CN108957529B - Attribute-based wellless wavelet estimation method - Google Patents
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Abstract
The invention discloses an attribute-based wellless wavelet estimation method, which utilizes welled wavelet energy and synthetic seismic record analysis to conjecture reasonable wavelet energy of a new area. The physical law of the far-well rock is guided into a target work area by changing the difference of wavelet amplitude energy, so that the inversion really realizes 'well participation'. The research of the technology can lead the wavelet analysis result to accord with the regional rock physical law; and the energy change of the sub-waves of the far and near channels is stable, so that the pre-stack inversion result is more reasonable. A new idea is provided for prestack wavelet analysis of numerous targets of deepwater non-well areas, powerful technical support is provided for geophysical reservoir prediction and hydrocarbon detection in deepwater exploration of sea oil, and the accuracy of reservoir and gas bearing prediction is improved.
Description
Technical Field
The invention mainly relates to the technical field of seismic inversion, in particular to an attribute-based wellhole-free wavelet estimation method.
Background
With the continuous development of seismic exploration data processing technology and interpretation technology, extracting seismic wavelets from seismic records has become one of the important research subjects of seismic exploration digital signal processing. Many wavelet extraction methods have been proposed for a long time by many scholars, but the wavelet extraction technology becomes one of the key restriction factors influencing the improvement of the seismic data processing quality because the actual seismic data often hardly meet some basic assumptions of wavelet extraction.
Under the condition of logging data, a reflection coefficient sequence can be calculated by using the logging data, and seismic wavelets can be estimated by using well-side seismic traces based on a convolution model. Under the condition of no logging data, a statistical wavelet extraction method is used, firstly, a certain assumption is made on the distribution of a reflection coefficient sequence, then, wavelet estimation is carried out by utilizing the statistical characteristics of seismic data, under the condition of no prior knowledge, one case is the common seismic convolution model assumption (second-order statistic assumption), the reflection coefficient sequence is assumed to be white, and the seismic wavelet sequence is the minimum phase, so that a zero-phase or minimum-phase seismic wavelet sequence can be obtained; the other situation is a high-order statistic hypothesis, a reflection coefficient sequence is a non-Gaussian, stable and statistically independent random process, wavelets are a non-causal and non-minimum phase system, additive noise is Gaussian noise, and a mixed phase seismic wavelet sequence can be obtained; when wavelet estimation is carried out by utilizing statistical information recorded by the earthquake, the second-order statistics has no phase information and only amplitude information, and the high-order statistics not only can keep the phase information of the earthquake wavelet, but also can better suppress Gaussian noise.
Robinson (1954) first proposed a time series convolution model in his doctor's paper, detailing a linear, time invariant seismic convolution model: a seismic trace can be viewed as the convolution of a sequence of white-noise reflection coefficients with a sequence of minimum-phase seismic wavelets. In the field of seismic exploration, the seismic convolution model has since become one of the most fundamental models in seismic exploration data processing. Conventional processing techniques such as seismic data deconvolution, seismic inversion, seismic wavelet extraction and the like are all based on a seismic convolution model.
Since the introduction of convolution models into seismic data processing, the estimation and extraction of seismic wavelets has become an important topic of seismic data processing methodology research. With the development need of seismic exploration and the development of digital signal processing theory, the method and the technology for extracting the seismic wavelet are greatly improved and developed. The method mainly comprises the following steps: the method comprises the steps of wave equation type non-convolution model seismic wavelet extraction, zero phase wavelet, homomorphism method, spectrum simulation method, high order statistical method, nonlinear method, well side channel and time-varying space-variant seismic wavelet extraction.
(1) Wave equation seismic wavelet extraction
The wave equation is another very important model in the field of seismic exploration, and is an important equation representing the relationship between the velocity of a subsurface medium (wave impedance) and a seismic source function (seismic wavelet). Many geophysicists seek to use wave equations to estimate seismic wavelets. It is also one of the most efficient methods for estimating seismic wavelets by non-convolution models.
The wave equation seismic wavelet extraction method mainly comprises the following steps: seismic wavelet estimation methods (Shtivelman, etc.) based on upward continuation of the wave field; seismic wavelet estimation methods (Loewenthal, et al) based on wave field continuation first up and then down. Based on multidimensional acoustic and seismic wavelet estimation methods in elastic media (Weglein et al). Seismic wavelet estimation methods (Carrion) based on wavefield downward continuation and reverse-time extrapolation. A method for estimating seismic wavelets using a weighted sum of all offset seismic traces between the shot and geophone points (Kagansky et al). And (3) simultaneously performing seismic source function and medium wave impedance inversion on the uniform layered elastic medium by using a one-dimensional wave equation characteristic line method (high, low, strong and the like). Wavelet estimation (Chen) is performed using a Bayesian method, assuming that the reflection coefficients obey a Bernoulli Gaussian distribution. Seismic wavelet estimation method based on Kirchhoff-Helmholtz integral equation (Osen et al).
(2) Zero phase wavelet extraction
After the convolution model is introduced into the field of seismic exploration, in order to perform deconvolution processing to improve the resolution of seismic data, two basic assumptions that the reflection coefficient is white and the phase of a seismic wavelet is the minimum phase are introduced. The reflection coefficient is white noise, which means that the amplitude spectrum (autocorrelation function) of the seismic data is equivalent to the amplitude spectrum (autocorrelation function) of the seismic wavelet (only by a constant scale factor), so that the seismic wavelet can be estimated by estimating the amplitude spectrum (autocorrelation function) of the seismic data. Seismic data with high signal-to-noise ratio and good transverse continuity are usually selected on a seismic section, a seismic wavelet amplitude spectrum (autocorrelation function) is obtained through multi-channel seismic data amplitude spectrum (autocorrelation function) estimation, and a zero-phase seismic wavelet estimation is obtained on the assumption that seismic wavelets are zero-phase. In the early stage of seismic data processing, seismic wavelet estimation adopts the method to estimate seismic wavelets, which is also the most common seismic wavelet extraction method. The method mainly comprises the following steps: a method for estimating seismic wavelet autocorrelation near and zero-phase seismic wavelet by seismic data autocorrelation; estimating minimum phase wavelet technique by orthogonal grid filter method; a method for estimating seismic wavelets in the time-space domain and a minimum entropy deconvolution method when reflection coefficients are known.
(3) Homomorphic theory extraction of seismic wavelets
An important concept of homomorphism theory is the cepstrum (log spectrum). The cepstrum of a stable sequence is the logarithm of its fourier transform. For homomorphism theory, it is believed that the log spectrum of the wavelet is distributed near the origin and the log spectrum of the reflection coefficient is distributed far away from the origin, thus separating the wavelet from the seismic data, which is the basis of the homomorphism theory method for estimating the seismic wavelet. The seismic wavelets are estimated by applying homomorphism theory, namely, a cepstrum of the reflection coefficient sequence is separated from a cepstrum of the seismic wavelets in a cepstrum domain, and then the cepstrum sequence of the seismic wavelets is obtained. And then transforming the seismic wavelet into a time domain to obtain a time domain mixed phase seismic wavelet, and estimating the wavelet by a common log spectrum average method. The zhongxing yuan firstly applies homomorphic theory to estimate the seismic wavelets in China, introduces a method for estimating the seismic wavelets by the homomorphic theory in detail, and provides a method for estimating the minimum phase seismic wavelets by the homomorphic theory in Huqiu. Blazing proposes a method for estimating seismic wavelets by weighting and filtering in a complex spectrum domain. Huiyu provides a method for estimating seismic wavelets by weighting the negative indexes of the cepstrum domain, and can estimate time-varying wavelets. Xu boh et al propose random time window selection, log spectrum averaging, wavelet shaping methods to estimate seismic wavelets. In the Ricken et al, it is proposed that in the cepstrum domain, the wavelet cepstrum is subjected to different separation according to a certain criterion to form a series of seismic wavelets, and the optimal seismic wavelets are determined according to the data variance modulus criterion after the wavelet deconvolution processing.
(4) Extraction of seismic wavelets by spectral simulation method
The spectrum simulation method simulates the amplitude spectrum of the seismic wavelet using the smooth amplitude spectrum of the seismic data assuming that the amplitude spectrum of the seismic wavelet is smooth. Spectral modeling techniques can estimate zero-phase wavelets, minimum-phase wavelets, and mixed-phase wavelets. In the time signals with the same amplitude spectrum, only the amplitude spectrum of the minimum phase time series has a definite relation with the phase spectrum, and the phase spectrum can be uniquely determined by the amplitude spectrum. The minimum phase wavelet also just meets the phase condition of deconvolution, so that a zero-phase or minimum-phase seismic wavelet can be obtained from the amplitude spectrum of the seismic data.
(5) High-order statistical method for extracting seismic wavelet
The seismic wavelet extraction method based on the high-order statistics assumes that the reflection coefficient sequence is an independent, identically distributed, non-Gaussian random process, and at the moment, the high-order cumulant of the seismic data is equivalent to the high-order cumulant of the seismic wavelet (only the phase difference is a constant proportionality factor), so that the high-order cumulant of the seismic data is estimated, and the purpose of estimating the seismic wavelet is further achieved. And the high-order moment, the high-order spectrum, the high-order cepstrum and the like are further operation results of the high-order cumulant. The high-order statistics comprise high-order cumulant, high-order moment, high-order spectrum and high-order cepstrum. Common high order statistics are fourth order cumulant, bispectrum and trispectrum, cepstrum and cepstrum, interbipartite and interobbipartite. The higher order statistics method extracts the seismic wavelets by using the higher order statistics. The higher-order statistical method for extracting the seismic wavelets is the main method for extracting the seismic wavelets since the 90 s of the 20 th century.
(6) Nonlinear theory extraction of seismic wavelets
Since the 90 s of the 20 th century, nonlinear theories including fractal, chaos, and neural networks began to be widely applied in the field of seismic exploration. In the aspect of extracting the seismic wavelets by applying the nonlinear theory, a plurality of geophysical workers do beneficial exploration and the like to find that the reflection coefficient sequence has fractal characteristics by researching the well-logging reflection coefficient sequence. Many scholars have studied wavelet extraction methods based on fractal theory. The extraction of the seismic wavelet by using the nonlinear theory just starts, and further research is needed by more technologists.
(7) Extracting seismic wavelets from well-side seismic channels
In oil field development, there are many well logs available. At this time, a seismic wavelet estimation method based on the logging information may be used. And (3) estimating seismic wavelets under the constraint condition of logging data by using a high-order cumulant and high-order moment method for beam light. The iterative inversion method based on the linear convolution model, such as Zhaosheng, can invert minimum phase property wavelets and zero phase wavelets. Summerhong and the like jointly solve wavelets by using seismic records and an initial model. The method for extracting the seismic wavelets by using the time-sharing window of the von hovel and the like for accurately extracting the seismic wavelets beside the well by using seismic and well logging data. Buland et al, using Bayesian theory, extract seismic wavelets in the presence of well log data. The method combines logging data and well-side seismic channel data to extract seismic wavelets, and particularly in the conventional wave impedance inversion technology, the method for extracting the seismic wavelets by using the well-side seismic channels is a very important technology.
(8) Time-varying space-variant seismic wavelet extraction
The seismic wavelet extraction method and the seismic wavelet extraction technology are various and different, but the seismic wavelets extracted by the seismic wavelet extraction method and the seismic wavelets are stable and have a certain length, and the seismic wavelets can meet the requirements of forward modeling, deconvolution and conventional seismic inversion processing of current seismic data based on a convolution model. However, due to the absorption and filtering action of the stratum on the seismic wavelets, the amplitude and energy of the seismic wavelets are gradually attenuated in the process of underground medium propagation, and high-frequency components are gradually absorbed. The seismic wavelets in the actual pre-stack seismic data are thus time and space varying, i.e., the seismic wavelets are time-varying and space-varying. Time-varying space-variant seismic wavelet extraction is a very difficult task. Generally, different time windows are selected in the time direction for extracting the time-varying seismic wavelet, and the seismic wavelet is considered to be time-invariant in each time window, so that the time-varying seismic wavelet is formed by the seismic wavelets on a series of different time windows in the time direction. The space-variant wavelet is that the seismic wavelet changes with different spatial positions, namely, each seismic channel corresponds to different seismic wavelets. The space-variant seismic wavelet sequence may be obtained by estimating seismic wavelets at different seismic traces. However, the space-variant time-varying seismic wavelets estimated in this way do not truly reflect the actual seismic wavelets propagated in the subsurface medium, and therefore, many practical problems arise in application. Although time-varying and space-varying seismic wavelets extracted from seismic data have just started, they are still the direction of seismic wavelet extraction technology and methods. With the development of seismic data processing and interpretation techniques with high resolution, high signal-to-noise ratio and high fidelity, time-varying and space-varying seismic wavelets are increasingly needed, and therefore, deep human research and exploration of a method and a technology for extracting the time-varying and space-varying seismic wavelets are needed.
Extracting wavelets from seismic records has become one of the classic research subjects of seismic exploration digital signal processing, and although people propose a plurality of wavelet extraction method technologies, because practical seismic data often hardly meet some basic assumptions of wavelet extraction, the most critical inversion is the wavelets, and the wavelet extraction technology is one of the key restriction factors influencing the further improvement of seismic data inversion quality.
At present, conventional methods for extracting wavelets include a deterministic wavelet extraction method and a statistical wavelet extraction method. Wherein, the determining method extracts the wavelet, and the generated wavelet is the extracted wavelet when the maximum energy of the cross correlation between the reflection coefficient sequence on the well and the seismic channel is utilized. The deterministic wavelet extraction method has the following disadvantages: first, the seismic data of the reference well is close to the target work area, and the wavelet frequency amplitude has certain stability. Second, there must be a high quality log and accurate time depth relationship. And (4) extracting the statistical wavelets, and finally obtaining the wavelets through the equal relation between the seismic amplitude spectrum and the wavelet amplitude spectrum. Similarly, the wavelet extraction has certain defects: first, the wavelet energy amplitude error is large and the inversion accuracy is not accurate. Second, the energy of near-far sub-waves is unstable, resulting in unstable pre-stack inversion results.
Disclosure of Invention
The invention provides an attribute-based wellless wavelet estimation method, which overcomes the space-variant phenomenon of amplitude and frequency of different batches of data by fusing an amplitude difference factor and a frequency spectrum, and has higher reliability in extracting wavelets in complex environments such as wellless and different batches of seismic data.
In order to solve the above technical problem, an embodiment of the present application provides an attribute-based method for estimating a wavelet without a well region, including the following steps:
s1, determining the expression of the seismic wavelet as:
the expression shows that the wavelet consists of three parts of amplitude, frequency and phase, wherein W (t) is the wavelet in time domain, A0Is the initial energy of the wavelet, i.e. the near-channel energy, e, for the prestack inversion-atFor the attenuation parameters, i.e. the energy relationship of the far and near channels,
is the phase;
s2, obtaining more accurate frequency spectrum and phase through generalized S transformation by utilizing seismic data of the non-well area, determining accurate wavelet energy of the non-well area, and extending the wavelet energy of the well area to the non-well area based on seismic attributes through formula derivation, so that the wavelet energy of the non-well area is 'well-available and parametrized';
the specific derivation process of the energy expression of the wavelet without the well region is as follows:
for a well area, the well point position seismic record of the well area is equal to the convolution of the wavelet and the reflection coefficient, and the time domain model is subjected to Fourier transform according to the convolution model to obtain:
wherein, omega is the frequency,
in order to record the frequency domain seismic data,
in the form of a frequency domain wavelet,
dividing the two formulas to establish the relationship between the well point position and any position:
further obtaining:
further simplifying to obtain a well-free wavelet energy calculation formula:
wherein the content of the first and second substances,
i.e. the wavelet energy representing an arbitrary position of the wellless region,
extracting deterministic wavelet energy for a well region from a log, andfor different positions of seismic energy proportion, can be divided based on the energy attribute of large time window areaIs obtained by analysis, and is called attribute factor X herein;
by the step, the reliable near channel wavelet energy of the well region is extended to the new well-free region, and the energy of the prestack near channel wavelet is obtained; for the post-stack inversion, the amplitude term obtained by the above formula is fused with the frequency spectrum to obtain a post-stack inversion wavelet;
for prestack inversion, wavelets of a middle and far channel are required to be obtained, and wavelet amplitude change rules under different angles can be fitted through far and near channel energy relation statistical rules:
A(θ)＝A0+A1θ+A2θ2+A3θ3，
therefore, an angle-dependent computation formula for the energy of the far-mid wavelet can be derived:
where A (θ) is the AVO amplitude as a function of angle of incidence, and θ is the angle of incidence.
Based on the above formula, the far-near channel energy relational expression based on the AVO attribute is deduced through the large-time window AVO attribute statistics, and the intermediate-far channel wavelet energy calculation formula related to the angle is obtained to complete the calculation of the prestack wavelet energy. And finally, fusing the energy of the prestack wavelet with the frequency spectrum to finally obtain the far-channel wavelet in the prestack inversion.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: by fusing the amplitude difference factor and the frequency spectrum, the space-variant phenomenon of amplitude and frequency of different batches of data is overcome, and the wavelet extraction under the complex environments of no well region, different batches of seismic data and the like has higher reliability.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a technical flow chart of the present invention.
Detailed Description
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
As shown in fig. 1, the method for attribute-based estimation of a wavelet without well region in this embodiment includes the following steps:
s1, determining the expression of the seismic wavelet as:
the expression shows that the wavelet consists of three parts of amplitude, frequency and phase, wherein W (t) is the wavelet in time domain, A0Is the initial energy of the wavelet, i.e. the near-channel energy, e, for the prestack inversion-atFor the attenuation parameters, i.e. the energy relationship of the far and near channels,
is the phase;
s2, obtaining more accurate frequency spectrum and phase through generalized S transformation by utilizing seismic data of the non-well area, determining accurate wavelet energy of the non-well area, and extending the wavelet energy of the well area to the non-well area based on seismic attributes through formula derivation, so that the wavelet energy of the non-well area is 'well-available and parametrized';
the specific derivation process of the energy expression of the wavelet without the well region is as follows:
for a well area, the well point position seismic record of the well area is equal to the convolution of the wavelet and the reflection coefficient, and the time domain model is subjected to Fourier transform according to the convolution model to obtain:
wherein the content of the first and second substances,omega is the frequency of the wave to be measured,
in order to record the frequency domain seismic data,
in the form of a frequency domain wavelet,
dividing the two formulas to establish the relationship between the well point position and any position:
further obtaining:
further simplifying to obtain a well-free wavelet energy calculation formula:
wherein the content of the first and second substances,i.e. the wavelet energy representing an arbitrary position of the wellless region,
extracting deterministic wavelet energy for a well region from a log, and
the seismic energy proportions of different positions can be obtained based on the analysis of the energy attribute of a large time window area, and are called attribute factors X;
by the step, the reliable near channel wavelet energy of the well region is extended to the new well-free region, and the energy of the prestack near channel wavelet is obtained; for the post-stack inversion, the amplitude term obtained by the above formula is fused with the frequency spectrum to obtain a post-stack inversion wavelet;
for prestack inversion, wavelets of a middle and far channel are required to be obtained, and wavelet amplitude change rules under different angles can be fitted through far and near channel energy relation statistical rules:
A(θ)＝A0+A1θ+A2θ2+A3θ3，
therefore, an angle-dependent computation formula for the energy of the far-mid wavelet can be derived:
where A (θ) is the AVO amplitude as a function of angle of incidence, and θ is the angle of incidence.
Based on the above formula, the far-near channel energy relational expression based on the AVO attribute is deduced through the large-time window AVO attribute statistics, and the intermediate-far channel wavelet energy calculation formula related to the angle is obtained to complete the calculation of the prestack wavelet energy. And finally, fusing the energy of the prestack wavelet with the frequency spectrum to obtain the far-channel wavelet in the prestack inversion.
Although the present invention has been described with reference to a preferred embodiment, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (1)
1. An attribute-based wellless wavelet estimation method is characterized in that welled wavelet energy and synthetic seismic record analysis are utilized to estimate reasonable wavelet energy of a new area, and the method comprises the following steps:
s1, determining the expression of the seismic wavelet as:
the expression shows that the wavelet consists of three parts of amplitude, frequency and phase, wherein W (t) is the wavelet in time domain, A0Is the initial energy of the wavelet, i.e. the near-channel energy, e, for the prestack inversion-atFor attenuation parameters, i.e. far-near energy relation, frIs the frequency of the wave of the sub-wave,
is the phase;
s2, obtaining more accurate frequency spectrum and phase through generalized S transformation by utilizing seismic data of the non-well area, determining accurate wavelet energy of the non-well area, and extending the wavelet energy of the well area to the non-well area based on seismic attributes through formula derivation, so that the wavelet energy of the non-well area is 'well-available and parametrized';
the specific derivation process of the energy expression of the wavelet without the well region is as follows:
for a well area, the well point position seismic record of the well area is equal to the convolution of the wavelet and the reflection coefficient, and the time domain model is subjected to Fourier transform according to the convolution model to obtain:
wherein, omega is the frequency,
for frequency domain earthquakesThe information is recorded and recorded in a recording medium,
in the form of a frequency domain wavelet,
for frequencies there are reflection coefficients, the square represents the energy, and for any other position the time domain model is also fourier transformed:
dividing the two formulas to establish the relationship between the well point position and any position:
further obtaining:
further simplifying to obtain a well-free wavelet energy calculation formula:
wherein the content of the first and second substances,
i.e. the wavelet energy representing an arbitrary position of the wellless region,
for well zones with deterministic wavelet energy extracted through well logs, and
for different positions of seismic energy proportion, canObtained by analyzing the energy attribute based on the large time window area, and is called an attribute factor X;
by the step, the reliable near channel wavelet energy of the well region is extended to the new well-free region, and the energy of the prestack near channel wavelet is obtained; for the post-stack inversion, the amplitude term obtained by the above formula is fused with the frequency spectrum to obtain a post-stack inversion wavelet;
for prestack inversion, wavelets of a middle and far channel are required to be obtained, and wavelet amplitude change rules under different angles can be fitted through far and near channel energy relation statistical rules:
A(θ)＝A0+A1θ+A2θ2+A3θ3，
wherein A is1、A2、A3Respectively fitting a first power constant, a second power constant and a third power constant of a formula for the energy of the statistical prestack gather along with the change of the angle;
therefore, an angle-dependent computation formula for the energy of the far-mid wavelet can be derived:
wherein A (theta) is AVO amplitude changing with incident angle, theta is incident angle;
and (3) deriving a far-near channel energy relational expression based on the AVO attribute through large-time window AVO attribute statistics based on the above expression to obtain a far-near channel wavelet energy calculation formula related to an angle, completing the calculation of the energy of the pre-stack wavelet, and finally fusing the energy of the pre-stack wavelet with the frequency spectrum to obtain the pre-stack inversion far-near channel wavelet.
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| CN103713319A (en) * | 2013-12-31 | 2014-04-09 | 张远银 | Prestack inversion method based on seismic restrained modeling |
| CN104991272A (en) * | 2015-07-02 | 2015-10-21 | 河海大学 | Earthquake speed disturbance modeling method for well-free earthquake reversion |
| CN105301644A (en) * | 2015-11-18 | 2016-02-03 | 中国石油大学(北京) | Oil/gas detection method based on multi-parameter gradient vector and sea color matrix and device |
| CN105629310A (en) * | 2014-11-05 | 2016-06-01 | 中国石油天然气股份有限公司 | Well-constraint-free geostatistical inversion method and device for carbonate reservoir |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103713319A (en) * | 2013-12-31 | 2014-04-09 | 张远银 | Prestack inversion method based on seismic restrained modeling |
| CN105629310A (en) * | 2014-11-05 | 2016-06-01 | 中国石油天然气股份有限公司 | Well-constraint-free geostatistical inversion method and device for carbonate reservoir |
| CN104991272A (en) * | 2015-07-02 | 2015-10-21 | 河海大学 | Earthquake speed disturbance modeling method for well-free earthquake reversion |
| CN105301644A (en) * | 2015-11-18 | 2016-02-03 | 中国石油大学(北京) | Oil/gas detection method based on multi-parameter gradient vector and sea color matrix and device |
Non-Patent Citations (1)
| Title |
|---|
| 叠前AVO特征分析方法在惠州地区油检测中的应用;陈兆明;《中国海上油气》;20151031;第27卷(第5期);第43-48页 * |
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