A method for predicting the thickness of a buried hill weathering crust reservoir

Abstract

The present application relates to the technical field of seismic data interpretation, and discloses a method for predicting the thickness of buried hill weathering crust reservoirs, which comprises the following steps: obtaining the wave peak amplitude, wave trough amplitude and relative amplitude ratio of wave peak-wave trough of the weathering crust reservoir; inputting a buried hill weathering crust reservoir thickness prediction model to obtain the predicted value of the weathering crust reservoir thickness. The buried hill weathering crust reservoir thickness prediction model is constructed by the following method: obtaining multiple sets of well point data pairs through multiple wells passing through the weathering crust; constructing a first fitting function of the wave peak amplitude and the wellbore thickness, a second fitting function of the wave trough amplitude and the wellbore thickness, and a third fitting function of the relative amplitude ratio of wave peak-wave trough and the wellbore thickness according to the multiple sets of well point data pairs; and constructing the weathering crust reservoir thickness prediction model: weathering crust reservoir thickness=(α first fitting function+β second fitting function+γ third fitting function) / (α+β+γ), which can improve the accuracy of the predicted value of the weathering crust reservoir thickness.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of seismic data interpretation. More particularly, the present application relates to a method for predicting the thickness of a buried hill weathering crust reservoir. BACKGROUND

[0002] Seismic data acquisition and processing in the three links of seismic exploration involve the operation of basic data, and seismic data interpretation is to convert these data into abstract geological terms, that is, to determine the geological structure shape and spatial position according to the seismic data, to infer the lithology, thickness and interlayer contact relationship of the stratum, to find the oil and gas reservoir, and to provide accurate well sites for drilling, etc. It is generally believed that the stratum thickness that can be identified in the time domain by seismic is H = 4.6 times the main frequency of the stratum velocity V, and the reservoir thickness is considered to be thin when it is less than the thickness.

[0003] Buried hill is a form of ancient landform. After long-term weathering and erosion of strata after crustal movement, the surface morphology is uneven, and then it is covered by Cenozoic sedimentary strata. The protruding hills are called ancient buried hills, and the weathering crust is covered on them. The weathering crust, as a kind of oil and gas reservoir, has a certain thickness and distribution range in space, is buried by overlying strata in time, has a certain porosity and permeability, and can store and preserve oil and gas. The weathering crust reservoir is a geological body formed by long-term surface rock formation, and still has a certain oil and gas storage performance rock body after burial. In many cases, the weathering crust reservoir belongs to a thin layer, which is difficult to clearly image in the seismic data volume.

[0004] Due to the thin layer characteristics of the weathering crust reservoir, the top and bottom interfaces of the weathering crust reservoir partially offset or interfere with each other in amplitude, and it is difficult to accurately predict the thickness of the weathering crust reservoir by using the amplitude of a single point.

[0005] Therefore, it is urgent to provide a method for predicting the thickness of a weathering crust reservoir to accurately predict the thickness of the weathering crust reservoir according to seismic data. SUMMARY

[0006] To at least solve the problems as mentioned above, the present application provides a method for constructing a buried hill weathering crust reservoir thickness prediction model, comprising: obtaining a plurality of well point data pairs through a plurality of wells penetrating the weathering crust, the well point data pair comprising: a borehole thickness of the buried hill weathering crust reservoir, a peak amplitude of a wellside seismic trace, a trough amplitude of the wellside seismic trace, and a relative peak-trough amplitude ratio of the wellside seismic trace; cross-plotting the peak amplitude of the wellside seismic trace and the borehole thickness to obtain a first fitting function, cross-plotting the trough amplitude of the wellside seismic trace and the borehole thickness to obtain a second fitting function, and cross-plotting the relative peak-trough amplitude ratio of the wellside seismic trace and the borehole thickness to obtain a third fitting function according to the plurality of well point data pairs; and constructing the buried hill weathering crust reservoir thickness prediction model: weathering crust reservoir thickness = (α first fitting function + β second fitting function + γ third fitting function) / (α + β + γ), wherein α, β, and γ are weights, which are preset or obtained through training of the well point data pairs.

[0007] According to an embodiment of the present application, the method for constructing the buried hill weathering crust reservoir thickness prediction model further comprises: correcting the buried hill weathering crust reservoir thickness prediction model through the plurality of well point data pairs to obtain a systematic error value; and constructing an optimized prediction model: weathering crust reservoir thickness = (α first fitting function + β second fitting function + γ third fitting function) / (α + β + γ) + systematic error value, wherein α, β, and γ are weights, which are preset or obtained through training of the well point data pairs.

[0008] According to an embodiment of the present application, the systematic error value is a mean value of differences between the borehole thickness and a predicted value of the buried hill weathering crust reservoir thickness in the plurality of well point data pairs.

[0009] According to an embodiment of the present application, the optimized prediction model is: weathering crust reservoir thickness = (first fitting function + second fitting function + third fitting function) / 3 + error value.

[0010] According to an embodiment of the present application, the prediction model is: weathering crust reservoir thickness = (first fitting function + second fitting function + third fitting function) / 3.

[0011] According to an embodiment of the present application, the first fitting function, the second fitting function, and the third fitting function at least comprise one of the following functions: a polynomial function, a power function, and a logarithmic function.

[0012] According to another aspect of the present application, there is provided a method for predicting the thickness of a buried hill weathering crust reservoir, comprising: a first step of obtaining the peak amplitude, the trough amplitude and the relative amplitude ratio of the peak-trough of the buried hill weathering crust reservoir; and a second step of inputting the peak amplitude, the trough amplitude and the relative amplitude ratio of the peak-trough of the buried hill weathering crust reservoir into a buried hill weathering crust reservoir thickness prediction model to obtain a predicted value of the thickness of the weathering crust reservoir; wherein the buried hill weathering crust reservoir thickness prediction model is constructed by the method for constructing a buried hill weathering crust reservoir thickness prediction model.

[0013] According to an embodiment of the present application, the obtaining of the peak amplitude, the trough amplitude and the relative amplitude ratio of the peak-trough of the buried hill weathering crust reservoir comprises: obtaining a three-dimensional seismic data volume; obtaining logging data of a well passing through the weathering crust reservoir in the three-dimensional seismic data volume; calibrating the three-dimensional seismic data volume by using the logging data to obtain the horizon information of the weathering crust reservoir; and extracting the peak amplitude, the trough amplitude and the relative amplitude ratio of the peak-trough of the buried hill weathering crust reservoir according to the horizon information.

[0014] According to another aspect of the present application, there is provided a device for constructing a buried hill weathering crust reservoir thickness prediction model, comprising: a processor for executing program instructions; and a memory storing program instructions, which, when loaded and executed by the processor, cause the processor to execute the method for constructing a buried hill weathering crust reservoir thickness prediction model.

[0015] According to another aspect of the present application, there is provided a non-volatile computer readable storage medium having computer readable instructions stored thereon, which, when executed by one or more processors, implement the method for constructing a buried hill weathering crust reservoir thickness prediction model.

[0016] According to another aspect of the present application, there is provided a device for predicting the thickness of a buried hill weathering crust reservoir, comprising: a processor for executing program instructions; and a memory storing program instructions, which, when loaded and executed by the processor, cause the processor to execute the method for predicting the thickness of a buried hill weathering crust reservoir.

[0017] According to another aspect of the present application, there is provided a non-volatile computer readable storage medium having computer readable instructions stored thereon, which, when executed by one or more processors, implement the method for predicting the thickness of a buried hill weathering crust reservoir.

[0018] In the embodiments of the present application, the constructed prediction model partially eliminates the problem of unstable thickness prediction caused by the interference and cancellation of amplitude information with each other when the weathering crust reservoir is relatively thin, and the thickness prediction value of the weathering crust reservoir is more accurate through the prediction model.

[0019] In the method of the present application, the influence of wavelet sidelobes and the influence of thin layers are eliminated by introducing both peak and trough amplitudes in the prediction model.

[0020] In the method of the present application, the relative amplitude ratio of peak-to-trough is considered, and the influence of near-surface or overburden inhomogeneous media on the reflection amplitude of weathering crust is eliminated.

[0021] In the method of the present application, the peak and trough feature point multi-parameter method is used for prediction, and the thickness prediction accuracy of weathering crust reservoirs can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0022] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings in which a number of embodiments of the present application are shown by way of example, and like reference numerals are used to refer to like elements throughout. In the drawings:

[0023] Figure 1 A schematic diagram of a three-dimensional seismic exploration system is shown;

[0024] Figure 2 A schematic diagram of the steps of a method for constructing a buried hill weathering crust reservoir thickness prediction model is shown;

[0025] Figure 3 A schematic diagram of a well point data pair according to an embodiment of the present application is shown;

[0026] Figure 4 A schematic diagram of a system for buried hill weathering crust reservoir thickness prediction model according to an embodiment of the present application is shown;

[0027] Figure 5 A schematic diagram of the steps of a method for predicting buried hill weathering crust reservoir thickness according to an embodiment of the present application is shown;

[0028] Figure 6 A hardware schematic diagram of a method for constructing a buried hill weathering crust reservoir thickness prediction model according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0029] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0030] It should be understood that the terms "comprises" and "comprising," when used in the specification and claims of this application, indicate the presence of the described features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0031] It should also be understood that the terms used in the specification and the claims are for the purpose of describing specific embodiments and are not intended to be limiting. As used in this specification and the claims, the singular forms "a," "an" and "the" include plural referents unless the context clearly dictates otherwise. It should also be further understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items, and that the term "at least one of" followed by a list of two or more items means any single one of the items in the list individually, as well as any combination of two or more of the items in the list.

[0032] As used in this specification and claims, the terms "if' and "when" can each be interpreted to mean "upon determination" or "in response to a determination" or "upon detection" or "in response to a detection," depending on the context. Similarly, the phrase "if determined" or "if detected [a described condition or event]" can be interpreted to mean "upon determination" or "in response to a determination" or "upon detection" or "in response to a detection" of [a described condition or event], depending on the context.

[0033] A detailed description of specific embodiments of the application is provided below with reference to the accompanying drawings.

[0034] Seismic exploration is a method of geophysical exploration that uses artificially excited seismic waves to locate mineral deposits and obtain engineering geological information. The basic principle is that an artificial seismic source is used to generate seismic waves, and when the seismic waves propagate in the rock, they encounter the interface of the rock layer and produce reflected or refracted waves. When the reflected or refracted waves return to the ground, they are received by recording instruments such as geophones, forming seismic data. Through analysis and processing of the seismic data and comprehensive interpretation, the buried depth and shape of the rock layer section where the seismic waves are reflected or refracted are determined, and accurate imaging of the underground geological structure is achieved.

[0035] Figure 1 A schematic diagram of a three-dimensional seismic exploration system is shown.

[0036] As Figure 1As shown, in the system 100, a plurality of mutually spaced geophones 110 for detecting seismic waves are arranged on the surface 101 of the exploration target area, forming a geophone array covering the target area on a plane, and the geophones 110 are connected to a seismic information processing device through wired or wireless connection, and a plurality of seismic sources 120 are also provided. The seismic information processing device can perform preliminary processing on seismic data. The working process of the three-dimensional seismic exploration system is as follows: artificial excitation of the seismic sources 120 at multiple positions generates seismic waves, the seismic waves are reflected from the boundary of the stratum 102 and are received by the geophone array to form seismic information collected on a plane and changing with time. The seismic information received by the geophone array represents certain measurements of seismic wave energy as a function of time, such as displacement, velocity, wave impedance, pressure, etc. These information can be grouped in different ways, such as traces, gathers, etc., and then processed or format-converted according to the corresponding relationship between time and space to form a three-dimensional seismic data volume in the form of a three-dimensional array, which can also be said to be formed by spatially stacking interface points. Interpretation of the three-dimensional seismic data volume can observe the morphology of the geological interface from different directions, and study the changes of geological bodies in three-dimensional space through cross-sectional, longitudinal sectional and horizontal slice.

[0037] Figure 2 A step schematic diagram of a method for constructing a buried hill weathered crust reservoir thickness prediction model is shown.

[0038] As Figure 2 shown, the method 200 for constructing a buried hill weathered crust reservoir thickness prediction model includes: step S201, obtaining a plurality of well point data pairs through a plurality of wells passing through the weathered crust, the well point data pair including: wellbore thickness of the buried hill weathered crust reservoir, peak amplitude of the well seismic trace, trough amplitude of the well seismic trace, and relative amplitude ratio of the peak-trough of the well seismic trace. Step S202, according to the plurality of well point data pairs, the peak amplitude of the well seismic trace is intersected and fitted with the wellbore thickness to obtain a first fitting function, the trough amplitude of the well seismic trace is intersected and fitted with the wellbore thickness to obtain a second fitting function, and the relative amplitude ratio of the peak-trough of the well seismic trace is intersected and fitted with the wellbore thickness to obtain a third fitting function. Step S203, constructing a buried hill weathered crust reservoir thickness prediction model: weathered crust reservoir thickness = (α first fitting function + β second fitting function + γ third fitting function) / (α + β + γ), wherein α, β, γ are weights, which are pre-set or obtained through training of the well point data pair.

[0039] In the present application, the buried hill weathered crust reservoir thickness prediction model is constructed based on the data of the existing weathered crust reservoir, and the weathered crust reservoir thickness is predicted based on the waveform amplitude. In the process of constructing the model, the seismic data containing the weathered crust reservoir is obtained, the three-dimensional seismic data body is constructed, and a plurality of wells are set in the area or position where the weathered crust is located to obtain well logging data. The well logging data includes the actual thickness of the weathered crust reservoir at the well position, that is, the well hole thickness. The well side seismic trace is extracted from the seismic data, and a series of waveform amplitude information such as the peak amplitude, the trough amplitude and the relative amplitude ratio of the peak to the trough of the well side seismic trace are read. The waveform amplitude information is corresponded to the well hole thickness to form a well point data pair.

[0040] Figure 3 A schematic diagram of the well point data pair according to the embodiment of the present application is shown.

[0041] In Figure 3 In the present application, the buried hill weathered crust reservoir thickness prediction model is constructed based on the data of the existing weathered crust reservoir, and the weathered crust reservoir thickness is predicted based on the waveform amplitude. In the process of constructing the model, the seismic data containing the weathered crust reservoir is obtained, the three-dimensional seismic data body is constructed, and a plurality of wells are set in the area or position where the weathered crust is located to obtain well logging data. The well logging data includes the actual thickness of the weathered crust reservoir at the well position, that is, the well hole thickness. The well side seismic trace is extracted from the seismic data, and a series of waveform amplitude information such as the peak amplitude, the trough amplitude and the relative amplitude ratio of the peak to the trough of the well side seismic trace are read. The waveform amplitude information is corresponded to the well hole thickness to form a well point data pair.

[0042] When the weathered crust reservoir is relatively thin, the read amplitude information interferes and cancels each other, and there is a certain corresponding relationship between the read amplitude information and the thickness of the weathered crust reservoir. When the weathered crust reservoir is relatively thick, the interference and cancellation phenomenon disappears, and the read amplitude information has a different corresponding relationship with the weathered crust reservoir than when the weathered crust reservoir is relatively thin.

[0043] In the embodiment of the present application, a plurality of well point data pairs are constructed through a plurality of wells, and the least square method is used for fitting to obtain a first fitting function of the peak amplitude and the well hole thickness, a second fitting function of the trough amplitude and the well hole thickness, and a third fitting function of the relative amplitude ratio of the peak to the trough and the well hole thickness. Due to the thinness of the buried hill weathered crust reservoir, the series of fitting functions cannot reflect the true thickness of the weathered crust reservoir when used alone, and there is a certain deviation. Therefore, the inventor takes the weighted average of the three fitting functions, verifies different well point data pairs to obtain the weight value, and obtains the buried hill weathered crust reservoir thickness prediction model. Alternatively, the weight value is set artificially to obtain the buried hill weathered crust reservoir thickness prediction model.

[0044] According to the embodiment of the present application, the buried hill weathered crust reservoir thickness prediction model partially eliminates the problem of unstable thickness prediction caused by the interference and cancellation phenomenon between the amplitude information when the weathered crust reservoir is relatively thin, and the thickness prediction value of the weathered crust reservoir is relatively accurate through the buried hill weathered crust reservoir thickness prediction model.

[0045] According to an embodiment of the present application, the system error value is obtained by correcting the prediction model with the multiple well point data; and an optimized prediction model is constructed: weathered crust reservoir thickness = (α first fitting function + β second fitting function + γ third fitting function) / (α + β + γ) + error value, wherein α, β and γ are weights, which are pre-set or obtained by training with the well point data.

[0046] When the prediction model is corrected with the multiple well point data, the system error value is introduced to further optimize the prediction model, so that the thickness prediction value of the weathered crust reservoir output by the prediction model is more accurate.

[0047] According to an embodiment of the present application, the system error value can be the average of the differences between the well hole thicknesses and the predicted values of the buried hill weathered crust reservoir thicknesses in the multiple well point data.

[0048] The multiple well point data are substituted into the optimized prediction model to obtain multiple predicted thicknesses corresponding to the multiple well hole thicknesses, the differences between the predicted thicknesses and the well hole thicknesses are obtained, and the average of the differences is obtained, so that the system error value is obtained. The system error value obtained by the method has a statistical buffering effect, which can effectively improve the accuracy of the thickness prediction value of the weathered crust.

[0049] According to an embodiment of the present application, the optimized prediction model is: weathered crust reservoir thickness = (first fitting function + second fitting function + third fitting function) / 3 + error value. That is, by setting the weights of the fitting functions to 1, the three fitting functions have the same weight, which can effectively eliminate the problem of unstable thickness prediction, and also simplifies the operation process of the prediction model.

[0050] According to an embodiment of the present application, the weathered crust reservoir thickness prediction model is: weathered crust reservoir thickness = (first fitting function + second fitting function + third fitting function) / 3.

[0051] According to an embodiment of the present application, the first fitting function, the second fitting function and the third fitting function at least include one of the following functions: a polynomial function, a power function and a logarithmic function.

[0052] In the embodiments of the present application, the first fitting function, the second fitting function and the third fitting function, and the construction of the weathered crust reservoir thickness prediction model can also be realized by a neural network model.

[0053] Figure 4 A schematic diagram of a system of a buried hill weathered crust reservoir thickness prediction model according to an embodiment of the present application is shown.

[0054] As Figure 4As shown, the system 400 comprises a first module 401 configured to obtain a plurality of well point data pairs through a plurality of wells penetrating the weathering crust, the well point data pair comprising a borehole thickness of the weathering crust reservoir, a peak amplitude of a seismic trace beside the well, a trough amplitude of the seismic trace beside the well, and a relative amplitude ratio of the peak-trough of the seismic trace beside the well; a second module 402 configured to construct a first fitting function of the peak amplitude and the borehole thickness, a second fitting function of the trough amplitude and the borehole thickness, and a third fitting function of the relative amplitude ratio of the peak-trough and the borehole thickness, respectively, according to the plurality of well point data pairs; and a third module 403 configured to construct a buried hill weathering crust reservoir thickness prediction model: weathering crust reservoir thickness = (α first fitting function + β second fitting function + γ third fitting function) / (α + β + γ), wherein α, β, and γ are weights, which are pre-set or obtained through training of the well point data pairs.

[0055] Figure 5 A step schematic diagram of a method for predicting a buried hill weathering crust reservoir thickness according to an embodiment of the present application is shown.

[0056] As shown, Figure 5 The method 500 for predicting a buried hill weathering crust reservoir thickness comprises: a first step S501 of obtaining a peak amplitude, a trough amplitude, and a relative amplitude ratio of a peak-trough of a weathering crust reservoir; and a second step S502 of inputting the peak amplitude, the trough amplitude, and the relative amplitude ratio of the peak-trough of the weathering crust reservoir into a buried hill weathering crust reservoir thickness prediction model to obtain a predicted value of the weathering crust reservoir thickness; wherein the buried hill weathering crust reservoir thickness prediction model is obtained by using the method for constructing a buried hill weathering crust reservoir thickness prediction model.

[0057] According to an embodiment of the present application, the obtaining of the peak amplitude, the trough amplitude, and the relative amplitude ratio of the peak-trough of the weathering crust reservoir comprises: obtaining a three-dimensional seismic data volume; obtaining logging data of a well penetrating the weathering crust reservoir in the three-dimensional seismic data volume; calibrating the three-dimensional seismic data volume by using the logging data to obtain horizon information of the weathering crust reservoir; and extracting the peak amplitude, the trough amplitude, and the relative amplitude ratio of the peak-trough of the weathering crust reservoir according to the horizon information.

[0058] According to still another aspect of the present application, there is provided an apparatus for constructing a weathering crust reservoir thickness prediction model, comprising: a processor configured to execute program instructions; and a memory storing the program instructions, which, when loaded and executed by the processor, cause the processor to execute the method described above.

[0059] Figure 6 A hardware schematic diagram of a buried hill weathering crust reservoir thickness prediction model according to an embodiment of the present application is shown.

[0060] The system 600 may include a device 601 and its peripheral devices and external networks according to embodiments of the present invention, wherein the device 601 is used to perform the operation of constructing a weathering crust reservoir thickness prediction model to achieve the technical solution of the aforementioned embodiments of the present invention.

[0061] like Figure 6 As shown, device 601 may include a CPU 6011, which may be a general-purpose CPU, a dedicated CPU, or other information processing and program execution unit. Furthermore, device 601 may also include a mass storage device 6012 and a read-only memory (ROM) 6013. The mass storage device 6012 may be configured to store various types of data, such as seismic data and model data, as well as various programs required for performing various operations. The ROM 6013 may be configured to store data required for power-on self-test of device 601, initialization of various functional modules in the system, drivers for basic input / output of the system, and data required to boot the operating system.

[0062] Furthermore, device 601 also includes other hardware platforms or components, such as the TPU 6014, GPU 6015, FPGA 6016, and MLU 6017 shown. It is understood that although various hardware platforms or components are shown in device 600, they are merely exemplary and not limiting, and those skilled in the art can add or remove corresponding hardware as needed. For example, device 601 may include only a CPU as a known hardware platform and another hardware platform as the test hardware platform of this invention.

[0063] The device 601 of the present invention also includes a communication interface 6018, through which it can connect to a local area network / wireless local area network (LAN / WLAN) 605, and further through the LAN / WLAN to connect to a local server 606 or to the Internet (“Internet”) 607. Alternatively or additionally, the device 601 of the present invention can also directly connect to the Internet or a cellular network via the communication interface 6018 based on wireless communication technology, such as third-generation (“3G”), fourth-generation (“4G”), or fifth-generation (“5G”) wireless communication technology. In some application scenarios, the device 601 of the present invention can also access a server 608 on an external network and, possibly, a database 609, as needed to obtain various known algorithms, data, and modules, and can remotely store various data.

[0064] The peripherals of the device 601 can include a display device 602, an input device 603, and a data transmission interface 604. In an embodiment, the display device 602 can include, for example, one or more speakers and / or one or more visual displays configured to provide audio and / or visual image displays of the operation processes or results of the device of the present application. The input device 603 can include, for example, a keyboard, a mouse, a microphone, a gesture capture camera, or other input buttons or controls configured to receive input or user instructions. The data transmission interface 604 can include, for example, a serial interface, a parallel interface, or a universal serial bus interface (“USB”), a small computer system interface (“SCSI”), a serial ATA, a FireWire, a PCI Express, and a high-definition multimedia interface (“HDMI”), etc., configured for data transmission and interaction with other devices or systems.

[0065] The above-mentioned CPU 6011, mass storage 6012, read-only memory ROM 6013, TPU 6014, GPU 6015, FPGA 6016, MLU 6017, and communication interface 6018 of the device 601 of the present application can be connected to each other through a bus 6019, and data interaction with peripherals is achieved through the bus. In an embodiment, through the bus 6019, the CPU 6011 can control other hardware components in the device 601 and their peripherals.

[0066] In operation, the processor CPU 6011 of the device 601 of the present application can receive a three-dimensional seismic data volume through the input device 603 or the data transmission interface 604, and call the computer program instructions or codes stored in the storage 6012 (such as various programs for building a stratigraphic model) to process the received three-dimensional seismic data volume to build a prediction model. After the CPU 6011 determines the prediction model by executing the program instructions, it can be displayed on the display device 602 or output in the form of voice prompts. In addition, the device 601 can also upload the prediction model to a network, such as a remote database 609, through the communication interface 6018.

[0067] It should also be understood that any module, unit, component, server, computer, terminal, or device of the present application that executes instructions can include or otherwise have access to computer-readable media, such as storage media, computer storage media, or data storage devices (removable and / or non-removable) such as magnetic disks, optical disks, or tape. Computer storage media can include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules, or other data.

[0068] The computer-readable storage medium can be any suitable magnetic storage medium or magneto-optical storage medium, such as, for example, Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random-Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), and the like, or any other medium that can be used to store the desired information and that can be accessed by an application, module, or both. Any such computer storage media can be part of the device or accessible or connectable thereto. Any applications or modules described herein can be implemented using computer-readable / executable instructions that can be stored or otherwise held by such computer-readable media.

[0069] While several embodiments of the application have been shown and described herein, it is obvious that many changes and modifications can be made thereto without departing from the spirit and scope of the application. It is also to be understood that such changes and modifications can be made to the embodiments described herein, and further, that such changes and modifications are to be construed as being within the scope of the application. The claims hereinafter appended are meant to define the scope of the application and to cover all changes and modifications of the application that fall within the scope of the claims.

Claims

1. A method for predicting the thickness of a buried hill weathering crust reservoir, characterized in that, The method comprises the following steps: a first step of obtaining the peak amplitude, the trough amplitude and the relative amplitude ratio of the peak to the trough of the buried hill weathering crust reservoir; a second step of inputting the peak amplitude, the trough amplitude and the relative amplitude ratio of the peak to the trough into a buried hill weathering crust reservoir thickness prediction model to obtain a predicted value of the buried hill weathering crust reservoir thickness; wherein the buried hill weathering crust reservoir thickness prediction model is constructed by the following method: a plurality of well point data pairs are obtained through a plurality of wells passing through the weathering crust, the well point data pair comprising the borehole thickness of the buried hill weathering crust reservoir, the peak amplitude of the seismic trace beside the well, the trough amplitude of the seismic trace beside the well and the relative amplitude ratio of the peak to the trough of the seismic trace beside the well; the peak amplitude of the seismic trace beside the well is cross-fitted with the borehole thickness to obtain a first fitting function, the trough amplitude of the seismic trace beside the well is cross-fitted with the borehole thickness to obtain a second fitting function, and the relative amplitude ratio of the peak to the trough of the seismic trace beside the well is cross-fitted with the borehole thickness to obtain a third fitting function according to a plurality of the well point data pairs; a buried hill weathering crust reservoir thickness prediction model is constructed: weathering crust reservoir thickness=(α first fitting function+β second fitting function+γ third fitting function) / (α+β+γ), wherein α, β and γ are weights, which are pre-set or obtained by training the well point data pair.

2. The method of claim 1, wherein, Further comprising: a plurality of the well point data pairs are used to correct the buried hill weathering crust reservoir thickness prediction model to obtain a systematic error value; a plurality of the systematic error values are used to optimize the buried hill weathering crust reservoir thickness prediction model: weathering crust reservoir thickness=(α first fitting function+β second fitting function+γ third fitting function) / (α+β+γ)+systematic error value, wherein α, β and γ are weights, which are pre-set or obtained by training the well point data pair.

3. The method of claim 2, wherein the systematic error value is the average of the differences between the borehole thickness of the weathering crust reservoir in the plurality of well point data pairs and the predicted value of the buried hill weathering crust reservoir thickness.

4. The method of claim 1, wherein the first fitting function, the second fitting function and the third fitting function at least comprise a polynomial function.

5. The method of claim 1, wherein the first fitting function, the second fitting function and the third fitting function at least comprise a power function.

6. The method of claim 1, wherein the first fitting function, the second fitting function and the third fitting function at least comprise a logarithmic function.

7. The method of claim 1, wherein the obtaining of the peak amplitude, the trough amplitude and the relative amplitude ratio of the peak to the trough of the buried hill weathering crust reservoir comprises: obtaining a three-dimensional seismic data body; obtaining logging data passing through the weathering crust reservoir in the three-dimensional seismic data body; calibrating the three-dimensional seismic data body by using the logging data to obtain the horizon information of the weathering crust reservoir; extracting the peak amplitude, the trough amplitude and the relative amplitude ratio of the peak to the trough of the weathering crust reservoir according to the horizon information. comprising: a processor for executing program instructions; ​ ​ ​ 8. A device for constructing a buried hill weathering crust reservoir thickness prediction model, characterized in that, ​ ​ and a memory storing program instructions that, when loaded and executed by the processor, cause the processor to perform the method of any one of claims 1-7.

9. A non-transitory computer-readable storage medium, comprising: having computer-readable instructions stored thereon that, when executed by one or more processors, implement the method of any one of claims 1-7.

Images