CN113138413A - Reservoir boundary identification method, device, medium and equipment - Google Patents
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Abstract
The embodiment of the invention provides a reservoir boundary identification method, a device, a medium and equipment, which relate to the technical field of oilfield exploration, and the method comprises the following steps: adopting at least two biological heuristic algorithms to respectively identify reservoir boundaries of the three-dimensional seismic data volume of the region where the reservoir is located to obtain at least two boundary identification results to be selected; searching the three-dimensional seismic data volume based on search parameters set by target boundary identification results in at least two boundary identification results to be selected to obtain search results; judging whether the wave group characteristic change of the search result meets the condition of the search parameter; if so, determining the position of the wave group characteristic change meeting the conditions of the search parameters in the search result, and obtaining the boundary distribution of the reservoir. According to the technical scheme of the embodiment of the invention, the accuracy of reservoir boundary identification can be improved.
Description
Technical Field
The invention relates to the technical field of oilfield exploration, in particular to a reservoir boundary identification method, a reservoir boundary identification device, a reservoir boundary identification medium and reservoir boundary identification equipment.
Background
With the progress and development of the hydrocarbon reservoir exploration and development technology, in order to more accurately develop a reservoir, reservoir boundary identification becomes more and more the key of hydrocarbon reservoir exploration.
The stratum oil and gas reservoirs are usually distributed in a strip shape, generally have small oil content, and exploration is not benefited due to too high or too low drilling parts, so that the accurate identification of reservoir boundaries is particularly important in hidden oil and gas reservoir exploration. Currently, a single biological heuristic algorithm is generally used to calculate and identify reservoir boundaries for three-dimensional seismic data of a reservoir. But under the conditions of no obvious change of reservoir thickness, pore permeability, fluid properties and the like, the seismic response near the reservoir boundary is weakened, the characteristics are not obvious, and particularly, the existence of compound waves causes lower accuracy of the identified reservoir boundary.
Disclosure of Invention
The embodiment of the invention provides a reservoir boundary identification method, a reservoir boundary identification device, a reservoir boundary identification medium and reservoir boundary identification equipment, which are used for solving the problem of low accuracy of an identified reservoir boundary.
In a first aspect of the embodiments of the present invention, a reservoir boundary identification method is provided, including:
adopting at least two biological heuristic algorithms to respectively identify reservoir boundaries of the three-dimensional seismic data volume of the region where the reservoir is located to obtain at least two boundary identification results to be selected;
searching the three-dimensional seismic data volume based on search parameters set by target boundary identification results in the at least two boundary identification results to be selected to obtain search results;
judging whether the wave group characteristic change of the search result meets the condition of the search parameter;
if yes, determining the position of the wave group characteristic change meeting the conditions of the search parameters in the search result, and obtaining the boundary distribution of the reservoir.
In some example embodiments of the invention, based on the above, before performing the reservoir boundary identification, the method further comprises:
performing frequency spectrum decomposition on the three-dimensional seismic data volume to obtain low-frequency seismic data, high-frequency seismic data and medium-frequency seismic data;
performing gain calculation on the high-frequency seismic data;
and reconstructing the three-dimensional seismic data volume based on the low-frequency seismic data, the medium-frequency seismic data and the high-frequency seismic data after gain calculation.
In some example embodiments of the present invention, based on the above scheme, the searching the three-dimensional seismic data volume based on the search parameter set by the target candidate boundary identification result in the at least two candidate boundary identification results to obtain a search result includes:
selecting the target boundary identification result to be selected from the at least two boundary identification results to be selected according to the continuity of data in space and noise;
setting search parameters aiming at a target biological heuristic algorithm based on the target candidate boundary identification result;
and searching the three-dimensional seismic data volume through the target biological heuristic algorithm based on the search parameters to obtain a search result.
In some example embodiments of the present invention, based on the above scheme, the searching the three-dimensional seismic data volume through the target bio-heuristic algorithm based on the search parameter to obtain a search result includes:
and searching the three-dimensional seismic data volume for preset cycle times through a preset ant colony algorithm based on the ant number, the seismic sampling point and the attribute value of the sampling point in the search parameters to obtain a search result.
In some example embodiments of the present invention, based on the above scheme, the determining a location of a wave group characteristic change in the search result that satisfies a condition of the search parameter to obtain a boundary distribution of the reservoir includes:
tracking the positions of all wave group characteristic changes meeting the conditions of the search parameters in the search results;
connecting the tracked positions of the wave group characteristic changes meeting the conditions of the search parameters into a line to form a three-dimensional space;
and obtaining the boundary distribution of the reservoir based on the three-dimensional space.
In some example embodiments of the present invention, based on the above scheme, the method further includes:
if the wave group characteristic change of the search result does not meet the search parameter, resetting the search parameter based on the target boundary identification result to be selected;
searching the three-dimensional seismic data volume according to the reset search parameters to obtain a search result;
and continuously judging whether the wave group characteristic change of the current search result meets the condition of the search parameter.
In some example embodiments of the present invention, the at least two bio-heuristic algorithms comprise at least two of genetic algorithms, particle swarm optimization algorithms, ant colony algorithms, artificial bee colony algorithms, bacterial foraging algorithms, colony search algorithms, DNA calculations, membrane calculations, and self-organizing migration algorithms, based on the above-described approach.
In a second aspect of the embodiments of the present invention, there is provided a reservoir boundary identification apparatus, including:
the first identification module is used for respectively identifying reservoir boundaries of the three-dimensional seismic data body of the region where the reservoir is located by adopting at least two biological heuristic algorithms to obtain at least two boundary identification results to be selected;
the search module is used for searching the reconstructed frequency-extended three-dimensional seismic data volume based on search parameters set by a target boundary identification result in the at least two boundary identification results to be selected to obtain a search result;
the judging module is used for judging whether the wave group characteristic change of the search result meets the condition of the search parameter; if yes, triggering a second identification module;
and the second identification module is used for determining the position of the wave group characteristic change meeting the condition of the search parameter in the search result under the trigger of the judgment module to obtain the boundary distribution of the reservoir.
In some example embodiments of the present invention, based on the above scheme, the apparatus further includes:
the decomposition module is used for carrying out frequency spectrum decomposition on the three-dimensional seismic data volume to obtain low-frequency seismic data, high-frequency seismic data and intermediate-frequency seismic data before reservoir boundary identification is carried out;
the gain module is used for carrying out gain calculation on the high-frequency seismic data;
and the reconstruction module is used for reconstructing the three-dimensional seismic data volume based on the low-frequency seismic data, the medium-frequency seismic data and the high-frequency seismic data after gain calculation.
In some example embodiments of the present invention, based on the above scheme, the search module includes:
the selecting unit is used for selecting the target boundary identification result to be selected from the at least two boundary identification results to be selected according to the continuity of data in space and noise;
the setting unit is used for setting search parameters aiming at the target biological heuristic algorithm based on the target candidate boundary identification result;
and the searching unit is used for searching the three-dimensional seismic data volume through the target biological heuristic algorithm based on the searching parameters to obtain a searching result.
In some exemplary embodiments of the present invention, based on the above scheme, the search unit is configured to search the three-dimensional seismic data volume for a preset cycle number through a preset ant colony algorithm based on the number of ants in the search parameter, a seismic sampling point, and an attribute value of the sampling point, so as to obtain a search result.
In some example embodiments of the present invention, based on the above scheme, the second identification module includes:
the tracking unit is used for tracking the positions of all wave group characteristic changes meeting the conditions of the search parameters in the search results;
a space forming unit for connecting the tracked positions of the wave group characteristic changes satisfying the condition of the search parameter to a line to form a three-dimensional space;
and the identification unit is used for obtaining the reservoir boundary distribution based on the three-dimensional space.
In some example embodiments of the present invention, based on the above scheme, the search module is further configured to:
if the wave group characteristic change of the search result does not meet the condition of the search parameter, resetting the search parameter based on the target boundary identification result to be selected, and searching the three-dimensional seismic data volume according to the reset search parameter to obtain a search result;
the judging module is further configured to continuously judge whether the wave group characteristic change of the current search result obtained by the searching module meets the condition of the search parameter.
In a third aspect of the embodiments of the present invention, a computer-readable storage medium is provided, where the storage medium includes a stored program, and when the program runs, the apparatus on which the storage medium is located is controlled to execute the reservoir boundary identification method according to any one of the first aspect.
In a fourth aspect of the present invention, there is provided a reservoir boundary identifying apparatus comprising: a memory for storing a program; a processor, coupled to the memory, for executing the program to perform the reservoir boundary identification method of any of the first aspects.
According to the reservoir boundary identification method, the reservoir boundary identification device, the reservoir boundary identification medium and the reservoir boundary identification equipment, the reservoir boundary identification is carried out on the three-dimensional seismic data body in a biological heuristic algorithm fusion mode, the three-dimensional seismic data body is searched to obtain a search result based on the search parameters set by the identification result of the boundary to be selected, and the reservoir boundary is obtained based on the search result. According to the technical scheme of the embodiment of the invention, on one hand, reservoir boundary identification is carried out on the three-dimensional seismic data body in a biological heuristic algorithm fusion mode, and the advantages of various biological heuristic algorithms can be combined; on the other hand, search parameters are set based on the identified boundary result to be selected, the three-dimensional seismic data volume is searched to obtain a search result, and the search parameters can be set more accurately, so that the search result is more accurate; on the other hand, the reservoir boundary is obtained based on the search result, and the accurate search result can be obtained, and the search result comprises a plurality of reservoir prediction parameters including reservoir physical properties and reservoir thickness, so that the reservoir boundary can be more accurately identified when the reservoir boundary is identified based on the search result.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention will be described in detail below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more comprehensible.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow diagram of a reservoir boundary identification method provided by some embodiments of the invention;
FIG. 2 is a schematic flow chart of a reservoir boundary identification method according to further embodiments of the present invention;
FIG. 3 is a schematic diagram of a reservoir boundary identification apparatus according to some embodiments of the invention;
FIG. 4 is a schematic diagram of a reservoir boundary identification apparatus according to further embodiments of the present invention;
FIG. 5 is a schematic block diagram of a search module provided in accordance with some embodiments of the present invention;
FIG. 6 is a schematic block diagram of a second identification module provided in accordance with some embodiments of the present invention;
fig. 7 is a schematic block diagram of an embodiment of a reservoir boundary identification apparatus provided in accordance with some embodiments of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Currently, a single biological heuristic algorithm is typically used to compute three-dimensional seismic data for a reservoir to identify reservoir boundaries. But under the conditions of no obvious change of reservoir thickness, pore permeability, fluid properties and the like, the seismic response near the reservoir boundary is weakened, the characteristics are not obvious, and particularly, the existence of compound waves causes lower accuracy of the identified reservoir boundary.
Based on the above, the basic idea of the invention is: reservoir boundary identification is carried out on the three-dimensional seismic data body in a biological heuristic algorithm fusion mode, the three-dimensional seismic data body is searched to obtain a search result based on search parameters set by the identified boundary identification result to be selected, and the reservoir boundary is obtained based on the search result. According to the technical scheme of the embodiment of the invention, the advantages of various biological heuristic algorithms can be combined, so that the search result is more accurate; because an accurate search result can be obtained, and the search result comprises a plurality of reservoir prediction parameters including reservoir physical properties and reservoir thickness, the reservoir boundary can be more accurately identified when the reservoir boundary is identified based on the search result.
In practical application, the reservoir boundary identification method in the embodiment of the invention can be used for identifying reservoir boundaries of various reservoirs such as a stratum trap oil-gas reservoir, a glutenite reservoir, a sand-shale thin interbed type reservoir, a fluvial facies reservoir, a thin reservoir and the like, and the invention is not particularly limited to this.
Fig. 1 is a schematic flow diagram of a reservoir boundary identification method provided by some embodiments of the invention. The reservoir boundary identification method includes steps S110 to S140, and the reservoir boundary identification method in the example embodiment is described in detail below with reference to the drawings.
Referring to fig. 1, in step S110, reservoir boundary identification is performed on the three-dimensional seismic data volume of the region where the reservoir is located respectively by using at least two biological heuristic algorithms, so as to obtain at least two candidate boundary identification results.
In an example embodiment, the three-dimensional seismic data volume is the three-dimensional seismic data volume for which it is desired to identify the region in which the reservoir or reservoir boundary is located. Illustratively, if it is desired to identify reservoir boundaries in region a, the three-dimensional seismic data volume is the three-dimensional seismic data volume for region a. Specifically, the three-dimensional seismic data volume includes: the parameters are related to the geometrical, kinematic, dynamic and statistical characteristics of the seismic waves, including amplitude, energy, frequency and phase, and are related to geological phenomena such as reservoir, oil and gas, deposition and fracture. In particular, the three-dimensional seismic data volume may be, but is not limited to, a standard segy format file.
Further, considering that a single algorithm severely limits the speed and the accuracy of reservoir boundary identification, two or more than two biological heuristic algorithms can be selected to respectively identify the reservoir boundary of the reconstructed frequency-extended three-dimensional seismic data body. The speed or accuracy of reservoir boundary identification can be improved to a certain extent through the combined application of a plurality of biological heuristic algorithms. In particular, the at least two bio-heuristic algorithms may include, but are not limited to, at least two of genetic algorithms, particle swarm optimization algorithms, ant colony algorithms, artificial bee colony algorithms, bacterial foraging algorithms, colony search algorithms, DNA calculations, membrane calculations, and self-organizing migration algorithms. The algorithm fusion mode is that the calculation result of one biological heuristic algorithm is used as the initial value of the other biological heuristic algorithm, and then the reservoir boundary is obtained by the latter biological heuristic algorithm.
In an example embodiment, a particle swarm optimization algorithm and a genetic algorithm are selected to respectively perform reservoir boundary identification on the three-dimensional seismic data after reconstruction frequency expansion processing, and a boundary identification result to be selected corresponding to the particle swarm optimization algorithm and a boundary identification result to be selected corresponding to the genetic algorithm are obtained. Specifically, the candidate boundary identification result carries the approximate range of the reservoir boundary identified by the corresponding algorithm.
In addition, three-dimensional seismic data volumes typically include data that is high in frequency and low in energy. The existence of the part of data seriously affects the accuracy of reservoir boundary identification, and in order to reduce the influence of the part of data, in an example embodiment, the three-dimensional data body is subjected to reconstruction frequency broadening processing, so that the high-frequency and low-energy data in the three-dimensional seismic data body are reduced, and the purpose of expanding the frequency band of the three-dimensional seismic data body is achieved.
In step S120, the three-dimensional seismic data volume is searched based on the search parameter set by the target candidate boundary identification result in the at least two candidate boundary identification results, so as to obtain a search result.
In an example embodiment, the target candidate boundary recognition result is selected from the candidate boundary recognition results based on one of the following three ways:
firstly, when reservoir boundary identification needs to be performed at the fastest speed, candidate boundary identification results corresponding to all biological heuristic algorithms in at least two biological heuristic algorithms do not need to be obtained, and only the first candidate boundary identification result is selected as a target candidate boundary identification result.
Secondly, when the accuracy of reservoir boundary identification is high, the boundary identification results to be selected corresponding to all the biological heuristic algorithms in at least two biological heuristic algorithms need to be obtained, and the target boundary identification result to be selected is selected from all the boundary identification results to be selected according to the continuity of data on the space and noise. The data of the target candidate boundary identification result is continuous in space, and the noise is lower than the set noise requirement, such as lower than a preset noise value.
And thirdly, when the accuracy of reservoir boundary identification and the speed of reservoir boundary identification are required, obtaining a candidate boundary identification result corresponding to at least one biological heuristic algorithm in at least two biological heuristic algorithms, wherein the candidate boundary identification result corresponding to the at least one biological heuristic algorithm is obtained within a set time interval. And selecting a target boundary identification result to be selected from the boundary identification results to be selected corresponding to the at least one biological heuristic algorithm according to the continuity of the data in space and the noise.
Further, in the example embodiment, when the target candidate boundary identification result is selected, the search parameter needs to be set based on a target bio-heuristic algorithm, where the target bio-heuristic algorithm is used to obtain the search result. It should be noted that the target bio-heuristic described herein may be the same as or different from the bio-heuristic used to derive the candidate boundary identification result. For example, when considering the technical advantages of different algorithms and improving the accuracy of reservoir boundary identification, a biological heuristic algorithm different from the above one for obtaining the candidate boundary identification result may be used.
In an exemplary embodiment, an ant colony algorithm is selected to search the three-dimensional seismic data volume after reconstruction frequency broadening processing, and a search result is obtained. At this time, search parameters for the ant colony algorithm need to be set based on the target candidate boundary recognition result. The search parameters described herein may include, but are not limited to, the number of ants, seismic sampling points, and attribute values of the sampling points, where the seismic sampling points and the attribute values of the sampling points are extracted from the target candidate boundary identification result.
It should be noted that the search result may include a plurality of reservoir prediction parameters including the reservoir property and the reservoir thickness, and may also include other suitable reservoir prediction parameters such as the dip angle of the formation, the dip angle of the unconformity, and the like, which are also within the scope of the present invention.
In step S130, determining whether the wave group characteristic change of the search result satisfies a condition of the search parameter; if yes, proceed to step S140.
In an example embodiment, the condition or requirement of the search parameter may include, but is not limited to, a relation requirement or relation condition between the standard reflected wave and the upper and lower reflected waves, for example, the condition of the search parameter may further include a condition of reservoir thickness, a condition of reservoir properties, and the like. When it is determined that the wave group characteristic change of the search result does not meet the requirement of the search parameter, which indicates that the reservoir boundary cannot be accurately identified, the search parameter needs to be reset based on the target candidate boundary identification result, the reconstructed frequency-extended three-dimensional seismic data volume is searched based on the reset search parameter to obtain a search result, and step 130 is continuously performed. The above processes are repeated until the wave group characteristic change of the obtained search result meets the requirement of the search parameter.
Specifically, when it is determined that the wave group characteristic change of the search result satisfies the condition of the search parameter, the search result can clearly reflect the reservoir top dead center, so step 140 is executed.
In step S140, if yes, the position of the wave group characteristic change satisfying the condition of the search parameter in the search result is determined, and the boundary distribution of the reservoir is obtained.
In an example embodiment, locations of wave group feature changes or locations of wave group feature changes in search results that satisfy the search parameters are tracked, the tracked locations are all used as reservoir cusps, and the tracked reservoir cusps are recorded. The following steps are then performed for the tracked reservoir cusp vanishing point: and connecting the reservoir sharp extinction point with all the adjacent reservoir sharp extinction points on the periphery of the reservoir sharp extinction point, wherein the line of the connection between the reservoir sharp extinction point and the reservoir sharp extinction point is a sharp extinction line. And when the connection of all the sharp points of the reservoir is finished, outputting all sharp lines, wherein the space formed by the sharp lines is the reservoir boundary of the reservoir distributed on the plane.
According to the reservoir boundary identification method provided by the embodiment of the invention, the reservoir boundary identification is carried out on the three-dimensional seismic data body in a biological heuristic algorithm fusion mode, the three-dimensional seismic data body is searched to obtain a search result based on the search parameter set by the identification result of the boundary to be selected, and the reservoir boundary is obtained based on the search result. According to the technical scheme of the embodiment of the invention, on one hand, reservoir boundary identification is carried out on the three-dimensional seismic data body in a biological heuristic algorithm fusion mode, and the advantages of various biological heuristic algorithms can be combined; on the other hand, search parameters are set based on the identified boundary result to be selected, the three-dimensional seismic data volume is searched to obtain a search result, and the search parameters can be set more accurately, so that the search result is more accurate; on the other hand, the reservoir boundary is obtained based on the search result, and the accurate search result can be obtained, and the search result comprises a plurality of reservoir prediction parameters including reservoir physical properties and reservoir thickness, so that the reservoir boundary can be more accurately identified when the reservoir boundary is identified based on the search result.
Fig. 2 is a schematic flow chart of a reservoir boundary identification method according to another embodiment of the present invention. The reservoir boundary identification method includes step S210, and the reservoir boundary identification method in the example embodiment is described in detail below with reference to fig. 2.
Referring to fig. 2, in step S210, the three-dimensional seismic data volume is subjected to spectral decomposition to obtain low-frequency seismic data, high-frequency seismic data, and medium-frequency seismic data.
In an example embodiment, the process of performing spectral decomposition on a three-dimensional seismic data volume may be: a low-frequency interval, a medium-frequency interval and a high-frequency interval are preset. And decomposing the three-dimensional seismic data volume into low-frequency seismic data, high-frequency seismic data and intermediate-frequency seismic data based on the preset three frequency intervals.
In step S215, gain calculation is performed on the high-frequency seismic data.
In an exemplary embodiment, the high frequency seismic data is gained by increasing the energy of the high frequency seismic data to increase the degree of discrimination of the portion of data. The method for performing Gain calculation on the high-frequency seismic data may include, but is not limited to, an Automatic Gain Control (AGC) algorithm.
In step S220, a three-dimensional seismic data volume is reconstructed based on the low frequency seismic data, the intermediate frequency seismic data, and the gain-calculated high frequency seismic data.
Specifically, low-frequency seismic data, medium-frequency seismic data and high-frequency seismic data after gain calculation are recombined based on attributes such as amplitude, frequency and phase to form a reconstructed three-dimensional seismic data body, and the data volume of high-frequency and low-energy in the reconstructed three-dimensional seismic data body is greatly reduced, so that the reconstructed three-dimensional seismic data body can better express reservoir spreading characteristics on the whole, and the accuracy of reservoir boundary identification is improved.
In step 225, reservoir boundary identification is performed on the reconstructed frequency-extended three-dimensional seismic data volume by using at least two biological heuristic algorithms respectively, so as to obtain at least two boundary identification results to be selected.
Specifically, the at least two bio-heuristic algorithms may be selected based on business requirements, and may include, but are not limited to, at least two of genetic algorithms, particle swarm optimization algorithms, ant colony algorithms, artificial bee colony algorithms, bacterial foraging algorithms, colony search algorithms, DNA calculations, membrane calculations, and self-organizing migration algorithms.
Specifically, when the three-dimensional seismic data volume after reconstruction frequency broadening processing needs to be searched by the ant colony algorithm to obtain a search result in the following, the particle swarm optimization algorithm is high in speed and strong in global optimization capability, and when the obtained boundary identification result to be selected is used as an initial value of the ant colony algorithm, the calculation speed and the optimization capability of the ant colony algorithm can be improved, the iterative convergence times of the ant colony algorithm can be reduced, and the disadvantage of low operation speed of the ant colony algorithm is overcome. Thus, a particle group optimization algorithm may be selected. Specifically, genetic algorithms may be selected for better identification of reservoir boundaries.
Illustratively, a particle swarm optimization algorithm and a genetic algorithm are selected to respectively perform reservoir boundary identification on the three-dimensional seismic data after reconstruction frequency extension processing, so as to obtain a boundary identification result to be selected corresponding to the particle swarm optimization algorithm and a boundary identification result to be selected corresponding to the genetic algorithm.
In step 230, a target candidate boundary recognition result is selected from the at least two candidate boundary recognition results according to the spatial continuity of the data and the noise.
In an example embodiment, the target candidate boundary recognition result is selected from at least two candidate boundary recognition results according to the continuity of data in space and noise. The target candidate boundary recognition result is spatially continuous, and the noise is lower than the set noise requirement, for example, lower than a preset noise value. Because the target candidate boundary identification result meets the requirements of the set data on the continuity and the noise in space, the accuracy of reservoir boundary identification can be improved when the subsequent reservoir boundary identification is carried out based on the target candidate boundary identification result.
In step S235, search parameters for the target bio-heuristic algorithm are set based on the target candidate boundary identification result.
In an exemplary embodiment, an ant colony algorithm is selected to search the three-dimensional seismic data volume after reconstruction frequency broadening processing, and a search result is obtained. At this time, search parameters for the ant colony algorithm need to be set based on the target candidate boundary recognition result. The search parameters described herein may include, but are not limited to, the number of ants, seismic sampling points, and attribute values of the sampling points, where the seismic sampling points and the attribute values of the sampling points are extracted from the target candidate boundary identification result.
In step 240, based on the set search parameters, the reconstructed frequency-extended three-dimensional seismic data volume is searched through a target biological heuristic algorithm to obtain a search result.
In an example embodiment, when the target biological heuristic algorithm is an ant colony algorithm, the process of searching the reconstructed frequency-broadened three-dimensional seismic data volume through the target biological heuristic algorithm is as follows: and searching the reconstructed frequency-broadening processed three-dimensional seismic data volume for preset cycle times through a preset ant colony algorithm based on the ant number, the seismic sampling point and the attribute value of the sampling point in the search parameters to obtain a search result.
Specifically, m is the number of ants, n represents seismic sampling points, a is the attribute value of the sampling points, and Nc is the iteration number of the algorithm loop.
At the initial moment of the ant colony algorithm, m ants are randomly placed on n sampling points, and the ants complete the search step by step from one sampling point to another sampling point. 1<Nc<Ncmax, Ncmax is a set maximum number of loop iterations. In each iteration, t is taken as a scale, t is more than or equal to 0 and less than or equal to n, and taui，j(t) represents the pheromone quantity between the sampling point i and the sampling point j at the moment t, and a tabuk (k is 1, 2.. multidot.m) is forbidden to record the set of the sampling points which are currently walked by the ant k, so that the ant is prevented from repeatedly walking into the same earthquake sampling point. Then the probability that ant k moves from sample point i to sample point j at time t is as shown in equations (1) and (2):
wherein S isk(i) Is k permission set, Sk(i) The forbidden set C is a set {1,2, 3.., n } of all sampling points, alpha is an pheromone factor which represents the importance degree of pheromones, and the larger the numerical value, the more important the pheromones are in the process of the ant advancing. Eta ij represents visibility between sampling points i and j, namely the reciprocal of an attribute change value, eta ij is 1/(a (j) -a (i)), the smaller the attribute change, the greater the visibility, the higher the selected expectation, beta is an expectation heuristic factor, which represents the importance degree of visibility, and the larger the value, the prior information representing specific problems is in the prior artThe more important the ants are in the process of advancing. When all ants complete one cycle, the pheromone increases and decreases, so the intensity of the pheromone needs to be adjusted, and the volatilization coefficient of the pheromone is set
The pheromone intensity between the sampling points i, j after each cycle is as shown in equation (3) and equation (4):
τij(t+n)＝(1-ρ)×τij(t)+Δτij(t) (3)
The basic ant colony algorithm is implemented as follows:
and (5) an initialization phase. Let the current cycle number Nc be 0 and the time t be 0, and let the initialization information amount τ i, j (0) on each path be a constant. The pheromone increment Δ τ i, j (t) ═ 0; setting the maximum cycle number to be NCmax, placing m ants on n sampling points, and initializing parameters eta ij, alpha and beta.
And (5) a circulation stage. If Nc<NCmax, loop continues; if Nc>NCmax, the loop ends. Each ant k moves the next vertex j according to formula (1),
and (5) allowing the collection. And (4) placing the sampling point which is walked by each ant in the previous step into the taboo list of the ant, and updating the taboo list. The amount of information on each path is updated according to equation (2). Nc +1, returnAnd (5) a circulation stage.
And (5) ending the loop, and ending the algorithm to output the processing data volume.
In step S245, it is determined whether the wave group characteristic change of the search result satisfies a condition of the search parameter; if yes, go to step S250; otherwise, step 255 is performed.
In the example embodiment, when it is determined that the wave group characteristic change of the search result does not satisfy the condition or requirement of the search parameter, which indicates that the reservoir boundary cannot be accurately identified, the search parameter needs to be reset based on the target candidate boundary identification result, and step S255 is executed.
Specifically, when it is determined that the wave group characteristic change of the search result meets the requirement of the search parameter, the search result clearly reflects the reservoir top vanishing point, so step 250 is executed.
In step S250, reservoir top vanishing points satisfying the wave group characteristic change in the search result are tracked, reservoir boundary distribution is obtained, and the current process is ended.
In an example embodiment, the specific process of tracking the reservoir top vanishing points satisfying the wave group characteristic changes in the search results to obtain the reservoir boundary distribution may include: tracking all reservoir sharp vanishing points which meet the wave group characteristic change in the search result; connecting the tracked pinch-out points into a pinch-out line to form a three-dimensional space; and obtaining the reservoir boundary distribution based on the three-dimensional space.
Specifically, after all reservoir point vanishing points meeting the wave group characteristic change in the search result are tracked, the following steps are executed for any reservoir point vanishing point: and connecting the reservoir sharp extinction point with all the adjacent reservoir sharp extinction points on the periphery of the reservoir sharp extinction point, wherein the line of the connection between the reservoir sharp extinction point and the reservoir sharp extinction point is a sharp extinction line. And when the connection of all the sharp points of the reservoir is finished, outputting all sharp lines, wherein the space formed by the sharp lines is the reservoir boundary of the reservoir distributed on the plane.
It should be noted that, because the search result is obtained by adopting a mode of fusion of the bio-heuristic algorithm algorithms, that is, the calculation result of one bio-heuristic algorithm is used as an initial value of another bio-heuristic algorithm, and then the result is obtained by the latter bio-heuristic algorithm, the obtained search result includes a plurality of reservoir prediction parameters including reservoir physical properties and thickness, and when reservoir boundary identification is performed based on the search result, the operation speed is high, and the identification is more accurate.
In practical application, after the reservoir boundary is identified, drilling exploitation can be performed according to the reservoir boundary, and the cost and the accuracy of the drilling exploitation can be greatly reduced because the reservoir boundary is identified more accurately.
In step S255, the search parameter is reset based on the target candidate boundary identification result, and the reconstructed frequency-extended three-dimensional seismic data volume is searched according to the reset search parameter to obtain a search result, and the operation continues to be performed 245.
In an example embodiment, when the search parameter is reset, the reset search parameter should be different from the last set search parameter, so that a search result that meets the identification requirement may be obtained. Illustratively, an ant colony algorithm is selected to search the three-dimensional seismic data volume after the reconstruction frequency extension processing, so as to obtain a search result. At this time, search parameters for the ant colony algorithm need to be set based on the target candidate boundary recognition result. The search parameters described herein may include, but are not limited to, the number of ants, seismic sampling points, and attribute values of the sampling points, where the seismic sampling points and the attribute values of the sampling points are extracted from the target candidate boundary identification result.
In addition, according to the above method embodiments, in some embodiments of the present invention, a reservoir boundary identification apparatus is also provided. Fig. 3 is a schematic structural diagram of a reservoir boundary identification apparatus according to some embodiments of the present invention.
Referring to fig. 3, the apparatus 300 includes:
the first identification module 310 is configured to perform reservoir boundary identification on the three-dimensional seismic data volume of the region where the reservoir is located by using at least two biological heuristic algorithms to obtain at least two candidate boundary identification results;
the search module 320 is configured to search the reconstructed frequency-extended three-dimensional seismic data volume based on a search parameter set by a target candidate boundary identification result in the at least two candidate boundary identification results to obtain a search result;
a judging module 330, configured to judge whether a wave group characteristic change of the search result meets a condition of the search parameter; if yes, triggering the second identification module 340;
the second identifying module 340 is configured to determine, under the trigger of the determining module, a position of a wave group characteristic change in the search result, where the position meets the condition of the search parameter, to obtain the boundary distribution of the reservoir.
In some example embodiments of the present invention, based on the above scheme, referring to fig. 4, the apparatus 300 further includes:
a decomposition module 410, configured to perform spectral decomposition on the three-dimensional seismic data volume to obtain low-frequency seismic data, high-frequency seismic data, and medium-frequency seismic data before performing reservoir boundary identification;
a gain module 420, configured to perform gain calculation on the high-frequency seismic data;
a reconstruction module 430 configured to reconstruct the three-dimensional seismic data volume based on the low-frequency seismic data, the medium-frequency seismic data, and the gain-computed high-frequency seismic data.
In some example embodiments of the present invention, based on the above scheme, referring to fig. 5, the search module 320 includes:
a selecting unit 510, configured to select the target candidate boundary identification result from the at least two candidate boundary identification results according to continuity of data in space and noise;
a setting unit 520, configured to set a search parameter for a target bio-heuristic algorithm based on the target candidate boundary identification result;
and the searching unit 530 is configured to search the three-dimensional seismic data volume through the target biological heuristic algorithm based on the search parameter to obtain a search result.
In some example embodiments of the present invention, based on the above scheme, the searching unit 530 is configured to perform a search for a preset cycle number on the three-dimensional seismic data volume through a preset ant colony algorithm based on the number of ants in the search parameter, the seismic sampling point, and the attribute value of the sampling point, so as to obtain a search result.
In some example embodiments of the present invention, based on the above scheme, referring to fig. 6, the second identification module 340 includes:
a tracking unit 610, configured to track positions of all wave group feature changes that satisfy the condition of the search parameter in the search result;
a space forming unit 620 for connecting the tracked positions of the wave group characteristic changes satisfying the condition of the search parameter to a line to form a three-dimensional space;
an identifying unit 630, configured to obtain the reservoir boundary distribution based on the three-dimensional space.
In some example embodiments of the present invention, based on the above scheme, the search module is further configured to:
if the wave group characteristic change of the search result does not meet the condition of the search parameter, resetting the search parameter based on the target boundary identification result to be selected, and searching the three-dimensional seismic data volume according to the reset search parameter to obtain a search result;
the judging module is further configured to continuously judge whether the wave group characteristic change of the current search result obtained by the searching module meets the condition of the search parameter.
The reservoir boundary identification device provided by the embodiment of the application can realize the processes in the method embodiments, such as the method embodiments of fig. 1 and fig. 2, and achieve the same functions and effects, which are not repeated here.
Fig. 7 is a schematic structural diagram of a first embodiment of a reservoir boundary identification device according to some embodiments of the present invention, and as shown in fig. 7, a reservoir boundary identification device 700 according to this embodiment may include: memory 710, and processor 720.
Optionally, the reservoir boundary identifying device may further include a bus. Wherein, the bus is used for realizing the connection between each element.
The memory 710 is used for storing computer programs and data, and the processor 720 calls the computer programs stored in the memory 710 to execute the technical solution of the reservoir boundary identification method provided by any one of the foregoing method embodiments.
Wherein the memory 710 and the processor 720 are electrically connected directly or indirectly to enable data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines, such as a bus. The memory 710 stores computer-executable instructions for implementing the data access control method, including at least one software functional module, which may be stored in the memory 710 in the form of software or firmware, and the processor 720 executes various functional applications and reservoir boundary identification by running the computer programs and modules stored in the memory 710.
The Memory 710 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 710 is used for storing programs, and the processor 720 executes the programs after receiving the execution instructions. Further, the software programs and modules within the memory 710 may also include an operating system, which may include various software components and/or drivers for managing system tasks (e.g., memory management, storage device control, power management, etc.), and may communicate with various hardware or software components to provide an operating environment for other software components.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, may implement the reservoir boundary identification method provided in any of the above method embodiments.
The computer-readable storage medium in this embodiment may be any available medium that can be accessed by a computer or a data storage device such as a server, a data center, etc. that is integrated with one or more available media, and the available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., SSDs), etc.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
It will be appreciated that the relevant features of the method and apparatus described above are referred to one another. In addition, "first", "second", and the like in the above embodiments are for distinguishing the embodiments, and do not represent merits of the embodiments.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Claims (15)
1. A method for reservoir boundary identification, comprising:
adopting at least two biological heuristic algorithms to respectively identify reservoir boundaries of the three-dimensional seismic data volume of the region where the reservoir is located to obtain at least two boundary identification results to be selected;
searching the three-dimensional seismic data volume based on search parameters set by target boundary identification results in the at least two boundary identification results to be selected to obtain search results;
judging whether the wave group characteristic change of the search result meets the condition of the search parameter;
if yes, determining the position of the wave group characteristic change meeting the conditions of the search parameters in the search result, and obtaining the boundary distribution of the reservoir.
2. The method of claim 1, wherein prior to performing the reservoir boundary identification, the method further comprises:
performing frequency spectrum decomposition on the three-dimensional seismic data volume to obtain low-frequency seismic data, high-frequency seismic data and medium-frequency seismic data;
performing gain calculation on the high-frequency seismic data;
and reconstructing the three-dimensional seismic data volume based on the low-frequency seismic data, the medium-frequency seismic data and the high-frequency seismic data after gain calculation.
3. The method according to claim 1, wherein the searching the three-dimensional seismic data volume based on the search parameter set by the target candidate boundary recognition result in the at least two candidate boundary recognition results to obtain a search result comprises:
selecting the target boundary identification result to be selected from the at least two boundary identification results to be selected according to the continuity of data in space and noise;
setting search parameters aiming at a target biological heuristic algorithm based on the target candidate boundary identification result;
and searching the three-dimensional seismic data volume through the target biological heuristic algorithm based on the search parameters to obtain a search result.
4. The method of claim 3, wherein said searching said three-dimensional seismic data volume by said target bio-heuristic algorithm based on said search parameters, resulting in search results, comprises:
and searching the three-dimensional seismic data volume for preset cycle times through a preset ant colony algorithm based on the ant number, the seismic sampling point and the attribute value of the sampling point in the search parameters to obtain a search result.
5. The method of claim 1, wherein the determining the location of the variation of the wave group characteristics in the search results that satisfy the condition of the search parameter results in a boundary distribution of the reservoir, comprises:
tracking the positions of all wave group characteristic changes meeting the conditions of the search parameters in the search results;
connecting the tracked positions of the wave group characteristic changes meeting the conditions of the search parameters into a line to form a three-dimensional space;
and obtaining the boundary distribution of the reservoir based on the three-dimensional space.
6. The method according to any one of claims 1-5, further comprising:
if the wave group characteristic change of the search result does not meet the search parameter, resetting the search parameter based on the target boundary identification result to be selected;
searching the three-dimensional seismic data volume according to the reset search parameters to obtain a search result;
and continuously judging whether the wave group characteristic change of the current search result meets the condition of the search parameter.
7. The method of any one of claims 1-5, wherein the at least two bio-heuristic algorithms comprise at least two of genetic algorithms, particle swarm optimization algorithms, ant colony algorithms, artificial bee colony algorithms, bacterial foraging algorithms, colony search algorithms, DNA calculations, membrane calculations, and self-organizing migration algorithms.
8. A reservoir boundary identification apparatus, comprising:
the first identification module is used for respectively identifying reservoir boundaries of the three-dimensional seismic data body of the region where the reservoir is located by adopting at least two biological heuristic algorithms to obtain at least two boundary identification results to be selected;
the search module is used for searching the reconstructed frequency-extended three-dimensional seismic data volume based on search parameters set by a target boundary identification result in the at least two boundary identification results to be selected to obtain a search result;
the judging module is used for judging whether the wave group characteristic change of the search result meets the condition of the search parameter; if yes, triggering a second identification module;
and the second identification module is used for determining the position of the wave group characteristic change meeting the condition of the search parameter in the search result under the trigger of the judgment module to obtain the boundary distribution of the reservoir.
9. The apparatus of claim 8, further comprising:
the decomposition module is used for carrying out frequency spectrum decomposition on the three-dimensional seismic data volume to obtain low-frequency seismic data, high-frequency seismic data and intermediate-frequency seismic data before reservoir boundary identification is carried out;
the gain module is used for carrying out gain calculation on the high-frequency seismic data;
and the reconstruction module is used for reconstructing the three-dimensional seismic data volume based on the low-frequency seismic data, the medium-frequency seismic data and the high-frequency seismic data after gain calculation.
10. The apparatus of claim 8, wherein the search module comprises:
the selecting unit is used for selecting the target boundary identification result to be selected from the at least two boundary identification results to be selected according to the continuity of data in space and noise;
the setting unit is used for setting search parameters aiming at the target biological heuristic algorithm based on the target candidate boundary identification result;
and the searching unit is used for searching the three-dimensional seismic data volume through the target biological heuristic algorithm based on the searching parameters to obtain a searching result.
11. The apparatus according to claim 10, wherein the search unit is configured to perform search for the three-dimensional seismic data volume for a preset number of cycles through a preset ant colony algorithm based on the number of ants in the search parameter, a seismic sampling point, and an attribute value of the sampling point, so as to obtain a search result.
12. The apparatus of claim 8, wherein the second identification module comprises:
the tracking unit is used for tracking the positions of all wave group characteristic changes meeting the conditions of the search parameters in the search results;
a space forming unit for connecting the tracked positions of the wave group characteristic changes satisfying the condition of the search parameter to a line to form a three-dimensional space;
and the identification unit is used for obtaining the reservoir boundary distribution based on the three-dimensional space.
13. The apparatus according to any of claims 8-12, wherein the search module is further configured to:
if the wave group characteristic change of the search result does not meet the condition of the search parameter, resetting the search parameter based on the target boundary identification result to be selected, and searching the three-dimensional seismic data volume according to the reset search parameter to obtain a search result;
the judging module is further configured to continuously judge whether the wave group characteristic change of the current search result obtained by the searching module meets the condition of the search parameter.
14. A computer-readable storage medium, comprising a stored program, wherein the program, when executed, controls an apparatus in which the storage medium is located to perform the reservoir boundary identification method of any one of claims 1 to 7.
15. A reservoir boundary identification device, comprising: a memory for storing a program; a processor, coupled to the memory, for executing the program to perform the reservoir boundary identification method of any one of claims 1-7.
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