WO2025035685A1 - 构建速度模型的方法、控制装置和存储介质 - Google Patents

构建速度模型的方法、控制装置和存储介质 Download PDF

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WO2025035685A1
WO2025035685A1 PCT/CN2023/142794 CN2023142794W WO2025035685A1 WO 2025035685 A1 WO2025035685 A1 WO 2025035685A1 CN 2023142794 W CN2023142794 W CN 2023142794W WO 2025035685 A1 WO2025035685 A1 WO 2025035685A1
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velocity
frequency
target layer
well
inversion
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French (fr)
Inventor
尹继全
窦立荣
朱秋影
段海岗
罗贝维
王震
杨沛广
肖萌
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Petrochina Co Ltd
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Petrochina Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/30—Analysis
    • G01V1/303—Analysis for determining velocity profiles or travel times
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/30—Analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/40—Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging

Definitions

  • the invention relates to the technical field of geological exploration, and in particular to a method for constructing a velocity model, a control device and a storage medium.
  • Velocity parameters are the basic physical properties of underground strata and important parameters of seismic exploration. They affect all aspects of seismic exploration and play a vital role in oil and gas exploration and development. Strata or geological bodies of different ages and lithologies usually have different layer velocities; underground reservoir pores will also show different degrees of velocity differences due to different fluids (gas, oil, water) or abnormalities and high-pressure closure of geological bodies. Therefore, an accurate velocity model that conforms to geological laws is not only the key to prestack migration imaging and time-depth conversion, but also affects many aspects of exploration and development, such as structural accuracy, well depth implementation, stratum thickness, reservoir prediction, and pressure interface prediction.
  • the methods for establishing layer velocity models mainly include: 1) using pre-stack time migration velocity or stacking velocity to calculate layer velocity through the Dix formula. This method is based on a uniform medium or horizontal layered medium model and is estimated from the seismic processing velocity spectrum. The velocity change trend and relative relationship can be obtained. This method has poor accuracy for sedimentary stratigraphic sequences with widespread spatial anisotropy. 2) Grid tomography velocity modeling method. This method uses data-driven and computer-automatic iterative solution methods to obtain more detailed velocity changes, but this method has a large amount of calculations and is difficult to control velocity errors under complex geological conditions due to the lack of human intervention.
  • the purpose of the embodiment of the present invention is to provide a method for constructing a velocity model.
  • the velocity model constructed by the method for constructing a velocity model can accurately characterize the velocity variation characteristics inside and at the boundary of a special geological body.
  • an embodiment of the present invention provides a method for constructing a velocity model, the method for constructing a velocity model comprising: acquiring a three-dimensional seismic data volume and logging data of a pre-selected well in a preset area; processing the three-dimensional seismic data volume and the logging data to obtain the time-depth relationship and layer interpretation data of the pre-selected well of the target layer; based on the obtained time-depth relationship and layer interpretation data of the pre-selected well of the target layer, inverting the three-dimensional seismic data volume through a preset frequency band inversion strategy.
  • the velocity bodies of different frequency bands are inverted by frequency bands to obtain velocity bodies of different frequency bands; and the velocity bodies of different frequency bands are fused to obtain a velocity model of the target layer.
  • the method for constructing the velocity model before obtaining the three-dimensional seismic data body of the preset area and the logging data of the pre-selected wells, the method for constructing the velocity model also includes: determining the range of the target layer; obtaining the logging data of the wells in the preset area; and for the wells drilled through the target layer, screening the wells with acoustic wave curves, density curves and well diameter curves as the pre-selected wells according to the corresponding logging data.
  • the method for constructing the velocity model further includes: converting the acoustic wave curve of the pre-selected well into a velocity curve.
  • the method for constructing a velocity model also includes: for the pre-selected wells of the target layer, analyzing the bar chart of the single well through the logging data to determine the lithology and longitudinal variation characteristics of the velocity of the target layer; comparing and analyzing the velocity curves of connected wells through the logging data to determine the lateral variation characteristics of the velocity of the target layer; and determining the velocity variation characteristics and geological body type of the target layer through the longitudinal variation characteristics of the velocity of the target layer and the lateral variation characteristics of the velocity of the target layer.
  • the logging data of the preselected well is processed to obtain the time-depth relationship of the preselected well in the target layer, including: obtaining the acoustic wave curve and the density curve in the logging data of the preselected well; and making a synthetic seismic record through the acoustic wave curve and the density curve, establishing a corresponding relationship between the seismic reflection interface in the time domain and the geological stratification in the depth domain, and obtaining the time-depth relationship of the preselected well.
  • the three-dimensional seismic data body and the well logging data are processed to obtain stratigraphic interpretation data, including: analyzing the well logging data of the pre-selected well to obtain velocity variation characteristics of the target layer; determining T geological layers of the target layer by analyzing the velocity variation characteristics of the target layer and based on the velocity difference of the velocity interface; calibrating the T geological layers of the target layer by making synthetic seismic records, and determining the characteristics of the seismic reflection event axes corresponding to each geological layer; and completing the tracking and interpretation of the seismic reflection event axes corresponding to each geological layer based on the three-dimensional seismic data body to obtain stratigraphic interpretation data of the T geological layers of the target layer.
  • the three-dimensional seismic data volume is inverted in frequency bands by a preset frequency band inversion strategy to obtain velocity volumes in different frequency bands, including: inverting the three-dimensional seismic data volume by Kriging interpolation method to obtain a low-frequency band velocity volume; inverting the three-dimensional seismic data volume by spectral simulation inversion method to obtain a mid-frequency band velocity volume; and inverting the three-dimensional seismic data volume by waveform indication inversion method to obtain a high-frequency band velocity volume.
  • the method for constructing a velocity model before performing frequency band inversion on the three-dimensional seismic data volume through a preset frequency band inversion strategy, the method for constructing a velocity model also includes: determining T interpretation layers of the target layer from bottom to top; between each two adjacent interpretation layers, according to the contact relationship between the upper and lower layers, and according to a preset subdivision ratio C, interpolating between each two adjacent interpretation layers to obtain C interpolated sublayers; and constructing a stratigraphic grid model of the target layer from the T interpretation layers and the C interpolated sublayers between each two adjacent interpretation layers.
  • the three-dimensional seismic data body is inverted by the Kriging interpolation method to obtain a low-frequency velocity body, including: obtaining a velocity curve in the logging data of the pre-selected well, and low-pass filtering the velocity curve to retain the low-frequency velocity curve within a first cutoff frequency value; based on the obtained time-depth relationship of the pre-selected well, the filtered low-frequency velocity curve is converted from the depth domain to the time domain to obtain a low-frequency velocity curve in the time domain; and based on the stratigraphic grid model of the target layer, lateral constraints of the low-frequency velocity curve are established to obtain the low-frequency velocity body by performing logging interpolation on the three-dimensional seismic data body under the lateral constraints by the Kriging interpolation method.
  • the lateral constraint condition of the low-frequency velocity curve is established based on the stratigraphic frame model of the target layer, so as to perform well logging interpolation under the lateral constraint condition on the three-dimensional seismic data volume through the Kriging interpolation method,
  • Obtaining the low-frequency velocity body comprises: for each layer in the layer grid model, obtaining an estimated value at each CDP of the layer based on the observed value of each well of the preselected wells, wherein the low-frequency velocity value read at each well is used as the observed value of each well, and the low-frequency velocity value of the layer is obtained through the estimated value at each CDP of the layer; and obtaining the low-frequency velocity body through the low-frequency velocity value of each layer, wherein each layer includes the T interpreted layers and the C interpolated sublayers between each two adjacent interpreted layers.
  • the three-dimensional seismic data volume is inverted by the spectral simulation inversion method to obtain a mid-frequency velocity volume, including: obtaining the velocity curve in the logging data of the preselected well and constructing a corresponding wave impedance curve; based on the obtained time-depth relationship of the preselected well in the target layer, the wave impedance curve of the preselected well is converted from the depth domain to the time domain to obtain a corresponding time-domain wave impedance curve; and the time-domain wave impedance curve is superimposed on the three-dimensional seismic data volume, and inverted by the spectral simulation inversion method to obtain the mid-frequency velocity volume.
  • the time domain wave impedance curve is superimposed on the three-dimensional seismic data volume, and inverted by spectral simulation inversion method to obtain the mid-frequency band velocity body, including: for each preselected well of the target layer, spectral analysis is performed on the seismic trace beside the well and the time domain wave impedance curve of the preselected well, and a frequency matching operator is calculated, and the frequency matching operator is applied to the three-dimensional seismic data volume to obtain an inversion result of spectral simulation, and according to the corresponding relationship between reflection coefficient and wave impedance, a wave impedance inversion result is obtained from the inversion result of the spectral simulation; and the mid-frequency band velocity body is obtained by the ratio of the wave impedance inversion result of the target layer to the first density mean.
  • the three-dimensional seismic data body is inverted by a waveform indication inversion method to obtain a high-frequency velocity body, including: for the wellbore seismic traces of each well in n pre-selected wells, under the stratigraphic grid model of the target layer, the seismic waveform to be predicted is compared with the waveform of the wellbore seismic traces of each well, and K waveform simulation samples of the wellbore seismic traces are selected according to two factors: waveform similarity and spatial distance, and an initial impedance model is constructed through corresponding logging data; based on the characteristics of the initial impedance model and according to a preset high-pass frequency parameter value and a preset high-cutoff frequency parameter value, high-frequency band information is continuously filtered out, and comparisons are made between the waveform simulation samples of each wellbore seismic trace to retain a preset deterministic frequency band component; and based on the waveform simulation samples and spatial structure characteristics of the K wellbore seismic traces, an estimated value of an unknown point is determined to obtain a waveform
  • fusing the velocity bodies of the different frequency bands to obtain the velocity model of the target layer includes: obtaining the velocity model of the target layer by taking the weighted sum of the low-frequency band velocity body, the mid-frequency band velocity body and the high-frequency band velocity body.
  • An embodiment of the present invention also provides a control device for constructing a velocity model, the control device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to implement the above-mentioned method for constructing a velocity model.
  • An embodiment of the present invention further provides a machine-readable storage medium, on which instructions are stored, and the instructions enable a machine to execute the above-mentioned method for constructing a velocity model.
  • the embodiment of the present invention provides a method for constructing a velocity model for more accurate comprehensive seismic geological research such as time-depth conversion, thickness calculation, reservoir prediction, and well location implementation related to special geological bodies. Based on the analysis of the geological characteristics of special geological bodies, fine time-depth relationship and layer interpretation data are obtained through fine seismic geological layer calibration.
  • the well logging data and three-dimensional seismic data body are fully utilized, and the three-dimensional seismic data body is subjected to frequency band inversion through the preset frequency band inversion strategy and data fusion technology to obtain velocity bodies of different frequency bands, and the frequency band velocities of low frequency, medium frequency and high frequency bands are used to construct a velocity model that can finely characterize the velocity change characteristics of the interior and boundary of the special geological body, and provide reliable velocity data for comprehensive geological research such as time-depth conversion, fine structural implementation, and well location deployment, so as to help expand and efficiently promote exploration in related fields of special geological bodies.
  • FIG1 is a schematic flow chart of a method for constructing a velocity model provided by an embodiment of the present invention
  • FIG. 2 is a schematic diagram illustrating velocity variation characteristics of an example geological body.
  • FIG3 is a schematic cross-sectional view of example migration stacked seismic data
  • FIG4 is a schematic diagram of a velocity profile of an exemplary target layer low-frequency velocity body
  • FIG5 is a schematic diagram of a velocity profile of a mid-frequency velocity body of an exemplary target layer
  • FIG6 is a schematic diagram of a velocity profile of an example high-frequency velocity body of a target layer
  • FIG7 is a schematic diagram of a velocity profile of an example velocity model.
  • FIG. 8 is a schematic diagram of a velocity profile illustrating an example conventional velocity model.
  • the existing velocity model is suitable for geological conditions with relatively stable lateral distribution of strata.
  • geological conditions with relatively complex geological structures for example, with the development of special geological bodies such as salt domes, belt reef structures and carbonate rock anomalies, the existing velocity modeling cannot accurately characterize the velocity change characteristics inside and at the boundaries of special geological bodies, which affects the accuracy of structural characteristics and restricts the exploration of related fields of special geological bodies. Therefore, for exploration areas with the development of special geological bodies, a velocity model that can accurately characterize the velocity changes inside and at the boundaries of special geological bodies is needed.
  • FIG1 is a flow chart of a method for constructing a velocity model provided by an embodiment of the present invention.
  • the method for constructing a velocity model may include the following steps:
  • Step S110 Acquire a three-dimensional seismic data volume of a preset area and logging data of a pre-selected well.
  • the three-dimensional seismic data volume is offset stacked seismic data after amplitude preservation processing.
  • the method for constructing a velocity model further includes: determining the range of the target layer; for wells drilled through the target layer, screening wells having acoustic wave curves, density curves and well diameter curves as the pre-selected wells based on corresponding logging data.
  • wells that penetrate the target layer are selected in a preset area, and among these wells, n wells that have acoustic wave curves, density curves and well diameter curves are preferably used as the pre-selected wells.
  • the method for constructing a velocity model further comprises: converting the acoustic wave curve of the preselected well into a velocity curve.
  • DT represents the sound wave curve
  • the unit is microseconds/meter
  • Vel represents the velocity curve
  • the unit is meter/second.
  • Step S120 Process the three-dimensional seismic data volume and the well logging data to obtain the time-depth relationship and stratigraphic interpretation data of the pre-selected wells of the target layer.
  • the construction speed also includes: for the pre-selected wells of the target layer, analyzing the histogram of the single well through the logging data to determine the longitudinal variation characteristics of the velocity of the target layer; comparing and analyzing the velocity curves of the connected wells through the logging data to determine the lateral variation characteristics of the velocity of the target layer; and determining the velocity variation characteristics and geological body type of the target layer through the longitudinal variation characteristics of the velocity of the target layer and the lateral variation characteristics of the velocity of the target layer.
  • the velocity variation characteristics of the target layer and the type of special geological body can be obtained through comparative analysis of single wells and wells in the target layer.
  • the lithology and vertical velocity variation characteristics of the target layer are determined through the histogram analysis of the single well; the lateral velocity variation characteristics of the target layer are analyzed through well-by-well comparative analysis of the velocity curve of the target layer, and the type of special geological bodies (for example, special geological bodies such as salt domes, belt-like reef structures, and carbonate rock anomalies) in the target layer are determined, and their velocity variation characteristics are obtained, providing a basis for the establishment of a velocity model.
  • special geological bodies for example, special geological bodies such as salt domes, belt-like reef structures, and carbonate rock anomalies
  • step S120 may include: 1. Analyzing the logging data of the preselected well to obtain the time-depth relationship of the preselected well in the target layer, including: obtaining the acoustic wave curve and the density curve in the logging data of the preselected well; and making a synthetic seismic record through the acoustic wave curve and the density curve, establishing a corresponding relationship between the seismic reflection interface in the time domain and the geological stratification in the depth domain, and obtaining the time-depth relationship of the preselected well.
  • step S120 may include: 2. Analyzing the three-dimensional seismic data body and the logging data to obtain stratigraphic interpretation data, including: analyzing the logging data of the preselected well to obtain the velocity change characteristics of the target layer; determining T geological layers of the target layer by analyzing the velocity change characteristics of the target layer and based on the velocity difference of the velocity interface; calibrating the T geological layers of the target layer by making synthetic seismic records, and determining the characteristics of the seismic reflection oscillators corresponding to each geological layer; and based on the three-dimensional seismic data body, completing the tracking and interpretation of the seismic reflection oscillators corresponding to each geological layer to obtain the stratigraphic interpretation data of the T geological layers of the target layer.
  • the fine time-depth relationship of the pre-selected wells in the target layer can be achieved through the method of synthetic seismic records.
  • synthetic seismic records are made through the acoustic wave curves and density curves of the pre-selected wells in the target layer, and the corresponding curves in the depth domain are converted to the time domain, that is, a one-to-one correspondence is established between the seismic reflection interface in the time domain and the geological stratification in the depth domain, so as to obtain a more detailed time-depth relationship for each pre-selected well.
  • Step S130 Based on the obtained time-depth relationship of the preselected wells of the target layer and the stratum interpretation data, the three-dimensional seismic data volume is subjected to frequency-band inversion through a preset frequency-band inversion strategy to obtain velocity volumes in different frequency bands.
  • the method for constructing a velocity model also includes: determining T interpretation layers of the target layer from bottom to top; between each two adjacent interpretation layers, according to the contact relationship between the upper and lower layers, and according to a preset subdivision ratio C, interpolating between each two adjacent interpretation layers to obtain C interpolated sublayers; and constructing a stratigraphic grid model of the target layer from the T interpretation layers and the C interpolated sublayers between each two adjacent interpretation layers.
  • the layer grid model (Layer_Model) of the target layer is composed of T interpretation layers and the interpolated sublayers between each two adjacent interpretation layers. Layer_Model can be used as a lateral constraint.
  • step S130 may include: 1. Inverting the three-dimensional seismic data volume by the Kriging interpolation method to obtain a low-frequency velocity volume; 2. Inverting the three-dimensional seismic data volume by the spectral simulation inversion method to obtain a mid-frequency velocity volume; 3. Inverting the three-dimensional seismic data volume by the waveform indication inversion method to obtain a high-frequency velocity volume.
  • the inversion of the three-dimensional seismic data volume by the Kriging interpolation method to obtain a low-frequency velocity volume comprises: obtaining a velocity curve in the well logging data of the preselected well, and performing low-pass filtering on the velocity curve to retain a low-frequency velocity curve within a first cutoff frequency value; based on the obtained time-depth relationship of the preselected well, converting the filtered low-frequency velocity curve from the depth domain to the time domain to obtain a low-frequency velocity curve in the time domain; and establishing a lateral constraint condition of the low-frequency velocity curve based on the stratigraphic framework model of the target layer, so as to perform well logging interpolation under the lateral constraint condition on the three-dimensional seismic data volume by the Kriging interpolation method to obtain the low-frequency velocity volume.
  • the lateral constraints of the low-frequency velocity curve are established based on the stratum grid model of the target layer, so as to perform well logging interpolation under the lateral constraints on the three-dimensional seismic data body through the Kriging interpolation method to obtain the low-frequency band velocity body, including: for each stratum in the stratum grid model, based on the observation value of each well of the preselected wells, obtaining the estimated value at each CDP of the stratum, wherein the low-frequency velocity value read at each well is used as the observation value of each well, and the low-frequency velocity value of the stratum is obtained through the observation value at each CDP of the stratum and the estimated value at each CDP of each stratum; and the low-frequency band velocity body is obtained through the low-frequency velocity value of each stratum, wherein each stratum includes the T interpreted strata and the C interpolated sublayers between each two adjacent interpreted strata.
  • the low-frequency velocity value at each CDP along the layer LayerM in the preset area is regarded as a regional variable Z(x).
  • a low-frequency velocity value can be read at each preselected well along the layer.
  • the low-frequency velocity values read from the n preselected wells can be regarded as n observation values, i.e., Z( x1 ), Z( x2 ), Z( x3 ), ..., Z( xn ).
  • the estimated value of the low-frequency velocity at a certain CDP along the layer LayerM in the preset area is recorded as Z( x0 ), which can be obtained by the weighted sum of the observation values of n known sampling points, i.e., it can be expressed by the following formula:
  • n weight coefficient values can be obtained by solving the following set of equations.
  • the estimated value at each CDP along each layer LayerM in the preset area can be calculated, and the low-frequency velocity value along each layer LayerM in the layer grid model can be obtained in turn, thereby obtaining the low-frequency band velocity body Vel_Low of the target layer.
  • the three-dimensional seismic data volume is inverted by the spectral simulation inversion method to obtain a mid-frequency velocity volume, including: obtaining the velocity curve in the logging data of the pre-selected well and constructing a corresponding wave impedance curve; based on the obtained time-depth relationship of the pre-selected well in the target layer, the wave impedance curve of the pre-selected well is converted from the depth domain to the time domain to obtain a corresponding time-domain wave impedance curve; and the time-domain wave impedance curve is superimposed on the three-dimensional seismic data volume, and inverted by the spectral simulation inversion method to obtain the mid-frequency velocity volume.
  • the inversion layer velocity body within the mid-frequency band of the target layer can be obtained by spectral simulation inversion method.
  • Spectral simulation inversion is a frequency domain logging constrained wave impedance inversion technology.
  • the well logging data and the three-dimensional seismic data body data are subjected to spectral analysis respectively, and a matching operator is designed in the frequency domain to match the seismic spectrum with the wave impedance spectrum of the well. Then, the frequency matching operator is applied to the three-dimensional seismic data body, and the time domain operator is inversely calculated to participate in the inversion operation to obtain the inversion data body.
  • an impedance curve of the pre-selected well is constructed and used as input data for the spectrum simulation inversion method.
  • the density value is taken as a constant, that is, the average value Den_average of the density logging value of the target layer, and the velocity curve value of the pre-selected well is multiplied by the constant density value Den_average to obtain the impedance curve of the pre-selected well, and the impedance curve of the pre-selected well is converted from the depth domain to the time domain using the time-depth relationship of each pre-selected well that has been obtained to obtain the time domain impedance curve IMP_time.
  • the time domain impedance curves of the n pre-selected wells and the three-dimensional seismic data volume can be inverted by spectrum simulation to obtain the mid-frequency band velocity volume of the target layer (represented by Vel_Medium).
  • the time domain wave impedance curve is superimposed on the three-dimensional seismic data volume, and inverted by spectral simulation inversion method to obtain the mid-frequency band velocity volume, including: for each pre-selected well of the target layer, a spectrum analysis is performed on the seismic trace beside the well and the time domain wave impedance curve of the pre-selected well, and a frequency matching operator is calculated, and the frequency matching operator is applied to the three-dimensional seismic data volume to obtain an inversion result of spectral simulation, and according to the corresponding relationship between reflection coefficient and wave impedance, a wave impedance inversion result is obtained from the inversion result of the spectral simulation; and the mid-frequency band velocity volume is obtained by the ratio of the wave impedance inversion result of the target layer to the first density mean.
  • S i ( ⁇ ) is the amplitude spectrum of the seismic trace
  • ⁇ i ( ⁇ ) is the phase spectrum of the seismic trace
  • R i ( ⁇ ) is the amplitude spectrum of the reflection coefficient
  • ⁇ i ( ⁇ ) is the phase spectrum of the reflection coefficient
  • B i ( ⁇ ) is the amplitude spectrum of the wavelet
  • ⁇ i ( ⁇ ) is the phase spectrum of the wavelet.
  • Zi ( ⁇ ) is the wave impedance amplitude spectrum at the preselected well
  • ⁇ i ( ⁇ ) is the wave impedance phase spectrum of the preselected well.
  • the reflection coefficient amplitude spectrum Ri ( ⁇ ) can be obtained by using the amplitude spectrum of wave impedance. Substituting the amplitude spectrum of reflection coefficient Ri ( ⁇ ) and the amplitude spectrum of seismic trace Si ( ⁇ ) into equation (7), the frequency matching operator Bi ( ⁇ ) can be obtained.
  • a i (t) is the time domain function of A i ( ⁇ ), which can be obtained by the frequency matching operator B i ( ⁇ ).
  • the inversion result of the reflection coefficient can be obtained by convolution of the time domain operator a i (t) and the seismic trace si (t) through formula (10), and then the reflection coefficient is converted into a wave impedance value by using the general correspondence between the reflection coefficient and the wave impedance to obtain the wave impedance inversion result of the seismic trace.
  • the wave impedance obtained at this time is a relative wave impedance.
  • the obtained mid-frequency band wave impedance inversion result is added with the low-frequency background to obtain the final spectrum simulation absolute wave impedance.
  • the above calculation is performed on each seismic trace in the preset area to obtain the spectral simulated wave impedance inversion volume of the target layer, and then the wave impedance inversion volume is divided by the density average Den_average to obtain the medium-frequency velocity volume Vel_Medium of the target layer.
  • the three-dimensional seismic data volume is inverted by the waveform indication inversion method to obtain a high-frequency velocity volume, including: for the wellside seismic traces of each well in the n pre-selected wells, under the stratigraphic frame model of the target layer, the seismic waveform to be predicted is compared with the waveform of the wellside seismic traces of each well, by analyzing the two factors of waveform similarity and spatial distance, K (K ⁇ n) waveform simulation samples of the wellside seismic traces are selected, and an initial impedance model is constructed through corresponding logging data; based on the characteristics of the initial impedance model and according to a preset high-pass frequency parameter value and a preset high-cutoff frequency parameter value, high-frequency band information is continuously filtered out, and the waveform simulation samples of each wellside seismic trace are compared to retain a preset deterministic frequency band component; and according to the waveform simulation samples and spatial structure characteristics of the K wellside seismic traces, an estimated value of an unknown point is determined to obtain a wave impedance
  • Waveform indication inversion is a new high-precision inversion method developed on the basis of traditional geostatistical inversion. Its theoretical basis is that the amplitude, phase, frequency and other information contained in the seismic waveform represent the characteristics of the reservoir. Although the effective seismic frequency band is narrow and the high-frequency components cannot be directly obtained, the lateral changes in the seismic waveform reflect the changing characteristics of the reservoir. Therefore, the lateral changes in the seismic waveform can be fully utilized to reflect the phase change characteristics of the reservoir space, and then the high-frequency structural characteristics of the reservoir lithology combination can be analyzed to achieve high-precision prediction of well-seismic combination.
  • the basic idea of waveform indication inversion is: based on the two basic factors of reference spatial distribution distance and seismic waveform similarity, all wells are sorted according to the correlation, and the well with the highest correlation with the predicted point is selected as the initial model to perform unbiased optimal estimation of the high-frequency components, and ensure that the final inverted seismic waveform is consistent with the original seismic waveform.
  • the process of obtaining the high-frequency band velocity body of the target layer by the waveform indication inversion method in the embodiment of the present invention is as follows:
  • the waveform indication inversion needs to carry out three processes in sequence: 1) sample optimization, 2) sample curve (i.e., waveform simulation sample) analysis, and 3) spatial valuation.
  • the seismic waveform to be predicted is compared with the well bypass waveforms of n pre-selected wells under the layer framework model Layer_Model. Based on the waveform similarity and spatial distance dual variables, K well bypasses with the most similar waveforms are selected as simulation samples, and their logging data are used to build the initial impedance model.
  • the impedance characteristics of the sample wells are statistically analyzed.
  • the high-frequency band information is continuously filtered out using the frequency division method and compared between samples to retain the deterministic frequency band components.
  • M(x 0 ) is the estimated value of the unknown point
  • ⁇ i is the weight of the ith sample point to the unknown point
  • K is the number of valid samples.
  • the wave impedance body obtained by waveform indication inversion can be obtained by formula (11), and it is divided by the density mean Den_average to obtain the layer velocity body.
  • the inversion result contains certain low-frequency and medium-frequency components.
  • the waveform indication inversion can be high-pass filtered to obtain a data body that only reflects the high-frequency components, that is, the high-frequency band velocity body obtained by waveform indication inversion (represented by Vel_High).
  • the three key inversion parameters of smoothing radius, effective sample number and cutoff frequency are tested to optimize the inversion result.
  • the smoothing radius mainly affects the lateral connectivity of the inversion body.
  • the cutoff frequency controls the frequency band range of the inversion, including high-pass and high-cutoff frequencies in the low-frequency band, and low-cut, low-pass, high-pass and high-cutoff frequencies in the high-frequency band.
  • the value of the cutoff frequency can be selected according to the amplitude spectrum of the three-dimensional seismic data body, and its value principle is to effectively compensate for the low-frequency and high-frequency parts of the seismic data.
  • the values of the three parameters are constantly fine-tuned to find the parameters that can minimize the waveform indication inversion error of the target layer and optimize the inversion result.
  • the key parameters are determined as follows: the smoothing coefficient is 2, the number of valid samples is 5, the high-pass and high-cut frequencies of the low-frequency band are 5Hz and 10Hz respectively, and the low-cut, low-pass, high-pass and high-cut frequencies of the high-frequency band are 40Hz, 50Hz, 90Hz and 100Hz respectively.
  • Den_average density average
  • Step S140 fusing the velocity bodies of the different frequency bands to obtain a velocity model of the target layer.
  • step S140 may include: obtaining the velocity model of the target layer by weighted sum of the low-frequency velocity body, the mid-frequency velocity body and the high-frequency velocity body.
  • the velocity model of the target layer can be obtained by fusing the inverted velocity bodies of the low frequency, medium frequency and high frequency bands, that is, the (layer) velocity model Vel_Model is calculated by the low frequency band velocity body Vel_Low, the medium frequency band velocity body Vel_Medium and the high frequency band velocity body Vel_High of the target layer obtained by segmented inversion.
  • the process of mid-frequency inversion of the target layer inverting the three-dimensional seismic data volume to obtain the mid-frequency velocity volume by spectral simulation inversion method, in order to more intuitively analyze the vertical and horizontal variation characteristics of the reservoir and to conduct effective
  • a certain amount of low-frequency information is added, so the low-frequency information in the medium-frequency velocity body Vel_Medium can be removed.
  • the medium-frequency velocity body Vel_Medium is subjected to high-pass filtering (the parameters of the high-pass filtering can be determined based on the spectrum of the 3D seismic data body, for example, the high-pass frequency is 15Hz), and through high-pass filtering, a (layer) velocity body Vel_Mediumfilter that only retains the medium-frequency information is obtained.
  • the low-frequency velocity body Vel_Low, the medium-frequency velocity body Vel_Medium and the high-frequency velocity body Vel_High of the target layer are weighted to obtain the fused velocity model.
  • the preset weight values can be determined by analyzing the consistency between the inverted layer velocity data and the well. For example, it can be a value between 1-2, with a high weight for a large consistency and a low weight for a small consistency. For example, by analyzing the consistency between the inversion result and the well, the weight value W_Low of the low-frequency velocity body, the weight value W_Medium of the medium-frequency velocity body and the weight value W_High of the high-frequency velocity body are determined.
  • the embodiment of the present invention can also analyze the reliability of the velocity model obtained in steps S110-S140 through 1) velocity model comparison and/or 2) depth error comparison at the well point after time-to-depth conversion.
  • the (layer) velocity body Vel_tradition obtained by conventional model-based inversion is compared with the (layer) velocity model Vel_Model of the embodiment of the present invention: the (layer) velocity body Vel_tradition cannot accurately characterize the longitudinal and lateral variation characteristics of the formation velocity in the preset area, and the (layer) velocity body Vel_tradition cannot identify the velocity changes inside and at the boundaries of special geological bodies; while the (layer) velocity model Vel_Model of the embodiment of the present invention can not only reflect the velocity characteristics between layers and between different lithofacies in the vertical direction, and accurately reflect the geological laws of the region, but also can well characterize the development zone boundaries of special geological bodies, and has a high degree of consistency with the velocity on the drilling, thereby verifying the reliability of the velocity model Vel_Model.
  • the target layer is converted into time-depth using the (layer) velocity body Vel_tradition and the (layer) velocity model Vel_Model of the embodiment of the present invention respectively; the depth error at each well point after the conversion between the (layer) velocity body Vel_tradition and the (layer) velocity model Vel_Model of the embodiment of the present invention is calculated respectively; the comparative analysis of the depth errors at the well points obtained after the conversion of the two different velocity models shows that the depth error value at the well point obtained after the time-depth conversion of the (layer) velocity model Vel_Model of the embodiment of the present invention is significantly lower than the depth error value after the conversion of the (layer) velocity body Vel_tradition, which verifies the reliability of the (layer) velocity model Vel_Model of the embodiment of the present invention.
  • the embodiment of the present invention provides a method for constructing a velocity model for more accurate comprehensive seismic geological research such as time-depth conversion, thickness calculation, reservoir prediction, and well location implementation related to special geological bodies.
  • fine drilling time-depth relationship and layer interpretation data are obtained through fine seismic geological layer calibration.
  • the logging data and three-dimensional seismic data body are fully utilized, and the three-dimensional seismic data body is subjected to frequency band inversion through a preset frequency band inversion strategy and data fusion technology to obtain velocity bodies of different frequency bands.
  • the frequency band velocity bodies of low frequency, medium frequency, and high frequency bands are used to construct a velocity model that can finely characterize the velocity change characteristics of the interior and boundary of the special geological body, and provide reliable velocity data for comprehensive geological research such as time-depth conversion, fine structural implementation, and well location deployment, so as to help expand and efficiently promote exploration in related fields of special geological bodies.
  • the offset stacking seismic data after amplitude preservation processing is obtained; and the well logging data of the target layer in the area is obtained.
  • Select all wells that penetrate the target layer, and select n wells that have acoustic wave curves, density curves, and well diameter curves according to the corresponding logging data as pre-selected wells. For example, select 28 wells that penetrate the target layer in the current area, analyze the logging data of these 28 wells that penetrate the target layer, and select 25 wells that have acoustic wave curves, density curves, and well diameter curves, that is, pre-selected wells n 25.
  • the acoustic wave curve of each pre-selected well is converted into a velocity curve by using formula (1), that is, the acoustic wave curves, density curves, velocity curves and caliper curves of the 25 pre-selected wells are obtained, thereby completing the acquisition of the logging data of the pre-selected wells.
  • the velocity change characteristics of the target layer and the type of special geological body are obtained.
  • the lithological characteristics and longitudinal velocity change characteristics of the target layer are obtained.
  • the target layer in this area is mainly carbonate sedimentary strata, and the lithology is mainly mudstone, limestone and marl-limestone interlayers.
  • the mudstone velocity is low and the limestone velocity is high.
  • the vertical velocity of the target layer shows the characteristics of interlayers of high-speed areas and low-speed areas.
  • the lateral variation characteristics of the velocity of the target layer are analyzed, and the types of special geological bodies in the target layer and their velocity characteristics are determined, providing a geological basis for the establishment of the velocity model.
  • the thickness and velocity change characteristics of the three layers of P1, KT1 and MKT from top to bottom in the vertical direction of the target layer can be obtained.
  • the strata of KT1 and MKT are relatively stable in the horizontal distribution, and the velocity and thickness changes are not very obvious, while the thickness and velocity differences of the P1 section at the top of the target layer are relatively obvious.
  • the velocity of the P1 section has obvious differences both vertically and horizontally.
  • the P1 section has the characteristics of low velocity in the lower part and high velocity in the middle and upper parts.
  • the velocity of the lower part of the P1 section changes horizontally relatively steadily, with an average velocity of about 3200m/s, while the velocity of the middle and upper part of the P1 section changes horizontally greatly, with the velocity value varying between 3300m/s-5600m/s, the low velocity area is about 3300m/s, and the high velocity area can reach 5600m/s, and the formation thickness of the P1 section in the high-speed area increases significantly.
  • the velocity in the upper and middle parts of the P1 section of the target layer varies greatly laterally and has strong heterogeneity.
  • special geological bodies in some parts and the type of special geological bodies is limestone high-speed anomaly bodies.
  • the velocity changes in the lower P1 section, KT1 and MKT sections of the target layer are relatively stable.
  • the detailed time-depth relationship of the pre-selected wells is determined.
  • synthetic records are made for 25 pre-selected wells, and the target layer is finely calibrated by well seismic, so that the detailed time-depth relationship of the 25 pre-selected wells can be obtained.
  • T seismic interpretation horizons of the target layer are determined.
  • the reflection characteristics of each geological layer on the seismic profile are calibrated, and the seismic reflection horizons corresponding to the five geological layers are tracked and interpreted within the three-dimensional seismic data body.
  • the naming of each seismic interpretation horizon is consistent with its corresponding geological layer.
  • the horizon interpretation data of the five seismic reflection horizons corresponding to the target layer and the geological layer are determined through tracking and interpretation.
  • the low-frequency velocity body is obtained through the Kriging interpolation method.
  • the velocity curves of the pre-selected wells in the area are low-pass filtered, and the first cutoff frequency value FL of the low-frequency band is determined according to the spectrum analysis of the 3D seismic data body, that is, the frequency components with frequency components greater than the first cutoff frequency value FL are filtered out, and only the velocity values within the cutoff frequency value are retained to obtain the filtered low-frequency velocity curve, and according to the time-depth relationship of each pre-selected well, the filtered low-frequency velocity curve is converted from the depth domain to the time domain.
  • the layer grid model Layer_Model of the target layer is established based on the layer interpretation data as the lateral constraint condition of the velocity model.
  • T interpretation layers of the target layer segment are selected from bottom to top.
  • the layer grid model (Layer_Model) of the target layer is composed of each interpreted layer and the interpolated sublayers between each two adjacent interpreted layers.
  • the layer grid model can be used as the lateral constraint condition of the low-frequency velocity body.
  • the low-frequency velocity curve in the filtered time domain and the layer grid model Layer_Model are used to obtain the low-frequency velocity body of the target layer segment through the ordinary Kriging interpolation method.
  • the low-frequency velocity value at each CDP along the layer in the region is regarded as the regional variable Z(x).
  • n pre-selected wells in the region a low-frequency velocity value can be read at each well along the layer.
  • the low-frequency velocity values read from the n pre-selected wells can be regarded as n observations.
  • the estimated value of the low-frequency velocity at a certain CDP can be obtained and recorded as Z(x0), that is, the estimated value of a certain layer LayerM at a certain CDP along the layer grid model Layer_Model.
  • Z(x0) the estimated value of a certain layer LayerM at a certain CDP along the layer grid model Layer_Model.
  • the estimated value at each CDP along the layer LayerM in the area is calculated to obtain the low-frequency velocity value along the layer LayerM; for each layer in the layer grid model Layer_Model, according to the calculation process of the low-frequency velocity value along the layer LayerM, the low-frequency velocity value along each layer in the layer grid model Layer_Model can be obtained in turn, thereby obtaining the low-frequency layer velocity body Vel_Low of the target layer.
  • the velocity logging curves (denoted as Vp) of 25 pre-selected wells are low-pass filtered.
  • Vp the velocity logging curves
  • the frequency band range of the target layer is 15Hz-45Hz
  • the main frequency is 30Hz
  • the three-dimensional seismic data body lacks frequency components less than 15Hz. Therefore, the low-frequency components on the well can be used to supplement.
  • a low-frequency velocity curve within 15Hz is obtained, and according to the time-depth relationship of the 25 pre-selected wells obtained above, the low-frequency velocity curve in the depth domain is converted to the time domain.
  • the low-frequency velocity values of 25 pre-selected wells at the layer are read in turn, and then the Kriging interpolation method is used to calculate the low-frequency velocity value at each CDP along the layer in turn through the above formula (2) and equation group (3) to obtain the velocity value of the layer.
  • the velocity value of each layer in the layer grid model Layer_Model is calculated, and thus the low-frequency layer velocity body Vel_Low of the target layer is obtained.
  • the example profile reflects the overall change trend of the velocity of the target layer and reflects the background velocity characteristics of the high-speed anomaly body of the limestone in the target layer.
  • the inversion layer velocity body within the mid-frequency band of the target layer can be obtained by the spectral simulation inversion method.
  • the impedance curve of the pre-selected well is constructed accordingly, and it is used as the input data of the spectral simulation inversion.
  • the density can be taken as a constant, that is, the average value Den_average of the density logging value of the target layer segment (in this example, Den_average can be set to 2.5 according to the statistical results of the density logging value of the target layer segment), and the velocity curve value of the pre-selected well is multiplied by the constant density value Den_average to obtain the impedance curve of the pre-selected well, and according to the obtained time-depth relationship of each pre-selected well, the impedance curve of the pre-selected well is converted from the depth domain to the time domain to obtain the time domain impedance curve IMP_time.
  • the time domain impedance curves of n pre-selected wells and the three-dimensional seismic data volume are used for spectral simulation inversion to obtain the mid-frequency velocity volume Vel_Medium of the target layer.
  • a frequency matching operator is obtained that achieves the best match between the seismic spectrum and the wave impedance spectrum of the well logging.
  • the earthquake amplitude spectrum Si ( ⁇ ) disclosed in formula (6) is equal to the product of the amplitude spectrum Ri ( ⁇ ) of the reflection coefficient and the amplitude spectrum Bi ( ⁇ ) of the wavelet.
  • the relationship formula (7) between the earthquake amplitude spectrum Si ( ⁇ ), the amplitude spectrum Ri ( ⁇ ) of the reflection coefficient and the amplitude spectrum Bi ( ⁇ ) of the wavelet, which is easier to calculate.
  • the earthquake amplitude spectrum is known, we only need to find the amplitude spectrum Ri ( ⁇ ) of the reflection coefficient to find the amplitude spectrum Bi ( ⁇ ) of the wavelet, where Bi ( ⁇ ) is the frequency matching operator of spectral simulation inversion.
  • the reflection coefficient amplitude spectrum Ri ( ⁇ ) can be obtained based on the corresponding relationship between the universal wave impedance and the reflection coefficient and the amplitude spectrum of the wave impedance.
  • the amplitude spectrum of the reflection coefficient Ri ( ⁇ ) and the amplitude spectrum of the seismic trace Si ( ⁇ ) are substituted into formula (7) to obtain the frequency matching operator Bi ( ⁇ ).
  • the frequency matching operator is applied to the three-dimensional seismic data volume to obtain the spectrum simulation inversion data volume.
  • equation (9) can be obtained, and equation (9) is transformed into the time domain by Fourier inverse, and the calculation formula of the reflection coefficient is obtained, that is, equation (10).
  • the inversion result of the reflection coefficient of the seismic trace can be obtained by convolution of the time domain operator a i (t) and the seismic trace s i (t), and then the reflection coefficient is converted to the wave impedance value through the general correspondence between the reflection coefficient and the wave impedance to obtain the wave impedance inversion result of the seismic trace.
  • the obtained wave impedance is the relative wave impedance.
  • the obtained mid-frequency band relative wave impedance inversion result can be added with the low-frequency background, that is, the relative impedance inversion value obtained by the spectrum simulation inversion is added with the low-frequency background value on the logging, that is, according to the logging analysis results, a low-frequency impedance value is assigned to each layer to obtain the final spectrum simulation absolute wave impedance value.
  • the above calculation is performed for each seismic trace in the area to obtain the spectral simulated wave impedance inversion body of the target layer in the area, and then the wave impedance inversion body is divided by the density average Den_average to obtain the mid-frequency velocity body Vel_Medium of the target layer.
  • the density average Den_average of the target layer is calculated as 2.5, and the velocity curve values of the 25 pre-selected wells are multiplied by the constant density value Den_average, then the impedance curve of the pre-selected wells can be obtained, and the impedance curve of the pre-selected wells can be converted from the depth domain to the time domain through the obtained time-depth relationship of each pre-selected well.
  • the logging wave impedance amplitude spectrum of the 25 pre-selected wells is converted into the reflection coefficient amplitude spectrum
  • the well side channel amplitude spectrum and the logging wave impedance amplitude spectrum R i ( ⁇ ) of the 25 selected wells are extracted, and the well side channel amplitude spectrum S i ( ⁇ ) of the 25 pre-selected wells is calculated.
  • the frequency matching operator B i ( ⁇ ) of the spectrum simulation inversion is obtained according to formula (7).
  • the frequency matching operator is reciprocated and transformed to the time domain through inverse Fourier transform to obtain the operator ai (t) in the time domain.
  • the inversion result of the reflection coefficient can be obtained by convolution of the time domain operator ai (t) and each seismic trace si (t) in the three-dimensional seismic data body. Then, the reflection coefficient is converted to the wave impedance value by using the general correspondence between the reflection coefficient and the wave impedance to obtain the relative wave impedance inversion result. Then, according to the statistical results of the logging data, a low-frequency background value is set for each layer of the target layer. For example, the low-frequency impedance background values from top to bottom are 10050, 8250, 11250 and 6875 respectively, and the absolute wave impedance body of the final spectrum simulation is obtained.
  • the wave impedance inversion body is divided by the density mean 2.5 to obtain the medium-frequency band velocity body Vel_Medium within the medium-frequency band of the target layer.
  • the example profile reflects the velocity change characteristics of the target layer in the same frequency band as the seismic.
  • the waveform indication inversion method is used to obtain the high-frequency velocity body of the target layer.
  • the key parameter test of the waveform indication inversion is carried out based on the obtained three-dimensional amplitude-preserving offset stacking data, the obtained layer grid model Layer_Model, and the time domain logging impedance curve IMP_time.
  • the key inversion parameters include smoothing radius, effective sample number and cutoff frequency.
  • the smoothing radius mainly affects the lateral connectivity of the inversion body.
  • the larger the smoothing radius the better the connectivity of the inversion body.
  • the value range is, for example, between 2-3.
  • the effective number of samples mainly characterizes the degree of influence of the spatial variation of the seismic waveform on the reservoir.
  • the cutoff frequency controls the frequency band of the inversion, including high-pass and high-cutoff frequencies in the low-frequency band, and low-cutoff, low-pass, high-pass and high-cutoff frequencies in the high-frequency band.
  • the cutoff frequency value should be selected according to the amplitude spectrum of the three-dimensional seismic data body. The principle of its value is to effectively compensate for the low-frequency and high-frequency parts of the seismic data.
  • the cutoff frequency value is fine-tuned to find the parameters that minimize the inversion error.
  • the key parameters that can minimize the waveform indication inversion error of the target layer and achieve the best inversion results are found.
  • the optimal wave impedance estimation is performed on each sampling point data of the target layer, thereby obtaining the wave impedance body of the waveform indication inversion, and dividing it by the density average Den_average, the layer velocity body of the waveform indication inversion can be obtained.
  • the result of the waveform indication inversion contains certain low-frequency and medium-frequency components.
  • the waveform indication inversion can be high-pass filtered, that is, the frequency components with a frequency value below FH (FH can be determined based on the amplitude spectrum of the three-dimensional seismic data body) are filtered out, and only the high-frequency part is retained, so as to obtain a data body that only reflects the high-frequency components, that is, the high-frequency band velocity body Vel_High obtained by the waveform indication inversion.
  • the inversion parameters are tested based on the time domain impedance curves IMP_time of 25 pre-selected wells, the three-dimensional seismic data body, and the layer grid model Layer_Model created by 5 interpretation layers.
  • the key parameters of inversion are determined as follows: the smoothing coefficient is 2, the number of effective samples is 5, the high-pass and high-cut frequencies of the low-frequency band are 5Hz and 10Hz respectively, and the low-cut, low-pass, high-pass and high-cut frequencies of the high-frequency band are 40Hz, 50Hz, 90Hz and 100Hz respectively.
  • the waveform indication inversion is performed with the above inversion parameters to obtain the wave impedance data body of the waveform indication inversion, and then it is divided by the density mean 2.5 to obtain the velocity body.
  • the example section reflects the high-frequency change characteristics of the target layer velocity, which can fully identify the longitudinal and lateral change characteristics of the velocity of some thin layers.
  • the low-frequency velocity body Vel_Low, medium-frequency velocity body Vel_Medium and high-frequency velocity body Vel_High of the target layer obtained by frequency band inversion are used to calculate the (layer) velocity model Vel_Model, and the (layer) velocity model Vel_Model of the target layer is established by using data fusion technology.
  • the three-dimensional seismic data body is inverted to obtain the mid-frequency band velocity body
  • a certain amount of low-frequency information is added, so the low-frequency information in the mid-frequency band velocity body Vel_Medium can be removed.
  • the mid-frequency band velocity body Vel_Medium is high-pass filtered (the parameters of the high-pass filter can be determined based on the spectrum of the three-dimensional seismic data body, for example, the high-pass frequency is 15Hz), and through high-pass filtering, a (layer) velocity body Vel_Mediumfilter that only retains the mid-frequency band information is obtained.
  • the low-frequency velocity body Vel_Low, the medium-frequency velocity body Vel_Medium and the high-frequency velocity body Vel_High of the target layer are weighted to obtain the fused velocity model.
  • the preset weight values can be determined by analyzing the consistency between the inverted layer velocity data and the well, for example, it can be a value between 1-2, the weight of the large consistency is high, and the weight of the small consistency is low. For example, by analyzing the consistency between the inversion result and the well, the weight value W_Low of the low-frequency velocity body, the weight value W_Medium of the medium-frequency velocity body and the weight value W_High of the high-frequency velocity body are determined.
  • the corresponding layer velocity values of the low-frequency velocity body Vel_Low(i), the medium-frequency velocity body Vel_Mediumfilter(i) and the high-frequency velocity body Vel_High(i) are read in turn.
  • the layer velocity value Vel_Model(i) after the data of the point is fused can be obtained.
  • the obtained mid-frequency velocity body Vel_Medium is high-pass filtered, that is, the frequencies below 15Hz are filtered. The frequency components are filtered out, and only the frequency components higher than 15Hz in the Vel_Medium layer velocity body are retained.
  • the high-pass filtered mid-frequency velocity body Vel_Mediumfilter is obtained.
  • the weight values of low frequency W_Low, mid-frequency W_Medium and high frequency W_High are determined by analyzing the consistency between the inversion results and the well (for example, all are 1). Then, using formula (12), the low-frequency velocity body Vel_Low, the mid-frequency velocity body Vel_Mediumfilter and the high-frequency velocity body Vel_High are fused to obtain the velocity model Vel_Model of the target layer.
  • the example profile can not only clearly reflect the longitudinal and lateral variation characteristics of the velocity of the target layer, but also can accurately describe the velocity variation characteristics of the interior and boundary of the limestone high-speed anomaly body, and can also accurately reveal the velocity changes of some thin layers.
  • the reliability of the velocity model is analyzed by comparing the velocity model Vel_Model and the depth error at the well point after time-depth conversion.
  • the (layer) velocity model Vel_tradition obtained by conventional methods is compared with the (layer) velocity model Vel_Model of the embodiment of the present invention.
  • the (layer) velocity model Vel_tradition cannot identify the velocity changes of special geological bodies (in this example, the limestone high-speed anomaly body), and the vertical and horizontal changes of the target layer velocity are not finely characterized; while the (layer) velocity model Vel_Model of the embodiment of the present invention can reflect the fine changes in velocity in the vertical and horizontal directions, reflect the geological laws of the study area, and can also well characterize the internal and boundary velocity change characteristics of the special geological body (in this example, the limestone high-speed anomaly body), and the degree of consistency with the velocity on the drilling is high, thereby verifying the reliability of the velocity model Vel_Model.
  • the target layer is converted into a time-depth.
  • the KT1 top surface interpretation layer of the target layer is selected for time-depth conversion, and the depth errors at each well point after the conversion between the layer velocity model Vel_tradition and the (layer) velocity model Vel_Model of the embodiment of the present invention are calculated respectively, as shown in Table 1.
  • Table 1 shows the drilling depth above sea level and time-depth conversion depth corresponding to the KT1 geological layer of 14 wells in this area. Error value.
  • the method for constructing a velocity model in the embodiment of the present invention effectively realizes the fine establishment of a velocity model of a special geological body development stratum, improves the velocity model's representation of the vertical and horizontal variation characteristics of the reservoir velocity, and the velocity model can not only reflect geological laws, but also well characterize the velocity variation characteristics inside and at the boundary of a special geological body (in this example, a limestone high-speed anomaly body), providing reliable velocity data for comprehensive geological studies such as time-depth conversion, fine structural implementation, and well location deployment.
  • the embodiment of the present invention is easy to implement and has high credibility, and has positive significance for the expansion and efficient advancement of exploration in related fields of special geological bodies.
  • An embodiment of the present invention also provides a control device for constructing a velocity model, the control device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to implement the above-mentioned method for constructing a velocity model.
  • An embodiment of the present invention further provides a machine-readable storage medium, on which instructions are stored, and the instructions enable a machine to execute the above-mentioned method for constructing a velocity model.
  • the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.
  • a computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
  • These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.
  • These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.
  • a computing device includes one or more processors (CPU), input/output interfaces, network interfaces, and memory.
  • processors CPU
  • input/output interfaces network interfaces
  • memory volatile and non-volatile memory
  • Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and/or non-volatile memory in the form of read-only memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
  • RAM random access memory
  • ROM read-only memory
  • flash RAM flash memory
  • Computer-readable media include permanent and non-permanent, removable and non-removable media that can store information by any method or technology.
  • the information can be computer-readable instructions, data structures, program modules or other data.
  • Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.
  • computer-readable media does not include transitory media such as modulated data signals and carrier waves.

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Abstract

本发明实施例提供一种构建速度模型的方法、控制装置和存储介质，属于地质勘探技术领域。该方法包括：获取预设区域的三维地震数据体和预选井的测井数据；对三维地震数据体和测井数据进行处理，得到目的层的预选井的时深关系和层位解释数据；基于所得到目的层的预选井的时深关系和层位解释数据，通过预设的分频段反演策略，对所述三维地震数据体进行分频段反演，得到不同频段的速度体；对不同频段的速度体进行融合，得到目的层的速度模型。充分利用测井数据和三维地震数据体，通过预设的分频段反演策略和数据融合技术，对所述三维地震数据体进行分频段反演，得到不同频段的速度体，来构建能够精细表征特殊地质体内部及边界速度变化特征的速度模型。

Description

构建速度模型的方法、控制装置和存储介质
相关申请的交叉引用
本申请要求2023年08月16日提交的中国专利申请202311033453.X的权益，该申请的内容通过引用被合并于本文。
技术领域
本发明涉及地质勘探技术领域，具体地涉及一种构建速度模型的方法、控制装置和存储介质。
背景技术
速度参数是地下地层的基本物理属性，也是地震勘探的重要参数，影响着地震勘探的各个环节，在油气勘探开发中发挥着举足轻重的作用。不同时代、不同岩性地层或地质体通常具有不同的层速度；地下储层孔隙因所含流体(气、油、水)不同或地质体因异常、高压封闭也会表现出不同程度的速度差异。因此，准确并符合地质规律的速度模型不仅是叠前偏移成像和时深转换的关键，也影响着构造精度、探井深度落实、地层厚度、储层预测、压力界面预测等勘探开发的诸多方面。
随着勘探开发的不断深入，面临的地质条件也越来越复杂，常面临着伴有盐丘、带状礁建造、火成岩侵入体以及碳酸盐岩异常体等一些局部分布的特殊地质体，这些特殊地质体通常在岩性、速度及厚度等方面与围岩存在较大差异，具有强非均质性。目前的速度建模方法由于不能反映特殊地质体速度的局部变化，从而导致时深转换的误差大、构造落实精度低，且容易形成构造假象等，严重制约了与特殊地质体相关的地质目标的勘探开发进程。因此，复杂的地质条件对速度建模提出了新的挑战。
目前，层速度模型的建立方法主要包括：1)利用叠前时间偏移速度或叠加速度，通过Dix公式来计算层速度，该方法基于均匀介质或水平层状介质模型，由地震处理速度谱估算得来，可以得到速度变化趋势和相对关系，该方法对于普遍存在空间各向异性的沉积地层序列，精度较差。2)网格层析速度建模方法，该方法采用数据驱动和计算机自动迭代求解的方法，可以得到速度较精细的变化，但该方法计算量大，且因缺少人工干预，多解性大，在复杂地质条件下，难以控制速度误差。3)利用已钻井的地震测井和声波测井资料，结合地质层位的解释，进行层速度模型的建立，该种方法在有控制井的位置，可以得到真实反应地层岩性特征的高精度层速度数据，但是受控于已钻井的平面分布和钻井密度，特别是在少井、无井区的速度预测精度较差。
发明内容
本发明实施例的目的是提供一种构建速度模型的方法，该构建速度模型的方法所构建的速度模型能够精细表征特殊地质体内部及边界速度变化特征。
为了实现上述目的，本发明实施例提供一种构建速度模型的方法，所述构建速度模型的方法包括：获取预设区域的三维地震数据体和预选井的测井数据；对所述三维地震数据体和所述测井数据进行处理，得到目的层的预选井的时深关系和层位解释数据；基于所得到目的层的预选井的时深关系和层位解释数据，通过预设的分频段反演策略，对所述三维地震数 据体进行分频段反演，得到不同频段的速度体；以及对所述不同频段的速度体进行融合，得到目的层的速度模型。
可选的，在所述获取预设区域的三维地震数据体和预选井的测井数据之前，所述构建速度模型的方法还包括：确定所述目的层的范围；获取所述预设区域的井的测井数据；以及对于钻穿所述目的层的井，根据对应的测井数据，筛选具有声波曲线、密度曲线和井径曲线的井作为所述预选井。
可选的，在所述获取预设区域的三维地震数据体和预选井的测井数据之后，所述构建速度模型的方法还包括：将所述预选井的声波曲线转换成速度曲线。
可选的，在所述得到目的层的预选井的时深关系和层位解释数据之前，所述构建速度模型的方法还包括：对于所述目的层的预选井，通过所述测井数据对单井的柱状图进行分析，确定所述目的层的岩性及速度的纵向变化特征；通过所述测井数据对连井的速度曲线进行对比分析，确定所述目的层的速度的横向变化特征；以及通过所述目的层的速度的纵向变化特征和所述目的层的速度的横向变化特征，确定所述目的层的速度变化特征及地质体类型。
可选的，对所述预选井的测井数据进行处理，得到目的层预选井的时深关系，包括：获取所述预选井的测井数据中的声波曲线和密度曲线；以及通过所述声波曲线和所述密度曲线制作合成地震记录，将时间域的地震反射界面与深度域的地质分层建立对应的关系，得到所述预选井的时深关系。
可选的，所述对所述三维地震数据体和所述测井数据进行处理，得到层位解释数据，包括：对所述预选井的测井数据进行分析，得到所述目的层的速度变化特征；通过对所述目的层的速度变化特征进行分析，并根据速度界面的速度差，确定所述目的层的T个地质分层；通过制作合成的地震记录，标定所述目的层的所述T个地质分层，确定与每个地质分层对应的地震反射同相轴的特征；以及基于所述三维地震数据体，完成与所述每个地质分层对应的地震反射同相轴的追踪和解释，以得到所述目的层的所述T个地质分层的层位解释数据。
可选的，所述通过预设的分频段反演策略，对所述三维地震数据体进行分频段反演，得到不同频段的速度体，包括：通过克里金插值法，对所述三维地震数据体进行反演，得到低频段速度体；通过谱模拟反演法，对所述三维地震数据体进行反演，得到中频段速度体；以及通过波形指示反演法，对所述三维地震数据体进行反演，得到高频段速度体。
可选的，在所述通过预设的分频段反演策略，对所述三维地震数据体进行分频段反演之前，所述构建速度模型的方法还包括：由下至上确定所述目的层的T个解释层位；在每相邻的两个解释层位之间，根据上下两层之间的接触关系，按照预设的细分比例C，在每相邻的两个解释层位之间内插得到C个内插子层；以及由所述T个解释层位和所述每相邻的两个解释层位之间的C个内插子层，构建目的层的层位格架模型。
可选的，所述通过克里金插值法，对所述三维地震数据体进行反演，得到低频段速度体，包括：获取所述预选井的测井数据中的速度曲线，对所述速度曲线进行低通滤波，以保留第一截止频率值以内的低频速度曲线；基于所得到的预选井的时深关系，将滤波后的低频速度曲线由深度域转到时间域，得到时间域的低频速度曲线；以及基于所述目的层的层位格架模型，建立所述低频速度曲线的横向约束条件，以通过克里金插值法，对所述三维地震数据体进行所述横向约束条件下的测井插值，来得到所述低频段速度体。
可选的，所述基于所述目的层的层位格架模型，建立所述低频速度曲线的横向约束条件，以通过克里金插值法，对所述三维地震数据体进行所述横向约束条件下的测井插值，来 得到所述低频段速度体，包括：对于所述层位格架模型中的每个层位，基于所述预选井的每口井的观测值，得到该层位每个CDP处的估计值，其中，将在所述每口井处读取的低频速度值，作为所述每口井的观测值，通过该层位每个CDP处的估计值，得到该层位的低频速度值；以及通过所述每个层位的低频速度值，得到所述低频段速度体，所述每个层位包括所述T个解释层位和所述每相邻的两个解释层位之间的C个内插子层。
可选的，所述通过谱模拟反演法，对所述三维地震数据体进行反演，得到中频段速度体，包括：获取所述预选井的测井数据中的速度曲线，构建对应的波阻抗曲线；基于所得到的目的层预选井的时深关系，将所述预选井的波阻抗曲线由深度域转到时间域，得到对应的时间域波阻抗曲线；以及将所述时间域波阻抗曲线叠加到所述三维地震数据体，并通过谱模拟反演法进行反演，得到所述中频段速度体。
可选的，所述将所述时间域波阻抗曲线叠加到所述三维地震数据体，并通过谱模拟反演法进行反演，得到所述中频段速度体，包括：对于所述目的层的每口预选井，对井旁地震道与该预选井的时间域波阻抗曲线进行频谱分析，计算得到频率匹配算子，将所述频率匹配算子施加到所述三维地震数据体，得到谱模拟的反演结果，根据反射系数与波阻抗的对应关系，由所述谱模拟的反演结果，得到波阻抗反演结果；通过所述目的层的波阻抗反演结果与第一密度均值的比值，得到所述中频段速度体。
可选的，所述通过波形指示反演法，对所述三维地震数据体进行反演，得到高频段速度体，包括：对于n口预选井中每口井的井旁地震道，在所述目的层的层位格架模型下，将待预测的地震波形与所述每口井的井旁地震道的波形进行对比，根据波形相似性以及空间距离两个因素，选取K个井旁地震道的波形模拟样本，并通过对应的测井数据构建初始阻抗模型；基于所述初始阻抗模型的特征，并根据预设的高通频率参数值和预设的高截频率参数值，不断滤除高频段信息，并在每个井旁地震道的波形模拟样本之间进行对比，保留预设的确定性频带成分；以及根据所述K个井旁地震道的波形模拟样本和空间结构特点，确定未知点的估计值，以得到波形指示反演的波阻抗反演结果，通过所述目的层的波阻抗反演结果与第二密度均值的比值，得到所述高频段速度体。
可选的，所述对所述不同频段的速度体进行融合，得到所述目的层的速度模型，包括：通过所述低频段速度体、所述中频段速度体和所述高频段速度体的加权和，得到所述目的层的速度模型。
本发明实施例还提供一种用于构建速度模型的控制装置，所述控制装置包括：存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序，以实现上述的构建速度模型的方法。
本发明实施例还提供一种机器可读存储介质，该机器可读存储介质上存储有指令，该指令使得机器执行上述的构建速度模型的方法。
通过上述技术方案，本发明实施例为更准确进行特殊地质体相关的时深转换、厚度计算、储层预测以及井位落实等地震地质综合研究，提供了一种构建速度模型的方法。以对特殊地质体的地质特征分析为前提，通过精细的地震地质层位标定获得精细的时深关系和层位解释数据，以此为基础，充分利用测井数据和三维地震数据体，通过预设的分频段反演策略和数据融合技术，对所述三维地震数据体进行分频段反演，得到不同频段的速度体，并采用低频、中频和高频段的分频段速度，来构建能够精细表征特殊地质体内部及边界速度变化特征的速度模型，为时深转换、构造精细落实、井位部署等综合地质研究提供可靠的速度数据，以助力特殊地质体相关领域勘探的拓展和高效推进。
本发明实施例的其它特征和优点将在随后的具体实施方式部分予以详细说明。
附图说明
附图是用来提供对本发明实施例的进一步理解，并且构成说明书的一部分，与下面的具体实施方式一起用于解释本发明实施例，但并不构成对本发明实施例的限制。在附图中：
图1是本发明实施例提供的构建速度模型的方法流程示意图；
图2是示例地质体的速度变化特征的示意图。
图3是示例偏移叠加地震数据的剖面示意图；
图4是示例目的层低频段速度体的一条速度剖面示意图；
图5是示例目的层中频段速度体的一条速度剖面示意图；
图6是示例目的层高频段速度体的一条速度剖面示意图；
图7是示例速度模型的一条速度剖面示意图；以及
图8是示例常规速度模型的一条速度剖面示意图。
具体实施方式
以下结合附图对本发明实施例的具体实施方式进行详细说明。应当理解的是，此处所描述的具体实施方式仅用于说明和解释本发明实施例，并不用于限制本发明实施例。
现有的速度模型适用于地层横向分布相对比较稳定的地质条件，而对于地质结构相对复杂，例如，伴有盐丘、带状礁建造以及碳酸盐岩异常体等特殊地质体发育的地质条件，现有的速度建模不能精细刻画特殊地质体内部及边界的速度变化特征，影响了构造特征的落实精度，制约了特殊地质体相关领域的勘探，因此，针对伴有特殊地质体发育的勘探区，需要一种能够精细表征特殊地质体内部及边界速度变化的速度模型。
图1是本发明实施例提供的构建速度模型的方法流程示意图，请参考图1，所述构建速度模型的方法可以包括以下步骤：
步骤S110：获取预设区域的三维地震数据体和预选井的测井数据。
其中，所述三维地震数据体数据为保幅处理后的偏移叠加地震数据。
优选的，在步骤S110之前，所述构建速度模型的方法还包括：确定所述目的层的范围；对于钻穿所述目的层的井，根据对应的测井数据，筛选具有声波曲线、密度曲线和井径曲线的井作为所述预选井。
以示例说明，在预设区域选择钻穿目的层的井，在这些井中，优选同时具有声波曲线、密度曲线和井径曲线的n口井作为所述预选井。
优选的，在步骤S110之后，所述构建速度模型的方法还包括：将所述预选井的声波曲线转换成速度曲线。
优选的，通过下式，将上述n口预选井对应的声波曲线转换成速度曲线，
Vel＝1000000/DT         (1)
其中，DT表示声波曲线，单位是微秒/米，Vel表示速度曲线，单位是米/秒。
如上所述，得到预设区域的三维地震数据体和预选井的测井数据。
步骤S120：对所述三维地震数据体和所述测井数据进行处理，得到目的层的预选井的时深关系和层位解释数据。
优选的，在所述得到目的层的预选井的时深关系和层位解释数据之前，所述构建速度 模型的方法还包括：对于所述目的层的预选井，通过所述测井数据对单井的柱状图进行分析，确定所述目的层的速度的纵向变化特征；通过所述测井数据对连井的速度曲线进行对比分析，确定所述目的层的速度的横向变化特征；以及通过所述目的层的速度的纵向变化特征和所述目的层的速度的横向变化特征，确定所述目的层的速度变化特征及地质体类型。
以示例说明，所述目的层的速度变化特征和特殊地质体的类型，可以通过目的层段单井及连井的对比分析获得。通过单井的柱状图分析，确定目的层的岩性及速度的纵向变化特征；通过目的层速度曲线的连井对比分析，分析目的层速度的横向变化特征，确定目的层内特殊地质体(例如，伴有盐丘、带状礁建造以及碳酸盐岩异常体等特殊地质体)的类型，获取其速度变化特征，为速度模型的建立提供基础。
优选的，步骤S120可以包括：1、对所述预选井的测井数据进行分析，得到目的层预选井的时深关系，包括：获取所述预选井的测井数据中的声波曲线和密度曲线；以及通过所述声波曲线和所述密度曲线制作合成地震记录，将时间域的地震反射界面与深度域的地质分层建立对应的关系，得到所述预选井的时深关系。
优选的，步骤S120可以包括：2、所述对所述三维地震数据体和所述测井数据进行分析，得到层位解释数据，包括：对所述预选井的测井数据进行分析，得到所述目的层的速度变化特征；通过对所述目的层的速度变化特征进行分析，并根据速度界面的速度差，确定所述目的层的T个地质分层；通过制作合成地震记录，标定所述目的层的所述T个地质分层，确定与每个地质分层对应的地震反射同相轴的特征；以及基于所述三维地震数据体，完成与所述每个地质分层对应的地震反射同相轴的追踪和解释，以得到所述目的层的所述T个地质分层的层位解释数据。
1、目的层预选井的精细时深关系可以通过合成地震记录的方法实现，以示例说明，通过目的层预选井的声波曲线和密度曲线制作合成地震记录，将深度域的相应的曲线转换到时间域上，即，将时间域的地震反射界面与深度域的地质分层建立一一对应的关系，来获得每口预选井更加精细的时深关系。
2、根据上述所确定的目的层的速度变化特征的分析结果，根据速度界面的速度差，确定目的层的速度界面变化明显的T个地质分层；通过1)所制作合成地震记录，标定目的层段的T个地质分层，确定与各地质分层对应的地震反射同相轴的特征，并基于所述三维地震数据体进行与T个地质分层对应的地震反射同相轴的追踪和解释，得到目的层的T个地质分层的层位解释数据。
步骤S130：基于所得到目的层的预选井的时深关系和层位解释数据，通过预设的分频段反演策略，对所述三维地震数据体进行分频段反演，得到不同频段的速度体。
优选的，在步骤S130之前，所述构建速度模型的方法还包括：由下至上确定所述目的层的T个解释层位；在每相邻的两个解释层位之间，根据上下两层之间的接触关系，按照预设的细分比例C，在每相邻的两个解释层位之间内插得到C个内插子层；以及由所述T个解释层位和所述每相邻的两个解释层位之间的C个内插子层，构建所述目的层的层位格架模型。
以示例说明，通过目的层的T个解释层位建立目的层的框架模型，包括：自下至上选择目的层段的T个解释层位(例如，T＝5)，在每相邻的两个解释层位之间，根据上下两层之间的接触关系，按照预设的细分比例C(例如，C＝25)，在每相邻的两个解释层位之间内插出C个内插子层，则共有C*(T-1)个(子)层位。由T个解释层位和每相邻的两个解释层位之间的内插子层构成了目的层的层位格架模型(Layer_Model)，该层位格架模型 Layer_Model可以作为横向约束条件。
优选的，步骤S130可以包括：1、通过克里金插值法，对所述三维地震数据体进行反演，得到低频段速度体；2、通过谱模拟反演法，对所述三维地震数据体进行反演，得到中频段速度体；3、通过波形指示反演法，对所述三维地震数据体进行反演，得到高频段速度体。
1、所述通过克里金插值法，对所述三维地震数据体进行反演，得到低频段速度体，包括：获取所述预选井的测井数据中的速度曲线，对所述速度曲线进行低通滤波，以保留第一截止频率值以内的低频速度曲线；基于所得到的预选井的时深关系，将滤波后的低频速度曲线由深度域转到时间域，得到时间域的低频速度曲线；以及基于所述目的层的层位格架模型，建立所述低频速度曲线的横向约束条件，以通过克里金插值法，对所述三维地震数据体进行所述横向约束条件下的测井插值，来得到所述低频段速度体。
以示例说明，对预设区域的n口预选井的速度曲线(如上文所述，通过声波曲线转换成的速度曲线)进行低通滤波，即将频率成分大于第一截止频率值FL(例如，FL＝15Hz)的曲线数据滤除，只保留FL以内的低频速度曲线；并根据上述获得的每口预选井的时深关系，将滤波后的低频速度曲线由深度域转到时间域，得到时间域的低频速度曲线；通过目的层的地震解释层位建立横向约束条件，即，通过目的层的层位格架模型Layer_Model作为横向约束条件；基于滤波后的低频速度曲线和层位格架模型Layer_Model，通过普通克里金插值方法，可以得到目的层的低频(层)速度体(通过Vel_Low表示)。
优选的，所述基于所述目的层的层位格架模型，建立所述低频速度曲线的横向约束条件，以通过克里金插值法，对所述三维地震数据体进行所述横向约束条件下的测井插值，来得到所述低频段速度体，包括：对于所述层位格架模型中的每个层位，基于所述预选井的每口井的观测值，得到该层位每个CDP处的估计值，其中，将在所述每口井处读取的低频速度值，作为所述每口井的观测值，通过该层位每个CDP处的观测值，通过所述每个层位的每个CDP处的估计值，得到该层位的低频速度值；以及通过所述每个层位的低频速度值，得到所述低频段速度体，所述每个层位包括所述T个解释层位和所述每相邻的两个解释层位之间的C个内插子层。
承接上述示例，对于层位格架模型中的每个层位LayerM(包括所述T个解释层位和所述每相邻的两个解释层位之间的C个内插子层)，将该预设区域内沿着该层位LayerM的每个CDP处的低频速度值视为区域变量Z(x)，对于n口预选井(例如，n＝25)，沿着该层位在每口预选井处可以读取到低频速度值，则n口预选井读取的低频速度值可以视为n个观测值，即，Z(x1)，Z(x2)，Z(x3)，…，Z(xn)，对于该预设区域内沿着该层位LayerM的某个CDP处的低频速度的估计值记为Z(x0)，可以通过n个已知采样点的观测值加权和获得，即，可以通过下式表示：
其中，αi(i＝1,2,…,n)为待求的权系数。根据克里金插值法的原则，保证估计量无偏且估计方差最小的前提下，通过求解如下方程组可以得出n个权系数值。
其中，γ(xi,xj)为采样点xi和xj之间的线性变异函数值。由方程组(3)，可以得到 加权系数αi(i＝1,2,…,n)的值，将其代入式(2)，可以求得某个CDP处的低频速度的估计值记为Z(x0)。
由此可以计算得到该预设区域内沿着每个层位LayerM的每个CDP处的估计值，依次可以得到该层位格架模型中的沿着每个层位LayerM的低频速度值，由此得到目的层的低频段速度体Vel_Low。
2、所述通过谱模拟反演法，对所述三维地震数据体进行反演，得到中频段速度体，包括：获取所述预选井的测井数据中的速度曲线，构建对应的波阻抗曲线；基于所得到的目的层预选井的时深关系，将所述预选井的波阻抗曲线由深度域转到时间域，得到对应的时间域波阻抗曲线；以及将所述时间域波阻抗曲线叠加到所述三维地震数据体，并通过谱模拟反演法进行反演，得到所述中频段速度体。
所述目的层的中频段以内反演层速度体，可以通过谱模拟反演法获得。谱模拟反演是一种频率域测井约束波阻抗反演技术，将测井数据和三维地震数据体数据分别进行频谱分析，在频率域设计一个匹配算子，使地震频谱和井的波阻抗频谱相匹配，然后将频率匹配算子施加到三维地震数据体，反算成时间域算子参与反演运算，得到反演数据体。
以示例说明，根据预设区域内的n口预选井的速度曲线，构建预选井的阻抗曲线，将其作为谱模拟反演法的输入数据。优选的，为了便于将反演得到的阻抗数据体转换为速度数据体，在阻抗曲线构建时，优选将密度取值为常数，即取为目的层的密度测井值的均值Den_average，将预选井的速度曲线值乘以常数密度值Den_average，得到预选井的阻抗曲线，并利用已经得到的每口预选井的时深关系，将预选井的阻抗曲线由深度域转到时间域，得到时间域阻抗曲线IMP_time。将n口预选井的时间域阻抗曲线和三维地震数据体(即，保幅偏移叠加数据)，通过谱模拟反演，可以得到目的层中频段速度体(通过Vel_Medium表示)。
优选的，所述将所述时间域波阻抗曲线叠加到所述三维地震数据体，并通过谱模拟反演法进行反演，得到所述中频段速度体，包括：对于所述目的层的每口预选井，对井旁地震道与该预选井的时间域波阻抗曲线进行频谱分析，计算得到频率匹配算子，将所述频率匹配算子施加到所述三维地震数据体，得到谱模拟的反演结果，根据反射系数与波阻抗的对应关系，由所述谱模拟的反演结果，得到波阻抗反演结果；通过所述目的层的波阻抗反演结果与第一密度均值的比值，得到所述中频段速度体。
承接上述示例，在n口预选井处对应提取n个井旁地震道，每个地震道记为si(t)，(i＝1,2,…,n)，将地震子波记为b(t)，反射系数记为ri(t)(i＝1,2,…,n)，则地震道的褶积模型可以表示如下：
si(t)＝ri(t)*b(t)     (4)
将其变换到频率域，得到下式：
其中，Si(ω)为地震道的振幅谱，Фi(ω)为地震道的相位谱，Ri(ω)为反射系数的振幅谱，Ψi(ω)为反射系数的相位谱，Bi(ω)为子波的振幅谱，φi(ω)为子波的相位谱，则根据式(5)可以得到下式：
Si(ω)＝Ri(ω)Bi(ω)     (6)
对式(6)取对数，可以得到下式：
lnSi(ω)＝lnRi(ω)+lnBi(ω)      (7)
假设每口预选井处的波阻抗为zi(t)，(i＝1,2,…,n)，则，将对应的波阻抗zi(t)变换 到频率域，可以得到下式：
其中，Zi(ω)为预选井处的波阻抗振幅谱，θi(ω)为预选井的波阻抗相位谱。根据通用的波阻抗与反射系数之间的关系，通过波阻抗的振幅谱可以用求得反射系数振幅谱Ri(ω)，将反射系数的振幅谱Ri(ω)和地震道的振幅谱Si(ω)代入式(7)中，即可得到频率匹配算子Bi(ω)。
将频率域得到的匹配算子施加到三维地震数据体，便得到谱模拟的反演结果。令Ai(ω)＝1/Bi(ω)，根据式(6)，可以得到下式：
将式(9)作傅里叶反变换到时间域，得到反射系数的计算公式，通过下式表示：
ri(t)＝ai(t)*si(t)     (10)
其中，ai(t)为Ai(ω)的时间域函数，可以通过频率匹配算子Bi(ω)求得。
对于该预设区域内的某个CDP处的地震道，通过式(10)，通过时间域算子ai(t)和地震道si(t)的褶积，即可得到反射系数的反演结果，再利用通用的反射系数与波阻抗的对应关系，将反射系数转换为波阻抗值，得到该地震道的波阻抗反演结果。此时得到的波阻抗为相对波阻抗，为了更直观的分析谱模拟波阻抗的反演效果，便于用井进行反演质控，将得到的中频段波阻抗反演结果加上低频背景，得到最终的谱模拟的绝对波阻抗。
对该预设区域内的每个地震道进行如上计算，得到目的层的谱模拟的波阻抗反演体，再将波阻抗反演体除以密度均值Den_average，得到目的层的中频段速度体Vel_Medium。
3、所述通过波形指示反演法，对所述三维地震数据体进行反演，得到高频段速度体，包括：对于n口预选井中每口井的井旁地震道，在所述目的层的层位格架模型下，将待预测的地震波形与所述每口井的井旁地震道的波形进行对比，通过分析波形相似性和空间距离两个因素，选取K(K≤n)个井旁地震道的波形模拟样本，并通过对应的测井数据构建初始阻抗模型；基于所述初始阻抗模型的特征，并根据预设的高通频率参数值和预设的高截频率参数值，不断滤除高频段信息，并在每个井旁地震道的波形模拟样本之间进行对比，保留预设的确定性频带成分；以及根据所述K个井旁地震道的波形模拟样本和空间结构特点，确定未知点的估计值，以得到波形指示反演的波阻抗反演结果，通过所述目的层的波阻抗反演结果与第二密度均值的比值，得到所述高频段速度体。
波形指示反演是在传统地质统计学反演基础上发展起来的一种新的高精度反演方法，其理论基础是：地震波形所包含的振幅、相位和频率等信息代表了储层的特征，虽然地震有效频带窄，无法直接获取高频成分，但地震波形的横向变化反映了储层的变化特征。因此，可以充分利用地震波形的横向变化来反映储层空间的相变特征，进而分析储层岩性组合的高频结构特征，实现井震结合的高精度预测。波形指示反演的基本思想是：在参照空间分布距离和地震波形相似性两个基本因素基础上，对所有井按照相关度排序，优选与预测点关联度最高的井作为初始模型对高频成分进行无偏最优估计，并保证最终反演的地震波形与原始地震波形一致。本发明实施例通过波形指示反演法获得所述目的层的高频段速度体的过程如下：
有效样本数为K(即，上文从n个井旁地震道的地震波形选出的K个井旁地震道的波形模拟样本，例如，K＝5)，波形指示反演需要依次进行1)样本优选、2)样本曲线(即，波形模拟样本)分析和3)空间估值三个流程。
1)对于样本优选，将待预测的地震波形与n口预选井的井旁道波形在层位框架模型Layer_Model下进行对比，基于波形相似性和空间距离双变量优选出波形最相似的K个井旁道作为模拟样本，利用其测井数据构建初始阻抗模型。
2)对于样本曲线分析，统计样本井的阻抗特征，根据高通频率R1和高截频率R2两个参数值，利用分频的方法不断滤除高频段信息并在样本间进行对比，保留确定性频带成分。
3)对于空间估值，通过样本井的原始数据和空间结构特点，对未知点进行线性无偏、最优估计。
设M(x0)为未知点的估计值，M(xi)(i＝1,2,…,K)为波形优选的样本点的值，λi为第i个样本点对未知点的权重，K为有效样本数，则通过下式，计算未知点的估计值：
由式(11)可以得到波形指示反演得到的波阻抗体，将其除以密度均值Den_average，得到层速度体。但基于波形指示反演的原理，在其反演过程中应用了地震的低频和中频信息，因此反演结果中包含了一定的低频和中频成分，为了将低频和中频成分去除，只保留波形指示反演的高频成分，优选的，可以对波形指示反演进行高通滤波处理，得到只反映高频成分的数据体，即为波形指示反演得到的高频段速度体(通过Vel_High表示)。
优选的，以三维地震数据体、层位格架模型和时间域阻抗曲线为基础，进行平滑半径、有效样本数和截止频率这三个反演关键参数的测试，以使反演结果达到最优。平滑半径主要影响反演体的横向连通性，平滑半径越大，反演体的连通性越好；有效样本数主要表征地震波形空间变化对储层的影响程度，当有效样本数增加到一定程度后，地震波形对储层的表征度不再变化时，此时的样本数即为有效样本数；截止频率控制了反演的频带范围，包括低频段的高通和高截频率，高频段低截、低通、高通和高截频率。其中，截止频率的取值可以根据三维地震数据体的振幅谱进行选择，其取值原则是能够有效地补偿地震数据的低频部分和高频部分。通过反复的参数测试，不断对三个参数的取值进行微调，寻找到能够使目的层的波形指示反演误差最小、反演结果最优的参数。例如，关键参数确定为：平滑系数为2，有效样本数为5，低频段的高通和高截频率分别为5Hz和10Hz，高频段的低截、低通、高通和高截频率分别为40Hz、50Hz、90Hz和100Hz。基于上述确定的反演参数，进行波形指示反演，得到波形指示反演的波阻抗数据体，再对其除以密度均值Den_average(例如，Den_average＝2.5)，得到波形指示反演的层速度体，但由于波形指示反演在计算过程中包含了一定的低频和中频的信息，为了获得反演的高频段速度值，需要对反演结果进行高通滤波，即将频率值为FH(FH可以根据地震的振幅谱确定，例如，FH＝60Hz)以下的频率成分滤除，只保留高频部分，则可以得到高频段速度体Vel_High。
步骤S140：对所述不同频段的速度体进行融合，得到目的层的速度模型。
优选的，步骤S140可以包括：通过所述低频段速度体、所述中频段速度体和所述高频段速度体的加权和，得到所述目的层的速度模型。
如步骤S130所述，可以通过低频、中频和高频段的反演速度体融合，得到所述目的层的速度模型。即，通过分段反演得到的目的层的低频段速度体Vel_Low、中频段速度体Vel_Medium和高频段速度体Vel_High，计算(层)速度模型Vel_Model。
优选的，由于在目的层中频段的反演(通过谱模拟反演法，对所述三维地震数据体进行反演，得到中频段速度体)过程中，为了更直观的分析储层的纵、横向变化特征和进行有 效的反演质控，加入了一定的低频的信息，因此，可以将中频段速度体Vel_Medium中的低频信息去除掉。即，对中频段速度体Vel_Medium进行高通滤波(高通滤波的参数可以基于三维地震数据体的频谱确定，例如，高通频率为15Hz)，通过高通滤波，得到只保留中频段信息的(层)速度体Vel_Mediumfilter。
接着，按照预设的权重值，对目的层的低频段速度体Vel_Low、中频段速度体Vel_Medium和高频段速度体Vel_High进行加权，得到融合后的速度模型。其中，预设的权重值可以通过反演的层速度数据与井的吻合度的分析来确定，例如，可以为介于1-2之间的值，吻合度大的权重高，吻合度小的权重低。例如，通过反演结果与井吻合度的分析，确定低频段速度体的权重值W_Low、中频段速度体的权重值W_Medium和高频段速度体的权重值W_High。对于目的层段的任意一个采样点i，依次读取其对应的低频段速度体Vel_Low(i)、中频段速度体Vel_Mediumfilter(i)和高频段速度体Vel_High(i)的层速度值，通过下式，得到该点数据融合后的层速度值Vel_Model(i)：
Vel_Model(i)＝Vel_Low(i)×W_Low+Vel_Mediumfilter(i)×W_Medium+
Vel_High(i)×W_High     (12)
基于此，可以实现对目的层段每个采样点的数据融合，即可得到目的层段层速度模型Vel_Model。
优选的，本发明实施例还可以通过1)速度模型对比和/或2)时深转换后井点处深度误差对比，分析步骤S110-S140所得到的速度模型的可靠性。
1)通过常规基于模型的反演得到的(层)速度体Vel_tradition与本发明实施例的(层)速度模型Vel_Model进行对比：(层)速度体Vel_tradition不能精细表征预设区域地层速度的纵横向变化特征，(层)速度体Vel_tradition不能识别特殊地质体内部及边界的速度变化；而本发明实施例的(层)速度模型Vel_Model不仅能够反映出层间和纵向不同岩相之间的速度特征，精细反映该区域的地质规律，同时也能够很好的刻画特殊地质体的发育区边界，且与钻井上的速度吻合度高，从而验证速度模型Vel_Model的可靠性。
2)分别利用(层)速度体Vel_tradition与本发明实施例的(层)速度模型Vel_Model对目的层进行时深转换；分别计算(层)速度体Vel_tradition与本发明实施例的(层)速度模型Vel_Model转换后，各井点处的深度误差；通过两个不同速度模型转换后得到的井点处深度误差的对比分析表明：本发明实施例的(层)速度模型Vel_Model进行时深转换后得到的井点处的深度误差值，明显低于(层)速度体Vel_tradition转换后的深度误差值，验证了本发明实施例的(层)速度模型Vel_Model的可靠性。
据此，本发明实施例为更准确进行特殊地质体相关的时深转换、厚度计算、储层预测以及井位落实等地震地质综合研究，提供了一种构建速度模型的方法。以对特殊地质体的地质特征分析为前提，通过精细的地震地质层位标定获得精细的钻井时深关系和层位解释数据，以此为基础，充分利用测井数据和三维地震数据体，通过预设的分频段反演策略和数据融合技术，对所述三维地震数据体进行分频段反演，得到不同频段的速度体，采用低频、中频和高频段的分频段速度体，来构建能够精细表征特殊地质体内部及边界速度变化特征的速度模型，为时深转换、构造精细落实、井位部署等综合地质研究提供可靠的速度数据，以助力特殊地质体相关领域勘探的拓展和高效推进。
以某盆地某勘探区发育有特殊地质体的层段为例，详细描述本发明实施例的一种实施过程。
如图3所示，获取保幅处理后的偏移叠加地震数据；获取该区域目的层的测井数据。 选择所有钻穿目的层的井，根据对应的测井数据，筛选同时具有声波曲线、密度曲线和井径曲线的n口井，作为预选井。例如，选择当前区钻穿目的层的井28口，对这28口钻穿目的层的井的测井数据进行分析，筛选同时具有声波曲线、密度曲线和井径曲线的井25口，即预选井n＝25。
接着，通过式(1)，将每口预选井的声波曲线转换成速度曲线，即，获得25口预选井的声波曲线、密度曲线、速度曲线和井径曲线，由此完成获取预选井的测井数据。
根据目的层单井及连井的对比分析，获得目的层的速度变化特征和特殊地质体的类型。通过对预选井的单井柱状图分析，获得目的层的岩性特征和速度纵向变化特征。本示例中，该区域目的层主要为碳酸盐岩沉积地层，岩性主要为泥岩、灰岩和泥灰岩互层，泥岩速度低，灰岩速度高，目的层的纵向上速度呈现高速区和低速区互层的特征。通过目的层的速度曲线的连井对比，分析目的层速度的横向变化特征，确定目的层内特殊地质体类型及其速度特征，为速度模型的建立提供地质基础。
其中，通过部分预选井的连井对比，如图2所示，可以获取目的层段纵向自上而下P1、KT1和MKT三个层段的厚度和速度变化特征。其中，KT1段和MKT段地层横向上分布相对稳定，速度和厚度变化不是很明显，而目的层顶部P1段的厚度和速度差异比较明显，P1段的速度纵向和横向均有明显的差异，P1段纵向上具有下部低速、中上部高速的特征。P1段下部的速度横向变化相对稳定，平均速度在3200m/s左右，而P1段中上部的速度横向变化较大，速度值在3300m/s-5600m/s之间变化，低速区在3300m/s左右，高速区速度可达5600m/s，且高速区P1段的地层厚度明显增加。经过分析可以确定，目的层的P1段中上部速度横向变化大，非均质性强，局部存在明显的特殊地质体，特殊地质体的类型为灰岩高速异常体，而目的层的P1段下部、KT1和MKT段速度变化相对稳定。
通过合成地震记录标定，确定预选井的精细的时深关系。本示例中，对25口预选井制作合成记录，对目的层进行了精细的井震标定，可以得到了25口预选井的精细的时深关系。
接着，根据上述所确定的目的层的速度变化特征的分析结果，确定目的层的T个地震解释层位。本示例中，根据上述所确定的目的层的速度变化特征的分析结果，确定目的层段速度变化明显的地质分层主要有5个，即T＝5，分别为P1顶、泥岩顶、KT1顶、MKT顶和KT2顶5个地质分层。根据25口预选井的合成地震记录，标定各个地质分层在地震剖面上的反射特征，并在三维地震数据体范围内，对5个地质分层对应的地震反射层位进行了追踪和解释。为了进行地震地质分析，将各地震解释层位的命名与其对应的地质分层一致，如图3所示，通过追踪和解释，确定目的层与地质分层相对应的5个地震反射层位的层位解释数据。
根据测井数据、时深关系和目的层的层位解释数据，通过克里金插值法，得到低频段速度体。对该区域预选井的速度曲线进行低通滤波，根据三维地震数据体的频谱分析，确定低频段的第一截止频率值FL，即将频率成分大于第一截止频率值FL的频率成分滤除，只保留截止频率值以内的速度值，得到滤波后的低频速度曲线，并根据每口预选井的时深关系，将滤波后的低频速度曲线由深度域转到时间域。
接着，基于层位解释数据建立目的层的层位格架模型Layer_Model，作为速度模型的横向约束条件。本示例中，自下至上选择目的层段的T个解释层位，在每相邻的两个解释层位之间，根据上下两层之间的接触关系，按照一定的细分比例C(例如，C＝25)，在每相邻的两个解释层位之间内插出C个内插子层，可以得到C*(T-1)个(子)层位，由T 个解释层位和每相邻的两个解释层位之间的内插子层构成了目的层的层位格架模型(Layer_Model)，该层位格架模型可以作为低频段速度体的横向约束条件。
以此为基础，利用滤波后的时间域的低频速度曲线和层位格架模型Layer_Model，通过普通克里金插值方法，得到目的层段的低频段速度体。本示例中，对于层位格架模型中的每个层位LayerM(包括所述T个解释层位和所述每相邻的两个解释层位之间的C个内插子层)，将该区域内沿着该层位的每个CDP处的低频速度值视为区域变量Z(x)，对于该区域内n口预选井，沿着该层位在每口井处可以读取到一个低频速度值，n口预选井读取的低频速度值可以视为n个观测值，通过上述公式(2)和方程组(3)，即可以求出某个CDP处的低频速度的估计值记为Z(x0)，即，沿着层位格架模型Layer_Model中的某个层位LayerM在某一个CDP处的估计值。根据上述计算过程，计算该区域内沿着层位LayerM的每个CDP处的估计值，得到沿着层位LayerM的低频速度值；对于层位格架模型Layer_Model中的每个层位，按照沿着层位LayerM的低频速度值的计算过程，依次可以得到该层位格架模型Layer_Model中的沿着每个层位的低频速度值，由此得到目的层的低频段层速度体Vel_Low。
本示例中，对25口预选井的速度测井曲线(记为Vp)进行低通滤波，通过对目的层段的三维地震数据体的频谱分析结果，可以得到目的层的频带范围在15Hz-45Hz，主频为30Hz，即，该三维地震数据体缺少小于15Hz的频率成分。因此，可以利用井上的低频成分进行补充。确定第一截止频率值FL＝15Hz，即从井上提取小于15Hz的频率成分。通过对速度曲线的低通滤波，得到15Hz以内的低频速度曲线，并根据上述得到的25口预选井的时深关系，将深度域的低频速度曲线转换到时间域。接着，根据该区域目的层的T个地震解释层位(T＝5)，选定细分比例C＝25，自下而上依次在每相邻的两个解释层位之间内插出25个子层，得到由5个解释层位和每相邻的两个解释层位之间的C个内插子层构成的层位格架模型Layer_Model。基于时间域低频速度曲线和层位格架模型Layer_Model，对于层位格架模型Layer_Model中的某个层位，依次读取25口预选井在该层位处的低频速度值，然后采用克里金插值法，通过上述公式(2)和方程组(3)依次计算沿着该层位的每个CDP处的低频速度值，得到该层的速度值。按照同样的计算过程，计算得到层位格架模型Layer_Model中每个层位的速度值，由此，得到目的层低频段层速度体Vel_Low。如图4所示，示例剖面反映了目的层速度的总体变化趋势，体现了目的层灰岩高速异常体的背景速度特征。
所述目的层的中频段以内反演层速度体，可以通过谱模拟反演法获得。根据预选井的速度曲线，对应构建预选井的阻抗曲线，将其作为谱模拟反演的输入数据。为了便于将反演得到的阻抗数据体转换到速度数据体，在阻抗曲线构建时，可以将密度取值为常数，即取为目的层段密度测井值的均值Den_average(本示例中，根据目的层段密度测井值的统计结果可以设置Den_average＝2.5)，将预选井的速度曲线值乘以常数密度值Den_average，可以得到预选井的阻抗曲线，并根据所得到的每口预选井的时深关系，将预选井的阻抗曲线由深度域转到时间域，得到时间域阻抗曲线IMP_time。
接着，将n口预选井的时间域阻抗曲线和三维地震数据体(即，保幅偏移叠加数据)，通过谱模拟反演，可以得到目的层中频段速度体Vel_Medium。通过测井数据和三维地震数据体的频谱分析，得到使地震频谱和测井的波阻抗频谱达到最佳匹配的频率匹配算子。在n口预选井处对应提取n个井旁地震道，每个地震道记为si(t)，(i＝1,2,…,n)，将地震子波记为b(t)，反射系数记为ri(t)(i＝1,2,…,n)，则根据上述式(4)和式(5)，可以得到地震的振幅谱Si(ω)、反射系数的振幅谱Ri(ω)和子波的振幅谱Bi(ω)之间的关系。即，上述 式(6)中所揭示的地震的振幅谱Si(ω)等于反射系数的振幅谱Ri(ω)和子波的振幅谱Bi(ω)的乘积。对式(6)取对数，可以得到更加便于计算的关于地震的振幅谱Si(ω)、反射系数的振幅谱Ri(ω)和子波的振幅谱Bi(ω)之间的关系式(7)。对于式(7)，由于地震的振幅谱是已知的，只需要求出反射系数的振幅谱Ri(ω)，就可以求得子波的振幅谱Bi(ω)，其中，Bi(ω)即为谱模拟反演的频率匹配算子。由于预选井的波阻抗是已知的，可以根据通用的波阻抗与反射系数之间的对应关系，并利用波阻抗的振幅谱求得反射系数振幅谱Ri(ω)，并将反射系数的振幅谱Ri(ω)和地震道的振幅谱Si(ω)代入式(7)，即可得到频率匹配算子Bi(ω)。
将频率匹配算子施加到三维地震数据体，得到谱模拟反演数据体。根据地震的振幅谱Si(ω)、反射系数的振幅谱Ri(ω)和子波的振幅谱Bi(ω)的关系，取频率匹配算子的倒数为Ai(ω)，即Ai(ω)＝1/Bi(ω)，则可以得到式(9)，将式(9)作傅里叶反变换到时间域，则得到反射系数的计算公式，即式(10)。根据式(10)，对于该区域内的某个CDP处的地震道，通过时间域算子ai(t)和地震道si(t)的褶积，即可以得到该地震道的反射系数的反演结果，再通过通用的反射系数与波阻抗的对应关系，将反射系数转换到波阻抗值，得到该地震道的波阻抗反演结果。所得到的波阻抗为相对波阻抗，为了更直观的分析谱模拟波阻抗的反演效果，便于用井进行反演质控，可以将得到的中频段相对波阻抗反演结果加上低频背景，即将谱模拟反演得到的相对阻抗反演值加上测井上的低频背景值，即，根据测井分析结果，对每个层位分配一个低频阻抗值，得到最终的谱模拟的绝对波阻抗值。
对该区域内的每个地震道进行如上计算，得到该区域内目的层的谱模拟的波阻抗反演体，再将波阻抗反演体除以密度均值Den_average，则可以得到目的层的中频段速度体Vel_Medium。
本示例中，根据25口预选井的密度曲线，统计目的层段的密度均值Den_average＝2.5，将25口预选井的速度曲线值乘以常数密度值Den_average，则可以得到预选井的阻抗曲线，并通过所得到的每口预选井的时深关系，将预选井的阻抗曲线由深度域转到时间域。接着，根据通用的反射系数与波阻抗的对应关系，将25口预选井的测井波阻抗振幅谱转换成反射系数振幅谱，提取25口优选井的井旁道振幅谱和测井波阻抗振幅谱Ri(ω)，计算25口预选井的井旁道振幅谱Si(ω)。根据式(7)求得谱模拟反演的频率匹配算子Bi(ω)。接着，将频率匹配算子取倒数并通过傅里叶反变换到时间域，得到时间域的算子ai(t)，利用式(10)，通过时间域算子ai(t)和三维地震数据体中的每个地震道si(t)的褶积，即可得到反射系数的反演结果，再利用通用的反射系数与波阻抗的对应关系，将反射系数转换到波阻抗值，得到相对波阻抗反演结果。再根据测井数据的统计结果，对目的层的每个层位设置低频背景值，例如，自上而下的低频阻抗背景值依次为10050，8250，11250和6875，得到最终的谱模拟的绝对波阻抗体，再将波阻抗反演体除以密度均值2.5，得到目的层的中频段以内的中频段速度体Vel_Medium。如图5所示，示例剖面反映了与地震同频带的目的层速度变化特征。
利用波形指示反演法获得目的层的高频段速度体。根据波形指示反演法的基本原理，以所获得的三维保幅处理偏移叠加数据、所得到的层位格架模型Layer_Model，以及时间域测井阻抗曲线IMP_time为基础，进行波形指示反演的关键参数测试。关键反演参数包括平滑半径、有效样本数和截止频率。其中，平滑半径主要影响反演体的横向连通性，平滑半径越大，反演体的连通性越好，取值范围例如在2-3之间。有效样本数主要表征地震波形空间变化对储层的影响程度，随着有效样本数的增加，地震波形对储层的表征程度越好，但当有效样本数增加到一定程度后，地震波形对储层的表征度不再变化，此时的样本数即为有效样 本数。截止频率控制了反演的频带范围，包括低频段的高通和高截频率，高频段低截、低通、高通和高截频率，截止频率的取值要根据三维地震数据体的振幅谱进行选择，其取值原则是能够有效地补偿地震数据的低频部分和高频部分，在此原则下，再对截止频率值进行微调，寻找使得反演的误差达到最小的参数。通过反复的参数测试，寻找到能够使目的层的波形指示反演误差最小、反演结果达到最优的关键参数。接着，利用参数测试中确定的关键反演参数，按照上述的波形指示反演法，对目的层的每个采样点数据进行最优的波阻抗估计，由此得到波形指示反演的波阻抗体，将其除以密度均值Den_average，则可以得到波形指示反演的层速度体。但基于波形指示反演法的原理，由于在其反演过程中应用了三维地震数据体的低频和中频信息，因此，得到的波形指示反演的结果中包含了一定的低频和中频成分，为了将低频和中频成分去除，只保留波形指示反演的高频成分，可以对波形指示反演进行高通滤波处理，即将频率值为FH(FH可以根据三维地震数据体的振幅谱确定)以下的频率成分滤除，只保留高频部分，得到只反映高频成分的数据体，即为波形指示反演得到的高频段速度体Vel_High。
本示例中，根据25口预选井的时间域阻抗曲线IMP_time、三维地震数据体以及由5个解释层位创建的层位格架模型Layer_Model，进行反演参数的测试。根据反演误差最小的原则，确定反演的关键参数如下：平滑系数为2，有效样本数为5，低频段的高通和高截频率分别为5Hz和10Hz，高频段的低截、低通、高通和高截频率分别为40Hz、50Hz、90Hz和100Hz。接着，通过以上反演参数进行波形指示反演，得到波形指示反演的波阻抗数据体，再对其除以密度均值2.5，得到速度体。对该速度体进行高通滤波，即将60Hz(FH＝60Hz)以下的频率成分滤除，只保留高频部分，得到高频段速度体Vel_High，如图6所示，示例剖面反映了目的层速度的高频变化特征，能够充分识别一些薄层的速度纵向和横向变化特征。
通过分频段反演得到的目的层的低频段速度体Vel_Low、中频段速度体Vel_Medium和高频段速度体Vel_High，计算(层)速度模型Vel_Model，采用数据融合技术，建立目的层的(层)速度模型Vel_Model。
其中，由于在目的层中频段的反演(通过谱模拟反演法，对所述三维地震数据体进行反演，得到中频段速度体)过程中，为了更直观的分析储层的纵、横向变化特征和进行有效的反演质控，加入了一定的低频的信息，因此，可以将中频段速度体Vel_Medium中的低频信息去除掉。即，对中频段速度体Vel_Medium进行高通滤波(高通滤波的参数可以基于三维地震数据体的频谱确定，例如，高通频率为15Hz)，通过高通滤波，得到只保留中频段信息的(层)速度体Vel_Mediumfilter。
接着，按照预设的权重值，对目的层的低频段速度体Vel_Low、中频段速度体Vel_Medium和高频段速度体Vel_High进行加权，得到融合后的速度模型。其中，预设的权重值可以通过反演的层速度数据与井的吻合度的分析来确定，例如，可以为介于1-2之间的值，吻合度大的权重高，吻合度小的权重低。例如，通过反演结果与井吻合度的分析，确定低频段速度体的权重值W_Low、中频段速度体的权重值W_Medium和高频段速度体的权重值W_High。对于目的层段的任意一个采样点i，依次读取其对应的低频段速度体Vel_Low(i)、中频段速度体Vel_Mediumfilter(i)和高频段速度体Vel_High(i)的层速度值，通过上式(12)，则可以得到该点数据融合后的层速度值Vel_Model(i)。
基于此，可以实现对目的层段每个采样点的数据融合，即可得到目的层段层速度模型Vel_Model。
本示例中，对所得到的中频段速度体Vel_Medium进行高通滤波，即将低于15Hz的 频率成分滤除，只保留Vel_Medium层速度体中高于15Hz的频率成分，通过高通滤波得到高通滤波后的中频段速度体Vel_Mediumfilter。通过反演结果与井吻合度的分析，确定低频W_Low、中频W_Medium和高频W_High的权重值(例如，都为1)。接着，利用式(12)，进行低频段速度体Vel_Low、中频段速度体Vel_Mediumfilter和高频段速度体Vel_High的融合，可以得到目的层的速度模型Vel_Model，如图7所示，示例剖面不仅能够清晰地反映了目的层速度的纵向和横向变化特征，而且能够精细描述灰岩高速异常体内部和边界的速度变化特征，同时也能精细揭示一些薄层的速度变化。
通过速度模型Vel_Model对比和时深转换后井点处深度误差对比，对速度模型的可靠性进行分析。将通过常规方法得到的(层)速度模型Vel_tradition与本发明实施例的(层)速度模型Vel_Model进行对比。如图7和图8对比所示，(层)速度模型Vel_tradition不能识别特殊地质体(本示例为灰岩高速异常体)的速度变化，对目的层速度的纵横向变化表征不精细；而本发明实施例的(层)速度模型Vel_Model则能够反映出速度在纵向和横向上的精细变化，反映研究区的地质规律，同时也能够很好地刻画特殊地质体(本示例为灰岩高速异常体)的内部及边界速度变化特征，且与钻井上的速度吻合度高，从而验证速度模型Vel_Model的可靠性。
接着，根据所得到的(层)速度模型Vel_tradition与本发明实施例的(层)速度模型Vel_Model，对目的层进行时深转换，本示例中，选取目的层的KT1顶面解释层位进行时深转换，分别计算层速度模型Vel_tradition与本发明实施例的(层)速度模型Vel_Model转换后各井点处的深度误差，如表1所示。
表1-时深转换后各井点在目的层处的深度误差对比表
表1示出了该区域14口井在KT1地质分层处对应的钻井海拔深度与时深转换深度的 误差值。通过两个不同速度模型在各井点处时深转换误差的对比分析，本发明实施例的(层)速度模型Vel_Model进行时深转换后得到的井点处的深度误差值为0.71％，明显低于(层)速度模型Vel_tradition转换后的深度误差值2.86％。由此进一步验证了本发明实施例根据分频段反演建立的速度模型的可靠性。
据此，本发明实施例构建速度模型的方法，有效地实现了特殊地质体发育地层的速度模型的精细建立，提高了速度模型对于储层速度在纵向和横向上变化特征的表征度，速度模型不仅能够反映地质规律，同时也能够很好地刻画特殊地质体(本示例为灰岩高速异常体)内部及边界的速度变化特征，为时深转换、构造精细落实、井位部署等综合地质研究提供可靠的速度数据。本发明实施例便于实施，可信度高，对于特殊地质体相关领域勘探的拓展和高效推进具有积极意义。
本发明实施例还提供一种用于构建速度模型的控制装置，所述控制装置包括：存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序，以实现上述的构建速度模型的方法。
本发明实施例还提供一种机器可读存储介质，该机器可读存储介质上存储有指令，该指令使得机器执行上述的构建速度模型的方法。
本领域内的技术人员应明白，本申请的实施例可提供为方法、系统、或计算机程序产品。因此，本申请可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本申请是参照根据本申请实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
在一个典型的配置中，计算设备包括一个或多个处理器(CPU)、输入/输出接口、网络接口和内存。
存储器可能包括计算机可读介质中的非永久性存储器，随机存取存储器(RAM)和/或非易失性内存等形式，如只读存储器(ROM)或闪存(flash RAM)。存储器是计算机可读介质的示例。
计算机可读介质包括永久性和非永久性、可移动和非可移动媒体可以由任何方法或技术来实现信息存储。信息可以是计算机可读指令、数据结构、程序的模块或其他数据。计算 机的存储介质的例子包括，但不限于相变内存(PRAM)、静态随机存取存储器(SRAM)、动态随机存取存储器(DRAM)、其他类型的随机存取存储器(RAM)、只读存储器(ROM)、电可擦除可编程只读存储器(EEPROM)、快闪记忆体或其他内存技术、只读光盘只读存储器(CD-ROM)、数字多功能光盘(DVD)或其他光学存储、磁盒式磁带，磁带磁磁盘存储或其他磁性存储设备或任何其他非传输介质，可用于存储可以被计算设备访问的信息。按照本文中的界定，计算机可读介质不包括暂存电脑可读媒体(transitory media)，如调制的数据信号和载波。
还需要说明的是，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、商品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、商品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括要素的过程、方法、商品或者设备中还存在另外的相同要素。
以上仅为本申请的实施例而已，并不用于限制本申请。对于本领域技术人员来说，本申请可以有各种更改和变化。凡在本申请的精神和原理之内所作的任何修改、等同替换、改进等，均应包含在本申请的权利要求范围之内。

Claims (16)

  1. 一种构建速度模型的方法，其特征在于，所述构建速度模型的方法包括：
    获取预设区域的三维地震数据体和预选井的测井数据；
    对所述三维地震数据体和所述测井数据进行处理，得到目的层的预选井的时深关系和层位解释数据；
    基于所得到目的层的预选井的时深关系和层位解释数据，通过预设的分频段反演策略，对所述三维地震数据体进行分频段反演，得到不同频段的速度体；以及
    对所述不同频段的速度体进行融合，得到目的层的速度模型。
  2. 根据权利要求1所述的构建速度模型的方法，其特征在于，在所述获取预设区域的三维地震数据体和预选井的测井数据之前，所述构建速度模型的方法还包括：
    确定所述目的层的范围；
    获取所述预设区域的井的测井数据；以及
    对于钻穿所述目的层的井，根据对应的测井数据，筛选具有声波曲线、密度曲线和井径曲线的井作为所述预选井。
  3. 根据权利要求2所述的构建速度模型的方法，其特征在于，在所述获取预设区域的三维地震数据体和预选井的测井数据之后，所述构建速度模型的方法还包括：
    将所述预选井的声波曲线转换成速度曲线。
  4. 根据权利要求1所述的构建速度模型的方法，其特征在于，在所述得到目的层的预选井的时深关系和层位解释数据之前，所述构建速度模型的方法还包括：
    对于所述目的层的预选井，通过所述测井数据对单井的柱状图进行分析，确定所述目的层的岩性及速度的纵向变化特征；
    通过所述测井数据对连井的速度曲线进行对比分析，确定所述目的层的速度的横向变化特征；以及
    通过所述目的层的速度的纵向变化特征和所述目的层的速度的横向变化特征，确定所述目的层的速度变化特征及地质体类型。
  5. 根据权利要求1所述的构建速度模型的方法，其特征在于，对所述预选井的测井数据进行处理，得到目的层预选井的时深关系，包括：
    获取所述预选井的测井数据中的声波曲线和密度曲线；以及
    通过所述声波曲线和所述密度曲线制作合成地震记录，将时间域的地震反射界面与深度域的地质分层建立对应的关系，得到所述预选井的时深关系。
  6. 根据权利要求5所述的构建速度模型的方法，其特征在于，所述对所述三维地震数据体和所述测井数据进行处理，得到层位解释数据，包括：
    对所述预选井的测井数据进行分析，得到所述目的层的速度变化特征；
    通过对所述目的层的速度变化特征进行分析，并根据速度界面的速度差，确定所述目的层的T个地质分层；
    通过制作合成的地震记录，标定所述目的层的所述T个地质分层，确定与每个地质分层对应的地震反射同相轴的特征；以及
    基于所述三维地震数据体，完成与所述每个地质分层对应的地震反射同相轴的追踪和解释，以得到所述目的层的所述T个地质分层的层位解释数据。
  7. 根据权利要求1所述的构建速度模型的方法，其特征在于，所述通过预设的分频段反演策略，对所述三维地震数据体进行分频段反演，得到不同频段的速度体，包括：
    通过克里金插值法，对所述三维地震数据体进行反演，得到低频段速度体；
    通过谱模拟反演法，对所述三维地震数据体进行反演，得到中频段速度体；以及
    通过波形指示反演法，对所述三维地震数据体进行反演，得到高频段速度体。
  8. 根据权利要求7所述的构建速度模型的方法，其特征在于，在所述通过预设的分频段反演策略，对所述三维地震数据体进行分频段反演之前，所述构建速度模型的方法还包括：
    由下至上确定所述目的层的T个解释层位；
    在每相邻的两个解释层位之间，根据上下两层之间的接触关系，按照预设的细分比例C，在每相邻的两个解释层位之间内插得到C个内插子层；以及
    由所述T个解释层位和所述每相邻的两个解释层位之间的C个内插子层，构建目的层的层位格架模型。
  9. 根据权利要求8所述的构建速度模型的方法，其特征在于，所述通过克里金插值法，对所述三维地震数据体进行反演，得到低频段速度体，包括：
    获取所述预选井的测井数据中的速度曲线，对所述速度曲线进行低通滤波，以保留第一截止频率值以内的低频速度曲线；
    基于所得到的预选井的时深关系，将滤波后的低频速度曲线由深度域转到时间域，得到时间域的低频速度曲线；以及
    基于所述目的层的层位格架模型，建立所述低频速度曲线的横向约束条件，以通过克里金插值法，对所述三维地震数据体进行所述横向约束条件下的测井插值，来得到所述低频段速度体。
  10. 根据权利要求9所述的构建速度模型的方法，其特征在于，所述基于所述目的层的层位格架模型，建立所述低频速度曲线的横向约束条件，以通过克里金插值法，对所述三维地震数据体进行所述横向约束条件下的测井插值，来得到所述低频段速度体，包括：
    对于所述层位格架模型中的每个层位，基于所述预选井的每口井的观测值，得到该层位每个CDP处的估计值，其中，将在所述每口井处读取的低频速度值，作为所述每口井的观测值，
    通过所述每个层位的每个CDP处的估计值，得到所述低频段速度体，
    所述每个层位包括所述T个解释层位和所述每相邻的两个解释层位之间的C个内插子层。
  11. 根据权利要求7所述的构建速度模型的方法，其特征在于，所述通过谱模拟反演 法，对所述三维地震数据体进行反演，得到中频段速度体，包括：
    获取所述预选井的测井数据中的速度曲线，构建对应的波阻抗曲线；
    基于所得到的目的层预选井的时深关系，将所述预选井的波阻抗曲线由深度域转到时间域，得到对应的时间域波阻抗曲线；以及
    将所述时间域波阻抗曲线叠加到所述三维地震数据体，并通过谱模拟反演法进行反演，得到所述中频段速度体。
  12. 根据权利要求11所述的构建速度模型的方法，其特征在于，所述将所述时间域波阻抗曲线叠加到所述三维地震数据体，并通过谱模拟反演法进行反演，得到所述中频段速度体，包括：
    对于所述目的层的每口预选井，对井旁地震道与该预选井的时间域波阻抗曲线进行频谱分析，计算得到频率匹配算子，将所述频率匹配算子施加到所述三维地震数据体，得到谱模拟的反演结果，
    根据反射系数与波阻抗的对应关系，由所述谱模拟的反演结果，得到波阻抗反演结果；以及
    通过所述目的层的波阻抗反演结果与第一密度均值的比值，得到所述中频段速度体。
  13. 根据权利要求8所述的构建速度模型的方法，其特征在于，所述通过波形指示反演法，对所述三维地震数据体进行反演，得到高频段速度体，包括：
    对于n口预选井中每口井的井旁地震道，在所述目的层的层位格架模型下，将待预测的地震波形与所述每口井的井旁地震道的波形进行对比，根据波形相似性以及空间距离两个因素，选取K个井旁地震道的波形模拟样本，并通过对应的测井数据构建初始阻抗模型；
    基于所述初始阻抗模型的特征，并根据预设的高通频率参数值和预设的高截频率参数值，不断滤除高频段信息，并在每个井旁地震道的波形模拟样本之间进行对比，保留预设的确定性频带成分；以及
    根据所述K个井旁地震道的波形模拟样本和空间结构特点，确定未知点的估计值，以得到波形指示反演的波阻抗反演结果，通过所述目的层的波阻抗反演结果与第二密度均值的比值，得到所述高频段速度体。
  14. 根据权利要求7所述的构建速度模型的方法，其特征在于，所述对所述不同频段的速度体进行融合，得到所述目的层的速度模型，包括：
    通过所述低频段速度体、所述中频段速度体和所述高频段速度体的加权和，得到所述目的层的速度模型。
  15. 一种用于构建速度模型的控制装置，其特征在于，所述控制装置包括：存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序，以实现根据权利要求1-14中任意一项所述的构建速度模型的方法。
  16. 一种机器可读存储介质，其特征在于，该机器可读存储介质上存储有指令，该指令使得机器执行根据权利要求1-14中任意一项所述的构建速度模型的方法。
PCT/CN2023/142794 2023-08-16 2023-12-28 构建速度模型的方法、控制装置和存储介质 Pending WO2025035685A1 (zh)

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