Lithology trap identification method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention relates to a lithologic trap identification method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the geologic horizon interpretation according to the seismic data of a target work area, and determining the stratum thickness; extracting at least one seismic fracture attribute in the seismic data, and identifying strike-slip fracture of a target work area according to the at least one seismic fracture attribute; establishing a sedimentary geologic model according to the stratum thickness, the strike-slip fracture and the drilled data of the target work area; carrying out forward modeling based on the sedimentary geologic model, and determining seismic waveform templates corresponding to a reservoir and a lithologic body respectively; determining distribution conditions of different seismic waveforms in the seismic data based on the seismic waveform templates corresponding to the reservoir and the lithologic body respectively; according to the stratum thickness, the strike-slip fracture and the distribution condition of the different seismic waveforms, the lithologic trap of the target work area is identified; and accurate identification of the lithologic trap of the carbonate rock is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration, and particularly to a method, device, electronic device and storage medium for identifying lithologic traps. Background Art

[0002] With the transformation of exploration from structural anticline areas to structural slope areas, the exploration targets have gradually become more complex, and the identification of lithologic traps has become the main goal of oil and gas exploration.

[0003] Lithologic exploration is divided into continental facies sandbody lithologic exploration and marine facies carbonate rock lithologic exploration. At present, the exploration of continental facies sandbody lithologic traps is relatively mature, mainly including seismic sensitive attribute technology and reservoir inversion technology. Since continental facies strata are usually buried relatively shallow and the wave impedance difference between sandstone and mudstone is large, the exploration effect of sandbody lithologic traps is good.

[0004] However, for marine facies carbonate rocks with relatively large burial depths, due to complex sedimentary environments, complex diagenetic processes, developed reservoir fractures and cavities, low signal-to-noise ratio and resolution of seismic data, there are lack of effective means for fine characterization of lithologic traps, it is difficult to identify lithologic boundaries with low reliability, and it is difficult to quickly and accurately characterize carbonate rock lithologic traps. Summary of the Invention

[0005] Embodiments of the present invention provide a method, device, electronic device and storage medium for identifying lithologic traps to solve the technical problem that it is difficult to quickly and accurately identify lithologic traps of marine facies carbonate rocks.

[0006] In a first aspect, embodiments of the present invention provide a method for identifying lithologic traps, including: carrying out geological horizon interpretation based on seismic data of a target work area to determine formation thickness; extracting at least one seismic fracture attribute from the seismic data, and identifying strike-slip faults in the target work area according to the at least one seismic fracture attribute; establishing a sedimentary geological model according to the formation thickness, the strike-slip faults and the drilled well data of the target work area; carrying out forward modeling based on the sedimentary geological model to determine seismic waveform templates corresponding to reservoirs and lithologic bodies respectively; determining the distribution of different seismic waveforms in the seismic data based on the seismic waveform templates corresponding to the reservoirs and lithologic bodies respectively; and identifying lithologic traps in the target work area according to the formation thickness, strike-slip faults and the distribution of different seismic waveforms.

[0007] In some embodiments, the extracting at least one seismic fracture attribute from the seismic data and identifying strike-slip faults in the target work area according to the at least one seismic fracture attribute includes: extracting coherence attribute, curvature attribute and azimuth angle from the seismic data; inputting the coherence attribute, curvature attribute and azimuth angle into a pre-trained first neural network model to perform fusion of the at least one seismic fracture attribute, and identifying strike-slip faults in the target work area.

[0008] In some embodiments, determining the distribution of different seismic waveforms in the seismic data based on the seismic waveform templates corresponding to the reservoir and the lithologic body respectively includes: identifying the planar distribution of different seismic waveforms in the seismic data based on a pre-trained second neural network model and the seismic waveform templates corresponding to the reservoir and the lithologic body respectively.

[0009] In some embodiments, the seismic data includes a well log acoustic curve, a seismic profile, and 3D seismic data; performing geological horizon interpretation based on the seismic data and determining the formation thickness includes: calibrating the top, bottom, and internal key geological horizons on the seismic profile according to the well log acoustic curve, and performing geological horizon interpretation on the 3D seismic data; calculating the formation thickness according to the interpretation of the top, bottom, and internal key geological horizons.

[0010] In some embodiments, performing forward modeling based on the sedimentary geological model and determining the seismic waveform templates corresponding to the reservoir and the lithologic body respectively includes: when the drilled well data in the target work area includes typical wells, determining the seismic waveform template corresponding to the reservoir or the lithologic body based on the seismic waveforms of the typical wells; when the drilled well data in the target work area does not include typical wells, performing forward modeling based on the sedimentary geological model to determine the seismic waveform templates corresponding to the reservoir and the lithologic body respectively.

[0011] In a second aspect, an embodiment of the present invention provides a lithologic trap identification device, including: a horizon interpretation module for performing geological horizon interpretation based on the seismic data of the target work area and determining the formation thickness; a fault identification module for extracting at least one seismic fault attribute from the seismic data and identifying the strike-slip fault of the target work area according to the at least one seismic fault attribute; a model establishment module for establishing a sedimentary geological model according to the formation thickness, the strike-slip fault, and the drilled well data of the target work area; a model forward modeling module for performing forward modeling based on the sedimentary geological model to determine the seismic waveform templates corresponding to the reservoir and the lithologic body respectively; a seismic waveform module for determining the distribution of different seismic waveforms in the seismic data based on the seismic waveform templates corresponding to the reservoir and the lithologic body respectively; a trap identification module for identifying the lithologic trap of the target work area according to the formation thickness, the strike-slip fault, and the distribution of different seismic waveforms.

[0012] In some embodiments, the fault identification module is specifically configured to: extract the coherence attribute, the curvature attribute, and the azimuth from the seismic data; input the coherence attribute, the curvature attribute, and the azimuth into a pre-trained first neural network model to perform fusion of the at least one seismic fault attribute and identify the strike-slip fault of the target work area.

[0013] In some embodiments, the seismic waveform module is specifically configured to: identify the planar distribution of different seismic waveforms in the seismic data based on a pre-trained second neural network model and seismic waveform templates corresponding to the reservoir and the lithological body respectively.

[0014] In a third aspect, an embodiment of the present invention provides an electronic device, including a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete communication with each other through the communication bus; the memory is used to store a computer program; when the processor executes the program stored on the memory, it implements the steps of the lithologic trap identification method according to any one of the first aspects.

[0015] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the lithologic trap identification method according to any one of the first aspects.

[0016] The embodiments of the present invention have the following beneficial effects:

[0017] The trench and highland geomorphic distribution characteristics of carbonate rock deposition can be identified by using the strike-slip fault and formation thickness characteristics. Then, forward modeling of the model is carried out using the drilled wells to establish seismic waveform identification templates for the reservoir and the tight lithological body, and then the reservoir and the tight lithological body are identified to achieve the accurate identification of lithologic traps. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present invention, and are used together with the specification to explain the principles of the present invention.

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0020] Figure 1 It is a flowchart of a lithologic trap identification method provided by an embodiment of the present invention;

[0021] Figure 2 It is a sedimentary geomorphic distribution map provided by an embodiment of the present invention;

[0022] Figure 3 It is a schematic diagram of a strike-slip fault provided by an embodiment of the present invention;

[0023] Figure 4 It is a schematic diagram of a sedimentary geological model of the Dengying Formation provided by an embodiment of the present invention;

[0024] Figure 5 A forward section view of a sedimentary geological model provided by an embodiment of the present invention;

[0025] Figure 6 A schematic diagram of seismic classification of sedimentary facies provided by an embodiment of the present invention;

[0026] Figure 7 A profile view for identifying different seismic waveforms provided by an embodiment of the present invention;

[0027] Figure 8 A diagram for identifying different seismic waveforms provided by an embodiment of the present invention;

[0028] Figure 9 A schematic diagram of a lithologic trap identified by an embodiment of the present invention;

[0029] Figure 10 For Figure 1 A detailed flowchart of step S102 in the illustrated embodiment;

[0030] Figure 11 A schematic structural diagram of a lithologic trap identification device provided by an embodiment of the present invention;

[0031] Figure 12 A schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0032] To make the objectives, 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 accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0033] With the transformation of exploration from structural anticline areas to structural slope areas, the exploration targets have gradually become more complex, and lithologic trap identification has become the main goal of oil and gas exploration.

[0034] Lithologic exploration is divided into continental facies sand body lithologic exploration and marine facies carbonate rock lithologic exploration. Currently, the exploration of continental facies sand body lithologic traps is relatively mature, mainly including seismic sensitive attribute technology and reservoir inversion technology. Since continental facies strata are usually shallowly buried and there is a large difference in wave impedance between sandstone and mudstone, the exploration effect of sand body lithologic traps is good. However, for marine facies carbonate rocks with a large burial depth, due to the complex sedimentary environment, complex diagenetic process, developed reservoir fractures and caves, low signal-to-noise ratio and resolution of seismic data, there are no effective means for fine characterization of lithologic traps. Mainly relying on inversion technology, it is difficult to identify lithologic boundaries, with low reliability, and it is difficult to quickly and accurately characterize carbonate rock lithologic traps.

[0035] In view of the above technical problems, the technical concept of the present invention lies in: using the characteristics of strike-slip faults and formation thickness to identify the distribution characteristics of grooves and highland landforms in carbonate sedimentation, and using the drilled wells and forward modeling of the model in the sedimentary highlands to establish seismic waveform recognition templates for reservoirs and tight lithologic bodies, and then identifying the reservoirs and tight lithologic bodies to achieve the accurate identification of lithologic traps.

[0036] Figure 1 The flowchart of a lithologic trap identification method provided by an embodiment of the present invention is as Figure 1 shown, and the lithologic trap identification method includes:

[0037] Step S101: Carry out geological horizon interpretation based on the seismic data of the target work area to determine the formation thickness.

[0038] Specifically, the target work area can be a marine carbonate rock area where lithologic traps are to be identified; before carrying out this embodiment, all relevant seismic data of the target work area can be collected, including logging curves, 3D seismic data, seismic profiles, etc.; then through step S101, use the collected seismic data to carry out geological horizon interpretation, and calculate the corresponding formation thickness according to the interpreted geological horizons.

[0039] In some embodiments, the seismic data includes logging acoustic curves, seismic profiles and 3D seismic data; step S101 includes: calibrating the top, bottom and internal key geological horizons on the seismic profile according to the logging acoustic curves, and carrying out geological horizon interpretation on the 3D seismic data; calculating the formation thickness according to the interpretation of the top, bottom and internal key geological horizons.

[0040] Specifically, seismic simulation can be carried out using the collected logging acoustic curves to clarify the seismic characteristics of geological horizons, find the corresponding top, bottom and internal key seismic geology on the collected seismic profiles, carry out key seismic geological horizon interpretation on the 3D seismic data, and calculate the formation thickness according to the top, bottom and internal key seismic geological horizons of the target layer.

[0041] In addition, the calculated formation thickness can be used to analyze the characteristics of the sedimentary paleogeomorphology, and the thicker area is the high part of the paleogeomorphology. Taking the carbonate rocks in a certain area as an example, Figure 2 A sedimentary geomorphic distribution map provided by an embodiment of the present invention is to analyze the main spatial positions where reservoirs develop according to the main drilled wells in the research section, finely interpret the top and bottom seismic horizons, compile the formation thickness, and use the formation thickness to analyze the sedimentary high zones and sedimentary depressions.

[0042] Step S102: Extract at least one seismic fault attribute from the seismic data, and identify the strike-slip faults in the target work area according to the at least one seismic fault attribute.

[0043] Specifically, multiple seismic attributes such as coherence, curvature, and azimuth that can sensitively reflect faults can be calculated using 3D seismic data in seismic data, and strike-slip faults can be identified on a plane using these multiple fault attributes. Taking the carbonate rocks in the aforementioned area as an example, Figure 3 This is a schematic diagram of a strike-slip fault provided by an embodiment of the present invention, which is identified through the fusion of multiple attributes of curvature + azimuth + dip angle.

[0044] Step S103: Establish a sedimentary geological model of the target work area according to the formation thickness, the strike-slip fault, and the drilled well data of the target work area.

[0045] Specifically, a sedimentary geological model is constructed based on the formation thickness, other faults, and seismic waveform characteristics extracted from the actual well positions. Taking the carbonate rocks in the aforementioned area as an example, Figure 4 This is a schematic diagram of a sedimentary geological model of the Dengying Formation provided by an embodiment of the present invention. According to the carbonate rock sedimentation theory and actual drilling conditions, combined with the actual seismic profile, a sedimentary geological model is established, and it is clear that the main controlling factors for reservoir development are strike-slip faults and sedimentary landforms. Through Figure 4 It can also be seen that the carbonate rock sedimentary environment is controlled by sedimentary landforms such as grooves. The strike-slip fault mainly reflects the control of the grooves, indirectly controlling the differences in sedimentary landforms. The matching of the strike-slip fault with the formation thickness can predict the planar law of sedimentary micro-landforms. Therefore, based on the favorable area for sedimentary micro-landform prediction, a corresponding sedimentary geological model is constructed using the drilled wells.

[0046] Step S104: Carry out forward modeling based on the sedimentary geological model to determine the seismic waveform templates corresponding to the reservoir and the tight lithologic body respectively.

[0047] Specifically, model forward modeling analysis is carried out on the sedimentary geological model to simulate the waveform characteristics of different beach facies reservoirs in different sedimentary thickness areas. According to the results of the model forward modeling, combined with the seismic waveforms of the drilled wells, seismic waveform recognition templates for the reservoir and the tight lithologic body are established. Taking the carbonate rocks in the aforementioned area as an example, Figure 5 This is a forward modeling cross-section diagram of a sedimentary geological model provided by an embodiment of the present invention. According to the actual drilling conditions and the actual seismic profile, a sedimentary geological model is established to carry out seismic simulation, and the seismic facies characteristics under different reservoir thicknesses and longitudinal combinations of reservoirs are summarized. It can be seen that there are three seismic facies differences in the mound-beach facies, and one seismic facies difference in the sedimentary depression facies; Figure 6 This is a schematic diagram of seismic classification of sedimentary facies provided by an embodiment of the present invention. According to the actual drilling conditions, different longitudinal combination relationships of reservoirs are summarized, and seismic models of different mound-beach facies and depression tight zones are established in combination with the actual drilled seismic profile.

[0048] In some embodiments, step S104 includes: when the drilled well data in the target work area includes typical wells, determining the seismic waveform templates corresponding to the reservoir or lithologic bodies based on the seismic waveforms of the typical wells; when the drilled well data in the target work area does not include typical wells, conducting forward modeling based on the sedimentary geological model to determine the seismic waveform templates corresponding to the reservoir and lithologic bodies respectively. Specifically, the classification of seismic waveforms needs to be complex. When there are actual drilled wells, the seismic waveforms of typical wells are preferably selected. When there are no drilled wells, the seismic waveforms are selected through forward modeling of the sedimentary geological model.

[0049] Step S105: Determine the distribution of different seismic waveforms in the seismic data based on the seismic waveform templates corresponding to the reservoir and lithologic bodies respectively.

[0050] Specifically, according to the seismic waveforms of the reservoir and tight lithologic bodies, identify the profiles and planar distributions of different seismic waveforms in the 3D seismic data. Taking the carbonate rocks in the aforementioned area as an example, Figure 7 FIG. is a cross-sectional view for identifying different seismic waveforms provided by an embodiment of the present invention. According to the seismic patterns of different mound-beach facies and depression tight zones, conduct comprehensive interpretation of the seismic profile; Figure 8 FIG. is a map for identifying different seismic waveforms provided by an embodiment of the present invention. According to the seismic patterns of different mound-beach facies and depression tight zones identified on the profile, compile the profile distribution of different seismic facies, and determine the distribution boundaries of the mound-beach facies and the tight zone, that is, the tight zone boundary.

[0051] In some embodiments, step S105 includes: based on the pre-trained second neural network model and the seismic waveform templates corresponding to the reservoir and lithologic bodies respectively, identify the planar distribution of different seismic waveforms in the seismic data.

[0052] Specifically, according to the obtained different seismic waveform templates, analyze the planar distribution of different types of seismic waveforms based on the 3D seismic data using the conventional neural network method.

[0053] Step S106: Identify the lithologic traps in the target work area according to the formation thickness, strike-slip faults and the distribution of different seismic waveforms.

[0054] Specifically, superimpose the formation thickness, the faults identified by multi-attributes and the favorable waveform boundaries as the lithologic trap boundaries. In the area with thick sedimentation values, regard the identified beach facies waveform area as the reservoir, and regard the tight zone waveform area in the area with thin sedimentation values as the barrier layer, and depict the boundaries of the reservoir and the barrier tight zone. Taking the carbonate rocks in the aforementioned area as an example, Figure 9Schematic diagram of a lithologic trap identified according to an embodiment of the present invention. Practice has shown that effective predictions have been made for different formations and strata at different depths in this area, and the coincidence rate of verification wells is greater than 80%. The prediction results are consistent with geological laws. Among the 3 wells drilled into lithologic trap gas reservoirs, industrial gas flows have been obtained, achieving a breakthrough in the exploration of lithologic gas reservoirs in the slope area and providing a geological basis for increasing reserves and production in this area.

[0055] The lithologic trap identification method provided by the embodiment of the present invention includes: carrying out geological horizon interpretation based on seismic data of a target work area to determine formation thickness; extracting at least one seismic fracture attribute from the seismic data, and identifying strike-slip faults in the target work area according to the at least one seismic fracture attribute; establishing a sedimentary geological model according to the formation thickness, the strike-slip faults and the drilled well data of the target work area; carrying out forward modeling based on the sedimentary geological model to determine seismic waveform templates corresponding to reservoirs and lithologic bodies respectively; determining the distribution of different seismic waveforms in the seismic data based on the seismic waveform templates corresponding to the reservoirs and lithologic bodies respectively; identifying lithologic traps in the target work area according to the formation thickness, strike-slip faults and the distribution of different seismic waveforms; using the characteristics of strike-slip faults and formation thickness to identify the trench and highland geomorphic distribution characteristics of carbonate rock deposition, and then using the drilled wells to carry out model forward modeling to establish seismic waveform identification templates for reservoirs and tight lithologic bodies, and further identifying reservoirs and tight lithologic bodies to achieve accurate identification of lithologic traps. It can effectively identify lithologic traps and improve the reliability of prediction results.

[0056] Figure 10 For Figure 1 The detailed flowchart of step S102 in the embodiment shown is as Figure 10 shown. On the basis of the foregoing embodiment, step S102 includes the following steps:

[0057] Step S1021: Extract coherence attribute, curvature attribute and azimuth angle from the seismic data.

[0058] Step S1022: Input the coherence attribute, curvature attribute and azimuth angle into a pre-trained first neural network model to perform fusion of the at least one seismic fracture attribute and identify strike-slip faults in the target work area.

[0059] Specifically, multiple seismic attributes that can sensitively reflect faults, such as coherence, curvature, and azimuth angle, can be extracted from the three-dimensional seismic data in the seismic data, and then these multiple sensitive seismic attributes are fused through a conventional neural network algorithm to obtain a comprehensive plan view to identify strike-slip faults, which can be referred to Figure 3 as shown.

[0060] Based on the foregoing embodiments, by extracting coherence attributes, curvature attributes, and azimuth angles from the seismic data; inputting the coherence attributes, curvature attributes, and azimuth angles into a pre-trained first neural network model to perform fusion of the at least one seismic fracture attribute, and identifying strike-slip fractures in the target work area, accurate and rapid identification of strike-slip fractures is achieved.

[0061] Figure 11 The following is a schematic structural diagram of a lithologic trap identification device provided by an embodiment of the present invention. As Figure 11 shown, the device includes:

[0062] A horizon interpretation module 1101, configured to perform geological horizon interpretation based on seismic data of the target work area to determine formation thickness; a fault identification module 1102, configured to extract at least one seismic fracture attribute from the seismic data, and identify strike-slip fractures in the target work area according to the at least one seismic fracture attribute; a model establishment module 1103, configured to establish a sedimentary geological model according to the formation thickness, the strike-slip fractures, and the drilled well data of the target work area; a model forward modeling module 1104, configured to perform forward simulation based on the sedimentary geological model to determine seismic waveform templates corresponding to the reservoir and the lithologic body respectively; a seismic waveform module 1105, configured to determine the distribution of different seismic waveforms in the seismic data based on the seismic waveform templates corresponding to the reservoir and the lithologic body respectively; a trap identification module 1106, configured to identify lithologic traps in the target work area according to the formation thickness, strike-slip fractures, and the distribution of different seismic waveforms.

[0063] In some embodiments, the fault identification module 1102 is specifically configured to: extract coherence attributes, curvature attributes, and azimuth angles from the seismic data; input the coherence attributes, curvature attributes, and azimuth angles into a pre-trained first neural network model to perform fusion of the at least one seismic fracture attribute, and identify strike-slip fractures in the target work area.

[0064] In some embodiments, the seismic waveform module 1105 is specifically configured to: identify the planar distribution of different seismic waveforms in the seismic data based on a pre-trained second neural network model and the seismic waveform templates corresponding to the reservoir and the lithologic body respectively.

[0065] In some embodiments, the seismic data includes logging acoustic curves, seismic profiles, and 3D seismic data; the horizon interpretation module 1101 is specifically configured to: perform calibration of key geological horizons at the top, bottom, and inside on the seismic profile according to the logging acoustic curves, and perform geological horizon interpretation on the 3D seismic data; calculate the formation thickness according to the interpretation of the key geological horizons at the top, bottom, and inside.

[0066] In some embodiments, the forward modeling module 1104 of the model is specifically configured to: when the drilled well data in the target work area includes typical wells, determine the seismic waveform templates corresponding to the reservoirs or lithological bodies based on the seismic waveforms of the typical wells; when the drilled well data in the target work area does not include typical wells, perform forward modeling based on the sedimentary geological model to determine the seismic waveform templates corresponding to the reservoirs and lithological bodies respectively.

[0067] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process and corresponding beneficial effects of the above-described lithologic trap identification device can refer to the corresponding process in the foregoing method examples, and will not be elaborated here.

[0068] Figure 12 The following is a schematic hardware structure diagram of an electronic device provided by an embodiment of the present invention. As Figure 12 shown, the electronic device includes: a processor 1201, a communication interface 1202, a memory 1203, and a communication bus 1204. Among them, the processor 1201, the communication interface 1202, and the memory 1203 communicate with each other through the communication bus 1204.

[0069] The memory 1203 is used to store computer programs.

[0070] In an embodiment of the present application, when the processor 1201 executes the program stored on the memory 1203, it implements the steps of the lithologic trap identification method provided in any one of the foregoing method embodiments.

[0071] The electronic device provided by the embodiment of the present application has the same implementation principle and technical effects as the above embodiment, and will not be elaborated here.

[0072] The above-mentioned memory 1203 may be an electronic memory such as a flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. The memory 1203 has a storage space for program codes for executing any of the method steps in the above-mentioned method. For example, the storage space for program codes may include respective program codes for implementing each of the steps in the above method. These program codes may be read from or written into one or more computer program products. These computer program products include program code carriers such as hard disks, optical discs (CDs), memory cards, or floppy disks. Such computer program products are usually portable or fixed storage units. The storage unit may have a storage segment or storage space arranged similarly to the memory 1203 in the above-mentioned electronic device. The program codes may be compressed in an appropriate form, for example. Generally, the storage unit includes a program for executing the method steps according to the embodiments of the present application, that is, codes that can be read by a processor such as 1201, and when these codes are run by the electronic device, the electronic device is caused to execute each of the steps in the method described above.

[0073] Embodiments of the present application also provide a computer-readable storage medium. A computer program is stored on the above-mentioned computer-readable storage medium, and when the computer program is executed by a processor, the steps of the lithologic trap identification method described above are implemented.

[0074] The computer-readable storage medium may be included in the device / apparatus described in the above embodiments; or it may exist separately without being assembled into the device / apparatus. The above-mentioned computer-readable storage medium carries one or more programs, and when the one or more programs are executed, the method according to the embodiments of the present application is implemented.

[0075] According to the embodiments of the present application, the computer-readable storage medium may be a non-volatile computer-readable storage medium, and may include, for example, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present application, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program may be used by or in combination with an instruction execution system, device, or apparatus.

[0076] It should be noted that in this document, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the phrase "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising said element.

[0077] The above are only specific embodiments of the present application, enabling those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for identifying lithologic traps, characterized in that, including: conducting geological horizon interpretation based on the seismic data of the target work area to determine the formation thickness; extracting at least one seismic fracture attribute from the seismic data and identifying the strike-slip faults in the target work area according to the at least one seismic fracture attribute; establishing a sedimentary geological model based on the formation thickness, the strike-slip faults and the drilled well data of the target work area; conducting forward modeling based on the sedimentary geological model to determine the seismic waveform templates corresponding to the reservoir and the lithologic body respectively; determining the distribution of different seismic waveforms in the seismic data based on the seismic waveform templates corresponding to the reservoir and the lithologic body respectively; identifying the lithologic traps in the target work area according to the formation thickness, the strike-slip faults and the distribution of different seismic waveforms.

2. The method according to claim 1, characterized in that, The extracting at least one seismic fracture attribute from the seismic data and identifying the strike-slip faults in the target work area according to the at least one seismic fracture attribute includes: extracting the coherence attribute, the curvature attribute and the azimuth from the seismic data; inputting the coherence attribute, the curvature attribute and the azimuth into a pre-trained first neural network model to perform the fusion of the at least one seismic fracture attribute and identifying the strike-slip faults in the target work area.

3. The method according to claim 1, wherein The determining the distribution of different seismic waveforms in the seismic data based on the seismic waveform templates corresponding to the reservoir and the lithologic body respectively includes: identifying the planar distribution of different seismic waveforms in the seismic data based on a pre-trained second neural network model and the seismic waveform templates corresponding to the reservoir and the lithologic body respectively.

4. The method according to any one of claims 1 to 3, characterized in that The seismic data includes logging acoustic curves, seismic profiles and 3D seismic data; the conducting geological horizon interpretation based on the seismic data of the target work area to determine the formation thickness includes: calibrating the key geological horizons at the top, bottom and inside on the seismic profile according to the logging acoustic curves and conducting geological horizon interpretation on the 3D seismic data; calculating the formation thickness according to the interpretation of the key geological horizons at the top, bottom and inside.

5. The method according to any one of claims 1 to 3, characterized in that, The conducting forward modeling based on the sedimentary geological model to determine the seismic waveform templates corresponding to the reservoir and the lithologic body respectively includes: when the drilled well data in the target work area includes typical wells, determining the seismic waveform template corresponding to the reservoir or the lithologic body based on the seismic waveforms of the typical wells; when the drilled well data in the target work area does not include typical wells, conducting forward modeling based on the sedimentary geological model to determine the seismic waveform templates corresponding to the reservoir and the lithologic body respectively.

6. A lithologic trap identification device, characterized in that including: a horizon interpretation module for conducting geological horizon interpretation based on the seismic data of the target work area to determine the formation thickness; a fault identification module for extracting at least one seismic fracture attribute from the seismic data and identifying the strike-slip faults in the target work area according to the at least one seismic fracture attribute; a model establishment module for establishing a sedimentary geological model based on the formation thickness, the strike-slip faults and the drilled well data of the target work area; a model forward modeling module for conducting forward modeling based on the sedimentary geological model to determine the seismic waveform templates corresponding to the reservoir and the lithologic body respectively; An earthquake waveform module, configured to determine the distribution of different earthquake waveforms in the seismic data based on the earthquake waveform templates corresponding to the reservoir and the lithological body respectively; A trap identification module, configured to identify lithological traps in the target work area according to the formation thickness, strike-slip faults and the distribution of different earthquake waveforms.

7. The device according to claim 6, characterized in that, The fault identification module is specifically configured to: Extract coherence attributes, curvature attributes and azimuth angles from the seismic data; Input the coherence attributes, curvature attributes and azimuth angles into a pre-trained first neural network model to perform the fusion of the at least one seismic fault attribute, and identify the strike-slip faults in the target work area.

8. The device according to claim 6, characterized in that, The earthquake waveform module is specifically configured to: Based on a pre-trained second neural network model and the earthquake waveform templates corresponding to the reservoir and the lithological body respectively, identify the planar distribution of different earthquake waveforms in the seismic data.

9. An electronic device, characterized in that, It includes a processor, a communication interface, a memory and a communication bus. Among them, the processor, the communication interface and the memory complete mutual communication through the communication bus; The memory is used to store computer programs; The processor, when executing the programs stored on the memory, implements the steps of the lithological trap identification method according to any one of claims 1-5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the lithological trap identification method according to any one of claims 1-5.

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