Method for generating pseudo well based on seismic data and electronic equipment

Abstract

The invention provides a method for generating a pseudo well based on seismic data and electronic equipment. The method comprises the following steps: selecting a seed point with logging data in an exploration target area, and directly extracting a time domain interval velocity field from seismic data; converting the time domain interval velocity field to a depth domain to obtain a depth domain longitudinal wave velocity field; deducing a longitudinal wave velocity and a transverse wave velocity and an empirical relationship between the longitudinal wave velocity and the density by using the logging curve of the seed point, and constructing an initial model; based on the initial model and the depth domain longitudinal wave velocity field, utilizing a genetic algorithm to carry out inversion, and generating a depth domain interval velocity model containing the longitudinal wave velocity, the transverse wave velocity and the density; and obtaining an interval velocity model of each point location through single-point inversion, and determining the interval velocity model as a pseudo well. A pseudo well is generated through an algorithm process, dependence on actual drilling well data in a work area is reduced, and the geological modeling problem caused by lack of well control data in the early stage of exploration or a new block is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of carbon dioxide geological storage, in particular to a method for generating pseudo wells based on seismic data and an electronic device. BACKGROUND

[0002] In oil and gas field exploration and carbon dioxide geological storage engineering, it is crucial to accurately obtain the physical parameters (such as P-wave velocity, S-wave velocity, density, etc.) of the underground formation. These parameters are usually obtained through drilling wells, but in a wide exploration area, there are usually only a few or even no real drilled wells, resulting in great uncertainty and extrapolation error in reservoir prediction and modeling based on well data.

[0003] In the prior art, to solve the problem of lack of well data, seismic inversion technology is often used. However, the conventional seismic inversion method seriously depends on the existing well data in the work area as the initial model and constraint condition. In the "no well" or "few well" area, due to the lack of reliable initial model, the inversion result has strong multi-solution, and the accuracy is difficult to guarantee, which cannot provide reliable basis for drilling and completion engineering design and storage layer evaluation. Therefore, there is an urgent need for a method that can extract reliable formation physical parameters directly from widely covered seismic data without or with very few real drilled well data. SUMMARY

[0004] The purpose of the embodiments of the present application is to provide a method for generating pseudo wells based on seismic data and an electronic device to solve the above problems.

[0005] In a first aspect, the present application provides a method for generating pseudo wells based on seismic data, comprising the following steps: selecting a seed point with well logging data in the exploration target area, and directly extracting a time domain interval velocity field from seismic data; converting the time domain interval velocity field to a depth domain to obtain a depth domain P-wave velocity field; using the well logging curve of the seed point to derive the empirical relationship between P-wave velocity and S-wave velocity, and between P-wave velocity and density, and to construct an initial model; based on the initial model and the depth domain P-wave velocity field, using genetic algorithm for inversion to generate a depth domain interval velocity model containing P-wave velocity, S-wave velocity and density; selecting other points in the exploration target area, repeating the above steps, obtaining the interval velocity model of each point by single point inversion, and determining the pseudo wells.

[0006] In an optional embodiment, the step of directly extracting the time domain interval velocity field from the seismic data comprises: comprehensively using velocity analysis technology and travel time tomography technology, and directly extracting the time domain interval velocity field from the seismic data after time difference correction.

[0007] In an optional embodiment, the converting the time-domain interval velocity field to a depth domain P-wave velocity field comprises: The time-domain RMS velocity obtained in seismic processing is converted to a time-domain interval velocity by the Dix formula, and then converted to a depth domain P-wave velocity field.

[0008] In an optional embodiment, the time window range used in converting the time-domain interval velocity to a depth domain velocity field is not less than 50 milliseconds.

[0009] In an optional embodiment, the empirical relationship between P-wave velocity and S-wave velocity, and the empirical relationship between P-wave velocity and density are derived from the logging curves of the seed point to construct an initial model, comprising: The relationship between P-wave velocity and S-wave velocity, and the relationship between P-wave velocity and density are established by regression analysis using the logging data of the seed point to construct an initial model.

[0010] In an optional embodiment, the relationship between P-wave velocity and density is expressed by the Gardner relationship.

[0011] In an optional embodiment, the inversion by the genetic algorithm specifically comprises: Based on the initial model and the depth domain P-wave velocity field, an initial population of a depth domain model composed of P-wave velocity, S-wave velocity and density is generated; Forward modeling is performed on the initial population to generate synthetic seismic data; The matching degree calculation is performed on the synthetic seismic data and the real seismic data; According to the matching degree result, the population is updated by the selection, crossover and mutation operations of the genetic algorithm; The foregoing steps are iteratively executed until the termination condition is met, and the population individual with the highest matching degree is output as the final interval velocity model.

[0012] In an optional embodiment, the matching degree calculation is quantified by the normalized cross-correlation method; In the iteration process of the genetic algorithm, the crossover probability is set to a fixed value to control the evolution rate of population update.

[0013] In an optional embodiment, in the normalized cross-correlation method, the total number of ray parameters or angles involved is a key parameter participating in the fitness evaluation.

[0014] In a second aspect, the present application provides an electronic device comprising a processor and a memory, the memory storing machine executable instructions executable by the processor to implement the method of any of the preceding embodiments.

[0015] The core of the present application is to directly extract the velocity field from the seismic data and establish an initial model, and generate pseudo wells through a complete algorithm process, which greatly reduces the dependence on real drilling data in the work area, and solves the geological modeling problem caused by the lack of well control data in the early exploration or new blocks.

[0016] Through the combination of velocity analysis, Dix formula conversion and global optimization inversion based on genetic algorithm, the layer velocity model highly matched with the real seismic data can be obtained. The generated pseudo well data is closer to the real situation underground, which provides a more reliable data basis for reservoir identification, physical property prediction and cap rock evaluation.

[0017] The genetic algorithm used is a global optimization algorithm, which can effectively avoid falling into local optimal solution, has better fault tolerance for noise and uncertainty existing in seismic data, and thus ensures the stability and rationality of the inversion result.

[0018] This scheme makes full use of the conventional collected seismic data, and can generate pseudo wells in batches in the target area without additional drilling cost, forming a virtual "logging network", which greatly expands the data coverage range, and provides key basis for well deployment, geological design and CO2 storage layer optimization, and has significant economic benefits. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments of the present application. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope, and for those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0020] Figure 1 A method flowchart for generating pseudo wells based on seismic data is provided for the embodiments of the present application; Figure 2 A device structure diagram for generating pseudo wells based on seismic data is provided for the embodiments of the present application; Figure 3 An electronic device structure diagram is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present application will be described below in combination with the drawings in the embodiments of the present application.

[0022] The embodiment of the present application provides a method flow diagram for generating a pseudo well based on seismic data, which can be applied to an electronic device, such as Figure 1 As shown in the figure, the method comprises the following steps: S110, selecting a seed point with well logging data in an exploration target area, and directly extracting a time domain interval velocity field from seismic data; In some embodiments, a velocity analysis technique and a travel time tomography technique can be comprehensively utilized, and a time difference correction is performed to directly extract the time domain interval velocity field from the seismic data.

[0023] The seed point is preferably selected according to the following principles: selecting a well in the work area or close to the work area, with complete well logging curves (at least containing P-wave velocity, S-wave velocity and density), reliable quality, and drilled strata having good comparability with the target area as the seed point.

[0024] The refined processing of the velocity field extraction: a strategy combining tomographic inversion in pre-stack depth migration (PreSDM) and high resolution velocity analysis (HRVA) is adopted.

[0025] The tomographic inversion is to invert the underground velocity structure by using the first arrival time of seismic waves or the travel time of reflected waves. The mathematical essence is to solve a large linear equation set: L*m=t, where L is a ray path matrix, m is a disturbance model of the reciprocal of velocity to be solved, and t is a travel time residual vector. The model m is updated through iteration, so that the residual between the synthesized travel time and the actual travel time is minimized.

[0026] The high resolution velocity analysis is to select the velocity that makes the trace gather flatten best on the common imaging point gather (CIG) by scanning a series of velocity models. The surface wave decomposition (SWD) technology is introduced to denoise and enhance the signal of the seismic data, so that clearer and more focused spectral points are obtained on the velocity spectrum.

[0027] The time difference correction is to apply residual moveout correction (RMO) to the extracted preliminary velocity field to eliminate the phenomenon that the CIG trace gather is not flattened due to inaccurate velocity, and through multiple iterations, a high-precision time domain interval velocity field v p (t x , t y ) is finally obtained, where t x , t y represent the spatial position on the time slice.

[0028] S120, converting the time domain interval velocity field to the depth domain to obtain a P-wave velocity field in the depth domain; In some embodiments, the time-domain RMS velocity obtained in seismic processing can be converted to time-domain interval velocity, and then converted to depth-domain, to obtain the depth-domain P-wave velocity field.

[0029] When converting the time-domain interval velocity to the depth-domain velocity field, the time window range used is not less than 50 milliseconds. The dominant frequency of the seismic wavelet determines its vertical resolution. A Ricker wavelet with a dominant frequency of 30 Hz has a wavelength of approximately 33 ms. Selecting a time window of ≥50 ms means that the converted layer thickness is greater than one wavelength, which can effectively suppress the uncertainty in layer velocity calculation caused by seismic tuning effects.

[0030] wherein the Dix formula is as follows:

[0031] t n-1 and t n are two times from the seismic reflection time profile. represents the two-way travel time required for the seismic wave to propagate from the surface to the n-1th reflection horizon and the nth reflection horizon, and the unit is usually milliseconds (ms).

[0032] v rms (t n-1 ) and v rms (t n ) correspond to the RMS velocities at times t n-1 and t n . They are known quantities obtained directly through seismic velocity analysis (such as picking on a velocity spectrum), and the unit is usually meters per second (m / s).

[0033] v int (t n ) is the interval velocity to be solved. It specifically refers to the velocity of the seismic wave propagating in the nth layer (i.e., the formation between the n-1th reflection interface and the nth reflection interface). The unit is meters per second (m / s).

[0034] The principle of the Dix formula is based on a simple physical concept: distance = velocity x time.

[0035] From "total distance" and "total time" to "segmented distance": The total equivalent distance traveled by the seismic wave from the surface to time t n can be expressed in terms of RMS velocity as: v rms (t n )•t n .

[0036] Similarly, the total equivalent distance to time t n-1 is: v rms (t n-1 )•tn-1 .

[0037] So the distance traveled in the nth layer is the difference between the two: nth layer distance = nth layer travel - (n-1)th layer travel rms (t n )•t n -v rms (t n-1 )•t n-1 .

[0038] Solving for interval velocity: The time taken for the wave to travel in the nth layer is (t n -t n-1 ).

[0039] From velocity = distance / time, the interval velocity in the nth layer is: v int (t n )=nth layer distance / nth layer time = nth layer travel - (n-1)th layer travel rms (t n )•t n -v rms (t n-1 )•t n-1 / t n -t n-1 .

[0040] However, to transition from the definition of RMS velocity (which involves the sum of the squares of the individual interval velocities) to interval velocity, a square term appears in the formula. Therefore, the final form requires taking the square root, resulting in the Dix formula.

[0041] In some embodiments, the Dix formula is sensitive to noise in the input data, especially when the interval between layers (t n -t n-1 ) is small, and small errors are amplified. To address this, the following measures can be implemented: Data smoothing: Before applying the Dix formula, the RMS velocity v rms (t) curve is polynomial-fitted or median-filtered to suppress high-frequency jitter.

[0042] Geological constraints: Use major horizons from seismic interpretation as control points to ensure that the interval velocities change in accordance with geological rules at these marker layers.

[0043] In some embodiments, the internal mechanism of time-depth conversion and time-window selection: Conversion process: Time-depth conversion is essentially an integration operation . In the discrete case, starting from the surface (t=0), the depth is accumulated layer by layer: z i =z i-1 +v int (ti )•(t i -t i-1 )。

[0044] Stability assurance: Smaller time window will make the converted depth domain velocity curve oscillate dramatically, resulting in a large number of unrealistic high and low velocity thin layers, which seriously damage the quality of the initial model. The time window of ≥50ms acts as a low-pass filter, ensuring the correctness of the trend of the converted velocity field, providing a stable and reasonable starting point for subsequent inversion.

[0045] S130, using the logging curve of the seed point, the empirical relationship between P-wave velocity and S-wave velocity, and P-wave velocity and density is derived to construct the initial model; Using the logging data of the seed point, the relationship between P-wave velocity and S-wave velocity, and the relationship between P-wave velocity and density are established by regression analysis to construct the initial model.

[0046] The relationship between P-wave velocity and density is expressed by Gardner relationship.

[0047] This step aims to establish the quantitative petrophysical law of the work area to provide physical constraints for genetic algorithm.

[0048] v p -v s Relationship establishment: Read the vp and vs logging curves of the seed point. Not only simple linear regression is performed, but also the relationship is established according to the main lithology or formation group (such as Shiqianfeng group, Shihedzhi group). Among them, v p is the P-wave velocity, v s is the S-wave velocity, and p is the density.

[0049] For example, for the sand and mud interbedded section of Shihedzhi group, a relationship v s =0.862•v p -1176(m / s) can be obtained, and for the mudstone development section, the relationship can be v s =0.742•v p -1034(m / s). This processing according to lithology and stratigraphic position greatly improves the accuracy of the relationship.

[0050] v p -p relationship and local Gardner formula: Formula:

[0051] Local calibration: Put the v p and p data of the seed point on the double logarithmic coordinate axis, and refit by least squares method to obtain the most suitable coefficients c and d of this work area. We may get p=0.298•vp 0.265, which is different from the classic Gardner formula in both coefficient and exponent, but more consistent with the rock properties of the Paleozoic strata.

[0052] Initial model generation: the obtained full-area depth domain v p (z) data is substituted into the above-mentioned localized v p -v s and v p -ρ relationship, that is, the corresponding v s (z) and p(z) can be calculated, thereby constructing a three-dimensional, physically reasonable initial model (v p , v s , p) in the whole work area.

[0053] S140, based on the initial model and the depth domain P-wave velocity field, using genetic algorithm inversion to generate a depth domain interval velocity model containing P-wave velocity, S-wave velocity and density; The inversion by genetic algorithm specifically includes: based on the initial model and the depth domain P-wave velocity field, generating an initial population of the depth domain model composed of P-wave velocity, S-wave velocity and density; forward modeling is performed on the initial population to generate synthetic seismic data; the matching degree calculation is performed on the synthetic seismic data and the real seismic data; according to the matching degree result, the population is updated through the selection, crossover and mutation operations of the genetic algorithm; the foregoing steps are iteratively executed until the termination condition is met, and the population individual with the highest matching degree is output as the final interval velocity model.

[0054] The matching degree calculation is quantified by using the normalized cross-correlation method; in the iteration process of the genetic algorithm, the crossover probability is set to a fixed value, which is used to control the evolution rate of population updating.

[0055] In the normalized cross-correlation method, the total number of ray parameters or angles involved is a key parameter participating in the fitness evaluation.

[0056] Through the intelligent optimization algorithm, the final model most matched with the real seismic data is found on the basis of the initial model. It can be realized through the following steps: Initial population generation strategy: the population size can be set to 500 individuals. Each individual is a complete three-dimensional velocity-density model. It can not be generated completely randomly, but on the basis of the constructed initial model, Gaussian random perturbation is added. The perturbation amplitude is linked with the uncertainty range of seismic data interpretation, for example, the perturbation range of P-wave velocity can be set to ±5% of the initial value.

[0057] High-precision forward modeling: Convolution model (for post-stack data) or Aki & Richards approximation (for pre-stack angle gather data) can be used for fast forward modeling.

[0058] Aki & Richards approximation:

[0059] where R pp (θ) is the P-wave reflection coefficient as a function of the incidence angle θ, A, B, C are coefficients related to the incidence angle and Poisson's ratio. This formula expresses the relationship between the reflection seismic response and the changes in the underground elastic parameters (v p , v s , ρ), and is the physical bridge that connects the model and the seismic data.

[0060] Fine construction of fitness function: The normalized cross-correlation (NCC) formula is as follows:

[0061] For pre-stack data, the NCC values of all angle gathers can be calculated and averaged, S syn is the synthetic seismic data, S obs is the real observed seismic data. n p is the number of angle gathers involved in the calculation (for example, 5 angle gathers: 5°-10°, 11°-20°,...). The final fitness function FitnessFitness can be:

[0062] This ensures that the inversion process matches the seismic information at different offsets at the same time, better constraining the v p / v s parameters and reducing the multi-solution.

[0063] Genetic operation parameterization: Roulette wheel selection method is used, and the probability of an individual being selected is proportional to its fitness. Single-point crossover is used. The crossover probability p c is set to 0.85. This is an empirical value that has been tested a lot, balancing the relationship between "exploring new solutions" and "using known good solutions". Too high (such as >0.95) will cause the population to be too volatile, and too low (such as <0.7) will make the search inefficient. Uniform mutation is used. The mutation probability is set to a low value, such as 0.02, to prevent the destruction of good genes.

[0064] Convergence judgment and output: Set double termination conditions. a) Maximum number of iterations: 1000 generations; b) Convergence threshold: the optimal fitness of the population for 50 consecutive generations improves by less than 0.1%.

[0065] The individual with the highest fitness in the population when the termination condition is met is output as the final, optimal pseudo-well data (i.e., fine v p , v s , p curves).

[0066] S150, select other point positions in the exploration target area, repeat the previous steps, obtain the interval velocity model of each point position by single-point inversion, and determine the pseudo-well.

[0067] The process of S110 to S140 can be performed for each common reflection point (CRP) in the 3D seismic data volume of the work area. Since the inversion of each point is independent, this process is very suitable for parallel processing on a high-performance computing (HPC) cluster, greatly improving efficiency.

[0068] Finally, a dense pseudo-well data volume covering the entire work area can be obtained. Based on this, the porosity and shale content maps of the target layer (the upper part of the Heshanggou Formation and the upper part of the Liujiagou Formation) can be drawn. The sand body distribution map is generated, and the favorable injection area is accurately identified. The well structure design and injection horizon optimization provide quantitative geological basis far beyond the "no well" case.

[0069] Figure 2 A device structure schematic diagram for generating a pseudo-well based on seismic data provided by an embodiment of the present application is shown in Figure 2 The device can include: The extraction module 201 is configured to select a seed point with well logging data in the exploration target area, and directly extract a time domain interval velocity field from seismic data. The conversion module 202 is configured to convert the time domain interval velocity field to a depth domain, and obtain a depth domain P-wave velocity field. The construction module 203 is configured to use the well logging curve of the seed point to derive the empirical relationship between P-wave velocity and S-wave velocity, and between P-wave velocity and density, and construct an initial model. The generation module 204 is configured to use genetic algorithm inversion based on the initial model and the depth domain P-wave velocity field to generate a depth domain interval velocity model containing P-wave velocity, S-wave velocity and density. The determination module 205 is configured to select other point positions in the exploration target area, repeat the previous steps, obtain the interval velocity model of each point position by single-point inversion, and determine the pseudo-well.

[0070] In some embodiments, the extraction module 201 is specifically configured to comprehensively use velocity analysis technology and travel time tomography technology, and directly extract a time domain interval velocity field from seismic data after time difference correction.

[0071] In some embodiments, the conversion module 202 is specifically configured to: convert the time-domain root-mean-square velocity obtained in seismic processing into a time-domain interval velocity by using the Dix formula, and then convert the time-domain interval velocity to a depth domain to obtain a depth-domain P-wave velocity field.

[0072] In some embodiments, the construction module 203 is specifically configured to: By using the logging data of the seed points, a relationship between the P-wave velocity and the S-wave velocity, and a relationship between the P-wave velocity and the density are established by regression analysis to construct an initial model.

[0073] In some embodiments, the generation module 204 is specifically configured to: Based on the initial model and the depth-domain P-wave velocity field, an initial population of the depth-domain model composed of the P-wave velocity, the S-wave velocity and the density is generated; Forward modeling is performed on the initial population to generate synthetic seismic data; The synthetic seismic data and the real seismic data are matched to calculate a matching degree; According to the matching degree result, the population is updated by selection, crossover and mutation operations of the genetic algorithm; The foregoing steps are iteratively executed until a termination condition is met, and a population individual with the highest matching degree is output as a final interval velocity model.

[0074] The device embodiment corresponds to the foregoing method embodiment, and can be understood with reference to each other.

[0075] Referring to Figure 3 The electronic device 300 provided by the embodiments of the present application at least includes: a processor 301, a memory 302, and a computer program stored in the memory 302 and capable of running on the processor 301, and the processor 301 implements the method provided by the embodiments of the present application when executing the computer program.

[0076] The electronic device 300 provided by the embodiments of the present application can further include a bus 303 connecting different components (including the processor 301 and the memory 302). Among them, the bus 303 represents one or more of several types of bus structures, including a memory bus, a peripheral bus, a local bus, etc.

[0077] The memory 302 can include a readable storage medium in the form of volatile memory, such as a random access memory (RAM) 3021 and / or cache memory 3022, and further can include a read only memory (ROM) 3023. The memory 302 can also include program tools 3025 having a set of (at least one) program modules 3024, including but not limited to an operating system, one or more applications, other program modules, and program data, each of which can include implementation of a network environment, or some combination thereof.

[0078] The processor 301 can be one processing element or a collective term for a plurality of processing elements, for example, the processor 301 can be a central processing unit (CPU), or one or more integrated circuits configured to implement the method provided by the embodiments of the present application. Specifically, the processor 301 can be a general-purpose processor, including but not limited to a CPU, an application specific integrated circuit (ASIC), a ready programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc.

[0079] The electronic device 300 can communicate with one or more external devices 304 (such as a keyboard, a remote control, etc.), and can also communicate with one or more devices that enable a user to interact with the electronic device 300 (such as a mobile phone, a computer, etc.), and / or with any device that enables the electronic device 300 to communicate with one or more other electronic devices 300 (such as a router, a modem, etc.). Such communication can be carried out through an input / output (I / O) interface 305. Also, the electronic device 300 can communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 306. As shown, the network adapter 306 communicates with other modules of the electronic device 300 through the bus 303. It should be understood that although Figure 3 the network adapter 306 is shown as a separate component, the network adapter 306 can be an integral part of the bus 303, the processor 301 and / or the memory 302. Figure 3Other hardware and / or software modules can be used in conjunction with the electronic device 300, as shown, including, but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, Redundant Arrays of Independent Disks (RAID) subsystems, tape drives, and data backup storage subsystems, etc.

[0080] It should be noted that, Figure 3 The electronic device 300 shown is merely an example and should not limit the function and use range of the embodiments of the present application.

[0081] The computer readable storage medium provided by the embodiments of the present application is introduced as follows. The computer readable storage medium provided by the embodiments of the present application stores computer instructions, and the computer instructions are executed by a processor to implement the method provided by the embodiments of the present application. Specifically, the computer instructions can be built-in or installed in the processor, so that the processor can implement the method provided by the embodiments of the present application by executing the built-in or installed computer instructions.

[0082] In addition, the method provided by the embodiments of the present application can also be implemented as a computer program product, which includes program codes that implement the method provided by the embodiments of the present application when running on a processor.

[0083] The computer program product provided by the embodiments of the present application can adopt one or more computer readable storage media, and the computer readable storage media can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any suitable combination of the above. Specifically, more specific examples (non-exhaustive list) of the computer readable storage media include: electrical connections with one or more conductive wires, portable disks, hard disks, RAM, ROM, Erasable Programmable Read Only Memory (EPROM), optical fibers, portable Compact Disc Read-Only Memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0084] The computer program product provided by the embodiments of the present application can adopt a CD-ROM and include program codes, and can also run on an electronic device such as a computer. However, the computer program product provided by the embodiments of the present application is not limited to this. In the embodiments of the present application, the computer readable storage medium can be any tangible medium containing or storing program codes, which can be used or combined with an instruction execution system, device or component.

[0085] It should be noted that, although several units or sub-units of the apparatus are mentioned in the above detailed description, such division is merely exemplary and not mandatory. Indeed, according to an embodiment of the present application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided into several units to be embodied.

[0086] Moreover, although the operations of the method(s) herein are described in a particular, sequential order, this order is not meant to be a limitation and is not intended to imply that

[0087] Although preferred embodiments of the application have been described herein, with reference to the accompanying drawings, it is to be understood that the application is not limited to those preferred embodiments. Rather, additional modifications and variations of the preferred embodiments can be effected by those skilled in the art to which the application pertains. Accordingly, it should be understood that the application is not to be limited by the specific illustrative embodiments set forth above, but only by the scope of the appended claims and their equivalents.

[0088] It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application cover the modifications and variations of this application provided they come within the scope of the appended claims and their equivalents.

Claims

1. A method of generating a pseudo well based on seismic data, characterized in that, The method comprises the following steps: selecting a seed point with well logging data in a target exploration area, and directly extracting a time domain interval velocity field from seismic data; converting the time domain interval velocity field to a depth domain P-wave velocity field; deriving empirical relationships between P-wave velocity and S-wave velocity, and between P-wave velocity and density, based on the well logging curve of the seed point, and constructing an initial model; based on the initial model and the depth domain P-wave velocity field, performing inversion using a genetic algorithm to generate a depth domain interval velocity model comprising P-wave velocity, S-wave velocity and density; selecting other points in the target exploration area, and repeating the foregoing steps to obtain an interval velocity model for each point by single-point inversion, and determining the interval velocity model as a pseudo well.

2. The method of claim 1, wherein, The step of directly extracting the time domain interval velocity field from the seismic data comprises comprehensively using velocity analysis technology and travel time tomography technology, and directly extracting the time domain interval velocity field from the seismic data after time difference correction.

3. The method of claim 1, wherein, The step of converting the time domain interval velocity field to the depth domain P-wave velocity field comprises: converting the time domain root mean square velocity obtained in seismic processing to a time domain interval velocity, and then converting the time domain interval velocity to a depth domain P-wave velocity field.

4. The method of claim 3, wherein, When converting the time domain interval velocity to the depth domain velocity field, the time window range used is not less than 50 milliseconds.

5. The method of claim 1, wherein, The step of deriving empirical relationships between P-wave velocity and S-wave velocity, and between P-wave velocity and density, based on the well logging curve of the seed point, and constructing an initial model comprises: using the well logging data of the seed point to establish a relationship between P-wave velocity and S-wave velocity, and a relationship between P-wave velocity and density, by regression analysis, and constructing an initial model.

6. The method of claim 5, wherein, The relationship between P-wave velocity and density is expressed using a Gardner relationship.

7. The method of claim 1, wherein, The step of performing inversion using a genetic algorithm comprises: generating an initial population of a depth domain model comprising P-wave velocity, S-wave velocity and density, based on the initial model and the depth domain P-wave velocity field; performing forward modeling on the initial population to generate synthetic seismic data; calculating a matching degree of the synthetic seismic data and real seismic data; updating the population by selection, crossover and mutation operations of the genetic algorithm according to the matching degree result; iteratively performing the foregoing steps until a termination condition is met, and outputting a population individual with the highest matching degree as a final interval velocity model.

8. The method of claim 7, wherein, The matching degree calculation is quantified using a standardized cross-correlation method. In the iterative process of the genetic algorithm, a crossover probability is set as a fixed value to control the evolution rate of population updating.

9. The method of claim 8, wherein, In the standardized cross-correlation method, the total number of ray parameters or angles involved is used as a key parameter to participate in fitness evaluation.

10. An electronic device, comprising: The device comprises a processor and a memory, the memory stores machine executable instructions that can be executed by the processor, and the processor can execute the machine executable instructions to implement the method of any one of claims 1 to 9.

Images