CN107861149B - Based on the prestack P-S wave velocity ratio analogy method under drive waveform - Google Patents
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Abstract
The present invention provides a kind of prestack P-S wave velocity ratio analogy methods based under drive waveform.The analogy method includes: the input data body that the simulation of prestack P-S wave velocity ratio is calculated；Phased sampling is carried out to the input data body；According to the phased sampling as a result, going out pseudo- well data using fixed well digital simulation；Initial model is established according to the pseudo- well data and the fixed well data, and counts the probability distribution of known well logging sample as prior probability distribution；According to the initial model and the input data body, likelihood function is calculated；According to the initial model and the prior probability distribution and the likelihood function, Markov Chain-Monte Carlo stochastic simulation is carried out under Bayesian frame, obtains analog result.The problem of being able to solve traditional facies control analysis manual intervention according to the method for the present invention, can be improved simulation precision, be capable of increasing the scope of application of lithology, reservoir and fluid prediction.
Description
Technical Field
The invention belongs to the technical field of oil-gas seismic exploration, and particularly relates to a prestack elastic parameter simulation method.
Background
Inversion techniques have been developed for nearly 40 years to date and include a variety of different algorithms. Populations can be divided into two broad categories, seismic-based deterministic inversion and geostatistical stochastic inversion.
The deterministic inversion is mainly based on the earthquake, reflection coefficients are solved by utilizing a convolution model to remove wavelets, relative impedance is obtained, inversion resolution is basically equivalent to that of the earthquake, and an inversion result is determined. For example, the sparse impulse seismic inversion method provided by Lvey and the like is influenced by thin layer interference, seismic information and a thin reservoir layer do not have direct corresponding relation, effective prediction on transverse change of the thin layer cannot be made, and in addition, due to large change among wells of the reservoir layer and low seismic resolution, great uncertainty exists in prediction of the reservoir layer among the wells.
In order to make up for the defect of low resolution of deterministic inversion, statistical inversion methods were developed. The method starts from a well, combines a random simulation theory and seismic inversion, utilizes a geostatistical thought, represents the space variability of a reservoir through a variation function, performs random simulation constrained by seismic impedance on the basis, estimates lithology and a high-resolution distribution rule of the reservoir, and results in a group of equal probability random simulation solutions. If the Haas is equal to the 20 th century and the 90 s introduces the geostatistical inversion, the method is based on spatial domain sampling point distribution, and the requirement on well uniform distribution is high.
Although the statistical inversion has the advantage of high resolution, the elastic parameter Vp/Vs spatial distribution characteristics of the statistical inversion often do not conform to geological rules and knowledge due to uncertainty of a random simulation solution, and therefore, the phased statistical inversion method draws attention. Many foreign famous software companies develop corresponding functions, such as Jason, Petrel and the like, which essentially control the final inversion result by manually divided sedimentary facies, and have three main problems: (1) the subjectivity of sedimentary facies research is strong due to the fact that different people use different data to divide facies into different phases at different time, the system lacks an iterative correction function, the correctness and the accuracy of subjectively dividing sedimentary facies cannot be judged, and meanwhile the rationality problem of facies boundary division can be caused; (2) the phase analysis has a scale problem that the sedimentary phase is divided into different scales, and the expression form is a three-dimensional concept which represents a certain thickness although the expression form is generally a two-dimensional plane diagram. Different phase precisions, different expressed vertical dimensions including sedimentary phase, subphase, microphase, etc., represent different stratigraphic units, respectively. In the transverse distribution, the sand-to-ground ratio of different facies zones and the reservoir thickness are also changed continuously, so that the sedimentary facies diagram generally expresses the spatial distribution relation of a group of formations with cause relation in practice, and an accurate mathematical expression model is difficult to establish; (3) the ambiguity of the phase analysis is the concept that the sedimentary phase is actually a dominant phase, which means that the phases are shown as main environments after the transition of various sedimentary environments in the vertical direction, so that the ambiguity problem of the vertical phase zone is inevitably caused, the resolution of Vp/Vs prediction is influenced, and the lithology and the resolution of reservoir prediction are also influenced.
Disclosure of Invention
In view of the problems in the prior art, the present invention is directed to solving at least one of the above-mentioned deficiencies in the prior art. For example, the invention aims to solve the problem of manual intervention of the traditional phase control analysis and improve the Vp/Vs (pre-stack longitudinal-transverse wave velocity ratio) simulation precision.
In order to achieve the above object, the present invention provides a method for simulating a prestack longitudinal-transverse wave velocity ratio based on waveform driving, the method comprising: calculating to obtain an input data volume for simulating the velocity ratio of the longitudinal wave and the transverse wave before the stack; performing phase-controlled sampling on the input data volume; simulating pseudo well data according to the phase control sample selection result; establishing an initial model according to the pseudo well data and the known well data, and counting the probability distribution of the known well logging sample as prior probability distribution; calculating to obtain a likelihood function according to the initial model and the input data volume; and carrying out Markov chain-Monte Carlo random simulation under a Bayesian frame according to the initial model, the prior probability distribution and the likelihood function to obtain a simulation result.
In an exemplary embodiment of the invention, the input data volume may be obtained by convolution of a prestack elastic parameter compressional-shear velocity ratio inverse volume and a synthetic wavelet.
In an exemplary embodiment of the invention, the prestack elastic parameter compressional-shear velocity ratio inversion volume may be obtained by prestack simultaneous inversion.
In an exemplary embodiment of the invention, the pre-stack simultaneous inversion may be solved using a well-constrained sparse pulse algorithm according to the zernillitz equation or an approximation thereof.
In an exemplary embodiment of the invention, the synthetic wavelet may be extracted from the post-stack seismic data and the well data.
In an exemplary embodiment of the invention, the phased sampling may utilize seismic waveform similarity characteristics and maximum spatial distance.
In an exemplary embodiment of the present invention, the maximum spatial distance may be a vector distance calculated by a spatial function.
In an exemplary embodiment of the invention, the simulation of the pseudo-well data may include:
and calculating the inter-track relation function of the input data body well side track of the N known wells and the input data body well side track of the unknown well according to the formula 1.
Formula 1 is: snew＝ΣwiSi，i＝1,2,3...,N。
Wherein, wiIs an inter-track relation function of the input data volume, SnewThe input data volume well side-track, S, for an unknown welliThe input data volume well bypass is a known well.
And (4) solving the correlation matching relation functions of different waveform parameters according to the formula 2.
The formula 2 is: pb＝ΣλabPa。
Wherein λ isabFor correlation-matching functions of different waveform parameters, PbIs a waveform parameter, P, of the input data volumeaIs the known well data waveform parameter.
And (4) solving a spatial relation function between different waveform parameters according to the formula 3.
Formula 3 is:
wherein,as a function of the spatial relationship between different waveform parameters, wiIs an inter-track relation function of the input data volume, λabAnd the correlation matching relation function of the different waveform parameters.
And solving the pseudo well data according to the formula 4.
Formula 4 is:
wherein L isnewFor the purpose of the pseudo-well data,as a function of the spatial relationship between said different waveform parameters, LiIs the known well data.
In an exemplary embodiment of the invention, the pseudo-well data may be wave impedance data.
In an exemplary embodiment of the present invention, the initial model and the input data volume may be subjected to a matched filtering calculation to obtain a likelihood function.
Compared with the prior art, the invention has the beneficial effects that: the method for simulating the prestack elastic parameters under the waveform driving can avoid manual intervention, fully utilizes the transverse change of seismic waveforms to reflect the phase change characteristics of reservoir space, further analyzes the vertical lithology combination high-frequency structural characteristics of the reservoir, better embodies the thought of phase control, is a real well-seismic combination high-frequency simulation method, enables the inversion result to be determined from complete randomness to gradual determination, does not have strict requirements on the uniformity of well position distribution, improves the simulation precision, and also increases the application range of lithology, reservoir and fluid prediction.
Drawings
Fig. 1 shows a simulation flowchart of a prestack crossbar velocity ratio simulation method based on waveform driving according to an exemplary embodiment of the present invention.
Fig. 2 shows a phase control sampling schematic diagram based on a prestack longitudinal-transverse wave velocity ratio simulation method under waveform driving according to an exemplary embodiment of the invention.
FIG. 3 shows a pseudo-well data simulation schematic based on a prestack compressional velocity ratio simulation approach under waveform drive, according to an exemplary embodiment of the invention.
Fig. 4 shows a simulated well-tie profile based on a prestack compressional velocity ratio simulation method under waveform driving according to an exemplary embodiment of the present invention.
Description of reference numerals:
well a, well B, well C, well D, well E, well F indicate drilled wells, H8 indicates the bottom of the lower section of box 8, SX indicates the bottom of the hill section 1, Vp/Vs indicates the velocity ratio of the longitudinal and transverse waves.
Detailed Description
Hereinafter, a prestack longitudinal-transverse wave velocity ratio simulation method based on waveform driving according to the present invention will be described in detail with reference to the accompanying drawings and exemplary embodiments.
Fig. 1 shows a simulation flowchart of a prestack crossbar velocity ratio simulation method based on waveform driving according to an exemplary embodiment of the present invention. As shown in fig. 1, in an exemplary embodiment, the method for simulating the prestack longitudinal-transverse wave velocity ratio based on waveform driving according to the present invention can be implemented by the following steps:
and (1) obtaining a prestack longitudinal and transverse wave velocity ratio inversion body according to prestack simultaneous inversion, and performing convolution on the longitudinal and transverse wave velocity ratio inversion body and the comprehensive wavelets to obtain an input data volume for prestack longitudinal and transverse wave velocity ratio simulation.
And (2) performing phase control sampling on the input data volume according to the seismic waveform similarity characteristics and the maximum spatial distance.
And (3) simulating pseudo well impedance data by using known well impedance data according to the phase control sampling result. The simulation of the pseudo-well impedance data specifically comprises:
and calculating the inter-track relation function of the input data body well side channels of the N known wells and the input data body well side channel of the unknown well according to the formula 1.
Formula 1 is: snew＝ΣwiSi，i＝1,2,3...,N。
Wherein, wiAs a function of the inter-track relationship of the input data volume, SnewThe input data volume well side-track, S, for an unknown welliThe input data volume well bypass is a known well.
And (4) solving the correlation matching relation functions of different waveform parameters according to the formula 2.
The formula 2 is: pb＝ΣλabPa。
Wherein λ isabFor correlation-matching functions of different waveform parameters, PbIs a waveform parameter, P, of the input data volumeaIs the known well data waveform parameter.
And (4) solving a spatial relation function between different waveform parameters according to the formula 3.
Formula 3 is:
wherein,as a function of the spatial relationship between different waveform parameters, wiIs an inter-track relation function of the input data volume, λabAnd the correlation matching relation function of the different waveform parameters.
And solving the pseudo well data according to the formula 4.
Formula 4 is:
wherein L isnewFor the purpose of the pseudo-well data,as a function of the spatial relationship between said different waveform parameters, LiIs the known well data.
And (4) establishing an initial model according to the pseudo well impedance data and the known well impedance data, and counting the probability distribution of the known logging sample as prior probability distribution.
And (5) performing matched filtering on the initial model and the input data body, and calculating to obtain a likelihood function.
And (6) carrying out Markov chain-Monte Carlo random simulation under a Bayes frame according to the initial model, the prior probability distribution and the likelihood function to obtain a simulation result which accords with the medium-frequency impedance of the earthquake and the structural feature of the well curve.
The method for simulating the prestack longitudinal-transverse wave velocity ratio based on waveform driving according to the present invention will be further described with reference to specific examples.
Examples of the invention
The method for simulating the velocity ratio of the pre-stack longitudinal wave and the transverse wave based on waveform driving is described in detail by taking an X work area as an example.
And (1) performing prestack simultaneous inversion on the seismic data of the work area to obtain a prestack elastic parameter Vp/Vs inversion body, extracting synthetic wavelets through well-seismic combination, and performing convolution on the synthetic wavelets and the Vp/Vs inversion body to obtain a Vp/Vs synthetic data body which is used as an input data body for prestack elastic parameter simulation. The pre-stack simultaneous inversion is based on a Zoeppritz equation or an approximate expression thereof, a well constraint sparse pulse algorithm is applied, so that the impedance values of the acquired longitudinal waves and transverse waves can be ensured to be consistent with actual seismic information within a seismic frequency band range, the inversion technology is good in uniqueness, strong in noise resistance, small in dependence on a well and good in result stability, and a deterministic component respecting original seismic data is provided for a final Vp/Vs simulation result.
And (2) under the drive of an isochronous stratigraphic framework and an input data body, performing phased sampling by utilizing the similarity characteristics of seismic waveforms, and preferably selecting an effective sample well. Specifically, a point to be predicted is assumed, and a well with a waveform similar to the point is preferred as a sample well according to the waveform similarity characteristics, and the method comprehensively considers two factors of the waveform and the distance, rather than only a well close to the point to be predicted as the sample well. In order to avoid the sample optimization error of a large distance range, the optimization process additionally considers the maximum spatial distance obtained by a spatial function, and the well with similar waveform and spatial distance bivariate optimization medium-low frequency structure is used as a spatial estimation sample in the known well.
And (3) simulating a pseudo-well impedance curve by using the impedance curve of the preferred sample well, wherein the structure is determined to be the pseudo-well impedance curve as shown in fig. 3. The seismic data of the work area is assumed to have one well data corresponding to each track, wherein the well data is composed of known well data and unknown well pseudo-well data. The simulation of the pseudo-well impedance curve specifically comprises the following steps:
calculating a channel relation function between the input data body well side channels of the N known wells and the input data body well side channel of the unknown well according to the formula 1.
Formula 1 is: snew＝ΣwiSi，i＝1,2,3...,N。
Wherein, wiIs an inter-track relation function of the input data volume, SnewThe input data volume well side-track, S, for an unknown welliThe input data volume well bypass is a known well.
Secondly, obtaining related matching relation functions of different waveform parameters according to the formula 2, namely obtaining different waveform parameters P of the known wella、PbAnd well bypass waveform matching parameter function lambda thereofab。
The formula 2 is: pb＝ΣλabPa。
Wherein λ isabFor correlation-matching functions of different waveform parameters, PbFor inputting waveform parameters of a data volume, PaKnown well data waveform parameters.
And thirdly, solving a spatial relation function between different waveform parameters according to the formula 3. As well point seismic and well logging data with the same parameters are reflected by the same geological structure, the well point seismic and well logging data meet the same spatial function relationship. Spatial function relation between different waveform parametersThere may be differences.
Formula 3 is:
wherein,as a function of the spatial relationship between different waveform parameters, wiIs an inter-track relation function of the input data volume, λabAnd the correlation matching relation function of the different waveform parameters.
And fourthly, obtaining the data of the pseudo well according to the formula 4.
Formula 4 is:
wherein L isnewIn the form of pseudo-well data,as a function of the spatial relationship between different waveform parameters, LiKnown well data.
And (4) under a construction frame, comprehensively utilizing a known well impedance curve and a pseudo well impedance curve to establish an initial model, and counting probability distribution of a logging sample as prior probability distribution.
And (5) carrying out matched filtering on the initial model and the Vp/Vs synthetic body which is inverted simultaneously before the stack, and calculating to obtain a likelihood function. According to the definition of the likelihood function, the likelihood function obtained by the matched filtering calculation describes the probability that a certain space position value is a certain specific value. Matched filtering produces a new probability distribution space after fusing deterministic information (from the prestack simultaneous inversion) and stepwise deterministic information (from the waveform phase-controlled simulation).
Step (6), Markov Chain-Monte Carlo stochastic simulation (MCMC). And (3) simulating the high-frequency component of the initial model under a Bayes framework to enable the simulation result to accord with the seismic intermediate-frequency impedance and well curve structural characteristics (conditional distribution probability). As shown in fig. 4, Vp/Vs simulated well tie-in profile with ordinate representing time in ms; h8 denotes the bottom of the lower section of box 8, SX denotes the bottom of mountain 1; the black part is a simulated sand body development area, the effective Vp/Vs threshold value of the sand body is smaller than 1.7 based on rock physics analysis, and the transverse extensibility of the sand body can be seen from a simulation diagram to be better and accord with the sedimentary geological rule. The lower section of the box 8 is plaited river sediment, sand bodies are spread on the plane more, and the Shanxi group is meandering river sediment and thin interbed sediment, so that the Shanxi group has less sand bodies than the lower section of the box 8, the whole simulation section is well matched with a well, and the simulation reliability is high.
While the present invention has been described above in connection with the accompanying drawings and exemplary embodiments, it will be apparent to those of ordinary skill in the art that various modifications may be made to the above-described embodiments without departing from the spirit and scope of the claims.
Claims (10)
1. A prestack longitudinal and transverse wave velocity ratio simulation method based on waveform driving is characterized by comprising the following steps:
calculating to obtain an input data volume for simulating the velocity ratio of the longitudinal wave and the transverse wave before the stack;
performing phase-controlled sampling on the input data volume;
simulating pseudo well data according to the phase control sample selection result;
establishing an initial model according to the pseudo well data and the known well data, and counting the probability distribution of the known well logging sample as prior probability distribution;
calculating to obtain a likelihood function according to the initial model and the input data volume;
and performing Markov chain-Monte Carlo random simulation under a Bayesian framework according to the initial model, the prior probability distribution and the likelihood function to obtain simulation results conforming to the medium-frequency impedance of the earthquake and the structural characteristics of the well curve.
2. The waveform-drive-based prestack longitudinal-transverse wave velocity ratio simulation method of claim 1, wherein the input data volume is obtained by convolution of a prestack elastic parameter longitudinal-transverse wave velocity ratio inverse volume and a synthetic wavelet.
3. The waveform-drive-based prestack longitudinal-transverse wave velocity ratio simulation method of claim 2, wherein the prestack elastic parameter longitudinal-transverse wave velocity ratio inversion body is obtained by prestack simultaneous inversion.
4. The waveform-driven prestack compressional velocity ratio simulation method according to claim 3, wherein the prestack simultaneous inversion is solved by using a well-constrained sparse pulse algorithm according to the Zornia-Martz equation or an approximation thereof.
5. The method of waveform-driven prestack compressional-to-shear velocity ratio simulation according to claim 2, wherein the synthetic wavelet is extracted from the post-stack seismic data and well data.
6. The waveform-driven prestack compressional-shear velocity ratio simulation method of claim 1, wherein the phased sampling utilizes seismic waveform similarity features and maximum spatial distance.
7. The method according to claim 6, wherein the maximum spatial distance is a vector distance obtained by a spatial function.
8. The method according to claim 1, wherein the simulating of the pseudo-well data comprises:
calculating an inter-track relationship function of the input data volume well side-tracks of the N known wells and the input data volume well side-tracks of the unknown well according to equation 1,
formula 1 is: snew＝∑wiSi，i＝1,2,3...,N，
Wherein, wiIs an inter-track relation function of the input data volume, SnewThe input data volume well side-track, S, for an unknown welliThe input data volume well bypass for a known well;
the correlation matching relation function of different waveform parameters is obtained according to the formula 2,
the formula 2 is: pb＝∑λabPa，
Wherein λ isabFor correlation-matching functions of different waveform parameters, PbIs a waveform parameter, P, of the input data volumeaIs the known well data waveform parameter;
the spatial relationship function between different waveform parameters is found according to equation 3,
formula 3 is:
wherein,as a function of the spatial relationship between different waveform parameters, wiIs a track of the input data volumeFunction of relationship between, λabThe correlation matching relation function of the different waveform parameters is obtained;
the pseudo-well data is solved according to the formula 4,
formula 4 is:
wherein L isnewFor the purpose of the pseudo-well data,as a function of the spatial relationship between said different waveform parameters, LiIs the known well data.
9. The waveform-drive-based prestack compressional-to-shear velocity ratio simulation method of claim 1, wherein the pseudo-well data is wave impedance data.
10. The method according to claim 1, wherein the likelihood function is obtained by performing matched filtering calculation on the initial model and the input data volume.
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| CN113311483B (en) * | 2020-02-27 | 2024-04-30 | 中国石油天然气集团有限公司 | Pre-stack elastic parameter combination inversion method and system based on shale oil |
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