A method and device for removing sedimentation background based on stacked autoencoder

Abstract

The present invention discloses a method and device for removing sedimentary background based on a stacked autoencoder. The method comprises: performing well-controlled phase conversion and Wheeler domain conversion on an original seismic volume to obtain a Wheeler domain seismic volume after well-controlled phase conversion; using the Wheeler domain seismic volume after well-controlled phase conversion to train a pre-constructed initial sedimentary background prediction model to obtain a sedimentary background prediction model; wherein the initial sedimentary background prediction model is constructed based on a stacked autoencoder; inputting the Wheeler domain seismic volume after well-controlled phase conversion into the sedimentary background prediction model to obtain sedimentary background single-channel data; expanding the sedimentary background single-channel data based on the original seismic volume to obtain a sedimentary background volume; and performing volume operations on the Wheeler domain seismic volume after well-controlled phase conversion and the sedimentary background volume to obtain a lithologic volume after sedimentary background removal. The present invention uses a stacked autoencoder to extract the sedimentary background volume, thereby improving the accuracy of effective reservoir seismic analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of petroleum geophysical exploration, and in particular to a method and device for removing sedimentation background based on a stacked autoencoder. Background Art

[0002] In recent years, major oil and gas field exploration projects both domestically and internationally have frequently encountered strong reflection background seismic profiles. The low reflection coefficient of reservoirs often affects adjacent layers above and below them, making reservoir prediction difficult. Furthermore, due to the influence of structural and sedimentary factors, existing seismic exploration techniques present significant challenges in predicting small river channels. To address this, there is an urgent need to develop methods to remove sedimentary background (including the influence of strongly reflecting strata).

[0003] In order to remove the influence of sedimentary background, domestic and foreign researchers have conducted in-depth research. They mainly use matching pursuit and various derivative algorithms to extract sedimentary background from seismic data, and then subtract the sedimentary background from the seismic record to eliminate the influence of sedimentary background (and strong reflection background) and improve the accuracy of seismic interpretation and reservoir prediction. At the same time, domestic scholars have used linear PCA method and its deformation algorithm to study the method of removing sedimentary background. Summary of the Invention

[0004] In order to better remove the deposition background, an embodiment of the present invention provides a method and device for removing the deposition background based on a stacked autoencoder.

[0005] In a first aspect, an embodiment of the present invention provides a method for removing deposition background based on a stacked autoencoder, the method comprising:

[0006] Perform well-controlled phase conversion on the original seismic volume and perform Wheeler domain conversion to obtain the Wheeler domain seismic volume after well-controlled phase conversion;

[0007] Using the Wheeler domain seismic volume after the well-controlled phase conversion to train a pre-built initial sedimentary background prediction model, a sedimentary background prediction model is obtained; wherein the initial sedimentary background prediction model is based on a stacked autoencoder and includes multiple encoders and multiple decoders;

[0008] Inputting the Wheeler domain seismic volume after the well-controlled phase conversion into the sedimentary background prediction model to obtain sedimentary background single-channel data;

[0009] Expanding the sedimentary background single-channel data based on the original seismic volume to obtain a sedimentary background volume;

[0010] Volume operations are performed on the Wheeler domain seismic volume after the well-controlled phase conversion and the sedimentary background volume to obtain a lithologic volume after removing the sedimentary background.

[0011] In one or some optional implementations of the embodiments of the present application, the initial deposition background prediction model includes a plurality of encoders connected in sequence and a plurality of decoders having the same number as the encoders;

[0012] The method of using the Wheeler domain seismic volume after the well-controlled phase conversion to train a pre-built initial sedimentary background prediction model to obtain a sedimentary background prediction model includes:

[0013] Using the Wheeler domain seismic volume after the well-controlled phase conversion, training an autoencoder consisting of a first encoder and a last decoder to obtain a trained first encoder;

[0014] Encoding the Wheeler domain seismic volume after the well-controlled phase conversion using the trained first encoder to obtain a first latent vector data set;

[0015] Using the first hidden vector data set to train an autoencoder consisting of a second encoder and a penultimate decoder to obtain a trained second encoder;

[0016] Encoding the first latent vector data set using the trained second encoder to obtain a second latent vector data set;

[0017] The above process of training the autoencoder and using the corresponding encoder to encode the input hidden vector data set is repeated until the deposition background prediction model consisting of multiple trained encoders connected in sequence is obtained.

[0018] In one or some optional implementations of the embodiment of the present application, the step of expanding the sedimentary background single-channel data based on the original seismic volume to obtain the sedimentary background volume includes:

[0019] Obtaining the total number of dimensions of the original seismic volume;

[0020] The sedimentary background single-channel data is copied and expanded based on the total number of dimensions to obtain a sedimentary background volume with the same total number of dimensions as the original seismic volume.

[0021] In one or some optional implementations of the embodiment of the present application, performing volume operations on the Wheeler domain seismic volume after well-controlled phase conversion and the sedimentary background volume to obtain a lithologic volume after removing the sedimentary background includes:

[0022] Based on the following formula 1, a volume operation is performed on the Wheeler domain seismic volume after the well-controlled phase conversion and the sedimentary background volume, and the sedimentary background volume in the Wheeler domain seismic volume after the well-controlled phase conversion is removed to obtain a lithologic volume after the sedimentary background is removed:

[0023] S l =SS b Formula 1;

[0024] Where, S is the Wheeler domain seismic volume after well-controlled phase conversion, S b is the sedimentary background body, S l Lithologic body data.

[0025] In one or some optional implementations of the embodiment of the present application, performing well-controlled phase conversion on the original seismic volume and performing Wheeler domain conversion to obtain the Wheeler domain seismic volume after well-controlled phase conversion includes:

[0026] Perform well-controlled phase conversion on the original seismic volume to obtain a 90-degree phased seismic volume;

[0027] The 90-degree phased seismic volume is subjected to a Wheeler domain transformation to obtain a well-controlled phase-converted Wheeler domain seismic volume.

[0028] In one or some optional implementations of the embodiment of the present application, before using the Wheeler domain seismic volume after the well-controlled phase conversion to train a pre-built initial sedimentary background prediction model to obtain the sedimentary background prediction model, the method further includes:

[0029] Based on the following formula 2, the Wheeler domain seismic volume after the well-controlled phase conversion is normalized:

[0030] S=-1+(seisdata w -seisdata w min ) / (seisdata w max -seisdata w min )×(1-(-1))Formula 2;

[0031] Where, seisdata w is the Wheeler domain seismic volume after well-controlled phase conversion, seisdata w min is the minimum value of the Wheeler domain seismic volume after well-controlled phase conversion, seisdata w max It is the maximum value of the Wheeler domain seismic volume after well-controlled phase conversion.

[0032] In a second aspect, an embodiment of the present invention provides a device for removing deposition background based on a stacked autoencoder, the device comprising:

[0033] The first conversion module is used to perform well-controlled phase conversion on the original seismic volume and perform Wheeler domain conversion to obtain a Wheeler domain seismic volume after well-controlled phase conversion;

[0034] A first training module is configured to train a pre-built initial sedimentation background prediction model using the Wheeler domain seismic volume after the well-controlled phase conversion to obtain a sedimentation background prediction model; wherein the initial sedimentation background prediction model is based on a stacked autoencoder and includes multiple encoders and multiple decoders;

[0035] A first prediction module is configured to input the Wheeler domain seismic volume after the well-controlled phase conversion into the sedimentary background prediction model to obtain sedimentary background single-channel data;

[0036] A first operation module is used to expand the sedimentary background single-channel data based on the original seismic volume to obtain a sedimentary background volume;

[0037] The second operation module is used to perform volume operation on the Wheeler domain seismic volume after the well-controlled phase conversion and the sedimentary background volume to obtain a lithologic volume after removing the sedimentary background.

[0038] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for removing deposition background based on a stacked autoencoder.

[0039] In a fourth aspect, an embodiment of the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for removing deposition background based on the stacked autoencoder as described above is implemented.

[0040] In a fifth aspect, an embodiment of the present invention provides a computer program product comprising instructions, which, when executed on a computer device, enables the computer device to execute the above-mentioned method for removing deposition background based on a stacked autoencoder.

[0041] In a sixth aspect, an embodiment of the present invention provides a chip, which includes a processor and a communication interface, wherein the communication interface and the processor are coupled, and the processor is used to run a computer program or instruction to implement the above-mentioned method for removing deposition background based on the stack autoencoder.

[0042] The beneficial effects of the above technical solutions provided by the embodiments of the present invention include at least:

[0043] The stacked autoencoder-based sedimentary background removal method provided in this embodiment uses phase-transformed Wheeler-domain seismic volume data as a training dataset. This method, trained through a stacked autoencoder, obtains a sedimentary background volume, ultimately yielding a more accurate lithologic volume. Using the stacked autoencoder for dimensionality reduction extraction enables more efficient and stable feature extraction, overcoming the impact of strong reflectors and sedimentary background on reservoirs. This significantly improves the precision and accuracy of effective reservoir seismic analysis using seismic data, enabling more detailed characterization of sedimentary reservoirs.

[0044] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0045] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0047] Figure 1 A schematic diagram of a flow chart of a method for removing deposition background based on a stacked autoencoder provided in an embodiment of the present invention;

[0048] Figure 2 Schematic diagram of a seismic volume section in the Wheeler domain after well-controlled phase conversion provided in an embodiment of the present application;

[0049] Figure 3 is a schematic diagram of the initial deposition background prediction model provided in an embodiment of the present application;

[0050] Figure 4 Schematic diagram of the network structure of the specific initial sedimentation background prediction model provided in the embodiment of the present application;

[0051] Figure 5 Schematic diagram of the extracted sedimentation background provided in the embodiment of the present application;

[0052] Figure 6 This is a schematic diagram of the lithologic body obtained after removing the sedimentary background provided in the embodiment of the present application;

[0053] Figure 7 A schematic structural diagram of a device for removing deposition background based on a stacked autoencoder provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0055] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0056] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0057] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0058] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0059] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0060] It should be understood that the size of the serial numbers of the steps in the following embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0061] In order to illustrate the technical solution of the present application, specific embodiments are provided below.

[0062] The inventor finds, in the prior art, mainly utilize matching pursuit and various derivative algorithms, strong reflection waveform is extracted from seismic data, then strong reflection waveform is subtracted from seismic record, to complete the influence of eliminating strong reflection waveform, improve the precision of seismic interpretation and reservoir prediction.But matching pursuit algorithm also has weak points, such as there is discontinuous strong axis and multiple strong axis removal effect poor, and substantially can not be solved, wavelet library easily redundant causes the problems such as the amount of calculation is larger.Due to the influence of structure and sedimentary background, cause seismic reservoir prediction analysis difficulty to increase, need to carry out sedimentary background (comprising strong reflection waveform) removal, domestic scholars have utilized linear PCA method and deformation algorithm thereof to attempt simultaneously, but effect is unstable.

[0063] In summary, existing methods cannot fully and effectively remove sedimentary background (including removing strongly reflective strata). Furthermore, the increasing volume of seismic data also places higher demands on sedimentary background removal methods. Based on this, the inventors, after further research and development, have developed the present invention, which provides a method and apparatus for removing sedimentary background based on a stacked autoencoder.

[0064] Example 1

[0065] The embodiment of the present invention provides a method for removing sedimentation background based on a stacked autoencoder, referring to Figure 1 As shown, the method includes:

[0066] S101: Perform well-controlled phase conversion on the original seismic volume and perform Wheeler domain conversion to obtain a Wheeler domain seismic volume after well-controlled phase conversion.

[0067] In the embodiment of the present application, in the above step S101, the original seismic volume is subjected to well-controlled phase conversion and Wheeler domain conversion to obtain the Wheeler domain seismic volume after well-controlled phase conversion, including:

[0068] The original seismic volume is subjected to well-controlled phase conversion to obtain a 90-degree phased seismic volume; the 90-degree phased seismic volume is subjected to Wheeler domain transformation to obtain a Wheeler domain seismic volume after well-controlled phase conversion.

[0069] In an embodiment of the present application, seismic wave data collected by a multi-channel scanning method is obtained, a phase angle step is defined, the seismic body is corrected with different phase angle shifts, the waveform similarity or maximum energy criterion is used to determine the optimal phase shift, and finally the original seismic body is converted into a 90-degree phased seismic body, which is conducive to the interpretation of the sand body. Among them, the steps of performing well-controlled phase conversion on the original seismic volume to obtain a 90-degree phased seismic volume include: a first step, calculating the Hilbert transform, performing the Hilbert transform on the original seismic data X(t) to convert it into a complex signal H(t), and dividing the original seismic data into a real part and an imaginary part through the Hilbert transform to form a complex signal, wherein the real part contains the amplitude information of the original data, and the imaginary part contains the 90-degree phase data; a second step, calculating the amplitude and phase, and from the complex signal H(t), the amplitude A(t) and the phase Φ(t) can be calculated, wherein the amplitude A(t) represents the amplitude information of the data, and the phase Φ(t) represents the phase information of the data; a third step, creating a 90-degree phased seismic volume, by combining the amplitude A(t) and the phase Φ(t), that is, increasing the phase by 90 degrees, to obtain a 90-degree phased seismic volume.

[0070] After obtaining the 90-degree phased seismic volume, a 3D Wheeler transform is performed on the 90-degree phased seismic volume to obtain the well-controlled phase-converted Wheeler domain seismic volume. The steps include: first, using the horizon tracing method to determine the sequence boundaries of the subsurface strata. The goal of this step is to determine the layered structure of the subsurface strata; second, selecting the controlling sequence boundaries. After determining the sequence boundaries, it is necessary to select control points. The continuous strong reflection axes above and below the target layer are selected as the controlling sequence boundaries; third, proportional division. This step evenly divides the strata between the controlling sequence boundaries into several parts and assigns depth values ​​to each stratum; fourth, horizon leveling. The depth values ​​of the subsurface strata are adjusted according to the controlling sequence boundaries to establish a uniform sequence model in the subsurface strata. After completing the above steps, the Wheeler transform is completed, and the 90-degree phased seismic volume is finally converted into the well-controlled phase-converted Wheeler domain seismic volume.

[0071] In the embodiment of the present application, the method further includes normalizing the Wheeler domain seismic volume after the well-controlled phase conversion, and normalizing the Wheeler domain seismic volume after the well-controlled phase conversion to between [-1, 1] based on the following formula 2:

[0072] S=-1+(seisdata w -seisdata w min ) / (seisdata w max -seisdata wmin )×(1-(-1))Formula 2;

[0073] Where, seisdata w is the Wheeler domain seismic volume after well-controlled phase conversion, seisdata w min is the minimum value of the Wheeler domain seismic volume after well-controlled phase conversion, seisdata w max It is the maximum value of the Wheeler domain seismic volume after well-controlled phase conversion.

[0074] S102: Using the Wheeler domain seismic volume after the well-controlled phase conversion to train a pre-built initial sedimentation background prediction model to obtain a sedimentation background prediction model; wherein the initial sedimentation background prediction model is based on a stacked autoencoder and includes multiple encoders and multiple decoders.

[0075] In the embodiment of the present application, in step S102, the normalized Wheeler domain seismic volume after well-controlled phase conversion is input as a training dataset into a pre-built initial sedimentation background prediction model for training. The Wheeler domain seismic volume after well-controlled phase conversion also serves as a sample label dataset. The Wheeler domain seismic volume after well-controlled phase conversion has n seismic traces, each with m sampling points. The dimension n in the seismic volume is large. To obtain compressed encoded feature data, an initial sedimentation background prediction model is constructed based on a stacked autoencoder. The initial sedimentation background prediction model includes multiple encoders and multiple decoders, with the number of encoders being equal to the number of decoders.

[0076] The initial sedimentary background prediction model is greedily trained layer by layer: First, the first encoder and the last encoder form the first shallow autoencoder, and the first shallow autoencoder is trained using the Wheeler domain seismic volume after well-controlled phase conversion to obtain the trained first encoder. Next, the trained first encoder is used to encode the Wheeler domain seismic volume after well-controlled phase conversion to obtain the first latent vector dataset. Then, the second encoder and the second-to-last encoder form the second autoencoder, and the second autoencoder is trained using the first latent vector dataset to obtain the trained second encoder. Next, the trained second encoder is used to encode the first latent vector dataset to obtain the second latent vector dataset. The above process of forming a new deep autoencoder and training it using the latent vector data obtained from the upper layer encoding is repeated until the training of the autoencoder composed of the last encoder and the first decoder in the initial sedimentary background prediction model is completed. The multiple trained encoders obtained in the above initial sedimentary background prediction model training process are sequentially connected to form the sedimentary background prediction model.

[0077] S103: Inputting the Wheeler domain seismic volume after the well-controlled phase conversion into the sedimentary background prediction model to obtain sedimentary background single-channel data.

[0078] S104: Expanding the sedimentary background single-channel data based on the original seismic volume to obtain a sedimentary background volume.

[0079] S105: performing volume operations on the Wheeler domain seismic volume after the well-controlled phase conversion and the sedimentary background volume to obtain a lithologic volume after removing the sedimentary background.

[0080] In the embodiment of the present application, in step S103, the Wheeler domain seismic volume after well-controlled phase conversion is input into the sedimentation background prediction model to obtain sedimentation background single-channel data. The total number of dimensions of the original seismic volume data is obtained, and the sedimentation background single-channel data is replicated and expanded based on the total number of dimensions to obtain a sedimentation background volume with the same dimensions as the original seismic volume.

[0081] Based on the following formula 1, volume operations are performed on the Wheeler domain seismic volume and the sedimentary background volume after the well-controlled phase conversion. The sedimentary background volume in the Wheeler domain seismic volume after the well-controlled phase conversion is removed to obtain the lithologic volume after the sedimentary background is removed:

[0082] S l =SS b Formula 1;

[0083] Where, S is the Wheeler domain seismic volume after well-controlled phase conversion, S b is the sedimentary background body, S l Lithologic body data.

[0084] In order to explain the embodiment of the present invention more clearly, in a specific example, see Figure 2-Figure 6 As shown, the method for removing deposition background based on the stacked autoencoder according to the embodiment of the present invention is described in detail:

[0085] The specific example data comes from the Sulige area of ​​the Ordos Basin, a typical low-porosity, low-permeability tight gas field. The main gas reservoirs are the He-8, Shan-1, and Shan-2 sandstone formations of the Upper Paleozoic Permian, which are thin reservoirs with strong heterogeneity. The sedimentary facies of the Shan-2 and Shan-1 phases, influenced by the Taiyuan Formation coal seams, are: the Shan-2 phase is characterized by meandering river deltaic deposits, with predominantly developed delta-plain distributary channels and floodplain mudstone deposits between the channels; the Shan-1 phase is a delta-front subfacies, with relatively well-developed underwater distributary channel microfacies and inter-distributary bay mudstone deposits between the channels. However, coal seams are prevalent in the Shan-2 and Taiyuan Formations in this area, manifesting as strong amplitude seismic signals, which strongly influence the Shan-1 and Shan-2 sandstone reservoirs. Furthermore, background sedimentation obscures some reservoir characteristics, making sedimentary facies characterization by conventional seismic data very unclear. Therefore, the present invention was applied to this region. The data was 201 channels, each with 101 sampling points. The method of the present invention was used to remove the sedimentary background and finely characterize the lithologic body. The steps are as follows:

[0086] The first step is to perform a well-controlled 90-degree phase conversion on the original seismic volume and perform a Wheeler domain transformation, such as Figure 2 The figure shows the cross section of a line in the work area after 90-degree phase conversion and Wheeler domain. Figure 2 As can be seen from the figure, due to the strong reflection of the Taiyuan Formation coal seam, the Shan 2 reservoir is located in the trough and its reservoir characteristics are obscured. At the same time, due to the influence of background sedimentation, the Shan 1 reservoir is too continuous and inconsistent with the actual sedimentary characteristics. This Wheeler domain profile cannot be directly used for subsequent reservoir prediction analysis, and the influence of the background sedimentary body needs to be removed.

[0087] The second step is to use the stack autoencoder to extract the sedimentary background. First, the Wheeler domain seismic volume after 90-degree phase processing is normalized, and then the stack autoencoder is used to train and extract the final sedimentary background. A stack autoencoder with a 7-layer symmetrical structure is designed as the initial sedimentary background prediction model, such as Figure 3 As shown, it contains 3 encoders and 3 decoders. In the designed symmetrical structure stacked autoencoder deep network, there are a total of n types of features as the network's training sample parameters (because the Wheeler domain seismic data is used as the training feature set input of n features, generally speaking, the dimension of n is large, hundreds or even tens of thousands are relatively normal). In order to obtain the compressed coded feature data body, a 7-layer deep network model with a network structure of 201-25-10-1-10-25-201 was constructed. The number of neurons gradually decreases from the 1st layer to the 4th layer, and the number of neurons in the 4th layer is reduced to one, which plays a role in compression coding in the network; the number of neurons in the remaining 3 layers is the same as the number of neurons in the first 3 layers. The number of nodes in the entire network is in a trumpet-like stacked shape, and a total of 3 decoders are set. The specific network structure of the initial sedimentary background prediction model is as follows: Figure 4As shown in Figure 2, the activation function for network training is set to the ReLU function. After training, the output of the third encoder is extracted as the single-channel sedimentary background data and expanded to the original seismic volume dimension to obtain the final sedimentary background volume. Figure 5 is the sedimentary background body obtained. Figure 2 By comparison, it is found that the coal seam at sampling point 40 was extracted, and some other sedimentary backgrounds were also extracted.

[0088] The third step is to perform volume operations on the Wheeler domain seismic volume after the well-controlled phase conversion and the sedimentary background volume to obtain a lithologic volume after removing the sedimentary background.

[0089] Perform volume operations on the Wheeler domain seismic volume after well-controlled phase conversion and the obtained sedimentary background volume to obtain the lithologic volume. Figure 6 The figure shows the lithologic body obtained after removing the sedimentary background. Figure 2 It can be seen that the sand body characteristics of the Shan 2 and Shan 1 sections are prominent. This lithologic body can be used for detailed reservoir prediction and other work in the future.

[0090] The method for removing sedimentary background based on a stacked autoencoder provided in an embodiment of the present invention uses phase-transformed Wheeler domain seismic volume data as a training data set and a sample data set, performs training and learning through a stacked autoencoder, performs dimensionality reduction extraction of ultra-high-dimensional features, obtains a sedimentary background volume, and extracts features more effectively and stably, overcoming the problem of the influence of strong reflection layers and sedimentary background on reservoirs, and ultimately obtains a more accurate lithologic body. Seismic reservoir prediction research is carried out based on the more accurate lithologic body, which can improve the precision and accuracy of effective reservoir seismic analysis using seismic data and can more finely characterize sedimentary reservoirs.

[0091] Example 2

[0092] Based on the same inventive concept, the embodiment of the present invention also provides a device for removing deposition background based on a stacked autoencoder, referring to Figure 7 As shown, the device includes:

[0093] The first conversion module 101 is used to perform well-controlled phase conversion on the original seismic volume and perform Wheeler domain conversion to obtain a Wheeler domain seismic volume after well-controlled phase conversion;

[0094] A first training module 102 is configured to train a pre-built initial sedimentation background prediction model using the Wheeler domain seismic volume after the well-controlled phase conversion to obtain a sedimentation background prediction model; wherein the initial sedimentation background prediction model is constructed based on a stacked autoencoder and includes multiple encoders and multiple decoders;

[0095] The first prediction module 103 is configured to input the Wheeler domain seismic volume after the well-controlled phase conversion into the sedimentary background prediction model to obtain sedimentary background single-channel data;

[0096] A first operation module 104 is configured to expand the sedimentary background single-channel data based on the original seismic volume to obtain a sedimentary background volume;

[0097] The second operation module 105 is configured to perform volume operation on the Wheeler domain seismic volume after the well-controlled phase conversion and the sedimentary background volume to obtain a lithologic volume after removing the sedimentary background.

[0098] Example 3

[0099] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method for removing deposition background based on a stacked autoencoder as described in the first embodiment above is implemented.

[0100] Example 4

[0101] Based on the same inventive concept, an embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and run on the processor. When the processor executes the computer program, it implements the method for removing deposition background based on the stacked autoencoder as described in the above embodiment 1.

[0102] Example 5

[0103] Based on the same inventive concept, an embodiment of the present invention further provides a computer program product comprising instructions. When the computer program product is run on a computer device, the computer device executes the method for removing deposition background based on a stacked autoencoder as described in the first embodiment above.

[0104] Example 6

[0105] Based on the same inventive concept, an embodiment of the present invention also provides a chip, which includes a processor and a communication interface, the communication interface and the processor are coupled, and the processor is used to run a computer program or instruction to implement the method for removing deposition background based on the stacked autoencoder as described in the above embodiment one.

[0106] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage, etc.) containing computer-usable program code.

[0107] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0108] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0109] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0110] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for removing sedimentation background based on stacked autoencoders, characterized in that: include: Perform well-controlled phase conversion on the original seismic volume and perform Wheeler domain conversion to obtain the Wheeler domain seismic volume after well-controlled phase conversion; Using the Wheeler domain seismic volume after the well-controlled phase conversion to train a pre-built initial sedimentary background prediction model, a sedimentary background prediction model is obtained; wherein the initial sedimentary background prediction model is based on a stacked autoencoder and includes multiple encoders and multiple decoders; Inputting the Wheeler domain seismic volume after the well-controlled phase conversion into the sedimentary background prediction model to obtain sedimentary background single-channel data; Expanding the sedimentary background single-channel data based on the original seismic volume to obtain a sedimentary background volume; Performing volume operations on the Wheeler domain seismic volume after the well-controlled phase conversion and the sedimentary background volume to obtain a lithologic volume after removing the sedimentary background; The initial deposition background prediction model comprises a plurality of encoders connected in sequence and a plurality of decoders having the same number as the encoders; The method of using the Wheeler domain seismic volume after the well-controlled phase conversion to train a pre-built initial sedimentary background prediction model to obtain a sedimentary background prediction model includes: Using the Wheeler domain seismic volume after the well-controlled phase conversion, training an autoencoder consisting of a first encoder and a last decoder to obtain a trained first encoder; Encoding the Wheeler domain seismic volume after the well-controlled phase conversion using the trained first encoder to obtain a first latent vector data set; Using the first hidden vector data set to train an autoencoder consisting of a second encoder and a penultimate decoder to obtain a trained second encoder; Encoding the first latent vector data set using the trained second encoder to obtain a second latent vector data set; The above process of training the autoencoder and using the corresponding encoder to encode the input hidden vector data set is repeated until the deposition background prediction model consisting of multiple trained encoders connected in sequence is obtained.

2. The method according to claim 1, wherein The step of expanding the sedimentary background single-channel data based on the original seismic volume to obtain a sedimentary background volume includes: Obtaining the total number of dimensions of the original seismic volume; The sedimentary background single-channel data is copied and expanded based on the total number of dimensions to obtain a sedimentary background volume with the same total number of dimensions as the original seismic volume.

3. The method according to claim 1, wherein The performing of volume operations on the Wheeler domain seismic volume after the well-controlled phase conversion and the sedimentary background volume to obtain a lithologic volume after removing the sedimentary background includes: Based on the following formula 1, a volume operation is performed on the Wheeler domain seismic volume after the well-controlled phase conversion and the sedimentary background volume, and the sedimentary background volume in the Wheeler domain seismic volume after the well-controlled phase conversion is removed to obtain a lithologic volume after the sedimentary background is removed: Where, is the Wheeler domain seismic volume after well-controlled phase conversion, S b is the sedimentary background body, S l Lithologic body data.

4. The method according to claim 1, wherein The performing of well-controlled phase conversion and Wheeler domain conversion on the original seismic volume to obtain the Wheeler domain seismic volume after well-controlled phase conversion includes: Perform well-controlled phase conversion on the original seismic volume to obtain a 90-degree phased seismic volume; The 90-degree phased seismic volume is subjected to a Wheeler domain transformation to obtain a well-controlled phase-converted Wheeler domain seismic volume.

5. The method according to claim 1, wherein Before obtaining the sedimentation background prediction model by using the Wheeler domain seismic volume after the well-controlled phase conversion to train the pre-built initial sedimentation background prediction model, the method further includes: Based on the following formula 2, the Wheeler domain seismic volume after the well-controlled phase conversion is normalized: Where S is the normalized Wheeler domain seismic volume, is the Wheeler domain seismic volume after well-controlled phase conversion, is the minimum value of the Wheeler domain seismic volume after well-controlled phase conversion, It is the maximum value of the Wheeler domain seismic volume after well-controlled phase conversion.

6. A device for removing sedimentation background based on a stacked autoencoder, characterized in that: include: The first conversion module is used to perform well-controlled phase conversion on the original seismic volume and perform Wheeler domain conversion to obtain a Wheeler domain seismic volume after well-controlled phase conversion; A first training module is configured to train a pre-constructed initial sedimentation background prediction model using the Wheeler domain seismic volume after the well-controlled phase conversion to obtain a sedimentation background prediction model; wherein the initial sedimentation background prediction model is constructed based on a stacked autoencoder and includes a plurality of encoders and a plurality of decoders; the initial sedimentation background prediction model includes a plurality of encoders connected in sequence and a plurality of decoders having the same number as the encoders; the training of the pre-constructed initial sedimentation background prediction model using the Wheeler domain seismic volume after the well-controlled phase conversion to obtain a sedimentation background prediction model comprises: using the Wheeler domain seismic volume after the well-controlled phase conversion to train an autoencoder consisting of a first encoder and a last decoder to obtain a trained first encoder; using the trained first encoder to encode the Wheeler domain seismic volume after the well-controlled phase conversion to obtain a first latent vector data set; using the first latent vector data set to train an autoencoder consisting of a second encoder and a second-to-last decoder to obtain a trained second encoder; using the trained second encoder to encode the first latent vector data set to obtain a second latent vector data set; repeating the above process of training the autoencoder and using the corresponding encoder to encode the input latent vector data set until the sedimentation background prediction model consisting of a plurality of trained encoders connected in sequence is trained; A first prediction module is configured to input the Wheeler domain seismic volume after the well-controlled phase conversion into the sedimentary background prediction model to obtain sedimentary background single-channel data; A first operation module is used to expand the sedimentary background single-channel data based on the original seismic volume to obtain a sedimentary background volume; The second operation module is used to perform volume operation on the Wheeler domain seismic volume after the well-controlled phase conversion and the sedimentary background volume to obtain a lithologic volume after removing the sedimentary background.

7. A computer-readable storage medium storing instructions, which, when executed on a terminal, causes the terminal to execute the method for removing deposition background based on a stacked autoencoder according to any one of claims 1 to 5.

8. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for removing deposition background based on a stacked autoencoder according to any one of claims 1 to 5 is implemented.

9. A computer program product comprising instructions, which, when executed on a computer device, enables the computer device to execute the method for removing deposition background based on a stacked autoencoder according to any one of claims 1 to 5.

10. A chip comprising a processor and a communication interface, wherein the communication interface and the processor are coupled, and the processor is configured to execute a computer program or instruction to implement the method for removing deposition background based on a stacked autoencoder according to any one of claims 1 to 5.

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