A river channel sand body identification method and system
By establishing a framework model and performing spatial interpolation of logging curves and lithological velocity assignment, the problem of inaccurate channel sand body identification in conventional geological modeling was solved, achieving efficient channel sand body identification and reservoir prediction.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA UNIV OF PETROLEUM (BEIJING)
- Filing Date
- 2023-12-29
- Publication Date
- 2026-06-09
Smart Images
Figure CN117784236B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to a method and system for identifying riverbed sand bodies, belonging to the field of petroleum exploration and development technology. Background Technology
[0002] The basic idea and implementation method of establishing a conventional theoretical geological model is as follows: starting from a single well, firstly, a theoretical model of channel sand geology of the wells is drawn by filling in different lithological elastic parameters of the single well. Then, the principle of folding is applied to extract the dominant frequency of seismic data and perform forward modeling on the theoretical model.
[0003] However, this modeling method has the following problems:
[0004] ①From a modeling perspective, due to the different mud content of sand bodies in single wells, there are certain differences in velocity. The theoretical model cannot accurately assign different sand and mudstone velocities to each well, and cannot well reflect the lithological changes of single wells in the vertical direction.
[0005] ② It cannot reflect the changes in the lateral velocity of sand bodies in areas with no wells or few wells, and cannot accurately depict the distribution morphology of sand bodies between wells. Its applicable conditions are harsh and its practicality is limited. Summary of the Invention
[0006] To address the aforementioned problems, the present invention aims to provide a method and system for identifying channel sand bodies. By defining the contact relationship of the target layer and the sedimentary pattern of the stratigraphic unit to establish a framework model, and by changing the inter-well interpolation rate model through facies modeling, the consistency between forward modeling results and seismic response is improved, thereby achieving a new method for accurate identification of channel sand bodies.
[0007] To achieve the above objectives, the present invention adopts the following technical solution:
[0008] In a first aspect, the present invention provides a method for identifying riverbed sand bodies, comprising the following steps:
[0009] Based on well seismic data and field outcrop data, a well-to-well velocity model for channel sand bodies was established.
[0010] Forward modeling of the well velocity model was performed to generate a synthetic seismic record. By comparing and analyzing the similarity of the reflection characteristics of the synthetic seismic record with the actual seismic record of the channel sand body, the actual reservoir distribution of the channel sand body was predicted.
[0011] Furthermore, the well-to-well velocity model for channel sand bodies, based on well seismic data and field outcrop data, includes:
[0012] A framework model was established based on well seismic data, and spatial interpolation of logging curves was performed within the framework model to obtain the initial well connection velocity model.
[0013] Based on field outcrop data, pseudo-well lithology is filled into the frame model to obtain the pseudo-well lithology model;
[0014] The lithological velocity values were filled into the pseudo-well lithology model using rock physics chart analysis, and the initial well-connection velocity model was updated.
[0015] Furthermore, the step of establishing a framework model based on well seismic data and performing spatial interpolation of logging curves within the framework model to obtain the initial well-connection velocity model includes:
[0016] Preprocessing is performed on the logging curves in the seismic data, wherein the logging curves include at least sonic curves and gamma curves;
[0017] The preprocessed acoustic waveform and gamma curve are fitted to obtain the pseudo-acoustic waveform.
[0018] Based on the pseudo-acoustic curve, a framework model is established using the extracted optimal reservoir geophysical parameters, and spatial interpolation of well logging curves is performed to generate an initial well connection velocity model.
[0019] Furthermore, the well logging curves are preprocessed, including environmental correction and standardization.
[0020] Furthermore, the process of establishing a framework model based on pseudo-acoustic curves using extracted optimal reservoir geophysical parameters, and performing spatial interpolation of logging curves to generate an initial well-connection velocity model includes:
[0021] Well vibration calibration is performed using pseudo-acoustic curves;
[0022] Based on the calibration results, the extracted optimal reservoir geophysical parameters are used to control the morphology of the target layer and establish a framework model; among which, the optimal reservoir geophysical parameters include the unconformity contact relationship of the target layer and the sedimentary model of the stratigraphic unit.
[0023] Under the constraints of the framework model, spatial interpolation of logging curves is performed to generate an initial well connection rate model.
[0024] Furthermore, based on field outcrop data, the pseudo-well lithology model is obtained by filling the framework model with pseudo-well lithology, including:
[0025] In well-free areas, lithology is filled into the framework model using data such as field geological outcrops and modern channel sediments. The boundary of the channel sand body is quantitatively characterized by combining the seismic response characteristics of the channel with wells in this area, and a pseudo-well lithology model for the well-free area is established.
[0026] In sparse well regions, pseudo-well lithology models are established based on the logging curves and lithological characteristics of adjacent wells.
[0027] Furthermore, the step of filling the pseudo-well lithology model with lithological velocity values through rock physics chart analysis and updating the initial well velocity model includes:
[0028] A phase model was established, and the rate at which the lithology was filled into the pseudo-well lithology model was analyzed using a rock physics chart.
[0029] Under phase control, the location, thickness, and physical property parameters of the lithology filling the pseudo-well lithology model are adjusted to determine the reasonable velocity of each filling lithology in the pseudo-well lithology model. The initial well connection velocity model is then updated to obtain the final well connection velocity model.
[0030] Secondly, the present invention provides a riverbed sand body identification system, comprising:
[0031] The model building module is used to build a well-to-well velocity model of channel sand bodies based on well seismic data and field outcrop data;
[0032] The sand body identification and prediction module is used to perform forward modeling of the well velocity model to generate synthetic seismic records, and compare and analyze the similarity of the reflection characteristics of the synthetic seismic records with the actual seismic records of channel sand bodies to predict the actual distribution of channel sand bodies in the reservoir.
[0033] Thirdly, the present invention provides a computer-readable storage medium for storing one or more programs, said one or more programs including instructions that, when executed by a computing device, cause the computing device to perform any method.
[0034] Fourthly, the present invention provides a computing device, characterized in that it includes: one or more processors and a memory, wherein the memory stores one or more programs and is configured to be executed by the one or more processors, and the one or more programs include instructions for performing any method.
[0035] The present invention has the following advantages due to the adoption of the above technical solutions:
[0036] 1. The predicted development of the target sand body in this invention has a high degree of consistency with the actual drilling. At the same time, by comparing the reflection characteristics of synthetic earthquakes and actual earthquakes, the possible lithological combinations and distribution of the target layer can also be obtained.
[0037] 2. This invention utilizes high-frequency gamma information combined with low-frequency acoustic information to synthesize pseudo-acoustic curves, which can better distinguish between sandstone and mudstone, thereby improving the resolution and accuracy of seismic forward modeling.
[0038] This invention is fast, efficient, and easy to operate, making it suitable for accurate reservoir prediction during the exploration, reservoir evaluation, and early development stages. It can be widely applied in the field of petroleum exploration and development technology. Attached Figure Description
[0039] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. In the drawings:
[0040] Figure 1 This is a flowchart of the river sand body identification method provided in the embodiments of the present invention;
[0041] Figure 2 The mountain 2 provided in the embodiment of the present invention 3 +Intersection diagram of acoustic wave and wave impedance and lithology of Taiyuan Formation;
[0042] Figure 3 The mountain 2 provided in the embodiment of the present invention 3 + Cross-sectional diagram of pseudo-acoustic waves, wave impedance, and lithology of the Taiyuan Formation;
[0043] Figure 4 This is the logging curve environmental correction provided in the embodiments of the present invention;
[0044] Figure 5 This is the standardization of logging curves provided in the embodiments of the present invention;
[0045] Figure 6 This is a schematic diagram of the construction of the pseudo-acoustic wave curve provided in an embodiment of the present invention;
[0046] Figure 7 This is a wellbore velocity model profile provided in an embodiment of the present invention;
[0047] Figure 8 The phase velocity of the model medium provided in the embodiments of the present invention;
[0048] Figure 9 This is a schematic diagram illustrating how phase modeling alters the distribution morphology of sand bodies, provided in an embodiment of the present invention.
[0049] Figure 10 This is the velocity model updated after phase modeling provided in the embodiments of the present invention;
[0050] Figure 11 This is a forward modeling profile of well-connected geological folds provided in an embodiment of the present invention;
[0051] Figure 12 This is a forward modeling profile of phase velocities in different media provided in the embodiments of the present invention.
[0052] Figure 13 This is a comparison diagram of the seismic profile and the forward modeling profile provided in the embodiments of the present invention. Detailed Implementation
[0053] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.
[0054] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.
[0055] In some embodiments of the present invention, a method for identifying channel sand bodies is provided. Based on well seismic data and field outcrop data, the optimal reservoir geophysical parameters are extracted, and a well-to-well velocity model is established. A synthetic seismic record is generated through forward modeling, and the similarity between the synthetic seismic record and the actual seismic record of the channel sand body is compared and analyzed. This establishes a bridge between geology and seismology, enabling accurate identification of channel sand bodies and reservoir prediction.
[0056] Correspondingly, in other embodiments of the present invention, a riverbed sand body identification system, device, and storage medium are provided.
[0057] Example 1
[0058] like Figure 1 As shown, this embodiment provides a method for identifying riverbed sand bodies, including the following steps:
[0059] 1) Based on well seismic data and field outcrop data, establish a well-to-well velocity model for channel sand bodies.
[0060] Specifically, it includes the following steps:
[0061] 1.1) Based on well seismic data, establish an initial well-to-well velocity model.
[0062] The method for establishing the above-mentioned initial well-connection velocity model includes the following steps:
[0063] 1.1.1) Preprocess the logging curves in the well seismic data, including at least sonic curves and gamma curves.
[0064] To ensure good consistency and comparability when combining well logging and seismic data, this embodiment requires preprocessing of the logging curves. The main preprocessing measures include environmental correction and standardization. The methods for environmental correction and standardization are well-known to those skilled in the art, and will not be elaborated upon here.
[0065] 1.1.2) Fit the preprocessed acoustic wave curve and gamma curve to obtain the pseudo-acoustic wave curve.
[0066] By fitting environmentally corrected and standardized logging curves and gamma curves to generate pseudo-acoustic curves, it is possible to better distinguish between sandstone and mudstone, improve the vertical resolution of logging, and provide an accurate initial velocity model for forward modeling. The method for fitting the logging curves and gamma curves can employ techniques known to those skilled in the art, and this invention does not impose any limitations on this.
[0067] 1.1.3) Based on the pseudo-acoustic curve, an initial well-connection velocity model is established using the extracted optimal reservoir geophysical parameters.
[0068] Specifically, it includes the following steps:
[0069] ① Use pseudo-acoustic curves for fine calibration of well seismic data.
[0070] The purpose of using pseudo-acoustic curves for fine-grained well-seismic calibration is to clarify the geological significance of seismic reflection layers. During calibration, the dominant frequency of the seismic data from the work area is used, and the Ricker wavelet or well-side seismic wavelet convolution is adjusted to ensure that the characteristics of the synthesized seismic wave group at the marker layer are consistent with those of the actual seismic wave group. The specific calibration method is well-known to those skilled in the art and will not be elaborated upon in this invention.
[0071] ②Based on the calibration results, the extracted optimal reservoir geophysical parameters are used to control the morphology of the target layer and a framework model is established.
[0072] The optimal geophysical parameters extracted mainly include parameters such as the unconformity contact relationship (normal deposition or erosion) of the target stratigraphic unit and the sedimentary model (parallel top surface, parallel bottom surface, equal scale).
[0073] ③ Under the constraints of the framework model, spatial interpolation of logging curves is performed to generate an initial well connection rate model.
[0074] 1.2) Conduct collaborative geological modeling of pseudo-wells and actual drilled wells, that is, based on field outcrop data, fill the framework model with pseudo-well lithology to obtain pseudo-well lithology model.
[0075] The above-mentioned collaborative geological modeling of dummy wells and actual drilled wells includes the following steps:
[0076] 1.2.1) In well-free areas, lithology is filled into the framework model using data such as field geological outcrops and modern channel sediments. The boundary of the channel sand body is quantitatively characterized by combining the seismic response characteristics of the channel with wells in this area, and a pseudo-well lithology model for the well-free area is established.
[0077] 1.2.2) In sparse well areas, a pseudo-well lithology model is established based on the logging curves and lithological characteristics of adjacent wells.
[0078] 1.3) Perform co-velocity modeling of pseudo-wells and actual drilled wells, that is, fill the pseudo-well lithology model with lithological velocity values through rock physics chart analysis, and update the initial well-connection velocity model.
[0079] Specifically, it includes the following steps:
[0080] 1.3.1) Establish a lithofacies model and analyze the rate at which the lithology is filled into the pseudo-well lithology model using a rock physics chart.
[0081] Specifically, the lithofacies model is mainly established based on seismic facies identification and sedimentary division. Sedimentary division is mainly defined by the stratigraphic unit sedimentary model to define the contact relationship of the target layer (overlap, parallel to the top; cutoff, parallel to the bottom; normal deposition, proportional). Seismic facies identification is mainly based on the seismic response characteristics (seismic waveform, amplitude) of the channel revealed by the completed wells in the work area, such as the lenticular shape of the channel sand body on the seismic profile.
[0082] 1.3.2) Under phase control, the position, thickness, physical properties and other parameters of the lithology filled in the pseudo-well lithology model are adjusted to determine the reasonable velocity of each filling lithology in the pseudo-well lithology model, and the initial well connection velocity model is updated to obtain the final well connection velocity model.
[0083] 2) Use the well velocity model to perform forward modeling to generate synthetic seismic records, and predict the actual reservoir distribution of the channel sand bodies by comparing and analyzing the similarity of the reflection characteristics between the synthetic seismic records and the actual seismic records of the channel sand bodies.
[0084] Specifically, forward modeling is performed using the convolution principle. Wavelet extracted from actual seismic data of the work area is used for forward modeling. By adjusting the model parameters, the forward modeled seismic response is made as similar as possible to the actual seismic data. When the two are consistent, the geological significance of the actual seismic reflection characteristics can be determined, thereby predicting the distribution of river sand bodies.
[0085] Example 2
[0086] This embodiment uses Mountain 2 3 The method of the present invention will be further described using the identification of river sand bodies in the Taiyuan Group as an example.
[0087] like Figure 2 , Figure 3As shown in the figure, cross-plot analysis reveals that conventional acoustic waveforms can only distinguish between low-impedance coal seams and high-impedance limestone. 3 The acoustic impedances of sand and mudstone in the section of strata overlap significantly, and the pseudo-acoustic curves can effectively distinguish the mountain 2. 3 The sand and mudstone in the strata improve the resolution and accuracy of the seismic forward model.
[0088] like Figure 4 , Figure 5 As shown, after environmental correction and standardization of the logging curves, good consistency and comparability can be ensured when combining well logging and seismic logging.
[0089] like Figure 6 As shown, by combining high-frequency gamma information with low-frequency acoustic information to synthesize pseudo-acoustic curves, sandstone and mudstone can be better distinguished, thus improving the resolution and accuracy of the seismic forward model.
[0090] like Figure 7 As shown, under the constraints of the frame model, the well logging curve spatial interpolation generates a well connection velocity model with high vertical resolution.
[0091] like Figure 8 , Figure 9 , Figure 10 As shown, by using data such as field geological outcrops and the scale of field distributary channels, the effective width and length of individual sand bodies in the channel are determined, the boundaries of channel sand bodies are quantitatively characterized, and a facies model is established by combining seismic facies identification and sedimentary division. The rate of filling different facies is analyzed by rock physics. Figure 8 Then, the position, thickness, and physical properties of the phase are continuously adjusted. Figure 9 ) and other parameters change the initial velocity model, and update the velocity model under phase control ( Figure 10 ).
[0092] like Figure 11 , Figure 12 , Figure 13 As shown, the forward modeling results of well-connected geological folding reveal that when the peaks and lower troughs exhibit moderate to weak reflection characteristics, the mountain 2... 3 The bottom sandstone is relatively well-developed; after the sandstone contains gas, the peak amplitude increases, and when the bottom sandstone is not well-developed, the peaks and troughs show strong amplitude reflections. Figure 11 Through forward modeling analysis, the following conclusions were drawn regarding the mountain 2. 3 The development characteristics of the lower sand body are basically of two types: one is localized lenticular reflection, with the peaks and troughs exhibiting moderate to weak reflection characteristics; the other is tri-phase reflection, with two moderate to weak troughs sandwiching a moderate to weak peak. Figure 12 Forward modeling of geological folding in connected wells and mountains 2 3 There is a good correlation between the seismic reflection characteristics of the lower sand body and the seismic reflection characteristics. Figure 13 This enabled accurate identification and tracking of sandstone in the river channel of the work area.
[0093] Example 3
[0094] The above-described embodiment 1 provides a method for identifying river sand bodies. Correspondingly, this embodiment provides a system for identifying river sand bodies. The system provided in this embodiment can implement the river sand body identification method of embodiment 1. The system can be implemented through software, hardware, or a combination of both. For example, the system may include integrated or separate functional modules or units to execute the corresponding steps in the methods of embodiment 1. Since the system in this embodiment is basically similar to the method embodiment, the description process in this embodiment is relatively simple. For relevant details, please refer to the description of embodiment 1. The system embodiment provided in this embodiment is merely illustrative.
[0095] The river sand body identification system provided in this embodiment includes:
[0096] The model building module is used to build a well-to-well velocity model of channel sand bodies based on well seismic data and field outcrop data;
[0097] The sand body identification and prediction module is used to perform forward modeling of the well velocity model to generate synthetic seismic records, and compare and analyze the similarity of the reflection characteristics of the synthetic seismic records with the actual seismic records of channel sand bodies to predict the actual distribution of channel sand bodies in the reservoir.
[0098] Example 4
[0099] This embodiment provides a processing device corresponding to the river sand body identification method provided in Embodiment 1. The processing device can be a client-side processing device, such as a mobile phone, laptop, tablet computer, desktop computer, etc., to execute the method of Embodiment 1.
[0100] The processing device includes a processor, a memory, a communication interface, and a bus. The processor, memory, and communication interface are connected via the bus to communicate with each other. The memory stores a computer program that can run on the processor. When the processor runs the computer program, it executes the riverbed sand body identification method provided in Embodiment 1.
[0101] Preferably, the memory may be high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk storage device.
[0102] Preferably, the processor can be any type of general-purpose processor such as a central processing unit (CPU) or a digital signal processor (DSP), and there is no limitation herein.
[0103] Example 5
[0104] The river sand body identification method of this embodiment 1 can be specifically implemented as a computer program product. The computer program product may include a computer-readable storage medium on which computer-readable program instructions for executing the river sand body identification method of this embodiment 1 are loaded.
[0105] A computer-readable storage medium can be a tangible device that holds and stores instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof.
[0106] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0107] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0108] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0109] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.
Claims
1. A method for identifying riverbed sand bodies, characterized in that... Includes the following steps: Based on well seismic data and field outcrop data, a well-to-well velocity model for channel sand bodies was established. Forward modeling of the well velocity model was performed to generate synthetic seismic records. By comparing and analyzing the similarity of the reflection characteristics of the synthetic seismic records with those of the actual seismic records of the channel sand bodies, the actual reservoir distribution of the channel sand bodies was predicted. The well-to-well velocity model for channel sand bodies, based on well seismic data and field outcrop data, includes: A framework model was established based on well seismic data, and spatial interpolation of logging curves was performed within the framework model to obtain the initial well connection velocity model. Based on field outcrop data, pseudo-well lithology is filled into the frame model to obtain the pseudo-well lithology model; The lithological velocity values were filled into the pseudo-well lithology model by analyzing the rock physics chart and the initial well-connection velocity model was updated. By filling the framework model with pseudo-well lithology data, a pseudo-well lithology model is obtained, including: In well-free areas, lithology is filled into the framework model using field geological outcrops and modern channel sedimentary data. The channel sand body boundaries are quantitatively characterized by combining the seismic response characteristics of well-calibrated channels in this area, and a pseudo-well lithology model for well-free areas is established. In sparse well regions, a pseudo-well lithology model is established based on the logging curves and lithological characteristics of adjacent wells. The process of filling the pseudo-well lithology model with lithological velocity values through rock physics chart analysis and updating the initial well velocity model includes: A phase model was established, and the rate at which the lithology was filled into the pseudo-well lithology model was analyzed using a rock physics chart. Under phase control, the location, thickness, and physical property parameters of the lithology filling the pseudo-well lithology model are adjusted to determine the reasonable velocity of each filling lithology in the pseudo-well lithology model. The initial well connection velocity model is then updated to obtain the final well connection velocity model.
2. The method for identifying riverbed sand bodies as described in claim 1, characterized in that, The process of establishing a framework model based on well seismic data and performing spatial interpolation of logging curves within the framework model to obtain an initial well-connection velocity model includes: Preprocessing is performed on the logging curves in the seismic data, wherein the logging curves include at least sonic curves and gamma curves; The preprocessed acoustic waveform and gamma curve are fitted to obtain the pseudo-acoustic waveform. Based on the pseudo-acoustic curve, a framework model is established using the extracted optimal reservoir geophysical parameters, and spatial interpolation of well logging curves is performed to generate an initial well connection velocity model.
3. The method for identifying riverbed sand bodies as described in claim 2, characterized in that, The preprocessing of the logging curves includes environmental correction and standardization.
4. The method for identifying riverbed sand bodies as described in claim 2, characterized in that, The process involves establishing a framework model based on pseudo-acoustic curves and extracting optimal reservoir geophysical parameters, then performing spatial interpolation of logging curves to generate an initial well-connection velocity model, including: Well vibration calibration is performed using pseudo-acoustic curves; Based on the calibration results, the extracted optimal reservoir geophysical parameters are used to control the morphology of the target layer and establish a framework model; among which, the optimal reservoir geophysical parameters include the unconformity contact relationship of the target layer and the sedimentary model of the stratigraphic unit. Under the constraints of the framework model, spatial interpolation of logging curves is performed to generate an initial well connection rate model.
5. A riverbed sand body identification system, characterized in that, include: The model building module is used to build a well-to-well velocity model of channel sand bodies based on well seismic data and field outcrop data; The sand body identification and prediction module is used to perform forward modeling of the well velocity model to generate synthetic seismic records, and compare and analyze the similarity of the reflection characteristics of the synthetic seismic records with the actual seismic records of channel sand bodies to predict the actual distribution of channel sand bodies in the reservoir. The well-to-well velocity model for channel sand bodies, based on well seismic data and field outcrop data, includes: A framework model was established based on well seismic data, and spatial interpolation of logging curves was performed within the framework model to obtain the initial well connection velocity model. Based on field outcrop data, pseudo-well lithology is filled into the frame model to obtain the pseudo-well lithology model; The lithological velocity values were filled into the pseudo-well lithology model by analyzing the rock physics chart and the initial well-connection velocity model was updated. By filling the framework model with pseudo-well lithology data, a pseudo-well lithology model is obtained, including: In well-free areas, lithology is filled into the framework model using field geological outcrops and modern channel sedimentary data. The channel sand body boundaries are quantitatively characterized by combining the seismic response characteristics of well-calibrated channels in this area, and a pseudo-well lithology model for well-free areas is established. In sparse well regions, a pseudo-well lithology model is established based on the logging curves and lithological characteristics of adjacent wells. The process of filling the pseudo-well lithology model with lithological velocity values through rock physics chart analysis and updating the initial well velocity model includes: A phase model was established, and the rate at which the lithology was filled into the pseudo-well lithology model was analyzed using a rock physics chart. Under phase control, the location, thickness, and physical property parameters of the lithology filling the pseudo-well lithology model are adjusted to determine the reasonable velocity of each filling lithology in the pseudo-well lithology model. The initial well connection velocity model is then updated to obtain the final well connection velocity model.
6. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods described in claims 1 to 4.
7. A computing device, characterized in that, include: One or more processors and a memory, wherein the memory stores one or more programs and is configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods described in claims 1 to 4.