CN104297785B - Petrofacies constraint reservoir physical parameter inversion method and device - Google Patents
Petrofacies constraint reservoir physical parameter inversion method and device Download PDFInfo
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Abstract
The invention discloses a kind of petrofacies constraint reservoir physical parameter inversion method and device, wherein method includes：According to Bayes's inverting framework, with reference to petrofacies information, the object inversion function based on petrofacies constraint is set up；Petrofacies information is introduced, statistics rock physics lithographic model is set up；Using stochastic simulation technology, elastic parameter and reservoir physical parameter training sample set comprising petrofacies information are produced based on statistics rock physics lithographic model；Input pre-stack seismic inversion performance data and petrofacies data, using training sample set, solve to the object inversion function based on petrofacies constraint.Petrofacies information as inverting constraint information by adding the means of petrofacies constraint so that inversion principle is more rigorous, refutation process is more reasonable, can be greatly reduced the multi-solution of inversion result by the present invention；The object inversion function derived incorporates petrofacies information, and solution procedure has more specific aim, as a result relatively reliable；The vantage point of reservoir can accurately be identified.
Description
Technical field
The present invention relates to technical field of geological exploration, more particularly to petrofacies constraint reservoir physical parameter inversion method and dress
Put.
Background technology
The emphasis of China's oil-gas exploration in recent years is gradually shifted to lithological reservoir exploration.Different from structural deposit, this
A little novel reservoirs are influenceed by construction and reservoir heterogeneity, and accumulating condition is complicated, and identification difficulty is big, and investment risk is high.
Oil and gas reservoir often shows as strong reflectance signature on seismic data, such as " bright spot ".This seismic response features
It is probably that high porosity, the high hydrocarbon-containing saturation degree reservoir of great business extraction value causes, it is also possible to without business exploitation valency
The low-porosity of value, low hydrocarbon saturation reservoir cause.Reservoir physical parameter such as porosity, saturation degree etc., are geology, earth thing
Science and engineering author carries out evaluating reservoir, estimates oil and gas reserves, determines the important evidence of development wells.Therefore, exploitation is a set of can determine
The geophysics solution for measuring predicting reservoir information seems particularly urgent.
The way most commonly seen for reservoir physical parameter inverting is modeled by Multivariate statistical techniques or rock physicses
Technology sets up certain relational expression between elastic parameter and reservoir physical parameter, can be by pre-stack seismic inversion using this relational expression
The elastic parameter data that technology is obtained is converted into reservoir physical parameter data.Wherein, Multivariate statistical techniques are mainly united using mathematics
The mode of meter obtains studying the statistical relationship in area between elastic parameter and reservoir physical parameter, but this statistical relationship based on pure
Pure relationship, physical significance is indefinite and accuracy of this statistical relationship depends on the quantity of training sample, and this is just
Causing the inversion result of reservoir physical parameter has greatly uncertainty；Rock physics modeling technique mainly uses equivalent Jie
Matter theory sets up the rock physicses transforming relationship between elastic parameter and reservoir physical parameter, this transforming relationship phase in research area
To stabilization, training samples number and explicit physical meaning are not relied on, be the technology being most widely used at present.
Inventor realize it is of the invention during, it is found that above-mentioned prior art is present following not enough：Conventional method is being built
When vertical petrophysical model, the petrophysical model of foundation is larger with actual difference.In addition, elastic parameter and reservoir properties
Relation between parameter can produce larger uncertainty, be unfavorable for the inverting of reservoir physical parameter.
The content of the invention
The embodiment of the present invention provides a kind of petrofacies constraint reservoir physical parameter inversion method, is used to reduce the rock thing of foundation
Reason model and actual difference, and beneficial to the inverting of reservoir physical parameter, the method includes：
According to Bayes's inverting framework, with reference to petrofacies information, the object inversion function based on petrofacies constraint is set up；
Petrofacies information is introduced, statistics rock physics lithographic model is set up；
Using stochastic simulation technology, based on statistics rock physics lithographic model produce the elastic parameter comprising petrofacies information with
Reservoir physical parameter training sample set；
Input pre-stack seismic inversion performance data and petrofacies data, using training sample set, to the mesh based on petrofacies constraint
Mark inverting function is solved；
It is described that the object inversion function based on petrofacies constraint, with reference to petrofacies information, is set up according to Bayes's inverting framework, wrap
Include：
Using petrofacies information as constraint information along with pre-stack seismic inversion performance data as known input data, according to
Bayes's inverting framework, is reservoir physical parameter R by object inversion valuei=[R1,R2,...,Rn] it is defined as known input data
When the corresponding value of maximum posteriori probability valueObtaining object inversion function isWherein, F is
The set that petrofacies information, m are pre-stack seismic inversion performance data, R is reservoir physical parameter value.
In one embodiment, the introducing petrofacies information sets up statistics rock physics lithographic model, including：
Point petrofacies set up certainty petrophysical model m=f (R, F), on the basis of the model, are provided with actual according to model
Difference degree between material, adds random error ε, constitutes statistics rock physics lithographic model m=f (R, F)+ε.
In one embodiment, the utilization stochastic simulation technology is produced based on statistics rock physics lithographic model and includes rock
The elastic parameter and reservoir physical parameter training sample set of phase information, including：
Based on statistics rock physics lithographic model, using Monte-Carlo Simulation technology point petrofacies simulation elastic parameter with
Reservoir physical parameter Joint Distribution sample space { (mk,Rk)F}K=1,2 ..., NsAs training sample set.
In one embodiment, the pre-stack seismic inversion performance data includes：Velocity of longitudinal wave, shear wave velocity, density, Poisson
Than, one of Lame parameter, p-wave impedance, S-wave impedance or any combination；
The petrofacies data includes：The data of image study area Lithofacies Types.
In one embodiment, input pre-stack seismic inversion performance data and the petrofacies data are right using training sample set
Object inversion function is solved, including：
Point petrofacies count corresponding reservoir physical parameter prior distribution probability density function P (R) and corresponding elasticity ginseng
The likelihood function P (m, F | R) of number and petrofacies；
According to Bayesian formula, posterior probability density function P (R are obtainedi| m, F)=P (Ri)P(m,F|Ri)；
The corresponding reservoir physical parameter of maximum a posteriori probability density value is taken for final inversion result：
The embodiment of the present invention also provides a kind of petrofacies constraint reservoir physical parameter inverting device, is used to reduce the rock of foundation
Physical model and actual difference, and beneficial to the inverting of reservoir physical parameter, the device includes：
Object inversion function module, for according to Bayes's inverting framework, with reference to petrofacies information, sets up and is based on petrofacies constraint
Object inversion function；
Physics lithographic model module, for introducing petrofacies information, sets up statistics rock physics lithographic model；
Stochastic simulation module, for utilizing stochastic simulation technology, is produced based on statistics rock physics lithographic model and includes rock
The elastic parameter and reservoir physical parameter training sample set of phase information；
Module is solved, for being input into pre-stack seismic inversion performance data and petrofacies data, using training sample set, to being based on
The object inversion function of petrofacies constraint is solved；
The object inversion function module specifically for：
Using petrofacies information as constraint information along with pre-stack seismic inversion performance data as known input data, according to
Bayes's inverting framework, is reservoir physical parameter R by object inversion valuei=[R1,R2,...,Rn] it is defined as known input data
When the corresponding value of maximum posteriori probability valueObtaining object inversion function isWherein, F is
The set that petrofacies information, m are pre-stack seismic inversion performance data, R is reservoir physical parameter value.
In one embodiment, the physics lithographic model module specifically for：
Point petrofacies set up certainty petrophysical model m=f (R, F), on the basis of the model, are provided with actual according to model
Difference degree between material, adds random error ε, constitutes statistics rock physics lithographic model m=f (R, F)+ε.
In one embodiment, the stochastic simulation module specifically for：
Based on statistics rock physics lithographic model, using Monte-Carlo Simulation technology point petrofacies simulation elastic parameter with
Reservoir physical parameter Joint Distribution sample space { (mk,Rk)F}K=1,2 ..., NsAs training sample set.
In one embodiment, the pre-stack seismic inversion performance data includes：Velocity of longitudinal wave, shear wave velocity, density, Poisson
Than, one of Lame parameter, p-wave impedance, S-wave impedance or any combination；
The petrofacies data includes：The data of image study area Lithofacies Types.
In one embodiment, it is described solution module specifically for：
Point petrofacies count corresponding reservoir physical parameter prior distribution probability density function P (R) and corresponding elasticity ginseng
The likelihood function P (m, F | R) of number and petrofacies；
According to Bayesian formula, posterior probability density function P (R are obtainedi| m, F)=P (Ri)P(m,F|Ri)；
The corresponding reservoir physical parameter of maximum a posteriori probability density value is taken for final inversion result：
Due to not accounting for petrofacies influence, the petrophysical model that conventional method is set up is larger with actual difference, and existing
Some rock physicses theoretical models are mostly based on a certain special petrofacies stratum and set up, the corresponding rock physicses of different petrofacies stratums
Theoretical model also can be different.And in technical scheme provided in an embodiment of the present invention, petrofacies information as inverting constraint information is led to
Cross and add the means of petrofacies constraint to cause that inversion principle is more rigorous, refutation process is more reasonable, inversion result can be greatly reduced
Multi-solution；The object inversion function derived incorporates petrofacies information, and solution procedure has more specific aim, as a result relatively reliable,
Accurately；The vantage point of reservoir can accurately be identified by real data reservoir inversion.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
The accompanying drawing to be used needed for having technology description is briefly described, it should be apparent that, drawings in the following description are only this
Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, can be with
Other accompanying drawings are obtained according to these accompanying drawings.In the accompanying drawings：
Fig. 1 is petrofacies constraint reservoir physical parameter inversion method implementing procedure schematic diagram in the embodiment of the present invention；
Fig. 2 is object inversion function solution room schematic diagram in the embodiment of the present invention；
Fig. 3 is the instantiation figure of petrofacies constraint reservoir physical parameter inversion method in the embodiment of the present invention；
Fig. 4 is reservoir physical parameter point petrofacies statistics prior distribution feature schematic diagram in the embodiment of the present invention；
Fig. 5 is that embodiment of the present invention research area A wells elastic parameter is illustrated with reservoir physical parameter log, petrofacies
Figure；
Fig. 6 is embodiment of the present invention A well prior art conventional rock physical modeling result schematic diagrams；
Fig. 7 is embodiment of the present invention rock physics petrofacies modeling result schematic diagram；
Fig. 8 is embodiment of the present invention A well individual well model gas sandstone phases gas saturation, porosity inversion result schematic diagram；
Fig. 9 crosses A well lithofacies successions schematic diagrams for embodiment of the present invention research area；
Figure 10 crosses A well gas saturation inverting generalized sections for embodiment of the present invention research area；
Figure 11 is gas saturation inverting section well lie inversion result and gas saturation log result in Figure 10
Contrast schematic diagram；
Figure 12 crosses A well porosity inversion generalized sections for embodiment of the present invention research area；
Figure 13 is that Figure 10 porosities inverting section well lie inversion result contrasts signal with porosity logging Dependence Results
Figure；
Figure 14 is petrofacies constraint reservoir physical parameter inverting apparatus structure schematic diagram in the embodiment of the present invention.
Specific embodiment
For the purpose, technical scheme and advantage for making the embodiment of the present invention become more apparent, below in conjunction with the accompanying drawings to this hair
Bright embodiment is described in further details.Here, schematic description and description of the invention is used to explain the present invention, but simultaneously
It is not as a limitation of the invention.
Inventor realize it is of the invention during find, conventional method do not have when petrophysical model is set up
Consider petrofacies influence, the petrophysical model of foundation is larger with actual difference.In fact, different petrofacies stratums, such as sandstone, shale
Its petrophysical property of the stratum such as sandstone, saturation water sandstone, mud stone is also different, and existing rock physicses theoretical model is mostly based on
A certain special petrofacies stratum and set up, the corresponding rock physicses theoretical model of different petrofacies stratums also can be different.Therefore, petrofacies are divided
Carry out petrophysical model structure more reasonable.Further, since the presence of different petrofacies can cause elastic parameter and reservoir properties
Relation between parameter produces larger uncertainty, is unfavorable for the inverting of reservoir physical parameter.Using petrofacies information as inverting about
Beam information, can greatly reduce the multi-solution of inversion result.
Based on this, a kind of petrofacies constraint reservoir physical parameter inversion method is provided in the embodiment of the present invention, below to this
The implementation of method is illustrated.
Fig. 1 is petrofacies constraint reservoir physical parameter inversion method implementing procedure schematic diagram in the embodiment of the present invention, such as Fig. 1 institutes
Show, can include：
Step 101, according to Bayes's inverting framework, with reference to petrofacies information, set up the object inversion letter based on petrofacies constraint
Number；
Step 102, introducing petrofacies information, set up statistics rock physics lithographic model；
Step 103, using stochastic simulation technology, the bullet comprising petrofacies information is produced based on statistics rock physics lithographic model
Property parameter and reservoir physical parameter training sample set；
Step 104, input pre-stack seismic inversion performance data and petrofacies data, using training sample set, to based on petrofacies
The object inversion function of constraint is solved.
During specific implementation, pre-stack seismic inversion performance data, such as elastic parameter data, including p-wave impedance, horizontal stroke can be remembered
Wave impedance and density etc., are m.Note reservoir physical parameter, such as porosity, gas saturation etc. are R, by reservoir physical parameter according to
Certain interval is divided into n classes, i.e. Ri=[R1,R2,...,Rn].According to Bayes's inverting framework, target componentBe to
Determine the class gas saturation value corresponding to maximum a posteriori probability density under conditions of elastic parameter data m, be expressed as：
Wherein P (Ri) be the i-th class gas saturation parameter value priori probability density function；P(m|Ri) it is to contain given
Gas saturation parameter value is RiOn the premise of, the conditional probability density function of m claims likelihood function；P (m) is constant, can in implementation
To omit.
In implementation, during petrofacies information included into object inversion function, new object inversion function can be obtained：
After adding petrofacies information F, definitely, multi-solution greatly reduces the solution room of object inversion function.Fig. 2 is this
Object inversion function solution room schematic diagram in inventive embodiments, as illustrated in fig. 2, it is assumed that research area has three kinds of petrofacies：Mud stone,
Water sand rock, gas sandstone, then in the solution room such as Fig. 2 of conventional method shown in (a), technical scheme provided in an embodiment of the present invention is asked
In solution space such as Fig. 2 shown in (b).It can be seen that, solution room is by three part groups in technical scheme provided in an embodiment of the present invention
Into, different petrofacies are corresponded to respectively, solution room is more specific, can carry out point petrofacies according to research needs and solve.Such as, researcher
Need to obtain the gas saturation of gas sandstone reservoir, then solution room just can greatly reduce specific to gas sandstone space, scope, many
Solution property just greatly reduces.
Technical scheme provided in an embodiment of the present invention, whole inverting flow process is substantially the solution exhibition of surrounding target inverting function
Open.Including reservoir physical parameter prior distribution probability density function P (Ri) solution and likelihood function P (m, F | Ri) solution.
Fig. 3 is the instantiation figure of petrofacies constraint reservoir physical parameter inversion method in the embodiment of the present invention.Such as Fig. 3 institutes
Show, can include in this example：
Step 301, point petrofacies statistics reservoir physical parameter prior distribution feature.
In implementation, the information such as Logging information, LITHOFACIES DATA, point petrofacies statistics reservoir physical parameter prior distribution is special
Levy.Fig. 4 is reservoir physical parameter point petrofacies statistics prior distribution feature schematic diagram in this example, short dash line in Fig. 4, pecked line, short
Pecked line represents gas sandstone phase, water sand petrofacies, the distribution characteristics of mud stone phase porosity respectively.By taking porosity parameter as an example, its
Prior distribution feature histogram schematic diagram is as shown in Figure 4.
Step 302, determine priori probability density function.
In implementation, according to reservoir physical parameter prior distribution feature, using being uniformly distributed, Gaussian Profile, exponential distribution etc.
The distribution function of known expression formula is approached, and obtains specific reservoir physical parameter priori probability density function P (Ri).As schemed
Shown in 4, in gas sandstone phase reservoir, reservoir physical parameter prior distribution feature meets Gaussian Profile, then using Gaussian Profile
Approaching the reservoir physical parameter priori probability density function for obtaining is：
Whereinσ is respectively sample average, variance, can be asked for according to log.
In embodiment, can using petrofacies information as constraint information along with pre-stack seismic inversion performance data as
Known input data, is reservoir physical parameter R by object inversion value according to Bayes's inverting frameworki=[R1,R2,...,
Rn] the corresponding value of maximum posteriori probability value when being defined as known input dataObtaining object inversion function isWherein, F be petrofacies information, m be pre-stack seismic inversion performance data.
Step 303, determine petrophysical model.
In implementation, reservoir physical parameter is expressed as conventional rock method of physical modeling the function of elastic parameter：M=f
(R), consider influence of the petrofacies to model accuracy, the embodiment of the present invention on the basis of conventional rock physical modeling, root
According to Reservoir Lithofacies feature, a point petrofacies modeling is carried out, propose the concept of rock physics lithographic model：M=f (R, F).Present invention modeling
Method, petrophysical model is individually established equivalent to each petrofacies, is different from and is used identical rock to all petrofacies
The traditional modeling method of physical model.
Step 304, composition statistics rock physics lithographic model.
In implementation, in order that rock physics lithographic model more accurately reflects between reservoir physical parameter and elastic parameter
True relation, the embodiment of the present invention on the basis of rock physics lithographic model, according to the difference between model and practical logging data
DRS degree, determines random error ε, constitutes statistics rock physics lithographic model m=f (R, F)+ε.
Step 305, reservoir parameter and elastic parameter point petrofacies joint sample space.
In implementation, the reservoir physical parameter elder generation according to statistics petrophysical model m=f (R, F)+ε and step 302
Test distribution probability density function P (Ri), produce Ns reservoir using based on markovian Monte Carlo stochastic simulation technology
Physical parameter, elastic parameter data, constitute reservoir physical parameter with elastic parameter point petrofacies joint sample space { (mk,Rk
)F}K=1,2 ..., Ns.It is different from the reservoir physical parameter and elastic parameter joint sample space of conventional method：(mk,
Rk)K=1,2 ..., Ns, the embodiment of the present invention in the reservoir physical parameter, elastic parameter Joint Distribution sample space of conventional method plus
Entered petrofacies information so that the corresponding relation of reservoir physical parameter and elastic parameter it is more specific be subordinated to a certain petrofacies.
Step 306, a point petrofacies statistics for likelihood function ask for strategy.
In implementation, divide petrofacies joint sample space as training sample according to elastic parameter, in the embodiment of the present invention, no
The different sample of same petrofacies correspondence.Likelihood function is asked in the embodiment of the present invention, can be using a point petrofacies training samples statistics
Ask for reservoir physical parameter strategy：
Wherein, n (Ri) represent that reservoir physical parameter value is equal to RiNumber of samples；n(mj)FRepresent the training for F in petrofacies
In sample, reservoir physical parameter value is equal to RiAnd elastic parameter value is mjNumber of samples.
Step 307, petrofacies constraint inverting object inversion function.
In implementation, according to reservoir physical parameter prior distribution probability density letter striked in step 302 and step 306
Number is multiplied with likelihood function, obtains the calculating formula of posterior probability density function：Ri=P (m, F | Ri)×P(Ri)。
Step 308, input pre-stack seismic inversion performance data and petrofacies data.
In implementation, pre-stack seismic inversion performance data and petrofacies volume data are input into, pre-stack seismic inversion performance data includes：
One of velocity of longitudinal wave, shear wave velocity, density, Poisson's ratio, Lame parameter, p-wave impedance, S-wave impedance or any combination etc.
Elastic parameter data, these data can be obtained by existing various pre-stack seismic inversion technologies and commercial software in specific implementation.
Petrofacies data includes：The data of image study area Lithofacies Types；Petrofacies data can be soft by existing lithology prediction technology and commercialization
Part is obtained.
Step 309, the corresponding reservoir parameter of output maximum a posteriori probability density are used as final inversion result.
In implementation, reservoir physical parameter is divided into n classes, i.e. R according to certain intervali=[R1,R2,...,Rn], point
Petrofacies are calculated per class reservoir physical parameter RiCorresponding posterior probability density function, takes the corresponding class of maximum a posteriori probability density
Reservoir physical parameter is final inversion result：
In embodiment, can a point petrofacies count corresponding reservoir physical parameter prior distribution probability density function P (Ri)
And corresponding elastic parameter and petrofacies likelihood function P (m, F | Ri)；According to Bayesian formula, posterior probability density function is obtained
P(Ri| m, F)=P (Ri)P(m,F|Ri)；The corresponding reservoir physical parameter of maximum a posteriori probability density value is taken for final inverting knot
Really：
Fig. 5 is that embodiment of the present invention research area A wells elastic parameter is illustrated with reservoir physical parameter log, petrofacies
Figure；The research area interval of interest Lithofacies Types are divided into three kinds：Mud stone phase, water sand petrofacies, gas sandstone phase.By Fig. 5 it can be seen that,
In different petrofacies, the elastic parameter such as the reservoir physical parameter such as porosity, water saturation and velocity of longitudinal wave, shear wave velocity, density
Corresponding relation it is also different.Compared with other petrofacies, water saturation is higher in gas sandstone phase, porosity is larger, density is smaller,
Velocity of longitudinal wave is also smaller.In practical application, often more concerned with payzone phase (e.g., gas sandstone phase or the equal layer of oil gas production of oil sandses)
Reservoir physical parameter.
Fig. 6 is embodiment of the present invention A well prior art conventional rock physical modeling result schematic diagrams, and dotted line represents that model is bent
Line, realizes representing measured curve, as shown in fig. 6, model curve (dotted line) is poor with measured curve (solid line) Integral-fit degree.Figure
It is coincide only at gas sandstone phase higher, this explanation conventional method is modeled merely using single rock physical model, and model is simultaneously
It is not suitable for other petrofacies.
Fig. 7 is embodiment of the present invention rock physics petrofacies modeling result schematic diagram, and dotted line represents model curve, realizes representing
Measured curve, the method that point petrofacies proposed using the embodiment of the present invention are modeled, respectively to mud stone phase, water sand petrofacies, gas sandstone
Corresponding rock physics lithographic model is mutually set up, as shown in Figure 7.It can be seen that, model curve is with measured curve in all petrofacies
Place coincide preferable.
Fig. 8 be embodiment of the present invention A well individual well model gas sandstone phases gas saturation, porosity inversion result schematic diagram,
Where the dotted line signifies that inversional curve of the present invention, realizes representing measured curve；As shown in Figure 8, it can be seen that, in gas sandstone phase,
Gas saturation is coincide preferable with porosity inversion result and measured curve.
Fig. 9 crosses A well lithofacies successions schematic diagrams for embodiment of the present invention research area, and its intermediate value is gas sandstone phase more than 3.5,
It is mud stone phase less than 2.5, remaining is water sand petrofacies, as a example by predicting gas sand facies reservoir gas-bearing saturation degree, porosity, |input paramete
It is prestack inversion performance data：The elastic parameters such as p-wave impedance, S-wave impedance and density, the embodiment of the present invention is to crossing A well profiles
Example inverting is carried out.
Figure 10 crosses A well gas saturation invertings generalized section, Figure 11 in Figure 10 for embodiment of the present invention research area
Gas saturation inverting section well lie inversion result and gas saturation log Comparative result schematic diagram, such as Figure 11 institutes
Show, it can be seen that in gas sandstone phase, gas saturation inversion result coincide preferably with measured curve, different gas saturation
Interval is correctly shown.
Figure 12 crosses A well porosity inversions generalized section, Figure 13 for Figure 10 mesopores for embodiment of the present invention research area
Degree inverting section well lie inversion result and porosity logging Dependence Results contrast schematic diagram, as shown in figure 13, it can be seen that
In gas sandstone phase, porosity result is coincide preferably with measured curve, and different aperture degree interval is correctly shown.
During specific implementation, the worker of explanation can be according to gas saturation, porosity inversion result, preferably with business valency
Value well location is exploited：High gas saturation, high porosity interval.
A kind of petrofacies constraint reservoir physical parameter inverting dress is additionally provided based on same inventive concept, in the embodiment of the present invention
Put, because the principle of the device solve problem is similar to petrofacies constraint reservoir physical parameter inversion method, therefore the device reality
The implementation that may refer to corresponding method is applied, part is repeated and is repeated no more.
Figure 14 is petrofacies constraint reservoir physical parameter inverting apparatus structure schematic diagram in the embodiment of the present invention, such as Figure 14 institutes
Show, including：
Object inversion function module 1401, for according to Bayes's inverting framework, with reference to petrofacies information, sets up and is based on petrofacies
The object inversion function of constraint；
Physics lithographic model module 1402, for introducing petrofacies information, sets up statistics rock physics lithographic model；
Stochastic simulation module 1403, for utilizing stochastic simulation technology, bag is produced based on statistics rock physics lithographic model
Elastic parameter and reservoir physical parameter training sample set containing petrofacies information；
Module 1404 is solved, it is right using training sample set for being input into pre-stack seismic inversion performance data and petrofacies data
Object inversion function based on petrofacies constraint is solved.
In implementation, object inversion function module specifically can be used for：
Using petrofacies information as constraint information along with pre-stack seismic inversion performance data as known input data, according to
Bayes's inverting framework, is reservoir physical parameter R by object inversion valuei=[R1,R2,...,Rn] it is defined as known input data
When the corresponding value of maximum posteriori probability valueObtaining object inversion function isWherein, F is
Petrofacies information, m are pre-stack seismic inversion performance data.
In implementation, physics lithographic model module specifically can be used for：
Point petrofacies set up certainty petrophysical model m=f (R, F), on the basis of the model, are provided with actual according to model
Difference degree between material, adds random error ε, constitutes statistics rock physics lithographic model m=f (R, F)+ε.
In implementation, stochastic simulation module specifically can be used for：
Based on statistics rock physics lithographic model, using Monte-Carlo Simulation technology point petrofacies simulation elastic parameter with
Reservoir physical parameter Joint Distribution sample space { (mk,Rk)F}K=1,2 ..., NsAs training sample set.
In implementation, the pre-stack seismic inversion performance data can include：Velocity of longitudinal wave, shear wave velocity, density, Poisson
Than, one of Lame parameter, p-wave impedance, S-wave impedance or any combination；
The petrofacies data can include：The data of image study area Lithofacies Types.
In implementation, solve module and specifically can be used for：
Point petrofacies count corresponding reservoir physical parameter prior distribution probability density function P (R) and corresponding elasticity ginseng
The likelihood function P (m, F | R) of number and petrofacies；
According to Bayesian formula, posterior probability density function P (R are obtainedi| m, F)=P (Ri)P(m,F|Ri)；
The corresponding reservoir physical parameter of maximum a posteriori probability density value is taken for final inversion result：
The purpose of technical scheme provided in an embodiment of the present invention is in rock for traditional reservoir physical parameter inversion method
Petrofacies difference is not considered during stone physical modeling, cause model and real data to differ greatly, inverting multi-solution it is strong, as a result
The problems such as precision is relatively low, realizes high-precision reservoir physical parameter quantitative inversion method.The program is built using rock physicses are counted
The thinking that mould is combined with Bayes's inversion principle, proposes the concept of statistics rock physicses petrofacies modeling, then derives and is based on
The object inversion function of petrofacies constraint and point petrofacies solution strategies, scheme have practicality higher, can efficiently reduce inverting
The multi-solution of result, can obtain quantitative reservoir physical parameter inversion result, so as to improve reservoir prediction success rate.
This have the advantage that：1) take into account influence of the petrofacies to inversion result, it is proposed that statistics rock
Physics lithographic model, a point petrofacies set up petrophysical model, and theory is more rigorous, more conform to reality；2) mesh of petrofacies constraint
Mark inverting function, definitely, multi-solution is significantly less for process, and precision is higher；3) the statistics strategy of petrofacies is divided so that using instruction
Practice sample set solution object inversion function more efficient；4) by the practical application of real data, favourable storage can accurately be described
The position of layer.
It should be understood by those skilled in the art that, embodiments of the invention can be provided as method, system or computer program
Product.Therefore, the present invention can be using the reality in terms of complete hardware embodiment, complete software embodiment or combination software and hardware
Apply the form of example.And, the present invention can be used and wherein include the computer of computer usable program code at one or more
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) is produced
The form of product.
The present invention is the flow with reference to method according to embodiments of the present invention, equipment (system) and computer program product
Figure and/or block diagram are described.It should be understood that every first-class during flow chart and/or block diagram can be realized by computer program instructions
The combination of flow and/or square frame in journey and/or square frame and flow chart and/or block diagram.These computer programs can be provided
The processor of all-purpose computer, special-purpose computer, Embedded Processor or other programmable data processing devices is instructed to produce
A raw machine so that produced for reality by the instruction of computer or the computing device of other programmable data processing devices
The device of the function of being specified in present one flow of flow chart or multiple one square frame of flow and/or block diagram or multiple square frames.
These computer program instructions may be alternatively stored in can guide computer or other programmable data processing devices with spy
In determining the computer-readable memory that mode works so that instruction of the storage in the computer-readable memory is produced and include finger
Make the manufacture of device, the command device realize in one flow of flow chart or multiple one square frame of flow and/or block diagram or
The function of being specified in multiple square frames.
These computer program instructions can be also loaded into computer or other programmable data processing devices so that in meter
Series of operation steps is performed on calculation machine or other programmable devices to produce computer implemented treatment, so as in computer or
The instruction performed on other programmable devices is provided for realizing in one flow of flow chart or multiple flows and/or block diagram one
The step of function of being specified in individual square frame or multiple square frames.
Particular embodiments described above, has been carried out further in detail to the purpose of the present invention, technical scheme and beneficial effect
Describe in detail bright, should be understood that and the foregoing is only specific embodiment of the invention, the guarantor being not intended to limit the present invention
Shield scope, all any modification, equivalent substitution and improvements within the spirit and principles in the present invention, done etc., should be included in this
Within the protection domain of invention.
Claims (10)
1. a kind of petrofacies constraint reservoir physical parameter inversion method, it is characterised in that including：
According to Bayes's inverting framework, with reference to petrofacies information, the object inversion function based on petrofacies constraint is set up；
Petrofacies information is introduced, statistics rock physics lithographic model is set up；
Using stochastic simulation technology, elastic parameter and reservoir comprising petrofacies information are produced based on statistics rock physics lithographic model
Physical parameter training sample set；
Input pre-stack seismic inversion performance data and petrofacies data, it is anti-to the target based on petrofacies constraint using training sample set
Function is drilled to be solved；
It is described that the object inversion function based on petrofacies constraint, with reference to petrofacies information, is set up according to Bayes's inverting framework, including：
Using petrofacies information as constraint information along with pre-stack seismic inversion performance data as known input data, according to pattra leaves
This inverting framework, is reservoir physical parameter R by object inversion valuei=[R1,R2,...,Rn] when being defined as known input data
The corresponding value of maximum posteriori probability valueObtaining object inversion function isWherein, F is petrofacies
The set that information, m are pre-stack seismic inversion performance data, R is reservoir physical parameter value.
2. the method for claim 1, it is characterised in that the introducing petrofacies information, sets up statistics rock physicses petrofacies
Model, including：
Point petrofacies set up certainty petrophysical model m=f (R, F), on the basis of the model, according between model and real data
Difference degree, add random error ε, constitute statistics rock physics lithographic model m=f (R, F)+ε.
3. method as claimed in claim 2, it is characterised in that the utilization stochastic simulation technology, based on statistics rock physicses
Lithographic model produces elastic parameter and reservoir physical parameter training sample set comprising petrofacies information, including：
Based on statistics rock physics lithographic model, using Monte-Carlo Simulation technology point petrofacies simulation elastic parameter and reservoir
Physical parameter Joint Distribution sample space { (mk,Rk)F}K=1,2 ..., NsAs training sample set.
4. the method as described in any one of claims 1 to 3, it is characterised in that the pre-stack seismic inversion performance data includes：
One of velocity of longitudinal wave, shear wave velocity, density, Poisson's ratio, Lame parameter, p-wave impedance, S-wave impedance or any combination；
The petrofacies data includes：The data of image study area Lithofacies Types.
5. method as claimed in claim 3, it is characterised in that the input pre-stack seismic inversion performance data and petrofacies number
According to, using training sample set, object inversion function is solved, including：
Point petrofacies count corresponding reservoir physical parameter prior distribution probability density function P (Ri) and corresponding elastic parameter and
Petrofacies likelihood function P (m, F | Ri)；
According to Bayesian formula, posterior probability density function P (R are obtainedi| m, F)=P (Ri)P(m,F|Ri)；
The corresponding reservoir physical parameter of maximum a posteriori probability density value is taken for final inversion result：
6. a kind of petrofacies constraint reservoir physical parameter inverting device, it is characterised in that including：
Object inversion function module, for according to Bayes's inverting framework, with reference to petrofacies information, setting up the mesh based on petrofacies constraint
Mark inverting function；
Physics lithographic model module, for introducing petrofacies information, sets up statistics rock physics lithographic model；
Stochastic simulation module, for utilizing stochastic simulation technology, is produced comprising petrofacies letter based on statistics rock physics lithographic model
The elastic parameter and reservoir physical parameter training sample set of breath；
Module is solved, for being input into pre-stack seismic inversion performance data and petrofacies data, using training sample set, to based on petrofacies
The object inversion function of constraint is solved；
The object inversion function module specifically for：
Using petrofacies information as constraint information along with pre-stack seismic inversion performance data as known input data, according to pattra leaves
This inverting framework, is reservoir physical parameter R by object inversion valuei=[R1,R2,...,Rn] when being defined as known input data
The corresponding value of maximum posteriori probability valueObtaining object inversion function isWherein, F is petrofacies
The set that information, m are pre-stack seismic inversion performance data, R is reservoir physical parameter value.
7. device as claimed in claim 6, it is characterised in that the physics lithographic model module specifically for：
Point petrofacies set up certainty petrophysical model m=f (R, F), on the basis of the model, according between model and real data
Difference degree, add random error ε, constitute statistics rock physics lithographic model m=f (R, F)+ε.
8. device as claimed in claim 7, it is characterised in that the stochastic simulation module specifically for：
Based on statistics rock physics lithographic model, using Monte-Carlo Simulation technology point petrofacies simulation elastic parameter and reservoir
Physical parameter Joint Distribution sample space { (mk,Rk)F}k=1,2,...,NsAs training sample set.
9. the device as described in any one of claim 6 to 8, it is characterised in that the pre-stack seismic inversion performance data includes：
One of velocity of longitudinal wave, shear wave velocity, density, Poisson's ratio, Lame parameter, p-wave impedance, S-wave impedance or any combination；
The petrofacies data includes：The data of image study area Lithofacies Types.
10. device as claimed in claim 8, it is characterised in that the solution module specifically for：
Point petrofacies count corresponding reservoir physical parameter prior distribution probability density function P (R) and corresponding elastic parameter and
The likelihood function P (m, F | R) of petrofacies；
According to Bayesian formula, posterior probability density function P (R are obtainedi| m, F)=P (Ri)P(m,F|Ri)；
The corresponding reservoir physical parameter of maximum a posteriori probability density value is taken for final inversion result：
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| CN109389154B (en) * | 2018-09-07 | 2021-08-03 | 中国石油天然气集团有限公司 | Method and device for identifying lithofacies of conglomerate reservoir of flood fan |
| CN111596978A (en) * | 2019-03-03 | 2020-08-28 | 山东英才学院 | Web page display method, module and system for lithofacies classification by artificial intelligence |
| CN110333534B (en) * | 2019-06-19 | 2020-10-30 | 中国地质大学（武汉） | Bayes time-shifting AVO inversion method and system based on Biot theory |
| CN113970787B (en) * | 2020-07-22 | 2023-05-16 | 中国石油化工股份有限公司 | Physical property parameter inversion method, device, computer equipment and storage medium |
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| CN114185090B (en) * | 2020-09-15 | 2024-04-05 | 中国石油化工股份有限公司 | Lithology and elastic parameter synchronous inversion method and device, electronic equipment and medium |
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