A method and device for inverting saturation by seismic-electric fusion, electronic equipment and medium

Abstract

The application discloses a method and device for seismic-electromagnetic fusion inversion of saturation, electronic equipment and medium. The method comprises the following steps: time-depth conversion is performed on time-domain seismic data to obtain depth-domain seismic data; the depth-domain seismic data and depth-domain resistivity data are matched to obtain depth-domain resistivity data in a target data format; time-depth conversion is performed on the depth-domain resistivity data in the target data format to obtain time-domain resistivity data; target seismic wavelets are extracted from the time-domain seismic data; data conversion is performed on the time-domain resistivity data to obtain wave impedance data, and a wave impedance low-frequency model is determined according to the wave impedance data; seismic-electromagnetic fusion inversion is performed according to the target seismic wavelets and the wave impedance low-frequency model to obtain a seismic-electromagnetic fusion inversion result; and the seismic-electromagnetic fusion inversion result comprises hydrate saturation data. The application can predict more accurate hydrate saturation through fusion inversion of electromagnetic data and seismic data, and can be widely applied to the technical field of geophysical exploration.

Description

Technical Field

[0001] This application relates to the field of geophysical exploration technology, and in particular to a method, apparatus, electronic device and medium for seismic-electric fusion inversion saturation. Background Technology

[0002] Natural gas hydrates are among the most promising unconventional natural gas resources, currently untapped and possessing immense potential. They represent a promising next-generation energy source after shale gas, coalbed methane, and tight gas, and numerous hydrate exploration and development areas exist globally. Hydrate exploration and evaluation are fundamental to hydrate development. While seismic exploration systems have achieved significant success in identifying hydrates, they still face challenges in accurately evaluating physical properties. Seismic exploration systems can only obtain information on relative differences at stratigraphic interfaces, leading to inaccurate acquisition of physical properties such as saturation.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The embodiments of this application aim to at least partially solve one of the technical problems in the related art. Therefore, the main objective of the embodiments of this application is to propose a method, apparatus, electronic device, and medium for seismic-electromagnetic fusion inversion of saturation, which can predict more accurate hydrate saturation through the fusion inversion of electromagnetic and seismic data in the absence of well logging data, greatly improving the success rate of exploration.

[0005] To achieve the above objectives, one aspect of this application proposes a method for seismoelectric fusion inversion saturation, the method comprising the following steps:

[0006] Acquire pre-drilling seismic electrical data; the pre-drilling seismic electrical data includes time-domain seismic data and depth-domain resistivity data;

[0007] The time-domain seismic data is subjected to time-depth conversion to obtain depth-domain seismic data;

[0008] The depth-domain seismic data and the depth-domain resistivity data are subjected to seismoelectric matching processing to obtain depth-domain resistivity data with the target data format.

[0009] The depth domain resistivity data with the target data format is subjected to time-depth conversion processing to obtain time domain resistivity data;

[0010] Wavelet extraction processing is performed on the time-domain seismic data to obtain the target seismic wavelet;

[0011] The time-domain resistivity data is converted to obtain wave impedance data, and a low-frequency wave impedance model is determined based on the wave impedance data.

[0012] Seismic-electric fusion inversion is performed based on the target seismic wavelet and the wave impedance low-frequency model to obtain seismic-electric fusion inversion results; the seismic-electric fusion inversion results include hydrate saturation data.

[0013] In some embodiments, the time-depth conversion processing of the time-domain seismic data to obtain depth-domain seismic data includes:

[0014] The time-domain seismic data is processed to obtain time-domain wave velocity data; wherein, the time-domain wave velocity data is used to treat the strata of corresponding thickness at each sampling interval in the seismic profile as sub-layers.

[0015] The root mean square velocity of each of the sub-layers is calculated using the root mean square velocity formula.

[0016] The layer velocity of each sub-layer is calculated based on the root mean square velocity of each sub-layer and the layer velocity formula.

[0017] Calculate the layer thickness of each of the sub-layers based on the layer velocity of each sub-layer;

[0018] The layer thicknesses of each sub-layer are accumulated and calculated according to the top-down time-depth conversion rule to obtain interface depth data, and the interface depth data is used as the depth domain seismic data.

[0019] In some embodiments, performing seismoelectric matching processing on the depth-domain seismic data and the depth-domain resistivity data to obtain depth-domain resistivity data with a target data format includes:

[0020] Based on the sampling interval of the depth domain seismic data, the depth domain resistivity data is subjected to encrypted sampling processing to obtain target sampling depth domain resistivity data; wherein, the sampling interval of the target sampling depth domain resistivity data is consistent with the sampling interval of the depth domain seismic data;

[0021] According to the target data format corresponding to the depth domain seismic data, the target sampled depth domain resistivity data is formatted to obtain the depth domain resistivity data with the target data format.

[0022] In some embodiments, performing time-depth conversion on the depth-domain resistivity data having the target data format to obtain time-domain resistivity data includes:

[0023] Based on the layer velocity and layer thickness corresponding to each of the sub-layers, the depth domain resistivity data with the target data format is subjected to time-depth conversion processing using a time-depth conversion formula to obtain the time domain resistivity data.

[0024] In some embodiments, the step of performing wavelet extraction processing on the time-domain seismic data to obtain the target seismic wavelet includes:

[0025] Based on the spectral characteristics of the time-domain seismic data, a predefined wavelet is determined;

[0026] Based on the predefined wavelet and well logging data, an initial synthetic seismic record is generated;

[0027] The target strata in the initial synthetic seismic record are calibrated to obtain candidate synthetic seismic records;

[0028] The predefined wavelet is updated based on the candidate synthetic seismic records and the time-domain seismic data to obtain candidate seismic wavelets;

[0029] If the correlation between the candidate synthetic seismic record and the time-domain seismic data does not meet the preset correlation threshold, then the candidate seismic wavelet is used as the predefined wavelet, and the process returns to the step of generating an initial synthetic seismic record based on the predefined wavelet and well logging data, until the correlation between the candidate synthetic seismic record and the time-domain seismic data meets the preset correlation threshold, and the candidate synthetic seismic record that meets the preset correlation threshold is used as the target synthetic seismic record.

[0030] Based on the target synthetic seismic record and the time-domain seismic data, the candidate seismic wavelet corresponding to the target synthetic seismic record is updated to obtain the target seismic wavelet.

[0031] In some embodiments, the step of performing data conversion processing on the time-domain resistivity data to obtain wave impedance data, and determining a low-frequency wave impedance model based on the wave impedance data, includes:

[0032] The time-domain resistivity data were converted and processed using the Alzer formula to obtain hydrate saturation profile data.

[0033] The hydrate saturation profile data is converted and processed according to the physical relationship of the reservoir rocks to obtain the wave impedance data, and the wave impedance low-frequency model is determined based on the wave impedance data.

[0034] In some embodiments, the step of performing data conversion processing on the hydrate saturation profile data based on reservoir rock physical relationships to obtain the wave impedance data, and determining the wave impedance low-frequency model based on the wave impedance data, includes:

[0035] Based on the physical relationship of the reservoir rocks, the hydrate saturation profile data were analyzed and processed to obtain the bulk modulus and shear modulus of the hydrate-bearing sediments.

[0036] The density of the hydrate-containing sediments was calculated based on the hydrate saturation of the hydrate saturation profile data.

[0037] Calculate the P-wave and S-wave velocities of the hydrate-bearing sediment based on the bulk modulus, the shear modulus, and the density;

[0038] Based on the longitudinal and transverse wave velocities and the density of the hydrate-bearing sediment, the wave impedance data corresponding to the hydrate-bearing sediment is calculated, and the wave impedance low-frequency model is determined based on the wave impedance data.

[0039] To achieve the above objectives, another aspect of this application proposes an apparatus for seismoelectric fusion inversion saturation, the apparatus comprising the following modules:

[0040] The pre-drilling seismic-electric data acquisition module is used to acquire pre-drilling seismic-electric data; the pre-drilling seismic-electric data includes time-domain seismic data and depth-domain resistivity data;

[0041] The first time-depth conversion processing module is used to perform time-depth conversion processing on the time-domain seismic data to obtain depth-domain seismic data.

[0042] The seismoelectric matching processing module is used to perform seismoelectric matching processing on the depth domain seismic data and the depth domain resistivity data to obtain depth domain resistivity data with the target data format.

[0043] The second time-depth conversion processing module is used to perform time-depth conversion processing on the depth domain resistivity data having the target data format to obtain time domain resistivity data.

[0044] The wavelet extraction and processing module is used to perform wavelet extraction processing on the time-domain seismic data to obtain the target seismic wavelet;

[0045] The wave impedance low-frequency model construction module is used to perform data conversion processing on the time domain resistivity data to obtain wave impedance data, and determine the wave impedance low-frequency model based on the wave impedance data.

[0046] The seismoelectric fusion inversion module is used to perform seismoelectric fusion inversion based on the target seismic wavelet and the wave impedance low-frequency model to obtain seismoelectric fusion inversion results; the seismoelectric fusion inversion results include hydrate saturation data.

[0047] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0048] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described above.

[0049] The embodiments of this application include at least the following beneficial effects: This application provides a method, apparatus, electronic device, and medium for seismoelectric fusion inversion of saturation. This scheme acquires pre-drilling seismoelectric data; the pre-drilling seismoelectric data includes time-domain seismic data and depth-domain resistivity data; the time-domain seismic data undergoes time-depth conversion processing to obtain depth-domain seismic data; the depth-domain seismic data and depth-domain resistivity data undergo seismoelectric matching processing to obtain depth-domain resistivity data with a target data format; the depth-domain resistivity data with the target data format undergoes time-depth conversion processing to obtain time-domain resistivity data; the time-domain seismic data undergoes wavelet extraction processing to obtain a target seismic wavelet; the time-domain resistivity data undergoes data conversion processing to obtain wave impedance data, and a wave impedance low-frequency model is determined based on the wave impedance data; seismoelectric fusion inversion is performed based on the target seismic wavelet and the wave impedance low-frequency model to obtain the seismoelectric fusion inversion result; the seismoelectric fusion inversion result includes hydrate saturation data. This application embodiment converts low-frequency electromagnetic data into wave impedance data by utilizing the relationship between resistivity, hydrate saturation, and wave impedance. Based on the wave impedance data, a low-frequency wave impedance model is determined, and then this low-frequency model is used for seismic inversion. This allows for the acquisition of more accurate elastic parameter information such as hydrate saturation. Furthermore, by fusing electromagnetic and seismic data, the seismic inversion results not only reflect the characterization of the background field by electromagnetic data but also retain the advantages of seismic data in vertical resolution. Therefore, this application embodiment can predict more accurate hydrate saturation through the fusing of electromagnetic and seismic data in the absence of well logging data, greatly improving the success rate of exploration. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating the steps of a method for seismoelectric fusion inversion saturation provided in an embodiment of this application;

[0051] Figure 2 yes Figure 1 The flowchart of step S106 in the process;

[0052] Figure 3 yes Figure 2 The flowchart of step S202 in the document;

[0053] Figure 4 This is a schematic diagram of the overall process of a method for seismoelectric fusion inversion saturation provided in an embodiment of this application;

[0054] Figure 5 This is a schematic diagram of a wave impedance profile converted from electromagnetic data, provided in an embodiment of this application.

[0055] Figure 6 This is a schematic diagram of a seismic inversion result provided in an embodiment of this application;

[0056] Figure 7 This is a schematic diagram of a seismoelectric fusion inversion result provided in an embodiment of this application;

[0057] Figure 8 This is a schematic diagram comparing the seismoelectric fusion inversion saturation and well logging saturation provided in an embodiment of this application;

[0058] Figure 9 This is a schematic diagram of the structure of a device for seismoelectric fusion inversion saturation provided in an embodiment of this application;

[0059] Figure 10 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0061] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0062] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0064] Natural gas hydrate resources have enormous potential and represent an important alternative energy source for the future. Currently, seismic technology is primarily used for resource exploration. However, long-term exploration experience has shown that seismic technology alone cannot accurately obtain physical properties such as saturation. A core issue is the lack of low-frequency data in the seismic data band. The low-frequency information required for inversion can only come from the seismic data itself, and low frequencies are crucial for predicting physical properties through seismic inversion because they provide background trends. Seismic technology can only obtain relative differences at stratigraphic interfaces, leading to unsatisfactory inversion results. In recent years, researchers have attempted to combine electromagnetic and seismic data for pre-drilling prediction of physical properties. However, current techniques have not truly integrated the two datasets organically. Currently, electromagnetic and seismic data are inverted separately and then compared and constrained, which also fails to accurately obtain physical properties such as saturation.

[0065] For example, seismic inversion is one of the most widely used and effective methods for predicting hydrate physical parameters. However, the construction of the low-frequency model is crucial in seismic impedance inversion. Seismic data provides the impedance difference above and below the interface, while the low-frequency model provides the background field. Understandably, assuming the impedance value at a certain depth is IP1, which is the impedance of the normal formation (considered the background value), and below this depth is the hydrate layer with an impedance value of IP2, then the seismic data reflects the difference between IP2 and IP1. To obtain the accurate impedance value IP2 of the hydrate layer, the impedance value IP1 needs to be added to this difference. Currently, the most accurate and commonly used low-frequency model construction utilizes well logging data interpolation and then extracts its low-frequency component. However, well logging data is often lacking before drilling. Therefore, the low-frequency model for seismic inversion mainly comes from the low-frequency velocity field obtained during seismic data migration or full waveform processing. However, due to the lack of a low-frequency band in seismic data, the obtained low-frequency velocity field has significant uncertainties, leading to unsatisfactory inversion results. In addition, electromagnetic exploration is receiving increasing attention in both onshore and offshore oil and gas exploration, and is considered an effective auxiliary means of seismic exploration. Currently, the most widely used methods in oil and gas exploration are magnetotelluric (MT) and marine controlled-source electromagnetic methods. Electromagnetic Spectroscopy (MCSEM) methods differ in that the Medium-Range Electromagnetic (MT) method is primarily based on low-frequency induced electromagnetic fields, thus it cannot effectively image relatively thin oil and gas reservoirs. MCSEM, on the other hand, uses an artificial source, typically a horizontal electric dipole. Since electromagnetic signals are highly sensitive to high-resistivity oil and gas reservoirs, reservoir rock physics models can link reservoir resistivity with porosity and fluid saturation. Therefore, when seismic data has established the reservoir's structural framework, MCSEM can be used to predict the fluid type and saturation within the reservoir, reducing exploration risks. The fundamental equations controlling the propagation of seismic and electromagnetic waves are the wave equation and the diffusion equation, respectively. Although both types of waves exhibit attenuation and scattering, their degrees of attenuation and scattering differ; that is, seismic waves exhibit relatively weaker attenuation and scattering. Therefore, electromagnetic exploration has relatively low resolution.

[0066] In view of this, this application provides a method, apparatus, electronic device, and medium for seismoelectric fusion inversion of saturation. This scheme involves acquiring pre-drilling seismoelectric data, including time-domain seismic data and depth-domain resistivity data; performing time-depth conversion on the time-domain seismic data to obtain depth-domain seismic data; performing seismoelectric matching on the depth-domain seismic data and depth-domain resistivity data to obtain depth-domain resistivity data with a target data format; performing time-depth conversion on the depth-domain resistivity data with the target data format to obtain time-domain resistivity data; performing wavelet extraction on the time-domain seismic data to obtain a target seismic wavelet; performing data conversion on the time-domain resistivity data to obtain wave impedance data, and determining a low-frequency wave impedance model based on the wave impedance data; performing seismoelectric fusion inversion based on the target seismic wavelet and the low-frequency wave impedance model to obtain the seismoelectric fusion inversion result; the seismoelectric fusion inversion result includes hydrate saturation data. This application embodiment converts low-frequency electromagnetic data into wave impedance data by utilizing the relationship between resistivity, hydrate saturation, and wave impedance. Based on the wave impedance data, a low-frequency wave impedance model is determined, and then this low-frequency model is used for seismic inversion. This allows for the acquisition of more accurate elastic parameter information such as hydrate saturation. Furthermore, by fusing electromagnetic and seismic data, the seismic inversion results not only reflect the characterization of the background field by electromagnetic data but also retain the advantages of seismic data in vertical resolution. Therefore, this application embodiment can predict more accurate hydrate saturation through the fusing of electromagnetic and seismic data in the absence of well logging data, greatly improving the success rate of exploration.

[0067] This application provides a method for seismic-electric fusion inversion saturation retrieval, relating to the field of geophysical exploration technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or vehicle terminal, but is not limited thereto. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing a seismic-electric fusion inversion saturation retrieval method, but is not limited to the above forms.

[0068] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframes, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0069] Please see Figure 1 , Figure 1 This is an optional flowchart of a method for seismoelectric fusion inversion saturation provided in an embodiment of this application. Figure 1 The method may include, but is not limited to, steps S101 to S107.

[0070] Step S101: Obtain pre-drilling seismic data; the pre-drilling seismic data includes time-domain seismic data and depth-domain resistivity data;

[0071] Pre-drilling seismoelectric data refers to comprehensive geophysical data obtained through methods such as seismic exploration and resistivity exploration before drilling work begins. Pre-drilling seismoelectric data reflects various physical characteristics of the subsurface medium and provides important basis for subsequent geological interpretation and resource assessment.

[0072] Optionally, pre-drilling seismoelectric data includes time-domain seismic data and depth-domain resistivity data. Time-domain seismic data represents the information on the changes in seismic waves over time recorded during seismic exploration. Specifically, when seismic waves propagate underground, they encounter interfaces between strata of different lithologies and densities, resulting in reflection and refraction. These reflected and refracted signals are received by seismic detectors installed on the surface or underground, and converted into electrical signals for recording, thus obtaining time-domain seismic data. This seismic data has a specific frequency band, mainly depending on the acquisition parameters, but it generally lacks low-frequency information, such as information below 3 Hz. Depth-domain resistivity data is obtained by establishing a correspondence between measured resistivity information and depth through resistivity exploration methods. Resistivity exploration is a method of probing underground geological conditions by utilizing the electrical differences in the underground medium. By supplying current underground and measuring the potential difference at different locations, the resistivity of the underground medium can be calculated. Resistivity data can also be referred to as electromagnetic data. It should be noted that the methods for acquiring time-domain seismic data and depth-domain resistivity data can refer to the specific implementation schemes in related technologies, and will not be elaborated here in the embodiments of this application.

[0073] Step S102: Perform time-depth conversion processing on the time-domain seismic data to obtain depth-domain seismic data;

[0074] In some embodiments, step S102 may include: performing data extraction processing on time-domain seismic data to obtain time-domain wave velocity data; wherein, the time-domain wave velocity data is used to take the strata of corresponding thickness at each sampling interval in the seismic profile as sub-layers; calculating the root mean square velocity of each sub-layer using the root mean square velocity formula; calculating the layer velocity of each sub-layer based on the root mean square velocity of each sub-layer and the layer velocity formula; calculating the layer thickness of each sub-layer based on the layer velocity of each sub-layer; accumulating the layer thickness of each sub-layer according to the top-down time-depth conversion rule to obtain interface depth data, and using the interface depth data as depth-domain seismic data.

[0075] Time-domain wave velocity data is information related to wave velocity extracted from time-domain seismic data. While time-domain seismic data records the changes in seismic waves over time, wave velocity data focuses on the velocity characteristics of seismic waves propagating in the subsurface medium. Time-domain wave velocity data reflects the propagation speed of seismic waves in different strata and serves as the basis for subsequent calculations of parameters such as root-mean-square velocity and layer velocity. Specifically, time-domain wave velocity data is used to classify strata of thickness corresponding to each sampling interval in a seismic profile as sub-layers. A sub-layer is a relatively small stratigraphic unit within a seismic profile, dividing the strata of thickness corresponding to each sampling interval. This sub-layer division facilitates a more detailed analysis of the velocity, thickness, and other characteristics of subsurface strata, as different sub-layers may possess different geological attributes and physical properties.

[0076] Interface depth data represents the depth of each stratigraphic interface relative to the Earth's surface. It is calculated by summing the thicknesses of each sublayer using a top-down time-depth conversion rule. Interface depth data is a crucial component of depth-domain seismic data, reflecting the depth, location, and spatial morphology of subsurface strata. Depth-domain seismic data, on the other hand, uses depth as the coordinate system. Unlike time-domain seismic data, depth-domain seismic data more intuitively displays the characteristics and distribution of subsurface strata along the depth direction, making it more significant for geological interpretation and resource assessment.

[0077] In practical implementation, time-depth conversion of seismic data is the process of transforming seismic data from the time domain to the depth domain, primarily relying on velocity information to reveal subsurface structural conditions. Specifically, the process of converting time-domain seismic data to depth-domain seismic data is as follows: First, time-domain wave velocity data is extracted from the time-domain seismic data. This time-domain wave velocity data is used to treat the strata corresponding to each sampling interval in the seismic profile as a small layer. Then, the root mean square velocity (RMS) formula is used to calculate the root mean square velocity of each small layer. The expression for the RMS velocity formula is as follows:

[0078]

[0079] Among them, v R,n The root mean square velocity at sampling point n is represented by Δt, which represents the sampling interval. s,i This represents the seismic stacking velocity at sampling point i (i = 1, 2, ..., n); then, based on the root mean square velocity of each sub-layer and combined with the layer velocity formula, the layer velocity v of each sub-layer is calculated. n The expression for the layer velocity formula is as follows:

[0080]

[0081] Among them, v n v represents the layer velocity of the nth layer. R,n and v R,n-1 Let t represent the root mean square velocity of the nth layer and the (n-1)th layer, respectively. n and t n-1 These represent the round-trip travel times for the nth and (n-1)th layers, respectively. Furthermore, the layer thickness of each sub-layer can be calculated based on its layer velocity. Finally, the layer thicknesses of each sub-layer are accumulated according to the top-down time-depth conversion rule to obtain the interface depth data. This interface depth data is then used as depth-domain seismic data. The formula for accumulating the layer thicknesses of each sub-layer is as follows:

[0082]

[0083] Among them, h n Let v represent the thickness of the nth layer, Δt represent the round-trip travel time difference between two adjacent layers, and v i This represents the layer velocity of the i-th layer.

[0084] Step S103: Perform seismoelectric matching processing on the depth domain seismic data and the depth domain resistivity data to obtain depth domain resistivity data with the target data format.

[0085] In some embodiments, step S103 may include: performing encrypted sampling processing on depth domain resistivity data according to the sampling interval of depth domain seismic data to obtain target sampling depth domain resistivity data; wherein the sampling interval of the target sampling depth domain resistivity data is consistent with the sampling interval of the depth domain seismic data; and performing formatting processing on the target sampling depth domain resistivity data according to the target data format corresponding to the depth domain seismic data to obtain depth domain resistivity data with the target data format.

[0086] Optionally, depth-domain resistivity data refers to resistivity data with depth as the coordinate, reflecting the electrical characteristics of the underground medium at different depths.

[0087] The encrypted sampling process is primarily used to adjust the sampling interval of the data to achieve the required resolution or match the sampling interval of other datasets. In this embodiment, to facilitate the fusion of resistivity data and seismic data, it is also necessary to correlate the resistivity data with the seismic data. This requires encrypted sampling of the resistivity data to ensure its sampling interval matches that of the seismic data. For example, assuming the seismic data sampling interval is 2 meters, the resistivity data is interpolated at 2-meter intervals. Optionally, the target sampling depth domain resistivity data is the depth domain resistivity data after encrypted sampling, and the sampling interval of the target sampling depth domain resistivity data is consistent with the sampling interval of the depth domain seismic data.

[0088] Seismoelectric matching is the process of matching depth-domain seismic data and depth-domain resistivity data. Its aim is to match the depth-domain seismic data and depth-domain resistivity data in terms of sampling intervals, data formats, etc., improving data compatibility and comparability, and providing a more accurate and reliable data foundation for comprehensive geological interpretation, thus facilitating subsequent comprehensive analysis and interpretation. In essence, seismoelectric data matching refers to adjusting the sampling interval and format of resistivity data to be consistent with seismic data, and then transferring the resistivity data (depth domain) to the time domain. Subsequent seismoelectric fusion and inversion are performed in the time domain.

[0089] The target data format is SGY, which is an abbreviation for SEG-Y. SEG-Y is a standard file format for storing seismic data, developed by the Society of Exploration Geophysicists (SEG). The SEG-Y format is a widely used standard format in seismic data processing, facilitating data storage, exchange, and processing.

[0090] In this embodiment of the application, since resistivity data does not have trace information, in order to make resistivity data consistent with seismic data, this embodiment of the application extracts the trace information and data storage format of seismic SGY data, and rewrites the marine electromagnetic resistivity data based on the trace information and data storage format of seismic SGY data to form a resistivity file based on SGY data format.

[0091] Step S104: Perform time-depth conversion processing on the depth domain resistivity data with the target data format to obtain time domain resistivity data.

[0092] In some embodiments, step S104 may include: performing time-depth conversion on the depth domain resistivity data with the target data format according to the layer velocity and layer thickness corresponding to each sub-layer, and obtaining time domain resistivity data.

[0093] In practical implementation, based on the seismic velocity, the corresponding electromagnetic data (depth-domain resistivity data with the target data format) is converted from the depth domain to the time domain. Specifically, based on the layer velocity and layer thickness corresponding to each sub-layer, a time-depth conversion formula is used to perform time-depth conversion processing on the depth-domain resistivity data with the target data format to obtain time-domain resistivity data. The specific conversion process is as follows: For each stratigraphic unit, the depth-domain resistivity data is converted into time-domain resistivity data using its layer velocity and layer thickness. The conversion formula used to perform time-depth conversion processing on the depth-domain resistivity data to obtain time-domain resistivity data is as follows:

[0094] t i =2h i / v i

[0095] Among them, t i h represents the round-trip travel time at the i-th level. i v represents the thickness of the i-th layer. i This represents the layer velocity of the i-th layer. In the specific implementation, the time-domain resistivity data of all stratigraphic units are summed to obtain the time-domain resistivity data corresponding to the entire depth-domain resistivity data.

[0096] Step S105: Perform wavelet extraction processing on the time-domain seismic data to obtain the target seismic wavelet;

[0097] In some embodiments, step S105 may include: determining a predefined wavelet based on the spectral characteristics of time-domain seismic data; generating an initial synthetic seismic record based on the predefined wavelet and well logging data; calibrating the target strata in the initial synthetic seismic record to obtain candidate synthetic seismic records; updating the predefined wavelet based on the candidate synthetic seismic records and time-domain seismic data to obtain candidate seismic wavelets; if the correlation between the candidate synthetic seismic record and the time-domain seismic data does not meet a preset correlation threshold, then using the candidate seismic wavelet as a predefined wavelet, returning to the step of generating an initial synthetic seismic record based on the predefined wavelet and well logging data, until the correlation between the candidate synthetic seismic record and the time-domain seismic data meets a preset correlation threshold, and using the candidate synthetic seismic record that meets the preset correlation threshold as the target synthetic seismic record; updating the candidate seismic wavelet corresponding to the target synthetic seismic record based on the target synthetic seismic record and time-domain seismic data to obtain the target seismic wavelet.

[0098] In practical implementation, wavelet extraction and synthetic record calibration are the primary steps in seismic inversion, and the calibration results directly affect the accuracy of the inversion results. This application embodiment achieves more accurate time-depth relationships and seismic wavelets through fine calibration of the strata. The fine reservoir calibration in this application embodiment mainly utilizes Jason inversion software. For the specific execution principle of Jason inversion software, please refer to the technical solutions in related technologies; this application embodiment will not elaborate further. Furthermore, the selection of inversion software can be based on actual application conditions, and this application embodiment does not impose any restrictions on this.

[0099] In this embodiment, the specific implementation process for extracting the seismic wavelet is as follows: First, based on the spectral characteristics (dominant frequency, length) of the time-domain seismic data, a Ricker wavelet is predefined. The Ricker wavelet is a zero-phase wavelet in the field of seismic exploration and signal processing. Then, using Jason inversion software, a synthetic seismic record is generated based on the predefined Ricker wavelet and well logging data. The synthetic seismic record will not be identical to the actual seismic record; therefore, it is necessary to adjust the key stratigraphic levels. Specifically, this embodiment uses the three most important and typical stratigraphic levels: the seafloor, the top of the natural gas hydrate layer, and the bottom interface. The marker layers are calibrated to ensure that the three layers in the synthetic seismic record are time-consistent with the three layers in the actual seismic profile. Based on this, the seismic wavelet is re-estimated according to the synthetic seismic record and time-domain seismic data, resulting in an updated seismic wavelet. Then, the Jason inversion software generates a new synthetic seismic record based on the updated seismic wavelet, and then readjusts the time-depth relationship of the aforementioned three key marker layers. This process is repeated until the synthetic seismic record and the well-side seismic data achieve the best match. Generally, a correlation between the synthetic seismic record and the seismic data of more than 80% is considered reasonable.

[0100] In step S105, the logging data required for wavelet extraction is longitudinal wave impedance data. However, the logging data here is not the logging data of the study area, but the logging data near the study area. Moreover, the logging data generally uses historical logging data from the vicinity of the study area.

[0101] It should be noted that the above process of extracting seismic wavelets is for a single location. To improve the applicability of seismic wavelets, wavelet extraction can be performed at multiple locations. By repeating the above steps for extracting seismic wavelets, seismic wavelets at different locations can be obtained. The final seismic wavelet is the average of the seismic wavelets at different locations.

[0102] Step S106: Perform data conversion processing on the time domain resistivity data to obtain wave impedance data, and determine the wave impedance low-frequency model based on the wave impedance data.

[0103] Please see Figure 2 , Figure 2 yes Figure 1 The flowchart of step S106 in the example; Figure 2 As shown, in some embodiments, step S106 includes, but is not limited to, steps S201 to S202:

[0104] Step S201: The time-domain resistivity data is converted using the Alzer formula to obtain hydrate saturation profile data.

[0105] In practical implementation, the resistivity is converted into a saturation profile using the Alzer formula, which is expressed as follows:

[0106]

[0107] Among them, S w Expressed as water saturation; hydrate saturation S h =1-S w ;R t Resistivity represented by electromagnetic data acquisition; R w The value is expressed as the resistivity of pure water and can be 0.3; a, m, and n are all empirical parameters obtained by fitting the resistivity of pure water with the resistivity of saturated water formations. In this embodiment, a = 1.1, m = 2.07, and n = 2.0. It is expressed as the total porosity of the sediment.

[0108] Step S202: The hydrate saturation profile data is converted and processed according to the physical relationship of the reservoir rocks to obtain the wave impedance data, and the wave impedance low-frequency model is determined based on the wave impedance data.

[0109] In some specific embodiments, please refer to Figure 3 , Figure 3 yes Figure 2 The flowchart of step S202 in the example; Figure 3 As shown, in some embodiments, step S202 includes, but is not limited to, steps S301 to S304:

[0110] Step S301: Based on the physical relationship of the reservoir rocks, perform data analysis and processing on the hydrate saturation profile data to obtain the bulk modulus and shear modulus of the hydrate-bearing sediments;

[0111] Step S302: Calculate the density of the hydrate-containing sediment based on the hydrate saturation of the hydrate saturation profile data;

[0112] Step S303: Calculate the longitudinal and transverse wave velocities of the hydrate-bearing sediment based on the bulk modulus, the shear modulus, and the density;

[0113] Step S304: Calculate the wave impedance data corresponding to the hydrate-bearing sediment based on the longitudinal and transverse wave velocities and the density of the hydrate-bearing sediment, and determine the wave impedance low-frequency model based on the wave impedance data.

[0114] The low-frequency model refers to the initial wave impedance model that needs to be constructed in seismic impedance inversion. In general seismic inversion techniques, this model mainly comes from the low-frequency range of well logging data or from seismic full waveform inversion. That is, seismic inversion in related technologies lacks low-frequency information, a problem that cannot be solved by seismic technology alone. However, in the embodiments of this application, based on the relationship between resistivity, hydrate saturation, and wave impedance, low-frequency electromagnetic data is converted into wave impedance data, and the wave impedance data is used as the low-frequency model for wave impedance inversion. This low-frequency model is then used for seismic inversion, making up for the deficiency of lacking low-frequency information in seismic inversion techniques. This application implements seismoelectric fusion inversion, which can predict hydrate saturation even without well logging data. That is, it can achieve pre-drilling prediction of hydrate saturation in areas without well logging data, providing technical support for new area exploration and greatly improving the success rate of exploration.

[0115] In practical implementation, the hydrate saturation profile data is transformed based on the reservoir rock physical relationship to obtain wave impedance data. Specifically, the saturation profile is converted into a wave impedance profile using the relationship between resistivity, hydrate saturation, and wave impedance. The specific calculation process for converting the saturation profile into a wave impedance profile is as follows:

[0116] Assume the hydrate saturation is S h Therefore, the three-phase Biot equation (TPBE) can be used for modeling. TPBE assumes an idealized arrangement in which hydrates, matrix, and pore fluids form three homogeneous and interconnected frameworks, each influencing the P-wave and S-wave velocities of the sediment. Thus, it is necessary to calculate the P-wave and S-wave velocities of the formation. P-wave and S-wave velocities refer to the velocities of the formation and are inherent properties of the formation. The specific process for calculating the wave impedance data is as follows:

[0117] First, calculate the bulk modulus and shear modulus of hydrate-bearing sediments. The formulas for calculating the bulk modulus and shear modulus of hydrate-bearing sediments are as follows:

[0118]

[0119] Where k and μ refer to the bulk modulus and shear modulus of hydrate-bearing sediments, respectively. dry The bulk modulus, μ, is expressed as the bulk modulus of the dry rock skeleton. dry The shear moduli β1, β2, and K are expressed as the dry rock skeleton. av These are all intermediate variables in the calculation process. β1, β2, and K av The definition is as follows:

[0120]

[0121] Among them, K ma The bulk modulus used to represent the rock framework is the weighted average of the bulk moduli of the content of each mineral component in the rock framework; μ ma The shear modulus used to represent the rock framework is the weighted average of the shear moduli of the mineral components within the rock framework. In a specific implementation, assuming that the hydrate sediments consist only of quartz and argillaceous sediments, with contents of V1 and V2 respectively, then K... ma =V1K 石英 +V2K 泥质 .

[0122] Wherein, ω represents the consolidation coefficient, which can be obtained by fitting the relationship between velocity and depth. In the embodiments of this application, it is used... d represents the depth below the seabed (in meters); ε represents an empirical parameter that provides an empirical parameter for measuring the support of hydrates to the mineral matrix, ranging from 0 to 1, and generally taking a value of 0.12. Expressed as total porosity; It is expressed as the porosity occupied by water; k represents the porosity occupied by hydrates. w Expressed as the bulk modulus of water; k h Expressed as the bulk modulus of the hydrate; It is represented as pseudoporosity; γ is a user-defined parameter; where ma, w, and h represent sediment particles, water, and hydrates, respectively.

[0123] For example, the values ​​of each modeling constant are shown in Table 1 below:

[0124]

[0125] Table 1. Schematic diagram of the values ​​of various modeling constants in the calculation of P-wave and S-wave velocities. Among them, the porosity occupied by water... Porosity occupied by hydrates The calculation formula is as follows:

[0126]

[0127] The density ρ of hydrate-bearing sediments can be calculated using the following formula:

[0128]

[0129] Where ρ represents the density of the hydrate-bearing sediment; ρ ma The density ρ is expressed as the density of the rock skeleton. ma It is a weighted average of the composition of various minerals; ρ w Expressed as the density of water; Represented as total porosity; ρh It is expressed as the density of the hydrate.

[0130] Finally, the wave impedance data I P It can then be calculated using the following formula:

[0131]

[0132] It should be noted that the P and S wave velocities are implicitly calculated when calculating the P wave impedance data. Based on the bulk modulus, shear modulus, and density, the P and S wave velocities of hydrate-bearing sediments can be calculated. Then, based on the P and S wave velocities and density of the hydrate-bearing sediments, the corresponding wave impedance data I can be calculated. P And based on the wave impedance data, the low-frequency model of wave impedance is determined, that is, the wave impedance data is used as the low-frequency model of wave impedance for seismoelectric fusion inversion.

[0133] Using the above calculation formula for wave impedance data, the hydrate saturation profile can be converted into a longitudinal wave impedance profile. It can be understood that the electromagnetic data has been converted into wave impedance data. Finally, the wave impedance data is used as the wave impedance low-frequency model for seismoelectric fusion inversion.

[0134] Step S107: Perform seismic-electric fusion inversion based on the target seismic wavelet and the wave impedance low-frequency model to obtain the seismic-electric fusion inversion result; the seismic-electric fusion inversion result includes hydrate saturation data.

[0135] For seismo-electromagnetic fusion, seismic data refers to seismic data and electromagnetic data refers to electromagnetic data. The aim is to fuse these two different types of data. Seismic and electromagnetic data contain complementary information, and joint inversion of electromagnetic and seismic data can effectively improve the imaging resolution of underground geological structures and the accuracy of reservoir description. For wave impedance inversion, it refers to the process of converting seismic data into wave impedance through inversion algorithms (such as sparse pulse inversion). This involves three main steps: extraction of seismic wavelets, construction of low-frequency models, and inversion. The overall process of the embodiments in this application mainly belongs to sequential joint inversion. Sequential joint inversion refers to: first, performing single inversion on seismic and electromagnetic data; then, using rock physics relationships, converting the seismic and electromagnetic attribute inversion results into electromagnetic-seismic attribute data, which serves as the constraint condition or reference model for single electromagnetic-seismic inversion; then, using rock physics relationships, converting back to seismic-electromagnetic attribute data; and performing multiple iterative inversions until a given iteration termination condition is met.

[0136] In practical implementation, electromagnetic data acquires the absolute value of resistivity of underground sediments, which belongs to the low-frequency band; while seismic data provides information on the relative impedance difference of stratigraphic interfaces, with a wider bandwidth and higher frequency. Therefore, this application embodiment converts resistivity data into seismic impedance data as a low-frequency input for seismic inversion, and then uses this low-frequency model to carry out seismic impedance inversion, achieving seismoelectric fusion and compensating for the lack of low-frequency seismic data. Resistivity is directly related to hydrates and gas saturation, as shown by the Alchian formula; hydrate / gas saturation also has a quantitative relationship with seismic impedance, which can be derived from theoretical models, such as the three-phase Biot model, or from statistical analysis of well logging data in the study area. The seismoelectric fusion inversion method for predicting hydrate saturation provided in this application embodiment can be applied to pre-drilling property prediction in new oil and gas and hydrate exploration areas.

[0137] In this embodiment, the constrained sparse pulse inversion algorithm is mainly used for inversion. The inversion is primarily implemented using commercial inversion software such as Jason and HRS. Quality control of the inversion mainly aims to minimize the error between the forward model record and the measured seismic data. While low-frequency models in related technologies originate from the velocity during seismic data processing, the low-frequency model used in this application for seismic inversion is derived from electromagnetic data.

[0138] Steps S101 to S107, as illustrated in this embodiment, involve acquiring pre-drilling seismic-electric data, including time-domain seismic data and depth-domain resistivity data; performing time-depth conversion on the time-domain seismic data to obtain depth-domain seismic data; performing seismic-electric matching on the depth-domain seismic data and depth-domain resistivity data to obtain depth-domain resistivity data with the target data format; performing time-depth conversion on the depth-domain resistivity data with the target data format to obtain time-domain resistivity data; performing wavelet extraction on the time-domain seismic data to obtain the target seismic wavelet; performing data conversion on the time-domain resistivity data to obtain wave impedance data, and determining the wave impedance low-frequency model based on the wave impedance data; performing seismic-electric fusion inversion based on the target seismic wavelet and the wave impedance low-frequency model to obtain the seismic-electric fusion inversion result; the seismic-electric fusion inversion result includes hydrate saturation data. This application embodiment converts low-frequency electromagnetic data into wave impedance data by utilizing the relationship between resistivity, hydrate saturation, and wave impedance. Based on the wave impedance data, a low-frequency wave impedance model is determined, and then this low-frequency model is used for seismic inversion. This allows for the acquisition of more accurate elastic parameter information such as hydrate saturation. Furthermore, by fusing electromagnetic and seismic data, the seismic inversion results not only reflect the characterization of the background field by electromagnetic data but also retain the advantages of seismic data in vertical resolution. Therefore, this application embodiment can predict more accurate hydrate saturation through the fusing of electromagnetic and seismic data in the absence of well logging data, greatly improving the success rate of exploration.

[0139] To explain in detail the principle of the technical solution of the present invention, the overall process of the present invention will be described below with reference to some specific embodiments. It is easy to understand that the following is an explanation of the technical principle of the present invention and should not be regarded as a limitation of the present invention.

[0140] Please see Figure 4 , Figure 4 This is a schematic diagram of the overall process of a method for seismoelectric fusion inversion saturation provided in an embodiment of this application; as shown below. Figure 4As shown, the overall process of the seismoelectric fusion inversion method for saturation is as follows: First, time-domain seismic data and depth-domain resistivity data are acquired. Then, time-depth conversion is performed on the time-domain seismic data to obtain depth-domain seismic data. Next, seismoelectric matching processing is performed on the depth-domain seismic data and depth-domain resistivity data to obtain depth-domain resistivity data with the target data format. Time-depth conversion is then performed on the depth-domain resistivity data with the target data format to obtain time-domain resistivity data. Further, rock physics modeling is performed based on the relationship between time-domain resistivity data, water and saturation, and wave impedance. Then, the time-domain resistivity data is converted to obtain wave impedance data. A low-frequency wave impedance model is determined based on the wave impedance data and used as a constraint for seismoelectric fusion inversion. At the same time, wavelet extraction processing is also required from the time-domain seismic data to obtain seismic wavelets. Finally, seismoelectric fusion inversion is performed based on the seismic wavelets and the low-frequency wave impedance model to obtain the seismoelectric fusion inversion results, which include physical property data such as hydrate saturation.

[0141] In this embodiment, to achieve the fusion of electromagnetic and seismic data, seismo-electromagnetic fusion processing is performed to ensure consistency between the seismic and electromagnetic data observation systems. Seismic velocity is used to perform time-depth conversion of the electromagnetic data, guaranteeing consistency in the time-depth relationship between the two. Since electromagnetic data reflects the absolute value of subsurface resistivity, while seismic data effectively demonstrates the relative relationships of local stratigraphic interfaces, this embodiment proposes a seismic inversion method based on electromagnetic data constraints. First, the resistivity profile is converted into a velocity profile using the relationship between resistivity, hydrate saturation, and wave impedance as a low-frequency constraint for seismic inversion. Under this constraint, seismic inversion is performed to obtain elastic parameter information such as wave impedance. The resulting inversion reflects both the characterization of the background field by electromagnetic data and the advantage of seismic data in vertical resolution. Therefore, the seismo-electromagnetic fusion inversion method provided in this embodiment can effectively preserve the advantages of both seismic and electromagnetic data, obtaining a more accurate wave impedance. This wave impedance is then used to calculate a more accurate hydrate saturation, providing technical support for new area exploration and significantly improving the success rate of exploration.

[0142] It should be noted that this embodiment is only a brief illustrative description of the general process of a method for seismoelectric fusion inversion saturation. Detailed descriptions of each step can be found in the relevant content of the foregoing embodiments, and will not be repeated here. It is understood that the present invention does not limit this.

[0143] In the embodiments of this application, experimental tests and verifications were also conducted. For details, please refer to [link / reference]. Figure 5 , Figure 5 This is a schematic diagram of a wave impedance profile converted from electromagnetic data, provided in an embodiment of this application. Figure 5 The horizontal axis represents the track number, and the vertical axis represents time (Time[ms]). Figure 5 The color-changing bar on the right side represents the wave impedance value (I). p [g / cm 3 *m / s])(as Figure 5 (From blue 2000 to red 26000), in practical implementation, the resistivity data of a certain survey line in a certain area can first be converted into a wave impedance profile (e.g., Figure 5 (As shown), and use it as the low-frequency model for inversion. Figure 5 This is the wave impedance profile derived from electromagnetic data; then, wavelet extraction and seismic inversion are performed, and the inversion results are as follows: Figure 6 and Figure 7 As shown, Figure 6 This is a schematic diagram of a seismic inversion result provided in an embodiment of this application. Figure 7 This is a schematic diagram of a seismoelectric fusion inversion result provided in an embodiment of this application. Figure 6 and Figure 7 The horizontal axis represents the track number, and the vertical axis represents time (Time[ms]). Figure 6 and Figure 7 The color-changing bars on the right side of the image represent wave impedance values ​​(I). p [g / cm 3 *m / s])(as Figure 6 and Figure 7 (From 18,000 in blue to 30,000 in red), such as Figure 6 and Figure 7 As shown, comparing the results of seismoelectric fusion inversion with the results of inversion using only seismic data, the following two improvements can be clearly observed:

[0144] (1) At A1 and A2, the seismoelectric fusion inversion results can well distinguish between thick hydrates and thinner hydrates, and the wave impedance inside the thick hydrate at W11 is well characterized. The wave impedance at A2 is even lower, indicating that the hydrate saturation is lower. This result is closer to the actual situation of well logging data. These characteristics are not well reflected in the pure seismic inversion results.

[0145] (2) Compared with the pure seismic inversion results, the impedance anomaly at A3 is significantly weakened. Overall, the high-value anomalies below BSR (Bottom Simulating Reflector) are significantly weakened, which is more consistent with the actual geological conditions. At A3, there is a gas layer. The resistivity of the gas layer is lower than that of the hydrate layer, and the impedance value is also lower than that of the hydrate layer. Therefore, high impedance anomalies below BSR are theoretically less common, and the seismoelectric fusion inversion results are more consistent with the geological conditions.

[0146] Furthermore, the hydrate saturation at W11 was calculated using the wave impedance obtained from the seismoelectric fusion inversion. Please refer to [link to relevant documentation]. Figure 8 , Figure 8 This is a schematic diagram comparing the seismoelectric fusion inversion saturation and well logging saturation provided in an embodiment of this application, as shown below. Figure 8 As shown, Figure 8 The horizontal axis represents the trace number, and the vertical axis represents time (Time[ms]). The red line represents the seismoelectric fusion inversion saturation, and the black line represents the logging saturation. Overall, the seismoelectric fusion inversion saturation and the logging saturation are in good agreement. This result shows that seismoelectric fusion technology can truly achieve saturation inversion, thereby enabling detailed evaluation of hydrates in a region. The seismoelectric fusion inversion saturation method provided in this application is expected to significantly reduce exploration costs and improve operational efficiency.

[0147] This application embodiment acquires pre-drilling seismoelectric data, which includes time-domain seismic data and depth-domain resistivity data. The time-domain seismic data undergoes time-depth conversion to obtain depth-domain seismic data. The depth-domain seismic data and depth-domain resistivity data are then subjected to seismoelectric matching to obtain depth-domain resistivity data with the target data format. The depth-domain resistivity data with the target data format undergoes time-depth conversion to obtain time-domain resistivity data. Wavelet extraction is performed on the time-domain seismic data to obtain the target seismic wavelet. The time-domain resistivity data undergoes data conversion to obtain wave impedance data, and a low-frequency wave impedance model is determined based on the wave impedance data. Seismoelectric fusion inversion is performed based on the target seismic wavelet and the low-frequency wave impedance model to obtain the seismoelectric fusion inversion result. The seismoelectric fusion inversion result includes hydrate saturation data. This application embodiment converts low-frequency electromagnetic data into wave impedance data by utilizing the relationship between resistivity, hydrate saturation, and wave impedance. Based on the wave impedance data, a low-frequency wave impedance model is determined, and then this low-frequency model is used for seismic inversion. This allows for the acquisition of more accurate elastic parameter information such as hydrate saturation. Furthermore, by fusing electromagnetic and seismic data, the seismic inversion results not only reflect the characterization of the background field by electromagnetic data but also retain the advantages of seismic data in vertical resolution. Therefore, this application embodiment can predict more accurate hydrate saturation through the fusing of electromagnetic and seismic data in the absence of well logging data, greatly improving the success rate of exploration.

[0148] Please see Figure 9 This application also provides a device 900 for seismoelectric fusion inversion saturation, which can realize the above-mentioned method for seismoelectric fusion inversion saturation. The device includes the following modules:

[0149] The pre-drilling seismic data acquisition module 901 is used to acquire pre-drilling seismic data; the pre-drilling seismic data includes time-domain seismic data and depth-domain resistivity data;

[0150] The first time-depth conversion processing module 902 is used to perform time-depth conversion processing on the time-domain seismic data to obtain depth-domain seismic data.

[0151] The seismoelectric matching processing module 903 is used to perform seismoelectric matching processing on the depth domain seismic data and the depth domain resistivity data to obtain depth domain resistivity data with the target data format.

[0152] The second time-depth conversion processing module 904 is used to perform time-depth conversion processing on the depth domain resistivity data having the target data format to obtain time domain resistivity data.

[0153] The wavelet extraction and processing module 905 is used to perform wavelet extraction processing on the time-domain seismic data to obtain the target seismic wavelet.

[0154] The wave impedance low-frequency model construction module 906 is used to perform data conversion processing on the time domain resistivity data to obtain wave impedance data, and determine the wave impedance low-frequency model based on the wave impedance data.

[0155] The seismoelectric fusion inversion module 907 is used to perform seismoelectric fusion inversion based on the target seismic wavelet and the wave impedance low-frequency model to obtain seismoelectric fusion inversion results; the seismoelectric fusion inversion results include hydrate saturation data.

[0156] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0157] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method for seismoelectric fusion inversion saturation. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0158] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0159] Please see Figure 10 , Figure 10 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0160] The processor 1001 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0161] The memory 1002 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 1002 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called and executed by the processor 1001 to execute a method for seismoelectric fusion inversion saturation according to an embodiment of this application.

[0162] Input / output interface 1003 is used to implement information input and output;

[0163] The communication interface 1004 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0164] Bus 1005 transmits information between various components of the device (e.g., processor 1001, memory 1002, input / output interface 1003, and communication interface 1004);

[0165] The processor 1001, memory 1002, input / output interface 1003 and communication interface 1004 are connected to each other within the device via bus 1005.

[0166] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for seismoelectric fusion inversion saturation.

[0167] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0168] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0169] This application provides a method, apparatus, electronic device, and medium for seismoelectric fusion inversion of saturation, which acquires pre-drilling seismoelectric data, including time-domain seismic data and depth-domain resistivity data. The time-domain seismic data undergoes time-depth conversion to obtain depth-domain seismic data. The depth-domain seismic data and depth-domain resistivity data undergo seismoelectric matching to obtain depth-domain resistivity data with a target data format. The depth-domain resistivity data with the target data format undergoes time-depth conversion to obtain time-domain resistivity data. The time-domain seismic data undergoes wavelet extraction to obtain a target seismic wavelet. The time-domain resistivity data undergoes data conversion to obtain wave impedance data, and a low-frequency wave impedance model is determined based on the wave impedance data. Seismoelectric fusion inversion is performed based on the target seismic wavelet and the low-frequency wave impedance model to obtain the seismoelectric fusion inversion result. The seismoelectric fusion inversion result includes hydrate saturation data. This application embodiment converts low-frequency electromagnetic data into wave impedance data by utilizing the relationship between resistivity, hydrate saturation, and wave impedance. Based on the wave impedance data, a low-frequency wave impedance model is determined, and then this low-frequency model is used for seismic inversion. This allows for the acquisition of more accurate elastic parameter information such as hydrate saturation. Furthermore, by fusing electromagnetic and seismic data, the seismic inversion results not only reflect the characterization of the background field by electromagnetic data but also retain the advantages of seismic data in vertical resolution. Therefore, this application embodiment can predict more accurate hydrate saturation through the fusing of electromagnetic and seismic data in the absence of well logging data, greatly improving the success rate of exploration.

[0170] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0171] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0172] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0173] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0174] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application 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.

[0175] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0176] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0177] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0178] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0179] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0180] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for seismoelectric fusion inversion saturation, characterized in that, The method includes the following steps: Acquire pre-drilling seismic electrical data; the pre-drilling seismic electrical data includes time-domain seismic data and depth-domain resistivity data; The time-domain seismic data is subjected to time-depth conversion to obtain depth-domain seismic data; The depth-domain seismic data and the depth-domain resistivity data are subjected to seismoelectric matching processing to obtain depth-domain resistivity data with the target data format. The depth domain resistivity data with the target data format is subjected to time-depth conversion processing to obtain time domain resistivity data; Wavelet extraction processing is performed on the time-domain seismic data to obtain the target seismic wavelet; The time-domain resistivity data is converted to obtain wave impedance data, and a low-frequency wave impedance model is determined based on the wave impedance data. Seismic-electric fusion inversion is performed based on the target seismic wavelet and the wave impedance low-frequency model to obtain seismic-electric fusion inversion results; the seismic-electric fusion inversion results include hydrate saturation data.

2. The method according to claim 1, characterized in that, The process of performing time-depth conversion on the time-domain seismic data to obtain depth-domain seismic data includes: The time-domain seismic data is processed to obtain time-domain wave velocity data; wherein, the time-domain wave velocity data is used to treat the strata of corresponding thickness at each sampling interval in the seismic profile as sub-layers. The root mean square velocity of each of the sub-layers is calculated using the root mean square velocity formula. The layer velocity of each sub-layer is calculated based on the root mean square velocity of each sub-layer and the layer velocity formula. Calculate the layer thickness of each of the sub-layers based on the layer velocity of each sub-layer; The layer thicknesses of each sub-layer are accumulated and calculated according to the top-down time-depth conversion rule to obtain interface depth data, and the interface depth data is used as the depth domain seismic data.

3. The method according to claim 1, characterized in that, The step of performing seismoelectric matching processing on the depth-domain seismic data and the depth-domain resistivity data to obtain depth-domain resistivity data with the target data format includes: Based on the sampling interval of the depth domain seismic data, the depth domain resistivity data is subjected to encrypted sampling processing to obtain target sampling depth domain resistivity data; wherein, the sampling interval of the target sampling depth domain resistivity data is consistent with the sampling interval of the depth domain seismic data; According to the target data format corresponding to the depth domain seismic data, the target sampled depth domain resistivity data is formatted to obtain the depth domain resistivity data with the target data format.

4. The method according to claim 2, characterized in that, The step of performing time-depth conversion on the depth-domain resistivity data having the target data format to obtain time-domain resistivity data includes: Based on the layer velocity and layer thickness corresponding to each of the sub-layers, the depth domain resistivity data with the target data format is subjected to time-depth conversion processing using a time-depth conversion formula to obtain the time domain resistivity data.

5. The method according to claim 1, characterized in that, The step of performing wavelet extraction processing on the time-domain seismic data to obtain the target seismic wavelet includes: Based on the spectral characteristics of the time-domain seismic data, a predefined wavelet is determined; Based on the predefined wavelet and well logging data, an initial synthetic seismic record is generated; The target strata in the initial synthetic seismic record are calibrated to obtain candidate synthetic seismic records; The predefined wavelet is updated based on the candidate synthetic seismic records and the time-domain seismic data to obtain candidate seismic wavelets; If the correlation between the candidate synthetic seismic record and the time-domain seismic data does not meet the preset correlation threshold, then the candidate seismic wavelet is used as the predefined wavelet, and the process returns to the step of generating an initial synthetic seismic record based on the predefined wavelet and well logging data, until the correlation between the candidate synthetic seismic record and the time-domain seismic data meets the preset correlation threshold, and the candidate synthetic seismic record that meets the preset correlation threshold is used as the target synthetic seismic record. Based on the target synthetic seismic record and the time-domain seismic data, the candidate seismic wavelet corresponding to the target synthetic seismic record is updated to obtain the target seismic wavelet.

6. The method according to claim 1, characterized in that, The step of performing data conversion processing on the time-domain resistivity data to obtain wave impedance data, and determining the wave impedance low-frequency model based on the wave impedance data, includes: The time-domain resistivity data were converted and processed using the Alzer formula to obtain hydrate saturation profile data. The hydrate saturation profile data is converted and processed according to the physical relationship of the reservoir rocks to obtain the wave impedance data, and the wave impedance low-frequency model is determined based on the wave impedance data.

7. The method according to claim 6, characterized in that, The step of performing data conversion processing on the hydrate saturation profile data based on the reservoir rock physical relationship to obtain the wave impedance data, and determining the wave impedance low-frequency model based on the wave impedance data, includes: Based on the physical relationship of the reservoir rocks, the hydrate saturation profile data were analyzed and processed to obtain the bulk modulus and shear modulus of the hydrate-bearing sediments. The density of the hydrate-containing sediments was calculated based on the hydrate saturation of the hydrate saturation profile data. Calculate the P-wave and S-wave velocities of the hydrate-bearing sediment based on the bulk modulus, the shear modulus, and the density; Based on the longitudinal and transverse wave velocities and the density of the hydrate-bearing sediment, the wave impedance data corresponding to the hydrate-bearing sediment is calculated, and the wave impedance low-frequency model is determined based on the wave impedance data.

8. A device for seismoelectric fusion inversion saturation, characterized in that, The device includes the following modules: The pre-drilling seismic-electric data acquisition module is used to acquire pre-drilling seismic-electric data; the pre-drilling seismic-electric data includes time-domain seismic data and depth-domain resistivity data; The first time-depth conversion processing module is used to perform time-depth conversion processing on the time-domain seismic data to obtain depth-domain seismic data. The seismoelectric matching processing module is used to perform seismoelectric matching processing on the depth domain seismic data and the depth domain resistivity data to obtain depth domain resistivity data with the target data format. The second time-depth conversion processing module is used to perform time-depth conversion processing on the depth domain resistivity data having the target data format to obtain time domain resistivity data. The wavelet extraction and processing module is used to perform wavelet extraction processing on the time-domain seismic data to obtain the target seismic wavelet; The wave impedance low-frequency model construction module is used to perform data conversion processing on the time domain resistivity data to obtain wave impedance data, and determine the wave impedance low-frequency model based on the wave impedance data. The seismoelectric fusion inversion module is used to perform seismoelectric fusion inversion based on the target seismic wavelet and the wave impedance low-frequency model to obtain seismoelectric fusion inversion results; the seismoelectric fusion inversion results include hydrate saturation data.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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