Method and device for determining lithological trap boundary, electronic equipment and storage medium

Abstract

The application discloses a method and device for determining a lithologic trap boundary, electronic equipment and a storage medium. The method comprises the following steps: simulating a pseudo-well vertical lithologic combination based on a continuous-time Markov chain model, determining a pseudo-well lithofacies curve of different lithologic combinations according to a simulation result; determining a target elastic parameter through the pseudo-well lithofacies curve of the different lithologic combinations based on elastic parameter Monte Carlo random simulation with lithofacies constraints; determining pseudo-well simulation seismic data of the different lithologic combinations through the target elastic parameter based on a wave equation forward modeling method of a propagation matrix; and determining a lithologic trap boundary according to the pseudo-well simulation seismic data of the different lithologic combinations. The technical scheme of the application avoids the problems of strong subjectivity, large deviation of boundaries delineated by different personnel, and insufficient objective basis in the determination of a traditional lithologic trap boundary, thereby avoiding the situation of deviation of the delineated boundary caused by human factors.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration technology, and in particular to a method, apparatus, electronic device and storage medium for determining lithological trap boundaries. Background Technology

[0002] Turbidite sandstone reservoirs deposited in deep-water environments form lithological traps with the surrounding mudstone. Determining the trap boundaries is not only a major issue in judging the effectiveness of lithological traps, but also directly affects the size of the traps.

[0003] However, the thickness of sand bodies near trap boundaries is usually much lower than the seismic resolution, making it impossible to directly obtain lithological pinch-out points through seismic interpretation. Deep-water sedimentary sand bodies are widely developed, with rapid lateral facies transitions and more complex and diverse lithological assemblages. Furthermore, the number of exploration wells in deep-water areas is small, the well-controlled area is large, and the amplitude threshold for characterizing trap boundaries is difficult to determine. Seismic responses are complex, and manually delineated boundaries have many possible solutions. Therefore, a technique is needed to accurately and quantitatively characterize lithological trap boundaries. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and storage medium for determining lithological trap boundaries, in order to solve the problem of high uncertainty in trap boundary identification.

[0005] According to one aspect of the present invention, a method for determining the boundary of a lithological trap is provided, the method comprising:

[0006] The vertical lithological combination of the pseudo-well was simulated based on the continuous-time Markov chain model, and the lithofacies curves of different lithological combinations of the pseudo-well were determined based on the simulation results.

[0007] Monte Carlo stochastic simulation of elastic parameters based on lithofacies constraints determines the target elastic parameters through pseudo-well lithofacies curves of different lithological combinations;

[0008] The wave equation forward modeling method based on the propagation matrix determines the pseudo-well simulated seismic data for different lithological combinations by using the target elastic parameters;

[0009] Lithological trap boundaries were determined based on simulated seismic data from pseudo-wells with different lithological combinations.

[0010] According to another aspect of the present invention, an apparatus for determining lithological trap boundaries is provided, the apparatus comprising:

[0011] The lithofacies curve determination module is used to simulate the vertical lithological combination of pseudo-wells based on the continuous-time Markov chain model, and determine the lithofacies curves of pseudo-wells for different lithological combinations based on the simulation results.

[0012] The elastic parameter determination module is used for Monte Carlo stochastic simulation of elastic parameters based on lithofacies constraints. It determines the target elastic parameters through pseudo-well lithofacies curves of different lithological combinations.

[0013] The seismic data determination module is used for wave equation forward modeling based on propagation matrix to determine pseudo-well simulated seismic data for different lithological combinations through target elastic parameters;

[0014] The trap boundary determination module is used to determine the lithological trap boundaries based on pseudo-well simulated seismic data with different lithological combinations.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] At least one processor; and

[0017] A memory that is communicatively connected to at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to perform the method for determining the lithological trap boundary according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute a method for determining the boundary of a lithological trap according to any embodiment of the present invention.

[0020] According to the technical solution of this invention, the vertical lithological combination of a pseudo-well is simulated based on a continuous-time Markov chain model. The lithofacies curves of different lithological combinations are determined based on the simulation results, enabling more accurate simulation results when simulating pseudo-wells with different lithological combinations, thereby reducing simulation errors. Through Monte Carlo stochastic simulation of elastic parameters based on lithofacies constraints, target elastic parameters are determined using the lithofacies curves of different lithological combinations. A forward modeling method based on the wave equation of the propagation matrix is ​​used to determine the simulated seismic data of different lithological combinations using the target elastic parameters, facilitating calculation and reducing the computational burden on computing equipment. By determining the lithological trap boundaries based on the simulated seismic data of different lithological combinations, the final confirmation results of the lithological trap boundaries can best reflect real-world conditions, improving the accuracy of the system's determination results. By adopting the above method, the delineation of trap boundaries has been developed from a qualitative stage to a quantitative stage, which solves the problem of strong uncertainty in trap boundary identification and avoids the problems of strong subjectivity, large deviations in the boundaries delineated by different personnel, and insufficient objective basis in the determination of traditional lithological trap boundaries. This avoids the situation of deviations in the delineation of boundaries caused by human factors.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 The flowchart illustrates a method for determining the boundary of a lithological trap, as provided in Embodiment 1 of the present invention.

[0024] Figure 2 This is a schematic diagram showing the simulation results of the lithological thickness of each layer in different lithological combinations used in the embodiments of the present invention;

[0025] Figure 3 A flowchart illustrating another method for determining the boundary of a lithological trap provided in Embodiment 2 of the present invention;

[0026] Figure 4 This is a schematic diagram of a simulated lithofacies pattern applicable to an embodiment of the present invention;

[0027] Figure 5 This is a schematic diagram of the longitudinal wave velocity applicable to the embodiments of the present invention;

[0028] Figure 6 This is a schematic diagram of the shear wave velocity applicable to the embodiments of the present invention;

[0029] Figure 7 This is a schematic diagram illustrating the density applicable to the embodiments of the present invention;

[0030] Figure 8 A flowchart illustrating another method for determining the boundary of a lithological trap provided in Embodiment 2 of the present invention;

[0031] Figure 9 A schematic diagram of the seismic amplitude of the target lithology to which this invention is applicable;

[0032] Figure 10 This is a schematic diagram of the lithological trap boundary to which the embodiments of the present invention apply;

[0033] Figure 11 This is a schematic diagram of a device for determining the boundary of a lithological trap provided in Embodiment 4 of the present invention;

[0034] Figure 12This is a schematic diagram of the structure of an electronic device that implements the method for determining the boundary of a lithological trap according to an embodiment of the present invention. Detailed Implementation

[0035] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0036] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0037] Example 1

[0038] Figure 1 This is a flowchart illustrating a method for determining the boundary of a lithological trap, as provided in Embodiment 1 of the present invention. This embodiment is applicable to accurately and quantitatively determining the boundary of a lithological trap when drilling is lacking. This method can be executed by a device for determining the boundary of a lithological trap, which can be implemented in hardware and / or software and can be configured in an electronic device with data processing capabilities. Figure 1 As shown, the method includes:

[0039] S110. Based on the continuous-time Markov chain model, the vertical lithology combination of the pseudo-well is simulated, and the lithological curves of different lithology combinations of the pseudo-well are determined according to the simulation results.

[0040] The continuous-time Markov chain model can be used to represent the transition probability of lithology categories at adjacent time points, and can be expressed using the following formula (1).

[0041] p(π t |π t+1 ,π t+2 ,…,π T )=p(πt |π t+1 (1)

[0042] In the formula, π t Let be the lithology at time t, and p be the transition probability matrix between different lithologies.

[0043] Lithological categories can include, but are not limited to, mudstone, cemented sandstone, limestone, and sandstone. The vertical lithological assemblage of the simulated well can be used to simulate the lithological composition of the well in the vertical direction, where the lithological assemblage can describe the combination of different rocks in their distribution range. The lithofacies curve of the simulated well can be used to describe the lithological assemblage of the simulated well. The simulated well can be a simulated well obtained based on a continuous-time Markov chain model.

[0044] Because geological processes treat stratigraphic profiles as sequences of different rock assemblages, and geological profiles of a given region often conform to Markov properties, and Markov chains have no aftereffects, the probability distribution of lithology at a given time t depends only on the lithology at the previous time t+1, and is independent of other times.

[0045] p(π t |π t+1 ,π t+2 ,…,π T )=p(π t |π t+1 )

[0046] Therefore, the development of lithology in the vertical direction can be simulated using Markov chains. Based on the continuous-time Markov chain model, the vertical lithology combination of the pseudo-well can be simulated to obtain the pseudo-well lithofacies curves of different lithology combinations.

[0047] In one alternative approach, before determining the pseudo-well lithofacies curves for different lithological combinations based on simulation results, the following steps are also included:

[0048] The thickness of each lithology layer in different lithological combinations was simulated based on an exponential probability model.

[0049] Figure 2 This is a schematic diagram showing the simulation results of the lithological thickness of each layer in different lithological combinations used in embodiments of the present invention. See also... Figure 2 Since not only are there different lithological combinations in drilling, but the thickness of different lithological layers in the same lithological combination is also different, it is necessary to simulate the vertical lithological combination of the pseudo-well based on the continuous time Markov chain model, and then simulate the thickness of each layer in different lithological combinations based on the exponential probability model, so as to determine the pseudo-well lithofacies curves of different lithological thicknesses of different lithological combinations.

[0050] The exponential probability model is as follows:

[0051] λe-λx

[0052] In the formula, the λ parameter can adjust the mean of the probability.

[0053] S120, Monte Carlo stochastic simulation of elastic parameters based on lithofacies constraints, determines the target elastic parameters through pseudo-well lithofacies curves of different lithological combinations.

[0054] The target elastic parameter can be an elastic parameter used to determine different lithologies.

[0055] After obtaining pseudo-well lithofacies curves with different lithological thicknesses for different lithological combinations, the target elastic parameters for different lithological thicknesses can be obtained by using the Monte Carlo stochastic simulation method for lithofacies-constrained elastic parameters.

[0056] S130, a forward modeling method based on the wave equation of the propagation matrix, determines the simulated seismic data of different lithological combinations using the target elastic parameters.

[0057] The simulated seismic data from a pseudo-well can be the seismic amplitudes of different lithologies obtained by simulating a given actual seismic wavelet. The actual seismic wavelet can be the seismic wavelet used to simulate seismic conditions.

[0058] After obtaining the target elastic parameters, the simulation is carried out using the forward modeling method of the wave equation of the propagation matrix to determine the quantitative response relationship between the pseudo-well lithology profile and the seismic amplitude, thereby obtaining pseudo-well simulated seismic data for different lithology combinations.

[0059] S140. Determine the lithological trap boundary based on simulated seismic data from pseudo-wells with different lithological combinations.

[0060] A lithological trap boundary can be used to describe the boundary of the area enclosed by the target lithology within the monitoring area. The monitoring area can be the area where the lithological trap boundary needs to be determined.

[0061] After obtaining simulated seismic data from pseudo-wells with different lithological combinations, the seismic amplitudes of different lithologies are statistically analyzed based on the simulated seismic data to determine the distribution of different lithologies and thus the lithological trap boundaries.

[0062] According to the technical solution of this invention, the vertical lithological combination of a pseudo-well is simulated based on a continuous-time Markov chain model. The lithofacies curves of different lithological combinations are determined based on the simulation results, enabling more accurate simulation results when simulating pseudo-wells with different lithological combinations, thereby reducing simulation errors. Through Monte Carlo stochastic simulation of elastic parameters based on lithofacies constraints, target elastic parameters are determined using the lithofacies curves of different lithological combinations. A forward modeling method based on the wave equation of the propagation matrix is ​​used to determine the simulated seismic data of different lithological combinations using the target elastic parameters, facilitating calculation and reducing the computational burden on computing equipment. By determining the lithological trap boundaries based on the simulated seismic data of different lithological combinations, the final confirmation results of the lithological trap boundaries can best reflect real-world conditions, improving the accuracy of the system's determination results. By adopting the above method, the delineation of trap boundaries has been developed from a qualitative stage to a quantitative stage, which solves the problem of strong uncertainty in trap boundary identification and avoids the problems of strong subjectivity, large deviations in the boundaries delineated by different personnel, and insufficient objective basis in the determination of traditional lithological trap boundaries. This avoids the situation of deviations in the delineation of boundaries caused by human factors.

[0063] Example 2

[0064] Figure 3 This is a flowchart of another method for determining the boundary of a lithological trap provided in Embodiment 2 of the present invention. Based on the above embodiments, this embodiment further optimizes the process of determining the target elastic parameter through Monte Carlo random simulation of elastic parameters based on lithofacies constraints and pseudo-well lithofacies curves of different lithological combinations, as described in the previous embodiments. This embodiment can be combined with various optional schemes in one or more of the above embodiments. Figure 3 As shown, the method includes:

[0065] S210. Based on the continuous-time Markov chain model, the vertical lithology combination of the pseudo-well is simulated, and the pseudo-well lithofacies curves for different lithology combinations are determined according to the simulation results.

[0066] S220, Monte Carlo stochastic simulation of elastic parameters based on lithofacies constraints, determines candidate elastic parameters for different lithofacies combinations by using pseudo-well lithofacies curves of different lithofacies combinations.

[0067] The candidate elastic parameters include longitudinal wave velocity, transverse wave velocity, and density.

[0068] Candidate elastic parameters can be used to describe the characteristics of different lithological combinations, including but not limited to P-wave velocity, S-wave velocity, and density.

[0069] After obtaining pseudo-well lithofacies curves for different lithological combinations, Monte Carlo random simulations of elastic parameters constrained by lithofacies are carried out to obtain candidate elastic parameters for different lithologies.

[0070] S230. Determine the first linear regression relationship between P-wave velocity and density and the second linear regression relationship between P-wave velocity and S-wave velocity based on the candidate elastic parameters of different lithological combinations.

[0071] S240. Randomly sample the longitudinal wave velocity based on the first linear regression relationship and the second linear regression relationship.

[0072] S250. Based on the sampled values ​​of the longitudinal wave velocity and the linear regression relationship, the transverse wave velocity and density are sampled to obtain the target elastic parameters.

[0073] Figure 4 This is a schematic diagram of a simulated lithofacies pattern applicable to an embodiment of the present invention. Figure 5 This is a schematic diagram of the longitudinal wave velocity applicable to the embodiments of the present invention. Figure 6 This is a schematic diagram of the transverse wave velocity applicable to the embodiments of the present invention. Figure 7 This is a schematic diagram illustrating the density applicable to embodiments of the present invention. See also... Figure 4 , 5 For parameters 6 and 7, since there is a certain correlation between the candidate elastic parameters, it is necessary to determine the relationship between them. Therefore, the first linear regression relationship between P-wave velocity and density and the second linear regression relationship between P-wave velocity and S-wave velocity are established for the candidate elastic parameters of different lithological combinations. The P-wave velocity is randomly sampled, and then the S-wave velocity and density are sampled based on the sampled P-wave velocity values ​​and the first and second linear regression relationships.

[0074] The target elastic parameters are determined based on the sampled values ​​of longitudinal wave velocity, transverse wave velocity, and density.

[0075] In one alternative approach, the target elastic parameters are obtained by sampling the longitudinal wave velocity and density based on the sampled values ​​of the longitudinal wave velocity and a linear regression relationship, which may include steps A1-A2:

[0076] Step A1: Based on the sampled values ​​of the longitudinal wave velocity and the linear regression relationship, sample the transverse wave velocity and density to obtain the sampled elastic parameters.

[0077] Step A2: Determine the target elastic parameter based on the sampled elastic parameter.

[0078] In one alternative approach, the target elasticity parameter is determined according to the following formula:

[0079]

[0080] Where EEI(χ) is the target elasticity parameter, GI is the gradient impedance, AI0 = ρ0V p0 AI0 is the reference longitudinal wave impedance; AI = ρV p AI is the longitudinal wave impedance, where V p V s ρ represents the target's longitudinal wave velocity, transverse wave velocity, and density, respectively, which are the target's elastic parameters; V p0 V s0 ρ0 represents the reference P-wave velocity, reference S-wave velocity, and reference density, K represents the P-wave velocity ratio constant, and χ represents the CHI angle, with an angle range of -90° < χ < 90°.

[0081] After obtaining the sampled values ​​of longitudinal wave velocity, transverse wave velocity and density, the target elastic parameters can be calculated using the following formula (2).

[0082]

[0083] Where EEI(χ) is the target elasticity parameter, GI is the gradient impedance, AI0 = ρ0V p0 AI0 is the reference longitudinal wave impedance; AI = ρV p AI is the longitudinal wave impedance, where V p V s ρ represents the target's longitudinal wave velocity, transverse wave velocity, and density, respectively, which are the target's elastic parameters; V p0 V s0 ρ0 represents the reference P-wave velocity, reference S-wave velocity, and reference density, K represents the P-wave velocity ratio constant, and χ represents the CHI angle, with an angle range of -90° < χ < 90°.

[0084] S260, a wave equation forward modeling method based on propagation matrix, determines pseudo-well simulated seismic data for different lithological combinations through target elastic parameters.

[0085] S270. Determine the lithological trap boundary based on simulated seismic data from pseudo-wells with different lithological combinations.

[0086] According to the technical solution of this invention, Monte Carlo random simulation of elastic parameters based on lithofacies constraints is used to determine candidate elastic parameters for different lithological combinations through pseudo-well lithofacies curves of different lithological combinations. Based on the candidate elastic parameters of different lithological combinations, a first linear regression relationship between P-wave velocity and density and a second linear regression relationship between P-wave velocity and S-wave velocity are determined. Based on the first and second linear regression relationships, P-wave velocity is randomly sampled. Based on the sampled P-wave velocity values ​​and the linear regression relationships, S-wave velocity and density are sampled to obtain the target elastic parameters. This makes the determination of the target elastic parameters more accurate and avoids data that does not conform to the correlation of candidate elastic parameters when simple sampling occurs, thereby affecting the accuracy of the system's calculation results.

[0087] Example 3

[0088] Figure 8 This is a flowchart illustrating another method for determining lithological trap boundaries provided in Embodiment 2 of the present invention. This embodiment further optimizes the process of determining lithological trap boundaries based on simulated seismic data from pseudo-wells with different lithological combinations, building upon the aforementioned embodiments. This embodiment can be combined with various optional schemes from one or more of the above embodiments. Figure 8 As shown, the method includes:

[0089] S310. Based on the continuous-time Markov chain model, the vertical lithology combination of the pseudo-well is simulated, and the lithological curves of different lithology combinations of the pseudo-well are determined according to the simulation results.

[0090] S320, Monte Carlo stochastic simulation of elastic parameters based on lithofacies constraints, determines the target elastic parameters through pseudo-well lithofacies curves of different lithological combinations.

[0091] S330, a wave equation forward modeling method based on propagation matrix, determines pseudo-well simulated seismic data for different lithological combinations through target elastic parameters.

[0092] S340. Based on Monte Carlo simulation, determine the pseudo-well characteristic elastic curves for different lithological combinations.

[0093] S350. Wave equation forward modeling simulation method based on propagation matrix, which performs earthquake forward modeling simulation based on predetermined actual earthquake wavelets to determine the earthquake amplitude response data of the target lithology; the target lithology includes at least sandstone and surrounding rock.

[0094] Seismic amplitude response data can be seismic amplitude data of different lithological combinations. The target lithology includes at least sandstone and surrounding rock.

[0095] Monte Carlo simulation was used to obtain characteristic elastic curves of pseudo-wells with different lithological combinations. Given actual seismic wavelets, seismic forward modeling of pseudo-wells was carried out using the wave equation forward modeling method based on the propagation matrix. The quantitative response relationship between the lithological profile of the pseudo-well and the seismic amplitude was established, thereby determining the seismic amplitude response data of the target lithology.

[0096] S360. Based on the seismic amplitude response data of the target lithology, determine the seismic amplitude probability distribution of the target lithology.

[0097] S370. Determine the lithological trap boundary based on the seismic amplitude probability distribution of the target lithology.

[0098] The earthquake amplitude probability distribution can be used to describe the probability distribution of different amplitudes.

[0099] Figure 9 This is a schematic diagram of the seismic amplitude of the target lithology to which this invention is applicable. See also: Figure 9 Based on seismic amplitude response data, the seismic amplitude of the target lithology is statistically analyzed to obtain the probability distribution of seismic amplitude in the sandstone section and the probability distribution of seismic amplitude in the surrounding rock.

[0100] Figure 10 This is a schematic diagram of the lithological trap boundary to which this invention applies in the embodiments. See also... Figure 10 By fitting probability function curves to the two probability distributions, the seismic amplitude value corresponding to the intersection of the two probability distribution function curves is the seismic amplitude threshold for quantitatively distinguishing lithological trap pinch-out lines. Using the lithological trap threshold as the cutoff value, areas with amplitude values ​​below the cutoff value are filtered out, and the remaining amplitude anomaly areas are the trap ranges, with the boundaries of the amplitude anomaly areas being the lithological trap boundaries.

[0101] According to the technical solution of the present invention, the wave equation forward modeling method based on the propagation matrix is ​​used to perform earthquake forward modeling based on a predetermined actual seismic wavelet to determine the seismic amplitude response data of the target lithology. Based on the seismic amplitude response data of the target lithology, the seismic amplitude probability distribution of the target lithology is determined. Based on the seismic amplitude probability distribution of the target lithology, the lithological trap boundary is determined, making the determination result of the lithological trap boundary more accurate and improving the stability of the overall system calculation results.

[0102] Example 4

[0103] Figure 11 This is a schematic diagram of a device for determining the boundary of a lithological trap according to Embodiment 4 of the present invention. This embodiment is applicable to situations where drilling is lacking, enabling accurate quantitative determination of the boundary of a lithological trap. This device for determining the boundary of a lithological trap can be implemented in hardware and / or software, and can be configured in an electronic device with data processing capabilities. Figure 11 As shown, the lithological trap boundary determination device of this embodiment may include: a lithofacies curve determination module 410, an elastic parameter determination module 420, a seismic data determination module 430, and a trap boundary determination module 440. Wherein:

[0104] The lithofacies curve determination module 410 is used to simulate the vertical lithological combination of the pseudo-well based on the continuous-time Markov chain model, and to determine the lithofacies curve of the pseudo-well for different lithological combinations based on the simulation results.

[0105] The elastic parameter determination module 420 is used for Monte Carlo stochastic simulation of elastic parameters based on lithofacies constraints, and determines the target elastic parameters through pseudo-well lithofacies curves of different lithological combinations.

[0106] The seismic data determination module 430 is used for the wave equation forward modeling method based on the propagation matrix to determine pseudo-well simulated seismic data with different lithological combinations through the target elastic parameters;

[0107] The trap boundary determination module 440 is used to determine the lithological trap boundary based on pseudo-well simulated seismic data with different lithological combinations.

[0108] Based on the above embodiments, optionally, before the lithofacies curve determination module 410, the following further includes:

[0109] The thickness simulation and determination module is used to simulate the lithological thickness of each layer in different lithological combinations based on an exponential probability model.

[0110] Based on the above embodiments, the elastic parameters may optionally include longitudinal wave velocity, transverse wave velocity, and density.

[0111] Based on the above embodiments, optionally, the elastic parameter determination module 420 includes:

[0112] Candidate elasticity determination unit is used for Monte Carlo stochastic simulation of elastic parameters based on lithofacies constraints. Candidate elastic parameters for different lithofacies combinations are determined by pseudo-well lithofacies curves of different lithofacies combinations.

[0113] The regression relationship determination unit is used to determine the first linear regression relationship between P-wave velocity and density and the second linear regression relationship between P-wave velocity and S-wave velocity based on candidate elastic parameters of different lithological combinations.

[0114] The random sampling unit is used to randomly sample the longitudinal wave velocity based on the first linear regression relationship and the second linear regression relationship.

[0115] The target elasticity determination unit is used to sample the shear wave velocity and density based on the sampled values ​​of the longitudinal wave velocity and the linear regression relationship to obtain the target elasticity parameters.

[0116] Based on the above embodiments, optionally, the target elasticity determination unit includes:

[0117] The sampling elasticity determination sub-unit is used to sample the shear wave velocity and density based on the sampled value of the longitudinal wave velocity and the linear regression relationship to obtain the sampling elasticity parameters.

[0118] The target elasticity determination sub-unit is used to determine the target elasticity parameters based on the sampled elasticity parameters.

[0119] Based on the above embodiments, optionally, the target elasticity parameter can be determined according to the following formula:

[0120]

[0121] Where EEI(χ) is the target elasticity parameter, GI is the gradient impedance, AI0 = ρ0V p0 AI0 is the reference longitudinal wave impedance; AI = ρV p AI is the longitudinal wave impedance, where V p V s ρ represents the target's longitudinal wave velocity, transverse wave velocity, and density, respectively, which are the target's elastic parameters; V p0 V s0 ρ0 represents the reference P-wave velocity, reference S-wave velocity, and reference density, K represents the P-wave velocity ratio constant, and χ represents the CHI angle, with an angle range of -90° < χ < 90°.

[0122] Based on the above embodiments, optionally, the closed boundary determination module 440 includes:

[0123] The elastic curve determination unit is used to determine the pseudo-well characteristic elastic curves based on Monte Carlo simulations for different lithological combinations.

[0124] The response data determination unit is used for the wave equation forward modeling method based on the propagation matrix. It performs seismic forward modeling based on a pre-determined actual seismic wavelet to determine the seismic amplitude response data of the target lithology; the target lithology includes at least sandstone and surrounding rock.

[0125] The probability distribution determination unit is used to determine the seismic amplitude probability distribution of the target lithology based on the seismic amplitude response data of the target lithology.

[0126] The lithological boundary determination unit is used to determine the lithological trap boundary based on the seismic amplitude probability distribution of the target lithology.

[0127] The lithological trap boundary determination device provided in the embodiments of the present invention can execute the lithological trap boundary determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0128] Example 5

[0129] Figure 12 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0130] like Figure 12 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0131] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0132] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as methods for determining lithological trap boundaries.

[0133] In some embodiments, the method for determining lithological trap boundaries may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for determining lithological trap boundaries described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the method for determining lithological trap boundaries by any other suitable means (e.g., by means of firmware).

[0134] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0135] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0136] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0137] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0138] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0139] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0140] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and no limitation is imposed herein.

[0141] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for determining the boundary of a lithological trap, characterized in that, include: The vertical lithological combination of the pseudo-well was simulated based on the continuous-time Markov chain model, and the lithofacies curves of different lithological combinations of the pseudo-well were determined based on the simulation results. Monte Carlo stochastic simulation of elastic parameters based on lithofacies constraints determines the target elastic parameters through pseudo-well lithofacies curves of different lithological combinations; The wave equation forward modeling method based on the propagation matrix determines the pseudo-well simulated seismic data for different lithological combinations through the target elastic parameters; The lithological trap boundaries are determined based on the simulated seismic data from the pseudo-wells of the different lithological combinations. The elastic parameters include longitudinal wave velocity, transverse wave velocity, and density. Among them, the Monte Carlo stochastic simulation of elastic parameters based on lithofacies constraints determines the target elastic parameters through pseudo-well lithofacies curves of different lithological combinations, including: Monte Carlo stochastic simulation of elastic parameters based on lithofacies constraints is used to determine candidate elastic parameters for different lithofacies combinations through pseudo-well lithofacies curves of the different lithofacies combinations. Based on the candidate elastic parameters of the different lithological combinations, a first linear regression relationship between the P-wave velocity and the density and a second linear regression relationship between the P-wave velocity and the S-wave velocity are determined; Based on the first linear regression relationship and the second linear regression relationship, the longitudinal wave velocity is randomly sampled; The target elastic parameters are obtained by sampling the longitudinal wave velocity and the linear regression relationship, and sampling the transverse wave velocity and density.

2. The method according to claim 1, characterized in that, Before determining the pseudo-well lithofacies curves for different lithological combinations based on simulation results, the following steps are also included: The thickness of each lithology layer in different lithological combinations was simulated based on an exponential probability model.

3. The method according to claim 1, characterized in that, Based on the sampled values ​​of the longitudinal wave velocity and the linear regression relationship, the transverse wave velocity and the density are sampled to obtain the target elastic parameters, including: Based on the sampled values ​​of the longitudinal wave velocity and the linear regression relationship, the transverse wave velocity and the density are sampled to obtain the sampling elasticity parameters; The target elastic parameter is determined based on the sampling elastic parameter.

4. The method according to claim 3, characterized in that, The target elasticity parameter is determined according to the following formula: in, The target elasticity parameter is... GI is the gradient impedance. , For reference longitudinal wave impedance; , For the longitudinal wave impedance, in the formula, and The target elastic parameters are the target longitudinal wave velocity, the target transverse wave velocity, and the target density. and For reference P-wave velocity, reference S-wave velocity, and reference density, The ratio of transverse to longitudinal wave velocities is a constant. The angle is CHI, and the angle range is... .

5. The method according to claim 1, characterized in that, Based on the simulated seismic data from different lithological combinations, the lithological trap boundaries are determined, including: The pseudo-well characteristic elastic curves for the different lithological combinations were determined based on Monte Carlo simulations. The wave equation forward modeling method based on the propagation matrix performs seismic forward modeling based on a predetermined actual seismic wavelet to determine the seismic amplitude response data of the target lithology; the target lithology includes at least sandstone and surrounding rock; Based on the seismic amplitude response data of the target lithology, the seismic amplitude probability distribution of the target lithology is determined; The lithological trap boundary is determined based on the seismic amplitude probability distribution of the target lithology.

6. A device for determining the boundary of a lithological trap, characterized in that, include: The lithofacies curve determination module is used to simulate the vertical lithological combination of pseudo-wells based on the continuous-time Markov chain model, and determine the lithofacies curves of pseudo-wells for different lithological combinations based on the simulation results. The elastic parameter determination module is used for Monte Carlo stochastic simulation of elastic parameters based on lithofacies constraints, and determines the target elastic parameters through pseudo-well lithofacies curves of different lithological combinations. The seismic data determination module is used for wave equation forward modeling based on the propagation matrix to determine pseudo-well simulated seismic data for different lithological combinations through the target elastic parameters; The trap boundary determination module is used to determine the lithological trap boundary based on the pseudo-well simulated seismic data of the different lithological combinations; The elastic parameters include longitudinal wave velocity, transverse wave velocity, and density. The elastic parameter determination module is specifically used for: Monte Carlo stochastic simulation of elastic parameters based on lithofacies constraints is used to determine candidate elastic parameters for different lithofacies combinations through pseudo-well lithofacies curves of the different lithofacies combinations. Based on the candidate elastic parameters of the different lithological combinations, a first linear regression relationship between the P-wave velocity and the density and a second linear regression relationship between the P-wave velocity and the S-wave velocity are determined; Based on the first linear regression relationship and the second linear regression relationship, the longitudinal wave velocity is randomly sampled; The target elastic parameters are obtained by sampling the longitudinal wave velocity and the linear regression relationship, and sampling the transverse wave velocity and density.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method for determining the lithological trap boundary as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method for determining the lithological trap boundary as described in any one of claims 1-5.

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