WO2019062655A1 - 薄夹层的确定方法和装置 - Google Patents

薄夹层的确定方法和装置 Download PDF

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WO2019062655A1
WO2019062655A1 PCT/CN2018/106872 CN2018106872W WO2019062655A1 WO 2019062655 A1 WO2019062655 A1 WO 2019062655A1 CN 2018106872 W CN2018106872 W CN 2018106872W WO 2019062655 A1 WO2019062655 A1 WO 2019062655A1
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data
thin interlayer
seismic
interlayer
determining
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PCT/CN2018/106872
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English (en)
French (fr)
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郭同翠
王红军
夏朝辉
孔祥文
马智
李昊宸
曲良超
赵文光
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中国石油天然气股份有限公司
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Priority to AU2018340369A priority Critical patent/AU2018340369B2/en
Priority to SG11202002483VA priority patent/SG11202002483VA/en
Priority to CA3076280A priority patent/CA3076280C/en
Publication of WO2019062655A1 publication Critical patent/WO2019062655A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V11/00—Prospecting or detecting by methods combining techniques covered by two or more of main groups G01V1/00 - G01V9/00

Definitions

  • the present application relates to the field of oil and gas exploration technology, and in particular, to a method and a device for determining a thin interlayer.
  • shale gas In the exploration and development of shale gas, due to the characteristics of shale gas itself, shale gas is mostly present in the shale interval in the form of free or adsorbed state.
  • the study shows that the thin carbonate interlayer in the shale interval is beneficial to strengthen the reversibility of the reservoir in the shale interval and plays an important role in the specific exploration and development of shale gas.
  • inversion is mostly performed by one-dimensional lithology ratio and two-dimensional phase control as a constraint to determine a specific thin interlayer.
  • the specific implementation often only makes the inversion result have a horizontal change trend, but can not distinguish the vertical change characteristics.
  • the obtained inversion results have a lower resolution, and the thin interlayers (i.e., thin sandwiches having a single layer thickness of 0.5-1.5 m) in the thin interlayer are less accurate.
  • the existing methods in the specific implementation, often have the technical problems of large thin interlayer error and low resolution.
  • the embodiment of the present application provides a method and a device for determining a thin interlayer to solve the technical problem that the determined thin interlayer has a large error and a low resolution, and the method can reflect the longitudinal change trend characteristic. It can reflect the technical effects of the lateral trend characteristics, so that the thin interlayer can be determined more accurately.
  • Embodiments of the present application provide a method for determining a thin interlayer, including:
  • the thin interlayer in the target region is determined by prestack geostatistical inversion.
  • the determining a high-frequency three-dimensional probability body of a thin interlayer of a target region according to the well logging data, the core assay data, the seismic overlay offset data, and the seismic interpretation horizon data include:
  • determining a first high frequency probability body for a thin interlayer distribution based on the well logging data, the core assay data, and the seismic interpretation horizon data including:
  • the logging response characteristics of the interlayer are determined by logging evaluation
  • rock physics analysis result data is obtained through petrophysical analysis
  • determining a second high frequency probability body for the thin interlayer distribution according to the well logging data, the seismic overlay offset data, the seismic interpretation horizon data comprising:
  • the seismic waveform difference simulation is performed on the seismic superposition offset data by using a probability curve of the in-situ interlayer distribution, and the second high frequency probability body about the thin interlayer distribution is obtained.
  • determining the high frequency three-dimensional probability body of the thin interlayer according to the first high frequency probability body and the second high frequency body including:
  • the first high frequency probability body and the second high frequency probability body are fused to obtain a high frequency three-dimensional probability body of the thin interlayer.
  • the thin interlayer in the target region is determined by pre-stack geologic statistical inversion, with the high-frequency three-dimensional probability body of the thin interlayer as a constraint, including:
  • the gather processing includes at least one of the following:
  • Denoising processing residual static correction processing, multiple wave attenuation processing, gather set leveling processing, gather set cutting processing, and superposition processing.
  • determining, in the target area, by pre-stack geostatistical inversion, based on the partially superimposed offset data and the fully superimposed offset data, with the high-frequency three-dimensional probability body of the thin interlayer as a constraint Thin sandwiches including:
  • the method further includes:
  • the embodiment of the present application further provides a device for determining a thin interlayer, comprising:
  • the obtaining module is configured to acquire logging data, core laboratory analysis data, seismic pre-stack gather data, seismic superposition offset data, and seismic interpretation horizon data of the target area;
  • a first determining module configured to determine a high-frequency three-dimensional probability body of the thin interlayer according to the logging data, the core test analysis data, the seismic superposition offset data, and the seismic interpretation horizon data;
  • a second determining module configured to determine a thin interlayer in the target region by pre-stack geologic statistical inversion, with the high-frequency three-dimensional probability body of the thin interlayer as a constraint.
  • the first determining module comprises:
  • a first determining unit configured to determine, according to the logging data and the core assay data, a first high frequency probability body about a thin interlayer distribution in a target horizon;
  • a second determining unit configured to determine a second high frequency probability body about the thin interlayer distribution according to the logging data, the seismic superposition offset data, and the seismic interpretation horizon data;
  • a third determining unit configured to determine a high frequency three-dimensional probability body of the thin interlayer according to the first high frequency probability body and the second high frequency body.
  • the second determining module comprises:
  • a processing unit configured to perform a gather process on the seismic pre-stack gather data to obtain partial superimposed offset data and full superimposed offset data
  • a fourth determining unit configured to determine the target area by pre-stack geologic statistical inversion based on the partially superimposed offset data and the full superimposed offset data, with the high-frequency three-dimensional probabilistic body of the thin interlayer as a constraint a thin interlayer in the middle.
  • a high-frequency three-dimensional probabilistic body of a thin interlayer having a high resolution, a good characterization effect, and capable of reflecting a longitudinal variation trend is first determined;
  • the high-frequency three-dimensional probabilistic body of the thin interlayer is used as a constraint to determine the specific thin interlayer by prestack geostatistical inversion, thereby solving the problem that the determined thin interlayer has a large error and low resolution, and surrounds the well.
  • the technical problem of the circle phenomenon appears to achieve the technical effect that can reflect the characteristics of the longitudinal change trend and the characteristics of the horizontal change trend, so that the thin interlayer can be determined more accurately.
  • FIG. 1 is a process flow diagram of a method of determining a thin interlayer provided in accordance with an embodiment of the present application
  • FIG. 2 is a structural diagram of a determining device for a thin interlayer provided according to an embodiment of the present application
  • FIG. 3 is a schematic diagram of playback of a well logging curve acquired in a scene example
  • FIG. 4 is a schematic diagram of seismic superposition offset data and synthetic records of the A well acquired in a scene example
  • FIG. 5 is a schematic diagram of a multi-well wavelet in a target area acquired in a scene example
  • FIG. 6 is a schematic diagram of an inversion profile (top) and a plan view (bottom) obtained by geostatistics using conventional logging constraints in a scene example;
  • FIG. 7 is a schematic cross-sectional view of a high-frequency three-dimensional probability body obtained by applying the method and apparatus for determining a thin interlayer provided by an embodiment of the present application;
  • FIG. 8 is a schematic diagram of applying a conventional geostatistical inversion effect diagram in a scene example
  • FIG. 9 is a schematic diagram of prestack geostatistical inversion of high frequency three-dimensional probabilistic body constraints obtained by applying the thin interlayer determination method and apparatus provided by the embodiments of the present application in one scene example;
  • FIG. 10 is a schematic cross-sectional view showing the probability inversion of the final calcareous interlayer (or limestone interlayer) obtained by applying the thin interlayer determination method and apparatus provided by the embodiment of the present application in a scene example;
  • FIG. 11 is a schematic diagram showing the analysis of the elastic parameter characteristics of four lithologies in the target region obtained by the method and apparatus for determining the thin interlayer provided by the embodiment of the present application;
  • Fig. 12 is a quantitative explanatory template of four lithologic elastic parameters in a target region obtained by the method and apparatus for determining a thin interlayer provided by an embodiment of the present application.
  • the existing methods do not fully combine the advantages of logging data with the advantages of seismic data, simply by two-dimensional phase control as a constraint, inversion is performed to determine the thin interlayer in the target area. Therefore, the specific implementation often only makes the inversion result have a horizontal change trend, and can not realize the vertical change characteristics; and the resolution of the obtained inversion result is relatively low, and the circle point phenomenon is easy to appear at the well point.
  • a thin sandwich with a thin thickness in a thin interlayer (for example, a thin interlayer having a thickness of 0.5 m to 1.5 m) has a relatively low recognition accuracy and a relatively large error.
  • the existing methods in the specific implementation, often have the technical problems of large thin interlayer error and low resolution.
  • the present application considers that the well logging data and the seismic data can be comprehensively utilized to determine a high-frequency three-dimensional probabilistic body of a thin interlayer with a high resolution, a good characterization effect, and a longitudinal variation tendency;
  • the high-frequency three-dimensional probability body of the thin interlayer is used as a constraint instead of the two-dimensional low-resolution data to determine a specific thin interlayer, thereby solving the problem that the determined thin interlayer error existing in the existing method is large and the resolution is large.
  • the lower technical problems achieve the technical effect of reflecting the characteristics of the longitudinal variation and accurately determining the thin interlayer.
  • the embodiment of the present application provides a method for determining a thin interlayer.
  • a method for determining a thin interlayer For details, please refer to the process flow chart of the method for determining a thin interlayer provided by the embodiment of the present application shown in FIG. 1 .
  • the method for determining the thin interlayer provided by the embodiment of the present application may include the following steps in the specific implementation.
  • S1 acquiring logging data, core laboratory analysis data, seismic pre-stack gather data, seismic superposition offset data, and seismic interpretation horizon data of the target area.
  • the target area may specifically be an area where a shale interval exists.
  • shale gas is mostly present in the organic or shale interval in the free or adsorbed state.
  • the matrix permeability of the shale interval is generally less than or equal to 0.001 ⁇ 10 -3 um 2 .
  • the above shale interval is mainly rich in matrix, and may contain a thin interlayer of carbonate or the like, also called a thin interlayer of limestone or a calcium interlayer.
  • the above thin interlayer is beneficial to enhance the revampability of shale gas reservoirs in the target area and contribute to the specific exploration and development of shale gas.
  • the above thin interlayer is further divided into a conventional thin interlayer and an ultra-thin thin interlayer (ie, a thin interlayer thinner than a conventional thin interlayer), wherein the thickness of the ultra-thin thin interlayer may specifically be 0.5 m to 1.5 m.
  • the existing thin interlayer determination method is limited by the method itself, resulting in lower resolution and poor precision, and often cannot accurately identify the above-mentioned ultra-thin thin interlayer. In addition, there are certain errors in identifying conventional thin interlayers.
  • the method of determining a thin interlayer provided by an embodiment of the present application can be applied to determine a conventional thin interlayer, in addition to being applicable to determining an ultra-thin thin interlayer.
  • the logging data may specifically be a logging data. In the specific implementation, it can be obtained by logging in the target area.
  • the logging data may specifically include: a logging curve, a logging response characteristic parameter, and the like.
  • the seismic pre-stack gather data may specifically be a kind of seismic data. In specific implementation, it can be obtained from seismic records in the target area.
  • the seismic pre-stack gather data may be a CRP (common reflection point) gather. It should be noted that the seismic pre-stack gather data includes the seismic pre-stack gather data corresponding to the logging data corresponding to the logging area.
  • the seismic superposition offset data may specifically be one type of seismic data.
  • the seismic interpretation results can be obtained based on the seismic data.
  • the above seismic superposition offset data may specifically be one type of data in the seismic interpretation result. It should be noted that the seismic superposition offset data passes through the area where the logging data corresponds to the logging.
  • the core test analysis data may specifically be data obtained by performing specific core analysis on the core sample collected in the target area.
  • the seismic interpretation horizon data may specifically be a seismic data used to characterize relevant information of the seismic horizon.
  • the log data of the well logging can better reflect the geological structure of the area where the well is located, but cannot be based on logging.
  • the data directly determines the geological structure of the logging area.
  • the above seismic data can better reflect the correlation of each location in the target area, but the effect of the characterization is not as fine as the logging data.
  • log data such as log data
  • seismic data such as seismic superimposed offset data and seismic pre-stack gather data
  • S2 Determine a high-frequency three-dimensional probability body of the thin interlayer according to the logging data, the core test analysis data, the seismic superposition offset data, and the seismic interpretation horizon data.
  • the above may include the following.
  • S2-1 Determine a first high frequency probability body about a thin interlayer distribution in the target horizon according to the logging data and the core analysis data.
  • the core test analysis data may specifically refer to the analysis of the mineral content of the core sample collected in the target area, and the obtained data for characterizing the lithology.
  • the data for characterizing the lithology may be obtained by: performing a chemical analysis on the core sample collected in the target region to obtain a mineral content characteristic of the core sample; and a mineral according to the core sample.
  • the content characteristics explain the lithology of the shale target interval in the target area, and divide the lithology of the shale target interval in the target area into four lithologies, namely: brittle shale, plastic shale, muddy Gray matter and limestone.
  • the lithology division may be performed as follows: the quartz mineral content is greater than or equal to 50%, and the organic carbon content is greater than or equal to 4%, which is classified as brittle shale; The lithology is greater than 50%, the clay mineral content is less than 20%, and the effective porosity is less than 2%. The lithology is divided into limestone; the carbonate mineral content is greater than 50%, the clay content is greater than 20%, and the effective porosity is greater than 2%. The lithology is divided into argillaceous limestone; the remaining lithology in the shale target interval is divided into plastic shale.
  • a lithology curve can be determined based on the above-mentioned four lithological mineral content characteristics, which can be recorded as Litho1.
  • the logging data may specifically include: a natural gamma logging curve, a sonic time difference logging curve, a neutron porosity curve, a resistivity logging curve, a density logging curve, and the like.
  • the following can be performed:
  • S2-1-1 determining logging response characteristics of the interlayer by logging evaluation according to the logging data
  • the logging response characteristic of the interlayer is determined by the logging evaluation according to the logging data, and specifically includes: by comprehensively comparing the curve characteristics of the plurality of logging curves, the following different lithologies can be determined.
  • Logging response characteristics of the interlayer For strata with lithologic shale, the log response characteristics are: high natural gamma, high acoustic time difference, high neutron porosity, high resistivity and low density.
  • the characteristics of the well logging response are: high resistivity, high density, low natural gamma, low acoustic time difference and low neutron porosity.
  • the rock physical analysis result data is obtained through petrophysical analysis, and specifically includes the following contents: combined with logging data, through the petrophysical (characteristic) analysis, the longitudinal wave impedance can be found
  • the intersection of the longitudinal and transverse wave velocity ratios can effectively distinguish four kinds of lithologies, that is, the interlayer of carbonate rock is the largest longitudinal wave impedance, the stratum of brittle shale is the smallest ratio of longitudinal wave velocity to transverse wave velocity, and the formation of plastic shale is The ratio of longitudinal wave velocity to shear wave velocity is the largest.
  • the interlayer of argillaceous limestone is the median longitudinal wave impedance and the median ratio of longitudinal wave velocity to shear wave velocity.
  • the corresponding petrophysical analysis results are obtained. For details, please refer to the related content shown in FIG. 11 and FIG.
  • the probability curve for establishing the distribution of the upper meridian layer based on the rock physical analysis result data, the log response characteristic, and the core test analysis data may be performed in the following manner.
  • Litho1 ie, core analysis data
  • quartz content + clay content + carbonate content + Organic carbon content + pore content 1.
  • the numerical probability approximation of limestone and argillaceous limestone can be expressed by numerical approximation of carbonate content. The larger the value of carbonate rock content, the greater the probability of limestone and the remaining brittleness.
  • the probability of shale and plastic shale is approximately 1 minus the probability of limestone and argillaceous limestone; then normalized according to quartz mineral content data to the above 1 minus limestone and argillaceous limestone The data between the obtained probability values is the probability value of the brittle shale and plastic shale.
  • the specific calculation formula can be expressed as the following form:
  • X can be expressed as the probability of brittle shale or plastic shale.
  • S can be expressed as the value of quartz mineral content
  • a can be expressed as quartz mineral content.
  • the minimum value, b can be expressed as the maximum value of quartz mineral content;
  • c can be expressed as the minimum value of 1 minus the probability of limestone and argillaceous limestone,
  • d can be expressed as 1 minus limestone and The maximum value of the value obtained from the probability of argillaceous limestone.
  • the probability values of four lithologies in the longitudinal direction of each well can be obtained.
  • a lithology probability can be formed for each well.
  • the curve that is, the probability curve of the distribution of interlayers on each well can be established.
  • the second method can also be used, that is, the normalization is performed according to the thickness of each lithology, that is, each lithology accounts for the total thickness of the interval.
  • the ratio is set to the probability value of the lithology distribution, and then the probability values of the four lithology intervals are calculated for each well in the longitudinal direction, so that each well can also form a lithology probability curve.
  • the inter-well interpolation may also be referred to as an inter-well difference method.
  • the inter-well difference may be performed by using an inverse distance weighting or a simple kriging method to obtain four kinds of target regions respectively.
  • the first high frequency probabilistic body of the distribution of lithology that is, the first high frequency probabilistic body with respect to the distribution of the thin interlayer.
  • S2-2 Determine a second high frequency probability body about the thin interlayer distribution according to the logging data and the seismic superposition offset data.
  • the following may be performed: according to the logging data, Through the numerical calculation of the results of the lithology distribution explained by the well, the lithology result curve of the upper interlayer distribution is obtained, which is divided into four kinds of lithology: brittle shale, plastic shale, argillaceous limestone and limestone, which can be expressed as 1, 2, 3, 4; further, according to the lithology result curve of the above-mentioned distribution of the upper hole, the seismic waveform indication simulation (or waveform difference simulation) is performed on the seismic superposition offset data, and the distribution about the thin interlayer is obtained.
  • the second high frequency probability body is performed: according to the logging data, Through the numerical calculation of the results of the lithology distribution explained by the well, the lithology result curve of the upper interlayer distribution is obtained, which is divided into four kinds of lithology: brittle shale, plastic shale, argillaceous limestone and limestone, which can be expressed as 1, 2, 3, 4; further, according to the lithology result curve of
  • the lithology result curve of the distribution of the upper hole interlayer may be utilized to perform a seismic waveform indication simulation on the seismic superposition offset data to obtain the second distribution regarding the thin interlayer distribution. High frequency probability body.
  • all the wells can be ranked according to the correlation degree of the well point sample distribution distance and the seismic waveform characteristics. It is preferred that the well with high correlation degree with the predicted point is used as the initial model to estimate the high frequency components without bias. And to ensure that the final simulated lithology probability distribution is consistent with the original seismic waveform.
  • the implementation mechanism of the above method is a well seismic synergistic simulation based on relative changes of waveforms.
  • the seismic waveform variable variance property is used as a feature vector describing the waveform change of the seismic wave.
  • the variational variance parameters of different lithologies of the adjacent wells are counted to characterize the “contribution” of the vertical structural changes of the wells to the seismic waveform changes.
  • the eigenvectors of the predicted seismic waveforms are statistically predicted, and the variability of the wells is predicted by the variable variance function to obtain the second high frequency probability body.
  • the variance function of the seismic waveform of the longitudinal limestone position after drilling through the well seismic calibration is used as the eigenvector to simulate the probability of predicting the limestone of the track.
  • the probability of predicting the argillaceous limestone is predicted.
  • the variational function of the seismic waveform at the location of the in-situ brittle shale is used as the eigenvector to simulate the probability of predicting the brittle shale of the track.
  • the probability body of the last plastic shale is calculated according to the following formula to obtain the final second high frequency probability body:
  • variable variance may specifically refer to a relationship between adjacent sampling points in the same time window and the amplitude between the previous sampling points and the variance of each sampling point in the time window, which may be used for Describe the amount of amplitude change.
  • the calculation formula can be expressed in the following form:
  • N(h) can be specifically expressed as the number of sampling points with the time window interval being h-1
  • the variance S can specifically represent
  • SN can be expressed as the variance of the adjacent two sampling points in the same time window
  • X(i) can be specifically expressed as the amplitude value of the sampling point numbered i, i can be Expressed as the number of the sampling point
  • h can be expressed as the sampling interval.
  • the lithological distribution of the known well can be analyzed according to the characteristics of the seismic waveform, and the initial model is preferably established with the well sample with high correlation degree with the waveform to be discriminated, and the lithological result is used as the prior information.
  • Wells with similar waveform similarity, difference, and spatial distance bivariate preferred waveforms are used in known wells as spatial estimation samples. Then the lithological results on the initial well are matched and filtered with the variance parameters of the seismic waveform, and the likelihood function is calculated.
  • the seismic waveforms of the two wells are similar, it indicates that the large lithology of the two wells is similar, and this characteristic can be used to constrain the range of high frequency values, so that the lithology probability simulation results are more certain, thus improving the The accuracy of the second high frequency probability body obtained.
  • the existing method uses the variability function of the well lithology result when the initial model is established, it is affected by the well position distribution, which makes it difficult to accurately characterize the heterogeneity of the lithological distribution, and the distribution Dense seismic waveforms accurately characterize changes in spatial structure and lithology. Therefore, the characteristics of the seismic waveform can be used, combined with the effective sample, smooth radius, target sampling rate and other data, to establish a reasonable stratigraphic framework model, complete the well logging calibration of the synthetic record in the target area to establish the initial model.
  • the effective sample number may specifically be one of the most important parameters in the seismic waveform indication simulation, and is mainly used to characterize the influence degree of the spatial variation of the seismic waveform on the lithology.
  • the setting of this parameter is mainly based on the results of known well statistics. Specifically, statistical analysis can be performed using “sample number” and “seismic correlation”, and the correlation increases with the increase of the number of samples. After reaching a certain level, the correlation no longer increases with the increase of the number of samples. It indicates that more samples do not contribute to the improvement of prediction accuracy, and the number of samples with the highest correlation is the optimal sample parameter. Its value can usually be set to 5.
  • this parameter is also related to the total number of samples. Generally, the number of samples is large, indicating that the lithological probability changes little, and the heterogeneity is weak. In areas with fast lateral changes and strong heterogeneity, the number of samples can be appropriately reduced.
  • the numerical value of the smoothing radius is greater than or equal to 0 and less than or equal to 5. In the present embodiment, it may be specifically set to 1. Generally, the larger the smoothing radius, the better the lateral continuity of the obtained lithological probability distribution data body. If the lithology of the target area changes rapidly, the width is narrow and the smoothing radius can be 1.
  • the value of the target sampling rate is smaller, the resolution of the simulated lithology probability result is higher, the smoother the data body, and the longer the calculation time, the thinner the lithology of the well (for example, 0.2 ms). It can meet the precise prediction requirements of the shale thin interlayer in the target area.
  • S2-3 Determine a high-frequency three-dimensional probability body of the thin interlayer according to the first high frequency probability body and the second high frequency body.
  • a high-frequency three-dimensional probabilistic body of a thin interlayer having a high resolution, a good characterization effect, and capable of reflecting a longitudinal change trend according to the first high frequency probability body, Determining, by the second high frequency body, a high frequency three-dimensional probability body of the thin interlayer, which may include: fusing the first high frequency probability body and the second by a global kriging method in a frequency domain A high-frequency probability body obtains a high-frequency three-dimensional probability body of the thin interlayer.
  • the first high frequency probability body and the second high frequency probability body are fused to obtain a high frequency three-dimensional probability body of the thin interlayer
  • the specific implementation may include the following content: according to the first high The frequency of the frequency probabilistic body is combined with the undulating characteristics of the target region stratum to determine the first weight of the first high frequency probabilistic body; and according to the frequency of the second high frequency probable body, the second high frequency probability is determined according to the undulating characteristics of the target region stratum The second weight of the body; combining the product of the first high frequency probability body with the first weight and the product of the second high frequency probability body and the second weight to obtain a high frequency three-dimensional probability body of the thin interlayer.
  • the fusion of the first high frequency probability body and the second high frequency probability body is completed.
  • the result data obtained by combining the first high-frequency probabilistic body and the second high-frequency probabilistic body may be used as a constraint condition, and the high-resolution longitudinal wave impedance and the longitudinal-to-transverse wave velocity ratio are better obtained by inversion. Parameter data.
  • the undulation feature of the target area formation layer may be specifically determined according to the overlay offset data. Specifically, when the determined fluctuation of the formation characteristic of the formation of the target area is relatively large, the specific value of the second weight may be appropriately increased; correspondingly, the relief of the formation characterized by the relief characteristics of the determined target area formation When the degree is relatively small, the specific value of the second weight can be appropriately reduced. While increasing the specific value of the second weight, the specific value of the first weight may also be appropriately reduced; correspondingly, while reducing the specific value of the second weight, the first weight may be appropriately increased. accurate value.
  • the first weight when the undulation characteristic of the determined target area formation is characterized by a very large degree of undulation, when the threshold is exceeded, the first weight may take a value of 0, and the second weight may take a value of 1.
  • the high-frequency three-dimensional probability body of the thin interlayer obtained by fusing the first high-frequency probabilistic body and the second high-frequency probabilistic body corresponds to the second high-frequency probabilistic body used alone.
  • the first high-frequency probability body is obtained by inter-well interpolation, the local layer is a horizontal stratum, the dip angle is small, and when the number of wells is large, the formation morphology changes little (ie, the stratum is undulating)
  • the first high-frequency probability body can be used when the degree of the local layer is large (ie, the formation undulation is relatively large), then the second high-frequency probability body can be used because the second high-frequency probability body It is realized by inter-well simulation considering the variation characteristics of the lateral seismic waveform.
  • the two high-frequency probabilistic bodies can be combined in the frequency domain data body according to the specific situation to further improve the effect of the simulated lithology probability body.
  • the high-frequency three-dimensional probabilistic body of the thin interlayer is obtained by the above method, and the well point consistency with the well is relatively better than that of the first high-frequency probabilistic body and the second high-frequency probabilistic body.
  • the spatial variation of the thin interlayer in the reaction stratum environment which better integrates the advantages of different data such as logging data and seismic data, can not only reflect the lateral trend characteristics, but also reflect the longitudinal trend characteristics. The higher resolution can more accurately and accurately reflect the specific structure of the stratum in the area.
  • S3 determining a thin interlayer in the target region by pre-stack geologic statistical inversion, with the high-frequency three-dimensional probability body of the thin interlayer as a constraint.
  • the high-frequency three-dimensional probability body of the thin interlayer is used as a constraint, and the target is determined by pre-stack geostatistical inversion.
  • the thin interlayer in the area may specifically include the following:
  • S3-2 determining, according to the partially superimposed offset data and the full superimposed offset data, a high-frequency three-dimensional probabilistic body of the thin interlayer, and determining a thinness in the target region by pre-stack geostatistical inversion Mezzanine.
  • the gather processing may specifically include at least one of the following: denoising processing, residual static correction processing. , multiple wave attenuation processing, gather set leveling processing, gather set cutting processing, superposition processing, and the like.
  • denoising processing residual static correction processing
  • multiple wave attenuation processing gather set leveling processing
  • gather set cutting processing gather set cutting processing
  • superposition processing and the like.
  • the above-mentioned partial superimposed offset data and the full superimposed offset data are constrained by the high-frequency three-dimensional probability body of the thin interlayer, and the pre-stack geology is adopted.
  • Statistical inversion determining a thin interlayer in the target area, and when implemented, may include the following:
  • the inversion result includes: a longitudinal wave impedance data body, a longitudinal and transverse wave velocity ratio data body, and a density data body;
  • the prestack geostatistical inversion may be a combination of traditional geostatistical simulation techniques with Bayesian inference, Monte Carlo simulation of Markov chains, and pre-stack simultaneous inversion techniques. Get up to get a series of high resolution reservoir properties. This implementation rationally predicts the uncertain solution set space of the reservoir model by integrating multiple sources of information (including geology, earthquakes, logging information, etc.).
  • the pre-stack geostatistical inversion can effectively improve the inversion resolution, accurately describe the spatial distribution of reservoir lithology and the distribution of physical properties, and more reliably depict the spatial distribution of reservoir lithology and physical properties.
  • the key to the above-mentioned prestack geostatistical inversion implementation is that the synthetic records obtained by the inversion of the longitudinal wave impedance and the shear wave impedance and the wavelet convolution should match the seismic data. Therefore, before performing prestack geostatistical inversion, a deterministic inversion is needed to ensure a high degree of uniformity between seismic information and logging information. In addition, if the input seismic data is a partial angle stack, then the full Zoeppritz equation needs to be selected for prestack geostatistical inversion.
  • S1 Quality control of partially superimposed offset data. Specifically, in the given window, the seismic data volume of the near, middle, and far offsets may be aligned and corrected according to the reference layer.
  • Synthetic seismic records are made for seismic data volumes (ie, fully superimposed offset data) with different offsets through well editing and well seismic calibration, and well seismic calibration is performed to obtain near offset, near and far offset data. A comprehensive wavelet of multiple wells.
  • the seismic horizon can be used as a constraint, and the longitudinal wave impedance, the shear wave impedance, and the density curve are selected on the well to establish a grid model of the formation.
  • S4 Perform geostatistical inversion. When implemented, it can include the following:
  • S4-1 Dividing lithology. Specifically, through the above analysis of multi-well lithology, it is determined that the lithology of geostatistical inversion in the target area is of four types: brittle shale, plastic shale, argillaceous limestone, and limestone.
  • S4-2 Determine the lithology percentage.
  • the conventional practice is to count the percentage of the four lithologies, which is one of the important parameters of geostatistics.
  • the first round of inversion can be performed by using the statistical lithology ratio to obtain the test parameters and the inversion effect; and the second round of inversion is performed by using the one-dimensional statistical lithology proportional curve. The test parameters and inversion effects are obtained.
  • the high-frequency three-dimensional probabilistic bodies of the four lithologies are used instead of the conventional lithology ratio as constraints, and the inversion results are obtained by inversion.
  • the geologic statistical inversion is performed here by using the three-dimensional high-frequency probabilistic body obtained above instead of the conventional lithology ratio as a constraint condition, which can greatly improve the resolution of the inversion.
  • the probability density distribution function of each lithology is calculated.
  • the probability density function of the Gaussian distribution, the longitudinal range and the lateral variation can be selected.
  • the process can estimate an approximate range based on the post-stack deterministic inversion results.
  • S4-5 Test the signal-to-noise ratio parameter to obtain the signal-to-noise ratio data of the near, medium and far offset seismic data volume.
  • geostatistical inversion parameters can be set, geostatistical inversion can be performed, and inversion results can be obtained to obtain longitudinal wave impedance, shear wave impedance, longitudinal wave velocity, shear wave velocity, density, and longitudinal and transverse wave velocity ratio data body.
  • the inversion result may specifically include a parameter data volume such as a longitudinal wave impedance, a longitudinal to transverse wave velocity ratio, and a density.
  • a parameter data volume such as a longitudinal wave impedance, a longitudinal to transverse wave velocity ratio, and a density.
  • the determining the thin interlayer in the target region according to the inversion result may include: determining the limestone in the target region by using the result of the physical analysis of the thin interlayer rock in the target region.
  • the interlayer is characterized by high longitudinal wave impedance, high longitudinal and transverse wave velocity ratio, and high density.
  • the longitudinal wave impedance data volume and the longitudinal and transverse wave velocity ratio data can be obtained by using the inversion results obtained by prestack geostatistical inversion.
  • the data body is analyzed by intersection and the intersection analysis results are obtained. According to the intersection analysis result and the characteristics of the thin interlayer, the data body with high longitudinal wave impedance and high aspect ratio can be further characterized, which is the data body of the thin interlayer distribution of limestone, thereby determining A thin interlayer in the target area.
  • the inversion obtained by the inversion is based on the high-frequency three-dimensional probabilistic body of the thin interlayer having the above-mentioned high resolution, good characterization effect, and capable of reflecting the longitudinal change tendency as a constraint.
  • the result has higher longitudinal resolution and can better conform to the spatial deposition law, so it can better characterize the specific stratigraphic structure in the stratum. Further, the above inversion result can be utilized to accurately identify and determine a thin interlayer in the target area.
  • the high-frequency three-dimensional of the thin interlayer with high resolution, good characterization effect and reflecting the longitudinal variation trend is determined.
  • Probabilistic body reusing the high-frequency three-dimensional probabilistic body of the thin interlayer as a constraint, and determining the specific thin interlayer by pre-stack geologic statistical inversion, thereby solving the problem that the determined thin interlayer error existing in the existing method is large
  • the technical problem of low resolution and circle phenomenon at the well point reaches the technical effect that can reflect the characteristics of the longitudinal change trend and the characteristics of the horizontal change trend, so that the thin interlayer can be determined more accurately.
  • the method may further include the following: according to the thin interlayer, the guiding pair The target area performs shale gas exploration.
  • the use of thin interlayers as a reference basis to guide specific shale gas exploration is only one of the specific applications of thin interlayers.
  • other corresponding geophysics can be carried out using the determined thin interlayers according to specific conditions. exploration. In this regard, the application is not limited.
  • the method for determining a thin interlayer determines that the resolution is high, the characterization effect is good, and the longitudinal change is reflected by comprehensively utilizing the logging data and the seismic data.
  • the high-frequency three-dimensional probabilistic body of the thin interlayer of the trend; the high-frequency three-dimensional probabilistic body of the thin interlayer is used as a constraint, and the pre-stack geologic statistical inversion is used to determine the specific thin interlayer, thereby solving the existing method.
  • the technical problem of large thin interlayer error and low resolution is achieved, which achieves the technical effect of reflecting both the longitudinal variation trend feature and the lateral change trend feature, so that the thin interlayer can be determined more accurately;
  • the logging data and the seismic data respectively, the first high frequency probability body about the thin interlayer distribution, the second high frequency probability body about the thin interlayer distribution, and the above two high frequency probability bodies are better integrated.
  • the comprehensive seismic data and the gradient of the logging data are used to determine the thin interlayer with good characterization and reflect the longitudinal variation. Frequency probability dimensional body for improved thin interlayer subsequent determination accuracy.
  • a thin interlayer determining device is also provided in the embodiment of the present invention, as described in the following embodiments. Since the principle of the device to solve the problem is similar to the method for determining the thin interlayer, the implementation of the determining device for the thin interlayer can be referred to the implementation of the method, and the repeated description will not be repeated.
  • the term "unit” or "module” may implement a combination of software and/or hardware of a predetermined function.
  • the devices described in the following embodiments are preferably implemented in software, hardware, or a combination of software and hardware, is also possible and contemplated.
  • 2 is a structural diagram of a device for determining a thin interlayer according to an embodiment of the present application.
  • the device may include: an acquisition module 21, a first determination module 22, and a second determination module 23, and the structure is performed below. Specific instructions.
  • the obtaining module 21 is specifically configured to acquire logging data, core laboratory analysis data, seismic pre-stack gather data, and seismic overlay offset data of the target area.
  • the first determining module 22 is specifically configured to determine a high-frequency three-dimensional probability body of the thin interlayer according to the logging data, the core test analysis data, the seismic superposition offset data, and the seismic interpretation horizon data.
  • the second determining module 23 is specifically configured to determine, by the high-frequency three-dimensional probabilistic body of the thin interlayer, a thin interlayer in the target region by pre-stack geostatistical inversion.
  • the first determining module 22 may specifically include the following structural units:
  • the first determining unit may be specifically configured to determine, according to the logging data and the core assay data, a first high frequency probability body about a thin interlayer distribution;
  • the second determining unit may be specifically configured to determine a second high frequency probability body about the thin interlayer distribution according to the logging data and the seismic superposition offset data;
  • the third determining unit may be specifically configured to determine a high frequency three-dimensional probability body of the thin interlayer according to the first high frequency probability body and the second high frequency body.
  • the first determining unit may specifically include the following structural subunits:
  • the first determining subunit may be specifically configured to determine a logging response characteristic of the interlayer by using the logging data according to the logging data;
  • the petrophysical analysis subunit may be specifically configured to obtain rock physics analysis result data through petrophysical analysis according to the logging data;
  • a subunit which may be specifically configured to establish a probability curve of the distribution of the interlayer on the well according to the rock physical analysis result data, the log response characteristic, and the core test analysis data;
  • the interpolation subunit may be specifically configured to obtain the first high frequency probability body about the thin interlayer distribution by performing interwell interpolation on the probability curve of the interlayer distribution according to the logging response characteristic of the interlayer.
  • the well in order to be able to determine a second high frequency probability body about the thin interlayer distribution according to the logging data and the seismic superposition offset data, when the second determining unit is specifically implemented, the well may be utilized A probability curve of the interlayer distribution, the seismic waveform indication simulation (ie, the difference simulation) is performed on the seismic superposition offset data, and the second high frequency probability body about the thin interlayer distribution is obtained.
  • the third determining unit may be implemented at a frequency when implemented.
  • the first high frequency probability body and the second high frequency probability body are fused by a global kriging method to obtain a high frequency three-dimensional probability body of the thin interlayer.
  • the second determining module 23 may specifically include the following structural units:
  • the processing unit may be specifically configured to perform a gather process on the seismic pre-stack gather data to obtain partial superimposed offset data and full superimposed offset data;
  • the fourth determining unit may be specifically configured to determine, according to the partially superimposed offset data and the full superimposed offset data, by using a high-frequency three-dimensional probabilistic body of the thin interlayer, and determining the pre-stack geostatistical inversion A thin interlayer in the target area.
  • the gather processing may specifically include at least one of the following: a noise removal process, a residual static correction process, a multiple wave attenuation process, a gather set leveling process, a gather set cut process, a superposition process, and the like.
  • the high-frequency three-dimensional probabilistic body of the thin interlayer is used as a constraint, and the target is determined by prestack geostatistical inversion.
  • the thin interlayer in the region, the fourth determining unit may specifically include the following structural subunits:
  • the inversion subunit may be specifically configured to perform prestack geostatistical inversion on the partially superimposed offset data and the full superimposed offset data, and determine the inversion result by using the high frequency three-dimensional probabilistic body of the thin interlayer as a constraint.
  • the inversion result includes: a longitudinal wave impedance data body, a longitudinal and transverse wave velocity ratio data body, and a density data body;
  • the second determining subunit may be specifically configured to determine a thin interlayer in the target area according to the inversion result.
  • the device may further include a construction module, which may be used to guide the target area according to the thin interlayer. Shale gas exploration.
  • system, device, module or unit illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product having a certain function.
  • the above devices are described separately in terms of functions divided into various units.
  • the functions of each unit may be implemented in the same software or software and/or hardware when implementing the present application.
  • the thin interlayer determining device provided by the embodiment of the present application
  • the thin interlayer determining method provided by the embodiment of the present application
  • the thin interlayer solves the technical problem that the determined thin interlayer error is large and the resolution is low in the existing method, and the technical effect that can reflect the longitudinal variation trend characteristic and the lateral change trend characteristic is achieved, thereby So that the thin interlayer can be determined more accurately; and the first determining unit and the second determining unit respectively determine the first high frequency probability body about the thin interlayer distribution of the logging according to the logging data and the seismic data, and about the thin interlayer
  • the second high frequency probability body of the distribution, and then the third high frequency probability body is merged
  • the present application provides a method and apparatus for determining a thin interlayer to specifically identify and determine a thin interlayer in a target area.
  • the specific implementation process can be performed by referring to the following content.
  • S1 acquiring logging data, seismic superposition offset data, and seismic pre-stack gather data in a target area.
  • the selected well may be specifically A well.
  • the obtained related logging data and seismic superposition offset data can be referred to the playback diagram of the A well logging curve obtained in a scene example shown in FIG. 3, specifically, the first track in the figure is geological stratification.
  • the second track (CAL is gamma energy spectrum, photoelectric absorption index and other lithology curve, the third track (MD) is the measured depth; the fourth track (RES) is the deep and shallow resistivity isoelectric curve; the fifth The road (XRD) is the whole rock analysis result profile, the sixth road (FRAC) is the StatMin optimization calculation lithology component result profile, and the seventh channel (VCL) is the shale content curve (in the 7th to the 3rd lane, the solid line is the calculation Curve, the point is the core analysis result; the 8th (QUA) is the quartz content curve; the 9th (CAR) is the carbonate content curve; the 10th (PYR) is the pyrite content curve; The 11th (TOC) is the TOC content curve; the 12th (POR) is the porosity curve; the 13th (SW) is the water saturation curve; the last (LITH) is the divided lithofacies, and the shale represents the mudstone.
  • CAL gam
  • sweet spot represents shale gas layer, also known as brittle shale, that is, dessert, limestone stands for limestone, shaly limestone generation Mud limestone.
  • shale gas layer also known as brittle shale
  • the locations of the thin interlayers in the area where the logging is located are: 3917.65-3198.73 meters, 3207.25-3208.77, 3209.12-4310.6 meters, and the above carbonate rock is thin.
  • the interlayer has a relatively high resistivity, higher density, higher impedance, etc. than the shale interval.
  • the wavelet extracted by the drilling in the whole region (where the dominant frequency of the wavelet is about 21 Hz, the frequency The width is 5-38Hz, the phase is close to zero phase. It is known that the target interval speed is 5100 meters, 38HZ corresponds to 1/4 wavelength of 33.6 meters, 1/8 wavelength is 16.8 meters, and the thin sandwich single layer has a thickness of 0.5-1.5 meters. The cumulative thickness of the thin interlayer in the well logging is ⁇ 8.5 m. Therefore, the conventional reservoir prediction method can be judged, that is, the existing method cannot effectively predict the thinner thin interlayer.
  • the control test can be carried out by a conventional means, that is, an existing method.
  • a conventional means that is, an existing method.
  • logging inversion inversion alone that is, inversion based on logging data alone as a constraint
  • S2 Determine a high-frequency three-dimensional probability body of the thin interlayer according to the logging data, the core test analysis data, and the seismic superposition offset data.
  • the information such as logging, petrophys, and sedimentary features can be well integrated, and multiple elastic waves, longitudinal and transverse wave velocity ratios, and density are obtained. parameter. Therefore, it is conceivable to use high-resolution three-dimensional probabilistic bodies (ie, high-frequency three-dimensional probabilistic bodies of thin interlayers) as inputs to carry out prestack geostatistical inversion, and the inversion results can better identify complex lithology. It solves the shortcomings of the traditional one-dimensional and two-dimensional constraints, achieves the improvement of the vertical resolution, better matches the well point data, is not easily affected by the range parameters, and has no effect of bulls eye at the well point.
  • This step mainly carries out the work of logging consistency processing, logging curve correction, multi-well logging evaluation, etc., and obtains well-consistent logging curves and logging evaluation results reflecting sedimentary characteristics (ie, mezzanine probability response of logging).
  • Characteristics, reservoir probability response characteristics of logging specifically, may include: key parameters such as shale content, calcareous content (ie limestone content), porosity, brittleness, etc.; A high frequency probability body.
  • S2-2 gather set processing and AVA wavelet extraction.
  • This step mainly performs processing such as gather processing, angle calculation, and resolution shift superposition for the original CRP gathers.
  • the gather processing may specifically include: denoising processing, residual static correction, multiple wave attenuation, etc.; Combined with the logging curve, the AVA wavelet extraction (equivalent to determining the partial offset superimposed data volume) is used to lay the foundation for the subsequent prestack inversion.
  • S2-3 3D seismic inversion and attribute analysis (corresponding to determining a second high frequency probability body about the thin interlayer distribution according to the logging data and the seismic superposition offset data).
  • This step is mainly to carry out the conventional post-stack impedance inversion and attribute analysis to obtain an initial model consistent with the distribution of limestone distribution (ie, calcareous distribution), that is, the second high-frequency probabilistic body with respect to the distribution of thin interlayers.
  • limestone distribution ie, calcareous distribution
  • S2-4 Establishing a three-dimensional volume model for the distribution probability of the thin interlayer (corresponding to determining the high-frequency three-dimensional probability body of the thin interlayer according to the first high-frequency probability body and the second high-frequency body).
  • the main purpose of this step is to establish an initial three-dimensional probability model of thin interbedded limestone (ie, high-frequency three-dimensional probabilistic body of thin interlayer). Specifically, it can use the results of multi-well logging evaluation, combined with conventional seismic inversion results and attributes. Using the global Kriging method to carry out three-dimensional volume modeling, a three-dimensional probability model of limestone thin interlayer conforming to spatial variation with good well point consistency is obtained. Obtaining the result data is relatively reasonable, and can maximize the fusion of multiple information such as logging and geology, and has the characteristics of high vertical resolution and reasonable space law. For details, refer to the cross-sectional schematic diagram of the three-dimensional probable body obtained by applying the thin interlayer determination method and apparatus provided by the embodiment of the present application in one scene example shown in FIG. 7 .
  • S3 determining a thin interlayer in the target region by pre-stack geologic statistical inversion, with the high-frequency three-dimensional probability body of the thin interlayer as a constraint.
  • the difference between this step and the conventional pre-stack geologic statistical inversion is that the three-dimensional probabilistic body of the limestone interlayer (ie, the high-frequency three-dimensional probability body of the thin interlayer) is introduced.
  • the probability body can be used as a similar “sand ratio”.
  • the attribute data, as a high-resolution soft constraint condition, and then as an input to geostatistics, can be obtained by pre-stack geostatistical inversion: high geologic statistical inverse with high vertical resolution and spatial sedimentary regularity
  • the result ie the second inversion result).
  • the inversion results may specifically include parameters such as longitudinal wave impedance, aspect ratio, and density.
  • results determined by the method and apparatus for determining the thin interlayer provided by the present application are specifically compared with the results obtained by the conventional method.
  • FIG. 8 a schematic diagram of applying a conventional geostatistical inversion effect diagram in one scene example and a three-dimensional method obtained by applying the thin interlayer determination method and apparatus provided by the embodiment of the present application in one scene example shown in FIG. 9 .
  • Probabilistic body-constrained prestack geostatistical inversion map shows that the latter is better integrated with logging and seismic information, and the results have high resolution and reasonable spatial law, which can carry out limestone interlayer (ie thin interlayer). Better portrayal.
  • a schematic cross-sectional view of the probability of inversion of the final limestone interlayer in the whole region obtained by applying the thin interlayer determination method and apparatus provided by the embodiment of the present application in a scene example shown in FIG. 10 can be seen: the inversion result and the drilling result
  • the high coincidence rate can better describe the distribution of limestone interlayers in the shale interval, and achieve the organic integration of logging and seismic information.
  • the method and device for determining the thin interlayer provided by the present application can better solve the problem of predicting thin interlayer in shale gas exploration, and has the characteristics of operability and applicability, especially for similar marine super.
  • the effect of the thin sandwich in the thin mud sand area is particularly obvious.
  • the method and device for determining the thin interlayer provided by the embodiment of the present application are verified.
  • the high-frequency three-dimensional probabilistic body of the thin interlayer is used as a constraint, and the pre-stack geologic statistical inversion is used to determine the specific thin interlayer, which truly solves the existing method.
  • the technical problems of large thin interlayer error and low resolution are achieved, and the technical effects of both the longitudinal variation trend feature and the lateral change trend feature are achieved, so that the thin interlayer can be determined more accurately.
  • the device or module and the like set forth in the above embodiments may be implemented by a computer chip or an entity, or by a product having a certain function.
  • the above devices are described as being separately divided into various modules by function.
  • the functions of the modules may be implemented in the same software or software and/or hardware, or the modules that implement the same function may be implemented by a combination of multiple sub-modules.
  • the device embodiments described above are merely illustrative.
  • the division of the modules is only a logical function division. In actual implementation, there may be another division manner.
  • multiple modules or components may be combined or integrated. Go to another system, or some features can be ignored or not executed.
  • the controller can be logically programmed by means of logic gates, switches, ASICs, programmable logic controllers, and embedding.
  • the application can be described in the general context of computer-executable instructions executed by a computer, such as a program module.
  • program modules include routines, programs, objects, components, data structures, classes, and the like that perform particular tasks or implement particular abstract data types.
  • the present application can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are connected through a communication network.
  • program modules can be located in both local and remote computer storage media including storage devices.
  • the present application can be implemented by means of software plus a necessary general hardware platform. Based on such understanding, the technical solution of the present application may be embodied in the form of a software product in essence or in the form of a software product, which may be stored in a storage medium such as a ROM/RAM or a disk. , an optical disk, etc., includes instructions for causing a computer device (which may be a personal computer, mobile terminal, server, or network device, etc.) to perform the methods described in various embodiments of the present application or portions of the embodiments.
  • a computer device which may be a personal computer, mobile terminal, server, or network device, etc.

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Abstract

一种薄夹层的确定方法，包括：获取目标区域的测井资料、岩心化验分析资料、地震叠前道集数据、地震叠加偏移数据、地震解释层位数据（S11）；根据上述资料数据，确定薄夹层的高频三维概率体（S12）；以薄夹层的高频三维概率体为约束，通过叠前地质统计学反演，确定目标区域中的薄夹层（S13）。通过综合利用测井数据和地震数据，确定出分辨率较高、表征效果较好、且能反映纵向变化趋势的薄夹层的高频三维概率体；再利用薄夹层的高频三维概率体作为约束，通过反演确定薄夹层，从而解决了现有方法中存在的所确定薄夹层误差较大、分辨率较低、会出现围绕井点的牛眼画圈现象的技术问题。还提供一种薄夹层的确定装置。

Description

薄夹层的确定方法和装置
本申请要求2017年9月27日递交的申请号为201710890153.1、发明名称为“薄夹层的确定方法和装置”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及油气勘探技术领域，特别涉及一种薄夹层的确定方法和装置。
背景技术
在页岩气的勘探、开发中，由于页岩气本身的特性，页岩气大多会以游离态或吸附态的形态赋存于页岩层段中。研究表明：页岩层段中的碳酸盐岩薄夹层有利于加强页岩层段中储层的可改造性，对页岩气具体的勘探、开发具有重要作用。
目前，为了识别、确定目标区域中的薄夹层，大多通过一维岩性比例和二维相控作为约束，进行反演，以确定出具体的薄夹层。但是，受限于方法本身，具体实施，往往只能使得反演结果具备横向的变化趋势，但无法辨别出纵向的变化特征。此外，所获得的反演结果分辨率较低，对于薄夹层中厚度较薄的薄夹层(即单层厚度为0.5-1.5米的薄夹层)识别精度较差。综上可知，现有方法，具体实施时，往往存在所确定薄夹层误差较大、分辨率较低的技术问题。
针对上述问题，目前尚未提出有效的解决方案。
发明内容
本申请实施方式提供了一种薄夹层的确定方法和装置，以解决现有方法中存在的所确定薄夹层误差较大、分辨率较低的技术问题，达到了既可以反映纵向变化趋势特征又可以反映横向变化趋势特征的技术效果，从而使得可以更为精准地确定薄夹层。
本申请实施方式提供了一种薄夹层的确定方法，包括：
获取目标区域的测井资料、岩心化验分析资料、地震叠前道集数据、地震叠加偏移数据、地震解释层位数据；
根据所述测井资料、所述岩心化验分析资料、所述地震叠加偏移数据、所述地震解释层位数据，确定薄夹层的高频三维概率体；
以所述薄夹层的高频三维概率体为约束，通过叠前地质统计学反演，确定所述目标区域中的薄夹层。
在一个实施方式中，所述根据所述测井资料、所述岩心化验分析资料、所述地震叠加偏移数据、所述地震解释层位数据，确定目标区域薄夹层的高频三维概率体，包括：
根据所述测井资料、所述岩心化验分析资料、所述地震解释层位数据，确定关于薄夹层分布的第一高频率概率体；
根据所述测井资料、所述地震叠加偏移数据、所述地震解释层位数据，确定关于薄夹层分布的第二高频率概率体；
根据所述第一高频率概率体、所述第二高频率体，确定所述薄夹层的高频三维概率体。
在一个实施方式中，根据所述测井资料、所述岩心化验分析资料、所述地震解释层位数据，确定关于薄夹层分布的第一高频率概率体，包括：
根据所述测井资料，通过测井评价，确定夹层的测井响应特征；
根据所述测井资料，通过岩石物理分析，得到岩石物理分析结果数据；
根据所述岩石物理分析结果数据、所述测井响应特征、所述岩心化验分析资料，在目标层位内建立井上夹层分布的概率曲线；
根据所述岩石物理分析结果根据所述夹层的测井响应特征，在目标层位内通过对所述井上夹层分布的概率曲线进行井间内插，获得所述关于薄夹层分布的第一高频率概率体。
在一个实施方式中，根据所述测井资料、所述地震叠加偏移数据、所述地震解释层位数据，确定关于薄夹层分布的第二高频率概率体，包括：
利用所述井上夹层分布的概率曲线，对所述地震叠加偏移数据进行地震波形差异模拟，得到所述关于薄夹层分布的第二高频率概率体。
在一个实施方式中，根据所述第一高频率概率体、所述第二高频率体，确定所述薄夹层的高频三维概率体，包括：
在频率域内，融合所述第一高频概率体和所述第二高频概率体，获得所述薄夹层的高频三维概率体。
在一个实施方式中，以所述薄夹层的高频三维概率体为约束，通过叠前地质统计学反演，确定所述目标区域中的薄夹层，包括：
对所述地震叠前道集数据进行道集处理，得到部分叠加偏移数据和全叠加偏移数据；
根据所述部分叠加偏移数据和全叠加偏移数据，以所述薄夹层的高频三维概率体为约束，通过叠前地质统计学反演，确定所述目标区域中的薄夹层。
在一个实施方式中，所述道集处理包括以下至少之一：
去噪处理、剩余静校正处理、多次波衰减处理、道集拉平处理、道集切除处理、叠加处理。
在一个实施方式中，根据所述部分叠加偏移数据和全叠加偏移数据，以所述薄夹层的高频三维概率体为约束，通过叠前地质统计学反演，确定所述目标区域中的薄夹层，包括：
以所述薄夹层的高频三维概率体为约束，对所述部分叠加偏移数据和全叠加偏移数据进行叠前地质统计学反演，确定反演结果；其中，所述反演结果包括：纵波阻抗数据体、纵横波速度比数据体、密度数据体；
根据所述反演结果，确定所述目标区域中的薄夹层。
在一个实施方式中，在确定所述目标区域中的薄夹层后，所述方法还包括：
根据所述薄夹层，指导对所述目标区域进行页岩气勘探。
本申请实施方式还提供了一种薄夹层的确定装置，包括：
获取模块，用于获取目标区域的测井资料、岩心化验分析资料、地震叠前道集数据、地震叠加偏移数据、地震解释层位数据；
第一确定模块，用于根据所述测井资料、所述岩心化验分析资料、所述地震叠加偏移数据、所述地震解释层位数据，确定薄夹层的高频三维概率体；
第二确定模块，用于以所述薄夹层的高频三维概率体为约束，通过叠前地质统计学反演，确定所述目标区域中的薄夹层。
在一个实施方式中，所述第一确定模块包括：
第一确定单元，用于根据所述测井资料、所述岩心化验分析资料，在目标层位内确定关于薄夹层分布的第一高频率概率体；
第二确定单元，用于根据所述测井资料、所述地震叠加偏移数据、所述地震解释层位数据，确定关于薄夹层分布的第二高频率概率体；
第三确定单元，用于根据所述第一高频率概率体、所述第二高频率体，确定所述薄夹层的高频三维概率体。
在一个实施方式中，所述第二确定模块包括：
处理单元，用于对所述地震叠前道集数据进行道集处理，得到部分叠加偏移数据和全叠加偏移数据；
第四确定单元，用于根据所述部分叠加偏移数据和全叠加偏移数据，以所述薄夹层 的高频三维概率体为约束，通过叠前地质统计学反演，确定所述目标区域中的薄夹层。
在本申请实施方式中，通过综合利用测井数据和地震数据，先确定出分辨率较高、表征效果较好、且能反映出纵向变化趋势的薄夹层的高频三维概率体；再利用上述薄夹层的高频三维概率体作为约束，通过叠前地质统计学反演确定具体的薄夹层，从而解决了现有方法中存在的所确定的薄夹层误差较大、分辨率较低、围绕井点出现画圈现象的技术问题，达到了既可以反映纵向变化趋势特征又可以反映横向变化趋势特征的技术效果，从而使得可以更为精准地确定薄夹层。
附图说明
为了更清楚地说明本申请实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请中记载的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1是根据本申请实施方式提供的薄夹层的确定方法的处理流程图；
图2是根据本申请实施方式提供的薄夹层的确定装置的组成结构图；
图3是在一个场景示例中获取的A井测井曲线回放的示意图；
图4是在一个场景示例中获取的过A井地震叠加偏移数据及合成记录示意图；
图5是在一个场景示例中获取的目标区多井子波示意图；
图6是在一个场景示例中应用常规测井约束的地质统计学获得的反演剖面(上)及平面图(下)示意图；
图7是在一个场景示例中应用本申请实施方式提供的薄夹层的确定方法和装置获得的高频三维概率体的剖面示意图；
图8是在一个场景示例中应用常规地质统计学反演效果图的示意图；
图9是在一个场景示例中应用本申请实施方式提供的薄夹层的确定方法和装置获得的高频三维概率体约束的叠前地质统计学反演示意图；
图10是在一个场景示例中应用本申请实施方式提供的薄夹层的确定方法和装置获得的全区过井最终钙质夹层(或灰岩夹层)概率反演成果剖面示意图；
图11是应用本申请实施方式提供的薄夹层的确定方法和装置获得的目标区域中四种岩性的弹性参数特征的分析示意图；
图12是应用本申请实施方式提供的薄夹层的确定方法和装置获得的目标区域中四 种岩性弹性参数的定量解释模板。
具体实施方式
为了使本技术领域的人员更好地理解本申请中的技术方案，下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都应当属于本申请保护的范围。
考虑到现有方法大多没有充分地将测井数据的优势和地震数据的优势相结合，只是简单地通过二维相控作为约束，进行反演，确定目标区域中的薄夹层。因此，具体实施，往往只能使得反演结果具备横向的变化趋势，无法变现出纵向的变化特征；且所获得的反演结果的分辨率相对较低、井点处容易出现画圈现象，对于薄夹层中厚度较薄的薄夹层(例如，厚度为0.5米至1.5米的薄夹层)识别精度相对较低、误差相对较大。综上可知，现有方法，具体实施时，往往存在所确定薄夹层误差较大、分辨率较低的技术问题。针对产生上述问题的根本原因，本申请考虑可以综合利用测井数据和地震数据，确定出分辨率较高、表征效果较好、且能反映出纵向变化趋势的薄夹层的高频三维概率体；再利用上述薄夹层的高频三维概率体而不是二维低分辨率的数据作为约束，以确定具体的薄夹层，从而解决了现有方法中存在的所确定的薄夹层误差较大、分辨率较低的技术问题，达到可以兼顾反映纵向变化趋势特征、精准地确定薄夹层的技术效果。
基于上述思考思路，本申请实施方式提供了一种薄夹层的确定方法。具体请参阅图1所示的根据本申请实施方式提供的薄夹层的确定方法的处理流程图。本申请实施方式提供的薄夹层的确定方法，具体实施时可以包括以下步骤。
S1：获取目标区域的测井资料、岩心化验分析资料、地震叠前道集数据、地震叠加偏移数据、地震解释层位数据。
在本实施方式中，所述目标区域具体可以是存在页岩层段的区域。其中，页岩气大多会以游离态或者吸附态赋存于富有机质的页岩层段。具体的，在地层条件下，上述页岩层段的基质渗透率一般小于等于0.001×10 -3um 2。通常上述页岩层段以富有基质为主，可以含有碳酸盐岩等材质的薄夹层，也称灰岩薄夹层或钙质夹层。其中，上述薄夹层有利于增强目标区域中页岩气储层的可改造性，有助于页岩气的具体勘探、开发。需要补充的是，上述薄夹层还分为常规薄夹层和超薄的薄夹层(即相对于常规薄夹层厚度更薄 的薄夹层)，其中，超薄的薄夹层的厚度具体可以是0.5米至1.5米。现有的薄夹层确定方法受限于方法本身，导致分辨率较低、精度较差，往往不能准确地识别出上述超薄的薄夹层。此外，识别常规薄夹层也会存在一定误差。本申请实施方式提供的薄夹层的确定方法除了可以适用于确定超薄的薄夹层，也可以适用于确定常规薄夹层。
在本实施方式中，上述测井资料具体可以是一种测井数据。具体实施时，可以通过目标区域中的测井获得。具体的，上述测井资料具体可以包括：测井曲线、测井响应特征参数等。
在本实施方式中，上述地震叠前道集数据具体可以是一种地震数据。具体实施时，可以从目标区域中的地震记录获得。具体的，上述地震叠前道集数据可以一种CRP(common reflection point，共反射点)道集。需要说明的是，上述地震叠前道集数据包括有上述测井数据对应测井所在区域的地震叠前道集数据。
在本实施方式中，上述地震叠加偏移数据具体可以是一种地震数据。具体实施时，可以根据地震数据，获取地震解释成果。上述地震叠加偏移数据具体可以是地震解释成果中的一种数据。需要说明的是，上述地震叠加偏移数据穿过上述测井数据对应测井所在的区域。
在本实施方式中，上述岩心化验分析资料具体可以是对目标区域采集的岩心样品进行具体的岩心化验分析得到的数据资料。上述地震解释层位数据具体可以是一种地震数据，用以表征地震层位的相关信息。
在本实施方式中，需要说明的是，在目标区域中只有有限数量的测井，通过上述测井的测井数据可以较好地反映出测井所在区域的地质结构情况，但无法根据测井数据直接确定没有测井区域的地质结构情况。相对的，通过上述地震数据可以较好反映出目标区域中各个位置的相关情况，但表征的效果没有测井数据精细。因此，在本实施方式中，为了能精确地确定出目标区域中薄夹层，可以将测井数据，例如测井资料，和地震数据，例如地震叠加偏移数据和地震叠前道集数据，相结合，以综合利用两种数据的优点，更加准确地表征出目标区域中各个位置具体的地质情况，精细地确定出目标区域中的薄夹层。
S2：根据所述测井资料、所述岩心化验分析资料、所述地震叠加偏移数据、所述地震解释层位数据，确定薄夹层的高频三维概率体。
在一个实施方式中，为了确定出分辨率较高、表征效果较好、能反映出纵向变化趋势特征、具有三维表征能力的薄夹层的高频三维概率体，具体实施时，上述根据所述测 井资料、所述岩心化验分析资料、所述地震叠加偏移数据、所述地震解释层位数据，确定薄夹层的高频三维概率体可以包括以下内容。
S2-1：根据所述测井资料、所述岩心化验分析资料，确定目标层位内关于薄夹层分布的第一高频率概率体。
在本实施方式中，上述岩心化验分析资料具体可以是指对目标区域采集的岩心样品进行矿物含量的化验分析，得到的用于表征岩性的数据资料。
在本实施方式中，上述用于表征岩性的数据资料，具体可以按照以下方式获取：对目标区域采集的岩心样品进行矿物含量的化验分析，得到岩心样品的矿物含量特征；根据岩心样品的矿物含量特征，对目标区域中页岩目的层段的岩性进行解释，并将目标区域中页岩目的层段的岩性划分为4种岩性，即：脆性页岩、塑性页岩、泥质灰质和灰岩。
在本实施方式中，具体实施时，可以按照以下方式进行岩性划分：将石英矿物含量大于等于50％，且有机碳含量大于等于4％的划分为脆性页岩；将碳酸盐岩矿物含量大于50％、粘土矿物含量小于20％，且有效孔隙度小于2％的岩性划分为灰岩；将碳酸盐岩矿物含量大于50％、粘土含量大于20％，且有效孔隙度大于2％的岩性划分为泥质灰岩；将页岩目的层段中的剩余岩性划分为塑性页岩。
在本实施方式中，在得到了上述用于表征岩性的数据资料后，进一步，还可以根据上述划分出的4种岩性的矿物含量特征，确定出一条岩性曲线，可以记为Litho1。
在本实施方式中，上述测井资料具体可以包括：自然伽马测井曲线、声波时差测井曲线、中子孔隙度曲线、电阻率测井曲线、密度测井曲线等测井曲线。
在一个实施方式中，为了能够较为准确地确定出关于薄夹层分布的第一高频率概率体，具体实施时，可以按照以下内容执行：
S2-1-1：根据所述测井资料，通过测井评价，确定夹层的测井响应特征；
在本实施方式中，上述根据所述测井资料，通过测井评价，确定夹层的测井响应特征，具体可以包括：通过综合比较多种测井曲线的曲线特征，可以确定出以下不同岩性的夹层的测井响应特征：对于岩性为脆性页岩的地层，测井响应特征表现为：自然伽马值高、声波时差值高、中子孔隙度值高、电阻率高和密度低的特征；对于岩性为碳酸盐岩的夹层，测井响应特征表现为：电阻率高、密度高、自然伽马值低、声波时差低和中子孔隙度值低的特征等。
S2-1-2：根据所述测井资料，通过岩石物理分析，得到岩石物理分析结果数据；
在本实施方式中，上述根据所述测井资料，通过岩石物理分析，得到岩石物理分析 结果数据，具体可以包括以下内容：结合测井资料，通过岩石物理(特征)分析，可以发现利用纵波阻抗和纵横波速度比的交汇可以有效地区分出4种岩性，即：碳酸盐岩的夹层是纵波阻抗最大，脆性页岩的地层是纵波速度与横波速度比最小，塑性页岩的地层是纵波速度与横波速度比最大，泥质灰岩的夹层是中值的纵波阻抗和中值的纵波速度与横波速度比，即得到了相应的岩石物理分析结果数据。具体可以参阅图11和图12所示的相关内容。
S2-1-3：根据所述岩石物理分析结果数据、所述测井响应特征、所述岩心化验分析资料，建立井上夹层分布的概率曲线；
在本实施方式中，上述根据所述岩石物理分析结果数据、所述测井响应特征、所述岩心化验分析资料，建立井上夹层分布的概率曲线，具体实施时，可以按照以下方式执行。
对于通过岩心矿物含量定义的岩性曲线Litho1(即岩心化验分析资料)，可以先根据每口井每个岩性近似归一化表示为以下形式：石英含量+粘土含量+碳酸盐岩含量+有机碳含量+孔隙含量＝1。其中，可以用碳酸盐岩含量的数值近似来表示灰岩和泥质灰岩两种岩性的含量概率曲线，碳酸盐岩含量的数值越大表示灰岩概率越大，剩下的脆性页岩和塑性页岩的概率近似为1减去灰岩和泥质灰岩概率后得到的数值；再根据石英矿物含量数据进行归一化处理到上述1减去灰岩与泥质灰岩后得到的概率数值之间的数据，就是脆性页岩和塑性页岩的概率数值。具体计算公式可以表示为以下形式：
Figure PCTCN2018106872-appb-000001
其中，X具体可以表示为脆性页岩或者塑性页岩的概率，上述X的数值越大表示脆性页岩概率越大；S具体可以表示为石英矿物含量的数值，a具体可以表示为石英矿物含量的最小数值，b具体可以表示为石英矿物含量的最大数值；c具体可以表示为1减去灰岩与泥质灰岩概率得到的数值的最小值，d具体可以表示为1减去灰岩与泥质灰岩概率得到的数值的最大值。
这样按照上述计算公式，通过碳酸盐岩矿物含量和石英矿物含量的标准化处理后，可以获取每口井纵向上4种岩性的概率值，具体的，对于每口井可以形成一条岩性概率曲线，即可以建立得到了各个井上夹层分布的概率曲线。具体的，对于通过岩心矿物含量定义的岩性曲线Litho1，也可以用第二种方法，就是根据每个岩性的厚度分别进行归一化处理，即确定每个岩性占该层段总厚度的比例设置为该岩性分布的概率值，再对每 口井纵向上分别计算4种岩性所占层段的概率值，这样每口井也可以形成一条岩性概率曲线。
S2-1-4：根据所述岩石物理分析结果根据所述夹层的测井响应特征，通过对所述井上夹层分布的概率曲线进行井间内插，获得所述关于薄夹层分布的第一高频率概率体。
在本实施方式中，上述井间内插也可以称为井间差值法，具体实施时，可以利用反距离加权或者简单克里金方法进行井间差值，以分别获得目标区域中4种岩性的分布的第一高频概率体，即关于薄夹层分布的第一高频概率体。
S2-2：根据所述测井资料、所述地震叠加偏移数据，确定关于薄夹层分布的第二高频率概率体。
在一个实施方式中，为了根据所述测井资料、所述地震叠加偏移数据，确定关于薄夹层分布的第二高频率概率体，具体实施时，可以按照以下内容执行：根据测井资料，通过井上解释的岩性分布的结果数值化，得到井上夹层分布的岩性结果曲线，即分为4种岩性：脆性页岩、塑性页岩、泥质灰岩和灰岩，分别可以表示为1、2、3、4；进而可以根据上述井上夹层分布的岩性结果曲线，对所述地震叠加偏移数据进行地震波形指示模拟(或称波形差异模拟)，得到所述关于薄夹层分布的第二高频率概率体。
在本实施方式中，具体实施时，可以按照以下方式利用所述井上夹层分布的岩性结果曲线，对所述地震叠加偏移数据进行地震波形指示模拟，得到所述关于薄夹层分布的第二高频率概率体。
根据测井资料、岩石物理分析结果数据，可以在目的层内地震波形数据指导下不断进行寻优。具体的，可以参照井点样本分布距离和地震波形特征两个因素对所有井按关联度排序，优选与预测点关联度较高的井作为初始模型对其高频成分进行无偏最优估计，并保证最终模拟的岩性概率分布与原始的地震波形一致。上述方式的实现机理是基于波形相对变化的井震协同模拟。
具体的，利用地震波形变方差属性作为描述地震波波形变化的特征向量。统计已钻井旁道不同岩性的变方差参数，用来表征井岩性垂向结构变化对地震波形变化的“贡献量”。统计预测道地震波形的特征向量，并利用变方差函数模拟预测道井的岩性概率，以得到上述第二高频率概率体。
例如，对目标区域用已钻井通过井震标定后的纵向上灰岩位置的地震波形的变方差函数作为特征向量，模拟预测道的灰岩的概率。同理，利用用井上泥质灰岩所在位置地震波形的变方差函数作为特征向量，模拟预测道的泥质灰岩的概率。再利用井上脆性页 岩所在位置地震波形的变方差函数作为特征向量，模拟预测道的脆性页岩的概率。最后塑性页岩的概率体是按照以下算式计算，以得到最终的第二高频率概率体：
1-灰岩概率体-泥质灰岩概率体-塑性灰岩概率体。
在本实施方式中，上述变方差，具体可以是指同一时窗内相邻采样点，及前一采样点之间的振幅与该时窗内各采样点的方差之间的关系，可以用于描述振幅变化的量。其计算公式具体可以表示为以下形式：
Figure PCTCN2018106872-appb-000002
Figure PCTCN2018106872-appb-000003
其中，
Figure PCTCN2018106872-appb-000004
具体可以表示为同一时窗间隔为h的两个采样点的振幅差的平方和的均值，N(h)具体可以表示为时窗间隔为h-1的采样点个数，方差S具体可以表示为采样点的振幅偏离其均值的程度，SN具体可以表示为相邻两采样点在同一时窗内的方差，X(i)具体可以表示为编号为i的采样点的振幅值，i具体可以表示为采样点的编号，h具体可以表示为采样间隔。
进一步的，可以按照地震波形特征对已知井岩性分布进行分析，优选与待判别道波形关联度较高的井样本建立初始模型，并统计其岩性结果作为先验信息。在已知井中利用波形相似性、差异性以及空间距离双变量优选波形相似的井作为空间估值样本。再将初始井上的岩性结果与地震波形变方差参数进行匹配滤波，计算得到似然函数。如果两口井的地震波形相似，则表明这两口井大的岩性是相似的，进而可以利用这一特性约束了高频的取值范围，使岩性概率模拟结果确定性更强，从而提高所获取的第二高频率概率体的精确度。
在本实施方式中，考虑到现有方法在建立初始模型时多采用井岩性结果的变差函数，会受井位分布的影响，导致难以精确表征岩性分布的非均质性，而分布密集的地震波形则可以精确表征空间结构和岩性的变化。因此，可以利用地震波形的特征，结合有效样本、平滑半径、目标采样率等数据，通过建立合理的地层格架模型，完成对目标区域中井的合成记录的井震标定，来建立初始模型。
在本实施方式中，有效样本数具体可以是地震波形指示模拟中非常重要的参数之一，主要用于表征地震波形空间变化对岩性的影响程度。该参数的设置主要参照对已知井统 计的结果。具体的，可以在利用“样本数”和“地震相关性”进行统计分析，相关性随着样本数的增加逐渐增大，达到一定程度后相关性不再随着样本数的增加而增加，则表明更多的样本无助于预测精度的提高，其相关性最大时的样本数就是最佳样本参数。其数值通常可以设置为5。此外，该参数也和总样本数有关。通常样本数较大，表明岩性概率变化小，非均质性弱，在横向变化快、非均质性强的地区，可适当减小样本数。
在本实施方式中，上述平滑半径的数值的取值范围为大于等于0，且小于等于5。在本实施方式中，具体可以设置为1。通常平滑半径越大，得到的岩性概率分布数据体横向连续性越好。如果目标区域岩性横向变化快，宽度窄，平滑半径可以为1。
在本实施方式中，如果上述目标采样率的数值越小，模拟岩性概率结果的分辨率越高，数据体越平滑，计算时间越长，井上岩性厚度较薄(例如为0.2ms)，则能满足目标区域中页岩薄夹层的精确预测的需求。
S2-3：根据所述第一高频率概率体、所述第二高频率体，确定所述薄夹层的高频三维概率体。
在一个实施方式中，为了确定出分辨率较高、表征效果较好、且能反映出纵向变化趋势的薄夹层的高频三维概率体，具体实施时，根据所述第一高频率概率体、所述第二高频率体，确定所述薄夹层的高频三维概率体，可以包括以下内容：在频率域内，通过全局克里金法，融合所述第一高频概率体和所述第二高频概率体，获得所述薄夹层的高频三维概率体。
在一个实施方式中，上述融合所述第一高频概率体和所述第二高频概率体，获得所述薄夹层的高频三维概率体，具体实施时可以包括以下内容：根据第一高频概率体的频率，结合目标区域地层的起伏特征，确定第一高频概率体的第一权重；根据第二高频概率体的频率，结合目标区域地层的起伏特征，确定第二高频概率体的第二权重；将第一高频概率体与第一权重的乘积、第二高频概率体与第二权重的乘积组合，得到所述薄夹层的高频三维概率体。即完成了对所述第一高频概率体和所述第二高频概率体的融合。后续可以利用上述融合了所述第一高频概率体和所述第二高频概率体得到的结果数据为约束条件，更好地通过反演获得高分辨率纵波阻抗和纵横波速度比等弹性参数数据。
在本实施方式中，上述目标区域地层的起伏特征具体可以根据叠加偏移数据确定。具体的，当所确定的目标区域地层的起伏特征表征的地层起伏程度相对较大时，可以适当地增大第二权重的具体数值；相应的，当所确定的目标区域地层的起伏特征表征的地层起伏程度相对较小时，可以适当地减小第二权重的具体数值。在增大第二权重的具体 数值的同时，还可以适当地减小第一权重的具体数值；相应的，在减小第二权重的具体数值的同时，还可以适当地增大第一权重的具体数值。具体的，例如，当所确定的目标区域地层的起伏特征表征的地层起伏程度非常大，超过阈值时，第一权重可以取值为0，第二权重可以取值为1。在这种情况下，融合第一高频概率体和第二高频概率体得到的薄夹层的高频三维概率体就相当于单独使用的第二高频概率体。
在本实施方式中，具体实施时，考虑到第一高频率概率体是通过井间插值获得的，当地层是水平地层，倾角较小，井数较多时，地层形态变化不大(即地层起伏程度相对较小)，可以使用第一高频概率体；当地层形态变化较大时(即地层起伏程度相对较大)，则可以使用第二高频率概率体，因为，第二高频率概率体是考虑了横向地震波形的变化特征进行井间模拟实现的。当然，根据具体情况也可以将这两个高频率概率体进行频率域数据体合并，以进一步提高模拟的岩性概率体的效果。
在本实施方式中，通过上述方法获得薄夹层的高频三维概率体，相较于第一高频概率体、第二高频概率体，与测井的井点一致性相对较好，也真实的反应地层环境中薄夹层的空间变化情况，从而较好地综合了测井数据、地震数据等不同数据的优势，不但能反映横向的变化趋势特征，还能反映出纵向的变化趋势特征，具有较高的分辨率，能够较为准确、精细地反映出所在区域中地层的具体结构情况。
S3：以所述薄夹层的高频三维概率体为约束，通过叠前地质统计学反演，确定所述目标区域中的薄夹层。
在一个实施方式中，为了准确地确定出目标区域中的薄夹层，具体实施时，上述以所述薄夹层的高频三维概率体为约束，通过叠前地质统计学反演，确定所述目标区域中的薄夹层，具体可以包括以下内容：
S3-1：对所述地震叠前道集数据进行道集处理，得到部分叠加偏移数据和全叠加偏移数据；
S3-2：根据所述部分叠加偏移数据和全叠加偏移数据，以所述薄夹层的高频三维概率体为约束，通过叠前地质统计学反演，确定所述目标区域中的薄夹层。
在一个实施方式中，为了能够得到符合施工要求的部分叠加偏移数据和全叠加偏移数据，具体实施时，所述道集处理具体可以包括以下至少之一：去噪处理、剩余静校正处理、多次波衰减处理、道集拉平处理、道集切除处理、叠加处理等。当然，需要说明的是，上述所列举的几种道集处理方式只是为了更好地说明本申请实施方式，具体实施时，也可以根据具体情况和施工要求引入其他类型的道集处理方式。
在一个实施方式中，为了能较为精准地确定出薄夹层，上述根据所述部分叠加偏移数据和全叠加偏移数据，以所述薄夹层的高频三维概率体为约束，通过叠前地质统计学反演，确定所述目标区域中的薄夹层，具体实施时，可以包括以下内容：
S3-2-1：以所述薄夹层的高频三维概率体为约束，对所述部分叠加偏移数据和全叠加偏移数据进行叠前地质统计学反演，确定反演结果；其中，所述反演结果包括：纵波阻抗数据体、纵横波速度比数据体、密度数据体；
在本实施方式中，上述叠前地质统计学反演具体可以是一种将传统的地质统计学模拟技术与贝叶斯推论、马尔科夫链的蒙特卡洛模拟以及叠前同时反演技术结合起来，以获得一系列高分辨率的油藏属性的实现方式。这种实现方式通过综合多种信息源(包括：地质、地震、测井信息等)来合理预测储层模型的不确定的解集空间。利用叠前地质统计学反演可以有效提高反演分辨率，较准确地描述出储层岩性的空间分布及物性的分布，较可靠地刻画出储层岩性和物性的空间展布。
具体实施时，考虑到上述叠前地质统计学反演实施的关键是反演得到的纵波阻抗与横波阻抗与子波褶积所得到的合成记录要与地震数据相匹配。因此在进行叠前地质统计学反演之前，需要做一个确定性反演，以保证地震信息与测井信息的高度统一。此外，如果输入的地震数据为部分角度叠加体，则需要选择使用全Zoeppritz方程进行叠前地质统计学反演。在进行高分辨率的岩性反演时，可以应用马尔科夫链-蒙特卡洛模拟同时生成高分辨率的岩石弹性参数体，然后合成各个偏移距的地震记录并与实际地震数据进行对比，以此控制单个岩性实现的合理性。最后获得高分辨率的岩性概率体，概率越大，就是该岩性越可靠。具体可以参阅图9和图10所示内容。
在本实施方式中，叠前地质统计学反演具体实施时，可以包括以下步骤：
S1：对部分叠加偏移数据进行质量控制。具体的，可以是在给定时窗内，根据参考层位把近、中、远不同偏移距的地震数据体对齐校正。
S2：通过井编辑和井震标定，为不同偏移距的地震数据体(即全叠加偏移数据)制作合成地震记录，并进行井震标定，以获得近、中、远不同偏移距数据体多井的综合子波。
S3；建立低频模型。具体的，可以以地震层位为约束，井上选取纵波阻抗、横波阻抗、密度曲线，建立地层的格架模型。
S4：进行地质统计学反演，具体实施时，可以包括以下内容：
S4-1：划分岩性。具体可以通过上述多井岩性的分析，确定目标区域地质统计学反 演的岩性为四类：脆性页岩、塑性页岩、泥质灰岩、和灰岩。
S4-2：确定岩性百分比。在划分岩性的基础上，常规做法是统计四种岩性所占的百分比，这也是地质统计学重要参数之一。在本实施方式中，具体实施时，可以先用统计的岩性比例进行第一轮反演，得到测试参数和反演效果；再用一维统计的岩性比例曲线进行第二轮反演，得到测试参数和反演效果；最后用四种岩性的高频三维概率体代替常规的岩性比例作为约束条件，进行反演得到反演结果。需要说明的是，这里使用前面获得的三维高频率概率体代替常规的岩性比例作为约束条件进行地质统计学反演，可以大幅度提高反演的分辨率。
S4-3：根据反演结果，统计每种岩性的概率密度分布函数，对于目标区域中的四种岩性变差函数的类型可以选择高斯型分布的概率密度函数，纵向变程和横向变程可以根据叠后确定性反演结果估计一个大概范围。
S4-4：统计每种岩性的变差函数，通过大量的系统测试分析，可得到每种岩性的纵向Z和横向X、Y三个方向的变程以及拟合参数。
S4-5：测试信噪比参数，可以获得近、中、远偏移距地震数据体的信噪比数据。
完成上述步骤后，可以设置好地质统计学反演参数，进行地质统计学反演，得到反演结果，从而获得纵波阻抗、横波阻抗、纵波速度、横波速度、密度以及纵横波速度比数据体等。
S3-2-2：根据所述反演结果，确定所述目标区域中的薄夹层。
在本实施方式中，上述反演结果具体可以包括：纵波阻抗、纵横波速度比、密度等参数数据体。当然需要说明的是，上述所列举的参数数据只是为了更好地说明本申请实施方式，具体实施时，也可以根据具体情况和施工要求引入其他相关的参数数据，作为上述反演结果。对此，本申请不作限定。
在本实施方式中，上述根据所述反演结果，确定所述目标区域中的薄夹层，具体实施时，可以包括：通过利用目标区域中薄夹层岩石物理分析结果，确定目标区域中灰岩薄夹层是高纵波阻抗、高纵横波速度比、高密度的特征；利用叠前地质统计学反演得到的反演结果，可以获得的纵波阻抗数据体、纵横波速度比数据体，对这两种数据体进行交汇分析，得到交汇分析结果；根据交汇分析结果和薄夹层的特征可以进一步刻画出高的纵波阻抗和高纵横波比的数据体，为灰岩薄夹层分布的数据体，从而确定出目标区域中的薄夹层。
在本实施方式中，需要说明的是，基于利用上述分辨率较高、表征效果较好、且能 反映出纵向变化趋势的薄夹层的高频三维概率体作为约束，通过反演得到的反演结果具有较高的纵向分辨率，且能更加符合空间沉积规律，因此能较好地表征出所处地层中具体的地层结构情况。进而可以利用上述反演结果，精准地识别、确定出目标区域中的薄夹层。
在本申请实施例中，相较于现有技术，通过综合利用测井数据和地震数据，确定出分辨率较高、表征效果较好、且能反映出纵向变化趋势的薄夹层的高频三维概率体；再利用上述薄夹层的高频三维概率体作为约束，通过叠前地质统计学反演以确定出具体的薄夹层，从而解决了现有方法中存在的所确定的薄夹层误差较大、分辨率较低、出现井点处画圈现象的技术问题，达到了既可以反映纵向变化趋势特征又可以反映横向变化趋势特征的技术效果，从而使得可以更为精准地确定薄夹层。
在一个实施方式中，为了对目标区域进行具体的页岩气的勘探开发，在确定所述目标区域中的薄夹层后，所述方法具体还可以包括以下内容：根据所述薄夹层，指导对所述目标区域进行页岩气勘探。当然，需要说明的是，利用薄夹层作为参考依据指导具体页岩气勘探只是薄夹层的具体用途之一，具体实施时，还可以根据具体情况，利用所确定的薄夹层进行其他相应的地球物理勘探。对此，本申请不作限定。
从以上的描述中，可以看出，本申请实施方式提供的薄夹层的确定方法，通过综合利用测井数据和地震数据，确定出分辨率较高、表征效果较好、且能反映出纵向变化趋势的薄夹层的高频三维概率体；再利用上述薄夹层的高频三维概率体作为约束，通过叠前地质统计学反演以确定出具体的薄夹层，从而解决了现有方法中存在的所确定的薄夹层误差较大、分辨率较低的技术问题，达到了既可以反映纵向变化趋势特征又可以反映横向变化趋势特征的技术效果，从而使得可以更为精准地确定薄夹层；又通过分别根据测井数据和地震数据分别确定出关于薄夹层分布的第一高频率概率体、关于薄夹层分布的第二高频率概率体，再将上述两种高频概率体进行融合，以较好地综合地震数据和测井数据的梯度，确定出表征效果较好、能反映纵向变化特征的薄夹层的高频三维概率体，以用于改善后续确定薄夹层的准确度。
基于同一发明构思，本发明实施方式中还提供了一种薄夹层的确定装置，如下面的实施方式所述。由于装置解决问题的原理与薄夹层的确定方法相似，因此薄夹层的确定装置的实施可以参见方法的实施，重复之处不再赘述。以下所使用的，术语“单元”或 者“模块”可以实现预定功能的软件和/或硬件的组合。尽管以下实施例所描述的装置较佳地以软件来实现，但是硬件，或者软件和硬件的组合的实现也是可能并被构想的。请参阅图2，是本申请实施方式的薄夹层的确定装置的一种组成结构图，该装置可以包括：获取模块21、第一确定模块22、第二确定模块23，下面对该结构进行具体说明。
获取模块21，具体可以用于获取目标区域的测井资料、岩心化验分析资料、地震叠前道集数据、地震叠加偏移数据。
第一确定模块22，具体可以用于根据所述测井资料、所述岩心化验分析资料、所述地震叠加偏移数据、地震解释层位数据，确定薄夹层的高频三维概率体。
第二确定模块23，具体可以用于以所述薄夹层的高频三维概率体为约束，通过叠前地质统计学反演，确定所述目标区域中的薄夹层。
在一个实施方式中，为了能够根据所述测井资料、所述岩心化验分析资料、所述地震叠加偏移数据、所述地震解释层位数据，确定薄夹层的高频三维概率体，所述第一确定模块22具体可以包括以下结构单元：
第一确定单元，具体可以用于根据所述测井资料、所述岩心化验分析资料，确定关于薄夹层分布的第一高频率概率体；
第二确定单元，具体可以用于根据所述测井资料、所述地震叠加偏移数据，确定关于薄夹层分布的第二高频率概率体；
第三确定单元，具体可以用于根据所述第一高频率概率体、所述第二高频率体，确定所述薄夹层的高频三维概率体。
在一个实施方式中，为了能够根据所述测井资料、所述岩心化验分析资料，确定关于薄夹层分布的第一高频率概率体，上述第一确定单元具体可以包括以下的结构子单元：
第一确定子单元，具体可以用于根据所述测井资料，通过测井评价，确定夹层的测井响应特征；
岩石物理分析子单元，具体可以用于根据所述测井资料，通过岩石物理分析，得到岩石物理分析结果数据；
建立子单元，具体可以用于根据所述岩石物理分析结果数据、所述测井响应特征、所述岩心化验分析资料，建立井上夹层分布的概率曲线；
内插子单元，具体可以用于根据所述夹层的测井响应特征，通过对所述井上夹层分布的概率曲线进行井间内插，获得所述关于薄夹层分布的第一高频率概率体。
在一个实施方式中，为了能够根据所述测井资料、所述地震叠加偏移数据，确定关 于薄夹层分布的第二高频率概率体，上述第二确定单元具体实施时，可以利用所述井上夹层分布的概率曲线，对所述地震叠加偏移数据进行地震波形指示模拟(即差异模拟)，得到所述关于薄夹层分布的第二高频率概率体。
在一个实施方式中，为了能够根据所述第一高频率概率体、所述第二高频率体，确定所述薄夹层的高频三维概率体，上述第三确定单元具体实施时，可以在频率域内，通过全局克里金法，融合所述第一高频概率体和所述第二高频概率体，获得所述薄夹层的高频三维概率体。
在一个实施方式中，为了能够根据所述地震叠前道集数据，以所述薄夹层的高频三维概率体为约束，通过叠前地质统计学反演，确定所述目标区域中的薄夹层，上述第二确定模块23具体可以包括以下的结构单元：
处理单元，具体可以用于对所述地震叠前道集数据进行道集处理，得到部分叠加偏移数据和全叠加偏移数据；
第四确定单元，具体可以用于根据所述部分叠加偏移数据和全叠加偏移数据，以所述薄夹层的高频三维概率体为约束，通过叠前地质统计学反演，确定所述目标区域中的薄夹层。
在一个实施方式中，所述道集处理具体可以包括以下至少之一：去噪处理、剩余静校正处理、多次波衰减处理、道集拉平处理、道集切除处理、叠加处理等。
在一个实施方式中，为了能够根据所述部分叠加偏移数据和全叠加偏移数据，以所述薄夹层的高频三维概率体为约束，通过叠前地质统计学反演，确定所述目标区域中的薄夹层，上述第四确定单元具体可以包括以下的结构子单元：
反演子单元，具体可以用于以所述薄夹层的高频三维概率体为约束，对所述部分叠加偏移数据和全叠加偏移数据进行叠前地质统计学反演，确定反演结果；其中，所述反演结果包括：纵波阻抗数据体、纵横波速度比数据体、密度数据体；
第二确定子单元，具体可以用于根据所述反演结果，确定所述目标区域中的薄夹层。
在一个实施方式中，为了能够对目标区域进行具体的页岩气勘探、开发，具体实施时，上述装置还可以包括施工模块，具体可以用于根据所述薄夹层，指导对所述目标区域进行页岩气勘探。
本说明书中的各个实施例均采用递进的方式描述，各个实施例之间相同相似的部分互相参见即可，每个实施例重点说明的都是与其他实施例的不同之处。尤其，对于系统实施例而言，由于其基本相似于方法实施例，所以描述的比较简单，相关之处参见方法 实施例的部分说明即可。
需要说明的是，上述实施方式阐明的系统、装置、模块或单元，具体可以由计算机芯片或实体实现，或者由具有某种功能的产品来实现。为了描述的方便，在本说明书中，描述以上装置时以功能分为各种单元分别描述。当然，在实施本申请时可以把各单元的功能在同一个或多个软件和/或硬件中实现。
此外，在本说明书中，诸如第一和第二这样的形容词仅可以用于将一个元素或动作与另一元素或动作进行区分，而不必要求或暗示任何实际的这种关系或顺序。在环境允许的情况下，参照元素或部件或步骤(等)不应解释为局限于仅元素、部件、或步骤中的一个，而可以是元素、部件、或步骤中的一个或多个等。
从以上的描述中，可以看出，本申请实施方式提供的薄夹层的确定装置，本申请实施方式提供的薄夹层的确定方法，通过综合利用测井数据和地震数据，确定出分辨率较高、表征效果较好、且能反映出纵向变化趋势的薄夹层的高频三维概率体；再利用上述薄夹层的高频三维概率体作为约束，通过叠前地质统计学反演以确定出具体的薄夹层，从而解决了现有方法中存在的所确定的薄夹层误差较大、分辨率较低的技术问题，达到了既可以反映纵向变化趋势特征又可以反映横向变化趋势特征的技术效果，从而使得可以更为精准地确定薄夹层；又通过第一确定单元、第二确定单元分别根据测井数据和地震数据以确定出测井的关于薄夹层分布的第一高频率概率体、关于薄夹层分布的第二高频率概率体，再利用第三确定单元将上述两种高频概率体进行融合，以较好地综合地震数据和测井数据的梯度，确定出表征效果较好、能反映纵向变化特征的薄夹层的高频三维概率体，以用于改善后续确定薄夹层的准确度。
在一个具体实施场景示例中，应用本申请提供薄夹层的确定方法和装置对某目标区域中的薄夹层进行具体的识别、确定。具体实施过程，可以参照以下内容执行。
S1：获取某目标区域中的测井资料、地震叠加偏移数据和地震叠前道集数据。
在本实施方式中，所选定的测井具体可以为A井。相应的，所获取的相关测井资料、地震叠加偏移数据可以参阅图3所示的在一个场景示例中获取的A井测井曲线回放示意图，具体的，图中第1道为地质分层，第2道(CAL为伽马能谱、光电吸收指数等岩性曲线道，第3道(MD)为测量深度；第4道(RES)为深、浅电阻率等电性曲线；第5道(XRD)为全岩分析成果剖面，第6道(FRAC)为StatMin最优化计算岩性成分成果剖面，第7道(VCL)为泥质含量曲线(第7-13道中，实线是计算的曲线，园点是岩心分 析结果)；第8道(QUA)为石英含量曲线；第9道(CAR)为碳酸盐岩含量曲线；第10道(PYR)为黄铁矿含量曲线；第11道(TOC)为TOC含量曲线；第12道(POR)为孔隙度曲线；第13道(SW)为含水饱和度曲线；最后一道(LITH)为划分的岩相成果，shale代表泥岩，也可称为塑性页岩、sweet spot代表页岩气层，也可称为脆性页岩，即甜点，limestone代表灰岩，shaly limestone代表泥质灰岩。通过具体分析图3中的数据可知：测井所在区域中的薄夹层分布的位置分别有：3197.65-3198.73米、3207.25-3208.77、3209.12-4310.6米，且上述碳酸盐岩薄夹层较页岩层段具有相对较高的电阻率、较高的密度、较高的阻抗等典型特征。图4所示的在一个场景示例中获取的过A井地震叠加偏移数据及合成记录示意图中的数据，可知：通过地震数据(即地震剖面)对于A测井所在的区域，由于页岩层段整体波阻抗相对较低(表现为：上下围岩均为高阻抗灰岩)，因此在地震剖面上的反射为强能量的一谷一峰特征，而薄夹层由于相对较薄，在地震剖面上无法直接识别、确定。结合参阅图5所示的在一个场景示例中获取的研究区多井子波示意图，由于该区域中薄夹层的厚度远低于地震分辨率的1/4波长(1/4.6波长)，通过全区域中钻井提取的子波(其中，子波主频约为21Hz，频宽5-38Hz，相位接近零相位)可知：目的层段速度为5100米，38HZ对应1/4波长为33.6米，1/8波长为16.8米，而薄夹层单层的厚度为0.5-1.5米，测井中薄夹层累计厚度<8.5米。因此，可以判断常规储层预测手段，即现有方法无法对上述较薄的薄夹层进行有效预测。
在本实施方式中，进一步地，可以采用常规手段，即现有方法进行对照试验。具体的，可以参阅图6所示的在一个场景示例中应用常规测井约束的地质统计学获得的反演剖面(上)及平面图(下)示意图，可知：针对常规薄层(例如，1/16波长<厚度<1/8波长)预测，单靠测井约束反演(即单独依据测井资料作为约束进行反演)能取得一定的确定效果，但存在一个无法逾越的问题：由于变程的影响，反演结果会存在牛眼的画圈现象，使得反演结果预测的可靠性降低。
S2：根据所述测井资料、所述岩心化验分析资料、所述地震叠加偏移数据，确定薄夹层的高频三维概率体。
在本实施方式中，考虑到叠前地质统计学反演具有高辨率特点，可以较好融合测井、岩石物理、沉积特征等信息，得到纵波阻抗、纵横波速度比、密度等多个弹性参数。因此，可以考虑使用高分辨率三维概率体(即薄夹层的高频三维概率体)作为输入，以便开展叠前地质统计学反演，其反演结果可以较好地识别复杂岩性，且较好解决了传统一维、二维约束的不足，达到提高纵向分辨率，较好匹配井点数据，不易受变程参数的影 响，井点处无牛眼等现象的效果。
在本实施方式中，为了确定出上述三维概率体，具体实施时，可以按照以下内容执行：
S2-1：对上述测井资料进行测井评价及岩石物理分析。
此步骤主要开展测井一致性处理、测井曲线校正、多井测井评价等工作，得到一致性规律较好的测井曲线及反映沉积特点的测井评价成果(即测井的夹层概率响应特征、测井的储层概率响应特征)，具体的，可以包括：泥质含量、钙质含量(即灰岩含量)、孔隙度、脆性等关键性参数；进而可以建立关于薄夹层分布的第一高频概率体。
S2-2：道集处理及AVA子波提取。
此步骤主要针对原始CRP道集开展道集处理、角度计算、分辨移距叠加等处理，其中，道集处理具体可以包括：去噪处理、剩余静校正、多次波衰减等；在此基础上，再结合测井曲线，进行AVA子波提取(相当于确定部分偏移叠加数据体)，为后续开展叠前反演奠定基础。
S2-3：三维地震反演及属性分析(相当于根据所述测井资料、所述地震叠加偏移数据，确定关于薄夹层分布的第二高频率概率体)。
此步骤主要是通过开展常规的叠后波阻抗反演及属性分析，得到与灰岩分布(即钙质分布)规律相一致的初始模型，即关于薄夹层分布的第二高频率概率体。
S2-4：建立针对薄夹层分布概率的三维体模型(相当于根据所述第一高频率概率体、所述第二高频率体，确定所述薄夹层的高频三维概率体)。
此步骤主要目的是为了建立初始的灰岩薄夹层三维概率模型(即薄夹层的高频三维概率体)，具体的，可以利用多井的测井评价成果，结合常规地震反演成果及属性，利用全局克里金方法开展三维体建模，得到与井点一致性较好的符合空间变化的灰岩薄夹层三维概率模型。获得该结果数据相对较合理，并能最大化地融合测井、地质等多重信息，具有纵向分辨率高、空间规律合理的特点。具体的，可以参阅图7所示的在一个场景示例中应用本申请实施方式提供的薄夹层的确定方法和装置获得的三维概率体的剖面示意图。
S3：以所述薄夹层的高频三维概率体为约束，通过叠前地质统计学反演，确定所述目标区域中的薄夹层。
此步骤与以往常规叠前地质统计反演的不同点在于，引入了灰岩夹层三维概率体(即薄夹层的高频三维概率体)，具体实施时，此概率体可以作为类似“砂地比”属性的数 据，作为一种高分辨率的软约束条件，进而作为地质统计学的输入，通过叠前地质统计学反演可以得：纵向分辨率高、符合空间沉积规律叠的前地质统计学反演成果(即第二反演结果)。该反演成果具体可以包括：纵波阻抗、纵横波速度比、密度等参数。再通过结合岩石物理研究认识开展解释，可以得到高分辨率的灰岩夹层，以及进一步确定出甜点分布、脆性等多种参数。
将应用本申请提供的薄夹层的确定方法和装置所确定的结果与常规方法所获取的结果进行具体的比较分析。参阅图8所示的在一个场景示例中应用常规地质统计学反演效果图的示意图和图9所示的在一个场景示例中应用本申请实施方式提供的薄夹层的确定方法和装置获得的三维概率体约束的叠前地质统计学反演示意图，可知：后者较好融合了测井与地震信息，其结果分辨率程度高，空间规律较为合理，能将灰岩夹层(即薄夹层)进行较好的刻画。结合图10所示的在一个场景示例中应用本申请实施方式提供的薄夹层的确定方法和装置获得的全区过井最终灰岩夹层概率反演成果剖面示意图，可知：反演成果与钻井的吻合率高，能较好地刻画出页岩层段内的灰岩夹层分布，做到了测井与地震信息的有机统一。
总得来说，应用本申请提供的薄夹层的确定方法和装置可较好解决页岩气勘探中薄夹层的预测问题，且具有操作性与适用性强的特点，尤其是针对类似的海相超薄泥包砂地区的中薄夹层，效果尤为明显。
通过上述场景示例，验证了本申请实施方式提供的薄夹层的确定方法和装置，通过综合利用测井数据和地震数据，确定出分辨率较高、表征效果较好、且能反映出纵向变化趋势的薄夹层的高频三维概率体；再利用上述薄夹层的高频三维概率体作为约束，通过叠前地质统计学反演以确定出具体的薄夹层，确实解决了现有方法中存在的所确定的薄夹层误差较大、分辨率较低的技术问题，达到了既可以反映纵向变化趋势特征又可以反映横向变化趋势特征的技术效果，从而使得可以更为精准地确定薄夹层。
尽管本申请内容中提到不同的具体实施方式，但是，本申请并不局限于必须是行业标准或实施例所描述的情况等，某些行业标准或者使用自定义方式或实施例描述的实施基础上略加修改后的实施方案也可以实现上述实施例相同、等同或相近、或变形后可预料的实施效果。应用这些修改或变形后的数据获取、处理、输出、判断方式等的实施例，仍然可以属于本申请的可选实施方案范围之内。
虽然本申请提供了如实施例或流程图所述的方法操作步骤，但基于常规或者无创造 性的手段可以包括更多或者更少的操作步骤。实施例中列举的步骤顺序仅仅为众多步骤执行顺序中的一种方式，不代表唯一的执行顺序。在实际中的装置或客户端产品执行时，可以按照实施例或者附图所示的方法顺序执行或者并行执行(例如并行处理器或者多线程处理的环境，甚至为分布式数据处理环境)。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、产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、产品或者设备所固有的要素。在没有更多限制的情况下，并不排除在包括所述要素的过程、方法、产品或者设备中还存在另外的相同或等同要素。
上述实施例阐明的装置或模块等，具体可以由计算机芯片或实体实现，或者由具有某种功能的产品来实现。为了描述的方便，描述以上装置时以功能分为各种模块分别描述。当然，在实施本申请时可以把各模块的功能在同一个或多个软件和/或硬件中实现，也可以将实现同一功能的模块由多个子模块的组合实现等。以上所描述的装置实施例仅仅是示意性的，例如，所述模块的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个模块或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。
本领域技术人员也知道，除了以纯计算机可读程序代码方式实现控制器以外，完全可以通过将方法步骤进行逻辑编程来使得控制器以逻辑门、开关、专用集成电路、可编程逻辑控制器和嵌入微控制器等的形式来实现相同功能。因此这种控制器可以被认为是一种硬件部件，而对其内部包括的用于实现各种功能的装置也可以视为硬件部件内的结构。或者甚至，可以将用于实现各种功能的装置视为既可以是实现方法的软件模块又可以是硬件部件内的结构。
本申请可以在由计算机执行的计算机可执行指令的一般上下文中描述，例如程序模块。一般地，程序模块包括执行特定任务或实现特定抽象数据类型的例程、程序、对象、组件、数据结构、类等等。也可以在分布式计算环境中实践本申请，在这些分布式计算环境中，由通过通信网络而被连接的远程处理设备来执行任务。在分布式计算环境中，程序模块可以位于包括存储设备在内的本地和远程计算机存储介质中。
通过以上的实施方式的描述可知，本领域的技术人员可以清楚地了解到本申请可借助软件加必需的通用硬件平台的方式来实现。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品可以存储在存储介质中，如ROM/RAM、磁碟、光盘等，包括若干指令用以使得一台 计算机设备(可以是个人计算机，移动终端，服务器，或者网络设备等)执行本申请各个实施例或者实施例的某些部分所述的方法。
本说明书中的各个实施例采用递进的方式描述，各个实施例之间相同或相似的部分互相参见即可，每个实施例重点说明的都是与其他实施例的不同之处。本申请可用于众多通用或专用的计算机系统环境或配置中。例如：个人计算机、服务器计算机、手持设备或便携式设备、平板型设备、多处理器系统、基于微处理器的系统、置顶盒、可编程的电子设备、网络PC、小型计算机、大型计算机、包括以上任何系统或设备的分布式计算环境等等。
虽然通过实施例描绘了本申请，本领域普通技术人员知道，本申请有许多变形和变化而不脱离本申请的精神，希望所附的实施方式包括这些变形和变化而不脱离本申请。

Claims (12)

  1. 一种薄夹层的确定方法，其特征在于，包括：
    获取目标区域的测井资料、岩心化验分析资料、地震叠前道集数据、地震叠加偏移数据、地震解释层位数据；
    根据所述测井资料、所述岩心化验分析资料、所述地震叠加偏移数据、所述地震解释层位数据，确定薄夹层的高频三维概率体；
    根据所述地震叠前道集数据，以所述薄夹层的高频三维概率体为约束，通过叠前地质统计学反演，确定所述目标区域中的薄夹层。
  2. 根据权利要求1所述的方法，其特征在于，所述根据所述测井资料、所述岩心化验分析资料、所述地震叠加偏移数据、所述地震解释层位数据，确定薄夹层的高频三维概率体，包括：
    根据所述测井资料、所述岩心化验分析资料、所述地震解释层位数据，确定关于薄夹层分布的第一高频率概率体；
    根据所述测井资料、所述地震叠加偏移数据、所述地震解释层位数据，确定关于薄夹层分布的第二高频率概率体；
    根据所述第一高频率概率体、所述第二高频率体，确定所述薄夹层的高频三维概率体。
  3. 根据权利要求2所述的方法，其特征在于，根据所述测井资料、所述岩心化验分析资料、所述地震解释层位数据，确定关于薄夹层分布的第一高频率概率体，包括：
    根据所述测井资料，通过测井评价，确定夹层的测井响应特征；
    根据所述测井资料，通过岩石物理分析，得到岩石物理分析结果数据；
    根据所述岩石物理分析结果数据、所述测井响应特征、所述岩心化验分析资料，建立井上夹层分布的概率曲线；
    根据所述夹层的测井响应特征，通过对所述井上夹层分布的概率曲线在目标层位内进行井间内插，获得所述关于薄夹层分布的第一高频率概率体。
  4. 根据权利要求3所述的方法，其特征在于，根据所述测井资料、所述地震叠加偏移数据、所述地震解释层位数据，确定关于薄夹层分布的第二高频率概率体，包括：
    利用所述井上夹层分布的概率曲线，对所述地震叠加偏移数据在目标层位内进行地震波形差异模拟，得到所述关于薄夹层分布的第二高频率概率体。
  5. 根据权利要求2所述的方法，其特征在于，所述根据所述第一高频率概率体、所 述第二高频率体，确定所述薄夹层的高频三维概率体，包括：
    在频率域内，通过全局克里金法，融合所述第一高频概率体和所述第二高频概率体，获得所述薄夹层的高频三维概率体。
  6. 根据权利要求1所述的方法，其特征在于，根据所述地震叠前道集数据，以所述薄夹层的高频三维概率体为约束，通过叠前地质统计学反演，确定所述目标区域中的薄夹层，包括：
    对所述地震叠前道集数据进行道集处理，得到部分叠加偏移数据和全叠加偏移数据；
    根据所述部分叠加偏移数据和全叠加偏移数据，以所述薄夹层的高频三维概率体为约束，通过叠前地质统计学反演，确定所述目标区域中的薄夹层。
  7. 根据权利要求6所述的方法，其特征在于，所述道集处理包括以下至少之一：
    去噪处理、剩余静校正处理、多次波衰减处理、道集拉平处理、道集切除处理、部分叠加处理。
  8. 根据权利要求6所述的方法，其特征在于，根据所述部分叠加偏移数据和全叠加偏移数据，以所述薄夹层的高频三维概率体为约束，通过叠前地质统计学反演，确定所述目标区域中的薄夹层，包括：
    以所述薄夹层的高频三维概率体为约束，对所述部分叠加偏移数据和全叠加偏移数据进行叠前地质统计学反演，确定反演结果；其中，所述反演结果包括：纵波阻抗数据体、纵横波速度比数据体、密度数据体；
    根据所述反演结果，确定所述目标区域中的薄夹层。
  9. 根据权利要求1所述的方法，其特征在于，在确定所述目标区域中的薄夹层后，所述方法还包括：
    根据所述薄夹层，指导所述目标区域的页岩气勘探。
  10. 一种薄夹层的确定装置，其特征在于，包括：
    获取模块，用于获取目标区域的测井资料、岩心化验分析资料、地震叠前道集数据、地震叠加偏移数据、地震解释层位数据；
    第一确定模块，用于根据所述测井资料、所述岩心化验分析资料、所述地震叠加偏移数据、所述地震解释层位数据，确定薄夹层的高频三维概率体；
    第二确定模块，用于根据所述地震叠前道集数据，以所述薄夹层的高频三维概率体为约束，通过叠前地质统计学反演，确定所述目标区域中的薄夹层。
  11. 根据权利要求10所述的装置，其特征在于，所述第一确定模块包括：
    第一确定单元，用于根据所述测井资料、所述岩心化验分析资料、所述地震解释层位数据，确定关于薄夹层分布的第一高频率概率体；
    第二确定单元，用于根据所述测井资料、所述地震叠加偏移数据、所述地震解释层位数据，确定关于薄夹层分布的第二高频率概率体；
    第三确定单元，用于根据所述第一高频率概率体、所述第二高频率体，确定所述薄夹层的高频三维概率体。
  12. 根据权利要求10所述的装置，其特征在于，所述第二确定模块包括：
    处理单元，用于对所述地震叠前道集数据进行道集处理，得到部分叠加偏移数据和全叠加偏移数据；
    第四确定单元，用于根据所述部分叠加偏移数据和全叠加偏移数据，以所述薄夹层的高频三维概率体为约束，通过叠前地质统计学反演，确定所述目标区域中的薄夹层。
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CA3076280C (en) 2023-07-18
CA3076280A1 (en) 2019-04-04
AU2018340369A1 (en) 2020-04-16
SG11202002483VA (en) 2020-04-29
AU2018340369B2 (en) 2021-11-04

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