A method for constructing a weathering crust reservoir thickness prediction model and predicting weathering crust reservoir thickness.

Abstract

This invention relates to the field of seismic data processing technology, and discloses a method for constructing a weathered crust reservoir thickness prediction model and predicting the thickness of weathered crust reservoirs. The method includes: calibrating three-dimensional seismic data using well logging data from multiple wells passing through the weathered crust to obtain first seismic data; performing adaptive dip-constrained median filtering on the first seismic data to obtain second seismic data; performing Gilbert transform on the second seismic data to obtain a three-dimensional instantaneous frequency data volume; acquiring multiple sets of well point data pairs; and fitting an expression for the weathered crust reservoir thickness based on the multiple sets of well point data pairs to obtain a weathered crust reservoir thickness prediction model. In the embodiments of this invention, the instantaneous frequencies corresponding to the reflection peaks, reflection troughs, and zero amplitude points are combined using linear equations, reducing the instability in thickness prediction caused by frequency phase interference when the weathered crust reservoir is thin, thus making the predicted thickness value of the weathered crust reservoir more accurate.

Description

Technical Field

[0001] This invention generally relates to the field of seismic exploration technology. More specifically, this invention relates to a method for constructing a weathering crust reservoir thickness prediction model and predicting the thickness of weathering crust reservoirs. Background Technology

[0002] Seismic data acquisition and processing, two of the three major stages of seismic exploration, involve the manipulation of basic data. Seismic data interpretation transforms this data into abstract geological terms, namely, determining the geological structure and spatial location based on seismic data, inferring the lithology, thickness, and interlayer contact relationships of strata, locating oil and gas reservoirs, and providing accurate well locations for drilling. It is generally believed that the thickness of a stratum that can be identified by seismic activity in the time domain is H = formation velocity V / 4.6 times the dominant frequency; reservoirs with a thickness less than this are considered thin layers.

[0003] Weathering crust, as a type of oil and gas reservoir, has a certain thickness and distribution range in space, and is covered and buried by overlying strata in time. It has a certain porosity and permeability, enabling it to store and preserve oil and gas. Weathering crust reservoirs are geological bodies formed by long-term supergene lithification. Even after later burial, they still retain a certain oil and gas storage capacity. In many cases, weathering crust reservoirs are thin layers, making them difficult to clearly image in seismic data volumes.

[0004] Due to the thin-layered nature of weathered crust reservoirs, the amplitudes at their top and bottom interfaces partially cancel each other out or interfere with each other, making it difficult to accurately predict the thickness of weathered crust reservoirs using the amplitude at a single point.

[0005] Therefore, there is an urgent need to provide a method for constructing a thickness prediction model for weathered crust reservoirs to accurately predict the thickness of weathered crust reservoirs based on seismic data. Summary of the Invention

[0006] To at least address the problems mentioned above, embodiments of this invention provide a method for constructing a weathered crust reservoir thickness prediction model, comprising: a first step, calibrating three-dimensional seismic data using well logging data from multiple wells passing through the weathered crust to obtain first seismic data; a second step, performing adaptive dip-constrained median filtering on the first seismic data to obtain second seismic data; a third step, performing Gilbert transform on the second seismic data to obtain a three-dimensional instantaneous frequency data volume; a fourth step, acquiring multiple sets of well point data pairs from the three-dimensional instantaneous frequency data volume, wherein each well point data pair includes: the wellbore thickness corresponding to the weathered crust reservoir, the instantaneous frequency corresponding to the reflection peak of the well-side seismic trace, the instantaneous frequency corresponding to the reflection trough of the well-side seismic trace, the instantaneous frequency corresponding to the zero amplitude point of the well-side seismic trace, and the two-way reflection time between the reflection peak and the reflection trough of the well-side seismic trace; a fifth step, fitting an expression for the weathered crust reservoir thickness based on the multiple sets of well point data pairs to obtain a weathered crust reservoir thickness prediction model; the expression for the weathered crust reservoir thickness is:

[0007] H = aF peak +bF trouth +cF0+dΔt,

[0008] Where H represents the predicted thickness of the weathering crust reservoir, and F peak F represents the instantaneous frequency corresponding to the reflected wave peak. trough F0 represents the instantaneous frequency corresponding to the trough of the reflected wave, Δt represents the two-way reflection time between the peak and the trough of the reflected wave, and a, b, c, and d represent the weight values, respectively.

[0009] According to one embodiment of the present invention, the method further includes correcting the weathering crust reservoir thickness prediction model using data from multiple well points to obtain a system error value; and constructing an optimized weathering crust reservoir thickness prediction model.

[0010] H′=aF peak +bF trough +cF0+dΔt+σ,

[0011] Where H′ represents the corrected predicted thickness of the weathering crust reservoir, and F peak F represents the instantaneous frequency corresponding to the reflected wave peak. trough F0 represents the instantaneous frequency corresponding to the trough of the reflected wave, Δt represents the two-way reflection time between the peak and trough of the reflected wave, a, b, c, and d represent the weight values, and σ represents the system error value.

[0012] According to one embodiment of the present invention, the systematic error value is the average of the differences between the wellbore thickness and the predicted thickness of the weathered crust reservoir in multiple well point data.

[0013] According to another aspect of the present invention, a method for predicting the thickness of a weathering crust reservoir is provided, comprising: obtaining the instantaneous frequency corresponding to the reflection peak of the weathering crust reservoir, the instantaneous frequency corresponding to the reflection trough, the instantaneous frequency corresponding to the amplitude zero point, and the two-way reflection time between the reflection peak and the reflection trough; inputting the weathering crust reservoir thickness prediction model obtained by the aforementioned method for constructing a weathering crust reservoir thickness prediction model; and obtaining the weathering crust reservoir thickness.

[0014] According to one embodiment of the present invention, obtaining the instantaneous frequency corresponding to the reflection peak of the weathered crust reservoir, the instantaneous frequency corresponding to the reflection trough, the instantaneous frequency corresponding to the amplitude zero point, and the two-way reflection time between the reflection peak and the reflection trough includes: acquiring a three-dimensional seismic data volume containing the weathered crust reservoir; acquiring well logging data of a well passing through the weathered crust; calibrating the three-dimensional seismic data volume using the well logging data to obtain the stratigraphic information of the weathered crust reservoir; and extracting the instantaneous frequency corresponding to the reflection peak, the instantaneous frequency corresponding to the reflection trough, the instantaneous frequency corresponding to the amplitude zero point, and the two-way reflection time between the reflection peak and the reflection trough based on the stratigraphic information.

[0015] According to another aspect of the present invention, an apparatus for constructing a weathering crust reservoir thickness prediction model is provided, comprising:

[0016] The first module calibrates 3D seismic data using logging data from multiple wells penetrating the weathered crust, obtaining first seismic data. The second module performs adaptive dip-constrained median filtering on the first seismic data to obtain second seismic data. The third module performs Gilbert transform on the second seismic data to obtain a 3D instantaneous frequency data volume. The fourth module acquires multiple wellpoint data pairs from the 3D instantaneous frequency data volume, where each wellpoint data pair includes: wellbore thickness corresponding to the weathered crust reservoir, instantaneous frequency corresponding to the reflection peak of the well-side seismic trace, instantaneous frequency corresponding to the reflection trough of the well-side seismic trace, instantaneous frequency corresponding to the zero amplitude point of the well-side seismic trace, and the two-way reflection time between the reflection peak and trough of the well-side seismic trace. The fifth module fits an expression for the weathered crust reservoir thickness based on the multiple wellpoint data pairs, obtaining a weathered crust reservoir thickness prediction model. The expression for the weathered crust reservoir thickness is:

[0017] H = aF peak +bF trouth +cF0+dΔt,

[0018] Where H represents the predicted thickness of the weathering crust reservoir, and F peak F represents the instantaneous frequency corresponding to the reflected wave peak. troughF0 represents the instantaneous frequency corresponding to the trough of the reflected wave, Δt represents the two-way reflection time between the peak and the trough of the reflected wave, and a, b, c, and d represent the weight values, respectively.

[0019] According to one embodiment of the present invention, the device further includes a sixth module, used to obtain a system error value by correcting the weathering crust reservoir thickness prediction model using data from multiple well points; and to construct an optimized weathering crust reservoir thickness prediction model.

[0020] H′=aF peak +bF trough +cF0+dΔt+σ,

[0021] Where H′ represents the corrected predicted thickness of the weathering crust reservoir, and F peak F represents the instantaneous frequency corresponding to the reflected wave peak. trough F0 represents the instantaneous frequency corresponding to the trough of the reflected wave, Δt represents the two-way reflection time between the peak and trough of the reflected wave, a, b, c, and d represent the weight values, and σ represents the system error value.

[0022] According to another aspect of the present invention, a system for predicting the thickness of a weathered crust reservoir is provided, comprising: a data acquisition module for acquiring the instantaneous frequency corresponding to the reflection peak of the weathered crust reservoir, the instantaneous frequency corresponding to the reflection trough, the instantaneous frequency corresponding to the amplitude zero point, and the two-way reflection time between the reflection peak and the reflection trough; and a prediction module for inputting the instantaneous frequency corresponding to the reflection peak of the weathered crust reservoir, the instantaneous frequency corresponding to the reflection trough, the instantaneous frequency corresponding to the amplitude zero point, and the two-way reflection time between the reflection peak and the reflection trough into a weathered crust reservoir thickness prediction model obtained by the aforementioned method for constructing a weathered crust reservoir thickness prediction model, thereby obtaining the weathered crust reservoir thickness.

[0023] According to another aspect of the invention, an apparatus for predicting the thickness of a weathering crust reservoir is provided, comprising: a processor for executing program instructions; and a memory storing program instructions that, when loaded and executed by the processor, cause the processor to perform the aforementioned method.

[0024] According to another aspect of the present invention, a non-volatile computer-readable storage medium is provided having computer-readable instructions stored thereon, which, when executed by one or more processors, implement the aforementioned method.

[0025] In the weathering crust reservoir thickness prediction model of the present invention, the instantaneous frequencies corresponding to the reflection peaks, reflection troughs and zero amplitude points are combined by linear equations, which reduces the problem of unstable thickness prediction caused by frequency phase interference when the weathering crust reservoir is thin, and makes the thickness prediction value of the weathering crust reservoir more accurate.

[0026] In the method of this invention, the reliability of the second seismic data can be improved by adaptive dip-constrained median filtering.

[0027] In the method of the present invention, the use of multiple parameters of reflected wave characteristic points can eliminate the uncertainty of single-factor prediction in the prior art.

[0028] In the method of the present invention, the accuracy of weathering crust reservoir thickness prediction can be improved by introducing a two-way reflection time parameter between the reflection peak and the reflection trough.

[0029] When using data from multiple well points to calibrate the weathering crust reservoir thickness prediction model, the concept of error value is introduced, which can further optimize the weathering crust reservoir thickness prediction model and improve the accuracy of the predicted thickness. Attached Figure Description

[0030] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent upon reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of the invention are illustrated by way of example and not limitation, and like or corresponding reference numerals denote like or corresponding parts, wherein:

[0031] Figure 1 A schematic diagram of a three-dimensional seismic exploration system is shown;

[0032] Figure 2 A schematic diagram illustrating the steps of constructing a weathering crust reservoir thickness prediction model is shown.

[0033] Figure 3 A schematic diagram of the cross-section of three-dimensional seismic data is shown;

[0034] Figure 4 A schematic cross-sectional view of the three-dimensional instantaneous frequency data volume after Gilbert transform is shown;

[0035] Figure 5 A schematic diagram of a single-channel seismic waveform before and after the Gilbert transform is shown;

[0036] Figure 6 A hardware schematic diagram of constructing a weathering crust reservoir thickness prediction model is shown. Detailed Implementation

[0037] 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] It should be understood that the terms "comprising" and "including" as used in the specification and claims of this invention indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0039] It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this specification and claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations.

[0040] As used in this specification and claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if [described condition or event] is detected" may be interpreted, depending on the context, as "once determined," "in response to determination," "once [described condition or event] is detected," or "in response to detection of [described condition or event]."

[0041] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0042] Seismic exploration is a geophysical exploration method that uses artificially generated seismic waves to locate mineral deposits and obtain engineering geological information. Its basic principle is as follows: Seismic waves are generated by artificially generating a seismic source. When these waves propagate through rock, they are reflected or refracted when they encounter interfaces between rock strata. These reflected or refracted waves are received by recording instruments such as geophones upon returning to the surface, forming seismic data. Through analysis, processing, and comprehensive interpretation of this seismic data, the burial depth and shape of the rock strata where the seismic waves were reflected or refracted are determined, thus achieving accurate imaging of underground geological structures.

[0043] Figure 1 A schematic diagram of a three-dimensional seismic exploration system is shown as an example.

[0044] like Figure 1As shown, in the system 100, multiple geophones 110, spaced apart from each other, are arranged on the surface 101 of the exploration target area to form a geophone array covering the target area on a plane. These geophones 110 are connected to the seismic information processing equipment via wired or wireless connections. Multiple seismic sources 120 are also provided. The seismic information processing equipment can perform preliminary processing on the seismic data. The working process of the three-dimensional seismic exploration system is as follows: seismic sources 120 located at multiple locations are artificially excited to generate seismic waves. The seismic waves are reflected from the boundaries of the strata 102 and received by the geophone array, forming seismic information that varies over time and is collected on a plane. The seismic information received by the geophone array represents certain measures of seismic wave energy as a function of time, such as displacement, velocity, wave impedance, and pressure. This information can be grouped in different ways, such as traces and sets, and then processed or converted according to the correspondence between time and space to form a three-dimensional seismic data volume in the form of a three-dimensional array. In other words, this three-dimensional seismic data volume is formed by the spatial stacking of interface points. Interpreting three-dimensional seismic data volumes allows for observation of geological interfaces from different directions, and the study of geological volume variations in three-dimensional space can be achieved by cutting cross sections, longitudinal sections, and horizontal slices.

[0045] Figure 2 A schematic diagram illustrating the steps of constructing a weathering crust reservoir thickness prediction model is shown.

[0046] like Figure 2 As shown, a method 200 for constructing a weathered crust reservoir thickness prediction model includes: Step S201, calibrating three-dimensional seismic data using well logging data from multiple wells passing through the weathered crust to obtain first seismic data. Step S202, performing adaptive dip-constrained median filtering on the first seismic data to obtain second seismic data. Step S203, performing Gilbert transform on the second seismic data to obtain a three-dimensional instantaneous frequency data volume. Step S204, acquiring multiple sets of well point data pairs from the three-dimensional instantaneous frequency data volume, wherein each well point data pair includes: wellbore thickness corresponding to the weathered crust reservoir, instantaneous frequency corresponding to the reflection peak of the well-side seismic trace, instantaneous frequency corresponding to the reflection trough of the well-side seismic trace, instantaneous frequency corresponding to the amplitude zero point of the well-side seismic trace, and two-way reflection time between the reflection peak and reflection trough of the well-side seismic trace. Step S205, fitting an expression for the weathered crust reservoir thickness based on the multiple sets of well point data pairs to obtain a weathered crust reservoir thickness prediction model.

[0047] The expression for the thickness of the weathering crust reservoir is:

[0048] H = aF peak +bF trough +cF0+dΔt,

[0049] Where H represents the predicted thickness of the weathering crust reservoir, and Fpeak F represents the instantaneous frequency corresponding to the reflected wave peak. trough F0 represents the instantaneous frequency corresponding to the trough of the reflected wave, Δt represents the two-way reflection time between the peak and the trough of the reflected wave, and a, b, c, and d represent the weight values, respectively.

[0050] The instantaneous frequency of an earthquake is the time derivative of its instantaneous phase, related to the spectrum of the formation velocity, and is the rate of change of the instantaneous phase over time. The instantaneous frequency is controlled by the properties of the formation through which the seismic wave passes; it is a physical property of the seismic wave signal, related to the density of the formation, and is often used as an indicator of formation thickness.

[0051] The instantaneous frequencies corresponding to the reflection peaks, troughs, and zero amplitude, as well as the two-way reflection time between the reflection peaks and troughs, are all directly related to the formation thickness. Therefore, the thickness of the weathered crust reservoir can be characterized by using the correspondence between the weathered crust reservoir thickness and the above parameters, and employing a polynomial regression fitting method.

[0052] The sample data for the weathered crust reservoir thickness prediction model includes training and validation data, both constructed based on existing well logging and seismic data of weathered crust reservoirs. First, seismic data containing the weathered crust reservoir is acquired to construct a three-dimensional seismic data volume. Multiple wells are then set up at the location of the weathered crust reservoir to obtain logging data. The logging data includes the actual thickness of the weathered crust reservoir at the wellbore location, i.e., the wellbore thickness corresponding to the weathered crust reservoir. After performing a Gilbert transform on the three-dimensional seismic data volume, based on the correspondence between the first seismic data and the three-dimensional instantaneous frequency data volume, well-side seismic traces are extracted. The instantaneous frequencies corresponding to the reflection peaks, reflection troughs, and zero amplitude points of the well-side seismic traces, as well as the two-way reflection time between the reflection peaks and troughs of the well-side seismic traces, are read. These parameters are then correlated with the wellbore weathered crust reservoir thickness to form wellpoint data pairs. Table 1 shows a feasible format for the wellpoint data pairs.

[0053] Table 1.

[0054] Wellbore thickness Peak data Valley data Peak time point Return to midnight trough time point Well 1 H1 <![CDATA[F peak 1]]> <![CDATA[F trough 1]]> <![CDATA[t1-1]]> <![CDATA[t2-1]]> <![CDATA[t3-1]]> Well 2 H2 <![CDATA[F peak 2]]> <![CDATA[F trough 2]]> <![CDATA[t1-2]]> <![CDATA[t2-2]]> <![CDATA[t3-2]]> Well 3 H3 <![CDATA[F peak 3]]> <![CDATA[F trough 3]]> <![CDATA[t1-3]]> <![CDATA[t2-3]]> <![CDATA[t3-3]]> Well 4 H4 <![CDATA[F peak 4]]> <![CDATA[F trough 4]]> <![CDATA[t1-4]]> <![CDATA[t2-4]]> <![CDATA[t3-4]]> Well 5 H5 <![CDATA[F peak 5]]> <![CDATA[F trough 5]]> <![CDATA[t1-5]]> <![CDATA[t2-5]]> <![CDATA[t3-5]]>

[0055] Based on the number of wells, multiple well point data pairs can be constructed to fit the expression for the thickness of the weathered crust reservoir mentioned above, and the weight values ​​can be obtained to obtain the weathered crust reservoir thickness prediction model.

[0056] Figure 3 A schematic cross-sectional view of three-dimensional seismic data according to an embodiment of the present invention is shown.

[0057] In step S201, the three-dimensional seismic data is calibrated using well logging data from multiple wells penetrating the weathered crust to obtain the first seismic data. For example... Figure 3 As shown, well logging information is used to calibrate the seismic profile. Well logging Qc2 can reflect the strata containing the weathering crust and its thickness, and has a corresponding well-side seismic trace on the first seismic data.

[0058] Figure 4 A schematic cross-sectional view of the three-dimensional instantaneous frequency data volume after Gilbert transform is shown.

[0059] In step S202, median filtering improves the signal-to-noise ratio of the reflection data in the second seismic data. The terminology of adaptive dip-constrained median filtering is a prior art and will not be elaborated upon here. For example: Yin Chuan, Du Xiangdong, Zhao Rumin, Wu Kui, Deng Jun, Shen Ye. 2014. Tectonic Steering Filtering Based on Dip Control and Its Application [J]. Progress in Geophysics, 29(6):2818-2822, doi:10.6038 / pg20140650.

[0060] In step S203, the second seismic data is subjected to a Gilbert transform to obtain a three-dimensional instantaneous frequency data volume. Figure 4 In the instantaneous frequency profile shown, the intensity of the frequency energy (i.e., amplitude) is represented by the color intensity. Since the data was calibrated using well logging in the first seismic data, there is a correspondence between the well depth corresponding to the well logging and the instantaneous frequency corresponding to the reflection peak, the instantaneous frequency corresponding to the reflection trough, and the two-way reflection time between the reflection peak and the reflection trough in the three-dimensional instantaneous data volume. The above data can be directly read from the three-dimensional instantaneous frequency data volume after Gilbert transformation.

[0061] Figure 5 A schematic diagram of a single-channel seismic waveform before and after the Gilbert transform is shown.

[0062] like Figure 5 As shown, the left side displays the original seismic trace waveform, and the right side displays the instantaneous frequency waveform after Gilbert transform. The instantaneous frequencies corresponding to the peaks, zeros, and troughs of the amplitude can be directly read from the transformed instantaneous frequency waveform. The two-way reflection time between the reflected wave peak and the reflected wave trough can be directly obtained from... Figure 4 The waveform information obtained from the data can represent the depth of a certain weathering crust.

[0063] In embodiments of the present invention, multiple sets of well point data pairs are constructed using multiple wells, and the least squares method is used to fit each pair to obtain the weight values ​​of the expression for the thickness of the weathered crust reservoir. The weathered crust reservoir thickness prediction model incorporates four elements: the instantaneous frequencies corresponding to the peak, zero point, and trough of the amplitude, and the two-way reflection time between the reflected peak and the reflected trough. This reduces the interference in thickness prediction when the weathered crust reservoir is thin, resulting in a more accurate predicted value for the thickness of the weathered crust reservoir.

[0064] In embodiments of the present invention, the factors reflecting thickness are integrated using linear polynomial expressions, which simplifies the computational complexity during model building and increases the speed of model creation.

[0065] According to one embodiment of the present invention, the method further includes obtaining systematic error values ​​by calibrating the model using data from multiple well points, and constructing an optimized weathering crust reservoir thickness prediction model:

[0066] H′=aF peak +bF trough +cF0+dΔt+σ,

[0067] Where H′ represents the corrected predicted thickness of the weathering crust reservoir, and F peak F represents the instantaneous frequency corresponding to the reflected wave peak. trough F0 represents the instantaneous frequency corresponding to the trough of the reflected wave, Δt represents the two-way reflection time between the peak and trough of the reflected wave, a, b, c, and d represent the weight values, and σ represents the system error value.

[0068] When using data from multiple well points to calibrate the weathered crust reservoir thickness prediction model, a systematic error value is introduced to correct the deviation of the prediction results, making the predicted thickness value of the weathered crust reservoir more accurate. In an embodiment of the present invention, the systematic error value is the average of the differences between the wellbore thickness and the predicted thickness of the weathered crust reservoir from multiple well point data.

[0069] By substituting multiple wellpoint data points into the weathered crust reservoir thickness prediction model, multiple predicted thicknesses can be obtained. These thicknesses are then correlated with multiple wellhead thicknesses. The differences between these thicknesses are calculated, and the average of these differences yields the error value. The error value obtained by this method has a statistically significant bias buffering effect, effectively improving the accuracy of the predicted weathered crust reservoir thickness.

[0070] According to an embodiment of the present invention, a method for predicting the thickness of a weathering crust reservoir is provided, comprising: obtaining the instantaneous frequency corresponding to the reflection peak of the weathering crust reservoir, the instantaneous frequency corresponding to the reflection trough, the instantaneous frequency corresponding to the amplitude zero point, and the two-way reflection time between the reflection peak and the reflection trough; inputting the weathering crust reservoir thickness prediction model to obtain the weathering crust reservoir thickness.

[0071] According to an embodiment of the present invention, a system for predicting the thickness of a weathered crust reservoir is also provided, comprising: a data acquisition module for acquiring the instantaneous frequency corresponding to the reflection peak, the instantaneous frequency corresponding to the reflection trough, the instantaneous frequency corresponding to the amplitude zero point, and the two-way reflection time between the reflection peak and the reflection trough of the weathered crust reservoir; and a prediction module for inputting the instantaneous frequencies corresponding to the reflection peak, the reflection trough, the amplitude zero point, and the two-way reflection time between the reflection peak and the reflection trough of the weathered crust reservoir into a weathered crust reservoir thickness prediction model to obtain the weathered crust reservoir thickness. This system can be implemented using hardware such as a computer, cloud processor, or circuitry with corresponding programs.

[0072] According to one embodiment of the present invention, obtaining the instantaneous frequency corresponding to the reflection peak, the instantaneous frequency corresponding to the reflection trough, the instantaneous frequency corresponding to the zero amplitude point, and the two-way reflection time between the reflection peak and the reflection trough of a weathered crust reservoir includes: acquiring a three-dimensional seismic data volume containing the weathered crust reservoir; acquiring well logging data of a well passing through the weathered crust; calibrating the three-dimensional seismic data volume using the well logging data to obtain the stratigraphic information of the weathered crust reservoir; and extracting the instantaneous frequency corresponding to the reflection peak, the instantaneous frequency corresponding to the reflection trough, the instantaneous frequency corresponding to the zero amplitude point, and the two-way reflection time between the reflection peak and the reflection trough of the weathered crust reservoir based on the stratigraphic information.

[0073] The present invention also provides an apparatus for constructing a weathering crust thickness prediction model, which can be implemented by hardware or a combination of hardware, wherein the hardware or combination of hardware contains a program for executing the above-described method. The system includes: a first module for calibrating 3D seismic data using logging data from multiple wells passing through the weathered crust to obtain first seismic data; a second module for performing adaptive dip-constrained median filtering on the first seismic data to obtain second seismic data; a third module for performing Gilbert transform on the second seismic data to obtain a 3D instantaneous frequency data volume; a fourth module for acquiring multiple sets of well point data pairs from the 3D instantaneous frequency data volume, wherein each well point data pair includes: wellbore thickness corresponding to the weathered crust reservoir, instantaneous frequency corresponding to the reflection peak of the well-side seismic trace, instantaneous frequency corresponding to the reflection trough of the well-side seismic trace, instantaneous frequency corresponding to the zero amplitude point of the well-side seismic trace, and two-way reflection time between the reflection peak and reflection trough of the well-side seismic trace; and a fifth module for fitting an expression for the weathered crust reservoir thickness based on the multiple sets of well point data pairs to obtain a weathered crust reservoir thickness prediction model; the expression for the weathered crust reservoir thickness is:

[0074] H = aF peak +bF trough +cF0+dΔt,

[0075] Where H represents the predicted thickness of the weathering crust reservoir, and F peak F represents the instantaneous frequency corresponding to the reflected wave peak. trough F0 represents the instantaneous frequency corresponding to the trough of the reflected wave, Δt represents the two-way reflection time between the peak and the trough of the reflected wave, and a, b, c, and d represent the weight values, respectively.

[0076] Modules one through five can be separate actuators logically connected to each other, or they can be computer processors that execute the above methods serially.

[0077] Furthermore, the device may also include: a sixth module, used to correct the weathering crust reservoir thickness prediction model using data from multiple well points to obtain system error values; and to construct an optimized weathering crust reservoir thickness prediction model.

[0078] H′=aF peak +bF trough +cF0+dΔt+σ,

[0079] Where H′ represents the corrected predicted thickness of the weathering crust reservoir, and F peak F represents the instantaneous frequency corresponding to the reflected wave peak. troughF0 represents the instantaneous frequency corresponding to the trough of the reflected wave, Δt represents the two-way reflection time between the peak and trough of the reflected wave, a, b, c, and d represent the weight values, and σ represents the system error value.

[0080] According to another aspect of the present invention, an apparatus for constructing a weathering crust reservoir thickness prediction model based on three-point instantaneous frequencies is provided, comprising: a processor for executing program instructions; and a memory storing program instructions that, when loaded and executed by the processor, cause the processor to perform the aforementioned method.

[0081] Figure 6 A hardware schematic diagram of constructing a weathering crust reservoir thickness prediction model is shown.

[0082] The system 600 may include device 601 and its peripheral devices and external networks, wherein 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.

[0083] 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.

[0084] 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.

[0085] 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 measured data.

[0086] Peripherals of 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, configured to provide voice prompts and / or display images and videos regarding the computation process or detection results of the device of the present invention. The input device 603 may include, for example, a keyboard, mouse, microphone, gesture capture camera, or other input buttons or controls, configured to receive input of training data or user instructions. The data transmission interface 604 may include, for example, a serial interface, parallel interface, or Universal Serial Bus interface (“USB”), Small Computer System Interface (“SCSI”), Serial ATA, FireWire (“FireWire”), PCI Express, and High Definition Multimedia Interface (“HDMI”), configured for data transmission and interaction with other devices or systems.

[0087] The 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 invention can be interconnected via bus 6019, and can interact with peripheral devices through this bus. In one embodiment, the CPU 6011 can control other hardware components in the device 601 and its peripheral devices through this bus 6019.

[0088] In operation, the processor CPU 6011 of the device 601 of this invention can receive three-dimensional seismic data volumes through the input device 603 or the data transmission interface 604, and retrieve computer program instructions or code (such as various programs for constructing stratigraphic models) stored in the memory 6012 to process the received three-dimensional seismic data volumes to construct a weathering crust reservoir thickness prediction model. After the CPU 6011 constructs the weathering crust reservoir thickness prediction model by executing the program instructions, it can be displayed on the display device 602 or output through voice prompts. In addition, the device 601 can also upload it to a network, such as a remote database 609, through the communication interface 6018.

[0089] It should also be understood that any module, unit, component, server, computer, terminal, or device that executes the instructions in this invention 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 discs, or magnetic 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.

[0090] Computer-readable storage media can be any suitable magnetic 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 desired information and can be accessed by an application, module, or both. Any such computer storage medium can be part of a device or accessible to or connected to a device. Any application or module described in this invention can be implemented using computer-readable / executable instructions that can be stored or otherwise retained by such a computer-readable medium.

[0091] While numerous embodiments of the 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. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of the invention. The appended claims are intended to define the scope of protection of the invention and therefore cover equivalents or alternatives within the scope of these claims.

Claims

1. A method for constructing a weathering crust reservoir thickness prediction model, characterized in that, include: The first step is to use well logging data from multiple wells that have penetrated the weathered crust to calibrate the three-dimensional seismic data and obtain the first seismic data. The second step is to perform adaptive dip-constrained median filtering on the first seismic data to obtain the second seismic data; The third step is to perform a Gilbert transform on the second seismic data to obtain a three-dimensional instantaneous frequency data volume; The fourth step is to acquire multiple sets of well point data pairs in the three-dimensional instantaneous frequency data volume. The well point data pairs include: the well thickness corresponding to the weathered crust reservoir, the instantaneous frequency corresponding to the reflection peak of the well-side seismic trace, the instantaneous frequency corresponding to the reflection trough of the well-side seismic trace, the instantaneous frequency corresponding to the zero amplitude point of the well-side seismic trace, and the two-way reflection time between the reflection peak and the reflection trough of the well-side seismic trace. The fifth step is to fit an expression for the thickness of the weathering crust reservoir based on multiple sets of well point data pairs, and obtain a weathering crust reservoir thickness prediction model. The expression for the thickness of the weathering crust reservoir is: H=aF peak +bF trough +cF0+dΔt, Where H represents the predicted thickness of the weathering crust reservoir, and F peak F represents the instantaneous frequency corresponding to the reflected wave peak. trough F0 represents the instantaneous frequency corresponding to the trough of the reflected wave, Δt represents the two-way reflection time between the peak and the trough of the reflected wave, and a, b, c, and d represent the weight values, respectively.

2. The method for constructing a weathering crust reservoir thickness prediction model according to claim 1, characterized in that, Also includes: The system error value is obtained by correcting the weathering crust reservoir thickness prediction model using data from multiple well points. Construct an optimized model for predicting the thickness of weathering crust reservoirs: H′=aF peak +bF trough +cF0+dΔt+σ, in, H′ represents the corrected predicted thickness of the weathering crust reservoir, F peak F represents the instantaneous frequency corresponding to the reflected wave peak. trough F0 represents the instantaneous frequency corresponding to the trough of the reflected wave, Δt represents the two-way reflection time between the peak and trough of the reflected wave, a, b, c, and d represent the weight values, and σ represents the system error value.

3. The method for constructing a weathering crust reservoir thickness prediction model according to claim 2, characterized in that, The system error value is the average of the differences between the borehole thickness and the predicted thickness of the weathered crust reservoir in multiple well point data.

4. A method for predicting the thickness of a weathering crust reservoir, characterized in that, include: The instantaneous frequency corresponding to the reflection peak of the weathered crust reservoir, the instantaneous frequency corresponding to the reflection trough, the instantaneous frequency corresponding to the zero amplitude, and the two-way reflection time between the reflection peak and the reflection trough are obtained. The weathered crust reservoir thickness prediction model obtained by the method of constructing a weathered crust reservoir thickness prediction model according to any one of claims 1-3 is then input to obtain the weathered crust reservoir thickness.

5. The method for predicting the thickness of weathering crust reservoirs according to claim 4, characterized in that, The instantaneous frequencies corresponding to the reflection peaks and troughs of the weathered crust reservoir, the instantaneous frequencies corresponding to the zero amplitude, and the two-way reflection time between the reflection peaks and troughs are obtained, including: Acquire a 3D seismic data volume containing weathered crust reservoirs; Obtain logging data from wells that penetrate the weathered crust; The three-dimensional seismic data volume is calibrated using the well logging data to obtain the stratigraphic information of the weathered crust reservoir; Based on the stratigraphic information, extract the instantaneous frequency corresponding to the reflection peak, the instantaneous frequency corresponding to the reflection trough, the instantaneous frequency corresponding to the zero amplitude point, and the two-way reflection time between the reflection peak and the reflection trough of the weathered crust reservoir.

6. An apparatus for constructing a predictive model for the thickness of weathered crust reservoirs, characterized in that, include: The first module is used to calibrate three-dimensional seismic data using well logging data from multiple wells that pass through the weathered crust, and obtain the first seismic data. The second module is used to perform adaptive dip-constrained median filtering on the first seismic data to obtain the second seismic data. The third module is used to perform a Gilbert transform on the second seismic data to obtain a three-dimensional instantaneous frequency data volume; The fourth module is used to acquire multiple sets of well point data pairs in the three-dimensional instantaneous frequency data volume. The well point data pairs include: the well thickness corresponding to the weathered crust reservoir, the instantaneous frequency corresponding to the reflection peak of the well-side seismic trace, the instantaneous frequency corresponding to the reflection trough of the well-side seismic trace, the instantaneous frequency corresponding to the zero amplitude point of the well-side seismic trace, and the two-way reflection time between the reflection peak and the reflection trough of the well-side seismic trace. The fifth module is used to fit an expression for the thickness of the weathering crust reservoir based on multiple sets of well point data pairs, and obtain a weathering crust reservoir thickness prediction model. The expression for the thickness of the weathering crust reservoir is: H=aF peak +bF trough +cF0+dΔt, Where H represents the predicted thickness of the weathering crust reservoir, and F peak F represents the instantaneous frequency corresponding to the reflected wave peak. trough F0 represents the instantaneous frequency corresponding to the trough of the reflected wave, Δt represents the two-way reflection time between the peak and the trough of the reflected wave, and a, b, c, and d represent the weight values, respectively.

7. The apparatus for constructing a weathering crust reservoir thickness prediction model according to claim 6, characterized in that, Also includes: The sixth module is used to correct the weathering crust reservoir thickness prediction model using data from multiple well points to obtain a system error value; Construct an optimized model for predicting the thickness of weathering crust reservoirs: H′=aF peak +bF trough +cF0+dΔt+σ, in, H′ represents the corrected predicted thickness of the weathering crust reservoir, F peak F represents the instantaneous frequency corresponding to the reflected wave peak. trough F0 represents the instantaneous frequency corresponding to the trough of the reflected wave, Δt represents the two-way reflection time between the peak and trough of the reflected wave, a, b, c, and d represent the weight values, and σ represents the system error value.

8. A system for predicting the thickness of weathered crust reservoirs, characterized in that, include: The data acquisition module is used to acquire the instantaneous frequency corresponding to the reflection peak of the weathered crust reservoir, the instantaneous frequency corresponding to the reflection trough, the instantaneous frequency corresponding to the amplitude zero point, and the two-way reflection time between the reflection peak and the reflection trough. The prediction module is used to input the instantaneous frequency corresponding to the reflection peak of the weathered crust reservoir, the instantaneous frequency corresponding to the reflection trough, the instantaneous frequency corresponding to the amplitude zero point, and the two-way reflection time between the reflection peak and the reflection trough into the weathered crust reservoir thickness prediction model obtained by the method of constructing the weathered crust reservoir thickness prediction model according to any one of claims 1-3, so as to obtain the weathered crust reservoir thickness.

9. A device for predicting the thickness of a weathering crust reservoir, characterized in that, include: A processor is used to execute program instructions; And a memory storing program instructions that, when loaded and executed by a processor, cause the processor to perform the method of any one of claims 4-5.

10. A non-volatile computer-readable storage medium, characterized in that, It stores computer-readable instructions that, when executed by one or more processors, implement the method of any one of claims 1-5.

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