CN104612660A - 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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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 based on MRF and implementation method.
Background technology
Petroleum reservoir is visual is combined with visualization technique by reservoir model, is shown the regularity of distribution of reservoir attribute by image intuitively.The visually storing layer software systems of domestic and international comparative maturity are only supplied to end users operation interface as GASOR, Petrel, RMS, are difficult to integrated new reservoir modeling algorithm.For a large amount of geological exploration data, though engineering calculation software such as MATLAB has application's expansibility and visualization function, but be faced with the multiple challenges such as computing time and memory space, it is coarse that its three-dimensional simulation result often has border, and internal construction is difficult to the inherent shortcomings such as sign.On the other hand, traditional reservoir modeling software is many based on Ke Lijin interpolation algorithm, and adopts the analogy method of Ke Lijin variant, and Ke Lijin algorithm has spatial symmetry, and actual geological structure often has anisotropy and non-homogeneity.Study a set of portable algorithm groupware that can characterize complicated reservoirs structure based on certain platform and become the problem needing solution in petroleum exploration technology badly.
Summary of the invention
The invention provides a kind of oil and gas reservoir stochastic simulation of lithofacies based on MRF and implementation method, its object is to, in order to overcome oil and gas reservoir stochastic simulation of lithofacies in prior art, to have border coarse, the problem that internal construction is difficult to characterize etc.
Based on oil and gas reservoir stochastic simulation of lithofacies and an implementation method of MRF, comprise following step:
Step 1: obtain the prospect pit petrofacies attribute data waiting the oil and gas reservoir of simulating the selected well section in area;
Step 2: treat simulation area and carry out gridding process, and carry out petrofacies figure notation;
According to waiting space size and the petrofacies Changing Pattern of simulating area, treat simulation area and carry out gridding process, make each grid have a kind of petrofacies attribute, namely each grid has unique petrofacies figure notation;
Step 3: treat the regional grid of simulation, the transition probability in each position in calculating Descartes direction between petrofacies, obtains the transition function figure in the Descartes direction of waiting to simulate regional grid;
Namely to wait to simulate in regional grid on each position from the probability of other petrofacies classifications of the class switching one-tenth of existing petrofacies;
Step 4: the transition function figure obtained according to step 3, calculates according to following goal condition new probability formula and treats that in simulated domain grid, each target location belongs to the probability of happening of all kinds of petrofacies:
Described goal condition new probability formula is the condition probability formula utilizing MRF, and choosing potential function is that the transition function of logarithmetics obtains;
MRF is the expansion spatially of Markov chain, and it is characterized in that the conditional probability of target location is only relevant with its neighbor system, the probability expression of its Joint Distribution can by Gibbs distributed problem solving;
Wherein, s
1..., s
nthe neighbours of target location s, and be according to target location between distance near to the sequence of arrangement far away; l
1... l
n, k, f are respectively petrofacies classification figure notation, span be 1,2,3,4 ..., K}, corresponding to the Lithofacies Types of the survey region obtained in step 1; h
idistance between neighbours' point of expression target location and target location, is called delayed distance, according to distance near to value successively far away;
expression is changed into by the Lithofacies Types k of target location and is spaced apart h
ithe Lithofacies Types l corresponding to neighbours
itransition probability;
represent the drilling well position s nearest with target location
1corresponding petrofacies l
1be converted into the transition probability of petrofacies k corresponding to the s of target location, described drilling well position s
1and the distance between the s of target location is h
1; K represents all petrofacies kind sums; N represents neighbours' number of target location; x
sthe petrofacies class formative at s place, target location, x
rit is the petrofacies class formative at r place, position.
In formula (1), by sample spot (drilling well position) s that first selected distance target location is the most contiguous
1, calculate by this position s
1corresponding petrofacies classification l
1be converted at a distance of h
1target location s corresponding to the transition probability of petrofacies classification k
in this formula, transition probability
direction and residue N-1 target location neighbour transition probability direction of putting just the opposite, the method effectively can solve large class in reservoir modeling and estimate excessively greatly and the excessive little estimation problem of group.
Step 5: what calculate according to step 4 treats that in simulated domain grid, each position belongs to the probability of happening of all kinds of petrofacies, utilizes 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 correlation; In order to make gathered prospect pit petrofacies attribute data meet geneva requirement, make the following judgment before using the prospect pit petrofacies attribute data gathered to carry out reservoir simulation:
According to setting step-length, the prospect pit petrofacies attribute data gathered step 1 in vertical direction, adopts the geneva of Chi-square Test prospect pit petrofacies attribute data; If prospect pit petrofacies attribute data has geneva, then according to waiting that the petrofacies attribute classification simulating area carries out figure notation to petrofacies attribute data, otherwise, if described prospect pit petrofacies attribute data does not have geneva, then again choose the well section waiting to simulate area, return step 1 and obtain prospect pit petrofacies attribute data.
By program compilation, dynamic link library is generated to the method described in described step 1-step 5, meanwhile, writes analog parameter inputting interface; Under dynamic link library and analog parameter inputting interface being incorporated into the plug-in unit catalogue of SGeMS platform, operational solution under debug and release pattern, obtain MRF algorithm groupware, utilize the SGeMS platform having merged MRF algorithm groupware to realize oil and gas reservoir stochastic simulation of lithofacies.
What calculate according to step 4 treats that in simulated domain grid, each position belongs to the probability of happening of all kinds of petrofacies, utilize Monte Carlo stochastic simulation, this position is given by the petrofacies attribute number word mark assignment of maximum a posteriori probability in each position, obtain the petrofacies analog result of this grid, and then obtain the lithofacies distribution simulation drawing of whole reservoir.By mouse drag, can realize target reservoir in the effect of three dimensions Arbitrary Rotation.The x of " Volume Explorer " under setting SGeMS platform " Preferences " column, 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 based on MRF and implementation method, the method utilizes the condition probability formula of MRF, choosing potential function is the petrofacies probability of happening that the transition function of logarithmetics obtains target location, consider the mathematical relationship between group potential function and transition function in neighborhood, utilize known point in neighborhood and simulation points, adopt the thought that sequential simulation and condition simulation combine, the probability of happening treating all kinds of petrofacies of simulated domain is calculated according to transition function figure, utilize Monte Carlo stochastic simulation, obtain the petrofacies analog result of this grid.Utilize this analogy method by compiling, application programming interfaces are utilized to be incorporated in SGeMS platform by MRF model with the form of algorithm groupware, by the x of " Volume Explorer " under setting this platform " Preferences " column, y, z value, can be implemented in Three dimensional Targets reservoir Arbitrary Rotation and carries out optional position section cutting.MRF algorithm can reflect well complex space continuity and in the application can the spatial distribution of more accurate description geologic body, avoided the complicated equation asked for and characterize reservoir form simultaneously.Due to the existence of transition probability directionality, for unidirectional distribution trend and the anisotropic character of portraying petrofacies transfer, SGeMS software platform simulate effect based on MRF algorithm groupware is better than the traditional visually storing layer system based on Ke Lijin algorithm, and the accuracy rate of petroleum reservoir petrofacies prediction is significantly improved.
Accompanying drawing explanation
Fig. 1 is the drilling well position view treating simulated domain;
Fig. 2 is that MRF algorithm data specifies interface;
Fig. 3 is MRF algorithm condition UI;
Fig. 4 is the simulate effect schematic diagram of application the method for the invention;
Fig. 5 is Three dimensional Targets reservoir section cutting drawing.
Detailed description of the invention
Below in conjunction with drawings and Examples, the present invention is described further.
Simulate for husky 75th district of system in Tahe Oilfield in this example, according to the drilling information of S66 well, S92 well, S67 well and S75 well, consider this district's reservoir characteristics, with 1 meter for step-length, on vertical, petrofacies replacement statistical analysis is carried out to S66 well, S92 well, S67 well and S75 well, by the geneva of Chi-square Test checking petrofacies sequence.The petrofacies sequence observed mainly contains four classes, is respectively sandstone, conglomerate, mud stone, limestone, uses numeral 1,2,3,4 to represent successively.
Based on oil and gas reservoir stochastic simulation of lithofacies and an implementation method of MRF, comprise following step:
Step 1: obtain the prospect pit petrofacies attribute data waiting the oil and gas reservoir of simulating the selected well section in area;
Step 2: treat simulation area and carry out gridding process, and carry out petrofacies figure notation;
According to waiting space size and the petrofacies Changing Pattern of simulating area, treat simulation area and carry out gridding process, make each grid have a kind of petrofacies attribute, namely each grid has unique petrofacies figure notation;
According to setting step-length, the prospect pit petrofacies attribute data gathered step 1 in vertical direction, adopts the geneva of Chi-square Test prospect pit petrofacies attribute data; If prospect pit petrofacies attribute data has geneva, then according to waiting that the petrofacies attribute classification simulating area carries out figure notation to petrofacies attribute data, otherwise, if described prospect pit petrofacies attribute data does not have geneva, then again choose the well section waiting to simulate area, return step 1 and obtain prospect pit petrofacies attribute data.
Husky 4000 meters long from south to north of 75th district of system in Tahe Oilfield, long 3000 meters of thing; Selection 5200-5300 rice is the interval degree of depth of simulation well section, and be SQ2 low stand system tract, the interval of vertical direction gets 1 meter, and lateral spacing is taken as 50 meters, finally obtains the virtual space be made up of grid system, as shown in Figure 1.
Step 3: treat the regional grid of simulation, the transition probability in each position in calculating Descartes direction between petrofacies, obtains the transition function figure in the Descartes direction of waiting to simulate regional grid;
Namely to wait to simulate in regional grid on each position from the probability of other petrofacies classifications of the class switching one-tenth of existing petrofacies;
Step 4: the transition function figure obtained according to step 3, calculates according to following goal condition new probability formula and treats that in simulated domain grid, each target location belongs to the probability of happening of all kinds of petrofacies:
Described goal condition new probability formula is the condition probability formula utilizing MRF, and choosing potential function is that the transition function of logarithmetics obtains;
MRF is the expansion spatially of Markov chain, and it is characterized in that the conditional probability of target location is only relevant with its neighbor system, the probability expression of its Joint Distribution can by Gibbs distributed problem solving.
(1) in formula, s
1..., s
nthe neighbours of target location s, and be according to target location between distance near to the sequence of arrangement far away; l
1... l
n, k, f are respectively petrofacies classification figure notation, span be 1,2,3,4 ..., K}, corresponding to the Lithofacies Types of the survey region obtained in step 1; h
idistance between neighbours' point of expression target location and target location, is called delayed distance, according to distance near to value successively far away.
expression is changed into by the Lithofacies Types k of target location and is spaced apart h
ithe Lithofacies Types l corresponding to neighbours
itransition probability;
represent the drilling well position s nearest with target location
1corresponding petrofacies l
1be converted into the transition probability of petrofacies k corresponding to the s of target location, described drilling well position s
1and the distance between the s of target location is h
1; K represents all petrofacies kind sums; N represents neighbours' number of target location; x
sthe petrofacies class formative at s place, target location, x
rit is the petrofacies class formative at r place, position.The probability of happening of petrofacies is products of normalized transition probability;
In formula (1), by sample spot (drilling well position) s that first selected distance target location is the most contiguous
1, calculate by this position s
1corresponding petrofacies classification l
1be converted at a distance of h
1target location s corresponding to the transition probability of petrofacies classification k
in this formula, transition probability
direction and the neighbours of residue N-1 target location to put transition probability just the opposite, the method effectively can solve large class in reservoir modeling and estimate excessively greatly and the excessive little estimation problem of group.
Step 5: method described in step 4 is generated dynamic link library by program compilation, meanwhile, writes analog parameter inputting interface; Under dynamic link library and analog parameter inputting interface being incorporated into the plug-in unit catalogue of SGeMS platform, operational solution under debug and release pattern, obtains MRF algorithm groupware.Described MRF algorithm groupware forms primarily of two parts: dynamic link library (DLL) and parameters input interface, and plug-in interface as shown in Figures 2 and 3; The concrete steps of design are as follows:
(1) write with the header file (MRF.h) of MRF name, with the master file (MRF.cpp) of MRF name.Provide the definition of each function in derived class, realize the acceptance of parameter and the initialization of parameter by initialization function initialize (), and the function of reception is stored in data member.Defined parameters interface.Algorithm tag is added at the top of Ui file.
(2) load GEOSTAT_PLUGIN at master file (MRF.cpp) grand, using class name (MRF) as parameter in GEOSTAT_PLUGIN is grand, carry out function call.
(3) compile C++ source file and obtain dynamic link library (.dll), under dynamic link library (.dll) and parameters input interface (.ui) are copied to plug-in unit catalogue, again operational solution under debug and release pattern, obtains MRF algorithm groupware.
In Fig. 2, " GRID " drop-down menu is for selecting the grid treating simulated domain, and " New Property Name " is for naming the attribute of newly specifying." object ", for selected hard data (as well data), " Property Name " is for the specifying information (as petrofacies attribute) in selected hard data.
Fig. 3 is given search condition." Min " and " Max " option under " Condition data " column is used to specify the neighbours' number scope in search ellipse." range " represents search radius, can set Max (maximum), Min (minimum) and Med (centre) value." angles " refers to the search angle in Descartes direction, uses " Azimuth " (azimuth, vertical z direction) respectively, and " Dip " (apparent dip, horizontal x direction) and " Rake " (apparent dip, horizontal y direction) represent." transiogram " is for importing existing transition probability model, the type sum in " Number of category " given selected work area, " Probability " requires the marginal probability (i.e. prior probability) of the various classification of input (as petrofacies).
What calculate according to step 4 treats that in simulated domain grid, each position belongs to the probability of happening of all kinds of petrofacies, utilize Monte Carlo stochastic simulation, this position is given by the petrofacies attribute number word mark assignment of maximum a posteriori probability in each position, obtain the petrofacies analog result of this grid, and then obtain the lithofacies distribution simulation drawing of whole reservoir, as shown in Figure 4.By mouse drag, can realize target reservoir in the effect of three dimensions Arbitrary Rotation.The x of " VolumeExplorer " under setting SGeMS platform " Preferences " column, y, z value, can obtain the section cutting drawing of objective body optional position, as shown in Figure 5.
Claims (3)
1., based on oil and gas reservoir stochastic simulation of lithofacies and an implementation method of MRF, it is characterized in that, comprise following step:
Step 1: obtain the prospect pit petrofacies attribute data waiting the oil and gas reservoir of simulating the selected well section in area;
Step 2: treat simulation area and carry out gridding process, and carry out petrofacies figure notation;
According to waiting space size and the petrofacies Changing Pattern of simulating area, treat simulation area and carry out gridding process, make each grid have a kind of petrofacies attribute, namely each grid has unique petrofacies figure notation;
Step 3: treat the regional grid of simulation, the transition probability in each position in calculating Descartes direction between petrofacies, obtains the transition function figure in the Descartes direction of waiting to simulate regional grid;
Step 4: the transition function figure obtained according to step 3, calculates according to following goal condition new probability formula and treats that in simulated domain grid, each target location belongs to the probability of happening of all kinds of petrofacies:
Described goal condition new probability formula is the condition probability formula utilizing MRF, and choosing potential function is that the transition function of logarithmetics obtains;
Wherein, s
1..., s
nthe neighbours of target location s, and be according to target location between distance near to the sequence of arrangement far away; l
1... l
n, k, f are respectively petrofacies classification figure notation, span be 1,2,3,4 ..., K}, corresponding to the Lithofacies Types of the survey region obtained in step 1; h
idistance between neighbours' point of expression target location and target location, is called delayed distance, according to distance near to value successively far away;
expression is changed into by the Lithofacies Types k of target location and is spaced apart h
ithe Lithofacies Types l corresponding to neighbours
itransition probability;
represent the drilling well position s nearest with target location
1corresponding petrofacies l
1be converted into the transition probability of petrofacies k corresponding to the s of target location, described drilling well position s
1and the distance between the s of target location is h
1; K represents all petrofacies kind sums; N represents neighbours' number of target location; x
sthe petrofacies class formative at s place, target location, x
rit is the petrofacies class formative at r place, position.
Step 5: what calculate according to step 4 treats that in simulated domain grid, each position belongs to the probability of happening of all kinds of petrofacies, utilizes Monte Carlo stochastic simulation, obtains lithofacies distribution simulation drawing.
2. a kind of oil and gas reservoir stochastic simulation of lithofacies based on MRF according to claim 1 and implementation method, it is characterized in that, according to setting step-length, the prospect pit petrofacies attribute data gathered step 1 in vertical direction, adopts the geneva of Chi-square Test prospect pit petrofacies attribute data;
If prospect pit petrofacies attribute data has geneva, then according to waiting that the petrofacies attribute classification simulating area carries out figure notation to petrofacies attribute data, otherwise, if described prospect pit petrofacies attribute data does not have geneva, then again choose the well section waiting to simulate area, return step 1 and obtain prospect pit petrofacies attribute data.
3. a kind of oil and gas reservoir stochastic simulation of lithofacies based on MRF according to claim 1 and 2 and implementation method, it is characterized in that, by program compilation, dynamic link library is generated to the method described in described step 1-step 5, meanwhile, writes analog parameter inputting interface; Under dynamic link library and analog parameter inputting interface being incorporated into the plug-in unit catalogue of SGeMS platform, operational solution under debug and release pattern, obtain MRF algorithm groupware, utilize the SGeMS platform having merged MRF algorithm groupware to realize oil and gas reservoir stochastic simulation of lithofacies.
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