NL2031350B1 - Method and system for determining geosteering irregular observation system - Google Patents
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Abstract
The present invention relates to a method and system for determining a geosteering irregular observation system. The method includes the following steps: acquiring subsurface formation information and constructing a geological model according to the 5 subsurface formation information; determining a Haximum sampling interval under regular sampling according to the geological model, wherein the maximum sampling interval is a maximum regular sampling interval, at which aliasing is not generated, during forward modeling of the geological model; determining a number of 10 sparse sampling points and candidate grids of sampling points according to the maximum sampling interval, a range of work area and, an average interval of sparse sampling; and, determining an irregular sparse observation system by a genetic algorithm according to the number of sparse sampling points and the 15 candidate grids of sampling points. Sensing seismic wave fields to the greatest extent is achieved by reducing spectrum energy leakage with fewer detector points. (+ Fig. l) 20
Description
METHOD AND SYSTEM FOR DETERMINING GEOSTEERING IRREGULAR
OBSERVATION SYSTEM
The present invention relates to the field of seismic explo- ration, in particular to a method and system for determining a ge- osteering irregular observation system.
Seismic exploration is the most important exploration method in geophysical exploration, and the most effective exploration technology in petroleum and natural gas exploration. Served as one of the three main links of seismic exploration, the field seismic data acquisition plays an important role in seismic exploration.
The quality of data acquired in the field directly determines the processing effect of seismic data, and also indirectly affects the correctness and reliability of seismic data interpretation.
Seismic data acquisition refers to the discrete recording of seismic wave fields generated by hypocenter excitation by arrang- ing detector points and shot points in a large area according to the designed observation system. For many years, the field seismic data acquisition has followed Nyquist sampling theorem, i.e., reg- ular dense sampling in time and space. In the actual seismic data acquisition, the time sampling interval can be small enough. Alt- hough regular high-density sampling in space can be technically realized, the resulting arrangement of a large number of detector points and shot points will greatly increase the acquisition cost and greatly reduce the exploration efficiency. Although regular sparse sampling can reduce the acquisition cost, a serious alias- ing phenomenon will occur, which will affect the subsequent seis- mic data processing and interpretation. In addition, with the deepening of oil and gas exploration, the data acquisition envi- ronment has become increasingly complex. Obstacles such as rivers, villages, mountains and roads do not enable detector points and shot points to be arranged regularly, and seismic wave field rec-
ords are prone to loss, thereby affecting subsequent seismic data processing and interpretation.
The compressed sensing theory, which has developed rapidly in the field of information technology in recent years, provides new ideas for solving the above-mentioned problems. According to the compressed sensing theory, when a signal is sparse or compressi- ble, with irregular sparse acquisition, the regular high-density original signal can be reconstructed by solving an optimization problem with sparsity constraints, even if the number of sampling points is far lower than the requirements of Nyquist sampling the- orem. Since seismic data are sparse in some transform domains, the compressed sensing theory can be applied to seismic exploration.
By designing a random sparse observation system, the seismic data acquisition and compression can be performed simultaneously, and a complete seismic wave field can be reconstructed by reconstruction algorithm. The random sparse observation system can effectively reduce the number of detector points and shot points and improve the efficiency of seismic data acquisition.
In the aspect of designing the irregular observation system based on compressed sensing theory, some people proposed a Jitter sampling method, some proposed a non-uniform optimal sampling method (NUOS), some proposed Poisson disk sampling, some proposed a segmented random sampling technique, and some proposed a greedy sequential algorithm to design a random irregular observation sys- tem. Although the observation systems designed by these methods can satisfy the requirements of compressed sensing theory for sam- pling matrix, and can satisfy the requirements of reducing the number of detector points and improving the quality of recon- structed seismic data, the actual underground structure is not taken into account. Therefore, the observation system satisfying the design of compressed sensing sampling matrix may not be able to sense underground structure information to the greatest extent.
According to the compressed sensing theory, the signal spectrum obtained by irregular acquisition at a sampling point far lower than Nyquist sampling theorem will transform the overlapped spa- tial aliasing into incoherent noise with a small amplitude, i.e., energy leakage. The smaller the leakage amplitude, the better the reconstruction of the original signal. Therefore, an observation method that can reduce a spectrum energy leakage is required.
The present invention is intended to provide a method and system for determining a geosteering irregular observation system, so as to achieve the purpose of sensing seismic wave fields to the greatest extent by reducing a spectrum energy leakage with fewer detector points.
To achieve the aforesaid purposes, the present invention pro- vides the following solutions:
A method for determining a geosteering irregular observation system includes the following steps: acquiring subsurface formation information and constructing a geological model according to the subsurface formation infor- mation; determining a maximum sampling interval under regular sam- pling according to the geological model, wherein the maximum sam- pling interval is a maximum regular sampling interval, at which aliasing is not generated, during forward modeling of the geologi- cal model; determining a number of sparse sampling points and candidate grids of sampling points according to the maximum sampling inter- val, a range of work area and an average interval of sparse sam- pling; and determining an irregular sparse observation system by a ge- netic algorithm according to the number of sparse sampling points and the candidate grids of sampling points, wherein the irregular sparse observation system includes locations of multiple detector points.
Optionally, the step of determining a maximum sampling inter- val under regular sampling according to the geological model in- cludes the following specific steps: determining trace spacings of detector points according to a dip angle, a velocity and a maximum freguency, at which spatial aliasing is not generated, of the geological model; and determining a maximum trace spacing of detector points as a maximum sampling interval under regular sampling.
Opticnally, the trace spacing of detector points is calculat- ed by the following formula: v
AS me Fos where Ax is a trace spacing, 8 is a dip angle, v is a veloci- ty, and fmax is a maximum frequency at which spatial aliasing is not generated.
Optionally, the step of determining an irregular sparse ob- servation system by a genetic algorithm according to the number of sparse sampling points and the candidate grids of sampling points includes the following specific steps: initializing gene strings of a genetic algorithm by the num- ber of sparse sampling points and the candidate grids of sampling points; decoding the initialized gene strings to obtain locations of detector points; performing forward modeling for an irregular sparse observa- tion system corresponding to all individuals in a population of the genetic algorithm to obtain sparse seismic wave field data; determining individual fitness according to a frequency- wavenumber spectrum of the sparse seismic wave field data and a freguency-wavenumber spectrum of regular seismic wave field data without aliasing; performing selection, inheritance and population regeneration by genetic operators according to the individual fitness to deter- mine a new population; and determining an irregular sparse observation system according to the new population and the locations of detector points, where- in the irregular sparse observation system is an individual with the highest individual fitness; and the individuals are the loca- tions of detector points.
A system for determining a geosteering irregular observation system includes: an acquisition module, configured to acquire subsurface for- mation information and construct a geological model according to the subsurface formation information;
a maximum sampling interval determination module, configured to determine a maximum sampling interval under regular sampling according to the geological model, wherein the maximum sampling interval is a maximum regular sampling interval, at which aliasing 5 is not generated, during forward modeling of the geological model; a sparse sampling point number and sampling point candidate grid determination module, configured to determine a number of sparse sampling points and candidate grids of sampling points ac- cording to the maximum sampling interval, a range of work area and an average interval of sparse sampling; and an irregular sparse observation system determination module, configured to determine an irregular sparse observation system by a genetic algorithm according to the number of sparse sampling points and the candidate grids of sampling points, wherein the ir- regular sparse observation system includes locations of multiple detector points.
Optionally, the maximum sampling interval determination mod- ule specifically includes: a detector point trace spacing determination unit, configured to determine trace spacings of detector points according to a dip angle, a velocity and a maximum frequency, at which spatial alias- ing is not generated, of the geological model; and a maximum sampling interval determination unit, configured to determine a maximum trace spacing of detector points as a maximum sampling interval under regular sampling.
Optionally, the trace spacing of detector points is calculat- ed by the following formula: v dx = 28in8 fax where Ax is a trace spacing, 8 is a dip angle, v is a veloci- ty, and {mar is a maximum frequency at which spatial aliasing is not generated.
Optionally, the irregular sparse observation system determi- nation module specifically includes: an initialization unit, configured to initialize gene strings of a genetic algorithm by the number of sparse sampling points and the candidate grids of sampling points;
a decoding unit, configured to decode the initialized gene strings to obtain locations of detector points; a forward modeling unit, configured to perform forward model- ing for an irregular sparse observation system corresponding to all individuals in a population of the genetic algorithm to obtain sparse seismic wave field data; an individual fitness determination unit, configured to de- termine individual fitness according to a frequency-wavenumber spectrum of the sparse seismic wave field data and a frequency- wavenumber spectrum of regular seismic wave field data without aliasing; a new population determination unit, configured to perform selection, inheritance and population regeneration by genetic op- erators according to the individual fitness to determine a new population; and an irregular sparse observation system determination unit, configured to determine an irregular sparse observation system ac- cording to the new population and the locations of detector points, wherein the irregular sparse observation system is an in- dividual with the highest individual fitness; and the individuals are the locations of detector points.
According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:
Subsurface formation information is acquired and a geological model is constructed according to the subsurface formation infor- mation; a maximum sampling interval under regular sampling is de- termined according to the geological model, wherein the maximum sampling interval is a maximum regular sampling interval, at which aliasing is not generated, during forward modeling of the geologi- cal model; a number of sparse sampling points and candidate grids of sampling points are determined according to the maximum sam- pling interval, a range of work area and an average interval of sparse sampling; and an irregular sparse observation system is de- termined by a genetic algorithm according to the number of sparse sampling points and the candidate grids of sampling points. By considering the underground formation information, an irregular sparse observation system with minimum spatial aliasing (i.e., the lowest energy leakage degree) is obtained by the genetic algo- rithm, so that the underground structure information can be sensed to the maximum extent with as few detector points as possible.
In order to explain the technical solutions in the embodi- ments of the present invention or in the prior art more clearly, the accompanying drawings required in the embodiments will be de- scribed below briefly. Apparently, the accompanying drawings in the following description show only some embodiments of the pre- sent invention, and other drawings can be derived from these ac- companying drawings by those of ordinary skill in the art without making creative efforts.
FIG. 1 is a flow chart of a method for determining a geo- steering irregular observation system provided in the present in- vention.
FIG. 2 is a flow diagram of a method for determining a geo- steering irregular observation system provided in the present in- vention.
FIG. 3 is a flow chart of a step of determining an irregular sparse observation system by a genetic algorithm provided in the present invention.
FIG. 4 is a seismogram of regular high-density single shots obtained by forward modeling of a simple geological model with a tilted formation.
FIG. 5 is a seismogram of regular sparse single shots ob- tained by 50% regular thinning of the regular seismic wave fields in FIG. 4.
FIG. 6 is a seismogram of irregular sparse single shots ob- tained by forward modeling of the irregular observation system de- signed in the present invention.
FIG. 7 is a convergence curve of errors between frequency- wavenumber spectrum in the irregular sparse single shot seismogram in FIG. 6 and in the regular high-density single shot seismogram in FIG. 4 vs. a number of evolutions.
FIG. 8 is a frequency-wavenumber spectrum corresponding to the regular seismic wave fields in FIG. 4.
FIG. 9 is a frequency-wavenumber spectrum corresponding to the regular sparse seismic wave fields in FIG. 5.
FIG. 10 is a frequency-wavenumber spectrum corresponding to the irregular sparse seismic wave fields in FIG. 6.
FIG. 11 is a difference image between the frequency- wavenumber spectrum in FIG. 9 and the frequency-wavenumber spec- trum in FIG. 8.
FIG. 12 is a difference image between the frequency- wavenumber spectrum in FIG. 10 and the frequency-wavenumber spec- trum in FIG. 8.
FIG. 13 is a regular high-density image obtained by forward modeling of a Marmousi velocity model.
FIG. 14 is a seismogram of single shots obtained by 50% regu- lar thinning of the corresponding regular seismic wave fields in
FIG. 13.
FIG. 15 is a seismogram of single shots obtained by forward modeling of a Marmousi velocity model using the observation system designed by the method in the present invention.
FIG. 16 is a convergence curve of errors between frequency- wavenumber spectrums corresponding to the irregular sparse single shot seismogram and to the regular high-density single shot seis- mogram in FIG. 13 vs. a number of evolutions.
FIG. 17 is a frequency-wavenumber spectrum corresponding to the regular seismic wave fields in FIG. 13.
FIG. 18 is a frequency-wavenumber spectrum corresponding to the regular sparse seismic wave fields in FIG. 14.
FIG. 19 is a frequency-wavenumber spectrum corresponding to the irregular sparse seismic wave fields in FIG. 15.
The technical solutions in the embodiments of the present in- vention will be described below clearly and completely in combina- tion with the accompanying drawings. Apparently, the embodiments described herein only constitute a part rather than all of the em- bodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts should fall within the protection scope of the present invention.
To make the aforesaid purposes, features and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in combination with the accompanying drawings and specific embodiments.
A seismic data acquisition process based on compressed sens- ing can be generally represented by the following formula: y= &x, where y is sparse seismic wave fields acquired, is a random sparse observation system, and x is a complete seismic wave field to be reconstructed. The compressed sensing theory requires that a signal acquired be sparse or compressible. Since seismic data are sparse in some transform domains, seismic wave field data can be represented by the following formula: x =W98, where Wis a sparse transform matrix, and 8 is a sparse coefficient of seismic data in transform domains. At this time, there is an expression y = ®W6 =
Af, where A=9%Wis called a sensing matrix. Since the random sparse observation system is OE RMN, M<N, this expression is an underdetermined problem with infinite solutions, and it is neces- sary to solve a sparse signal from the underdetermined equation by an effective sparsity constraint optimization algorithm.
An important application premise of the compressed sensing theory is a random and nonlinear measurement of a signal acquired.
A regular and sparse arrangement of sampling points easily causes spectrum leakage and forms spatial aliasing, while an irregular arrangement of sampling points will transform spatial aliasing in- to incoherent noise that is easy to remove. Therefore, when a sparse signal is acquired by applying the compressed sensing theo- ry, it is necessary to design a sensing matrix that satisfies cer- tain requirements to ensure that important information will not be lost in the process of sparse signal acquisition. For seismic ex- ploration based on compressed sensing, Hennenfent and Herrman pro- posed a jitter sampling method to avoid massive data loss caused by random undersampling, which can effectively control a maximum spacing between adjacent detectors. Moldoveanu made theoretical researches on a random sampling method of marine data. Mosher et al. proposed a non-uniform optimal sampling method (NUOS) that is more conducive to data reconstruction. Bhuiyan et al. proposed a continuous non-uniform sampling technology based on a cross corre- lation coefficient, which can fully consider a constraint of a maximum spacing between detector points when being used to design an irregular sparse observation system. Domestic research scholars also made extensive researches on irregular sparse acquisition methods. Tang Gang et al. introduced a Poisson disk sampling meth- od to improve the deficiencies of a simple random undersampling method in irregular observation system, which effectively solved the problem of too dense or scattered sampling points. Cao Jingjie et al. introduced a segmented random sampling technology into the design of irregular sparse observation system. Cai Rui et al. pro- posed a random undersampling method satisfying Bernoulli distribu- tion and its jitter form. Chen Shengchang et al. proposed an im- proved segmented sampling method for efficient seismic data acqui- sition. Zhou Song et al. proposed a greedy sequential strategy to design a random irregular observation system and achieved an ex- cellent application effect in the actual seismic data acquisition test. However, the above-mentioned design methods of irregular sparse observation system do not consider the underground situa- tion. Since the propagation of seismic waves is affected by such factors as formation structure, velocity and anisotropy, different observation systems under the same geological conditions will also get seismogram sections in different quality. Therefore, the geo- logical conditions of a work area should be fully considered in the design of irregular seismic observation system, and the obser- vation system should be designed based on a geological model, so that an irregular observation system designed can acquire more un- derground information with as few observation points as possible.
By summarizing the advantages and deficiencies of the above- mentioned irregular sparse observation system design methods, in the present invention, an irregular observation system with a max- imum spatial aliasing suppression degree is selected in combina- tion with forward modeling of seismic wave fields for sparse ac- quisition of actual seismic data. Compared with the previous sparse seismic data acquisition methods, in the present invention,
an irregular sparse observation system which can fully acquire seismic wave fields can be designed by making full use of geologi- cal information of a work area, so as to provide theoretical sup- port for accurate reconstruction of a complete seismic wave field.
As shown in FIG. 1, a method for determining a geosteering irregular observation system provided in the present invention in- cludes the following steps: 5101, acquiring subsurface formation information and con- structing a geological model according to the subsurface formation information; 5102, determining a maximum sampling interval under regular sampling according to the geological model, wherein the maximum sampling interval is a maximum regular sampling interval, at which aliasing is not generated, during forward modeling of the geologi- cal model; S102 includes the following specific steps: determining trace spacings of detector points according to a dip angle, a ve- locity and a maximum frequency, at which spatial aliasing is not generated, of the geological model; and determining a maximum trace spacing of detector points as a maximum sampling interval under regular sampling;
The trace spacing of detector points is calculated by the following formula: v
AS me Fos where Ax is a trace spacing, 8 is a dip angle, v is a veloci- ty, and fmax is a maximum frequency at which spatial aliasing is not generated. 5103, determining a number of sparse sampling points and can- didate grids of sampling points according to the maximum sampling interval, a range of work area and an average interval of sparse sampling, wherein the range of work area is a target area to be detected; and
S104, determining an irregular sparse observation system by a genetic algorithm according to the number of sparse sampling points and the candidate grids of sampling points, wherein the ir- regular sparse observation system includes locations of multiple detector points.
S104 includes the following specific steps: initializing gene strings of a genetic algorithm by the num- ber of sparse sampling points and the candidate grids of sampling points; decoding the initialized gene strings to obtain locations of detector points; performing forward modeling for an irregular sparse observa- tion system corresponding to all individuals in a population of the genetic algorithm to obtain sparse seismic wave field data; determining individual fitness according to a frequency- wavenumber spectrum of the sparse seismic wave field data and a frequency-wavenumber spectrum of regular seismic wave field data without aliasing; performing selection, inheritance and population regeneration by genetic operators according to the individual fitness to deter- mine a new population; and determining an irregular sparse observation system according to the new population and the locations of detector points, where- in the irregular sparse observation system is an individual with the highest individual fitness; and the individuals are the loca- tions of detector points.
According to the present invention, sensing of seismic wave fields is achieved by reducing spectrum energy leakage with fewer detector points, and seismic wave reconstruction is completed by the seismic wave fields sensed.
In order to make full use of a known geological model and sense underground structure information to the greatest extent with as few detector points and shot points as possible in the de- sign of observation system, the present invention also provides general ideas of the method for determining a geosteering irregu- lar observation system in a practical application: {1) A geological model is constructed by making full use of prior information, such as logging data, interval velocity, ab- sorption attenuation information and anisotropy; previous geologi- cal and geophysical data of a work area need to be collected as many as possible, because an accuracy degree of the geological model directly affects an accuracy of subsequent wave equation forward modeling. {2) A maximum trace spacing of detector points under regular sampling is calculated by using information such as a structural dip angle, a velocity and a maximum frequency, at which spatial aliasing is not generated, of the geological model; the maximum trace spacing of detector points at which spatial aliasing is not generated under regular acquisition is calculated according to a dip angle, a velocity and other information in the geological mod- el constructed in Step I, so as to provide a reference for deter- mining a number of sparse sampling points and selecting an optimal irregular observation system subsequently. (3) A number of candidate grid points and detector points is determined; a number of shot points and detector points needs to be determined according to the information calculated in Step IT.
High-density seismic wave field data are expected to be obtained by using the number of shot detector points less than the regular acquisition. (4) An irregular observation system is designed by a genetic algorithm, so that the irregular observation system obtained has a minimum spectrum leakage; the genetic algorithm is a global search optimization algorithm, which simulates phenomena such as replica- tion, crossover and mutation occurring in natural selection and inheritance. Starting from any initial population, a group of in- dividuals more suitable for survival are generated through random selection, crossover and mutation operations, in this way, a group of individuals most suitable for environment are converged from generation to generation, and thus a high-quality solution to a problem is obtained. Due to a need for a random selection in the reproductive process of a population, individuals who adapt to the environment have a higher probability of being selected, while in- dividuals who do not adapt to the environment have a higher proba- bility of not being selected. A degree which an individual adapts to the environment is described by fitness, and the higher the fitness, the more adaptable an individual to the environment and the easier to be retained. In this step, a sparse seismic wave field generated by the irregular observation system is subject to frequency-wavenumber transform, a difference degree of frequency-
wavenumber spectrums between the sparse seismic wave field and the high-density regular seismic wave field is contrasted, and more wave field information is recorded by a sparse seismic wave field corresponding to a frequency-wavenumber spectrum less different from a frequency-wavenumber spectrum of regular high-density seis- mic data, which is more conducive to wave field reconstruction, and thereby the fitness should be larger. From generation to gen- eration, excellent genetic genes are retained until an optimal in- dividual obtained after convergence is the most adaptable to the environment, i.e., a final high-quality solution is more conducive to sparse wave field reconstruction. (5) An optimal irregular observation system is outputted. A high-quality solution obtained by the genetic algorithm is output- ted, i.e., an optimal irregular sparse observation system.
As shown in FIG. 2, according to the above-mentioned general ideas, the present invention also provides the specific steps of the method for determining a geosteering irregular observation system in a practical application:
Step I, constructing a geological model. Based on early geo- physical exploration data, logging data, etc., the information such as shear wave velocity, longitudinal wave velocity, anisotro- py and absorption attenuation of subsurface formations and struc- tures of formation annihilation, lenticular body and complex thrust fault are collected to construct a high-precision geologi- cal model, so as to ensure an accuracy of subsequent seismic wave field forward modeling.
Step II, calculating a maximum sampling interval under regu- lar sampling. In an actual seismic data acquisition process, the gecological conditions of a work area are not the same, and differ- ences of formation velocity, dip angle, etc. will affect the qual- ity of a seismic section. For a certain work area, the smaller the trace spacing, the more the seismic data acquired, and the higher the quality of a final seismic profile. However, in an actual field data acquisition process, detector points cannot be densely arranged due to economy, time, field environment and other fac- tors, so it is easy to cause spatial aliasing. When a subsurface formation in the work area has the dip angle, to obtain seismic data without aliasing, the trace spacings of detector points should satisfy the following formula: v
AS iin fn where Ax is a detector point trace spacing, 8 is a dip angle, v is a velocity, and fmax 1s a maximum frequency at which spatial aliasing is not generated. The velocity is a root mean square ve- locity of all formations above the inclined formation. The maximum frequency is a frequency limit of the spatial aliasing. Therefore, a maximum sampling interval when regular sampling of the geologi- cal model does not generate aliasing can be obtained according to this formula.
Step III, determining a number of random sampling points and candidate grids. After the maximum sampling interval without ali- asing under regular sampling is determined, a number of sparse sampling points, candidate grids of sampling points and other in- formation can be determined according to a range of work area and an average trace spacing of sparse sampling, and one sampling point can be arranged in each grid point.
Step IV, designing an irregular observation system by a ge- netic algorithm. As there are a lot of possibilities to select a small number of irregular sampling points on the candidate grids of sampling points, if an irregular sparse observation system is designed by an exhaustive method and an application effect of each irregular sparse observation system needs to be evaluated, a lot of time and computing resources will be wasted. Therefore, design- ing an irregular sparse observation system by a genetic algorithm is proposed in the present invention, which hopes to obtain a high-quality solution through limited computing resources.
Designing an irregular sparse observation system by a genetic algorithm in SIV imitates the evolutional rules of “Natural selec- tion and survival of the fittest” in the biological world, and be- longs to an evolutionary algorithm. The algorithm is simple and easy to understand. The genetic algorithm simulates reproduction, crossover and genetic mutation in natural selection and natural inheritance. In each evolution, a group of candidate solutions are retained, and better individuals are selected according to a cer-
tain index, and these individuals are combined by genetic opera- tors such as selection, crossover and mutation to produce a new generation of individuals. From generation to generation, high- quality genes are retained, and the individuals obtained are well adapted to the environment. A flow of designing an irregular sparse observation system by a genetic algorithm, as shown in FIG. 3, includes the following steps:
Sl, encoding, i.e., mapping from a solution to a problem to a genotype is called encoding. A genetic algorithm first expresses a solution to a solution space in a form of gene strings before searching, wherein each gene string represents a solution. Before an evolution of the genetic algorithm, a certain number of indi- viduals need to be set, wherein each gene string represents an in- dividual. Common encoding methods include binary encoding, Gray encoding, float-point encoding, multi-parameter cross encoding, etc. In the present invention, the solution to the solution space is equivalent to arrangement locations of detector points in an irregular observation system.
S2, initialization of a population, i.e., a process of trans- forming gene strings into individuals. Each gene string represents an individual. The gene strings can be transformed into corre- sponding individuals through the decoding operation in S1. In the present invention, each individual represents specific arrangement locations of detector points. 83, forward modeling of seismic wave fields. The individuals acquired in S2 represent specific arrangement locations of detec- tor points, i.e., an irregular sparse observation system. In this step, forward modeling is performed on an irregular sparse obser- vation system corresponding to all individuals in a population to obtain corresponding sparse seismic wave field data.
S4, evaluation of individual fitness in the population. As the genetic algorithm imitates the evolutional rules of “Natural selection and survival of the fittest“ in the biological world, it is hopeful that individuals who do not adapt to the environment will be removed through evolutions and better individuals will be retained. A factor that determines whether an individual adapts to the environment is called individual fitness. In the present in-
vention, a similarity of frequency-wavenumber spectrums between the sparse seismic wave field data obtained in S3 and regular seismic wave field data without aliasing is taken as the individu- al fitness, so as to find an individual corresponding to a sparse seismic wave field less different from the frequency-wavenumber spectrum of the regular seismic wave field data without aliasing. 55, selection and inheritance. Selection and inheritance are performed on individuals in the population by genetic operators including selection, crossover and mutation. The selection opera- tion is to select excellent individuals from an old population with a certain probability to form a new population. A probability of individual selection is related to the individual fitness cal- culated in S4. The greater the individual fitness, the greater the probability of being selected. In the present invention, a rou- lette method is selected for selection operation. If a number of populations is set as M and the individual fitness is set as f£,, the probability P; of individuals being selected is:
Dii Ji
The crossover operation refers to a random selection of two individuals from the population. Through an exchange combination of two chromosomes, excellent characteristics of a parent string are transferred to a child string, so as to produce new excellent individuals. The mutation operation can effectively prevent the genetic algorithm from falling into a locally optimal solution in the process of optimization. The mutation operation on individuals makes the genetic algorithm jump out of the locally optimal solu- tion.
S6, population regeneration. The gene strings operated by the genetic operators will generate a group of new gene strings, which will be decoded to obtain a new population.
S7, Judgment of whether the new population satisfies condi- tions. When the individual fitness converges or reaches a maximum number of evolutions, an individual with the highest fitness is selected as a high-quality solution for outputting.
S8, outputting of the high-quality individual. In the present invention, the high-quality individual outputted is a target ir-
regular sparse observation system.
Step V, outputting the irregular sparse observation system obtained by the genetic algorithm. The irregular sparse observa- tion system obtained can fully acquire the underground information with as few detector points as possible and suppress the spatial aliasing to the maximum extent.
As shown in FIGS. 4-7, FIG. 4 shows a seismogram of single shots obtained by forward modeling of a simple geological model with a tilted formation; FIG. 5 shows a seismogram of single shots obtained by regular thinning of the seismogram of single shots, with only 50% detector points retained; FIG. 6 shows a seismogram of single shots corresponding to an irregular sparse observation system obtained by a genetic algorithm of the geological model; and FIG. 7 shows a convergence curve of errors between frequency- wavenumber spectrums of the irregular sparse seismogram corre- sponding to an optimal individual in each evolution and of com- plete regular seismogram vs. a number of evolutions, during design of an irregular random observation system by the genetic algo- rithm.
In the present invention, a simple stratified geological mod- el is designed, wherein a formation includes a dip angle, and a maximum trace spacing under regular sampling is calculated as 10 m; then a regular observation system is designed, with a trace spacing of 10 m, 320 detector points, and a time sampling interval of 2 ms; then forward modeling is performed for the geological model by the regular observation system to obtain a seismogram of single shots, as shown in FIG. 4. In order to compare advantages of the method in the present invention in design of irregular sparse observation system, the seismogram of single shots as shown in FIG. 4 is subject to 50% regular thinning to obtain regular sparse single shot seismogram with 160 detector points, as shown in FIG. 5. An irregular sparse observation system with 160 detec- tor points is designed by the irregular single shot seismogram de- sign method proposed in the present invention, and then forward modeling is performed for the geological model by the observation system. The irregular sparse single shot seismogram obtained is as shown in FIG. 6. In the present invention, 1,000 evolutions are set, and an optimal individual obtained by each evolution can ob- tain an irregular sparse seismic wave field, wherein a convergence curve of errors between frequency-wavenumber spectrums of these sparse wave fields and of the regular wave fields as shown in FIG. 4 vs. a number of evolutions is as shown in FIG. 7.
As shown in FIGS. 8-12, FIG. 8 shows a frequency-wavenumber spectrum corresponding to the regular seismic wave fields in FIG. 4, and it can be seen that no spatial aliasing appears. FIG. 3 shows a frequency-wavenumber spectrum corresponding to the regular sparse seismic wave fields in FIG. 5, and it can be seen that the spatial aliasing is serious, causing great influence on subsequent seismic data processing and interpretation. FIG. 10 shows a fre- quency-wavenumber spectrum corresponding to the irregular sparse seismic wave fields in FIG. 6, and it can be seen that the spatial aliasing is transformed into incoherent noise with a small ampli- tude. FIG. 11 and FIG. 12 show differences between FIG. 9 and FIG. 10 with FIG. 8 respectively, and it can be seen that an optimal irregular observation system can be designed based on the geologi- cal model, which can effectively suppress the spatial aliasing.
As shown in FIGS. 13-16, FIG. 13 shows a seismogram of single shots obtained by forward modeling of a Marmousi velocity model, with a trace spacing of 4 m, 2,000 detector points, a sampling in- terval of 8 ms, and a total sampling time of 4 s. FIG. 14 shows a seismogram of single shots obtained by 50% regular thinning of the corresponding regular seismic wave fields in FIG. 13, with a trace spacing of 8 m, 1,000 detector points, a sampling interval of 8 ms, and a total sampling time of 4 s. FIG. 15 shows a seismogram of single shots obtained by forward modeling with the observation system designed by the method proposed in the present invention, with 1,000 detector points, a sampling interval of 8 ms, and a to- tal sampling time of 4 s. In the present invention, 1,000 evolu- tions are set, and an optimal individual obtained by each evolu- tion is corresponding to a sparse seismic wave field, wherein a curve of errors between frequency-wavenumber spectrums of these sparse wave fields and of the regular seismic wave fields as shown in FIG. 13 vs. a number of evolutions is as shown in FIG. 16, and it can be seen that the spatial aliasing of the irregular seismic wave fields designed is also suppressed continuously with a con- stant evolution of the population.
As shown in FIGS. 17-19, FIG. 17 shows a frequency-wavenumber spectrum corresponding to the regular seismic wave fields in FIG. 13, and it can be seen that no spatial aliasing appears. FIG. 18 shows a frequency-wavenumber spectrum corresponding to the regular sparse seismic wave fields in FIG. 14, and it can be seen that the spatial aliasing is aliased with the real spectrums, affecting subsequent seismic data processing and interpretation. FIG. 19 shows a frequency-wavenumber spectrum corresponding to the irregu- lar sparse seismic wave fields in FIG. 15, and it can be seen that the spatial aliasing is transformed into incoherent noise with a small amplitude, and regularly complete seismic wave field records can be accurately reconstructed by an efficient sparsity con- straint optimization algorithm.
A system for determining a geosteering irregular observation system provided in the present invention includes: an acquisition module, configured to acquire subsurface for- mation information and construct a geological model according to the subsurface formation information; a maximum sampling interval determination module, configured to determine a maximum sampling interval under regular sampling according to the geological model, wherein the maximum sampling interval is a maximum regular sampling interval, at which aliasing is not generated, during forward modeling of the geological model; a sparse sampling point number and sampling point candidate grid determination module, configured to determine a number of sparse sampling points and candidate grids of sampling points ac- cording to the maximum sampling interval, a range of work area and an average interval of sparse sampling; and an irregular sparse observation system determination module, configured to determine an irregular sparse observation system by a genetic algorithm according to the number of sparse sampling points and the candidate grids of sampling points, wherein the ir- regular sparse observation system includes locations of multiple detector points.
In a practical application, the maximum sampling interval de-
termination module specifically includes: a detector point trace spacing determination unit, configured to determine trace spacings of detector points according to a dip angle, a velocity and a maximum frequency, at which spatial alias- ing is not generated, of the geological model; and a maximum sampling interval determination unit, configured to determine a maximum trace spacing of detector points as a maximum sampling interval under regular sampling.
In a practical application, the trace spacing of detector points is calculated by the following formula: v
Ax = 2 sin fmax where Ax is a trace spacing, 8 is a dip angle, v is a veloci- ty, and fmax 1s a maximum freguency at which spatial aliasing is not generated.
In a practical application, the irregular sparse observation system determination module specifically includes: an initialization unit, configured to initialize gene strings of a genetic algorithm by the number of sparse sampling points and the candidate grids of sampling points; a decoding unit, configured to decode the initialized gene strings to obtain locations of detector points; a forward modeling unit, configured to perform forward model- ing for an irregular sparse observation system corresponding to all individuals in a population of the genetic algorithm to obtain sparse seismic wave field data; an individual fitness determination unit, configured to de- termine individual fitness according to a frequency-wavenumber spectrum of the sparse seismic wave field data and a frequency- wavenumber spectrum of regular seismic wave field data without aliasing; a new population determination unit, configured to perform selection, inheritance and population regeneration by genetic op- erators according to the individual fitness to determine a new population; and an irregular sparse observation system determination unit, configured to determine an irregular sparse observation system ac-
cording to the new population and the locations of detector points, wherein the irregular sparse observation system is an in- dividual with the highest individual fitness; and the individuals are the locations of detector points.
According to the present invention, based on a known geologi- cal model, an irregular sparse observation system with minimum spatial aliasing (i.e., the lowest energy leakage degree) is ob- tained by a genetic algorithm, so that the underground structure information can be sensed to the maximum extent with as few obser- vation points as possible, and the spectrum energy leakage is min- imized, which provide support for accurate reconstruction of com- plete seismic wave field information. The existing irregular ob- servation system design methods do not consider an underground structure of an actual work area, so the observation systems de- signed may not be able to fully acquire the underground structure information. According to the technical solutions proposed in the present invention, based on an actual geological model, an irregu- lar observation system with a minimum spectrum energy leakage can be designed, so that a complete seismic wave field can be recon- structed more accurately.
All embodiments in the specification are described in a pro- gressive manner. Each embodiment focuses on the differences with other embodiments, and the same and similar parts among all embod- iments can be referred to each other. For the system disclosed in the embodiments, because it corresponds to the method disclosed in the embodiments, the description is relatively simple. For rele- vant points, refer to the description in the method section.
The principle and implementation mode of the present inven- tion are described with specific embodiments. The description of the above-mentioned embodiments is only used to help understand the method of the present invention and its core idea. Meanwhile, both specific implementation mode and scope of application will be changed by those of ordinary skill in the art based on the idea of the present invention. To sum up, the content of the specification should not be understood as a limitation to the present invention.
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