A method and device for describing a thick salt-underlying volcanic rock, an electronic device and a medium

Abstract

The application discloses a method and device for describing a thick salted volcanic rock, electronic equipment and a medium. The method comprises the following steps: determining a source fracture of a volcanic eruption channel; wherein the source fracture is used for controlling the development of the volcanic rock; analyzing and identifying the source fracture of the volcanic eruption channel to determine a seismic facies data body; wherein the seismic facies data body comprises a volcanic rock seismic facies data body and a non-volcanic rock seismic facies data body; taking the seismic facies data body as input, performing seismic facies analysis on the seismic facies data body based on a pre-determined neural network to obtain a volcanic facies data body; and performing seismic inversion on the volcanic facies data body to determine the three-dimensional spatial distribution of the volcanic rock. The technical scheme can minimize the multi-solution of thick salted volcanic rock prediction, greatly improve the volcanic rock description accuracy, and solve the problems of great difficulty in thick salted volcanic rock description and difficulty in oil and gas reservoir prediction.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration technology, and in particular to a method, apparatus, electronic device and medium for describing thick pre-salt volcanic rocks. Background Technology

[0002] Subsalt oil and gas deposits are a crucial resource with immense potential worldwide. However, subsalt deposits are often accompanied by complex lithology and the development of unique rock masses such as volcanic rocks. Furthermore, the quality of subsalt seismic data is generally poor, leading to significant ambiguity in lithofacies, lithology, and reservoir predictions. Therefore, effectively identifying the development scale and extent of volcanic facies and rock masses is key to reducing reservoir prediction ambiguity. Utilizing geophysical data to predict the distribution range of volcanic rocks before drilling is of great significance for subsalt oil and gas exploration.

[0003] Currently, the prediction of volcanic rocks mainly relies on the seismic facies method, and commonly uses methods such as seismic attribute analysis and post-stack seismic inversion.

[0004] However, current methods generally suffer from problems such as high ambiguity, poor determinism, and low prediction accuracy, especially in their poor adaptability to low-quality seismic data under thick salt layers. Therefore, it is essential to develop a new technology that integrates multiple information sources, is highly adaptable to low-quality seismic data under rocks, and can accurately determine the volcanic facies and distribution range of rock masses, thus providing technical support for efficient exploration of oil and gas under salt layers. Summary of the Invention

[0005] This invention provides a method, apparatus, electronic device, and medium for describing massive subsalt volcanic rocks, which minimizes the ambiguity in predicting massive subsalt volcanic rocks, significantly improves the accuracy of volcanic rock characterization, and solves the problems of high difficulty in characterizing massive subsalt volcanic rocks and difficulty in predicting oil and gas reservoirs.

[0006] According to one aspect of the present invention, a method for describing thick pre-salt volcanic rocks is provided, the method comprising:

[0007] Identify the source fractures of volcanic eruption channels; wherein the source fractures are used to control the development of volcanic rocks;

[0008] The source fractures of the volcanic eruption channels are analyzed and identified to determine the seismic facies data volume; wherein, the seismic facies data volume includes volcanic rock seismic facies data volume and non-volcanic rock seismic facies data volume;

[0009] Using the seismic facies data volume as input, seismic facies analysis is performed on the seismic facies data volume based on a pre-determined neural network to obtain the volcanic rock facies data volume;

[0010] Seismic inversion was performed on the volcanic rock facies data volume to determine the three-dimensional spatial distribution of the volcanic rocks.

[0011] According to another aspect of the present invention, a device for describing massive pre-salt volcanic rocks is provided, the device comprising:

[0012] A volcanic eruption channel determination module is used to determine the source fractures of volcanic eruption channels; wherein, the source fractures are used to control the development of volcanic rocks;

[0013] The seismic phase data volume determination module is used to analyze and identify the source fractures of the volcanic eruption channel and determine the seismic phase data volume; wherein, the seismic phase data volume includes volcanic rock seismic phase data volume and non-volcanic rock seismic phase data volume;

[0014] The volcanic facies data volume acquisition module is used to take the seismic facies data volume as input, perform seismic facies analysis on the seismic facies data volume based on a pre-determined neural network, and obtain the volcanic facies data volume.

[0015] The module for determining the three-dimensional spatial distribution of volcanic rocks is used to perform seismic inversion on the volcanic rock facies data volume to determine the three-dimensional spatial distribution of volcanic rocks.

[0016] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0017] At least one processor; and

[0018] A memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform a method for describing thick pre-salt volcanic rocks according to any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement a method for describing thick pre-salt volcanic rocks as described in any embodiment of the present invention.

[0021] The technical solution of this invention identifies the source fractures of volcanic eruption channels, analyzes and identifies these fractures to determine seismic facies data, and uses this data as input. Based on a pre-defined neural network, seismic facies analysis is performed on the seismic facies data to obtain volcanic rock facies data. Seismic inversion is then performed on this data to determine the three-dimensional spatial distribution of the volcanic rocks. This technical solution minimizes the ambiguity in predicting thick subsalt volcanic rocks, significantly improves the accuracy of volcanic rock characterization, and solves the problems of difficulty in characterizing thick subsalt volcanic rocks and the difficulty in predicting oil and gas reservoirs.

[0022] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of a method for describing a massive subsalt volcanic rock according to Embodiment 1 of the present invention;

[0025] Figure 2 This is a schematic diagram of the source fracture identification provided in Embodiment 1 of this application;

[0026] Figure 3 This is a schematic diagram of the volcanic rock facies boundary provided in Embodiment 1 of this application;

[0027] Figure 4 This is a schematic diagram of the planar distribution of volcanic rocks used in the embodiments of this application;

[0028] Figure 5 This is a schematic diagram of a device for describing extremely thick pre-salt volcanic rocks according to Embodiment 2 of the present invention;

[0029] Figure 6 This is a schematic diagram of the structure of an electronic device that implements a method for describing massive pre-salt volcanic rocks according to an embodiment of the present invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "primary," "secondary," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] Example 1

[0033] Figure 1 This is a flowchart of a method for describing a massive subsalt volcanic rock mass according to Embodiment 1 of the present invention. This embodiment is applicable to describing the distribution of massive subsalt volcanic rocks. The method can be executed by a device for describing massive subsalt volcanic rocks, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0034] S110, Determine the source fracture of the volcanic eruption channel.

[0035] In this embodiment, volcanic rock refers to small hills of different shapes formed by the hot gases, liquids, and solids ejected from the ground during a volcanic eruption. Because the magma ejected during the eruption contains gaseous debris and solid magma, the temperature and pressure drop rapidly, resulting in chemical and physical changes, and thus the magma becomes volcanic rock.

[0036] Among them, the source-connecting faults have the dual functions of source connection and reservoir control. The large-scale sand bodies developed in the high-energy water environment provide the basis for fracture development and dissolution. Local structures control the formation of complex fracture networks. The three factors together control the development of fractured reservoirs.

[0037] In this scheme, volcanic eruptions exhibit strong randomness, which is the fundamental reason for the difficulty in predicting and characterizing volcanic rocks. However, the distribution of volcanic rocks is not entirely without pattern. Following the basic logic of source control, finding the source of volcanic rocks significantly reduces the ambiguity of volcanic rock predictions. According to the source control approach, all volcanic rocks erupt along deep, large faults. Finding the source-conduit faults of deep volcanic rocks reveals the distribution area. Therefore, the distribution of volcanic rocks can be determined by identifying faults that can form eruption channels. These faults are called source-conduit faults. By conducting hierarchical and categorized interpretations of faults, identifying the source-conduit faults that can form eruption channels among numerous different types and levels of faults allows for the determination of the volcanic rock distribution.

[0038] Optionally, identifying the source fractures of volcanic eruption conduits includes:

[0039] Using seismic data interpretation and seismic data correlation techniques, faults in the target area are identified to determine the source faults of volcanic eruption channels.

[0040] Seismic data interpretation involves transforming the seismic data obtained from seismic exploration into an understanding of the underground geological conditions of the exploration area.

[0041] In this scheme, seismic data-related technologies may include seismic inversion, pre-stack time migration, noise cancellation, etc., which are not specifically limited in this embodiment.

[0042] Specifically, seismic data interpretation and related technologies are used to identify all faults in the target area. Then, based on the geological conditions of the target area, fault displacement, longitudinal scale, and transverse scale are comprehensively considered to classify the faults in the entire area. Faults with large displacement, longitudinal scale, and transverse scale are classified as through-source faults; faults with small displacement, longitudinal scale, and transverse scale are classified as non-through-source faults.

[0043] Furthermore, the identification and characterization of volcanic rocks only revolve around the Tongyuan Fault, while other areas are considered to be devoid of volcanic rock distribution, and no prediction or characterization of volcanic rocks is carried out.

[0044] Identifying the source fractures in volcanic eruption channels can significantly reduce the ambiguity of volcanic rock predictions.

[0045] In this plan, Figure 2 This is a schematic diagram of the source fracture identification provided in Embodiment 1 of this application, as shown below. Figure 2 As shown, by identifying the fractures in the target area, the source fractures of the volcanic eruption channel can be determined, which facilitates the prediction and characterization of the distribution of volcanic rocks.

[0046] S120. Analyze and identify the source fractures of the volcanic eruption channel to determine the seismic phase data volume; wherein, the seismic phase data volume includes volcanic rock seismic phase data volume and non-volcanic rock seismic phase data volume.

[0047] Among them, seismic facies is the sum of sedimentary facies as shown on a seismic profile. It is a seismic feature formed by the sedimentary environment and refers to a seismic reflection unit within a certain area.

[0048] In this scheme, a stepwise iterative algorithm can be used to analyze and identify the source fractures of volcanic eruption channels, and establish a seismic facies data volume of volcanic rocks.

[0049] Optionally, the source fractures of the volcanic eruption conduit are analyzed and identified to determine the seismic facies data volume, including:

[0050] The source fractures of the volcanic eruption channel are analyzed and identified using a pre-defined machine learning algorithm to determine the seismic phase data volume.

[0051] Specifically, the process begins by establishing easily identifiable volcanic seismic facies data around the source fault, and non-volcanic seismic facies data at locations far from the source fault. These two sets of data are combined to create primary label data. Using the labels from the primary label data as samples, machine learning is used to identify more volcanic and non-volcanic seismic facies data. The machine learning results are then merged with the samples from the primary label data to create secondary label data. Using the labels from the secondary label data as samples, machine learning continues to identify more difficult seismic facies. Based on the machine learning results and the samples from the secondary label data, tertiary label data is created. This process is iterated until the final seismic facies data volume is established.

[0052] S130. Using the seismic facies data volume as input, perform seismic facies analysis on the seismic facies data volume based on a pre-determined neural network to obtain a volcanic rock facies data volume.

[0053] Among them, artificial neural networks are the core of deep learning algorithms. They are inspired by neurons in the human brain, mimicking the way biological neurons transmit signals to each other to achieve learning. Optionally, the neural network can be a CNN (Convolutional Neural Network).

[0054] Specifically, seismic facies data volumes can be used as input, and CNN-based machine learning can be applied to train the seismic data volumes in three-dimensional space. This allows for the rapid conversion of seismic data volumes into lithofacies data volumes, with the boundaries of the volcanic lithofacies data volumes representing the approximate outline of the volcanic lithofacies distribution.

[0055] S140. Perform seismic inversion on the volcanic rock facies data volume to determine the three-dimensional spatial distribution of volcanic rocks.

[0056] Seismic inversion is a process of imaging (solving) the spatial structure and physical properties of underground rock strata using surface seismic observation data and constrained by known geological laws and drilling and logging data.

[0057] In this scheme, seismic inversion of volcanic rock facies data volume can yield the accurate boundaries of volcanic rock bodies and the three-dimensional spatial distribution of different types of volcanic rocks.

[0058] Optionally, seismic inversion is performed on the volcanic rock facies data volume to determine the three-dimensional spatial distribution of the volcanic rocks, including:

[0059] Seismic inversion is performed on the volcanic lithofacies data volume to determine the seismic impedance data volume; and rock physics analysis is performed on the volcanic lithofacies data volume to determine the volcanic lithofacies boundaries.

[0060] Based on the seismic impedance data volume and volcanic rock facies boundaries, the three-dimensional spatial distribution of volcanic rocks is determined.

[0061] When a seismic wave propagates in a medium, the ratio of the pressure acting on a certain area to the flow rate of particles passing perpendicularly through that area per unit time (i.e., area multiplied by particle vibration velocity) has the meaning of resistance and is called wave impedance.

[0062] In this scheme, the distribution of different types of volcanic rocks can be determined based on the seismic impedance data volume. After determining the seismic impedance data volume and the volcanic rock facies boundary, the three-dimensional spatial distribution of volcanic rocks can be characterized.

[0063] The volcanic rock characterization is achieved step by step by following the sequence of volcanic rock source fractures, volcanic rock facies, volcanic rock facies boundaries, and wave impedance. A novel approach of stepwise phase control is used in volcanic rock prediction, which minimizes the ambiguity in the prediction of thick subsalt volcanic rocks, significantly improves the accuracy of volcanic rock characterization, and solves the problems of high difficulty in characterizing thick subsalt volcanic rocks and difficulty in predicting oil and gas reservoirs.

[0064] In this embodiment, Figure 3 This is a schematic diagram of the volcanic rock facies boundary provided in Embodiment 1 of this application, as shown below. Figure 3 As shown, by performing rock physics analysis on the volcanic rock facies data volume, the boundaries of the volcanic rock facies can be determined, that is, the approximate outline of the distribution of the volcanic rock facies can be determined.

[0065] Optionally, based on the seismic impedance data volume and volcanic rock facies boundaries, the three-dimensional spatial distribution of volcanic rocks is determined, including:

[0066] Based on the seismic impedance data, the distribution of wave impedance within different lithofacies is determined;

[0067] Based on the distribution of wave impedance within different rock facies and the boundaries of volcanic rock facies, the three-dimensional spatial distribution of volcanic rocks is determined.

[0068] Specifically, under the constraint of the lithofacies body, seismic inversion is carried out to obtain seismic impedance data volume and obtain the distribution of wave impedance in different lithofacies bodies; based on the results of rock physics analysis, the boundaries of volcanic lithofacies can be further refined; at the same time, the wave impedance in different lithofacies bodies is transformed into different sub-phases, and finally the accurate boundaries of volcanic rock bodies and the three-dimensional spatial distribution of different types of volcanic rocks can be obtained.

[0069] By using volcanic facies boundaries as constraints, inversion calculations are performed only within the volcanic facies boundaries, excluding areas outside the boundary outlines. Finally, the facies-controlled inversion results are analyzed, and based on the distribution range of impedance of different types of volcanic rocks, the spatial distribution and internal structure of volcanic rocks are determined more accurately, enabling the prediction and characterization of volcanic rocks.

[0070] In this plan, Figure 4 This is a schematic diagram of the planar distribution of volcanic rocks used in an embodiment of this application, as shown below. Figure 4 As shown, after obtaining the accurate boundaries of the volcanic rock mass and the three-dimensional spatial distribution of different types of volcanic rocks, the distribution of volcanic rocks can be characterized.

[0071] The technical solution of this invention identifies the source fractures of volcanic eruption channels, analyzes and identifies these fractures to determine seismic facies data, and uses this data as input. Based on a pre-defined neural network, seismic facies analysis is performed to obtain volcanic rock facies data. Seismic inversion is then performed on this data to determine the three-dimensional spatial distribution of volcanic rocks. This technical solution progressively characterizes volcanic rocks by following the sequence of source fractures, volcanic rock facies, volcanic rock facies boundaries, and wave impedance. It utilizes a novel step-by-step phase control approach in volcanic rock prediction, minimizing the ambiguity in predicting thick subsalt volcanic rocks, significantly improving the accuracy of volcanic rock characterization, and solving the problems of difficulty in characterizing thick subsalt volcanic rocks and predicting oil and gas reservoirs.

[0072] Example 2

[0073] Figure 5 This is a schematic diagram of a device for describing extremely thick pre-salt volcanic rock according to Embodiment 2 of the present invention. Figure 5 As shown, the device includes:

[0074] The volcanic eruption channel determination module 510 is used to determine the source fractures of the volcanic eruption channel; wherein, the source fractures are used to control the development of volcanic rocks;

[0075] The seismic phase data volume determination module 520 is used to analyze and identify the source fractures of the volcanic eruption channel and determine the seismic phase data volume; wherein, the seismic phase data volume includes volcanic rock seismic phase data volume and non-volcanic rock seismic phase data volume;

[0076] The volcanic rock facies data volume obtaining module 530 is used to take the seismic facies data volume as input, perform seismic facies analysis on the seismic facies data volume based on a pre-determined neural network, and obtain the volcanic rock facies data volume;

[0077] The volcanic rock three-dimensional spatial distribution determination module 540 is used to perform seismic inversion on the volcanic rock facies data volume to determine the three-dimensional spatial distribution of the volcanic rocks.

[0078] Optional, the volcanic eruption channel determination module 510 is specifically used for:

[0079] Using seismic data interpretation and seismic data correlation techniques, faults in the target area are identified to determine the source faults of volcanic eruption channels.

[0080] Optional, the seismic phase data volume determination module 520 is specifically used for:

[0081] The source fractures of the volcanic eruption channel are analyzed and identified using a pre-defined machine learning algorithm to determine the seismic phase data volume.

[0082] Optionally, the three-dimensional spatial distribution determination module 540 for volcanic rocks includes:

[0083] The volcanic facies boundary determination unit is used to perform seismic inversion on the volcanic facies data volume to determine the seismic impedance data volume; and to perform rock physical analysis on the volcanic facies data volume to determine the volcanic facies boundary.

[0084] The three-dimensional spatial distribution determination unit of volcanic rocks is used to determine the three-dimensional spatial distribution of volcanic rocks based on the seismic impedance data volume and the volcanic rock facies boundary.

[0085] Optionally, the three-dimensional spatial distribution determination unit of volcanic rocks is specifically used for:

[0086] Based on the seismic impedance data, the distribution of wave impedance within different lithofacies is determined;

[0087] Based on the distribution of wave impedance within different rock facies and the boundaries of volcanic rock facies, the three-dimensional spatial distribution of volcanic rocks is determined.

[0088] The device for describing thick subsalt volcanic rocks provided in this embodiment of the invention can execute the method for describing thick subsalt volcanic rocks provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0089] Example 3

[0090] Figure 6 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0091] like Figure 6 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0092] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0093] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as a method for describing massive pre-salt volcanic rocks.

[0094] In some embodiments, a method for describing massive pre-salt volcanic rocks can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for describing massive pre-salt volcanic rocks described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform a method for describing massive pre-salt volcanic rocks by any other suitable means (e.g., by means of firmware).

[0095] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0096] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0097] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0098] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0099] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0100] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0101] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0102] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for describing extremely thick pre-salt volcanic rocks, characterized in that, include: Identify the source fractures of volcanic eruption channels; wherein the source fractures are used to control the development of volcanic rocks; The source fractures of the volcanic eruption channels are analyzed and identified to determine the seismic facies data volume; wherein, the seismic facies data volume includes volcanic rock seismic facies data volume and non-volcanic rock seismic facies data volume; Using the seismic facies data volume as input, seismic facies analysis is performed on the seismic facies data volume based on a pre-determined neural network to obtain the volcanic rock facies data volume; Seismic inversion was performed on the volcanic rock facies data volume to determine the three-dimensional spatial distribution of the volcanic rocks; The analysis and identification of the source fractures in the volcanic eruption channels, and the determination of seismic phase data volumes, include: The source fractures of the volcanic eruption channel are analyzed and identified using a pre-defined machine learning algorithm to determine the seismic phase data volume; The process involves several steps. First, easily identifiable volcanic seismic facies data are established around the source fault. Second, non-volcanic seismic facies data are established at locations far from the source fault. These two sets of data are combined to create primary label data. Using the labels from the primary label data as samples, machine learning is used to identify more volcanic and non-volcanic seismic facies data. The machine learning results are then merged with the samples from the primary label data to create secondary label data. Using the labels from the secondary label data as samples, machine learning continues to identify more difficult seismic facies. Finally, based on the machine learning results and the samples from the secondary label data, tertiary label data is created. This process is iterated until the final seismic facies data volume is established.

2. The method according to claim 1, characterized in that, Determining the source fractures of volcanic eruption channels, including: Using seismic data interpretation and seismic data correlation techniques, faults in the target area are identified to determine the source faults of volcanic eruption channels.

3. The method according to claim 1, characterized in that, Seismic inversion is performed on the volcanic rock facies data volume to determine the three-dimensional spatial distribution of the volcanic rocks, including: Seismic inversion is performed on the volcanic lithofacies data volume to determine the seismic impedance data volume; and rock physics analysis is performed on the volcanic lithofacies data volume to determine the volcanic lithofacies boundaries. Based on the seismic impedance data volume and volcanic rock facies boundaries, the three-dimensional spatial distribution of volcanic rocks is determined.

4. The method according to claim 3, characterized in that, Based on the seismic impedance data volume and volcanic rock facies boundaries, the three-dimensional spatial distribution of volcanic rocks is determined, including: Based on the seismic impedance data, the distribution of wave impedance within different lithofacies is determined; Based on the distribution of wave impedance within different rock facies and the boundaries of volcanic rock facies, the three-dimensional spatial distribution of volcanic rocks is determined.

5. A device for describing extremely thick pre-salt volcanic rocks, characterized in that, include: A volcanic eruption channel determination module is used to determine the source fractures of volcanic eruption channels; wherein, the source fractures are used to control the development of volcanic rocks; The seismic phase data volume determination module is used to analyze and identify the source fractures of the volcanic eruption channel and determine the seismic phase data volume; wherein, the seismic phase data volume includes volcanic rock seismic phase data volume and non-volcanic rock seismic phase data volume; The volcanic facies data volume acquisition module is used to take the seismic facies data volume as input, perform seismic facies analysis on the seismic facies data volume based on a pre-determined neural network, and obtain the volcanic facies data volume. The module for determining the three-dimensional spatial distribution of volcanic rocks is used to perform seismic inversion on the volcanic rock facies data volume to determine the three-dimensional spatial distribution of volcanic rocks. The seismic phase data volume determination module is specifically used for: The source fractures of the volcanic eruption channel are analyzed and identified using a pre-defined machine learning algorithm to determine the seismic phase data volume; The process involves several steps. First, easily identifiable volcanic seismic facies data are established around the source fault. Second, non-volcanic seismic facies data are established at locations far from the source fault. These two sets of data are combined to create primary label data. Using the labels from the primary label data as samples, machine learning is used to identify more volcanic and non-volcanic seismic facies data. The machine learning results are then merged with the samples from the primary label data to create secondary label data. Using the labels from the secondary label data as samples, machine learning continues to identify more difficult seismic facies. Finally, based on the machine learning results and the samples from the secondary label data, tertiary label data is created. This process is iterated until the final seismic facies data volume is established.

6. The apparatus according to claim 5, characterized in that, The volcanic eruption conduit determination module is specifically used for: Using seismic data interpretation and seismic data correlation techniques, faults in the target area are identified to determine the source faults of volcanic eruption channels.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform a method for describing massive pre-salt volcanic rocks according to any one of claims 1-4.

8. A computer-readable medium, characterized in that, The computer-readable medium stores computer instructions that cause a processor to execute and implement the method for describing thick pre-salt volcanic rocks according to any one of claims 1-4.

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