Method for predicting thickness of weathering crust reservoir of buried hill

Abstract

The invention relates to the technical field of seismic data interpretation, and discloses a method for predicting the thickness of a weathering crust reservoir of a buried hill, and the method comprises the steps: obtaining the wave crest amplitude, the wave trough amplitude and the wave crest-wave trough relative amplitude ratio of the weathering crust reservoir, inputting the wave crest amplitude, the wave trough amplitude and the wave crest-wave trough relative amplitude ratio into a thickness prediction model of the weathering crust reservoir of the buried hill, and obtaining a predicted value of the thickness of the weathering crust reservoir. The buried hill weathering crust reservoir thickness prediction model is constructed by adopting the following method: obtaining a plurality of groups of well point data pairs through a plurality of wells penetrating through a weathering crust; according to the multiple sets of well point data pairs, a first fitting function of the wave crest amplitude and the well hole thickness, a second fitting function of the wave trough amplitude and the well hole thickness and a third fitting function of the wave crest-wave trough relative amplitude ratio and the well hole thickness are constructed. The thickness of the weathering crust reservoir is equal to (alpha first fitting function + beta second fitting function + gamma third fitting function) / (alpha + beta + gamma), and the prediction model can improve the accuracy of the predicted value of the thickness of the weathering crust reservoir.

Description

Technical Field

[0001] The present invention generally relates to the technical field of seismic data interpretation and more particularly to a method for predicting the thickness of a buried hill weathering crust reservoir. Background Art

[0002] Seismic data acquisition and processing in the three major 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 and spatial position based on seismic data, infer the lithology, thickness and interlayer contact relationship of the stratum, find oil and gas reservoirs, provide accurate well locations for drilling, etc. It is generally believed that the stratum thickness that can be identified by earthquakes in the time domain is H = stratum velocity V / 4.6 times the main frequency, and when the reservoir thickness is less than this thickness, it is considered to be a thin layer.

[0003] Buried hill is a form of ancient landform. After long-term weathering and erosion of the crust movement, the surface morphology is uneven. Later, it sank again and was covered by the Cenozoic sedimentary layer. The protruding hill is called the ancient buried hill, which is covered by the weathering crust. As a reservoir containing oil and gas, the weathering crust has a certain thickness and distribution range in space. It is covered and buried by the overlying strata in time. It 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 epigenesis. After later burial, it still retains a certain rock mass with oil and gas storage properties. In many cases, the weathering crust reservoir is a thin layer and it is difficult to clearly image it in the seismic data body.

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

[0005] In view of this, there is an urgent need to provide a method for predicting the thickness of the weathering crust reservoir, and to accurately predict the thickness of the weathering crust reservoir based on seismic data. Summary of the invention

[0006] In order to at least solve the above-mentioned problems, the present invention provides a method for constructing a buried hill weathering crust reservoir thickness prediction model, comprising: obtaining multiple groups of well point data pairs through multiple wells passing through the weathering crust, the well point data pairs comprising: the wellbore thickness of the buried hill weathering crust reservoir, the peak amplitude of the wellside seismic trace, the trough amplitude of the wellside seismic trace and the relative amplitude ratio of the peak to the trough of the wellside seismic trace; according to the multiple groups of well point data pairs, the peak amplitude of the wellside seismic trace and the wellbore thickness are intersected and fitted to obtain a first fitting function, the trough amplitude of the wellside seismic trace and the wellbore thickness are intersected and fitted to obtain a second fitting function, and the relative amplitude ratio of the peak to the trough of the wellside seismic trace and the wellbore thickness are intersected and fitted to obtain a third fitting function; constructing 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, γ are weights, which are preset or obtained through training of the well point data pairs.

[0007] According to one embodiment of the present invention, the method for constructing a buried hill weathering crust reservoir thickness prediction model also includes: correcting the buried hill weathering crust reservoir thickness prediction model through multiple well point data pairs to obtain a systematic error value; 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, γ are weights, which are pre-set or obtained through training of well point data pairs.

[0008] According to one embodiment of the present invention, the system error value is the average of the differences between the wellbore thickness in the multiple well point data and the predicted value of the mountain weathering crust reservoir thickness.

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

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

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

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

[0013] According to one embodiment of the present invention, obtaining the peak amplitude, trough amplitude and relative amplitude ratio of the peak-to-trough of the buried hill weathering crust reservoir includes: 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 using the logging data to obtain stratigraphic information of the weathering crust reservoir; and extracting the peak amplitude, trough amplitude and relative amplitude ratio of the peak-to-trough of the buried hill weathering crust reservoir based on the stratigraphic information.

[0014] According to another aspect of the present invention, 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, wherein when the program instructions are loaded and executed by the processor, the processor executes the aforementioned method for constructing a buried hill weathering crust reservoir thickness prediction model.

[0015] According to another aspect of the present invention, a non-volatile computer-readable storage medium is provided, on which computer-readable instructions are stored. When the computer-readable instructions are executed by one or more processors, the aforementioned method of constructing a buried hill weathering crust reservoir thickness prediction model is implemented.

[0016] According to another aspect of the present invention, there is provided an apparatus for predicting the thickness of a buried hill weathering crust reservoir, comprising: a processor for executing program instructions; and a memory storing program instructions, wherein when the program instructions are loaded and executed by the processor, the processor executes the aforementioned method for predicting the thickness of a buried hill weathering crust reservoir.

[0017] According to another aspect of the present invention, a non-volatile computer-readable storage medium is provided, on which computer-readable instructions are stored. When the computer-readable instructions are executed by one or more processors, the aforementioned method for predicting the thickness of a buried hill weathering crust reservoir is implemented.

[0018] In the embodiment of the present invention, the constructed prediction model partially eliminates the problem of unstable thickness prediction caused by interference and offset between amplitude information when the weathering crust reservoir is thin. The prediction model can make the thickness prediction value of the weathering crust reservoir more accurate.

[0019] In the method of the present invention, by simultaneously introducing the peak and trough amplitudes into the prediction model, the influence of the sub-wave side lobes and the influence of the thin layer are eliminated.

[0020] In the method of the present invention, the relative amplitude ratio of the wave crest to the wave trough is taken into consideration, and the influence of the inhomogeneous medium near the surface or the overlying strata on the reflection amplitude of the weathering crust is eliminated.

[0021] In the method of the present invention, the prediction accuracy of the thickness of the weathering crust reservoir can be improved by using the multi-parameter method of peak and trough characteristic points for prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

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

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

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

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

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

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

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0030] It should be understood that the terms "include" and "comprising" used in the description and claims of the present invention indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0031] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the claims, the singular forms of "a", "an" and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in the specification of the present invention and the claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0032] As used in this specification and claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0033] The specific embodiments of the present invention are described in detail 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. Its basic principle is: artificially excited seismic sources generate seismic waves. When seismic waves propagate in rocks, if they encounter the interface of rock layers, they will generate reflected waves or refracted waves. When the reflected waves or refracted waves return to the ground, they are received by recording instruments such as geophones to form seismic data. Through the analysis, processing and comprehensive interpretation of seismic data, the burial depth and shape of the rock layer section where the seismic waves are reflected or refracted are determined, and accurate imaging of underground geological structures is achieved.

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

[0036] like Figure 1As shown, in the system 100, a plurality of mutually spaced detectors 110 for detecting seismic waves are arranged on the surface 101 of the exploration target area, forming a detector array covering the target area on a plane. These detectors 110 are connected to the seismic information processing device by wired or wireless connection, and a plurality of seismic sources 120 are also provided. The seismic information processing device can perform preliminary processing on the seismic data. The working process of the three-dimensional seismic exploration system is: artificially excite the seismic sources 120 located at multiple positions to generate seismic waves, which are reflected from the boundary of the stratum 102 and received by the detector array to form seismic information collected on a plane and changing with time. The seismic information received by the detector array represents certain measures of seismic wave energy as a function of time, such as displacement, velocity, wave impedance, pressure, etc. This information can be grouped in different ways, such as traces, sets, etc., and then processed or format-converted according to the corresponding relationship between time and space to form a three-dimensional seismic data body in the form of a three-dimensional array. It can also be said that the three-dimensional seismic data body is formed by stacking interface points in space. The interpretation of three-dimensional seismic data can observe the morphology of geological interfaces from different directions and study the changes of geological bodies in three-dimensional space by cutting cross sections, longitudinal sections and horizontal slices.

[0037] Figure 2 A schematic diagram showing the steps of a method for constructing a buried hill weathering crust reservoir thickness prediction model.

[0038] like Figure 2 As shown, the method 200 for constructing a buried hill weathering crust reservoir thickness prediction model comprises: step S201, obtaining multiple sets of well point data pairs through multiple wells that pass through the weathering crust, the well point data pairs comprising: the wellbore thickness of the buried hill weathering crust reservoir, the peak amplitude of the wellside seismic trace, the trough amplitude of the wellside seismic trace, and the relative amplitude ratio of the peak-trough of the wellside seismic trace. Step S202, according to the multiple sets of well point data pairs, performing intersection fitting of the peak amplitude of the wellside seismic trace and the wellbore thickness to obtain a first fitting function, performing intersection fitting of the trough amplitude of the wellside seismic trace and the wellbore thickness to obtain a second fitting function, and performing intersection fitting of the relative amplitude ratio of the peak-trough of the wellside seismic trace and the wellbore thickness to obtain a third fitting function. Step S203, constructing 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, γ are weights, which are preset or obtained through training of well point data.

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

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

[0041] exist Figure 3 In FIG. 1 , examples of well point data pairs are presented in a table form. According to the number of wells, multiple well point data pairs can be constructed to construct the above fitting function and verify the above fitting function.

[0042] When the weathering crust reservoir is thin, the amplitude information read out will interfere with and offset each other, and there will be a certain corresponding relationship with the thickness of the weathering crust reservoir. When the weathering crust reservoir is thick, this interference and offset phenomenon disappears, and the amplitude information read out will have a different corresponding relationship with the weathering crust reservoir than when the weathering crust reservoir is thin.

[0043] In an embodiment of the present invention, multiple groups of well point data pairs are constructed through multiple wells, and fitting is performed separately through the least square method to obtain a first fitting function of the peak amplitude and the wellbore thickness, a second fitting function of the trough amplitude and the wellbore thickness, and a third fitting function of the relative amplitude ratio of the peak-trough and the wellbore thickness. Since the buried hill weathering crust reservoir is relatively thin, this series of fitting functions cannot reflect the actual thickness of the weathering crust reservoir when used alone, and there is a certain deviation. Therefore, the inventor obtains a weighted average value of these three fitting functions, verifies them through different well point data pairs, obtains weight values, and obtains a buried hill weathering crust reservoir thickness prediction model or obtains a buried hill weathering crust reservoir thickness prediction model by artificially setting weight values.

[0044] According to an embodiment of the present invention, the buried hill weathering crust reservoir thickness prediction model partially eliminates the problem of unstable thickness prediction caused by the interference and offset between amplitude information when the weathering crust reservoir is thin. Through the buried hill weathering crust reservoir thickness prediction model, the thickness prediction value of the weathering crust reservoir can be made more accurate.

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

[0046] When using multiple well point data to calibrate the prediction model, introducing a systematic error value can further optimize the prediction model, making the predicted value of the thickness of the weathering crust reservoir output by the prediction model more accurate.

[0047] According to one embodiment of the present invention, the system error value may be the average of the differences between the wellbore thickness in the plurality of well point data and the predicted value of the buried hill weathering crust reservoir thickness.

[0048] Substituting multiple well point data pairs into the optimized prediction model, multiple predicted thicknesses can be obtained, corresponding to multiple wellhead thicknesses, and the difference between them is calculated, and the difference is averaged to obtain the system error value. The system error value obtained by this method has a statistically significant buffering effect and can effectively improve the accuracy of the predicted value of the weathering crust thickness.

[0049] According to one embodiment of the present invention, the optimized prediction model is: weathering crust reservoir thickness = (first fitting function + second fitting function + third fitting function) / 3 + error value. That is, by setting the weight of each fitting function to 1, the three fitting functions have the same weight, which can effectively eliminate the problem of unstable thickness prediction and simplify the calculation process of the above prediction model.

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

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

[0052] In the embodiment of the present invention, the construction of the first fitting function, the second fitting function, the third fitting function, and the construction of the weathering crust reservoir thickness prediction model can also be achieved through a neural network model.

[0053] Figure 4 A schematic diagram of a system for predicting the thickness of a buried hill weathering crust reservoir according to an embodiment of the present invention is shown.

[0054] like Figure 4As shown, the system 400 includes a first module 401, which is used to obtain multiple groups of well point data pairs through multiple wells passing through the weathering crust, and the well point data pairs include: the wellbore thickness of the weathering crust reservoir, the peak amplitude of the wellside seismic trace, the trough amplitude of the wellside seismic trace and the relative amplitude ratio of the peak-to-trough of the wellside seismic trace; a second module 402, which is used to construct a first fitting function of the peak amplitude and the wellbore thickness, a second fitting function of the trough amplitude and the wellbore thickness, and a third fitting function of the relative amplitude ratio of the peak-to-trough and the wellbore thickness according to the multiple groups of well point data pairs; a third module 403, which is used 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, γ are weights, which are preset or obtained through training of well point data pairs.

[0055] 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 invention is shown.

[0056] like Figure 5 As shown, a method 500 for predicting the thickness of a buried hill weathering crust reservoir comprises: a first step S501, obtaining the peak amplitude, trough amplitude and relative amplitude ratio of the peak-to-trough of the weathering crust reservoir; a second step S502, inputting the peak amplitude, trough amplitude and relative amplitude ratio 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 aforementioned method for constructing a buried hill weathering crust reservoir thickness prediction model.

[0057] According to one embodiment of the present invention, obtaining the peak amplitude, trough amplitude and relative amplitude ratio of the peak to the trough of the weathering crust reservoir includes: 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 using the logging data to obtain stratigraphic information of the weathering crust reservoir; and extracting the peak amplitude, trough amplitude and relative amplitude ratio of the peak to the trough of the weathering crust reservoir based on the stratigraphic information.

[0058] According to another aspect of the present invention, there is provided an apparatus for constructing a weathering crust reservoir thickness prediction model, comprising: a processor for executing program instructions; and a memory storing program instructions, wherein when the program instructions are loaded and executed by the processor, the processor executes the aforementioned method.

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

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

[0061] like Figure 6 As shown in , the 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. Further, the device 601 may also include a large-capacity memory 6012 and a read-only memory ROM 6013, wherein the large-capacity memory 6012 may be configured to store various types of data, seismic data, model data, etc., and various programs required for performing various operations, and the ROM 6013 may be configured to store the power-on self-test of the device 601, the initialization of various functional modules in the system, the basic input / output driver of the system, and the data required for booting the operating system.

[0062] Further, the 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 a variety of hardware platforms or components are shown in the device 600, this is only exemplary and not restrictive, and those skilled in the art can add or remove corresponding hardware according to actual needs. For example, the device 601 can include only a CPU as a well-known hardware platform and another hardware platform as a test hardware platform of the present invention.

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

[0064] The peripheral devices of the device 601 may include a display device 602, an input device 603, and a data transmission interface 604. In one embodiment, the display device 602 may include, for example, one or more speakers and / or one or more visual displays, which are configured to perform voice prompts and / or image video displays on the computing process or results of the device of the present invention. The input device 603 may include, for example, a keyboard, a mouse, a microphone, a gesture capture camera, or other input buttons or controls, which are configured to receive input or user instructions. The data transmission interface 604 may 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 ("FireWire"), a PCI Express, and a high-definition multimedia interface ("HDMI"), etc., which are configured for data transmission and interaction with other devices or systems.

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

[0066] In operation, the processor CPU 6011 of the device 601 of the present invention can receive the three-dimensional seismic data body through the input device 603 or the data transmission interface 604, and call the computer program instructions or codes (such as various programs for building a stratigraphic model) stored in the memory 6012 to process the received three-dimensional seismic data body 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 by 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 that executes instructions of the present invention examples may include or otherwise access computer-readable media, such as storage media, computer storage media or data storage devices (removable) and / or non-removable) such as disks, optical disks or tapes. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules or other data.

[0068] The computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, such as 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), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device. Any application or module described in the present invention may be implemented using computer-readable / executable instructions that may be stored or otherwise maintained by such a computer-readable medium.

[0069] Although multiple embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art may conceive of many changes, modifications, and alternatives without departing from the thought and spirit of the present invention. It should be understood that in the process of practicing the present invention, various alternatives to the embodiments of the present invention described herein may be adopted. The appended claims are intended to define the scope of protection of the present invention, and therefore cover equivalents or alternatives within the scope of these claims.

Claims

1. A method for predicting the thickness of buried hill weathering crust reservoir, characterized in that: include: The first step is to obtain the peak amplitude, trough amplitude and relative amplitude ratio of the peak to trough of the buried hill weathering crust reservoir; The second step is to input the peak amplitude, trough amplitude and 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; The buried hill weathering crust reservoir thickness prediction model is constructed using the following method: By passing through a plurality of wells in the weathering crust, a plurality of well point data pairs are obtained, wherein the well point data pairs include: the wellbore 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; According to the plurality of well point data pairs, the peak amplitude of the seismic trace beside the well is intersected and fitted with the wellbore thickness to obtain a first fitting function, the trough amplitude of the seismic trace beside the well is intersected and fitted with the wellbore thickness to obtain a second fitting function, and the relative amplitude ratio of the peak to trough of the seismic trace beside the well is intersected and fitted with the wellbore thickness to obtain a third fitting function; Constructing a prediction model for buried hill weathering crust reservoir thickness: Weathering 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 preset or obtained through training of the well point data.

2. The method according to claim 1, characterized in that Also includes: Correcting the buried hill weathering crust reservoir thickness prediction model through a plurality of well point data pairs to obtain a system error value; The majority system error value is 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) + system error value, wherein α, β, γ are weights, which are preset or obtained through training of the well point data.

3. The method according to claim 2, characterized in that The system error value is the average value of the difference between the wellbore thickness of the weathering crust reservoir in the plurality of well point data and the predicted value of the buried hill weathering crust reservoir thickness.

4. The method according to claim 1, characterized in that The first fitting function, the second fitting function and the third fitting function at least include polynomial functions.

5. The method according to claim 1, characterized in that The first fitting function, the second fitting function and the third fitting function at least include a power function.

6. The method according to claim 1, characterized in that The first fitting function, the second fitting function and the third fitting function at least include a logarithmic function.

7. The method according to claim 1, characterized in that Obtaining the peak amplitude, trough amplitude and relative amplitude ratio of the peak-trough of the buried hill weathering crust reservoir includes: Acquire three-dimensional seismic data volume; Acquire well logging data passing through the weathered crust reservoir in the three-dimensional seismic data volume; Using the well logging data to calibrate the three-dimensional seismic data volume, and obtain the stratigraphic information of the weathering crust reservoir; The peak amplitude, trough amplitude and the relative amplitude ratio of the peak to the trough of the weathering crust reservoir are extracted according to the stratigraphic information.

8. A device for constructing a buried hill weathering crust reservoir thickness prediction model, characterized in that: include: a processor for executing program instructions; and a memory storing program instructions, which, when loaded and executed by a processor, enables the processor to execute the method according to any one of claims 1 to 7.

9. A non-volatile computer-readable storage medium, characterized in that: Computer-readable instructions are stored thereon, and when the computer-readable instructions are executed by one or more processors, the method according to any one of claims 1 to 7 is implemented.

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