CN118426046A - Thin layer inversion technology based on local infinite norms - Google Patents
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Abstract
The invention discloses a thin layer inversion technology based on a local infinite norm, which adopts the technical scheme that a frequency division band inversion strategy is adopted, a low-frequency use model is used for supplementing, intermediate frequency inversion is carried out based on the local infinite norm, and high-frequency information is derived from seismic inversion and modeling constraint. The invention overcomes the limitation of L1 and L2 norms, gives consideration to high fidelity and high resolution, and realizes the fine reservoir characterization by respecting the hidden information in the seismic waveform. The thin layer inversion based on the local infinite norms is developed on the basis of petrophysical modeling, well earthquake fine calibration and wavelet optimization, so that effective information of a high-frequency range of an earthquake is conveniently mined, and the thin layer inversion precision is improved. The method is suitable for thin reservoir prediction in areas with low main frequency and complex lithology of earthquakes.
Description
Technical Field
The invention relates to the technical field of oil and gas reservoir exploration, in particular to a thin layer inversion technology based on local infinite norms.
Background
The thin layer is generally defined as a stratum smaller than the resolution limit of the earthquake, and is more and more paid attention to as the exploration and development are continued, and as a reservoir, the thin layer is used for accumulating a large-scale oil gas resource amount, and as a mudstone cover layer, the thin layer controls the migration and enrichment degree of oil gas under certain geological conditions. Aiming at the characteristics of thin thickness, large variation, poor transverse continuity and strong heterogeneity of the thin layer, how to improve the effectiveness of identification, tracking and evaluation of the thin layer is a current research hot spot.
Currently, thin layer identification is mainly a spectral decomposition technique based on time-frequency analysis. The time-frequency analysis comprises short-time Fourier transformation, wigner-Ville distribution, wavelet transformation, matching pursuit, S transformation, generalized S transformation and other technologies. In domestic terms Li Qing faithfully pointed out in 1987 that the structure of the sand and mudstone, the oil and gas properties and the wavelet waveform of the thin interbed determine the frequency characteristics of the seismic waves. Su Chengfu in 1988, a seismic thin layer quantitative interpretation chart was made to calculate the thin layer thickness. Yang Kai, lei Xiao have studied in 1993 methods for finding the thickness and reflectance of thin layers in the frequency domain. Dou Yi liters in 1995 the square ratio method of amplitude spectra was used for thin layer analysis. Shao Zhilong introduced wavelet transform in 1998 to make thin-layer high-resolution profiles and achieve good results. The thin layer thickness is analytically calculated by the frequency domain notch feature and the time domain relation. Gao Jinghuai et al in 2003 used an improved S-transform to identify thin layers, increasing resolution to one-eighth wavelength. Chen Xuehua is equal to 2006, the improved S transformation is used for thin layer time-frequency domain characteristic analysis, and the thin layer resolution is improved to be less than one eighth wavelength. The technology has strong theories, complex denoising calculation needs to be carried out in a time-frequency domain during actual operation, the efficiency is low, and the technology cannot be widely applied to actual production.
In actual exploration operation, an inversion technology is mainly adopted for stratum identification, and mainly comprises a sparse pulse prestack post-stack inversion technology based on seismic traces, a geostatistical inversion technology based on models and a multi-attribute fitting technology based on machine learning. The sparse pulse inversion is mostly based on inversion of L1 norms, the resolution is limited by the main frequency and the frequency band of the seismic data, and a thin layer cannot be inverted. Geostatistical inversion is a statistical probability inversion, is limited by the number of wells and probability distribution functions, is mostly used for reserve evaluation estimation, is not a deterministic thin layer prediction technology, and cannot be generally used for thin layer prediction. The multi-attribute fitting technology is a big data nonlinear fitting technology, is limited by the types and the number of samples, and has strong multi-solution.
Therefore, developing a thin layer identification technology suitable for practical exploration operation has become a current urgent problem in the field of oil and gas reservoir exploration.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention aims to provide a thin layer inversion technology based on local infinite norms, which supplements a low-frequency use model by adopting a frequency division inversion strategy, inverts based on the local infinite norms at a medium frequency, and obtains high-frequency information from seismic inversion and modeling constraint. The invention overcomes the limitation of L1 and L2 norms, gives consideration to high fidelity and high resolution, and realizes the fine reservoir characterization by respecting the hidden information in the seismic waveform.
In order to achieve the above purpose, the present invention provides the following technical solutions: a thin layer inversion technique based on a local infinite norm, comprising the steps of:
S1, building a rock physical model according to a target layer logging interpretation result, wherein the logging interpretation result comprises mineral components, porosity and fluid components of rock; after the model is built, all logging curves are forward developed, a geophysical four-dimensional quantitative relation is built, and the relations among lithology, physical properties, fluid and elasticity are realized;
s2, analyzing the final inversion wavelet morphology, frequency distribution and phase characteristics according to the seismic data of the target layer, constructing a space-variant wavelet body, and providing wavelet constraint for a next inversion engine;
s3, frequency division inversion, which comprises the following specific steps:
S31, the low-frequency information is derived from a three-dimensional low-frequency model; creating a construction grid according to the latest horizon fault interpretation, and constructing a three-dimensional low-frequency model by using logging impedance, density data and seismic processing velocity spectrum to restrict in the construction grid;
s32, the intermediate frequency information is derived from seismic inversion, the seismic inversion adopts a local infinite norm regularization algorithm, and the equation is as follows:
Wherein W is a seismic wavelet, r is a reflection coefficient, s is seismic data, and λ1 is an L2 norm regularization parameter; λ2 is an L1 norm regularization parameter; λ3 is a local infinite norm regularization parameter;
S33, the high-frequency information is derived from seismic inversion and modeling constraint, and logging impedance, density data and seismic processing velocity spectrum are added into the high-frequency information of the seismic inversion to obtain the high-frequency information;
S34, combining the low-frequency information, the medium-frequency information and the high-frequency information to obtain an elastic parameter body;
S4, analyzing the distribution ranges of elastic parameters of different reservoirs and non-reservoirs by combining the conclusions of the S1 and the S3, and determining the sensitive threshold value of the reservoirs; acquiring the spatial distribution of each lithology; and fitting out the relation between physical properties and oil and gas contents of different lithofacies according to the sensitive threshold value of the reservoir and the spatial distribution of lithofacies, and quantitatively describing the physical properties and the oil and gas contents of the reservoir.
The invention is further provided with: in step S31, a three-dimensional low-frequency model is created by using a low-frequency kriging modeling technique.
The invention is further provided with: the high-band information in step S33 is obtained by the kriging technique of the high-band variation function.
The invention is further provided with: the elastic parameters in step S34 include the longitudinal and transverse wave impedance, poisson ratio, longitudinal and transverse wave velocity ratio, and AVO intercept gradient.
The invention is further provided with: the analysis step of S2 comprises the following steps:
S21, acquiring a seismic signal-to-noise ratio of a target layer, acquiring footprint noise, and performing spectrum analysis;
S22, creating and primarily calibrating theoretical wavelets, and constructing zero-phase Rake wavelets according to the spectral characteristics of the seismic data of the target layer; secondly, utilizing the Rake wavelet and acoustic wave time difference curve and the density curve to manufacture a synthetic record, carrying out primary well earthquake calibration, and determining the general position of a target layer;
S23, a parawell seismic amplitude spectrum constraint wavelet and a parawell seismic phase spectrum constraint wavelet; extracting the amplitude spectrum and the phase spectrum of the well side earthquake of the target layer, constructing new wavelets which accord with the spectrum characteristics of the actual earthquake data, further carrying out fine calibration by utilizing the wavelets, determining the thin-layer earthquake response characteristics and rules of the target layer, and finishing the fine calibration of the reservoir.
The invention is further provided with: s24, multi-well comprehensive wavelet extraction and multi-well calibration time deep quality control are further included in the analysis step of S2; extracting multi-well comprehensive wavelets on the basis of fine calibration of a plurality of single wells; carrying out full-zone multi-well joint calibration by utilizing the comprehensive wavelet, and determining a final well vibration calibration position; and (3) utilizing the quality control and calibration effects of the deep relations in the multiple wells, wherein the deep relations in the multiple wells in the same work area are basically consistent, and if the deep relations in the individual wells are obviously abnormal, repeating the steps S22 and S23, and re-implementing the well earthquake calibration.
The invention is further provided with: s25, extracting and quality controlling angle superposition body pre-stack wavelets; for partial overlapped volume data before the stack, carrying out the extraction of the pre-stack wavelet, wherein the data required to be input comprises a longitudinal wave curve, a transverse wave curve, a density curve, a partial overlapped volume and a speed volume; and performing quality control on the extracted wavelets to reduce residual errors between the synthetic seismic records and the actual seismic data.
The invention is further provided with: the rock physical model construction in the S1 comprises the following specific steps:
s11, correcting logging data;
S12, analyzing mineral content, porosity, water saturation and lithology classification and interpretation conclusion in the logging interpretation result; dividing the intervals into two types of reservoir intervals and non-reservoir intervals according to logging interpretation results;
S13, constructing a petrophysical model according to the rock mineral components, the porosity and the fluid components in the logging interpretation result.
The invention is further provided with: : the specific steps of the petrophysical model construction in the S1 further comprise the following steps of S14: verifying the rationality of the petrophysical model; and (3) performing forward modeling experiments on the rock physical model constructed in the step (S13), wherein the forward modeling curve is consistent with the actually measured curve, so that the rock physical model is reasonable.
The invention is further provided with: in S11, not only is the logging data of a single well corrected, but also an area trend surface is established, so that logging curves within the same working area range have better inter-well consistency.
In summary, compared with the prior art, the invention has the following beneficial effects: the invention overcomes the limitation of L1 and L2 norms, gives consideration to high fidelity and high resolution, and realizes the fine reservoir characterization by respecting the hidden information in the seismic waveform. The thin layer inversion based on the local infinite norms is developed on the basis of petrophysical modeling, well earthquake fine calibration and wavelet optimization, so that effective information of a high-frequency range of an earthquake is conveniently mined, and the thin layer inversion precision is improved. The method is suitable for thin reservoir prediction in areas with low main frequency and complex lithology of earthquakes.
Drawings
FIG. 1 is a schematic flow chart of an embodiment;
FIG. 2 is a schematic diagram of a well logging environment correction;
FIG. 3 is a schematic diagram of a log consistency correction;
FIG. 4 is a petrophysical version;
FIG. 5 is a schematic diagram of comprehensive extraction and optimization analysis of seismic wavelets;
FIG. 6 is a schematic diagram of a well seismic reservoir fine calibration and well seismic response analysis;
FIG. 7 is a schematic diagram of a thin layer inversion effect analysis based on a local infinite norm;
FIG. 8 depicts a fine depiction of a thin layer spatial distribution schematic based on thin layer inversion results.
Detailed Description
The technical solutions of the present invention will be clearly described below with reference to the accompanying drawings, and it is obvious that the described embodiments are not all embodiments of the present invention, and all other embodiments obtained by a person skilled in the art without making any inventive effort are within the scope of protection of the present invention.
Examples
Referring to fig. 1, a flow chart of a preferred embodiment of the present invention is shown, which is a thin layer inversion technique based on local infinity, comprising the steps of:
S1, building a rock physical model according to a target layer logging interpretation result, wherein the logging interpretation result comprises mineral components, porosity and fluid components of rock; after the model is built, all logging curves are forward developed, a geophysical four-dimensional quantitative relation is built, and the relations among lithology, physical properties, fluid and elasticity are realized, so that sensitive petrophysical parameters of reservoir prediction are established, and reservoir prediction and hydrocarbon detection work of a research area are guided.
S1 comprises the following specific steps:
S11, correcting logging data. The well logging data measured on site have quality problems due to the influence of instruments, measuring environments, stratum factors and the like, and have certain requirements for quality of the well logging data in order to meet the requirements of thin layer inversion, and the problems mainly comprise the splicing problem of an inspection well logging curve, the depth matching problem, the influence of the measuring environments on the quality of a density acoustic wave curve, the influence of acoustic wave cycle hopping on time difference, the influence of a soft stratum on transverse wave time difference, the influence of extreme expansion on gamma and the influence of mud invasion and the like. Meanwhile, the well logging curve in the working area range should have better well consistency response, the average response of a plurality of key well standard layers is generally counted to be used as a well logging response standard at well points, and then an area trend surface is established through an interpolation algorithm, wherein the average response of the standard layers of each well is the same as the trend surface.
In this embodiment, the log is first analyzed for stitching, depth migration, and singularities, and the relationship and rationality before the curve is analyzed using various intersection graphs. Analysis finds that the borehole expansion of the local well section is serious, and the density curve is distorted, as shown in fig. 2, so that the density curve of the expanded well section is corrected by using a multiple regression mode, the density= 3.47983-0.015574 (acoustic time difference) +0.0010295 (gamma), and the corrected density curve can accurately reflect the real condition of the stratum. And then, carrying out multi-well curve consistency correction, analyzing the mean value and variance of curves such as gamma, acoustic time difference, neutrons and the like by means of a histogram and the like aiming at a standard layer, wherein the corrected curves have good consistency, as shown in figure 3.
S12, analyzing the logging interpretation results of the pre-examination people, and analyzing the mineral content, the porosity, the water saturation, lithology classification and interpretation conclusion in the logging interpretation results, wherein the parameter interpretation of the non-reservoir section is particularly noted; lithology and fluid type are reclassified for inversion purposes, such as logging interpretation including oil, water, dry, non-reservoir. However, only two types of reservoirs and non-reservoirs are usually needed in the thin layer inversion, so that logging interpretation results of the former are combined, and lithology intervals are divided again.
S13, constructing a rock physical model, wherein for clastic rock stratum, an Xu & White model differential effective medium theory method is generally adopted, and the logging interpretation results firstly input during modeling comprise mineral components, porosity and fluid components of the rock.
S14, performing forward modeling experiments, and finally enabling the forward curve to be consistent with the actually measured curve, so that the rock physical model is reasonable.
S15, forward modeling all logging curves, such as a longitudinal wave curve and a transverse wave curve and a density curve. Meanwhile, a four-dimensional relationship quantity edition of geophysics is established by utilizing a junction diagram mode, and as shown in fig. 4, the relationship between elastic parameters (longitudinal and transverse impedance, longitudinal and transverse wave speed ratio, poisson ratio and the like) and lithology, physical properties and fluid is searched, so that the relationship between lithology, physical properties, fluid and elasticity is realized. Studies have shown that local reservoirs are characterized by low impedance, low longitudinal-to-transverse wave velocity ratios, and that this conclusion will be used for later reservoir discrimination and interpretation.
S2, according to the seismic data of the target layer, the final inversion wavelet morphology, frequency distribution and phase characteristics are analyzed, high-precision seismic wavelets are obtained, and high-precision wavelet constraints are provided for a next inversion engine.
S2, gradually approaching and constructing accurate wavelets next to the first time, wherein the accurate wavelets comprise theoretical wavelet calibration, amplitude spectral wavelets beside a well, amplitude phase spectral wavelets beside a well, wavelet length judgment, multi-well comprehensive wavelets, deep relation quality control during multi-well calibration and pre-stack wavelet extraction, and finally, the final inversion wavelet morphology, frequency distribution and phase characteristics are obtained, as shown in fig. 5 and 6. S2, the specific steps are as follows:
s21, acquiring the earthquake signal-to-noise ratio of the target layer, acquiring footprint noise, and performing spectrum analysis.
S22, creating theoretical wavelets and primarily calibrating; constructing zero-phase Rake wavelets according to the frequency spectrum characteristics of the seismic data of the target layer; and secondly, utilizing the Rake wavelet and acoustic wave time difference curve and the density curve to manufacture a synthetic record, carrying out primary well earthquake calibration, and determining the general position of the target layer.
S23, a parawell seismic amplitude spectrum constraint wavelet and a parawell seismic phase spectrum constraint wavelet; extracting the amplitude spectrum and the phase spectrum of the well side earthquake of the target layer, constructing new wavelets which accord with the spectrum characteristics of the actual earthquake data, further carrying out fine calibration by utilizing the wavelets, determining the thin-layer earthquake response characteristics and rules of the target layer, and finishing the fine calibration of the reservoir.
S24, extracting multi-well comprehensive wavelets and controlling the deep quality during multi-well calibration; on the basis of fine calibration of a plurality of single wells, selecting wells with good well curve quality and high seismic signal to noise ratio, and extracting multi-well comprehensive wavelets; carrying out full-zone multi-well joint calibration by utilizing the comprehensive wavelet, and determining a final well vibration calibration position; at this time, the quality control and calibration effects of the deep relations of the multiple wells can be utilized, generally, the deep relations of the multiple wells are basically consistent in a region, and if obvious abnormality occurs in the deep relations of the individual wells, the process needs to be repeated, so that the well earthquake calibration is realized again.
S25, extracting and quality controlling angle superposition body pre-stack wavelets; for partial overlapped volume data before the stack, carrying out the extraction of the pre-stack wavelet, wherein the data required to be input comprises a longitudinal wave curve, a transverse wave curve, a density curve, a partial overlapped volume and a speed volume; and performing quality control on the extracted wavelets to reduce residual errors between the synthetic seismic records and the actual seismic data.
S26, constructing final inversion wavelet morphology, frequency distribution and phase characteristics, and optimizing the space-variant wavelet according to actual conditions.
S3, frequency division inversion, namely solving the problem of improving the resolution ratio of thin sandstone identification, and specifically comprising the following steps:
S31, the low-frequency information is derived from a three-dimensional low-frequency model, and the low-frequency information is lack of earthquake, so that the upper and lower boundaries of the thin layer are not clearly resolved, and the low-frequency signals need to be complemented during inversion. And creating a construction grid according to the latest horizon fault interpretation, using logging impedance, density data and a seismic processing speed spectrum to constrain in the construction grid, and constructing a three-dimensional low-frequency model by adopting a low-frequency kriging modeling technology. In the embodiment, based on the latest explained three-dimensional positions T23, T24, T25, T30, T32 and T50 of the library car, a construction grid is created, and then a three-dimensional low-frequency model is created in the construction grid by using logging impedance, density data and PSDM seismic processing speed spectrum constraints of the dental ha 10 and the dental ha 12 wells and adopting a low-frequency Kerling modeling technology.
S32, medium-frequency information is derived from seismic inversion, and compared with conventional sparse pulse inversion, a seismic spectrum constraint term is added, a local infinite norm regularization algorithm of nonlinear spectrum inversion is established, the thin layer identification precision is improved, and an algorithm equation is as follows:
Wherein W is a seismic wavelet; r is the reflection coefficient; s is seismic data; λ1 is an L2 norm regularization parameter, the width of an effective frequency band of the seismic data is controlled, and the higher the signal-to-noise ratio of the data is, the smaller the parameter is; λ2 is an L1 norm regularization parameter, and the smaller the λ2 is, the smaller the data proportion outside the effective frequency band of the seismic data is; λ3 is a local infinite norm regularization parameter, so that similarity of a reflection coefficient amplitude spectrum and an earthquake amplitude spectrum is controlled, and inversion accuracy is improved;
S33, high-frequency information is derived from seismic inversion and modeling constraint, and L1 norm inversion generates high-frequency information which is derived from a seismic high-frequency signal and inversion algorithm, but contains some interference components due to low high-frequency seismic signal-to-noise ratio. Therefore, the high-frequency information is obtained by using logging impedance, density data and seismic processing velocity spectrum, adding inversion high-frequency information and adopting a kriging technology based on a high-frequency range variation function.
S34, as shown in FIG. 7, combining the low-frequency information, the medium-frequency information and the high-frequency information to obtain elastic parameter bodies such as longitudinal and transverse wave impedance, poisson ratio and AVO intercept gradient bodies.
S4, interpretation of results, namely solving the quantitative characterization problem of physical properties and oil-gas properties of a thin reservoir, wherein the quantitative characterization problem comprises the following specific steps:
s41, combining conclusion based on petrophysical modeling in S1 and S3, analyzing distribution ranges of elastic parameters (longitudinal wave impedance and longitudinal and transverse wave speed ratio) of different reservoirs and non-reservoirs, and determining a sensitive threshold value of the reservoirs.
S42, quantitatively describing each lithofacies space distribution by adopting a full three-dimensional interpretation technology according to the conclusion of S1;
S43, adopting a machine learning means, fitting the relation between physical properties and oil and gas contents of different lithofacies according to the sensitive threshold value of the reservoir and the spatial distribution of the lithofacies, and quantitatively describing the physical properties and the oil and gas contents of the reservoir, as shown in fig. 8.
In summary, the present embodiment overcomes the limitation of L1 and L2 norms, and combines high fidelity and high resolution, thereby respecting the hidden information in the seismic waveform and realizing the fine reservoir characterization. The thin layer inversion based on the local infinite norms is developed on the basis of petrophysical modeling, well earthquake fine calibration and wavelet optimization, so that effective information of a high-frequency range of an earthquake is conveniently mined, and the thin layer inversion precision is improved. The method is suitable for thin reservoir prediction in areas with low main frequency and complex lithology of earthquakes.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A thin layer inversion technique based on local infinite norms is characterized in that: the method comprises the following steps:
S1, building a rock physical model according to a target layer logging interpretation result, wherein the logging interpretation result comprises mineral components, porosity and fluid components of rock; after the model is built, all logging curves are forward developed, a geophysical four-dimensional quantitative relation is built, and the relations among lithology, physical properties, fluid and elasticity are realized;
s2, analyzing the final inversion wavelet morphology, frequency distribution and phase characteristics according to the seismic data of the target layer, constructing a space-variant wavelet body, and providing wavelet constraint for a next inversion engine;
s3, frequency division inversion, which comprises the following specific steps:
S31, the low-frequency information is derived from a three-dimensional low-frequency model; creating a construction grid according to the latest horizon fault interpretation, and constructing a three-dimensional low-frequency model by using logging impedance, density data and seismic processing velocity spectrum to restrict in the construction grid;
s32, the intermediate frequency information is derived from seismic inversion, the seismic inversion adopts a local infinite norm regularization algorithm, and the equation is as follows:
Wherein, the wavelet is the seismic wavelet, the reflection coefficient is the seismic data, and lambda 1 is the regularization parameter of L2 norm; lambda 2 is the L1 norm regularization parameter; lambda 3 is a local infinite norm regularization parameter;
S33, the high-frequency information is derived from seismic inversion and modeling constraint, and logging impedance, density data and seismic processing velocity spectrum are added into the high-frequency information of the seismic inversion to obtain the high-frequency information;
S34, combining the low-frequency information, the medium-frequency information and the high-frequency information to obtain an elastic parameter body;
S4, analyzing the distribution ranges of elastic parameters of different reservoirs and non-reservoirs by combining the conclusions of the S1 and the S3, and determining the sensitive threshold value of the reservoirs; acquiring the spatial distribution of each lithology; and fitting out the relation between physical properties and oil and gas contents of different lithofacies according to the sensitive threshold value of the reservoir and the spatial distribution of lithofacies, and quantitatively describing the physical properties and the oil and gas contents of the reservoir.
2. A thin layer inversion technique based on local infinity according to claim 1, wherein: in step S31, a three-dimensional low-frequency model is created by using a low-frequency kriging modeling technique.
3. A thin layer inversion technique based on local infinity according to claim 1, wherein: the high-band information in step S33 is obtained by the kriging technique of the high-band variation function.
4. A thin layer inversion technique based on local infinity according to claim 1, wherein: the elastic parameters in step S34 include the longitudinal and transverse wave impedance, poisson ratio, longitudinal and transverse wave velocity ratio, and AVO intercept gradient.
5. A thin layer inversion technique based on local infinity according to claim 1, wherein: the analysis step of S2 comprises the following steps:
S21, acquiring a seismic signal-to-noise ratio of a target layer, acquiring footprint noise, and performing spectrum analysis;
S22, creating and primarily calibrating theoretical wavelets, and constructing zero-phase Rake wavelets according to the spectral characteristics of the seismic data of the target layer; secondly, utilizing the Rake wavelet and acoustic wave time difference curve and the density curve to manufacture a synthetic record, carrying out primary well earthquake calibration, and determining the general position of a target layer;
S23, a parawell seismic amplitude spectrum constraint wavelet and a parawell seismic phase spectrum constraint wavelet; extracting the amplitude spectrum and the phase spectrum of the well side earthquake of the target layer, constructing new wavelets which accord with the spectrum characteristics of the actual earthquake data, further carrying out fine calibration by utilizing the wavelets, determining the thin-layer earthquake response characteristics and rules of the target layer, and finishing the fine calibration of the reservoir.
6. A thin layer inversion technique based on local infinity according to claim 5 wherein: s24, multi-well comprehensive wavelet extraction and multi-well calibration time deep quality control are further included in the analysis step of S2; extracting multi-well comprehensive wavelets on the basis of fine calibration of a plurality of single wells; carrying out full-zone multi-well joint calibration by utilizing the comprehensive wavelet, and determining a final well vibration calibration position; and (3) utilizing the quality control and calibration effects of the deep relations in the multiple wells, wherein the deep relations in the multiple wells in the same work area are basically consistent, and if the deep relations in the individual wells are obviously abnormal, repeating the steps S22 and S23, and re-implementing the well earthquake calibration.
7. A thin layer inversion technique based on local infinity according to claim 6, wherein: s25, extracting and quality controlling angle superposition body pre-stack wavelets; for partial overlapped volume data before the stack, carrying out the extraction of the pre-stack wavelet, wherein the data required to be input comprises a longitudinal wave curve, a transverse wave curve, a density curve, a partial overlapped volume and a speed volume; and performing quality control on the extracted wavelets to reduce residual errors between the synthetic seismic records and the actual seismic data.
8. A thin layer inversion technique based on local infinity according to claim 1, wherein: the rock physical model construction in the S1 comprises the following specific steps:
s11, correcting logging data;
S12, analyzing mineral content, porosity, water saturation and lithology classification and interpretation conclusion in the logging interpretation result; dividing the intervals into two types of reservoir intervals and non-reservoir intervals according to logging interpretation results;
S13, constructing a petrophysical model according to the rock mineral components, the porosity and the fluid components in the logging interpretation result.
9. A thin layer inversion technique based on local infinity according to claim 8, wherein: the specific steps of the petrophysical model construction in the S1 further comprise the following steps of S14: verifying the rationality of the petrophysical model; and (3) performing forward modeling experiments on the rock physical model constructed in the step (S13), wherein the forward modeling curve is consistent with the actually measured curve, so that the rock physical model is reasonable.
10. A thin layer inversion technique based on local infinity according to claim 8, wherein: in S11, not only is the logging data of a single well corrected, but also an area trend surface is established, so that logging curves within the same working area range have better inter-well consistency.
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