CN104297785A - Lithofacies constrained reservoir physical property parameter inversion method and device - Google Patents

Abstract

The invention discloses a lithofacies constrained reservoir physical property parameter inversion method and device. The lithofacies constrained reservoir physical property parameter inversion method comprises the steps of establishing a target inversion function based on lithofacies constraint according to a Bayes inversion frame and lithofacies information; introducing the lithofacies information to establish a statistical rock physical lithofacies model; utilizing a stochastic simulation technology to produce an elastic parameter and reservoir physical property parameter training sample set including the lithofacies information based on the statistical rock physical lithofacies model; inputting pre-stack seismic inversion result data and lithofacies data, and utilizing the training sample set to solve the target inversion function based on the lithofacies constraint. The lithofacies constrained reservoir physical property parameter inversion method and device uses the lithofacies information as inversion constraint information, enables an inversion principle to be more rigorous and enables the inversion process to be more reasonable by adding the lithofacies constraint and can greatly decrease multiple solutions of an inversion result. The derived target inversion function includes the lithofacies information, the solving process is more targeted, the result is more reliable, and the favorable position of a reservoir can be accurately recognized.

Description

Petrofacies constraint reservoir physical parameter inversion method and device

Technical field

The present invention relates to technical field of geological exploration, particularly relate to petrofacies constraint reservoir physical parameter inversion method and device.

Background technology

The emphasis of China's oil-gas exploration in recent years shifts to lithological reservoir exploration gradually.Be different from structural deposit, these novel reservoirs are subject to the impact of structure and reservoir heterogeneity, and accumulating condition is complicated, and identify that difficulty is large, investment risk is high.

Oil and gas reservoir often shows as strong reflectance signature on seismic data, as " bright spot ".This seismic response features may be have the high porosity of business extraction value, high hydrocarbon-containing saturation degree reservoir causes, also may be there is no the low-porosity of business extraction value, low hydrocarbon saturation reservoir causes.Reservoir physical parameter, as factor of porosity, saturation degree etc., is geology, Some Comments On Geophysical Work person carries out evaluating reservoir, estimation oil and gas reserves, determines the important evidence developing well location.Therefore, develop and a set ofly the geophysics solution of quantitative forecast reservoir information can seem particularly urgent.

Be set up certain relational expression between elastic parameter and reservoir physical parameter by Multivariate statistical techniques or rock physics modeling technique for the way that reservoir physical parameter inverting is the most common, the elastic parameter data transformations utilizing this relational expression pre-stack seismic inversion technology can be obtained is reservoir physical parameter data.Wherein, the statistical relationship that Multivariate statistical techniques mainly utilizes the mode of mathematical statistics to obtain in study area between elastic parameter and reservoir physical parameter, but this statistical relationship is based on pure relationship, indefinite and the accuracy that is this statistical relationship of physical significance depends on the quantity of training sample, and this has great uncertainty with regard to causing the inversion result of reservoir physical parameter; Rock physics modeling technique mainly utilizes EFFECTIVE MEDIUM THEORY to set up rock physics transforming relationship in study area between elastic parameter and reservoir physical parameter, this transforming relationship is relatively stable, not relying on training sample quantity and explicit physical meaning, is the technology be most widely used at present.

Inventor is realizing in process of the present invention, finds that above-mentioned prior art exists following not enough: conventional method is when setting up petrophysical model, and petrophysical model and the actual difference of foundation are larger.In addition, the relation between elastic parameter and reservoir physical parameter can produce larger uncertainty, is unfavorable for the inverting of reservoir physical parameter.

Summary of the invention

The embodiment of the present invention provides a kind of petrofacies to retrain reservoir physical parameter inversion method, and in order to reduce the difference of petrophysical model and the reality set up, and be beneficial to the inverting of reservoir physical parameter, the method comprises:

According to Bayes's inverting framework, in conjunction with petrofacies information, set up the object inversion function based on petrofacies constraint;

Introduce petrofacies information, set up statistics rock physics petrofacies model;

Utilize stochastic simulation technology, Corpus--based Method rock physics petrofacies model produces the elastic parameter and the reservoir physical parameter training sample set that comprise petrofacies information;

Input pre-stack seismic inversion performance data and petrofacies data, utilize training sample set, solve the object inversion function retrained based on petrofacies.

In an embodiment, described according to Bayes's inverting framework, in conjunction with petrofacies information, set up the object inversion function based on petrofacies constraint, comprising:

Using petrofacies information as constraint information with pre-stack seismic inversion performance data as known input data, according to Bayes's inverting framework, by object inversion value and reservoir physical parameter R _i=[R ₁, R ₂..., R _n] value corresponding to maximum a posteriori probability value when being defined as known input data obtaining object inversion function is wherein, F is petrofacies information, m is pre-stack seismic inversion performance data.

In an embodiment, described introducing petrofacies information, set up statistics rock physics petrofacies model, comprising:

Point petrofacies set up determinacy petrophysical model m=f (R, F), on this model basis, according to the difference degree between model and real data, add stochastic error ε, form statistics rock physics petrofacies model m=f (R, F)+ε.

In an embodiment, describedly utilize stochastic simulation technology, Corpus--based Method rock physics petrofacies model produces the elastic parameter and the reservoir physical parameter training sample set that comprise petrofacies information, comprising:

Corpus--based Method rock physics petrofacies model, adopts Monte-Carlo Simulation technical point petrofacies simulation elastic parameter and reservoir physical parameter joint distribution sample space { (m _k, R _k) _f} _{k=1,2 ..., Ns}as training sample set.

In an embodiment, described pre-stack seismic inversion performance data comprises: velocity of longitudinal wave, shear wave velocity, density, Poisson ratio, Lame parameter, p-wave impedance, S-wave impedance one of them or combination in any;

Described petrofacies data comprise: the data of image study district Lithofacies Types.

In an embodiment, described input pre-stack seismic inversion performance data and petrofacies data, utilize training sample set, solves, comprising object inversion function:

Point petrofacies count the likelihood function P (m, F|R) of corresponding reservoir physical parameter prior distribution probability density function P (R) and corresponding elastic parameter and petrofacies;

According to Bayesian formula, obtain posterior probability density function P (R _i| m, F)=P (R _i) P (m, F|R _i);

Getting reservoir physical parameter corresponding to maximum a posteriori probability density value is final inversion result:

The embodiment of the present invention also provides a kind of petrofacies to retrain reservoir physical parameter inverting device, and in order to reduce the difference of petrophysical model and the reality set up, and be beneficial to the inverting of reservoir physical parameter, this device comprises:

Object inversion function module, for according to Bayes's inverting framework, in conjunction with petrofacies information, sets up the object inversion function based on petrofacies constraint;

Physics petrofacies model module, for introducing petrofacies information, sets up statistics rock physics petrofacies model;

Stochastic simulation module, for utilizing stochastic simulation technology, Corpus--based Method rock physics petrofacies model produces the elastic parameter and the reservoir physical parameter training sample set that comprise petrofacies information;

Solving module, for inputting pre-stack seismic inversion performance data and petrofacies data, utilizing training sample set, the object inversion function retrained based on petrofacies is solved.

In an embodiment, described object inversion function module specifically for:

Using petrofacies information as constraint information with pre-stack seismic inversion performance data as known input data, according to Bayes's inverting framework, by object inversion value and reservoir physical parameter R _i=[R ₁, R ₂..., R _n] value corresponding to maximum a posteriori probability value when being defined as known input data obtaining object inversion function is wherein, F is petrofacies information, m is pre-stack seismic inversion performance data.

In an embodiment, described physics petrofacies model module specifically for:

Point petrofacies set up determinacy petrophysical model m=f (R, F), on this model basis, according to the difference degree between model and real data, add stochastic error ε, form statistics rock physics petrofacies model m=f (R, F)+ε.

In an embodiment, described stochastic simulation module specifically for:

Corpus--based Method rock physics petrofacies model, adopts Monte-Carlo Simulation technical point petrofacies simulation elastic parameter and reservoir physical parameter joint distribution sample space { (m _k, R _k) _f} _{k=1,2 ..., Ns}as training sample set.

In an embodiment, described pre-stack seismic inversion performance data comprises: velocity of longitudinal wave, shear wave velocity, density, Poisson ratio, Lame parameter, p-wave impedance, S-wave impedance one of them or combination in any;

Described petrofacies data comprise: the data of image study district Lithofacies Types.

In an embodiment, described in solve module specifically for:

Point petrofacies count the likelihood function P (m, F|R) of corresponding reservoir physical parameter prior distribution probability density function P (R) and corresponding elastic parameter and petrofacies;

According to Bayesian formula, obtain posterior probability density function P (R _i| m, F)=P (R _i) P (m, F|R _i);

Getting reservoir physical parameter corresponding to maximum a posteriori probability density value is final inversion result:

Owing to not considering that petrofacies affect, petrophysical model and the actual difference of conventional method foundation are larger, and existing rock physics theoretical model is set up based on a certain special petrofacies stratum mostly, rock physics theoretical model corresponding to different petrofacies stratum also can be different.And in the technical scheme that the embodiment of the present invention provides, using petrofacies information as inverting constraint information, make that inversion principle is more rigorous by the means adding petrofacies constraint, refutation process is more reasonable, can greatly reduce the multi-solution of inversion result; The object inversion function derived incorporates petrofacies information, and solution procedure has more specific aim, and result is more reliable, accurate; Can real data reservoir inversion be passed through, identify the vantage point of reservoir accurately.

Accompanying drawing explanation

In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be 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 embodiment of the present invention study area A well elastic parameter and reservoir physical parameter logging trace, petrofacies schematic diagram;

Fig. 6 is embodiment of the present invention A well prior art conventional rock physical modeling result schematic diagram;

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 phase gas saturation, porosity inversion result schematic diagram;

Fig. 9 is that A well lithofacies successions schematic diagram is crossed in embodiment of the present invention study area;

Figure 10 is that A well gas saturation inverting diagrammatic cross-section is crossed in embodiment of the present invention study area;

Figure 11 is gas saturation inverting section well lie inversion result and gas saturation logging trace Comparative result schematic diagram in Figure 10;

Figure 12 is that A well porosity inversion diagrammatic cross-section is crossed in embodiment of the present invention study area;

Figure 13 is that Figure 10 porosity inverting section well lie inversion result and porosity log Dependence Results contrast schematic diagram;

Figure 14 is petrofacies constraint reservoir physical parameter inverting apparatus structure schematic diagram in the embodiment of the present invention.

Embodiment

For making the object of the embodiment of the present invention, technical scheme and advantage clearly understand, below in conjunction with accompanying drawing, the embodiment of the present invention is described in further details.At this, schematic description and description of the present invention is for explaining the present invention, but not as a limitation of the invention.

Inventor is realizing finding in process of the present invention, and conventional method, when setting up petrophysical model, does not consider that petrofacies affect, and petrophysical model and the actual difference of foundation are larger.In fact, different petrofacies stratum, as also different in its petrophysical properties of stratum such as sandstone, dirty sandstone, water-saturated sandstone, mud stone, existing rock physics theoretical model is set up based on a certain special petrofacies stratum mostly, and rock physics theoretical model corresponding to different petrofacies stratum also can be different.Therefore, point petrofacies carry out petrophysical model build more reasonable.In addition, the existence due to different petrofacies can make the relation between elastic parameter and reservoir physical parameter produce larger uncertainty, is unfavorable for the inverting of reservoir physical parameter.Using petrofacies information as inverting constraint information, the multi-solution of inversion result can be greatly reduced.

Based on this, provide a kind of petrofacies constraint reservoir physical parameter inversion method in the embodiment of the present invention, below the enforcement of the method is described.

Fig. 1 is petrofacies constraint reservoir physical parameter inversion method implementing procedure schematic diagram in the embodiment of the present invention, as shown in Figure 1, can comprise:

Step 101, according to Bayes's inverting framework, in conjunction with petrofacies information, set up based on petrofacies constraint object inversion function;

Step 102, introducing petrofacies information, set up statistics rock physics petrofacies model;

Step 103, utilize stochastic simulation technology, Corpus--based Method rock physics petrofacies model produces the elastic parameter and the reservoir physical parameter training sample set that comprise petrofacies information;

Step 104, input pre-stack seismic inversion performance data and petrofacies data, utilize training sample set, solve the object inversion function retrained based on petrofacies.

During concrete enforcement, can remember pre-stack seismic inversion performance data, as elastic parameter data, comprising p-wave impedance, S-wave impedance and density etc., is m.Note reservoir physical parameter, as factor of porosity, gas saturation etc. are R, reservoir physical parameter are divided into n class according to certain interval, i.e. R _i=[R ₁, R ₂..., R _n].According to Bayes's inverting framework, target component for the class gas saturation value under the condition of given elastic parameter data m corresponding to maximum a posteriori probability density, be expressed as:

$\tilde{R}=\arg \underset{R_i\∈R}{\mathrm{Max}}P\left(R_i\right|m)=\arg \underset{R_i\∈R}{\mathrm{Max}}\left\{\frac{P\left(m\right|R_i)\×P\left(R_i\right)}{P\left(m\right)}\right\}$

Wherein P (R _i) be the priori probability density function of the i-th class gas saturation parameter value; P (m|R _i) for being R at given gas saturation parameter value _iprerequisite under, the conditional probability density function of m, claim likelihood function; P (m) is constant, can omit in enforcement.

In enforcement, petrofacies information is included in object inversion function, new object inversion function can be obtained:

$\tilde{R}=\arg \underset{R_i\∈R}{\mathrm{Max}}P\left(R_i\right|m,F)=\arg \underset{R_i\∈R}{\mathrm{Max}}\{P(m,F|R_i)\×P\left(R_i\right)\}$

After adding petrofacies information F, definitely, multi-solution greatly reduces the solution room of object inversion function.Fig. 2 is object inversion function solution room schematic diagram in the embodiment of the present invention, as shown in Figure 2, suppose that study area has three kinds of petrofacies: mud stone, water sandstone, gas sandstone, then the solution room of classic method is as shown in (a) in Fig. 2, and the technical scheme solution room that the embodiment of the present invention provides is as shown in (b) in Fig. 2.Can see, in the technical scheme that the embodiment of the present invention provides, solution room is made up of three parts, petrofacies corresponding different respectively, and solution room is more concrete, can carry out point petrofacies solve according to research needs.As, researcher needs the gas saturation obtaining gas sandstone reservoir, then solution room just can specific to gas sandstone space, and scope greatly reduces, and multi-solution just greatly reduces.

The technical scheme that the embodiment of the present invention provides, whole inverting flow process essence be surrounding target inverting function solve expansion.Comprise reservoir physical parameter prior distribution probability density function P (R _i) solve and likelihood function P (m, F|R _i) solve.

Fig. 3 is the instantiation figure of petrofacies constraint reservoir physical parameter inversion method in the embodiment of the present invention.As shown in Figure 3, can comprise in this example:

Step 301, point petrofacies statistics reservoir physical parameter prior distribution feature.

In enforcement, the information such as Logging information, LITHOFACIES DATA, point petrofacies statistics reservoir physical parameter prior distribution feature.Fig. 4 divides petrofacies to add up prior distribution feature schematic diagram for reservoir physical parameter in this example, and short dash line in Fig. 4, pecked line, short pecked line represent the distribution characteristics of gas sandstone phase, water sandstone phase, mud stone phase porosity respectively.For factor of porosity parameter, its prior distribution feature histogram schematic diagram as shown in Figure 4.

Step 302, determine priori probability density function.

In enforcement, according to reservoir physical parameter prior distribution feature, the distribution function of the known expression formulas such as utilization is uniformly distributed, Gaussian distribution, exponential distribution approaches, and obtains concrete reservoir physical parameter priori probability density function P (R _i).As shown in Figure 4, in gas sandstone phase reservoir, reservoir physical parameter prior distribution feature meets Gaussian distribution, then the reservoir physical parameter priori probability density function that Gaussian distribution can be utilized to approach obtain is:

$P\left(R_i\right)=1/2{\mathrm{\πe}}^{-{(R_i-\stackrel{\‾}{R})}^2/{\mathrm{\σ}}^2}$

Wherein σ is respectively sample average, variance, can ask for according to logging trace.

In embodiment, can using petrofacies information as constraint information with pre-stack seismic inversion performance data as known input data, according to Bayes's inverting framework, by object inversion value and reservoir physical parameter R _i=[R ₁, R ₂..., R _n] value corresponding to maximum a posteriori probability value when being defined as known input data obtaining object inversion function is wherein, F is petrofacies information, m is pre-stack seismic inversion performance data.

Step 303, determine petrophysical model.

In enforcement; reservoir physical parameter is expressed as the function of elastic parameter by conventional rock Method of Physical Modeling: m=f (R); do not consider that petrofacies are on the impact of model accuracy; the embodiment of the present invention is on conventional rock physical modeling basis; according to Reservoir Lithofacies feature; carry out a point petrofacies modeling, propose the concept of rock physics petrofacies model: m=f (R, F).Modeling method of the present invention, is equivalent to establish petrophysical model separately to each petrofacies, is different from the traditional modeling method all petrofacies all being adopted to identical petrophysical model.

Step 304, formation statistics rock physics petrofacies model.

In enforcement, true relation between reservoir physical parameter and elastic parameter is more accurately reflected in order to make rock physics petrofacies model, the embodiment of the present invention is on the basis of rock physics petrofacies model, according to the difference degree between model and practical logging data, determine stochastic error ε, form statistics rock physics petrofacies model m=f (R, F)+ε.

Step 305, reservoir parameter and elastic parameter divide petrofacies joint sample space.

In enforcement, according to reservoir physical parameter prior distribution probability density function P (R described in statistics petrophysical model m=f (R, F)+ε and step 302 _i), adopt and produce Ns reservoir physical parameter, elastic parameter data based on markovian Monte Carlo stochastic simulation technology, formation reservoir physical parameter and elastic parameter divide petrofacies joint sample space { (m _k, R _k) _f} _{k=1,2 ..., Ns}.Be different from reservoir physical parameter and the elastic parameter joint sample space of classic method: (m _k, R _k) _{k=1,2 ..., Ns}, the embodiment of the present invention adds petrofacies information in the reservoir physical parameter, elastic parameter joint distribution sample space of classic method, make the corresponding relation of reservoir physical parameter and elastic parameter more concrete be subordinated to a certain petrofacies.

A point petrofacies statistics for step 306, likelihood function asks for strategy.

In enforcement, divide petrofacies joint sample space as training sample according to elastic parameter, in the embodiment of the present invention, the sample that different petrofacies are corresponding different.The asking for of likelihood function in the embodiment of the present invention, can adopt a point petrofacies training sample statistics to ask for reservoir physical parameter strategy:

$P(m_j,F|R_i)=\frac{n{\left(m_j\right)}_F}{n\left(R_i\right)}$

Wherein, n (R _i) represent that reservoir physical parameter value equals R _inumber of samples; N (m _j) _frepresent in petrofacies to be in the training sample of F, reservoir physical parameter value equals R _iand elastic parameter value is m _jnumber of samples.

Step 307, petrofacies constraint inverting object inversion function.

In enforcement, be multiplied with likelihood function according to reservoir physical parameter prior distribution probability density function striked in step 302 and step 306, obtain the calculating formula of posterior probability density function: R _i=P (m, F|R _i) × P (R _i).

Step 308, input pre-stack seismic inversion performance data and petrofacies data.

In enforcement, input pre-stack seismic inversion performance data and petrofacies volume data, pre-stack seismic inversion performance data comprises: the elastic parameter data such as velocity of longitudinal wave, shear wave velocity, density, Poisson ratio, Lame parameter, p-wave impedance, S-wave impedance one of them or combination in any, concrete implement in these data can be obtained by existing multiple pre-stack seismic inversion technology and commercial software.Petrofacies data comprise: the data of image study district Lithofacies Types; Petrofacies data can be obtained by existing lithology prediction technology and commercial software.

Step 309, export reservoir parameter corresponding to maximum a posteriori probability density as final inversion result.

In enforcement, reservoir physical parameter is divided into n class according to certain interval, i.e. R _i=[R ₁, R ₂..., R _n], point petrofacies calculate every class reservoir physical parameter R _icorresponding posterior probability density function, the class reservoir physical parameter getting maximum a posteriori probability density corresponding is final inversion result: $\tilde{R}=\arg \underset{R_i\∈R}{\mathrm{Max}}\left\{P(m,F|R_i\×P\left(R_i\right)\right\}.$

In embodiment, point petrofacies can count corresponding reservoir physical parameter prior distribution probability density function P (R _i) and likelihood function P (m, the F|R of corresponding elastic parameter and petrofacies _i); According to Bayesian formula, obtain posterior probability density function P (R _i| m, F)=P (R _i) P (m, F|R _i); Getting reservoir physical parameter corresponding to maximum a posteriori probability density value is final inversion result: $\tilde{R}=\arg \underset{R_i\∈R}{\mathrm{Max}}P\left(R_i\right|m,F).$

Fig. 5 is embodiment of the present invention study area A well elastic parameter and reservoir physical parameter logging trace, petrofacies schematic diagram; This study area objective interval Lithofacies Types is divided into three kinds: mud stone phase, water sandstone phase, gas sandstone phase.Can be seen by Fig. 5, in different petrofacies, the reservoir physical parameter such as factor of porosity, water saturation is also different from the corresponding relation of the elastic parameters such as velocity of longitudinal wave, shear wave velocity, density.Compared with other petrofacies, middle water saturation is higher mutually, factor of porosity is comparatively large, density is less, velocity of longitudinal wave is also less for gas sandstone.In practical application, be often more concerned about the reservoir physical parameter in payzone phase (e.g., gas sandstone phase or the equal layer of oil gas production of oil sands).

Fig. 6 is embodiment of the present invention A well prior art conventional rock physical modeling result schematic diagram, dotted line represents model curve, realize representing measured curve, as shown in Figure 6, model curve (dotted line) is poor with measured curve (solid line) Integral-fit degree.Figure only gets along identical higher at gas sandstone, and this illustrates that classic method adopts merely single rock physical model to carry out modeling, and model is also not suitable for other petrofacies.

Fig. 7 is embodiment of the present invention rock physics petrofacies modeling result schematic diagram, dotted line represents model curve, realize representing measured curve, the method of point petrofacies modeling adopting the embodiment of the present invention to propose, respectively corresponding rock physics petrofacies model is set up mutually to mud stone phase, water sandstone phase, gas sandstone, as shown in Figure 7.Can see, model curve and measured curve all coincide better at all petrofacies places.

Fig. 8 is embodiment of the present invention A well individual well model gas sandstone phase gas saturation, porosity inversion result schematic diagram, and wherein dotted line represents inversional curve of the present invention, realizes representing measured curve; As shown in Figure 8, can see, gas sandstone mutually in, gas saturation and porosity inversion result and measured curve coincide better.

Fig. 9 is that A well lithofacies successions schematic diagram is crossed in embodiment of the present invention study area, its intermediate value is greater than 3.5 for gas sandstone phase, be less than 2.5 for mud stone phase, all the other are water sandstone phase, to predict gas sand facies reservoir gas-bearing saturation degree, factor of porosity, input parameter is prestack inversion performance data: the elastic parameters such as p-wave impedance, S-wave impedance and density, and the embodiment of the present invention has carried out example inverting to crossing A well profile.

Figure 10 is that A well gas saturation inverting diagrammatic cross-section is crossed in embodiment of the present invention study area, Figure 11 is gas saturation inverting section well lie inversion result and gas saturation logging trace Comparative result schematic diagram in Figure 10, as shown in figure 11, can see, gas sandstone mutually in, gas saturation inversion result and measured curve coincide better, and different gas saturation interval is all correctly shown.

Figure 12 is that A well porosity inversion diagrammatic cross-section is crossed in embodiment of the present invention study area, Figure 13 is Figure 10 porosity inverting section well lie inversion result and porosity log Dependence Results contrasts schematic diagram, as shown in figure 13, can see, gas sandstone mutually in, factor of porosity result and measured curve coincide better, and different aperture degree interval is all correctly shown.

During concrete enforcement, interpretation work person according to gas saturation, porosity inversion result, preferably can have commercial value well location and exploits: high gas saturation, high porosity interval.

Based on same inventive concept, a kind of petrofacies constraint reservoir physical parameter inverting device is additionally provided in the embodiment of the present invention, it is similar that the principle of dealing with problems due to this device and petrofacies retrain reservoir physical parameter inversion method, therefore the enforcement of this device see the enforcement of corresponding method, can repeat part and repeats no more.

Figure 14 is petrofacies constraint reservoir physical parameter inverting apparatus structure schematic diagram in the embodiment of the present invention, as shown in figure 14, comprising:

Object inversion function module 1401, for according to Bayes's inverting framework, in conjunction with petrofacies information, sets up the object inversion function based on petrofacies constraint;

Physics petrofacies model module 1402, for introducing petrofacies information, sets up statistics rock physics petrofacies model;

Stochastic simulation module 1403, for utilizing stochastic simulation technology, Corpus--based Method rock physics petrofacies model produces the elastic parameter and the reservoir physical parameter training sample set that comprise petrofacies information;

Solving module 1404, for inputting pre-stack seismic inversion performance data and petrofacies data, utilizing training sample set, the object inversion function retrained based on petrofacies is solved.

In enforcement, object inversion function module specifically may be used for:

Using petrofacies information as constraint information with pre-stack seismic inversion performance data as known input data, according to Bayes's inverting framework, by object inversion value and reservoir physical parameter R _i=[R ₁, R ₂..., R _n] value corresponding to maximum a posteriori probability value when being defined as known input data obtaining object inversion function is wherein, F is petrofacies information, m is pre-stack seismic inversion performance data.

In enforcement, physics petrofacies model module specifically may be used for:

Point petrofacies set up determinacy petrophysical model m=f (R, F), on this model basis, according to the difference degree between model and real data, add stochastic error ε, form statistics rock physics petrofacies model m=f (R, F)+ε.

In enforcement, stochastic simulation module specifically may be used for:

Corpus--based Method rock physics petrofacies model, adopts Monte-Carlo Simulation technical point petrofacies simulation elastic parameter and reservoir physical parameter joint distribution sample space { (m _k, R _k) _f} _{k=1,2 ..., Ns}as training sample set.

In enforcement, described pre-stack seismic inversion performance data can comprise: velocity of longitudinal wave, shear wave velocity, density, Poisson ratio, Lame parameter, p-wave impedance, S-wave impedance one of them or combination in any;

Described petrofacies data can comprise: the data of image study district Lithofacies Types.

In enforcement, solve module and specifically may be used for:

Point petrofacies count the likelihood function P (m, F|R) of corresponding reservoir physical parameter prior distribution probability density function P (R) and corresponding elastic parameter and petrofacies;

According to Bayesian formula, obtain posterior probability density function P (R _i| m, F)=P (R _i) P (m, F|R _i);

Getting reservoir physical parameter corresponding to maximum a posteriori probability density value is final inversion result:

The object of the technical scheme that the embodiment of the present invention provides is in the process of rock physics modeling, not consider petrofacies difference for traditional reservoir physical parameter inversion method, cause that model and real data differ greatly, inverting multi-solution is strong, the problems such as result precision is lower, realize high-precision reservoir physical parameter quantitative inversion method.The thinking that the program utilizes the modeling of statistics rock physics to combine with Bayes's inversion principle, the concept of statistics rock physics petrofacies modeling is proposed, then the object inversion function based on petrofacies constraint and point petrofacies solution strategies is derived, scheme has higher practicality, effectively can reduce the multi-solution of inversion result, quantitative reservoir physical parameter inversion result can be obtained, thus improve reservoir prediction success ratio.

The advantage of the embodiment of the present invention is: 1) take into account the impact of petrofacies on inversion result, and propose statistics rock physics petrofacies model, point petrofacies set up petrophysical model, and theory is more rigorous, more realistic; 2) the object inversion function of petrofacies constraint, definitely, multi-solution is greatly less, and precision is higher for process; 3) divide the statistics strategy of petrofacies, make to utilize training sample set to solve object inversion function more efficient; 4) by the practical application of real data, the position of Favorable Reservoir can be described accurately.

Those skilled in the art should understand, embodiments of the invention can be provided as method, system or computer program.Therefore, the present invention can adopt the form of complete hardware embodiment, completely software implementation or the embodiment in conjunction with software and hardware aspect.And the present invention can adopt in one or more form wherein including the upper computer program implemented of computer-usable storage medium (including but not limited to magnetic disk memory, CD-ROM, optical memory etc.) of computer usable program code.

The present invention describes with reference to according to the process flow diagram of the method for the embodiment of the present invention, equipment (system) and computer program and/or block scheme.Should understand can by the combination of the flow process in each flow process in computer program instructions realization flow figure and/or block scheme and/or square frame and process flow diagram and/or block scheme and/or square frame.These computer program instructions can being provided to the processor of multi-purpose computer, special purpose computer, Embedded Processor or other programmable data processing device to produce a machine, making the instruction performed by the processor of computing machine or other programmable data processing device produce device for realizing the function of specifying in process flow diagram flow process or multiple flow process and/or block scheme square frame or multiple square frame.

These computer program instructions also can be stored in can in the computer-readable memory that works in a specific way of vectoring computer or other programmable data processing device, the instruction making to be stored in this computer-readable memory produces the manufacture comprising command device, and this command device realizes the function of specifying in process flow diagram flow process or multiple flow process and/or block scheme square frame or multiple square frame.

These computer program instructions also can be loaded in computing machine or other programmable data processing device, make on computing machine or other programmable devices, to perform sequence of operations step to produce computer implemented process, thus the instruction performed on computing machine or other programmable devices is provided for the step realizing the function of specifying in process flow diagram flow process or multiple flow process and/or block scheme square frame or multiple square frame.

Above-described specific embodiment; object of the present invention, technical scheme and beneficial effect are further described; be understood that; the foregoing is only specific embodiments of the invention; the protection domain be not intended to limit the present invention; within the spirit and principles in the present invention all, any amendment made, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.

Claims (12)

1. a petrofacies constraint reservoir physical parameter inversion method, is characterized in that, comprising:

According to Bayes's inverting framework, in conjunction with petrofacies information, set up the object inversion function based on petrofacies constraint;

Introduce petrofacies information, set up statistics rock physics petrofacies model;

Utilize stochastic simulation technology, Corpus--based Method rock physics petrofacies model produces the elastic parameter and the reservoir physical parameter training sample set that comprise petrofacies information;

Input pre-stack seismic inversion performance data and petrofacies data, utilize training sample set, solve the object inversion function retrained based on petrofacies.

2. the method for claim 1, is characterized in that, described according to Bayes's inverting framework, in conjunction with petrofacies information, sets up the object inversion function based on petrofacies constraint, comprising:

Using petrofacies information as constraint information with pre-stack seismic inversion performance data as known input data, according to Bayes's inverting framework, by object inversion value and reservoir physical parameter R _i=[R ₁, R ₂..., R _n] value corresponding to maximum a posteriori probability value when being defined as known input data obtaining object inversion function is wherein, F is petrofacies information, m is pre-stack seismic inversion performance data.

3. method as claimed in claim 2, is characterized in that, described introducing petrofacies information, sets up statistics rock physics petrofacies model, comprising:

Point petrofacies set up determinacy petrophysical model m=f (R, F), on this model basis, according to the difference degree between model and real data, add stochastic error ε, form statistics rock physics petrofacies model m=f (R, F)+ε.

4. method as claimed in claim 3, is characterized in that, describedly utilizes stochastic simulation technology, and Corpus--based Method rock physics petrofacies model produces the elastic parameter and the reservoir physical parameter training sample set that comprise petrofacies information, comprising:

Corpus--based Method rock physics petrofacies model, adopts Monte-Carlo Simulation technical point petrofacies simulation elastic parameter and reservoir physical parameter joint distribution sample space { (m _k, R _k) _f} _{k=1,2 ..., Ns}as training sample set.

5. the method as described in any one of Claims 1-4, is characterized in that, described pre-stack seismic inversion performance data comprises: velocity of longitudinal wave, shear wave velocity, density, Poisson ratio, Lame parameter, p-wave impedance, S-wave impedance one of them or combination in any;

Described petrofacies data comprise: the data of image study district Lithofacies Types.

6. method as claimed in claim 4, it is characterized in that, described input pre-stack seismic inversion performance data and petrofacies data, utilize training sample set, solves, comprising object inversion function:

Point petrofacies count corresponding reservoir physical parameter prior distribution probability density function P (R _i) and likelihood function P (m, the F|R of corresponding elastic parameter and petrofacies _i);

According to Bayesian formula, obtain posterior probability density function P (R _i| m, F)=P (R _i) P (m, F|R _i);

Getting reservoir physical parameter corresponding to maximum a posteriori probability density value is final inversion result:

7. a petrofacies constraint reservoir physical parameter inverting device, is characterized in that, comprising:

Object inversion function module, for according to Bayes's inverting framework, in conjunction with petrofacies information, sets up the object inversion function based on petrofacies constraint;

Physics petrofacies model module, for introducing petrofacies information, sets up statistics rock physics petrofacies model;

Stochastic simulation module, for utilizing stochastic simulation technology, Corpus--based Method rock physics petrofacies model produces the elastic parameter and the reservoir physical parameter training sample set that comprise petrofacies information;

Solving module, for inputting pre-stack seismic inversion performance data and petrofacies data, utilizing training sample set, the object inversion function retrained based on petrofacies is solved.

8. device as claimed in claim 7, is characterized in that, described object inversion function module specifically for:

Using petrofacies information as constraint information with pre-stack seismic inversion performance data as known input data, according to Bayes's inverting framework, by object inversion value and reservoir physical parameter R _i=[R ₁, R ₂..., R _n] value corresponding to maximum a posteriori probability value when being defined as known input data obtaining object inversion function is wherein, F is petrofacies information, m is pre-stack seismic inversion performance data.

9. device as claimed in claim 8, is characterized in that, described physics petrofacies model module specifically for:

Point petrofacies set up determinacy petrophysical model m=f (R, F), on this model basis, according to the difference degree between model and real data, add stochastic error ε, form statistics rock physics petrofacies model m=f (R, F)+ε.

10. device as claimed in claim 9, is characterized in that, described stochastic simulation module specifically for:

Corpus--based Method rock physics petrofacies model, adopts Monte-Carlo Simulation technical point petrofacies simulation elastic parameter and reservoir physical parameter joint distribution sample space { (m _k, R _k) _f} _{k=1,2 ..., Ns}as training sample set.

11. devices as described in any one of claim 7 to 10, it is characterized in that, described pre-stack seismic inversion performance data comprises: velocity of longitudinal wave, shear wave velocity, density, Poisson ratio, Lame parameter, p-wave impedance, S-wave impedance one of them or combination in any;

Described petrofacies data comprise: the data of image study district Lithofacies Types.

12. devices as claimed in claim 10, is characterized in that, described in solve module specifically for:

Point petrofacies count the likelihood function P (m, F|R) of corresponding reservoir physical parameter prior distribution probability density function P (R) and corresponding elastic parameter and petrofacies;

According to Bayesian formula, obtain posterior probability density function P (R _i| m, F)=P (R _i) P (m, F|R _i);

Getting reservoir physical parameter corresponding to maximum a posteriori probability density value is final inversion result: