A method and device for delineating a river course

Abstract

This invention provides a method and apparatus for characterizing river channels, solving the problem that conventional river channel characterization methods struggle to clearly characterize channels with weak reflectivity. One embodiment of the invention provides a method for characterizing river channels comprising: acquiring pre-stack gathers of the river channel and optimizing the pre-stack gathers; acquiring data and performing well-seismic calibration based on the data; analyzing the AVO response characteristics of different channels based on the well-seismic calibration results and obtaining weighting coefficients based on the AVO response characteristics; and characterizing the river channel based on the AVO response characteristics of the different channels and the weighting coefficients.

Description

Technical Field

[0001] This invention relates to the field of seismic exploration technology, specifically to a method and apparatus for depicting river channels. Background Technology

[0002] Fluvial sandstone reservoirs are the main oil and gas reservoirs in my country's continental basins. However, some channel sandstone reservoirs have weak reflection characteristics, and the impedance difference between conventional sandstone reservoirs and surrounding rocks is large, resulting in strong reflection characteristics on conventional superimposed profiles, exhibiting typical Type III AVO features. However, due to the superposition of channel sandstone bodies and variations in thickness and physical properties, some channels have weak seismic reflection characteristics and unclear channel boundaries, making it difficult to clearly characterize these "hidden" channels using conventional channel delineation methods. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a method and apparatus for depicting river channels, which solves the problem that conventional river channel depiction methods are unable to clearly depict river channels with weak reflective features.

[0004] An embodiment of the present invention provides a method for depicting a river channel, comprising:

[0005] Obtain the pre-stack set of the river channel and optimize the pre-stack set;

[0006] Acquire data and perform well-seismic calibration based on the data.

[0007] Based on the well seismic calibration results, the AVO response characteristics of different river channels were analyzed, and weighting coefficients were obtained based on the AVO response characteristics.

[0008] The river channels are characterized based on the AVO response characteristics of the different channels and the weighting coefficients.

[0009] In one embodiment, the step of optimizing the pre-stack gather includes: performing optimization processing on the pre-stack gather using spatial consistency techniques, while preserving the AVO features of the pre-stack gather.

[0010] In image segmentation, a point in an image has a high probability of sharing the same class attribute as points in its surrounding neighborhood. This characteristic is called the spatial consistency property of an image.

[0011] AVO (Amplitude Variation with Offset) technology is used to study the variation of seismic reflection amplitude with the distance between the shot point and receiver (i.e., the shot-receiver offset (or incident angle)). This allows for the exploration of the variation in reflection coefficient response with shot-receiver offset (or incident angle), thereby determining the lithological characteristics and physical properties of the overlying and underlying media at the reflection interface. With the help of AVO analysis, geophysicists can better assess the rock properties of hydrocarbon reservoirs, including porosity, density, lithology, and fluid content. The theoretical basis of AVO is Zoeppritz's equatorial equation. Based on pre-stack seismic inversion developed according to AVO theory, formation elastic parameters can be predicted through observed seismic data, making it an important quantitative interpretation technique for seismic data.

[0012] In one embodiment, the data includes at least one of seismic data, well logging data, and drilling data.

[0013] In one embodiment, the step of acquiring data and performing well seismic calibration based on the data includes:

[0014] Acquire seismic data and interpret seismic horizons based on the seismic data;

[0015] Acquire well logging data and interpret well logging sand bodies based on the well logging data;

[0016] The well-seismic calibration is performed based on the seismic stratigraphic interpretation and the well logging sand body interpretation.

[0017] In one implementation, the step of obtaining weighting coefficients based on the AVO response characteristics includes: analyzing the corresponding dominant channel sets according to the AVO response characteristics of different channels, thereby determining the weighting coefficients.

[0018] Based on the different AVO channel response characteristics, the corresponding dominant channels are analyzed to determine the weighting coefficients, as shown in the following formula.

[0019]

[0020] Where, x j a represents the amplitude value at the j-th angle. j This represents the weighted superposition coefficient for the j-th angle.

[0021] In one implementation, the step of characterizing the river channel based on the AVO response characteristics of the different river channels and the weighting coefficients includes: extracting the maximum trough attribute sensitive to the river channel based on the weighting coefficients, and characterizing the river channel based on the maximum trough attribute sensitive to the river channel.

[0022] A river channel sculpting device, comprising:

[0023] The acquisition module is used to acquire pre-stack gathers of the river channel and to acquire data.

[0024] An optimization module is used to optimize the pre-stack gather;

[0025] The calibration module is used to perform well vibration calibration based on the data.

[0026] The analysis module is used to analyze the AVO response characteristics of different channels based on the well seismic calibration results, and to obtain weighting coefficients based on the AVO response characteristics;

[0027] A characterization module is used to characterize the river channels based on the AVO response characteristics of the different river channels and the weighting coefficients.

[0028] After acquiring the pre-stack gathers of the river channel, the acquisition module transmits the data to the optimization module, which optimizes the pre-stack gathers. The acquisition module 10 also acquires data, which includes at least one of seismic data, well logging data, and drilling data. The acquisition module sends the acquired data to the calibration module, which performs well-seismic calibration based on the data. Then, the analysis module analyzes the AVO response characteristics of different river channels based on the well-seismic calibration results and obtains weighting coefficients based on the AVO response characteristics. Finally, the characterization module characterizes the river channel based on the AVO response characteristics of the different river channels and the weighting coefficients.

[0029] In one embodiment, the acquisition module is further configured to acquire seismic data and well logging data;

[0030] The channel characterization device also includes an interpretation module, used for interpreting seismic horizons based on the seismic data and interpreting well logging sand bodies based on the well logging data.

[0031] The calibration module is also used to perform well-seismic calibration based on the seismic horizon interpretation and the well logging sand body interpretation.

[0032] The acquisition module acquires data including seismic data and well logging data; the interpretation module performs seismic horizon interpretation based on the seismic data acquired by the acquisition module and well logging sand body interpretation based on the well logging data; subsequently, the calibration module is also used to perform well-seismic calibration based on the seismic horizon interpretation and the well logging sand body interpretation.

[0033] An electronic device includes a memory and a processor, the memory being used to store one or more computer instructions, wherein the one or more computer instructions, when executed by the processor, implement the river characterization method as described in any of the preceding claims.

[0034] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the river channel characterization method described in any one of the preceding claims.

[0035] The present invention provides a method and apparatus for characterizing a river channel. Starting from pre-stack data and based on AVO analysis technology, it determines the dominant channel set based on understanding the AVO type of the hidden channel, and performs a clear characterization of the hidden channel based on weighted stacking technology.

[0036] Compared with existing technologies, this invention has the following innovative features:

[0037] (1) This invention starts from pre-stack gathers and determines the dominant gathers based on AVO analysis, thus overcoming the balancing effect of conventional stacking methods.

[0038] (2) The present invention uses a weighted superposition method to highlight different AVO types of waterways and realize the depiction of hidden waterways. Attached Figure Description

[0039] Figure 1 The diagram shown is a flowchart of a river channel characterization method provided in an embodiment of the present invention.

[0040] Figure 2 The diagram shown is a flowchart of a well vibration calibration method provided in an embodiment of the present invention.

[0041] Figure 3 The diagram shown is a structural schematic of a river channel marking device provided in an embodiment of the present invention.

[0042] Figure 4a The diagram shown is a schematic diagram of an A1 well orthogonal set provided in an embodiment of the present invention.

[0043] Figure 4b The diagram shown is a schematic diagram of an A2 well forward set provided in an embodiment of the present invention.

[0044] Figure 5a The diagram shown is a schematic diagram of a pre-stack gather for the A1 well bypass provided in an embodiment of the present invention.

[0045] Figure 5b The diagram shown is a schematic diagram of a pre-stack set of the wellside track of well A2 provided by an embodiment of the present invention.

[0046] Figure 6 The image shown is a property map of the maximum valley with constant coefficient superposition provided in an embodiment of the present invention.

[0047] Figure 7 The image shown is a three-class weighted superposition maximum trough attribute map provided by an embodiment of the present invention.

[0048] Figure 8 The image shown is a four-type weighted superposition maximum valley attribute map provided by an embodiment of the present invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] Fluvial sandstone reservoirs are the main oil and gas reservoirs in my country's continental basins. Conventional sandstone reservoirs have a large difference in impedance with the surrounding rocks and exhibit strong reflection characteristics on conventional superimposed profiles, with typical Type III AVO characteristics.

[0051] The commonly used river channel delineation techniques mainly include the following methods:

[0052] (1) Waveform classification technology: The Stratimagic seismic phase analysis software developed by the French company CGG first implemented three-dimensional seismic waveform classification technology. It mainly utilizes the amplitude, frequency and phase information contained in the seismic waveform to avoid the multiple solutions caused by a single parameter, which can improve the resolution of the channel. However, it is difficult to determine the analysis window and the number of classification types, which has certain limitations in actual operation.

[0053] (2) Multi-attribute fusion technology: Based on RGB fusion technology, data from different frequency bands are fused to depict the shape and boundary of the channel of the Shaximiao Formation in the Zhongjiang Gas Field. However, the different frequency band data are manually selected, which has a certain degree of randomness. The selection of different frequency band data affects the channel depiction results.

[0054] (3) Stratigraphic slicing technology: Based on isochronous slicing technology, the amplitude characteristics on the stratigraphic slices and the waveform change characteristics on the seismic profile are applied to depict the river channel according to the principle of "seismic trend as guide and well point phase determination".

[0055] However, due to the superposition of river sand bodies and the changes in thickness and physical properties, some river channels have weak seismic reflection characteristics and unclear river boundaries, making it difficult to clearly depict such "hidden" river channels using conventional river delineation methods.

[0056] To address the aforementioned issues, this invention firstly performs AVO-preserving gather optimization processing on pre-stack gather data; secondly, it comprehensively utilizes seismic, logging, and drilling data to perform refined well-seismic calibration, clarify the AVO response characteristics of river locations, determine dominant gathers, and determine stacking weight coefficients based on typical well AVO forward simulation results; finally, based on the stacked data volume, it extracts the maximum trough attribute sensitive to river channels to characterize hidden river channels.

[0057] This method starts from pre-stack gathers, uses AVO analysis technology to identify dominant gathers based on understanding the AVO types of hidden channels, and uses weighted stacking technology to clearly characterize hidden channels.

[0058] The specific implementation methods are as described in the following embodiments:

[0059] Example 1:

[0060] Figure 1 The diagram shown is a flowchart of a river channel characterization method provided in an embodiment of the present invention.

[0061] like Figure 1 As shown.

[0062] This embodiment provides a method for depicting river channels, which includes:

[0063] Step 01: Obtain the pre-stack set of the river channel and optimize the pre-stack set.

[0064] The step of optimizing the pre-stack gather includes: using spatial consistency techniques to optimize the pre-stack gather while preserving the AVO features of the pre-stack gather.

[0065] In image segmentation, a point in an image has a high probability of sharing the same class attribute as points in its surrounding neighborhood. This characteristic is called the spatial consistency property of an image.

[0066] AVO (Amplitude Variation with Offset) technology is used to study the variation of seismic reflection amplitude with the distance between the shot point and receiver (i.e., the shot-receiver offset (or incident angle)). This allows for the exploration of the variation in reflection coefficient response with shot-receiver offset (or incident angle), thereby determining the lithological characteristics and physical properties of the overlying and underlying media at the reflection interface. With the help of AVO analysis, geophysicists can better assess the rock properties of hydrocarbon reservoirs, including porosity, density, lithology, and fluid content. The theoretical basis of AVO is Zoeppritz's equatorial equation. Based on pre-stack seismic inversion developed according to AVO theory, formation elastic parameters can be predicted through observed seismic data, making it an important quantitative interpretation technique for seismic data.

[0067] Step 02: Obtain data and perform well-seismic calibration based on the data.

[0068] The data includes at least one of seismic data, well logging data, and drilling data.

[0069] Figure 2 The diagram shown is a flowchart of a well vibration calibration method provided in an embodiment of the present invention.

[0070] like Figure 2 As shown.

[0071] The steps of acquiring data and performing well-seismic calibration based on the data include:

[0072] Step 021: Acquire seismic data and interpret seismic horizons based on the seismic data;

[0073] Step 022: Acquire logging data and interpret logging sand bodies based on the logging data.

[0074] Optionally, interpreting the logging sand body based on the logging data includes: using the logging data to interpret the top and bottom interfaces and the thickness of the channel sand body at the target layer.

[0075] Step 023: Perform well-seismic calibration based on the seismic stratigraphic interpretation and the well logging sand body interpretation. A detailed well-seismic calibration is performed by comprehensively utilizing seismic, well logging, and drilling data.

[0076] Step 03: Analyze the AVO response characteristics of different river channels based on the well seismic calibration results, and obtain weighting coefficients based on the AVO response characteristics.

[0077] Based on the different AVO channel response characteristics, the corresponding dominant channels are analyzed to determine the weighting coefficients, as shown in the following formula.

[0078]

[0079] Where, x j a represents the amplitude value at the j-th angle. j This represents the weighted superposition coefficient for the j-th angle.

[0080] Step 04: Characterize the river channels based on the AVO response characteristics of the different channels and the weighting coefficients. Based on the weighting coefficients of different overlay data volumes, extract the maximum trough attribute to characterize channels with different AVO types.

[0081] The method for characterizing river channels provided in this embodiment of the invention starts from pre-stack data, uses AVO analysis technology, determines the dominant channel set based on understanding the AVO type of the hidden channel, and performs a clear characterization of the hidden channel based on weighted stacking technology.

[0082] Compared with existing technologies, this invention has the following innovative features:

[0083] (1) This invention starts from pre-stack gathers and determines the dominant gathers based on AVO analysis, thus overcoming the balancing effect of conventional stacking methods.

[0084] (2) The present invention uses a weighted superposition method to highlight different AVO types of waterways and realize the depiction of hidden waterways.

[0085] Example 2:

[0086] Figure 3 The diagram shown is a structural schematic of a river channel marking device provided in an embodiment of the present invention.

[0087] like Figure 3 As shown.

[0088] This embodiment provides a river channel characterization device 100, which includes: an acquisition module 10, an optimization module 20, a calibration module 30, an analysis module 40, and a characterization module 50.

[0089] Among them, the acquisition module 10 is used to acquire the pre-stack gathers of the river channel and acquire data.

[0090] Optimization module 20 is used to optimize the pre-stack gather;

[0091] Calibration module 30 is used for well vibration calibration based on the data.

[0092] Analysis module 40 is used to analyze the AVO response characteristics of different channels based on the well seismic calibration results, and to obtain weighting coefficients based on the AVO response characteristics;

[0093] Characterization module 50 is used to characterize the river channel based on the AVO response characteristics of the different river channels and the weighting coefficients.

[0094] After acquiring the pre-stack gathers of the river channel, the acquisition module 10 transmits the data to the optimization module 20, which optimizes the pre-stack gathers. The acquisition module 10 also acquires data, which includes at least one of seismic data, well logging data, and drilling data. The acquisition module 10 sends the acquired data to the calibration module 30, which performs well-seismic calibration based on the data. Then, the analysis module 40 analyzes the AVO response characteristics of different river channels based on the well-seismic calibration results and obtains weighting coefficients based on the AVO response characteristics. Finally, the characterization module 50 characterizes the river channel based on the AVO response characteristics of the different river channels and the weighting coefficients.

[0095] In this invention, the acquisition module 10 can acquire the pre-stack gathers and data of the river channel simultaneously, or acquire the pre-stack gathers first and then the data; or acquire the data first and then the pre-stack gathers. The present invention does not limit the order in which the acquisition module 10 acquires the pre-stack gathers and data of the river channel.

[0096] In addition, the acquisition module 10 is also used to acquire seismic data and well logging data;

[0097] The river channel delineation device also includes an interpretation module 60;

[0098] The interpretation module 60 is used to interpret seismic horizons based on the seismic data and to interpret well logging sand bodies based on the well logging data.

[0099] The calibration module is also used to perform well-seismic calibration based on the seismic horizon interpretation and the well logging sand body interpretation.

[0100] The acquisition module 10 acquires data including seismic data and well logging data; the interpretation module 60 performs seismic horizon interpretation based on the seismic data acquired by the acquisition module 10 and well logging sand body interpretation based on the well logging data; subsequently, the calibration module 30 is also used to perform well-seismic calibration based on the seismic horizon interpretation and the well logging sand body interpretation.

[0101] The optimization module 20 is also used to perform pre-stack gather optimization processing using spatial consistency technology, while preserving the AVO characteristics of the pre-stack gather.

[0102] Analysis module 40 is also used to analyze the corresponding dominant channels based on the AVO response characteristics of different channels, thereby determining the weighting coefficients.

[0103] The characterization module 50 is also used to extract the maximum trough attribute that is sensitive to the river channel based on the weighting coefficient, and to characterize the river channel based on the maximum trough attribute that is sensitive to the river channel.

[0104] The method for characterizing river channels provided in this embodiment of the invention starts from pre-stack data, uses AVO analysis technology, determines the dominant channel set based on understanding the AVO type of the hidden channel, and performs a clear characterization of the hidden channel based on weighted stacking technology.

[0105] Compared with existing technologies, this invention has the following innovative features:

[0106] (1) This invention starts from pre-stack gathers and determines the dominant gathers based on AVO analysis, thus overcoming the balancing effect of conventional stacking methods.

[0107] (2) The present invention uses a weighted superposition method to highlight different AVO types of waterways and realize the depiction of hidden waterways.

[0108] Example 3:

[0109] Figure 4a The diagram shown is a schematic diagram of an A1 well orthogonal set provided in an embodiment of the present invention.

[0110] Figure 4b The diagram shown is a schematic diagram of an A2 well forward set provided in an embodiment of the present invention.

[0111] Figure 5a The diagram shown is a schematic diagram of a pre-stack gather for the A1 well bypass provided in an embodiment of the present invention.

[0112] Figure 5b The diagram shown is a schematic diagram of a pre-stack set of the wellside track of well A2 provided by an embodiment of the present invention.

[0113] Figure 6 The image shown is a property map of the maximum valley with constant coefficient superposition provided in an embodiment of the present invention.

[0114] Figure 7 The image shown is a three-class weighted superposition maximum trough attribute map provided by an embodiment of the present invention.

[0115] Figure 8 The image shown is a four-type weighted superposition maximum valley attribute map provided by an embodiment of the present invention.

[0116] This embodiment illustrates the implementation process and application effects of the present invention using an example. Taking data from a real work area in China as an example, a study on the characterization of hidden river channels is conducted on layer T1.

[0117] Step 1: Pre-stack gather optimization. Pre-stack gather optimization is performed using techniques such as spatial consistency to preserve the AVO characteristics of the gathers.

[0118] Step 2: Seismic stratum interpretation. The T1 layer is traced using seismic data.

[0119] Step 3: Well logging sand body interpretation. Use well logging data to interpret the top and bottom interfaces and thickness of the T1 layer channel sand body.

[0120] Step 4: Well-seismic calibration. A detailed well-seismic calibration is performed by comprehensively utilizing seismic, logging, and drilling data.

[0121] Step 5: Analysis of typical channel sand AVO types.

[0122] Combining well logging interpretation results with typical well forward modeling results, it can be seen that the channel sand bodies in the T1 layer of the work area are mainly composed of Class III and Class IV AVOs, such as Figure 4a and Figure 4b As shown.

[0123] Based on the well seismic calibration results, the gather characteristics of typical well-side channels were analyzed. The channels in this layer are all low-resistivity gas-bearing sandstone, exhibiting Class III and IV AVO characteristics, such as... Figure 5a and Figure 5b As shown.

[0124] Depend on Figures 5a-5b It can be seen that the amplitude intensity of the three types of AVO increases with the increase of the incident angle, and the amplitude intensity is stronger when the incident angle is perpendicular; while the amplitude intensity of the four types of AVO decreases with the increase of the incident angle, and the amplitude intensity is relatively weak when the incident angle is perpendicular.

[0125] When using conventional equal-weight coefficients for superposition, the three types of AVO channels may still have strong reflection intensity, appearing as bright spots on the superimposed profile, and can be characterized using conventional methods; however, due to the equalization effect during the superposition process, the four types of AVO channels exhibit moderate to weak reflection on the superimposed profile, making it difficult to characterize the channels using conventional methods.

[0126] Figure 6 The maximum trough attribute of the data is the sum of the conventional equal-weight coefficients. Figure 6 It can be seen that the river boundary is blurred within the red circle, making it difficult to clearly depict the river channel.

[0127] Step Six: Determine the weighting coefficients. Based on Step Five, in order to characterize the channel distribution characteristics of layer T1, especially the four types of AVO channels, the corresponding weighting coefficients are determined according to the response characteristics of different AVO channel types.

[0128] This invention is based on forward simulation results such as Figure 4a and Figure 4b As shown, the weighting coefficients for each channel are determined using formula (1).

[0129]

[0130] Where, x j a represents the amplitude value at the j-th angle. jThis represents the weighted superposition coefficient for the j-th angle.

[0131] Step 7: Delineate the hidden river channel.

[0132] Using the weighting coefficients determined in step six for both Type III and Type IV AVO channel reservoirs, the data were then stacked separately, resulting in Type III weighted stacked data volumes and Type IV weighted stacked data volumes, respectively. For each type of stacked data volume, the maximum trough attribute was extracted.

[0133] like Figure 7 and Figure 8 As shown, by Figure 7 It can be seen that, compared with conventional overlay, the three-type weighted overlay clearly depicts the river channels in region 1, while also adding the river channels in region 3; Figure 8 It can be seen that the river boundaries in region 2 are clearer in the four types of weighted overlay data, and at the same time, the river in region 4 is depicted.

[0134] Therefore, it can be seen that the present invention can depict hidden waterways.

[0135] Therefore, this invention has the following innovative aspects compared to existing technologies:

[0136] (1) This invention starts from pre-stack gathers and determines the dominant gathers based on AVO analysis, thus overcoming the balancing effect of conventional stacking methods.

[0137] (2) The present invention uses a weighted superposition method to highlight different AVO types of waterways and realize the depiction of hidden waterways.

[0138] Example 4:

[0139] This embodiment provides an electronic device that may include a memory and a processor. The memory stores a computer program, which, when executed by the processor, implements the reservoir parameter prediction method as described in Embodiment 1. It is understood that the electronic device may further include an input / output (I / O) interface and communication components.

[0140] The processor is used to perform the following steps:

[0141] Step 01: Obtain the pre-stack set of the river channel and optimize the pre-stack set.

[0142] The step of optimizing the pre-stack gather includes: using spatial consistency techniques to optimize the pre-stack gather while preserving the AVO features of the pre-stack gather.

[0143] In image segmentation, a point in an image has a high probability of sharing the same class attribute as points in its surrounding neighborhood. This characteristic is called the spatial consistency property of an image.

[0144] AVO (Amplitude Variation with Offset) technology is used to study the variation of seismic reflection amplitude with the distance between the shot point and receiver (i.e., the shot-receiver offset (or incident angle)). This allows for the exploration of the variation in reflection coefficient response with shot-receiver offset (or incident angle), thereby determining the lithological characteristics and physical properties of the overlying and underlying media at the reflection interface. With the help of AVO analysis, geophysicists can better assess the rock properties of hydrocarbon reservoirs, including porosity, density, lithology, and fluid content. The theoretical basis of AVO is Zoeppritz's equatorial equation. Based on pre-stack seismic inversion developed according to AVO theory, formation elastic parameters can be predicted through observed seismic data, making it an important quantitative interpretation technique for seismic data.

[0145] Step 02: Obtain data and perform well-seismic calibration based on the data.

[0146] The data includes at least one of seismic data, well logging data, and drilling data.

[0147] The steps of acquiring data and performing well-seismic calibration based on the data include:

[0148] Step 021: Acquire seismic data and interpret seismic horizons based on the seismic data;

[0149] Step 022: Acquire logging data and interpret logging sand bodies based on the logging data.

[0150] Optionally, interpreting the logging sand body based on the logging data includes: using the logging data to interpret the top and bottom interfaces and the thickness of the channel sand body at the target layer.

[0151] Step 023: Perform well-seismic calibration based on the seismic stratigraphic interpretation and the well logging sand body interpretation. A detailed well-seismic calibration is performed by comprehensively utilizing seismic, well logging, and drilling data.

[0152] Step 03: Analyze the AVO response characteristics of different river channels based on the well seismic calibration results, and obtain weighting coefficients based on the AVO response characteristics.

[0153] Based on the different AVO channel response characteristics, the corresponding dominant channels are analyzed to determine the weighting coefficients, as shown in the following formula.

[0154]

[0155] Where, x j a represents the amplitude value at the j-th angle. j This represents the weighted superposition coefficient for the j-th angle.

[0156] Step 04: Characterize the river channels based on the AVO response characteristics of the different channels and the weighting coefficients. Based on the weighting coefficients of different overlay data volumes, extract the maximum trough attribute to characterize channels with different AVO types.

[0157] The method for characterizing river channels provided in this embodiment of the invention starts from pre-stack data, uses AVO analysis technology, determines the dominant channel set based on understanding the AVO type of the hidden channel, and performs a clear characterization of the hidden channel based on weighted stacking technology.

[0158] Compared with existing technologies, this invention has the following innovative features:

[0159] (1) This invention starts from pre-stack gathers and determines the dominant gathers based on AVO analysis, thus overcoming the balancing effect of conventional stacking methods.

[0160] (2) The present invention uses a weighted superposition method to highlight different AVO types of waterways and realize the depiction of hidden waterways.

[0161] Memory is used to store various types of data, which may include, for example, instructions for any application or method in an electronic device, as well as application-related data.

[0162] The processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic components, and is used to execute the reservoir parameter prediction method in the above embodiments.

[0163] The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0164] Example 5:

[0165] This embodiment also provides a computer-readable storage medium. The functional units in the various embodiments of the present invention 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. If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0166] Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0167] The aforementioned storage media include: flash memory, hard disks, multimedia cards, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disks, optical discs, servers, APP application stores, and various other media capable of storing program verification codes, on which computer programs are stored. When the computer program is executed by a processor, it can implement the following method steps:

[0168] Step 01: Obtain the pre-stack set of the river channel and optimize the pre-stack set.

[0169] The step of optimizing the pre-stack gather includes: using spatial consistency techniques to optimize the pre-stack gather while preserving the AVO features of the pre-stack gather.

[0170] In image segmentation, a point in an image has a high probability of sharing the same class attribute as points in its surrounding neighborhood. This characteristic is called the spatial consistency property of an image.

[0171] AVO (Amplitude Variation with Offset) technology is used to study the variation of seismic reflection amplitude with the distance between the shot point and receiver (i.e., the shot-receiver offset (or incident angle)). This allows for the exploration of the variation in reflection coefficient response with shot-receiver offset (or incident angle), thereby determining the lithological characteristics and physical properties of the overlying and underlying media at the reflection interface. With the help of AVO analysis, geophysicists can better assess the rock properties of hydrocarbon reservoirs, including porosity, density, lithology, and fluid content. The theoretical basis of AVO is Zoeppritz's equatorial equation. Based on pre-stack seismic inversion developed according to AVO theory, formation elastic parameters can be predicted through observed seismic data, making it an important quantitative interpretation technique for seismic data.

[0172] Step 02: Obtain data and perform well-seismic calibration based on the data.

[0173] The data includes at least one of seismic data, well logging data, and drilling data.

[0174] The steps of acquiring data and performing well-seismic calibration based on the data include:

[0175] Step 021: Acquire seismic data and interpret seismic horizons based on the seismic data;

[0176] Step 022: Acquire logging data and interpret logging sand bodies based on the logging data.

[0177] Optionally, interpreting the logging sand body based on the logging data includes: using the logging data to interpret the top and bottom interfaces and the thickness of the channel sand body at the target layer.

[0178] Step 023: Perform well-seismic calibration based on the seismic stratigraphic interpretation and the well logging sand body interpretation. A detailed well-seismic calibration is performed by comprehensively utilizing seismic, well logging, and drilling data.

[0179] Step 03: Analyze the AVO response characteristics of different river channels based on the well seismic calibration results, and obtain weighting coefficients based on the AVO response characteristics.

[0180] Based on the different AVO channel response characteristics, the corresponding dominant channels are analyzed to determine the weighting coefficients, as shown in the following formula.

[0181]

[0182] Where, x j a represents the amplitude value at the j-th angle. j This represents the weighted superposition coefficient for the j-th angle.

[0183] Step 04: Characterize the river channels based on the AVO response characteristics of the different channels and the weighting coefficients. Based on the weighting coefficients of different overlay data volumes, extract the maximum trough attribute to characterize channels with different AVO types.

[0184] The method for characterizing river channels provided in this embodiment of the invention starts from pre-stack data, uses AVO analysis technology, determines the dominant channel set based on understanding the AVO type of the hidden channel, and performs a clear characterization of the hidden channel based on weighted stacking technology.

[0185] Compared with existing technologies, this invention has the following innovative features:

[0186] (1) This invention starts from pre-stack gathers and determines the dominant gathers based on AVO analysis, thus overcoming the balancing effect of conventional stacking methods.

[0187] (2) The present invention uses a weighted superposition method to highlight different AVO types of waterways and realize the depiction of hidden waterways.

[0188] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention. It will be clearly understood by those skilled in the art that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0189] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units 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.

[0190] Another point is that the displayed or discussed mutual couplings, direct couplings, or communication connections can be indirect couplings or communication connections through some interfaces, devices, or units, and can be electrical, mechanical, or other forms. The units described as separate components may or may not be physically separate; the components shown as units may or may not be physical units, i.e., 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.

[0191] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0192] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner.

[0193] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0194] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0195] In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications (such as up, down, left, right, front, back, top, bottom, etc.) in the embodiments of this application are only used to explain the relative positional relationships and movement of the components in a specific posture (as shown in the figures). If the specific posture changes, the directional indication will also change accordingly. 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 device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0196] Furthermore, the reference to "embodiment" herein means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0197] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0198] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method of river delineation, characterized by, The method comprises the following steps: acquiring prestack gathers of river channels and optimizing the prestack gathers; acquiring data, and calibrating wells and seismic data based on the data; analyzing AVO response characteristics of different river channels based on the calibration results, and obtaining weighting coefficients based on the AVO response characteristics; characterizing river channels based on the AVO response characteristics of different river channels and the weighting coefficients; the step of acquiring data and calibrating wells and seismic data based on the data comprises the following steps: acquiring seismic data, interpreting seismic horizons based on the seismic data; acquiring logging data, interpreting logging sand bodies based on the logging data; and calibrating wells and seismic data based on the seismic horizon interpretation and the logging sand body interpretation; the step of obtaining weighting coefficients based on the AVO response characteristics comprises the following step: analyzing corresponding dominant gathers according to the AVO response characteristics of different river channels, so as to determine the weighting coefficients; the step of characterizing river channels based on the AVO response characteristics of different river channels and the weighting coefficients comprises the following steps: extracting river channel sensitive maximum trough attributes based on the weighting coefficients, and characterizing river channels based on the river channel sensitive maximum trough attributes; the weighting coefficients are determined according to different AVO river channel response characteristics by analyzing corresponding dominant gathers, and are expressed as follows: （1） wherein, x j represents the amplitude value of the jth angle, a j represents the weighted superposition weight coefficient of the jth angle.

2. The riverway delineation method according to claim 1, characterized by, the step of optimizing the prestack gathers comprises the following step: performing prestack gather optimization processing by using spatial consistency technology, and preserving AVO characteristics of the prestack gathers.

3. The river carving method according to claim 1, wherein The data comprises at least one of seismic data, logging data and drilling data.

4. A river course delineation device characterized by comprising: The method comprises the following steps: an acquiring module, which is configured to acquire prestack gathers of river channels and acquire data; an optimizing module, which is configured to optimize the prestack gathers; a calibrating module, which is configured to calibrate wells and seismic data based on the data; an analyzing module, which is configured to analyze AVO response characteristics of different river channels based on the calibration results, and obtain weighting coefficients based on the AVO response characteristics; a characterizing module, which is configured to characterize river channels based on the AVO response characteristics of different river channels and the weighting coefficients; the acquiring module is further configured to acquire seismic data and acquire logging data; the river channel characterization device further comprises an interpreting module, which is configured to interpret seismic horizons based on the seismic data, and interpret logging sand bodies based on the logging data; and the calibrating module is further configured to calibrate wells and seismic data based on the seismic horizon interpretation and the logging sand body interpretation; the analyzing module is further configured to analyze corresponding dominant gathers according to the AVO response characteristics of different river channels, so as to determine the weighting coefficients; the characterizing module is further configured to extract river channel sensitive maximum trough attributes based on the weighting coefficients, and characterize river channels based on the river channel sensitive maximum trough attributes; the weighting coefficients are determined according to different AVO river channel response characteristics by analyzing corresponding dominant gathers, and are expressed as follows: （1） wherein, x j represents the amplitude value of the jth angle, a j represents the weighted superposition weight coefficient of the jth angle.

5. An electronic device, comprising: a device comprising a memory and a processor, wherein the memory is configured to store one or more computer instructions, and the one or more computer instructions are configured to be executed by the processor to implement the river channel characterization method according to any one of claims 1-3.

6. A computer readable storage medium characterized by The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the river channel delineation method according to any one of claims 1-3.

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