information of first arrival inversion, and pre-stack time migration velocity information;

[0122] The spatial distribution attribute determining unit 5012 is configured to determine spatial distribution attributes of a large set of strata and faults from shallow to deep according to surface information and structural interpretation information;

[0123] The preprocessing unit 5013 is configured to perform preset processing on the along-layer velocity based on a structural constraint condition, to obtain an initial shallow layer velocity model, where the velocity range of the strata and the spatial distribution attributes are taken as the structural constraint condition;

[0124] The initial velocity model determining unit 5014 is configured to perform velocity scanning and migration processing on the initial shallow layer velocity model at different preset scales to obtain shallow layer imaging results corresponding to each preset scale, to determine an initial shallow layer velocity model satisfying a preset condition according to the shallow layer imaging results, and to take the initial shallow layer velocity model satisfying the preset condition as the initial velocity model.

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

[0126] filling processing, interpolation processing, smoothing processing, and extrapolation processing.

[0127] Optionally, in the first optimization module 502, the optimization and iteration of the initial velocity model by the network tomography inversion method specifically comprises the following steps:

[0128] optimizing common imaging point gathers;

[0129] jointly controlling picking up residual curvatures by multiple parameters;

[0130] Multi-attribute constrained reflected wave grid tomography inversion

[0131] Using different scale velocity scanning in structure complex area with low signal to noise ratio;

[0132] Carrying out velocity updating and migration imaging;

[0133] The above steps are cycled for multiple iterations until the gathers are flattened and the imaging cannot be improved any more.

[0134] Optionally, in the ray tracing module 503, the structure imaging unreasonable area comprises:

[0135] In the shallow layer signal to noise ratio is not greater than a preset signal to noise ratio threshold and there is a fracture development inclined stratum under the stratum, the stratum imaging precision is not greater than a preset precision threshold area, and / or a local imaging abnormal area determined according to geological knowledge and gather characteristics.

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

[0137] The to-be-optimized area determination unit 5041 is configured to determine the area range involved by the ray path as the to-be-optimized area;

[0138] The optimization value determination unit 5042 is configured to determine a velocity optimization amount of the to-be-optimized area range according to a preset ideal imaging trend;

[0139] The local velocity optimization unit 5043 is configured to perform local velocity optimization on the velocity of the to-be-optimized area range in the optimized initial velocity model according to the velocity optimization amount, to obtain the shallow layer velocity model.

[0140] The local fine velocity modeling device provided in the embodiment first establishes 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, then optimizes and iterates the initial velocity model through a network tomography inversion method to obtain an optimized initial velocity model, performs local ray tracing on a structure imaging unreasonable area, determines a ray path corresponding to the structure imaging unreasonable area, and then performs local velocity optimization on the optimized initial velocity model according to the ray path to obtain a more accurate shallow layer velocity model after local correction, thereby improving the imaging quality of data in a double complex area, further implementing the structure position, and the entire modeling process does not completely depend on the first arrival, micro logging data, well data and other related near-surface information, and the application area and range are wide.

[0141] Example Three

[0142] In the third embodiment of the present application, a computer program product is also provided, which comprises a computer program or instructions, which, when executed by a processor, implement all or part of the steps of the local fine velocity modeling method described in the above embodiments.

[0143] The local fine velocity modeling method comprises:

[0144] An initial velocity model is established according to velocity information of well data, velocity information of first arrival inversion and pre-stack time migration velocity information;

[0145] The initial velocity model is iteratively optimized by a network tomography inversion method to obtain an optimized initial velocity model;

[0146] Local ray tracing is performed on the unreasonable structural imaging area to determine a 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 a shallow layer velocity model.

[0148] Optionally, the establishment of the initial velocity model according to the velocity information of the well data, the velocity information of the first arrival inversion and the pre-stack time migration velocity information comprises:

[0149] The velocity range of the stratum from shallow to deep is determined according to the velocity information of the well data, the velocity information of the first arrival inversion and the pre-stack time migration velocity information;

[0150] The spatial distribution properties of the large set of stratum horizons and faults from shallow to deep are determined according to the surface information and the structural interpretation information;

[0151] The velocity range and the spatial distribution properties of the stratum are taken as structural constraint conditions, preset processing is performed on the along-layer velocity based on the structural constraint conditions to obtain an initial shallow layer velocity model;

[0152] Velocity scanning and migration processing are performed on the initial shallow layer velocity model under different preset proportions to obtain shallow layer imaging results corresponding to each preset proportion, the initial shallow layer velocity model satisfying a preset condition is determined according to the shallow layer imaging results, and the initial shallow layer velocity model satisfying the preset condition is taken as the initial velocity model.

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

[0154] Optionally, the optimization and iteration of the initial velocity model by the network tomography inversion method comprises:

[0155] Optimized common imaging point gathers are obtained;

[0156] A plurality of parameters are jointly controlled to pick up residual curvature;

[0157] Multi-attribute constrained reflected wave grid tomography inversion

[0158] Adopting different scale velocity scanning in structure complex area with low signal to noise ratio

[0159] Carrying out velocity updating and migration imaging

[0160] Carrying out the above steps repeatedly for multiple iterations until the gathers are flattened and the imaging can not be improved any more.

[0161] Optionally, the unreasonable imaging area includes:

[0162] In the shallow layer, the signal to noise ratio is not greater than a preset signal to noise ratio threshold, and there is a fracture development in the inclined stratum under the cover, the imaging precision of the stratum in the area is not greater than a preset precision threshold, and / or a local imaging abnormal area determined according to geological knowledge and gather characteristics.

[0163] Optionally, the initial velocity model after optimization is locally optimized according to the ray path to obtain a shallow layer velocity model, including:

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

[0165] The velocity optimization amount of the area range to be optimized is determined according to a preset ideal imaging trend;

[0166] The velocity of the area range to be optimized in the initial velocity model after optimization is locally optimized according to the velocity optimization amount to obtain a shallow layer velocity model.

[0167] Further, the computer program product can include one or more computer executable components configured to perform the embodiments when the programs are run; the computer program product can also include a computer program tangibly embodied on a computer readable medium, the computer program containing program code for performing any of the methods in the embodiments of the application. In such embodiments, the computer program can be downloaded and installed from a network by a communication part, and / or installed from a detachable medium.

[0168] Example Four

[0169] In the fourth embodiment of the application, a computer readable storage medium is also provided, which stores a computer program. When executed by one or more processors, the computer program realizes all or part of the steps of the local fine velocity modeling method described in the above embodiments.

[0170] The local fine velocity modeling method includes:

[0171] The initial velocity model is established according to velocity information of well data, velocity information of first arrival inversion and pre-stack time migration velocity information;

[0172] The initial velocity model is iteratively optimized by a network tomography inversion method to obtain an optimized initial velocity model;

[0173] Local ray tracing is performed on the unreasonable structural imaging area to determine the ray path corresponding to the unreasonable structural imaging area;

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

[0175] Optionally, the initial velocity model is established according to velocity information of well data, velocity information of first arrival inversion and pre-stack time migration velocity information, comprising:

[0176] The velocity range of the stratum from shallow to deep is determined according to velocity information of well data, velocity information of first arrival inversion and pre-stack time migration velocity information;

[0177] The spatial distribution properties of large sets of strata and faults from shallow to deep are determined according to surface information and structural interpretation information;

[0178] The initial shallow layer velocity model is obtained by presetting the velocity along the layer based on the structural constraint condition, taking the velocity range and spatial distribution properties of the stratum as the structural constraint condition;

[0179] The initial shallow layer velocity model is scanned and migrated at different preset scales to obtain shallow layer imaging results corresponding to each preset scale, and the initial shallow layer velocity model meeting the preset condition is determined according to the shallow layer imaging results, and the initial shallow layer velocity model meeting the preset condition is taken 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 iteratively optimized by a network tomography inversion method, comprising:

[0182] Optimizing common imaging point gathers;

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

[0184] Multi-attribute constrained reflection wave grid tomography inversion;

[0185] Different scales of velocity scanning are used in the structural complex area with low signal-to-noise ratio;

[0186] Velocity updating and migration imaging are performed;

[0187] The above steps are iterated for multiple rounds until the gathers are flattened and imaging can not be improved any more.

[0188] Optionally, the unreasonable imaging area is configured to include:

[0189] In the shallow layer SNR is not greater than the preset SNR threshold and the underlying existence of fracture development in the inclined strata, the strata imaging accuracy is not greater than the preset accuracy threshold area, and / or, according to the geological knowledge and gather characteristics to determine the local imaging abnormal area.

[0190] Optionally, the initial velocity model after optimization is locally optimized according to the ray path to obtain a shallow layer velocity model, including:

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

[0192] The velocity optimization amount of the area range to be optimized is determined according to the preset ideal imaging trend;

[0193] The velocity of the area range to be optimized in the initial velocity model after optimization is locally optimized according to the velocity optimization amount to obtain a shallow layer velocity model.

[0194] The various functional units in the various embodiments of the present application can be integrated in one processing unit, or each unit can be physically present, or two or more units can be integrated in one unit. If the functions are realized in the form of software functional units and sold or used as independent products, they can be stored in a computer readable storage medium.

[0195] The computer readable storage medium can adopt 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 may, for example, include but is not limited to electric, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or components, or any combination of the above. More specific examples (non-exhaustive list) of readable storage medium include: electric connection with one or more conductors, portable disc, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.

[0196] Example Five

[0197] In the fifth embodiment of the present application, an electronic device 600 is also provided, which can be a mobile phone, a computer, a tablet computer or the like. Figure 6 The electronic device provided in the fifth embodiment of the present application has a structural schematic diagram as shown in Figure 6 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 the components. The user interface 603 can include a display screen, and the external communication interface 604 can include a standard wired interface and a wireless interface. The memory 605 stores a computer program, and the memory 605 and the one or more processors 601 are in communication connection with each other. When the computer program is executed by the one or more processors, the processor 601 is configured to execute the computer program stored in the memory to realize all or part of the steps of the local fine velocity modeling method in the above embodiments.

[0198] The local fine velocity modeling method includes:

[0199] establishing an initial velocity model according to velocity information of well data, velocity information of first arrival inversion, and pre-stack time migration velocity information;

[0200] optimizing and iterating the initial velocity model by a network tomography inversion method to obtain an optimized initial velocity model;

[0201] performing local ray tracing on the unreasonable area of structure imaging to determine a ray path corresponding to the unreasonable area of structure imaging;

[0202] performing local velocity optimization on the optimized initial velocity model according to the ray path to obtain a shallow layer velocity model.

[0203] Optionally, establishing the 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 includes:

[0204] determining a velocity range of strata from shallow to deep according to the velocity information of well data, the velocity information of first arrival inversion, and the pre-stack time migration velocity information;

[0205] determining spatial distribution attributes of a large set of strata and faults from shallow to deep according to surface information and structure interpretation information;

[0206] taking the velocity range of strata and the spatial distribution attributes as structure constraint conditions, performing preset processing on the velocity along the layer based on the structure constraint conditions to obtain an initial shallow layer velocity model;

[0207] The initial shallow velocity model is subjected to velocity scanning and migration processing under different preset ratios to obtain shallow imaging results corresponding to each preset ratio, and the initial shallow velocity model meeting the preset condition is determined according to the shallow imaging results, and the initial shallow velocity model meeting the preset condition is taken 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 iteratively optimized by a network tomography inversion method, including:

[0210] Optimizing common imaging point gathers;

[0211] Multi-parameter joint control picks up residual curvature;

[0212] Multi-attribute constrained reflection wave grid tomography inversion;

[0213] Different ratios of velocity scanning are used in a structure complex region with low signal-to-noise ratio;

[0214] Velocity updating and migration imaging are performed;

[0215] The above steps are cycled for multiple iterations until the gathers are flattened and the imaging cannot be improved.

[0216] Optionally, the unreasonable imaging region includes:

[0217] In a tilted stratum with fracture development under the shallow stratum, a region with stratum imaging precision not greater than a preset precision threshold, and / or a local imaging abnormal region determined according to geological understanding and gather characteristics.

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

[0219] The region range involved by the ray path is the region range to be optimized;

[0220] The velocity optimization amount of the region range to be optimized is determined according to a preset ideal imaging trend;

[0221] The velocity of the region range to be optimized in the initial velocity model after optimization is locally optimized according to the velocity optimization amount to obtain a shallow velocity model.

[0222] The processor can be an Application Specific Integrated Cricuit (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 elements, which are used to execute all or part of the steps of the local fine velocity modeling method described in the above embodiments, and the embodiments will not be repeated here.

[0223] The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, 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 application first establish 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, then optimize and iterate the initial velocity model by a network tomography inversion method to obtain an optimized initial velocity model, perform local ray tracing on a structure imaging unreasonable area, determine the ray path corresponding to the structure imaging unreasonable area, and then perform local velocity optimization on the optimized initial velocity model according to the ray path to obtain a more accurate shallow layer velocity model after local correction, thereby improving the imaging quality of data in double complex areas, further implementing the structure position, and the entire modeling process does not completely depend on the first arrival, micro logging data, well data and other related near-surface information, and the application area and range are wide.

[0225] The basic principles of the application are described above in combination with specific embodiments, but it should be pointed out that the advantages, advantages and effects mentioned in the application are only examples and cannot be considered as the must-have of each embodiment of the present disclosure.

[0226] In addition, the specific details of the above-disclosed implementation are not limiting, but rather are merely exemplary, as are the quantities, shapes, materials, and so forth associated therewith. The implementation described above can be implemented in any desired manner.

[0227] The block diagrams of the devices, apparatuses, equipment, systems referred to in the present disclosure are merely illustrative examples and are not intended to require or imply that the connections, arrangements, configurations must be as shown in the block diagrams. These devices, apparatuses, equipment, systems can be connected, arranged, configured in any manner as will be appreciated by those skilled in the art. Words such as "including," "containing," "comprising," and the like are to be construed in an inclusive fashion, indicating open-ended duration, and should be

[0228] It is also noted that the devices, equipment, and methods of the present disclosure can be embodied in a variety of other forms; all of which have been specifically contemplated herein. It is further noted that the claims can be drafted to exclude any elements or limit the scope of any elements insofar as such elements are specifically mentioned in the specification and / or claims. Other examples of equivalents are written description, filed claims, as well as "means-plus-function" claims (without correspondence to any described structure) and "step-plus-function" claims (without correspondence to any described acts). Any claims intending to cover any such equivalents should be stated as such.

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

[0230] The above description has been presented for the purpose of illustration and description. Furthermore, the description is not intended to limit the embodiments of the present application to forms disclosed herein. Although various example aspects and embodiments have been discussed above, those of skill in the art will recognize certain modifications, permutations, 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; Optimizing and iterating the initial velocity model 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; Performing local velocity optimization on the optimized initial velocity model according to the ray path to obtain a shallow velocity model; The method of establishing an initial velocity model based on velocity information of well data, velocity information of first arrival inversion and velocity information of pre-stack time migration includes: determining the velocity range of the stratum from shallow to deep according to the velocity information of the well data, the velocity information of first arrival inversion and the velocity information of pre-stack time migration; determining the spatial distribution attributes of a large set of stratum layers and faults from shallow to deep according to surface information and structural interpretation information; using the velocity range and the spatial distribution attributes of the stratum as structural constraints, performing preset processing on the layer-along velocity based on the structural constraints to obtain an initial shallow velocity model; performing 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, determining an initial shallow velocity model that meets the 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.

2. The local fine velocity modeling method according to claim 1, characterized in that: The preset processing includes: Filling, interpolation, smoothing, and extrapolation.

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

4. The local fine velocity modeling method according to claim 1, characterized in that: The performing local velocity optimization on the optimized initial velocity model according to the ray path to obtain a shallow velocity model includes: Determining the area range involved in the ray path as the area range to be optimized; Determining a speed optimization amount for the area to be optimized according to a preset ideal imaging trend; Local velocity optimization is performed on the velocity of the to-be-optimized area in the optimized initial velocity model according to the velocity optimization amount to obtain a shallow velocity model.

5. A local fine velocity modeling device, characterized in that: The device comprises: The initial velocity model building module is used to build the initial velocity model based on the velocity information of the well data, the velocity information of the first arrival inversion and the velocity information of the pre-stack time migration; A first optimization module is used to iteratively optimize the initial velocity model using 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; A second optimization module is configured to perform local velocity optimization on the optimized initial velocity model according to the ray path to obtain a shallow velocity model; The initial velocity model establishment module includes: a velocity range determination unit, which is used to determine the velocity range of the stratum from shallow to deep according to the velocity information of the well data, the velocity information of the first arrival inversion and the pre-stack time migration velocity information; a spatial distribution attribute determination unit, which is used to determine the spatial distribution attributes of a large set of stratum layers and faults from shallow to deep according to surface information and structural interpretation information; a preprocessing unit, which is used to use the velocity range and the spatial distribution attributes of the stratum as structural constraints, and perform preset processing on the layer-along velocity based on the structural constraints to obtain an initial shallow velocity model; an initial velocity model determination unit, which is used to perform velocity scanning and migration processing on the initial shallow velocity model at different preset scales to obtain shallow imaging results corresponding to each preset scale, determine an 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.

6. The local fine velocity modeling device according to claim 5, characterized in that: The second optimization module includes: a unit for determining a region to be optimized, configured to determine a region involved in the ray path as a region to be optimized; An optimization value determination unit, configured to determine a 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.

7. 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 4.

8. 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 4 are performed.

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