CN104612660B - MRF-based oil and gas reservoir lithofacies stochastic simulation and achievement method - Google Patents
MRF-based oil and gas reservoir lithofacies stochastic simulation and achievement method Download PDFInfo
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- CN104612660B CN104612660B CN201510035241.4A CN201510035241A CN104612660B CN 104612660 B CN104612660 B CN 104612660B CN 201510035241 A CN201510035241 A CN 201510035241A CN 104612660 B CN104612660 B CN 104612660B
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Abstract
The invention discloses an MRF-based oil and gas reservoir lithofacies stochastic simulation and achievement method. A contingent probability formula of an MRF is adopted, a potential function is selected to serve as a logarithmetic transition probability function to obtain the lithofacies occurrence probability of a target position, known points and simulated points in a neighborhood are used, the thought that sequential simulation and condition simulation are combined is adopted, and the occurrence probabilities of various lithofacies in a to-be-simulated area are calculated according to a transition probability function graph; Monte Carlo stochastic simulation is adopted for obtaining lithofacies simulation results of grids. The method is integrated into a SGeMS platform in an algorithm plug-in mode through an application program interface, and the effects of rotation by any angle in a target reservoir of three-dimensional space and cutting of profiles at any position can be achieved. As the existence of the transition probability directivity, the accuracy for depicting a one-way distribution trend and anisotropy features of lithofacies transferring and forecasting the oil reservoir lithofacies based on the MRF is obviously improved.
Description
Technical field
The present invention relates to a kind of oil and gas reservoir stochastic simulation of lithofacies and implementation method based on MRF.
Background technology
Petroleum reservoir visualization is that reservoir model is combined with visualization technique, intuitively shows that reservoir belongs to by image
The regularity of distribution of property.Visually storing layer software system such as GASOR, Petrel, RMS of comparative maturity is provided only to terminal both at home and abroad
User interface, it is difficult to integrated new reservoir modeling algorithm.For substantial amounts of geological exploration data, engineering calculation software is such as
Though MATLAB has application's expansibility and visualization function, the multiple challenges such as calculating time and memory space are faced with,
Its three-dimensional simulation result often has that border is coarse, the inherent shortcoming such as internal structure is difficult to characterize.On the other hand, traditional reservoir
It is based on Ke Lijin interpolation algorithms more modeling software, and using the analogy method of Ke Lijin variants, Ke Lijin algorithms has space pair
Title property, and actual geological structure often has anisotropy and non-homogeneity.A set of can characterize based on certain platform of research
The transplantation algorithm groupware of complicated reservoirs structure becomes the problem of urgent need to resolve in petroleum exploration technology.
The content of the invention
The invention provides a kind of oil and gas reservoir stochastic simulation of lithofacies and implementation method based on MRF, it is intended that being
In overcoming prior art, oil and gas reservoir stochastic simulation of lithofacies has border coarse, the problem that internal structure is difficult to characterize etc..
A kind of oil and gas reservoir stochastic simulation of lithofacies and implementation method based on MRF, including following step：
Step 1：Obtain the prospect pit petrofacies attribute data for waiting to simulate the oil and gas reservoir of well section selected by area；
Step 2：Treating simulation area carries out gridding process, and carries out petrofacies numeral mark；
According to waiting to simulate the space size and petrofacies Changing Pattern in area, treating simulation area carries out gridding process, makes
There is a kind of petrofacies attribute, i.e. each grid to have unique petrofacies numeral mark to obtain each grid；
Step 3：The regional grid of simulation is treated, the transition probability between petrofacies in each position in Descartes direction is calculated, is obtained
Take the transition function figure for waiting the Descartes direction for simulating regional grid；
Wait to simulate the probability for changing into other petrofacies attributes in regional grid on each position from existing petrofacies attribute；
Step 4：The transition function figure obtained according to step 3, calculates according to goal of condition probability formula and treats
In simulated domain grid, each target location belongs to the probability of happening of all kinds of petrofacies：
The goal condition new probability formula is the condition probability formula using MRF, chooses transfer of the potential function for logarithmetics
Probability function is obtained；
MRF is Markov chain expansion spatially, it is characterised in that the conditional probability of target location and its neighbours system
System is relevant, and the probability expression of its Joint Distribution can be by Gibbs distributed problem solvings；
Wherein, s1,…,sNThe neighbours of target location s, and be according to the distance between target location from closely to remote row
The sequence of row；l1,…lN, k, f respectively petrofacies attribute numeral mark, span is { 1,2,3,4 ..., K }, corresponding to step
The petrofacies attribute of the survey region obtained in 1；hiThe distance between neighbours' point of target location and target location is represented, it is referred to as stagnant
Afterwards away from according to distance from closely to far value successively；Expression is changed at intervals of h by the petrofacies attribute k of target locationi
Neighbours corresponding to petrofacies attribute liTransition probability；Represent and the closest drilling well position s in target location1It is right
Petrofacies l answered1It is converted into the transition probability of petrofacies k corresponding to the s of target location, the drilling well position s1Between the s of target location
Distance is h1；K represents all of petrofacies species sum；N represents neighbours' number of target location；xsIt is the petrofacies at the s of target location
Attribute mark, xrIt is the petrofacies attribute mark at the r of position.
In formula (1), by the closest sample spot in selected distance target location first (drilling well position) s1, calculate by this
Position s1Corresponding petrofacies attribute l1It is converted at a distance of h1Target location s corresponding to petrofacies attribute k transition probability
In the formula, transition probabilityDirection and remaining N-1 target location neighbours point the lucky phase in transition probability direction
Instead, the method excessively can be estimated greatly with big class in effectively solving reservoir modeling and group excessively little estimation problem；
Step 5：According to step 4 it is calculated treat each position in simulated domain grid belong to all kinds of petrofacies generation it is general
Rate, using Monte Carlo stochastic simulation, obtains lithofacies distribution simulation drawing.
Reservoir simulation based on transition probability requires that survey data meets geneva, i.e. short distance dependency；In order that institute
The prospect pit petrofacies attribute data of collection meets geneva requirement, uses gathered prospect pit petrofacies attribute data to carry out reservoir simulation
Before make the following judgment：
According to setting step-length, the prospect pit petrofacies attribute data gathered to step 1 in vertical direction, is visited using X 2 test
The geneva of well petrofacies attribute data；If prospect pit petrofacies attribute data has geneva, according to the petrofacies category for treating simulation area
Property classification numeral mark is carried out to petrofacies attribute data, otherwise, if the prospect pit petrofacies attribute data have geneva, weigh
The well section for waiting to simulate area is chosen newly, return to step 1 obtains prospect pit petrofacies attribute data.
Dynamic link library is generated by program compiling to the method described in the step 1- step 5, meanwhile, write simulation ginseng
Number inputting interface；Dynamic link library and analog parameter inputting interface are incorporated under the plug-in unit catalogue of SGeMS platforms, in debug
With operational solution under release patterns, MRF algorithm groupwares are obtained, using the SGeMS platforms for having merged MRF algorithm groupwares
Realize oil and gas reservoir stochastic simulation of lithofacies.
According to the calculated probability of happening for treating that each position in simulated domain grid belongs to all kinds of petrofacies of step 4, utilize
The petrofacies attribute numeral mark of maximum a posteriori probability in each position is assigned to the position, is obtained by Monte Carlo stochastic simulation
The petrofacies analog result of the grid, and then obtain the lithofacies distribution simulation drawing of whole reservoir.By mouse drag, it is possible to achieve mesh
Effect of the mark reservoir in three dimensions Arbitrary Rotation." Volume under setting SGeMS platforms " Preferences " column
The x of Explorer ", y, z value, can obtain the section cutting drawing of objective body optional position.
Beneficial effect
The invention provides a kind of oil and gas reservoir stochastic simulation of lithofacies and implementation method based on MRF, the method utilizes MRF
Condition probability formula, choose potential function for the transition function of logarithmetics and obtain the petrofacies probability of happening of target location, examine
Consider mathematical relationship between group potential function and transition function in neighborhood, simulated using the known point in neighborhood and a little,
The thought combined using sequential simulation and condition simulation, is calculated according to transition function figure and treats all kinds of petrofacies of simulated domain
Probability of happening, using Monte Carlo stochastic simulation, obtains the petrofacies analog result of the grid.Using the analogy method by compiling
Translate, MRF models are incorporated in SGeMS platforms in the form of algorithm groupware using application programming interfaces, by setting the platform
The x of " Volume Explorer " under " Preferences " column, y, z value, it is possible to achieve any in Three dimensional Targets reservoir
Angle rotates and carries out optional position section cutting.MRF algorithms can reflect the seriality of complex space and in the application well
Can more accurate description geologic body spatial distribution, while avoided ask for characterize reservoir form complicated equation.Due to transfer generally
The presence of rate directivity, for the unidirectional distribution trend and anisotropic character of portraying petrofacies transfer, based on MRF algorithm groupwares
SGeMS software platforms simulation effect is better than the traditional visually storing layer system based on Ke Lijin algorithms, and petroleum reservoir petrofacies are pre-
The accuracy rate of survey is significantly improved.
Description of the drawings
Fig. 1 is the drilling well position view for treating simulated domain；
Fig. 2 is that MRF algorithm datas specify interface；
Fig. 3 is MRF algorithm condition UIs；
Fig. 4 is the simulation effect diagram using the method for the invention；
Fig. 5 is Three dimensional Targets reservoir section cutting drawing.
Specific embodiment
Below in conjunction with drawings and Examples, the present invention is described further.
It is simulated for 75th area of system in Tahe Oilfield sand in this example, according to the drilling well of S66 wells, S92 wells, S67 wells and S75 wells
Information, it is considered to area's reservoir characteristics, with 1 meter as step-length, carries out petrofacies on vertical to S66 wells, S92 wells, S67 wells and S75 wells
Substitute statistical analysiss, the geneva of petrofacies sequence is verified with X 2 test.It was observed that petrofacies sequence mainly have four classes, respectively
Sandstone, conglomerate, mud stone, limestone are represented with numeral 1,2,3,4 successively.
A kind of oil and gas reservoir stochastic simulation of lithofacies and implementation method based on MRF, including following step：
Step 1：Obtain the prospect pit petrofacies attribute data for waiting to simulate the oil and gas reservoir of well section selected by area；
Step 2：Treating simulation area carries out gridding process, and carries out petrofacies numeral mark；
According to waiting to simulate the space size and petrofacies Changing Pattern in area, treating simulation area carries out gridding process, makes
There is a kind of petrofacies attribute, i.e. each grid to have unique petrofacies numeral mark to obtain each grid；
According to setting step-length, the prospect pit petrofacies attribute data gathered to step 1 in vertical direction, is visited using X 2 test
The geneva of well petrofacies attribute data；If prospect pit petrofacies attribute data has geneva, according to the petrofacies category for treating simulation area
Property classification numeral mark is carried out to petrofacies attribute data, otherwise, if the prospect pit petrofacies attribute data have geneva, weigh
The well section for waiting to simulate area is chosen newly, return to step 1 obtains prospect pit petrofacies attribute data.
75th area of system in Tahe Oilfield sand are long from south to north 4000 meters, 3000 meters of thing length；5200-5300 rice is selected to be the layer for simulating well section
Section depth, is SQ2 low stand system tracts, and the interval of vertical direction takes 1 meter, and lateral spacing is taken as 50 meters, finally gives by grid system
The simulation space of system composition, as shown in Figure 1.
Step 3：The regional grid of simulation is treated, the transition probability between petrofacies in each position in Descartes direction is calculated, is obtained
Take the transition function figure for waiting the Descartes direction for simulating regional grid；
Wait to simulate the probability for changing into other petrofacies attributes in regional grid on each position from existing petrofacies attribute；
Step 4：The transition function figure obtained according to step 3, calculates according to goal of condition probability formula and treats
In simulated domain grid, each target location belongs to the probability of happening of all kinds of petrofacies：
The goal condition new probability formula is the condition probability formula using MRF, chooses transfer of the potential function for logarithmetics
Probability function is obtained；
MRF is Markov chain expansion spatially, it is characterised in that the conditional probability of target location and its neighbours system
System is relevant, and the probability expression of its Joint Distribution can be by Gibbs distributed problem solvings.
(1) in formula, s1,…,sNThe neighbours of target location s, and be according to the distance between target location from closely to remote
The sequence of arrangement；l1,…lN, k, f respectively petrofacies attribute numeral mark, span is { 1,2,3,4 ..., K }, corresponding to step
The petrofacies attribute of the survey region obtained in rapid 1；hiThe distance between neighbours' point of target location and target location is represented, referred to as
It is delayed away from according to distance from closely to far value successively.Represent by the petrofacies attribute k of target location change at intervals of
hiNeighbours corresponding to petrofacies attribute liTransition probability；Represent and the closest drilling well position s in target location1
Corresponding petrofacies l1It is converted into the transition probability of petrofacies k corresponding to the s of target location, the drilling well position s1Between the s of target location
Distance be h1；K represents all of petrofacies species sum；N represents neighbours' number of target location；xsIt is the rock at the s of target location
Phase attribute mark, xrIt is the petrofacies attribute mark at the r of position.The probability of happening of petrofacies is the product of normalized transition probability；
In formula (1), by the closest sample spot in selected distance target location first (drilling well position) s1, calculate by this
Position s1Corresponding petrofacies attribute l1It is converted at a distance of h1Target location s corresponding to petrofacies attribute k transition probability
In the formula, transition probabilityDirection and remaining N-1 target location neighbours' point transition probability it is just the opposite, this
Method excessively can be estimated greatly with big class in effectively solving reservoir modeling and group excessively little estimation problem.
Step 5：Step 4 methods described is generated into dynamic link library by program compiling, meanwhile, write analog parameter input
Interface；Dynamic link library and analog parameter inputting interface are incorporated under the plug-in unit catalogue of SGeMS platforms, debug with
Operational solution under release patterns, obtains MRF algorithm groupwares.The MRF algorithm groupwares are mainly made up of two parts：It is dynamic
State chained library (DLL) and parameter inputting interface, plug-in interface is as shown in Figures 2 and 3；That what is designed comprises the following steps that：
(1) header file (MRF.h) named with MRF is write, with the master file (MRF.cpp) that MRF is named.Provide derived class
In each function definition, the acceptance of parameter and the initialization of parameter are realized by initialization function initialize (), and
The function for receiving is stored in data member.Defined parameters interface.In the top addition algorithm tag of Ui files.
(2) it is grand in master file (MRF.cpp) loading GEOSTAT_PLUGIN, with class name in GEOSTAT_PLUGIN is grand
(MRF) as parameter, carry out function call.
(3) C++ source files are compiled and obtains dynamic link library (.dll), dynamic link library (.dll) and parameter inputting interface
(.ui) copy under plug-in unit catalogue, the operational solution under debug with release patterns, obtains MRF algorithm groupwares again.
In Fig. 2, for selecting to treat the grid of simulated domain, " New Property Name " is for right for " GRID " drop-down menu
The attribute newly specified is named." object ", for selecting hard data (such as well data), " Property Name " is for selecting
Specifying information in hard data (such as petrofacies attribute).
Fig. 3 is given search condition." Min " and " Max " option under " Condition data " column is used to specify to be searched
Neighbours' number scope in rope ellipse." range " represents search radius, can set Max (maximum), Min (minimum) and Med
(centre) value." angles " refers to the search angle in Descartes direction, respectively with " Azimuth " (azimuth, vertical z directions),
" Dip " (apparent dip, horizontal x directions) and " Rake " (apparent dip, horizontal y directions) represents." transiogram " is for importing
Existing transition probability model, the type sum in " Number of category " given selected work area, " Probability " will
Seek the marginal probability (i.e. prior probability) for being input into various classifications (such as petrofacies).
According to the calculated probability of happening for treating that each position in simulated domain grid belongs to all kinds of petrofacies of step 4, utilize
The petrofacies attribute numeral mark of maximum a posteriori probability in each position is assigned to the position, is obtained by Monte Carlo stochastic simulation
The petrofacies analog result of the grid, and then the lithofacies distribution simulation drawing of whole reservoir is obtained, as shown in Figure 4.By mouse drag,
Effect of the target reservoir in three dimensions Arbitrary Rotation can be realized.Under setting SGeMS platforms " Preferences " column
The x of " Volume Explorer ", y, z value can obtain the section cutting drawing of objective body optional position, as shown in Figure 5.
Claims (3)
1. a kind of oil and gas reservoir stochastic simulation of lithofacies and implementation method based on MRF, it is characterised in that including following step
Suddenly：
Step 1：Obtain the prospect pit petrofacies attribute data for waiting to simulate the oil and gas reservoir of well section selected by area；
Step 2：Treating simulation area carries out gridding process, and carries out petrofacies numeral mark；
According to waiting to simulate the space size and petrofacies Changing Pattern in area, treating simulation area carries out gridding process so that every
There is individual grid a kind of petrofacies attribute, i.e. each grid to have unique petrofacies numeral mark；
Step 3：The regional grid of simulation is treated, the transition probability between petrofacies in each position in Descartes direction is calculated, acquisition is treated
The transition function figure in the Descartes direction of the regional grid of simulation；
Step 4：The transition function figure obtained according to step 3, calculates according to goal of condition probability formula and waits to simulate
In area grid, each target location belongs to the probability of happening of all kinds of petrofacies：
The goal condition new probability formula is the condition probability formula using MRF, chooses transition probability of the potential function for logarithmetics
Function is obtained；
Wherein, s1,…,sNThe neighbours of target location s, and be according to the distance between target location from closely to remote arrangement
Sequence；l1,…lN, k, f respectively petrofacies attribute numeral mark, span is { 1,2,3,4 ..., K }, corresponding in step 1
The petrofacies attribute of the survey region of acquisition；hiThe distance between neighbours' point of target location and target location is represented, it is referred to as delayed
Away from according to distance from closely to far value successively；Expression is changed at intervals of h by the petrofacies attribute k of target locationi's
Petrofacies attribute l corresponding to neighboursiTransition probability；Represent and the closest drilling well position s in target location1Correspondence
Petrofacies l1It is converted into the transition probability of petrofacies k corresponding to the s of target location, the drilling well position s1Between the s of target location away from
From for h1；K represents all of petrofacies species sum；N represents neighbours' number of target location；xsIt is the petrofacies category at the s of target location
Property mark, xrIt is the petrofacies attribute mark at the r of position；
Step 5：According to the calculated probability of happening for treating that each position in simulated domain grid belongs to all kinds of petrofacies of step 4, profit
Monte Carlo stochastic simulation is used, lithofacies distribution simulation drawing is obtained.
2. a kind of oil and gas reservoir stochastic simulation of lithofacies and implementation method based on MRF according to claim 1, its feature exist
In, according to setting step-length, to step 1 collection prospect pit petrofacies attribute data in vertical direction, using X 2 test prospect pit rock
The geneva of phase attribute data；
If prospect pit petrofacies attribute data has geneva, according to the petrofacies attribute classification for waiting to simulate area to petrofacies attribute data
Numeral mark is carried out, otherwise, if the prospect pit petrofacies attribute data does not have geneva, the well for waiting to simulate area is chosen again
Section, return to step 1 obtain prospect pit petrofacies attribute data.
3. a kind of oil and gas reservoir stochastic simulation of lithofacies and implementation method based on MRF according to claim 1 and 2, which is special
Levy and be, dynamic link library is generated by program compiling to the method described in the step 1- step 5, meanwhile, write simulation ginseng
Number inputting interface；Dynamic link library and analog parameter inputting interface are incorporated under the plug-in unit catalogue of SGeMS platforms, in debug
With operational solution under release patterns, MRF algorithm groupwares are obtained, using the SGeMS platforms for having merged MRF algorithm groupwares
Realize oil and gas reservoir stochastic simulation of lithofacies.
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| CN103206209A (en) * | 2013-03-26 | 2013-07-17 | 中国石油大学(华东) | Comprehensive simulation experiment device for reservoir heterogeneity |
| CN104090303A (en) * | 2014-07-10 | 2014-10-08 | 中国海洋石油总公司 | Seismic inversion method and device |
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| CN103206209A (en) * | 2013-03-26 | 2013-07-17 | 中国石油大学(华东) | Comprehensive simulation experiment device for reservoir heterogeneity |
| CN104090303A (en) * | 2014-07-10 | 2014-10-08 | 中国海洋石油总公司 | Seismic inversion method and device |
| CN104200115A (en) * | 2014-09-12 | 2014-12-10 | 中国石油集团川庆钻探工程有限公司地球物理勘探公司 | Geostatistics simulation based full-formation velocity modeling method |
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