CN117724151A - Construction method of river facies compact heterogeneous reservoir low-frequency model - Google Patents
Construction method of river facies compact heterogeneous reservoir low-frequency model Download PDFInfo
- Publication number
- CN117724151A CN117724151A CN202311673070.9A CN202311673070A CN117724151A CN 117724151 A CN117724151 A CN 117724151A CN 202311673070 A CN202311673070 A CN 202311673070A CN 117724151 A CN117724151 A CN 117724151A
- Authority
- CN
- China
- Prior art keywords
- wave impedance
- transverse wave
- lithofacies
- longitudinal wave
- phase
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 208000035126 Facies Diseases 0.000 title claims abstract description 60
- 238000010276 construction Methods 0.000 title claims description 13
- 238000000034 method Methods 0.000 claims abstract description 31
- 230000007480 spreading Effects 0.000 claims abstract description 11
- 238000005056 compaction Methods 0.000 claims description 89
- 238000004458 analytical method Methods 0.000 claims description 54
- 239000011148 porous material Substances 0.000 claims description 27
- 239000011435 rock Substances 0.000 claims description 13
- 238000012545 processing Methods 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 11
- 238000004590 computer program Methods 0.000 claims description 11
- 238000013211 curve analysis Methods 0.000 claims description 8
- 238000011160 research Methods 0.000 claims description 7
- 230000003993 interaction Effects 0.000 claims description 6
- 238000005070 sampling Methods 0.000 claims description 6
- 230000015572 biosynthetic process Effects 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 244000309464 bull Species 0.000 description 3
- 230000008859 change Effects 0.000 description 3
- 238000007796 conventional method Methods 0.000 description 3
- 241001522301 Apogonichthyoides nigripinnis Species 0.000 description 2
- 239000013589 supplement Substances 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 239000004927 clay Substances 0.000 description 1
- 230000008021 deposition Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000013213 extrapolation Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000004062 sedimentation Methods 0.000 description 1
- 239000004575 stone Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
Landscapes
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention relates to a method for constructing a river-phase compact heterogeneous reservoir low-frequency model, which relates to the technical field of geophysical reservoir prediction seismic inversion and comprises the following steps: dividing the river facies compact heterogeneous reservoir into lithofacies types and counting the proportion of each lithofacies; obtaining longitudinal wave impedance, transverse wave impedance and density parameter inversion three-dimensional data volume after the first inversion; obtaining a low-frequency model of longitudinal wave impedance, transverse wave impedance and density parameters after the last iteration and a three-dimensional data body of longitudinal wave impedance, transverse wave impedance and density parameters of pre-stack inversion after the last iteration; and calculating a longitudinal and transverse wave speed ratio data body to obtain a river-phase compact sandstone reservoir spreading rule. The method for constructing the low-frequency model of the river facies compact heterogeneous reservoir provides an accurate low-frequency model for reservoir prediction, and the pre-stack inversion result can better characterize river spreading.
Description
Technical Field
The invention relates to the technical field of geophysical reservoir prediction seismic inversion, in particular to a method for constructing a river-phase compact heterogeneous reservoir low-frequency model.
Background
The Erdos basin-shaped binary stone box group commonly develops a river-phase compact sandstone reservoir, and has rapid transverse change and strong heterogeneity. Petrophysical analysis shows that sandstone has low aspect ratio features and mudstone has high aspect ratio features. The parameters of the longitudinal wave and transverse wave speed ratio are calculated through pre-stack inversion, so that a tight sandstone reservoir can be effectively identified. Because the frequency bandwidth of the pre-stack inversion result is limited, generally from tens of hertz to tens of hertz, low-frequency information needs to be supplemented to obtain an absolute impedance value, and the description of the longitudinal and transverse boundaries of the underground geologic body is realized. Therefore, establishing a low-frequency model capable of reflecting the change rule of the river-phase heterogeneous reservoir is important for pre-stack inversion and tight sandstone reservoir prediction.
Conventional low frequency modeling methods fall into two main categories: one is obtained by means of inter-well interpolation and extrapolation of the log. The low-frequency model established by the method is easy to have the phenomenon of circling around well points, namely the problem of 'bull' and does not accord with the geological rule of a heterogeneous reservoir of a river phase. One is to build a low frequency model using regional formation compaction trends. The method only considers the compaction trend of mudstone, cannot consider the difference of compaction trends among different lithofacies (sandstone, mudstone and the like), and only supplements very low frequency information (less than 2 Hz), so that the longitudinal and transverse resolutions of the model are low, and the spatial recognition capability of the geologic body is poor. Ideally, the low-frequency modeling should consider compaction trend rules of different lithofacies, and the low-frequency model is built by dividing the lithofacies.
In conclusion, for a river-phase compact heterogeneous reservoir, the conventional low-frequency modeling method has the problems of low resolution, poor geologic body recognition capability, bullseye and the like.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a construction method of a river-phase compact heterogeneous reservoir low-frequency model, which is used for solving the problems of low model resolution, poor geologic body recognition capability, bulls eye and the like in the conventional method and providing an accurate low-frequency model for compact sandstone reservoir prediction.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
in a first aspect, the invention discloses a method for constructing a low-frequency model of a dense heterogeneous reservoir of a river facies, which comprises dividing the dense heterogeneous reservoir of the river facies into lithofacies types and counting the proportion of each lithofacies;
carrying out compaction trend curve analysis according to the lithofacies types of the river facies compact heterogeneous reservoir to obtain compaction trend three-dimensional bodies of longitudinal wave impedance, transverse wave impedance and density parameters of each lithofacies, respectively carrying out proportional weighted calculation on the results and the proportion occupied by the lithofacies of the corresponding type to obtain initial low-frequency models of the longitudinal wave impedance, the transverse wave impedance and the density parameters, respectively carrying out pre-stack inversion on the initial low-frequency models to obtain three-dimensional data bodies of longitudinal wave impedance, transverse wave impedance and density parameter inversion after first inversion;
according to the longitudinal wave impedance and the transverse wave impedance after the first inversion, performing intersection analysis and Bayesian discriminant analysis to obtain a first lithofacies probability three-dimensional body; updating the low-frequency model of the initial longitudinal wave impedance, the transverse wave impedance and the density parameters according to the first lithofacies probability three-dimensional body; taking the updated low-frequency models of the longitudinal wave impedance, the transverse wave impedance and the density parameters as initial values, performing iterative computation of pre-stack inversion, intersection analysis and Bayesian discriminant analysis until the longitudinal wave impedance, the transverse wave impedance and the density parameters participating in the iterative computation coincide with a logging curve or reach the set maximum iteration times, and stopping to obtain the low-frequency models of the longitudinal wave impedance, the transverse wave impedance and the density parameters after the last iteration and the three-dimensional data body of the longitudinal wave impedance, the transverse wave impedance and the density parameters of the pre-stack inversion after the last iteration;
and calculating a longitudinal wave and transverse wave speed ratio data body according to the longitudinal wave impedance, transverse wave impedance and density parameter three-dimensional data body of the pre-stack inversion after the last iteration to obtain a river phase tight sandstone reservoir spreading rule.
Preferably, the steps of dividing the river facies dense heterogeneous reservoir into lithofacies types and counting the proportion of each lithofacies include the following steps:
obtaining a shale content and porosity threshold value of a river facies compact heterogeneous reservoir according to a shale content and porosity curve of a drilled well in a research area, and dividing the river facies compact heterogeneous reservoir into a shale facies, a compact sandstone facies and a high-pore sandstone facies according to the shale content and the porosity threshold value;
and respectively counting the proportion of the mud lithofacies, the compact lithofacies and the high-pore lithofacies in the drilled stratum.
Preferably, obtaining the inverted three-dimensional data volume of the longitudinal wave impedance, the transverse wave impedance and the density parameter after the first inversion comprises the following steps:
according to the lithofacies types, determining compaction trend curves of longitudinal wave impedance, transverse wave impedance and density parameters of each lithofacies, and establishing a compaction trend three-dimensional body of the longitudinal wave impedance, transverse wave impedance and density parameters of each lithofacies under horizon constraint;
according to the proportion of each lithofacies and the compaction trend three-dimensional body of the longitudinal wave impedance, the transverse wave impedance and the density parameters of each lithofacies, obtaining an initial low-frequency model of the longitudinal wave impedance, the transverse wave impedance and the density parameters according to proportion weighted calculation;
and performing pre-stack inversion on the low-frequency model of the initial longitudinal wave impedance, the transverse wave impedance and the density parameter to obtain a three-dimensional data volume for inversion of the longitudinal wave impedance, the transverse wave impedance and the density parameter after the first inversion.
Preferably, the initial anti-low frequency model of longitudinal wave resistance:
Zp 0 ＝p 1 ×Zp f1 +p 2 ×Zp f2 +p 3 ×Zp f3 (1)
Wherein Zp 0 Representing an initial low frequency model of the longitudinal wave impedance,
Zp f1 represents a mudstone phase longitudinal wave impedance compaction trend three-dimensional body,
Zp f2 represents a compact sandstone phase longitudinal wave impedance compaction trend three-dimensional body,
Zp f3 representing a high Kong Shayan phase longitudinal wave impedance compaction trend three-dimensional body,
p 1 representing the phase ratio of the mud rock,
p 2 represents compact sandThe rock phase is in a ratio of that of the rock phase,
p 3 representing the high pore sandstone phase ratio;
the low frequency model of the initial transverse wave impedance:
Zs 0 ＝p 1 ×Zs f1 +p 2 ×Zs f2 +p 3 ×Zs f3 (2)
Wherein, zs 0 Representing an initial low frequency model of the transverse wave impedance,
Zs f1 represents a mudstone phase transverse wave impedance compaction trend three-dimensional body,
Zs f2 represents a compact sandstone phase transverse wave impedance compaction trend three-dimensional body,
Zs f3 representing a high Kong Shayan phase transverse wave impedance compaction trend three-dimensional body,
p 1 representing the phase ratio of the mud rock,
p 2 representing the proportion of the dense sandstone phase,
p 3 representing the high pore sandstone phase ratio;
the low frequency model of the initial density parameters:
ρ 0 ＝p 1 ×ρ f1 +p 2 ×ρ f2 +p 3 ×ρ f3 (3)
Wherein ρ is 0 Representing an initial low frequency model of the density parameter,
ρ f1 representing a mud lithofacies density parameter compaction trend three-dimensional body,
ρ f2 represents a compact sandstone phase density parameter compaction trend three-dimensional body,
ρ f3 representing a high pore sandstone phase density parameter compaction trend three-dimensional body,
p 1 representing the phase ratio of the mud rock,
p 2 representing the proportion of the dense sandstone phase,
p 3 representing the high pore sandstone phase ratio.
Preferably, obtaining the low-frequency model of the longitudinal wave impedance, the transverse wave impedance and the density parameter after the last iteration, and the three-dimensional data volume of the longitudinal wave impedance, the transverse wave impedance and the density parameter of the pre-stack inversion comprises the following steps:
step C1: calculating a longitudinal and transverse wave speed ratio according to the longitudinal wave impedance and the transverse wave impedance after the first inversion, performing intersection analysis according to the longitudinal and transverse wave speed ratio and the longitudinal wave impedance after the first inversion to obtain an interaction graph, performing Bayesian discriminant analysis on the obtained interaction graph, and calculating a lithofacies probability value of each sampling point position to obtain a first lithofacies probability three-dimensional body;
step C2: updating the low-frequency model of the initial longitudinal wave impedance, transverse wave impedance and density parameters according to the first lithofacies probability three-dimensional body to obtain the updated low-frequency model of the longitudinal wave impedance, transverse wave impedance and density parameters;
step C3: repeatedly performing pre-stack inversion work by taking the updated low-frequency models of the longitudinal wave impedance, the transverse wave impedance and the density parameters as initial values to obtain a three-dimensional data body inverted by the longitudinal wave impedance, the transverse wave impedance and the density parameters after the second inversion; repeatedly performing intersection analysis and Bayesian discriminant analysis to obtain a second lithofacies probability three-dimensional body, and completing one iteration;
step C4: and C3, repeating the step until the longitudinal wave impedance, the transverse wave impedance and the density parameters participating in iterative computation coincide with the logging curve or reach the set maximum iteration times, and then terminating to obtain a longitudinal wave impedance, transverse wave impedance and density parameter low-frequency model after the last iteration and a longitudinal wave impedance, transverse wave impedance and density parameter three-dimensional data body of pre-stack inversion.
Preferably, obtaining the low frequency model of the updated longitudinal wave impedance, transverse wave impedance and density parameters comprises the steps of:
calculating a longitudinal and transverse wave speed ratio according to the longitudinal wave impedance and the transverse wave impedance after the first inversion, and performing intersection analysis according to the longitudinal and transverse wave speed ratio and the longitudinal wave impedance after the first inversion to obtain an intersection graph;
performing Bayesian discriminant analysis on the intersection graph, and calculating a lithofacies probability value of each sampling point position to obtain a first lithofacies probability three-dimensional body;
the Bayesian discriminant analysis method comprises the following steps: computing lithofacies class f using bayesian formulas i The posterior probability p (f|d) of (a) is calculated by the following formula:
wherein p (f|d) is the lithofacies class f i Posterior probability of (2);
p (d|f) represents that the sample point is lithofacies f i The prior probability of the corresponding d;
p (f) represents a lithofacies type f i Is used for counting the lithology type f through logging data i I.e. the proportion of the facies type to all facies types;
p (d) represents a scale factor, and takes a constant value in Bayesian discriminant analysis;
wherein f i (i=1,., N) represents N different facies categories;
d represents observed single-parameter or multi-parameter sample values from the seismic attribute or log sample values.
Preferably, the updated low frequency model of longitudinal wave impedance:
Zp＝π(f 1 )×Zp f1 +π(f 2 )×Zp f2 +π(f 3 )×Zp f3 (5)
Where Zp represents the updated low frequency model of the longitudinal wave impedance,
Zp f1 represents a mudstone phase longitudinal wave impedance compaction trend three-dimensional body,
Zp f2 represents a compact sandstone phase longitudinal wave impedance compaction trend three-dimensional body,
Zp f3 representing a high Kong Shayan phase longitudinal wave impedance compaction trend three-dimensional body,
π(f 1 ) Represents the mudstone phase probability body of Bayesian discrimination,
π(f 2 ) Represents the dense sandstone phase probability body of Bayesian discrimination,
π(f 3 ) A high Kong Shayan phase probability body representing bayesian discrimination;
the updated low frequency model of transverse wave impedance:
Zs＝π(f 1 )×Zs f1 +π(f 2 )×Zs f2 +π(f 3 )×Zs f3 (6)
Where Zs represents the low frequency model of the updated transverse wave impedance,
Zs f1 represents a mudstone phase transverse wave impedance compaction trend three-dimensional body,
Zs f2 represents a compact sandstone phase transverse wave impedance compaction trend three-dimensional body,
Zs f3 representing a high Kong Shayan phase transverse wave impedance compaction trend three-dimensional body;
π(f 1 ) Represents the mudstone phase probability body of Bayesian discrimination,
π(f 2 ) Represents the dense sandstone phase probability body of Bayesian discrimination,
π(f 3 ) A high Kong Shayan phase probability body representing bayesian discrimination;
the updated low frequency model of the density parameter:
ρ＝π(f 1 )×ρ f1 +π(f 2 )×ρ f2 +π(f 3 )×ρ f3 (7)
Where ρ represents a low frequency model of the updated density parameter,
ρ f1 representing a mud lithofacies density parameter compaction trend three-dimensional body,
ρ f2 represents a compact sandstone phase density parameter compaction trend three-dimensional body,
ρ f3 representing a high pore sandstone phase density parameter compaction trend three-dimensional body,
π(f 1 ) Represents the mudstone phase probability body of Bayesian discrimination,
π(f 2 ) Represents the dense sandstone phase probability body of Bayesian discrimination,
π(f 3 ) Representing a bayesian-discriminated high Kong Shayan phase probability volume.
The second aspect of the invention discloses a construction device of a river-phase compact heterogeneous reservoir low-frequency model, which comprises
The first processing unit is used for dividing the river facies compact heterogeneous reservoir into lithofacies types and counting the proportion of each lithofacies;
the second processing unit is used for carrying out compaction trend curve analysis according to the lithofacies types of the river facies compact heterogeneous reservoir to obtain compaction trend three-dimensional bodies of longitudinal wave impedance, transverse wave impedance and density parameters of each lithofacies, respectively carrying out proportional weighted calculation on the results and the proportion occupied by the lithofacies of the corresponding type to obtain initial low-frequency models of the longitudinal wave impedance, the transverse wave impedance and the density parameters, respectively carrying out prestack inversion on the initial low-frequency models to obtain a longitudinal wave impedance, a transverse wave impedance and density parameter inversion three-dimensional data body after the first inversion;
the third processing unit is used for carrying out intersection analysis and Bayesian discriminant analysis according to the longitudinal wave impedance and the transverse wave impedance after the first inversion to obtain a first lithofacies probability three-dimensional body; updating the low-frequency model of the initial longitudinal wave impedance, the transverse wave impedance and the density parameters according to the first lithofacies probability three-dimensional body; taking the updated low-frequency models of the longitudinal wave impedance, the transverse wave impedance and the density parameters as initial values, performing iterative computation of pre-stack inversion, intersection analysis and Bayesian discriminant analysis until the longitudinal wave impedance, the transverse wave impedance and the density parameters participating in the iterative computation coincide with a logging curve or reach the set maximum iteration times, and stopping to obtain the low-frequency models of the longitudinal wave impedance, the transverse wave impedance and the density parameters after the last iteration and the three-dimensional data body of the longitudinal wave impedance, the transverse wave impedance and the density parameters of the pre-stack inversion after the last iteration;
and the fourth processing unit is used for calculating a longitudinal wave and transverse wave speed ratio data body according to the longitudinal wave impedance, the transverse wave impedance and the density parameter three-dimensional data body of the pre-stack inversion after the last iteration to obtain a river-phase tight sandstone reservoir spreading rule.
In a third aspect, the present invention also discloses a computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the above-mentioned method.
In a fourth aspect, the present invention also discloses a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a construction method of a river facies compact heterogeneous reservoir low-frequency model, which comprises the steps of firstly, sequentially carrying out lithofacies division and proportion statistics of lithofacies according to a porosity and clay content curve of a drilled well in a research area; then, compaction trend curve analysis, proportional weighting calculation and pre-stack inversion are carried out, and a three-dimensional data body is obtained through longitudinal wave impedance, transverse wave impedance and density parameter inversion after the first inversion; then carrying out iterative computation of intersection analysis and Bayesian discriminant analysis to obtain a longitudinal wave impedance, a transverse wave impedance and density parameter low-frequency model after the last iteration and a longitudinal wave impedance, transverse wave impedance and density parameter three-dimensional data body of pre-stack inversion after the last iteration; and finally, calculating a longitudinal wave speed ratio data body according to the longitudinal wave impedance, the transverse wave impedance and the density parameter three-dimensional data body of the pre-stack inversion after the last iteration to obtain a river facies tight sandstone reservoir spreading rule. The method for constructing the river phase compact heterogeneous reservoir low-frequency model disclosed by the invention provides an accurate low-frequency model for reservoir prediction, and the pre-stack inversion result can better characterize river spreading, and has the following advantages:
(1) On the basis of conventional single mudstone compaction trend modeling, the compaction trend difference between different lithofacies is considered, the low-frequency model is built according to the lithofacies, the influence of the compaction trend difference on the low-frequency modeling is eliminated, and the 'bullseye' phenomenon of well interpolation low-frequency modeling can be effectively avoided;
(2) According to the invention, through introducing Bayesian discrimination and lithofacies probability bodies, the loop of mutual iteration of pre-stack inversion and compaction trend low-frequency modeling is realized. The iterated low-frequency model can supplement low-frequency components (2-10 Hz) lacking in conventional compaction trend modeling, and the longitudinal and transverse resolutions of the model are improved;
(3) The low-frequency model constructed by the method can reflect the deposition characteristics of the river facies reservoir, improve the space recognition capability of the geologic body, and improve the inversion and characterization effects before the river facies heterogeneous reservoir is stacked.
Drawings
FIG. 1 is a schematic flow chart of a method for constructing a low-frequency model of a dense heterogeneous reservoir of a river phase, which is provided in embodiment 1 of the present invention;
FIG. 2 is a schematic diagram of the division results of the mud lithofacies, the dense lithofacies and the high Kong Shayan facies provided in example 1 of the present invention;
FIG. 3 is a graph showing compaction trend curves of longitudinal wave impedance, transverse wave impedance and density parameters for each lithofacies according to example 1 of the present invention, wherein FIG. 3 (a) is a mudstone phase, FIG. 3 (b) is a tight sandstone phase, and FIG. 3 (c) is a high-pore sandstone phase;
fig. 4 is a schematic iteration effect of the low-frequency longitudinal wave impedance model provided in embodiment 1 of the present invention, where fig. 4 (a) is an initial low-frequency longitudinal wave impedance model, fig. 4 (b) is a low-frequency longitudinal wave impedance model after a first iteration, fig. 4 (c) is a low-frequency longitudinal wave impedance model after a second iteration, and fig. 4 (d) is a final optimized low-frequency longitudinal wave impedance model;
FIG. 5 is a schematic plan view of the results of a longitudinal-to-transverse wave velocity ratio inversion using well interpolation low frequency modeling in a conventional approach;
FIG. 6 is a schematic plan view of the results of a longitudinal-to-transverse wave velocity ratio inversion using low frequency modeling in the disclosed method.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
In order to solve the problems of low model resolution, poor geologic body recognition capability, bulls-eye and the like of the conventional method, the invention discloses a construction method of a river facies compact heterogeneous reservoir low-frequency model, which comprises the steps of firstly, sequentially carrying out lithofacies division and proportion statistics of lithofacies according to a porosity and argillaceous content curve of a drilled well in a research area; then, compaction trend curve analysis, proportional weighting calculation and pre-stack inversion are carried out, and a three-dimensional data body is obtained through longitudinal wave impedance, transverse wave impedance and density parameter inversion after the first inversion; then carrying out iterative computation of intersection analysis and Bayesian discriminant analysis to obtain a longitudinal wave impedance, a transverse wave impedance and density parameter low-frequency model after the last iteration and a longitudinal wave impedance, transverse wave impedance and density parameter three-dimensional data body of pre-stack inversion after the last iteration; and finally, calculating a longitudinal wave and transverse wave speed ratio data body according to the longitudinal wave impedance, the transverse wave impedance and the density parameter three-dimensional data body of the pre-stack inversion after the last iteration to obtain a river phase tight sandstone reservoir spreading rule, and providing an accurate low-frequency model for tight sandstone reservoir prediction.
Example 1: construction method of river facies compact heterogeneous reservoir low-frequency model
The embodiment 1 of the invention provides a method for constructing a low-frequency model of a dense heterogeneous reservoir of a river, taking a small well exploration area at the east edge of an Erdos basin as an example, as shown in fig. 1, the method comprises the following steps:
step A: dividing a river facies compact heterogeneous reservoir into lithofacies types according to a porosity and argillaceous content curve of a drilled well in a research area and counting the proportion of each lithofacies, wherein the lithofacies types comprise a argillaceous facies, a compact sandstone facies and a high-pore sandstone facies, and the method comprises the following specific steps of:
step A1: obtaining a shale content and porosity threshold value of a river facies compact heterogeneous reservoir according to a shale content and porosity curve of a drilled well in a research area, and dividing the river facies compact heterogeneous reservoir into a shale facies, a compact sandstone facies and a high-pore sandstone facies according to the shale content and the porosity threshold value;
specifically, defining a shale content threshold value as a, dividing a stratum higher than the shale content threshold value a into a shale phase f 1 The formation below the threshold a is divided into sandstones; in sandstone, defining a porosity threshold value b, dividing a stratum below the porosity threshold value b into a dense sandstone phase f 2 The formation above the porosity threshold b is divided into high pore sandstone phases f 3 。
In particular, in this embodiment, the shale content threshold value a is 0.5, the porosity threshold value b is 0.06, and three different lithofacies including a shale phase, a dense sandstone phase and a high-pore sandstone phase are divided together, as shown in fig. 2.
Step A2: and respectively counting the proportion of the mud lithofacies, the compact lithofacies and the high-pore lithofacies in the drilled stratum.
Wherein p is 1 Representing the mud-rock phase ratio, p 2 Represents the compact sandstone phase duty ratio, p 3 Representing the high pore sandstone phase ratio;
in particular to this embodiment, the mud-rock phase ratio p 1 =0.67, dense sandstone phase ratio p 2 =0.18, high pore sandstone phase ratio p 3 ＝0.15。
And (B) step (B): carrying out compaction trend curve analysis according to the lithofacies types of the river facies compact heterogeneous reservoir, as shown in fig. 3, obtaining compaction trend three-dimensional bodies of longitudinal wave impedance, transverse wave impedance and density parameters of each lithofacies, and carrying out proportional weighted calculation on the results and the proportion occupied by the lithofacies of the corresponding type respectively to obtain an initial low-frequency model of the longitudinal wave impedance, the transverse wave impedance and the density parameters, as shown in fig. 4 (a); respectively carrying out pre-stack inversion on the three-dimensional data body to obtain longitudinal wave impedance, transverse wave impedance and density parameter inversion three-dimensional data body after first inversion, wherein the method comprises the following specific steps:
step B1: according to the lithofacies types, determining compaction trend curves of longitudinal wave impedance, transverse wave impedance and density parameters of each lithofacies, and establishing a compaction trend three-dimensional body of the longitudinal wave impedance, transverse wave impedance and density parameters of each lithofacies under horizon constraint;
the method for establishing the compaction trend curve comprises the following steps:
and utilizing the well logging curves to analyze intersections of longitudinal wave impedance and time, transverse wave impedance and time and density parameters and time of each lithofacies respectively, and obtaining corresponding compaction trend curves based on exponential functions.
Step B2: according to the proportion of each lithofacies and the compaction trend three-dimensional body of the longitudinal wave impedance, the transverse wave impedance and the density parameters of each lithofacies, obtaining an initial low-frequency model of the longitudinal wave impedance, the transverse wave impedance and the density parameters according to proportion weighted calculation;
specifically, an initial low frequency model of longitudinal wave impedance:
Zp 0 ＝p 1 ×Zp f1 +p 2 ×Zp f2 +p 3 ×Zp f3 (1)
Wherein Zp 0 Representing an initial low frequency model of the longitudinal wave impedance,
Zp f1 represents a mudstone phase longitudinal wave impedance compaction trend three-dimensional body,
Zp f2 represents a compact sandstone phase longitudinal wave impedance compaction trend three-dimensional body,
Zp f3 representing a high Kong Shayan phase longitudinal wave impedance compaction trend three-dimensional body,
p 1 representing the phase ratio of the mud rock,
p 2 representing the proportion of the dense sandstone phase,
p 3 representing the high pore sandstone phase ratio.
Specifically, the low frequency model of the initial transverse wave impedance:
Zs 0 ＝p 1 ×Zs f1 +p 2 ×Zs f2 +p 3 ×Zs f3 (2)
Wherein, zs 0 Representing an initial low frequency model of the transverse wave impedance,
Zs f1 represents a mudstone phase transverse wave impedance compaction trend three-dimensional body,
Zs f2 represents a compact sandstone phase transverse wave impedance compaction trend three-dimensional body,
Zs f3 representing a high Kong Shayan phase transverse wave impedance compaction trend three-dimensional body,
p 1 representing the phase ratio of the mud rock,
p 2 representing the proportion of the dense sandstone phase,
p 3 representing the high pore sandstone phase ratio.
Specifically, the low frequency model of the initial density parameters:
ρ 0 ＝p 1 ×ρ f1 +p 2 ×ρ f2 +p 3 ×ρ f3 (3)
Wherein ρ is 0 Representing an initial low frequency model of the density parameter,
ρ f1 representing a mud lithofacies density parameter compaction trend three-dimensional body,
ρ f2 represents a compact sandstone phase density parameter compaction trend three-dimensional body,
ρ f3 representing a high pore sandstone phase density parameter compaction trend three-dimensional body,
p 1 representing the phase ratio of the mud rock,
p 2 representing the proportion of the dense sandstone phase,
p 3 representing the high pore sandstone phase ratio.
Step B3: performing pre-stack inversion on the low-frequency model of the initial longitudinal wave impedance, the transverse wave impedance and the density parameters to obtain a longitudinal wave impedance, a transverse wave impedance and density parameter inversion three-dimensional data body after the first inversion;
specifically, the prestack inversion uses Fatti approximation equations based on longitudinal wave impedance, transverse wave impedance, and density parameters. Both the industrial software Jason and the HRS have modules of pre-stack inversion, and in the embodiment, a Strata module of the HRS software is adopted, and the specific implementation process refers to the use description of the Strata module of the HRS software.
Step C: according to the longitudinal wave impedance and the transverse wave impedance after the first inversion, performing intersection analysis and Bayesian discriminant analysis to obtain a first lithofacies probability three-dimensional body; updating the low-frequency model of the initial longitudinal wave impedance, the transverse wave impedance and the density parameters according to the first lithofacies probability three-dimensional body; taking the updated low-frequency models of the longitudinal wave impedance, the transverse wave impedance and the density parameters as initial values, performing iterative computation of pre-stack inversion, intersection analysis and Bayesian discriminant analysis until the longitudinal wave impedance, the transverse wave impedance and the density parameters participating in the iterative computation coincide with a logging curve or reach the set maximum iteration times, and stopping to obtain the low-frequency models of the longitudinal wave impedance, the transverse wave impedance and the density parameters after the last iteration and the three-dimensional data volume of the longitudinal wave impedance, the transverse wave impedance and the density parameters of the pre-stack inversion after the last iteration, wherein the method comprises the following specific steps:
step C1: calculating a longitudinal and transverse wave speed ratio according to the longitudinal wave impedance and the transverse wave impedance after the first inversion, performing intersection analysis according to the longitudinal and transverse wave speed ratio and the longitudinal wave impedance after the first inversion to obtain an interaction graph, performing Bayesian discriminant analysis on the obtained interaction graph, and calculating a lithofacies probability value of each sampling point position to obtain a first lithofacies probability three-dimensional body, wherein the method comprises the following specific steps of:
step C11: calculating a longitudinal and transverse wave speed ratio according to the longitudinal wave impedance and the transverse wave impedance after the first inversion, and performing intersection analysis according to the longitudinal and transverse wave speed ratio and the longitudinal wave impedance after the first inversion to obtain an intersection graph;
step C12: performing Bayesian discriminant analysis on the intersection graph, and calculating a lithofacies probability value of each sampling point position to obtain a first lithofacies probability three-dimensional body;
the Bayesian discriminant analysis is a tool for carrying out seismic quantitative interpretation by using statistical petrophysics, and the prior probability information of the drilled well can be fully utilized. The lithology probability value refers to: based on the seismic inversion data, the likelihood or probability of a formation being quantitatively interpreted as a certain lithology is determined by Bayesian discrimination.
The Bayesian discriminant analysis method comprises the following steps: computing lithofacies class f using bayesian formulas i The posterior probability p (f|d) of (a) is calculated by the following formula:
wherein p (f|d) is the lithofacies class f i Posterior probability of (2);
p (d|f) represents that the sample point is lithofacies f i The prior probability of the corresponding d;
p (f) represents a lithofacies type f i Is used for counting the lithology type f through logging data i I.e. the proportion of the facies type to all facies types;
p (d) represents a scale factor, and takes a constant value in Bayesian discriminant analysis;
wherein f i (i=1,., N) represents N different facies categories;
d represents observed single-parameter or multi-parameter sample values from the seismic attribute or log sample values.
Step C2: updating the low-frequency model of the initial longitudinal wave impedance, transverse wave impedance and density parameters according to the first lithofacies probability three-dimensional body to obtain the updated low-frequency model of the longitudinal wave impedance, transverse wave impedance and density parameters;
specifically, the updated low frequency model of the longitudinal wave impedance:
Zp＝π(f 1 )×Zp f1 +π(f 2 )×Zp f2 +π(f 3 )×Zp f3 (5)
Where Zp represents the updated low frequency model of the longitudinal wave impedance,
Zp f1 represents a mudstone phase longitudinal wave impedance compaction trend three-dimensional body,
Zp f2 represents a compact sandstone phase longitudinal wave impedance compaction trend three-dimensional body,
Zp f3 representing a high Kong Shayan phase longitudinal wave impedance compaction trend three-dimensional body,
π(f 1 ) Represents the mudstone phase probability body of Bayesian discrimination,
π(f 2 ) Represents the probability body of the dense sandstone phase,
π(f 3 ) Representing a high Kong Shayan phase probability volume.
Specifically, the updated low frequency model of the transverse wave impedance:
Zs＝π(f 1 )×Zs f1 +π(f 2 )×Zs f2 +π(f 3 )×Zs f3 (6)
Where Zs represents the low frequency model of the updated transverse wave impedance,
Zs f1 represents a mudstone phase transverse wave impedance compaction trend three-dimensional body,
Zs f2 represents a compact sandstone phase transverse wave impedance compaction trend three-dimensional body,
Zs f3 representing a high Kong Shayan phase transverse wave impedance compaction trend three-dimensional body;
π(f 1 ) Represents the mudstone phase probability body of Bayesian discrimination,
π(f 2 ) Represents the probability body of the dense sandstone phase,
π(f 3 ) Representing a high Kong Shayan phase probability volume.
Specifically, the updated low frequency model of the density parameter:
ρ＝π(f 1 )×ρ f1 +π(f 2 )×ρ f2 +π(f 3 )×ρ f3 (7)
Where ρ represents a low frequency model of the updated density parameter,
ρ f1 representing a mud lithofacies density parameter compaction trend three-dimensional body,
ρ f2 represents a compact sandstone phase density parameter compaction trend three-dimensional body,
ρ f3 representing a high pore sandstone phase density parameter compaction trend three-dimensional body,
π(f 1 ) Represents the mudstone phase probability body of Bayesian discrimination,
π(f 2 ) Represents the probability body of the dense sandstone phase,
π(f 3 ) Representing a high Kong Shayan phase probability volume.
Step C3: repeating the pre-stack inversion operation of the step B3 by taking the updated low-frequency model of the longitudinal wave impedance, the transverse wave impedance and the density parameters as initial values to obtain a three-dimensional data body inverted by the longitudinal wave impedance, the transverse wave impedance and the density parameters after the second inversion; repeating the intersection analysis and the Bayesian discriminant analysis of the step C1 to obtain a second lithofacies probability three-dimensional body, and completing one iteration;
step C4: and C3, repeating the step until the longitudinal wave impedance, the transverse wave impedance and the density parameters which participate in iterative computation coincide with the logging curve or are terminated after the set maximum iteration times are reached, and obtaining a longitudinal wave impedance, transverse wave impedance and density parameter low-frequency model after the last iteration and a longitudinal wave impedance, transverse wave impedance and density parameter three-dimensional data body of pre-stack inversion.
Specifically, the maximum iteration number is set to be preferably 5 to 10 times according to the calculated amount and time consumption of the pre-stack inversion.
Step D: and calculating a longitudinal wave speed ratio data body according to the longitudinal wave impedance, transverse wave impedance and density parameter low-frequency model after the last iteration and a longitudinal wave impedance, transverse wave impedance and density parameter three-dimensional data body of pre-stack inversion to obtain a river-phase tight sandstone reservoir spreading rule.
Comparing fig. 4 (b), fig. 4 (c) and fig. 4 (d), it can be found that, through inversion for multiple iterations, the longitudinal and transverse resolutions of the low-frequency model are improved, the frequency bandwidth is widened, the detail information is more abundant, the low-frequency information of the 2-10Hz part can be effectively compensated, and the accuracy of the inversion result is improved.
Comparing the construction method of the river facies compact heterogeneous reservoir low-frequency model disclosed by the invention with the prior art, as shown in fig. 5 and 6, as can be seen from fig. 5, the inversion of the longitudinal and transverse wave velocity ratio of the well interpolation low-frequency model in the conventional method is adopted, the obvious 'bull' phenomenon exists at the well point, the transverse resolution is low, the geological rule is unclear, and the river facies reservoir depiction requirement cannot be met; as can be seen from fig. 6, the inversion of the aspect ratio of the low-frequency modeling in the method disclosed by the invention effectively overcomes the 'bull' problem caused by well interpolation, highlights the transverse change rule of the heterogeneous reservoir of the river phase, enhances the space recognition capability of the geologic body, accords with the knowledge of geology and sedimentation rules, and improves the depicting effect of the reservoir of the river phase. Therefore, the construction method of the river-phase compact heterogeneous reservoir low-frequency model disclosed by the invention has a good effect.
Example 2: construction device of river facies compact heterogeneous reservoir low-frequency model
Example 2 provides an apparatus for constructing a low frequency model of a dense heterogeneous reservoir of a river phase, comprising
The first processing unit is used for dividing the lithofacies type of the river facies compact heterogeneous reservoir according to the porosity and argillaceous content curve of the drilled well in the research area and counting the proportion of each lithofacies;
the second processing unit is used for carrying out compaction trend curve analysis according to the lithofacies types of the river facies compact heterogeneous reservoir to obtain compaction trend three-dimensional bodies of longitudinal wave impedance, transverse wave impedance and density parameters of each lithofacies, respectively carrying out proportional weighted calculation on the results and the proportion occupied by the lithofacies of the corresponding type to obtain initial low-frequency models of the longitudinal wave impedance, the transverse wave impedance and the density parameters, respectively carrying out prestack inversion on the initial low-frequency models to obtain a longitudinal wave impedance, a transverse wave impedance and density parameter inversion three-dimensional data body after the first inversion;
the third processing unit is used for carrying out intersection analysis and Bayesian discriminant analysis according to the longitudinal wave impedance and the transverse wave impedance after the first inversion to obtain a first lithofacies probability three-dimensional body; updating the low-frequency model of the initial longitudinal wave impedance, the transverse wave impedance and the density parameters according to the first lithofacies probability three-dimensional body; taking the updated low-frequency models of the longitudinal wave impedance, the transverse wave impedance and the density parameters as initial values, performing iterative computation of pre-stack inversion, intersection analysis and Bayesian discriminant analysis until the longitudinal wave impedance, the transverse wave impedance and the density parameters participating in the iterative computation coincide with a logging curve or reach the set maximum iteration times, and stopping to obtain the low-frequency models of the longitudinal wave impedance, the transverse wave impedance and the density parameters after the last iteration and the three-dimensional data body of the longitudinal wave impedance, the transverse wave impedance and the density parameters of the pre-stack inversion after the last iteration;
and the fourth processing unit is used for calculating a longitudinal wave speed ratio data body according to the longitudinal wave impedance, the transverse wave impedance and the density parameter low-frequency model after the last iteration and the longitudinal wave impedance, the transverse wave impedance and the density parameter three-dimensional data body of pre-stack inversion to obtain a river-phase tight sandstone reservoir spreading rule.
Example 3: computer readable storage medium
Embodiment 3 provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method described in embodiment 1.
Example 4: computer equipment
Embodiment 4 provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, which processor implements the steps of the method of embodiment 1 when executing the computer program.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. The construction method of the river facies compact heterogeneous reservoir low-frequency model is characterized by comprising the following steps of
Dividing the river facies compact heterogeneous reservoir into lithofacies types and counting the proportion of each lithofacies;
carrying out compaction trend curve analysis according to the lithofacies types of the river facies compact heterogeneous reservoir to obtain compaction trend three-dimensional bodies of longitudinal wave impedance, transverse wave impedance and density parameters of each lithofacies, respectively carrying out proportional weighted calculation on the results and the proportion occupied by the lithofacies of the corresponding type to obtain initial low-frequency models of the longitudinal wave impedance, the transverse wave impedance and the density parameters, respectively carrying out pre-stack inversion on the initial low-frequency models to obtain three-dimensional data bodies of longitudinal wave impedance, transverse wave impedance and density parameter inversion after first inversion;
according to the longitudinal wave impedance and the transverse wave impedance after the first inversion, performing intersection analysis and Bayesian discriminant analysis to obtain a first lithofacies probability three-dimensional body; updating the low-frequency model of the initial longitudinal wave impedance, the transverse wave impedance and the density parameters according to the first lithofacies probability three-dimensional body; taking the updated low-frequency models of the longitudinal wave impedance, the transverse wave impedance and the density parameters as initial values, performing iterative computation of pre-stack inversion, intersection analysis and Bayesian discriminant analysis until the longitudinal wave impedance, the transverse wave impedance and the density parameters participating in the iterative computation coincide with a logging curve or reach the set maximum iteration times, and stopping to obtain the low-frequency models of the longitudinal wave impedance, the transverse wave impedance and the density parameters after the last iteration and the three-dimensional data body of the longitudinal wave impedance, the transverse wave impedance and the density parameters of the pre-stack inversion after the last iteration;
and calculating a longitudinal wave and transverse wave speed ratio data body according to the longitudinal wave impedance, transverse wave impedance and density parameter three-dimensional data body of the pre-stack inversion after the last iteration to obtain a river phase tight sandstone reservoir spreading rule.
2. The method of claim 1, wherein the dividing the river facies dense heterogeneous reservoir into facies types and counting the proportion of each facies comprises the steps of:
obtaining a shale content and porosity threshold value of a river facies compact heterogeneous reservoir according to a shale content and porosity curve of a drilled well in a research area, and dividing the river facies compact heterogeneous reservoir into a shale facies, a compact sandstone facies and a high-pore sandstone facies according to the shale content and the porosity threshold value;
and respectively counting the proportion of the mud lithofacies, the compact lithofacies and the high-pore lithofacies in the drilled stratum.
3. The method of claim 1, wherein obtaining the first inverted longitudinal wave impedance, transverse wave impedance, and density parameter inversion three-dimensional data volume comprises:
according to the lithofacies types, determining compaction trend curves of longitudinal wave impedance, transverse wave impedance and density parameters of each lithofacies, and establishing a compaction trend three-dimensional body of the longitudinal wave impedance, transverse wave impedance and density parameters of each lithofacies under horizon constraint;
according to the proportion of each lithofacies and the compaction trend three-dimensional body of the longitudinal wave impedance, the transverse wave impedance and the density parameters of each lithofacies, obtaining an initial low-frequency model of the longitudinal wave impedance, the transverse wave impedance and the density parameters according to proportion weighted calculation;
and performing pre-stack inversion on the low-frequency model of the initial longitudinal wave impedance, the transverse wave impedance and the density parameter to obtain a three-dimensional data volume for inversion of the longitudinal wave impedance, the transverse wave impedance and the density parameter after the first inversion.
4. The construction method according to claim 3, wherein,
the initial longitudinal wave resistance low frequency resistant model:
Zp 0 ＝p 1 ×Zp f1 +p 2 ×Zp f2 +p 3 ×Zp f3 (1)
Wherein Zp 0 Representing an initial low frequency model of the longitudinal wave impedance,
Zp f1 represents a mudstone phase longitudinal wave impedance compaction trend three-dimensional body,
Zp f2 represents a compact sandstone phase longitudinal wave impedance compaction trend three-dimensional body,
Zp f3 representing a high Kong Shayan phase longitudinal wave impedance compaction trend three-dimensional body,
p 1 representing the phase ratio of the mud rock,
p 2 representing the proportion of the dense sandstone phase,
p 3 representing the high pore sandstone phase ratio;
the low frequency model of the initial transverse wave impedance:
Zs 0 ＝p 1 ×Zs f1 +p 2 ×Zs f2 +p 3 ×Zs f3 (2)
Wherein, zs 0 Representing an initial low frequency model of the transverse wave impedance,
Zs f1 represents a mudstone phase transverse wave impedance compaction trend three-dimensional body,
Zs f2 represents a compact sandstone phase transverse wave impedance compaction trend three-dimensional body,
Zs f3 representing a high Kong Shayan phase transverse wave impedance compaction trend three-dimensional body,
p 1 representing the phase ratio of the mud rock,
p 2 representing the proportion of the dense sandstone phase,
p 3 representing the high pore sandstone phase ratio;
the low frequency model of the initial density parameters:
ρ 0 ＝p 1 ×ρ f1 +p 2 ×ρ f2 +p 3 ×ρ f3 (3)
Wherein ρ is 0 Representing an initial low frequency model of the density parameter,
ρ f1 representing a mud lithofacies density parameter compaction trend three-dimensional body,
ρ f2 represents a compact sandstone phase density parameter compaction trend three-dimensional body,
ρ f3 representing a high pore sandstone phase density parameter compaction trend three-dimensional body,
p 1 representing the phase ratio of the mud rock,
p 2 representing the proportion of the dense sandstone phase,
p 3 representing the high pore sandstone phase ratio.
5. A method of constructing as claimed in claim 3 wherein obtaining a low frequency model of the longitudinal wave impedance, the transverse wave impedance and the density parameters after the last iteration and a three dimensional data volume of the longitudinal wave impedance, the transverse wave impedance and the density parameters of the pre-stack inversion comprises the steps of:
step C1: calculating a longitudinal and transverse wave speed ratio according to the longitudinal wave impedance and the transverse wave impedance after the first inversion, performing intersection analysis according to the longitudinal and transverse wave speed ratio and the longitudinal wave impedance after the first inversion to obtain an interaction graph, performing Bayesian discriminant analysis on the obtained interaction graph, and calculating a lithofacies probability value of each sampling point position to obtain a first lithofacies probability three-dimensional body;
step C2: updating the low-frequency model of the initial longitudinal wave impedance, transverse wave impedance and density parameters according to the first lithofacies probability three-dimensional body to obtain the updated low-frequency model of the longitudinal wave impedance, transverse wave impedance and density parameters;
step C3: repeatedly performing pre-stack inversion work by taking the updated low-frequency models of the longitudinal wave impedance, the transverse wave impedance and the density parameters as initial values to obtain a three-dimensional data body inverted by the longitudinal wave impedance, the transverse wave impedance and the density parameters after the second inversion; repeatedly performing intersection analysis and Bayesian discriminant analysis to obtain a second lithofacies probability three-dimensional body, and completing one iteration;
step C4: and C3, repeating the step until the longitudinal wave impedance, the transverse wave impedance and the density parameters participating in iterative computation coincide with the logging curve or reach the set maximum iteration times, and then terminating to obtain a longitudinal wave impedance, transverse wave impedance and density parameter low-frequency model after the last iteration and a longitudinal wave impedance, transverse wave impedance and density parameter three-dimensional data body of pre-stack inversion.
6. The method of claim 5, wherein obtaining the updated low frequency model of longitudinal wave impedance, transverse wave impedance and density parameters comprises the steps of:
calculating a longitudinal and transverse wave speed ratio according to the longitudinal wave impedance and the transverse wave impedance after the first inversion, and performing intersection analysis according to the longitudinal and transverse wave speed ratio and the longitudinal wave impedance after the first inversion to obtain an intersection graph;
performing Bayesian discriminant analysis on the intersection graph, and calculating a lithofacies probability value of each sampling point position to obtain a first lithofacies probability three-dimensional body;
the Bayesian discriminant analysis method comprises the following steps: computing lithofacies class f using bayesian formulas i The posterior probability p (f|d) of (a) is calculated by the following formula:
wherein p (f|d) is the lithofacies class f i Posterior probability of (2);
p (d|f) represents that the sample point is lithofacies f i The prior probability of the corresponding d;
p (f) represents a lithofacies type f i Is used for counting the lithology type f through logging data i I.e. the proportion of the facies type to all facies types;
p (d) represents a scale factor, and takes a constant value in Bayesian discriminant analysis;
wherein f i (i=1,., N) represents N different facies categories;
d represents observed single-parameter or multi-parameter sample values from the seismic attribute or log sample values.
7. The construction method according to claim 5, wherein,
the updated low frequency model of longitudinal wave impedance:
Zp＝π(f 1 )×Zp f1 +π(f 2 )×Zp f2 +π(f 3 )×Zp f3 (5)
Where Zp represents the updated low frequency model of the longitudinal wave impedance,
Zp f1 represents a mudstone phase longitudinal wave impedance compaction trend three-dimensional body,
Zp f2 represents a compact sandstone phase longitudinal wave impedance compaction trend three-dimensional body,
Zp f3 representing a high Kong Shayan phase longitudinal wave impedance compaction trend three-dimensional body,
π(f 1 ) Represents the mudstone phase probability body of Bayesian discrimination,
π(f 2 ) Represents the dense sandstone phase probability body of Bayesian discrimination,
π(f 3 ) A high Kong Shayan phase probability body representing bayesian discrimination;
the updated low frequency model of transverse wave impedance:
Zs＝π(f 1 )×Zs f1 +π(f 2 )×Zs f2 +π(f 3 )×Zs f3 (6)
Where Zs represents the low frequency model of the updated transverse wave impedance,
Zs f1 represents a mudstone phase transverse wave impedance compaction trend three-dimensional body,
Zs f2 represents a compact sandstone phase transverse wave impedance compaction trend three-dimensional body,
Zs f3 representing a high Kong Shayan phase transverse wave impedance compaction trend three-dimensional body;
π(f 1 ) Represents the mudstone phase probability body of Bayesian discrimination,
π(f 2 ) Represents the dense sandstone phase probability body of Bayesian discrimination,
π(f 3 ) A high Kong Shayan phase probability body representing bayesian discrimination;
the updated low frequency model of the density parameter:
ρ＝π(f 1 )×ρ f1 +π(f 2 )×ρ f2 +π(f 3 )×ρ f3 (7)
Where ρ represents a low frequency model of the updated density parameter,
ρ f1 representing a mud lithofacies density parameter compaction trend three-dimensional body,
ρ f2 represents a compact sandstone phase density parameter compaction trend three-dimensional body,
ρ f3 representing a high pore sandstone phase density parameter compaction trend three-dimensional body,
π(f 1 ) Represents the mudstone phase probability body of Bayesian discrimination,
π(f 2 ) Represents the dense sandstone phase probability body of Bayesian discrimination,
π(f 3 ) Representing a bayesian-discriminated high Kong Shayan phase probability volume.
8. The device for constructing the low-frequency model of the river facies compact heterogeneous reservoir is characterized by comprising
The first processing unit is used for dividing the river facies compact heterogeneous reservoir into lithofacies types and counting the proportion of each lithofacies;
the second processing unit is used for carrying out compaction trend curve analysis according to the lithofacies types of the river facies compact heterogeneous reservoir to obtain compaction trend three-dimensional bodies of longitudinal wave impedance, transverse wave impedance and density parameters of each lithofacies, respectively carrying out proportional weighted calculation on the results and the proportion occupied by the lithofacies of the corresponding type to obtain initial low-frequency models of the longitudinal wave impedance, the transverse wave impedance and the density parameters, respectively carrying out prestack inversion on the initial low-frequency models to obtain a longitudinal wave impedance, a transverse wave impedance and density parameter inversion three-dimensional data body after the first inversion;
the third processing unit is used for carrying out intersection analysis and Bayesian discriminant analysis according to the longitudinal wave impedance and the transverse wave impedance after the first inversion to obtain a first lithofacies probability three-dimensional body; updating the low-frequency model of the initial longitudinal wave impedance, the transverse wave impedance and the density parameters according to the first lithofacies probability three-dimensional body; taking the updated low-frequency models of the longitudinal wave impedance, the transverse wave impedance and the density parameters as initial values, performing iterative computation of pre-stack inversion, intersection analysis and Bayesian discriminant analysis until the longitudinal wave impedance, the transverse wave impedance and the density parameters participating in the iterative computation coincide with a logging curve or reach the set maximum iteration times, and stopping to obtain the low-frequency models of the longitudinal wave impedance, the transverse wave impedance and the density parameters after the last iteration and the three-dimensional data body of the longitudinal wave impedance, the transverse wave impedance and the density parameters of the pre-stack inversion after the last iteration;
and the fourth processing unit is used for calculating a longitudinal wave and transverse wave speed ratio data body according to the longitudinal wave impedance, the transverse wave impedance and the density parameter three-dimensional data body of the pre-stack inversion after the last iteration to obtain a river-phase tight sandstone reservoir spreading rule.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1-7.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of any of claims 1-7 when the computer program is executed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311673070.9A CN117724151A (en) | 2023-12-07 | 2023-12-07 | Construction method of river facies compact heterogeneous reservoir low-frequency model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311673070.9A CN117724151A (en) | 2023-12-07 | 2023-12-07 | Construction method of river facies compact heterogeneous reservoir low-frequency model |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117724151A true CN117724151A (en) | 2024-03-19 |
Family
ID=90200870
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311673070.9A Pending CN117724151A (en) | 2023-12-07 | 2023-12-07 | Construction method of river facies compact heterogeneous reservoir low-frequency model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117724151A (en) |
-
2023
- 2023-12-07 CN CN202311673070.9A patent/CN117724151A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109709603B (en) | Seismic horizon identification and tracking method and system | |
| CA3076280C (en) | Method and apparatus for determining thin interlayers | |
| US11016214B2 (en) | Dolomite reservoir prediction method and system based on well and seismic combination, and storage medium | |
| CN110031896B (en) | Seismic random inversion method and device based on multi-point geostatistics prior information | |
| CN104297785B (en) | Petrofacies constraint reservoir physical parameter inversion method and device | |
| CN104516018B (en) | Porosity inversion method under lithological constraint in geophysical exploration | |
| CN111596978A (en) | Web page display method, module and system for lithofacies classification by artificial intelligence | |
| CN104502966A (en) | Thin reservoir prediction method and thin reservoir prediction system | |
| US11221425B1 (en) | Generating a model for seismic velocities in a subsurface region using inversion with lateral variations | |
| WO2016126453A1 (en) | Seismic attributes derived from the relative geological age property of a volume-based model | |
| CN117546051A (en) | Method and system for seismic imaging using an S-wave velocity model and machine learning | |
| CN103969685B (en) | A kind of processing method of thin interbed seismic signal | |
| CN112578475B (en) | Data mining-based dense reservoir dual dessert identification method | |
| CN112505754B (en) | Method for collaborative partitioning sedimentary microfacies by well-seismic based on high-precision sequence grid model | |
| CN112147677B (en) | Method and device for generating parameter tag data of oil and gas reservoir | |
| CN115130529A (en) | Pre-stack seismic inversion method and device | |
| Iltaf et al. | Facies and petrophysical modeling of Triassic Chang 6 tight sandstone reservoir, Heshui oil field, Ordos basin, China | |
| CN111273346B (en) | Method, device, computer equipment and readable storage medium for removing deposition background | |
| CN111859709A (en) | Geological statistical simulation method and device for variation and transition probability of aquifer structure | |
| Mehdipour et al. | The Best Scenario for Geostatistical Modeling of Porosity in the Sarvak Reservoir in an Iranian Oil Field, Using Electrofacies, Seismic Facies, and Seismic Attributes | |
| CN117724151A (en) | Construction method of river facies compact heterogeneous reservoir low-frequency model | |
| CN115880455A (en) | Three-dimensional intelligent interpolation method based on deep learning | |
| Huang et al. | A Novel Method of 3D Multipoint Geostatistical Inversion Using 2D Training Images | |
| CN104375171B (en) | A kind of High-resolution Seismic Inversion method | |
| CN117345208B (en) | Quantitative characterization method and device for fracturing advantage area, electronic equipment and medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |