First arrival wave travel time chromatography velocity inversion method, system, equipment and medium

Abstract

The invention belongs to the field of oil-gas exploration seismic data processing, and relates to a first-motion wave travel time chromatography velocity inversion method, system and device and a medium. According to the method, the first arrival travel time and the grid unit travel time are acquired, and the minimum grid unit travel time is selected; an initial velocity field closer to a real structure is established through micro-logging constraint so as to replace a conventionally used gradient velocity field. The initial velocity field and the first arrival travel time are combined to construct the target function, the slowness information and the regularization information are added in the target function, the multi-information constrained target function is obtained, and the inversion stability and the calculation precision are improved. And adopting a Gaussian-Newton method to minimize the target function of the multi-information constraint, solving the minimized target function of the multi-information constraint, and carrying out iterative updating on a solving result to obtain an inversion velocity field. Explicit calculation and sensitive kernel function storage are not needed, the method is more suitable for large-scale data processing, and the calculation time of speed inversion and the requirement for storage space are reduced.

Description

Technical Field

[0001] The invention belongs to the field of oil and gas exploration seismic data processing, and relates to a first arrival wave travel time tomography velocity inversion method, system, equipment and medium. Background Art

[0002] First-arrival travel time tomographic inversion is a near-surface velocity structure modeling technology that uses the travel time recorded by the shot and the forward ray path to calculate the slowness update in the data domain. It can provide accurate shallow low-wave number background velocity for static correction, pre-stack depth migration, waveform inversion and other links in seismic data processing. However, the traditional first-arrival travel time tomographic imaging algorithm requires too much memory and computing time, and is difficult to apply to the current large-data seismic exploration conditions. At the same time, the complex surface of the complex exploration area is complex and undulating. For ultra-large offset data and western complex piedmont data, the near-surface modeling accuracy is low, which seriously affects the subsequent mid-deep modeling and migration imaging quality.

[0003] Traditional first-arrival wave travel time tomography only fits the first-arrival travel time. Under undulating surface conditions, the inversion process of simply fitting the travel time residual cannot effectively distinguish two receiving points with different positions and the same true travel time residual, which limits the ray angle range involved in the tomography and leads to the loss of the projection angle of the observation data. For three-dimensional tomography, the travel time data is relatively insufficient, and the ray density is difficult to meet the requirements, which makes it difficult to converge in the inversion, aggravating the pathological nature of the inverse problem, resulting in the inversion results easily containing more high-speed anomalies, and the low and speed reduction zones and the high-speed layer top interfaces are not clear, and the inversion results are of low accuracy. With the increase of data volume, the ray-based traditional first-arrival wave tomography algorithm requires a lot of storage space and computing time, and it is difficult to meet production needs in terms of efficiency. Summary of the invention

[0004] In order to overcome the shortcomings of the above-mentioned prior art, the purpose of the present invention is to provide a first-arrival wave travel time tomography velocity inversion method, system, device and medium. The present invention adds apparent slowness information and regularization information constraints in the objective function to solve the problem of high-speed anomalies in complex near-surface modeling, improves the stability and calculation accuracy of the inversion, introduces an improved Gauss-Newton iterative algorithm, avoids the storage of large sensitive kernel functions, and reduces the calculation time and storage space requirements of velocity inversion.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention discloses a first arrival wave travel time tomographic velocity inversion method, comprising the following steps:

[0007] Get the real first arrival time;

[0008] The initial velocity field is established through micro-logging constraints, and the forward modeling first arrival travel time is obtained according to the initial velocity field;

[0009] The objective function is constructed by combining the real first arrival travel time and the forward first arrival travel time, and the apparent slowness information and regularization information are added to the objective function to obtain the objective function with multi-information constraints.

[0010] The Gauss-Newton method is used to minimize the objective function of the multi-information constraints, and the minimized objective function of the multi-information constraints is solved and the solution is iteratively updated to obtain the inverted velocity field.

[0011] In a second aspect, according to the method of the present invention, a first arrival wave travel time tomographic velocity inversion system is proposed, comprising: a data acquisition module, a velocity field construction module, a function construction module and an iterative solution module;

[0012] Data acquisition module: used to obtain the real first arrival time;

[0013] Velocity field construction module: used to establish the initial velocity field through micro-logging constraints and obtain the forward modeling first arrival travel time based on the initial velocity field;

[0014] Function construction module: used to construct the objective function by combining the real first arrival travel time and the forward first arrival travel time, adding the apparent slowness information and regularization information to the objective function to obtain the objective function with multi-information constraints;

[0015] Iterative solution module: used to minimize the objective function of multi-information constraints using the Gauss-Newton method, solve the minimized objective function of multi-information constraints and iteratively update the solution results to obtain the inverted velocity field.

[0016] In a third aspect, the present invention provides an electronic device, comprising: a processor; a memory for storing computer program instructions; and a memory for executing the steps of the first arrival wave travel time tomography velocity inversion method.

[0017] In a fourth aspect, the present invention provides a storage medium storing computer program instructions, wherein when the computer program instructions are loaded and executed by a processor, the first arrival wave travel time tomography velocity inversion method is implemented.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] The first arrival wave travel time tomographic velocity inversion method of the present invention obtains the true first arrival travel time. An initial velocity field is established through micro-logging constraints, and an initial velocity field closer to the real structure is established through micro-logging constraints to replace the conventionally used gradient velocity field. The objective function is constructed by combining the true first arrival travel time with the forward first arrival travel time, and the apparent slowness information and regularization information are added to the objective function to obtain the objective function of multi-information constraints. By adding prior information such as apparent slowness and regularization to the objective function, the velocity reversal and high-speed anomaly problems that appear in the first arrival wave travel time tomographic imaging of complex near-surface three-dimensional seismic data are solved, and the stability and calculation accuracy of the inversion are improved. The Gauss-Newton method is used to minimize the objective function of the multi-information constraints, and the minimized multi-information constraint objective function is solved and the solution result is iteratively updated to obtain the inverted velocity field. The improved Gauss-Newton iterative algorithm is introduced, and there is no need to explicitly calculate and store sensitive kernel functions, which is more suitable for large-scale data processing and reduces the calculation time and storage space requirements of velocity inversion.

[0020] The first arrival wave travel time tomography velocity inversion system of the present invention comprises a data acquisition module, a velocity field construction module, a function construction module and an iterative solution module connected in sequence, wherein the data acquisition module is used to obtain the true first arrival travel time. The velocity field construction module is used to establish the initial velocity field through micro-logging constraints. The function construction module is used to construct the objective function by combining the true first arrival travel time with the forward first arrival travel time, and to add the apparent slowness information and the regularization information to the objective function to obtain the objective function of the multi-information constraints. The iterative solution module is used to minimize the objective function of the multi-information constraints by using the Gauss-Newton method, solve the minimized objective function of the multi-information constraints and iteratively update the solution result to obtain the inverted velocity field. The modules cooperate with each other to solve the problem of high-speed anomalies in complex near-surface modeling, improve the stability and calculation accuracy of the inversion, avoid the storage of large sensitive kernel functions, and reduce the calculation time and storage space requirements of the velocity inversion.

[0021] The electronic device and storage medium provided by the present invention can also solve the problem of high-speed anomalies in complex near-surface modeling, improve the stability and calculation accuracy of inversion, avoid the storage of large sensitive kernel functions, and reduce the calculation time and storage space requirements of velocity inversion. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a flow chart of the method of the present invention;

[0023] Figure 2 A three-dimensional single Cartesian domain template of the present invention;

[0024] Figure 3 are 6 three-dimensional MSFM Cartesian domain templates of the present invention;

[0025] Figure 4It is a schematic diagram of interpolation along the layers of terrain undulations of the present invention;

[0026] Figure 5 is the real velocity field of the undulating surface model of the present invention;

[0027] Figure 6 An initial velocity field constructed by adding prior information to the undulating surface model of the present invention;

[0028] Figure 7 It is the tomographic inversion result of the same type of conventional first arrival wave travel time;

[0029] Figure 8 is the tomographic inversion result of the first arrival wave travel time of the present invention;

[0030] Fig. 9 It is the actual data processing routine of the present invention to establish the gradient velocity field;

[0031] Fig.10 It is the initial velocity field established by the actual data processing micro-logging constraint of the present invention;

[0032] Fig.11 It is the same type of conventional first arrival wave travel time tomographic inversion ray density field;

[0033] Fig.12 It is the ray density field of the first arrival wave travel time tomographic inversion after multi-information constraint optimization of the present invention;

[0034] Fig.13 It is an internal slice of the velocity field of the conventional first arrival wave travel time tomographic inversion;

[0035] Fig.14 It is the internal slice of the velocity field of tomographic inversion after multi-information constraint optimization of the present invention;

[0036] Fig.15 It is the iterative convergence curve of the multi-information constrained first arrival wave travel time tomographic inversion of the present invention;

[0037] Fig.16 It is a comparison chart of the extraction velocity curve and the well curve at the same position;

[0038] Fig.17 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0039] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0040] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0041] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0042] See also Figure 1 The present invention discloses a first arrival wave travel time tomographic velocity inversion method, comprising the following steps:

[0043] S1. Get the real first arrival time;

[0044] S2. Establish an initial velocity field through micro-logging constraints, and obtain the forward modeling first arrival travel time according to the initial velocity field;

[0045] The initial velocity field is established through micro-logging constraints as follows:

[0046] Calculate the layer velocity using the micro-logging calculation formula;

[0047] The micro-well logging calculation formula is:

[0048]

[0049] Among them, V i represents the interval velocity of the i-th layer, H i represents the burial depth of the i-th stratum, t i It represents the one-way travel time of the direct wave of the i-th formation, and h is the distance between the excitation point and the wellbore.

[0050] When the micro-logging data is deep, the velocity structure of the extremely shallow layer is constructed based on the micro-logging information, and then the gradient velocity field that increases with depth is made based on the velocity structure of the extremely shallow layer. Figure 4 It is a schematic diagram of the interpolation along the layer of the terrain undulation of the present invention, Figure 5 is the real velocity field of the undulating surface model of the present invention;

[0051] When the micro-logging data are extremely shallow, the interval velocity is used as the starting velocity to establish the velocity gradient field;

[0052] like Figure 4 As shown, taking into account the terrain undulation, interpolation along the layer is used. The boundary points of each layer at the control point are determined according to the thickness and velocity between the micro-well logging. The layer velocity is linearly interpolated to the gradient velocity field to obtain the initial velocity field, see.

[0053] The forward modeling first arrival travel time is obtained according to the initial velocity field as follows:

[0054] The multi-template rapid advance algorithm is used to perform forward simulation on the initial velocity field, realize global travel time calculation, and obtain the forward first arrival travel time.

[0055] The details are as follows:

[0056] To construct a 3D Cartesian domain template, see Figure 2 is a three-dimensional single Cartesian domain template of the present invention, Figure 3 The six three-dimensional MSFM Cartesian domain templates of the present invention construct the eikonal equation according to the three-dimensional Cartesian domain template. The eikonal equation is as follows:

[0057]

[0058] Wherein, x, y and z represent the three directions of the 3D template, s(x, y, z) represents the slowness along the three directions of x, y and z, U1 is the directional derivative of the unit vector along the x direction, U2 is the directional derivative of the unit vector along the y direction, U3 is the directional derivative of the unit vector along the z direction, α is the angle between the x direction and the y direction, β is the angle between the y direction and the z direction, and γ is the angle between the x direction and the z direction;

[0059] In the isotropic case, the travel time calculation formula for all grid cells in the three-dimensional Cartesian domain template can be obtained:

[0060]

[0061] Where, F is the speed;

[0062] According to the calculation formula of grid unit travel time, the first-order approximate solution and second-order approximate solution of directional derivatives U1, U2 and U3 in three-dimensional case are solved:

[0063] First-order approximate solution:

[0064]

[0065] Second-order approximate solution:

[0066]

[0067] Where v = 1, 2 and 3;

[0068]

[0069]

[0070]

[0071] Among them, T1, T2 and T3 are all the adjacent forward first arrival travel times at the grid node x.

[0072] S3. Construct an objective function by combining the true first arrival travel time and the forward first arrival travel time, add the apparent slowness information and regularization information to the objective function, and obtain the objective function with multi-information constraints;

[0073] The details are as follows:

[0074] The calculated travel time is subtracted from the initial arrival travel time to obtain the travel time residual. The objective function is constructed by combining the ray path with the travel time residual. The objective function is as follows:

[0075] Φ(m)=||C l (TG(m))|| 2

[0076] Where Φ(m) is the objective function, T is the first arrival travel time, G(m) is the travel time of model m calculated by MSFM, and C l is the normalization operator, m is the target to be solved, generally called the model, usually referring to the velocity of the underground medium;

[0077] The visual slowness information and regularization information are added to the objective function, the objective function Φ(m) is minimized, and the objective function after multi-information constraints is obtained. The objective function after multi-information constraints is as follows:

[0078]

[0079] Among them, Φ(m)′ is the objective function after multi-information constraints, R is the regularization operator, and D x is the differential operator of travel time with respect to distance, is the gradient of the travel time and offset curve, i.e., the apparent slowness, τ is the smoothing weight factor, ω is the weight factor between the average slowness mismatch and the apparent slowness mismatch; represents the average slowness data, Represents the apparent slowness data, and S is the smoothness of the model.

[0080] The model smoothness is explicitly expressed as an objective function after multi-information constraints. Generally, due to the poor initial inversion model, large nonlinearity will only appear in the early stage of inversion. When the data mismatch of S1 and S2 in the objective function is small and S3 accounts for a large proportion, the tomographic inversion objective function tends to be quadratic.

[0081] S4. Use the Gauss-Newton method to minimize the objective function of the multi-information constraints, solve the minimized objective function of the multi-information constraints and iteratively update the solution to obtain the inverted velocity field.

[0082] The details are as follows:

[0083] The Gauss-Newton method is used to minimize the objective function of multi-information constraints, and a variable damping parameter is added to the Gauss-Newton method to explain the nonlinearity, and the steady-state equation is obtained. The steady-state equation is as follows:

[0084] The steady-state equation is as follows:

[0085]

[0086]

[0087]

[0088] Where Φ(m) is the steady-state equation, ω is the apparent slowness weight factor, and A k is the average slowness kernel function, B k is the apparent slowness kernel function, m k is the slowness of the model at time k, R is the regularization operator, τ is the smoothing weight factor, ε k I is the variable damping parameter added to the steady-state equation, ε k =α×rhs, α is an empirical parameter, approximately 0.1, rhs is the root mean square adaptation norm of the steady-state equation, T represents the transpose, and τ is the smoothing weight factor.

[0089] The conjugate gradient algorithm is used to solve the steady-state equation, and the slowness of the model at time k is iteratively updated to obtain the inverted velocity field. This method does not need to store the tomographic kernel function during calculation, which improves the inversion efficiency.

[0090] The formula for iteratively updating the slowness of the model at time k is as follows:

[0091] m k+1 =m k +Δm k

[0092] Where k = 1, 2, 3, ..., N, Δm kThe slow update amount.

[0093] The tomographic inversion objective function of the present invention is not suitable for minimizing the data of average slowness and apparent slowness, which means that the tomographic inversion objective function is not only the inversion of the first arrival travel time, but also the inversion combined with the travel time curve. The velocity structure of the middle and deep layers can be inverted with the help of the apparent slowness information. The weight factor ω in the steady-state equation can control the proportion of different slowness information. The larger its value, the better the inversion effect of the middle and deep layers. In particular, when ω=0, it means that there is no apparent slowness constraint.

[0094] See also Figure 1 In another feasible embodiment of the present invention, the following is adaptively modified according to the situation. Obtain the true first arrival travel time. Establish an initial velocity field through micro-logging constraints, obtain the forward first arrival travel time according to the initial velocity field, and establish an initial velocity field that is closer to the real structure through micro-logging constraints to replace the conventionally used gradient velocity field. Construct an objective function by combining the true first arrival travel time with the forward first arrival travel time, add apparent slowness information and regularization information to the objective function, and obtain an objective function with multiple information constraints; by adding prior information such as apparent slowness and regularization to the objective function, the problem of high-speed anomalies in complex near-surface modeling is solved, and the velocity reversal and high-speed anomalies in the first arrival wave travel time tomography of complex near-surface three-dimensional seismic data are solved, and the stability and calculation accuracy of the inversion are improved. The Gauss-Newton method is used to minimize the objective function of multiple information constraints, solve the minimized objective function of multiple information constraints, and iteratively update the solution results to obtain the inversion velocity field, introduce an improved Gauss-Newton iterative algorithm, and there is no need to explicitly calculate and store sensitive kernel functions, which is more suitable for large-scale data processing. The storage of large sensitive kernel functions is avoided, and the calculation time and storage space requirements of velocity inversion are reduced. The present invention can adapt to complex near-surface conditions and has high calculation efficiency.

[0095] Embodiment 1:

[0096] This embodiment first tests the undulating surface model to illustrate the accuracy of the method; then tests it with actual data of complex near-surface in a certain area to prove the effectiveness of the proposed method in complex near-surface modeling.

[0097] This embodiment designs a model of undulating surface, with 56 excitation points, 600 receiving channels per shot, 33,600 detection points in total, and the shot points and detection points are evenly distributed on the undulating surface, with a height difference of more than 200 meters. Figure 5To model the real velocity field, the number of horizontal grid sampling points is 600, the grid spacing is 12.5m, the number of vertical grid sampling points is 160, and the grid spacing is 8m; two reflection layers are placed at depths of 640m and 1280m respectively, including a low-speed anomaly of 3500m / s, a background velocity of 4000m / s, placed at a depth of 800m, and a high-speed anomaly of 3150m / s, a background velocity of 2300m / s-2800m / s, placed at a depth of 320m.

[0098] First, a certain amount of information is extracted from the real model as prior information, and the initial velocity field is constructed by interpolation along the layer. Figure 6 The initial velocity field constructed to incorporate prior information constraints. Figure 8 The inverted velocity field, surface undulation trend and flat velocity structure obtained by the inversion method in this paper have obtained good results. Figure 7 The velocity field is obtained by the conventional unconstrained first-arrival wave travel time tomographic inversion. It can be seen that the inversion result is relatively smooth and can roughly invert the contour of the undulating surface. However, due to the factors such as the difficulty of deep rays to penetrate, the deep inversion result is not good, and there are illusions such as velocity reversal.

[0099] The actual data of a certain area are processed to test the applicability of the method. The loess mountain area in the southern part of the work area is undulating, with a maximum drop of more than 600m, and the speed of the low-speed reduction zone is about 300m / s to 1600m / s. The northwest and northeast parts of the work area are relatively flat and mainly have a three-layer structure. The speed of the high-speed layer is generally above 1800m / s, the speed of the speed reduction zone varies greatly between 1300m / s and 1600m / s, and the speed of the low-speed zone is generally between 600m / s and 1300m / s. The high-speed top varies with the undulation of the mountain surface. The maximum offset of the data is 3000m. The conventional and optimized inversions use the same grid size, which is Δx=15m, Δy=15m, and Δz=10m respectively; the number of grids is nx=55, ny=550, and nz=150, respectively, representing 55 grids in the x direction, 550 grids in the y direction, and 150 grids in the z direction.

[0100] First, since the actual data involved in this study do not involve many micro-logging control points in the work area, and there is only one micro-logging well near the target area, the interpolation along the layer is adopted to obtain the inversion initial velocity field containing micro-logging information through interpolation. Fig. 9 The figure shows the initial velocity field of the conventional inversion without micro-logging information. Fig.10 It is the inversion initial velocity field containing micro-logging information after interpolation along the layer.

[0101] The grid unit travel time is calculated according to the three-dimensional multi-template block fast walking method, and then the inverse problem is solved and each iterative update is completed. Fig.11 is the ray density distribution diagram of the conventional first arrival wave travel time tomography, Fig.12 This is the ray density distribution diagram of the method of the present invention. The ray path distribution corresponding to the optimized inversion result is also more reasonable and evenly distributed. It can be seen that there is an obvious velocity anomaly near the depth of 500m and an obvious refraction layer at the depth of 1000m. Fig.13 , Fig.14 The internal slices of the tomographic inversion results obtained by the conventional method and the method of the present invention are respectively. When the conventional first-arrival tomography without constraints is used to process complex near-surface problems, the inversion results are prone to the "bull's eye" phenomenon, that is, it contains more high-speed anomalies, and the low and speed reduction zones and the high-speed layer top interface are not clear. The first-arrival wave tomographic inversion results of the present invention basically eliminate the "bull's eye" phenomenon, and the inversion depth can reach 1000 to 1500m. It has a good effect on the depiction of low and speed reduction zones under undulating surface conditions. The speed of the low-speed zone is about 800m / s, the speed of the speed reduction zone is about 1300m / s, and the high-speed layer top interface is clear, with a speed of about 1800m / s, which are consistent with the surface survey results.

[0102] After a threshold value of the travel time residual between two iterations is met, the convergence iteration process is stopped. Fig.15 This is the travel time residual convergence iteration curve for tomographic inversion using the method in this paper. The horizontal axis is the number of iteration convergence and the vertical axis is the travel time residual.

[0103] The velocity curves are extracted at the same positions of the tomographic inversion results, such as Fig.16 As shown, the curve is the well curve, the dashed line is the velocity curve of the inversion result of the method of the present invention, and the scattered line is the velocity curve of the conventional first-arrival wave tomography inversion result. There is almost no difference in the inversion results of the two at a depth of 200m to 800m, but at a depth greater than 800m, the accuracy of the method of the present invention is higher, and the trend is basically consistent with the well curve.

[0104] A first arrival wave travel time tomographic velocity inversion system, comprising: a data acquisition module, a velocity field construction module, a function construction module and an iterative solution module;

[0105] Data acquisition module: used to obtain the real first arrival time;

[0106] Velocity field construction module: used to establish the initial velocity field through micro-logging constraints and obtain the forward modeling first arrival travel time based on the initial velocity field;

[0107] Function construction module: used to construct the objective function by combining the real first arrival travel time and the forward first arrival travel time, adding the apparent slowness information and regularization information to the objective function to obtain the objective function with multi-information constraints;

[0108] Iterative solution module: used to minimize the objective function of multi-information constraints using the Gauss-Newton method, solve the minimized objective function of multi-information constraints and iteratively update the solution results to obtain the inverted velocity field.

[0109] See also Fig.17 In another feasible embodiment of the present invention, the following is adaptively modified according to the situation. A first arrival wave travel time tomography velocity inversion system, including a data acquisition module, a velocity field construction module, a function construction module and an iterative solution module connected in sequence, the data acquisition module is used to obtain the true first arrival travel time. The velocity field construction module is used to establish an initial velocity field through micro-logging constraints, and obtain the forward first arrival travel time according to the initial velocity field. Function construction module. It is used to construct an objective function by combining the true first arrival travel time with the forward first arrival travel time, and add apparent slowness information and regularization information to the objective function to obtain a multi-information constrained objective function. The iterative solution module is used to minimize the multi-information constrained objective function using the Gauss-Newton method, solve the minimized multi-information constrained objective function and iteratively update the solution result to obtain an inverted velocity field. The modules cooperate with each other to solve the problem of high-speed anomalies in complex near-surface modeling, improve the stability and calculation accuracy of the inversion, avoid the storage of large sensitive kernel functions, and reduce the calculation time and storage space requirements of velocity inversion.

[0110] An electronic device comprises: a processor; a memory for storing computer program instructions; and a memory for executing the steps of the first arrival wave travel time tomography velocity inversion method.

[0111] A storage medium stores computer program instructions. When the computer program instructions are loaded and run by a processor, the processor executes a first arrival wave travel time tomography velocity inversion method.

[0112] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0113] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0114] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0115] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A first arrival wave travel time tomographic velocity inversion method, characterized in that: The steps include: Get the real first arrival time; The initial velocity field is established through micro-logging constraints, and the forward modeling first arrival travel time is obtained according to the initial velocity field; The objective function is constructed by combining the real first arrival travel time and the forward first arrival travel time, and the apparent slowness information and regularization information are added to the objective function to obtain the objective function with multi-information constraints. The Gauss-Newton method is used to minimize the objective function of the multi-information constraints, and the minimized objective function of the multi-information constraints is solved and the solution is iteratively updated to obtain the inverted velocity field.

2. The first arrival wave travel time tomographic velocity inversion method according to claim 1, characterized in that: The establishment of the initial velocity field through micro-logging constraints is specifically as follows: Calculate the layer velocity using the micro-logging calculation formula; When the micro-logging data is deep, the velocity structure of the extremely shallow layer is constructed based on the micro-logging information, and then the gradient velocity field that increases with depth is made based on the velocity structure of the extremely shallow layer; When the micro-logging data are extremely shallow, the interval velocity is used as the starting velocity to establish the velocity gradient field; The boundary points of each layer at the control point are determined according to the thickness and velocity between the micro-logging wells, and the layer velocity is linearly interpolated into the gradient velocity field to obtain the initial velocity field.

3. The first arrival wave travel time tomographic velocity inversion method according to claim 2, characterized in that: The micro-logging calculation formula is: Among them, V i represents the interval velocity of the i-th layer, H i represents the burial depth of the i-th stratum, t i It represents the one-way travel time of the direct wave of the i-th formation, and h is the distance between the excitation point and the wellbore.

4. The first arrival wave travel time tomographic velocity inversion method according to claim 1, characterized in that: The specific method of obtaining the forward modeling first arrival travel time according to the initial velocity field is as follows: The multi-template rapid advance algorithm is used to perform forward simulation on the initial velocity field, realize global travel time calculation, and obtain the forward first arrival travel time.

5. The first arrival wave travel time tomographic velocity inversion method according to claim 1, characterized in that: The objective function is constructed by combining the true first arrival travel time and the forward first arrival travel time, and the apparent slowness information and regularization information are added to the objective function to obtain the objective function with multi-information constraints as follows: The actual first arrival travel time is subtracted from the forward first arrival travel time to obtain the travel time residual. The objective function is constructed by combining the ray path with the travel time residual. The objective function is as follows: Φ(m)=||C l (T-G(m))|| 2 Where Φ(m) is the objective function, T represents the true first arrival travel time, G(m) is the forward first arrival travel time of model m calculated by MSFM, and C l is the normalization operator, m is the target to be solved, generally called the model, usually referring to the velocity of the underground medium; The visual slowness information and regularization information are added to the objective function, the objective function Φ(m) is minimized, and the objective function after multi-information constraints is obtained. The objective function after multi-information constraints is as follows: Among them, Φ(m)′ is the objective function after multi-information constraints, R is the regularization operator, and D x is the differential operator of travel time with respect to distance, is the gradient of the travel time and offset curve, i.e., the apparent slowness, τ is the smoothing weight factor, ω is the weight factor between the average slowness mismatch and the apparent slowness mismatch; represents the average slowness data, Represents the apparent slowness data, and S is the smoothness of the model.

6. The first arrival wave travel time tomography velocity inversion method according to claim 1, characterized in that: The Gauss-Newton method is used to minimize the objective function of the multi-information constraint, solve the minimized objective function of the multi-information constraint and iteratively update the solution result to obtain the inversion velocity field as follows: The Gauss-Newton method is used to minimize the objective function of multi-information constraints, and a variable damping parameter is added to the Gauss-Newton method to explain the nonlinearity, and the steady-state equation is obtained; The conjugate gradient algorithm is used to solve the steady-state equation, and the slowness of the model at time k is iteratively updated to obtain the inverted velocity field. The formula for iteratively updating the slowness of the model at time k is as follows: m k+1 =m k +Δm k Where k = 1, 2, 3, ..., N, Δm k The slow update amount.

7. The first arrival wave travel time tomographic velocity inversion method according to claim 6, characterized in that: The steady-state equation is as follows: Where Φ(m) is the steady-state equation, ω is the apparent slowness weight factor, and A k is the average slowness kernel function, B k is the apparent slowness kernel function, m k is the slowness of the model at time k, R is the regularization operator, τ is the smoothing weight factor, ε k I is the variable damping parameter added to the steady-state equation, ε k =α×rhs, α is an empirical parameter, approximately 0.1, rhs is the root mean square adaptation norm of the steady-state equation, T represents the transpose, and τ is the smoothing weight factor.

8. A first arrival wave travel time tomography velocity inversion system as claimed in claim 1, characterized in that: include: Data acquisition module, velocity field construction module, function construction module and iterative solution module; Data acquisition module: used to obtain the real first arrival time; Velocity field construction module: used to establish the initial velocity field through micro-logging constraints and obtain the forward modeling first arrival travel time based on the initial velocity field; Function construction module: used to construct the objective function by combining the real first arrival travel time and the forward first arrival travel time, adding the apparent slowness information and regularization information to the objective function to obtain the objective function with multi-information constraints; Iterative solution module: used to minimize the objective function of multi-information constraints using the Gauss-Newton method, solve the minimized objective function of multi-information constraints and iteratively update the solution results to obtain the inverted velocity field.

9. An electronic device, comprising: A processor; a memory for storing computer program instructions; characterized in that it is used to implement the steps of the first arrival wave travel time tomography velocity inversion method as described in any one of claims 1 to 7 when executing the computer program.

10. A storage medium storing computer program instructions, characterized in that: When the computer program instructions are loaded and executed by a processor, the processor executes the first arrival wave travel time tomography velocity inversion method according to any one of claims 1 to 7.

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