CN106875471B - Three-dimensional visual modeling method for coal-series water-containing or water-resisting layer - Google Patents
Three-dimensional visual modeling method for coal-series water-containing or water-resisting layer Download PDFInfo
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Abstract
The invention discloses a three-dimensional visual modeling method for coal-based coal-containing or water-resisting layers, which comprises the following steps: preparing research basic data; establishing a corresponding fault model according to the digital construction diagram and the fault data; dividing the aquifer and the water-resisting layer, identifying a corresponding water-containing or water-resisting layer interface, establishing a water-containing or water-resisting layer stratum framework, and establishing a corresponding layer model by a kriging interpolation method; researching the sedimentary geological features of the coal measure strata, and establishing a sedimentary microfacies model by combining the sequential indication simulation with the sedimentary microfacies spread feature technology; establishing a physical property model of the aquifer according to the collected and calculated physical property data on the basis of a phase control technology and theory; and displaying the spatial distribution rule of the coal-series water-containing or water-resisting layer according to the finally established related model. The invention applies modeling to objectively describe the spatial distribution of the water-containing or water-resisting layer, intuitively reflects the three-dimensional spatial distribution of the water-containing or water-resisting layer and the water-rich characteristic of the water-containing layer, and provides a three-dimensional visual basis for the research of the coal water-containing or water-resisting layer.
Description
Technical Field
The invention relates to a three-dimensional space distribution method for coal-series water-containing or water-resisting layers, in particular to a three-dimensional visual modeling method for coal-series water-containing or water-resisting layers.
background
The production and consumption of coal in China are in the forefront of the world, and the coal has great significance in domestic energy consumption. Along with the increase of the mining depth and the mining strength of the mine, the water damage of the mine occurs more and more frequently. The hydrogeological conditions of the coal mine are found, and the visual research of the aquifer distribution is particularly important.
in the traditional research of coal-series water-containing or water-resisting layers, a two-dimensional geological map is mainly applied, the principles of projection geometry, drawing geometry and ore body geometry are applied, the water-containing or water-resisting layer distribution is projected onto a plane, and the plane shape and the longitudinal distribution of the water-containing or water-resisting layer are described. However, due to the influence of complex coal measure water-containing or water-resisting layer distribution and geological structure, it is difficult to intuitively and reasonably express the spatial distribution of the coal measure water-containing or water-resisting layer through a two-dimensional and static plane graph, and the geological content difference between the two-dimensional graph and the three-dimensional space due to insufficient knowledge of the three-dimensional space distribution often occurs.
in order to prevent mine water damage, a coal system water-containing or water-resisting layer must be studied more intuitively and effectively, and the establishment of a three-dimensional geological model becomes an effective means for intuitively studying the spatial distribution of the coal system water-containing or water-resisting layer. GMS is the three-dimensional hydrogeological modeling software of coal-series water-containing or water-resisting layers commonly used in China at present, and can build three-dimensional stratum entities according to drilling stratums. However, GMS is deficient in practical applications in structural modeling and physical modeling. Due to the defects of the structure and physical modeling, the influence of the fault on the water-containing or water-resisting layer cannot be expressed in practical application, and the water-rich property of the aquifer cannot be expressed visually.
The doctor thesis of the Chinese geological academy of sciences, named research on heterogeneity of aquifer in North China plain-example Shijiazhuang Koelken county, journal of Jilin university (version of Earth science) 41, 09 month 2011 in No. 5, discloses the application of entropy weight coupling stochastic theory in research on heterogeneous comprehensive indexes of aquifers, named Marong, and the like, and both papers disclose that the heterogeneous comprehensive indexes of aquifers are used for quantitatively representing the comprehensive heterogeneity of aquifers, and the calculation process mainly comprises the following steps: (1) estimating the permeability coefficient of the sediment sample by using a cloud-Markov model; (2) simulating a distribution model of aquifer sedimentary microphase through a Markov principle; (3) on the basis, an improved sequential simulation technology is utilized to construct a permeability coefficient and porosity distribution model of the aquifer through a phase-controlled modeling principle.
Firstly, the technology for judging the sedimentary geological features by the public method is simple, analysis is carried out only according to a probability accumulation curve, and the analysis result is not very accurate aiming at the characteristic of complicated geological features; secondly, the Markov principle sedimentary microfacies modeling belongs to a stochastic modeling method, the stochastic simulation method has great uncertainty, and the sedimentary microfacies modeling is carried out only by mathematical calculation, so that the distribution rule of sedimentary microfacies can not be truly reflected. Thirdly, the application method does not use a structural modeling technology, and cannot embody the control of structural characteristics on coal series containing or water-resisting layers.
disclosure of Invention
The invention aims to overcome the defects of the existing single modeling method, the structural and physical modeling and the basic geological research, and provides a three-dimensional visual modeling method for a coal-series water-containing or water-resisting layer.
In order to achieve the purpose, the invention adopts the following technical scheme:
A three-dimensional visual modeling method for coal-series water-containing or water-resisting layers comprises the following steps:
1) collecting relevant drilling lithology, mudstone color, drilling rock core, drilling coordinates, orifice elevation, digital construction diagram, physical property and breakpoint data, and sorting and classifying the corresponding data;
2) identifying an interface between an aquifer and a water-resisting layer by using the lithological data of the drill hole in the step 1), establishing corresponding layered data, and neglecting the attribute of a relatively thin stratum when the thickness ratio of different strata is less than 1/10; establishing a stratum framework of a water-resisting layer and a water-bearing layer by combining peripheral drilling data;
3) establishing a fault model constrained by the breakpoint data and the structural diagram by using the breakpoint data in the step 1) and the fault intersection line in the digital structural diagram and using the digital structural diagram obtained by interpolation as a constraint;
4) after the fault model in the step 3) is established and before the structural layer model is established, the resolution and the horizontal and vertical recognition ranges of the whole model are specified, namely the model is gridded;
5) Obtaining an initial construction level model by using the hierarchical data in the step 2) and taking the results of the step 3) and the step 4) as constraint conditions and adopting a kriging interpolation method, and adjusting the construction level according to the hierarchical data;
6) Researching the sedimentary geological features of the coal measure strata by utilizing the lithology, mudstone color and core data of the drill hole in the step 1), establishing a sedimentary microfacies model by combining the sedimentary microfacies spread characteristic technology through sequential indication simulation, and enabling a simulation result to be closer to the reality by utilizing a method of combining random simulation and deterministic simulation;
7) And on the basis of establishing the layer model in the step 5), calculating physical property data used for modeling by combining the corresponding logging curves according to the physical property data arranged in the step 1), and establishing a physical property model of the coal-based water-containing or water-resisting layer on the basis of a phase control technology and a theoretical technology.
And 3) in the steps 3) -7), the established fault model, the structural layer model, the sedimentary microfacies model and the coal-series physical property model containing or separating the water layer are all established by applying Petrel software.
petrel is reservoir geological modeling software developed by Schlumberger, and a perfect structural modeling system is the characteristic of the software, and a three-dimensional visual fault model can be established by applying a deterministic modeling method according to a digitized structural diagram and fault data in each layer, and the faults can effectively control the distribution of formation fluid.
The phase control technology and theory in the step 7) take the sedimentary microfacies as constraint conditions, further perform phase splitting simulation on physical properties of the coal measure strata, and establish a relevant model; the starting point of the theory is to recognize the difference between different phases and is the basis for researching the heterogeneity of coal-series water-containing or water-resisting layers.
and in the step 1), the lithology data of the drill hole is mainly analyzed according to a drill hole histogram, or the rock core of the drill hole is directly observed, and the lithology of the rock core is divided to distinguish the type of the rock of the aquifer from the type of the rock of the water-resisting layer.
Fault intersecting surface lines generated by the breakpoint data exist in the digital construction diagram in the step 3), and the fault intersecting surface line of each small layer displays an ascending disc and a descending disc of the fault at the position; and if the fault cross line does not exist, controlling the fault according to the fault point data, combining the fault points of the same fault on the plane, and drawing the fault cross lines of different layers.
Judging the nature of the fault according to the difference of the construction lines of the construction diagram; the fault property is judged according to the condition of the regional stress, and the reverse fault generally does not appear under the condition of the regional tensile stress, and the thrust or reverse fault is mainly used under the condition of the compressive stress.
the gridding of the model in the step 4) is to set a grid of a pre-established model, and the accuracy of the model is set according to the resolution of the grid, wherein the gridding of the model is a foundation stone of the geological model.
In the step 5), the initial structural layer model obtained through the kriging interpolation is inconsistent with the actual geological conditions due to the mechanical operation problem, and the layer models are overlapped due to the lack of control of hierarchical data in some parts, so that the generated structural model has a leak; the layer model needs to be smoothed to fit the actual geological situation.
The kriging estimation in step 5) is an optimal unbiased estimation method, which is used for random simulation, and based on known variables, an optimal unbiased estimation is made on unknown values of points to be estimated by applying a variation function, and a random variable Z (x) at x of a regionalized variable Z (x) is represented by a linear combination: wherein Z x (x) -the Krigin estimated value of the point to be estimated; z (xi) -the observed value at a point xi around the point to be estimated, i ═ 1,2,3 …, n; n is a natural number; the weighting factor at λ i-xi represents the magnitude of the influence of xi on the estimate Z x (x).
in the step 6), the coal measure sedimentary geological features mainly comprise the research on the sedimentary environment, sediment sources and sedimentary microfacies of the coal measure stratum; judging the deposition environment of a research area by rock color index analysis, ancient biology and ancient water deep reduction methods, judging that the rock color is deeper to be a reduction environment, indicating that the water depth is deeper, the ancient biology fossil mostly appears to be suitable for the deep water biology fossil, and judging that the oxygen-rich environment is lighter in color, indicating that the water depth is shallower, and the ancient biology fossil mostly appears to be diving water biology fossil; analyzing the combination type and content of heavy minerals to indicate the nature of parent rock in the object source region, indicating a near object source region in a region with high content of heavy minerals such as garnet and the like, and taking a region with low content of various heavy minerals as a far object source region; dividing the sand thickness of the target area by combining the area sedimentary facies background and other logging information; in the aspect of phase distribution research, a well logging lithology section and a sand body distribution graph are fully utilized, phase marks are identified, sedimentary microfacies are distinguished, and meanwhile, the distribution characteristics of planes of the sedimentary microfacies are divided; the planar distribution of the deposited micro-phases is according to the phase sequence gradient rule, and the phase jump phenomenon should not occur; on the basis of depositing the microphase plane spread, the plane spread form of the water-containing or water-resisting layer is restrained by depositing microphase.
the sequential indication simulation in the step 6) is an indication method of sequential simulation, and compared with the conventional sequential simulation, the method can better process original samples in various distribution modes, and is favorable for establishing a corresponding model based on discrete sedimentary microfacies and aquifer sand body distribution data;
the specific steps of sequential indication simulation are as follows:
firstly, converting original data into an indicator variable; the sedimentary microphase data belong to discrete distribution data, and the threshold value of the data is all the discrete data; the overlying strata of the coal system in the mining area is a braided river channel deposition environment, the subphase comprises three deposition microphases which are respectively cardiac beach deposition, braided river channel retention deposition and river channel overflow deposition, the facies data are 1,2 and 3, all the facies data are one of the three values, and the threshold value is the three values; the corresponding indicator function is Z (u, 1); z (u, 2); z (u, 3);
Secondly, randomly simulating the indicated original phase data by adopting a sequential simulation method; the sequential simulation method comprises the following specific steps:
The mine area is gridded into N grid nodes, wherein a conditional joint probability model of N random variables Zi (i ═ 1,2, …, N):
F[Z,Z,…,Z/(n)]＝Prob{Z≤z,i＝1,2,…,N/(n)}
the conditional cumulative distribution function is known from the above equation:
Z-Prob{Z≤z/(n)}；
Z-Prob{Z≤z/(n+1)}
…
Z-Prob{Z≤z/(n+N-1)}；
Wherein, i is 1,2, …, N is a natural number, and N is a positive integer;
According to the conditional probability cumulative distribution function of various grid variables, the sequential simulation algorithm is realized by the following steps:
(1) extracting a sample from a condition accumulation distribution function of the variable under the condition of knowing n original data to obtain a first sample which is set as z 1;
(2) Adding Z1 into an original data set, changing the current original data into (n +1) ═ n ═ u { Z1 ═ Z1}, extracting a sample from a condition accumulation distribution function under a new condition, and obtaining a second sample which is set as Z2;
(3) Repeating the step (2) to obtain samples z3, …, zN, wherein the group of samples is a simulation result;
(4) repeating the steps (1) to (3) for n times to obtain n simulation results.
finally, different indicated variation function types are respectively selected for cardiac beach deposition, braided riverway retention deposition and riverway overflow deposition, and a standard spherical variation function model is applied to the cardiac beach deposition:
where a is a variable, and represents the magnitude of the influence of the variable. The change range is smaller than the original data, which shows that the continuity is good and the randomness is small; greater than the original data, the randomness is large.
Braided river channel retention sedimentation application index variation contains digital models:
where C0 is a block gold constant representing the magnitude of spatial variability, a representing the magnitude of the effect of the variable on the variation, and C representing the magnitude of the difference between the variables. Setting the direction of a main object source to be C0 to be 0, wherein the variable range a is smaller than the original data; setting the direction of the secondary object source to be C0 greater than the direction of the primary object source, and setting the variable range a greater than the original data;
the river overflow deposition adopts an intermittent variation function block gold effect model:
C0 is the size of the spatial variability represented by the gold constant; c is the arch height, indicating the magnitude of the variable difference, with greater arch height indicating greater difference. C + C0, called the base station value, characterizes the overall variability in the variables over space.
through variation function simulation of different deposition micro-phases, the goal of combining certainty with randomness simulation is achieved, and the simulation result is closer to reality.
The physical property data in the step 1) mainly comprise porosity data of an aquifer, and the physical property data is obtained by testing a rock core sample; performing porosity correction according to the corresponding logging data in the step 7) by combining with the core sample test to obtain a regression formula, and calculating physical property data for modeling by combining with the corresponding logging curve; the physical property data used for modeling in the step 7) mainly refers to a porosity value calculated according to a regression formula.
The invention uses Petrel software to apply a plurality of advanced technologies: such as structural modeling techniques, three-dimensional networking techniques, techniques that combine deterministic modeling with stochastic modeling. Particularly for stratums with strong heterogeneity, stochastic modeling can well make up for the disadvantage of deterministic modeling. The Petrel software is utilized to establish the three-dimensional visual geological model based on the phase control technology and theory and the stochastic-deterministic modeling theory, the three-dimensional spatial distribution of the aquifer or the aquifer and the water-rich property of the aquifer can be reflected visually, and a three-dimensional visual basis is provided for the research of the water hazard danger of the mine.
The invention has the beneficial effects that:
Compared with the paper in the background art, the invention has the following technical effects:
firstly, aiming at the characteristic that the sedimentary geological features of the coal-series coal-containing or water-resisting layer are complex, the sedimentary geological features are respectively analyzed by a sedimentary environment judging technology, a material source analyzing technology and a sedimentary microfacies spread and drawing technology, and the spatial spread and distribution trend of the coal-series coal-containing or water-resisting layer is more accurately described.
secondly, the invention applies a technical method combining random modeling and deterministic modeling, namely a technology combining sequential indication simulation and deposition micro-phase spread distribution characteristics, so that the model is more practical.
Thirdly, the invention emphasizes that other models are established on the basis of the construction model by combining the digital construction diagram and the breakpoint data, and the control of the construction characteristics on the coal containing or water-resisting layer is fully embodied.
The invention applies the techniques of structure modeling and the like in petroleum geological modeling to the modeling of coal-based aquifer. The coal-series aquifer has complicated deposition structure and various deposition environments, so that the lithology in the coal-series overburden stratum changes frequently; the heterogeneity of the stratum is strong due to the difference of the deposition environments of the coal bed and the overlying stratum; the basic data of coal-based formations is low relative to oil reservoirs. Therefore, the three-dimensional geological modeling difficulty of the coal-series aquifer is obviously higher than that of the petroleum reservoir.
Compared with the prior theory and other prior arts, the invention introduces the phase control technology and the theory into the research of coal series water-containing or water-resisting layers; by applying Petrel software, the advantages of fault modeling of the structure of the Petrel software are highlighted, and faults are important factors for controlling aquifers; the method combines random modeling and deterministic modeling, namely, the sedimentary microfacies model is established by combining sequential indication simulation with the spreading characteristics of sedimentary microfacies, and the physical property model of the aquifer is established according to corresponding physical property data on the basis of a phase control technology and a theory, so that the model is closer to reality and better conforms to the heterogeneity characteristics of the coal-series aquifer or the water-resisting layer.
The modeling means adopted by the invention is a point, line, surface and ring modeling means which are buckled with each other and are progressive. Controlling the trend and the tendency of the fault according to a digital construction diagram and fault data on the basis of strong construction modeling capability of Petrel software; establishing a stratum layer model by using the kriging estimation value; establishing a sedimentary microfacies model by combining sequential indication simulation with the spreading characteristics of sedimentary microfacies; the physical properties of the reservoir and the spreading of sand bodies are controlled by the deposition micro-phase, a corresponding model is established, the spatial spreading trend of the coal-series containing or water-resisting layer is described more accurately, and the model reliability is higher.
Drawings
FIG. 1 is a flow chart of a three-dimensional visual modeling method for coal-bearing or water-resisting layers of the coal-series of the invention;
FIG. 2 is a three-dimensional view of a fault in an H-mine site created in accordance with an embodiment of the present invention;
FIG. 3 is a diagram of an established H-mine grid system in one embodiment of the present invention;
FIG. 4 is an H-mine tectonic layer model established in one embodiment of the present invention;
FIG. 5 is a plan view of a sedimentary microphase of an H-mine created in one embodiment of the present invention;
FIG. 6 is a random simulation result of a sedimentary microfacies model built in one embodiment of the present invention;
FIG. 7 is a plot sedimentary microfacies model of an H-mine in an embodiment of the present invention;
FIG. 8 is a plot physical model of an H mine created in one embodiment of the present invention;
FIG. 9 is a regression plot of porosity of H-site rock versus acoustic moveout established in an embodiment of the present invention.
Detailed Description
the invention is further illustrated with reference to the following figures and examples.
The structures, proportions, sizes, and other dimensions shown in the drawings and described in the specification are for understanding and reading the present disclosure, and are not intended to limit the scope of the present disclosure, which is defined in the claims, and are not essential to the art, and any structural modifications, changes in proportions, or adjustments in size, which do not affect the efficacy and attainment of the same are intended to fall within the scope of the present disclosure. In addition, the terms "upper", "lower", "left", "right", "middle" and "one" used in the present specification are for clarity of description, and are not intended to limit the scope of the present invention, and the relative relationship between the terms and the terms is not to be construed as a scope of the present invention.
The method aims to solve the problems that the structural and physical modeling and the spatial distribution form of a coal-based water-containing (separation) layer cannot be fully reflected in the conventional aquifer modeling.
therefore, the invention provides a coal-series coal-containing or water-resisting layer modeling method based on a phase control technology and theory and applying Petrel software, as shown in figure 1, comprising the following steps:
1) And collecting data such as the lithology, the coordinates of the drill hole, the elevation of the hole opening, the digital construction drawing, the physical property, the breakpoint and the like of the related drill hole.
The lithology data of the drill hole is mainly analyzed according to a drill hole histogram, and the drill hole core can also be directly observed. And the rock properties of the rock core are reasonably divided, and the rock type of the aquifer and the rock type of the water-resisting layer are distinguished.
Table 1 shows the borehole coordinates and orifice elevations. The drilling coordinates refer to geodetic coordinates, X coordinates and Y coordinates of the drill hole; the orifice elevation indicates the elevation of the orifice; the digital construction diagram refers to coal measure stratum contour lines obtained by interpolation, and the construction diagram contains information such as fault cross-over lines; table 2 is breakpoint data. The fault data refers to the depth of the fault at each small layer.
2) Dividing the formation properties into a water-resisting layer and a water-bearing layer according to the rock properties of the drilled holes, identifying the interface of the water-bearing layer and the water-resisting layer, establishing corresponding layered data, and adjusting the formation properties according to the single-layer thickness and the rock properties of the drilled holes at the periphery.
in the division of the aquifer and the water-resisting layer, the rock property of the drilled hole is the main basis of the division, and if a thin layer of water-bearing rock is included in a large set of water-resisting rock, the influence of the water-bearing rock is not large. Can be classified as a water barrier.
The lithologic mutational surface, unconformity surface and granularity conversion surface can be used as marks for identifying the interface of the water-containing or water-resisting layer. When the thickness of the hydrous/water-resistant rock is less than 1/10, the hydrous rock is considered to have little effect on the formation properties.
3) fig. 2 is a fault model created using the fault data in step 1) and the digitized structural diagram obtained by interpolation.
The digital construction diagram has fault cross-section lines generated by the fault point data, and the fault cross-section line of each small layer displays the ascending disc and the descending disc of the fault at the layer. If the fault cross line does not exist, the fault can be controlled according to the fault data, the breakpoints of the same fault are combined on the plane, and the fault cross lines of different layers are drawn.
judging the nature of the fault according to the difference of the construction lines of the construction diagram; the fault property is judged according to the condition of the regional stress, and the reverse fault generally does not appear under the condition of the regional tensile stress, and the thrust or reverse fault is mainly used under the condition of the compressive stress.
4) After the fault model is built and before the structural layer model is built, the resolution and the horizontal and vertical recognition ranges of the whole model need to be specified, namely the model is gridded. The accuracy of the generated model is determined by the resolution of the mesh.
the gridding of the model is the foundation stone of the geological model, and the resolution of the grid directly influences the generation precision of the model. And (3) establishing a grid system of the H mine area by considering the geological characteristics and fault distribution of the H mine area and the computing capability of a computer.
5) and (4) obtaining an initial construction level model by using the hierarchical data and the drilling lithology data in the steps 1) and 2) and using the result of the step 3) and the step 4 as constraint conditions and adopting Krigin interpolation, and adjusting the construction level according to the hierarchical data.
the initial structure layer model obtained through the kriging interpolation is inconsistent with the actual geological conditions due to the mechanical operation problem, and the layer models are overlapped due to the lack of control of hierarchical data in some parts, so that the generated structure model has a leak. The layer model needs to be smoothed to fit the actual geological situation.
6) the kriging estimation in the step 5) is an optimal unbiased estimation method, and the method can be used for random simulation, and optimal unbiased estimation is carried out on unknown values of points to be estimated by applying a variation function based on known variables. Regionalized variable Z (x) random variable Z x (x) at x can be represented by a linear combination: z (x) -the estimated Krigin value of the point to be estimated; z (xi) -the observed value at a point xi around the point to be estimated, i ═ 1,2,3 …, n; the weighting factor at λ i-xi represents the magnitude of the influence of xi on the estimate Z x (x).
7) and (5) researching the sedimentary geological characteristics of the main aquifer of the coal measure strata of the H mining area by using the data of lithology, mudstone color, drill core and the like in the step 1), and (6) establishing a sedimentary microfacies model of the main aquifer of the coal measure strata of the H mining area by combining the spreading characteristics of sedimentary microfacies with sequential indication simulation. The method of combining random simulation and deterministic simulation makes the simulation result more practical.
the coal measure sedimentary geological features mainly comprise research on sedimentary environments, sediment sources and sedimentary microfacies of coal measure stratums. And judging the deposition environment of the research area by methods such as rock color index analysis, ancient biology, ancient water deep reduction and the like. The judgment of the source direction is the basis of the spreading of the next step of the sand body and the microphase, the analysis of the combination type and the content of the heavy minerals can directly indicate the property of the parent rock of the source area, and the source direction can also be judged according to the change of the content of the heavy minerals, thereby laying the foundation for the spreading of the next step of the deposition microphase. And dividing the sand thickness of the target area by combining the area sedimentary facies background and other logging information. On the basis of the sedimentary microfacies distribution mode, the well logging lithology section and the sand body distribution graph in the H mining area are fully utilized, the facies marks are identified, the sedimentary microfacies in the H mining area are distinguished, and meanwhile, the plane distribution characteristics of the sedimentary microfacies are divided. The planar distribution of the deposited micro-phase is according to the phase sequence gradient rule, and the phase jump phenomenon should not occur. And the plane spreading shape of the coal containing or water-resisting layer of the H mining area is restrained by the sedimentary microfacies of the H mining area.
figure (5) divides the braided river sedimentary environment of the main aquifer of the H-site into three sedimentary microphases: cardiac beach deposition, braided riverway retention deposition and riverway overflowing deposition. The three types of phase data are denoted 1,2,3, respectively. The sedimentary microfacies model in the graph (6) is used for constraining the sedimentary microfacies model of the aquifer in the graph (7) according to the result of the sedimentary microfacies plane distribution in the step 7) after different types of facies data are subjected to sequential indication simulation, and the effect of combining a sedimentary geology research by applying a sequential indication simulation method is achieved.
8) The sequential indication simulation in the step 7) is an indication method of sequential simulation, and compared with the conventional sequential simulation, the method can better process original samples in various distribution modes, and is favorable for establishing a corresponding model based on discrete sedimentary microfacies and aquifer sand body distribution data.
The specific steps of sequential indication simulation are as follows:
first, the original data is transformed into an indication variable. The deposition microphase data belong to discrete distribution data, and the threshold value of the data can be all the discrete data. The coal series overlying strata of the H mining area is a braided river channel deposition environment, the subphase comprises three deposition microphases which are respectively cardiac beach deposition, braided river channel retention deposition and river channel overflow deposition, the facies data are 1,2 and 3, all the facies data are one of the three values, and the threshold value is the three values. The corresponding indicator function is Z (u, 1); z (u, 2); z (u, 3).
and finally, randomly simulating the indicated original phase data by adopting a sequential simulation method. The sequential simulation method comprises the following specific steps:
H-site gridding was 362987 grid nodes with a conditional joint probability model of 362987 random variables Zi (i ═ 1,2, …, 362987):
F[Z,Z,…,Z/(362987)]＝Prob{Z≤z,i-1,2,…,362987/(362987)}
The conditional cumulative distribution function is known from the above equation:
Z-Prob{Z≤z/(362987)}；
Z-Prob{Z≤z/(362987+1)}
…
Z-Prob{Z≤z/(362987+362987-1)}
according to the conditional probability cumulative distribution function of various grid variables, the sequential simulation algorithm is realized by the following steps:
(1) Extracting a sample from a conditional cumulative distribution function of variables under the condition of known 362987 original data to obtain a first sample which is set as z 1;
(2) adding Z1 into an original data set, changing the current original data into (362987+1) ═ 362987 ═ Z1 ═ Z1}, extracting a sample from a condition accumulation distribution function under a new condition, and obtaining a second sample which is set as Z2;
(3) repeating the step (2) to obtain samples z3, … and z362987, wherein the group of samples is a simulation result;
(4) Repeating the step (1) -the step (3) and 362987 times to obtain 362987 simulation results.
Finally, different variation function types are respectively selected for cardiac beach deposition, braided riverway retention deposition and riverway overflowing deposition, and a standard spherical variation function model is applied to cardiac beach deposition:
Wherein, the change a takes 2, which is smaller than the original data, and shows good continuity.
braided river channel retention sedimentation application index variation contains digital models:
Wherein, the direction of the main object source is set to be C0 which tends to 0, and the variable range a is less than 3; the direction of the secondary source is set to be C0 larger than that of the primary source, and the variable range a is larger than 3;
the river overflow deposition adopts an intermittent variation function block gold effect model:
c0 the gold constant is 0.21; the height of the C arch is 0.94; the base number of C + C0 was 1.15.
Through variation function simulation of different deposition micro-phases, the goal of combining certainty with randomness simulation is achieved, and the simulation result is closer to reality.
9) And (8) on the basis of the establishment of the layer model in the step 5), establishing a physical property model of a coal containing or water-resisting layer by combining a Krigin interpolation method on the basis of a phase control technology and a theory according to the physical property data of the drill holes arranged in the step 1).
the lithology of the aquifer is mainly medium and coarse sandstone, and the aquifer also comprises most fine sandstone and a small amount of siltstone, and the physical properties are strong in heterogeneous degree due to different lithologies of the stratum. The method of random simulation is helpful for people to know the complexity of the aquifer, better reflects the discreteness of the reservoir property and has great advantages for the representation of heterogeneity.
The physical data mainly comprises porosity and permeability data of the aquifer. The physical property data can be obtained by testing a core sample, and then the porosity correction is carried out according to the logging data by combining with an indoor experiment, and the corresponding physical property data is obtained according to the logging data. The spreading boundary of the sand body on the plane is the boundary of physical modeling.
In order to better research the physical properties of a coal-based aquifer, the invention collects the physical property experimental data of the drill core, and a graph (9) establishes a correlation relationship according to a method for comparing the physical property data with an acoustic time difference logging curve to obtain a regression formula:
1.3222x-1.2577, wherein the correlation coefficient (R2) is 0.7601, and the correlation coefficient is stronger. And establishing physical property distribution characteristics of all drilling control in the research area based on the acoustic time difference logging data. And according to the physical property plane distribution characteristics, carrying out constraint by using the deposition micro-phase spread on the plane to finally obtain the physical property model of the aquifer.
Compared with the existing theory and technology, the invention introduces the research of coal series water-containing or water-resisting layer into the phase control technology and the theory; by applying Petrel software, the advantages of fault modeling of the structure of the Petrel software are highlighted, and faults are important factors for controlling aquifers; the method is characterized in that a layer model is established by combining random modeling and deterministic modeling, and a physical property model of a water-bearing layer is established according to corresponding physical property data on the basis of a phase control technology and theory, so that the model is closer to the reality and better conforms to the heterogeneity characteristics of a coal-bearing or water-resisting layer.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. a three-dimensional visual modeling method for coal-based water-containing or water-resisting layers is characterized by comprising the following steps:
1) Collecting relevant drilling lithology, mudstone color, drilling rock core, drilling coordinates, orifice elevation, digital construction diagram, physical property and breakpoint data, and sorting and classifying the corresponding data;
2) Identifying an interface of an aquifer and a water-resisting layer by using the lithological data of the drill hole in the step 1), establishing corresponding layered data, and neglecting relatively thin stratum properties when the thickness ratio of different stratum layers is less than 1/10; establishing a stratum framework of a water-resisting layer and a water-bearing layer by combining peripheral drilling data;
3) establishing a fault model constrained by the breakpoint data and the structural diagram by using the breakpoint data in the step 1) and the fault intersection line in the digital structural diagram and using the digital structural diagram obtained by interpolation as a constraint;
4) after the fault model in the step 3) is established and before the structural layer model is established, the resolution and the horizontal and vertical recognition ranges of the whole model are specified, namely the model is gridded;
5) obtaining an initial construction level model by using the hierarchical data in the step 2) and taking the results of the step 3) and the step 4) as constraint conditions and adopting a kriging interpolation method, and adjusting the construction level according to the hierarchical data;
6) Researching the sedimentary geological features of the coal measure strata by using the lithology, mudstone color and core data of the drill hole in the step 1), establishing a sedimentary microfacies model by combining the spread characteristics of sedimentary microfacies with sequential indication simulation, and enabling the simulation result to be closer to the reality by using a method of combining random simulation and deterministic simulation;
the method comprises the following specific steps:
Firstly, converting original data into an indicator variable; the sedimentary microfacies data belong to discrete distribution data, the overlying strata of the coal system in the mining area are in a braided river channel sedimentary environment, the subphase comprises three sedimentary microfacies which are respectively cardiac beach sedimentary, braided river channel retention sedimentary and river channel overflow sedimentary, the facies data are 1,2 and 3, all the facies data are one of the three values, and the threshold value is the three values; the corresponding indicator function is Z (u, 1); z (u, 2); z (u,3), u being the argument of the function;
Secondly, randomly simulating the indicated original phase data by adopting a sequential simulation method;
Finally, different indicated variation function types are respectively used for cardiac beach deposition, braided riverway retention deposition and riverway overflow deposition, and a standard spherical variation function model is applied to cardiac beach deposition; braided river retention and deposition application index variation content model; selecting an intermittent variation function block gold effect model for river overflow deposition;
Through variation function simulation of different deposition microphase indicators, the goal of combining certainty with randomness simulation is achieved, and the simulation result is closer to reality;
7) and (3) on the basis of establishing the layer model in the step 5), calculating physical property data used for modeling by combining the corresponding logging curves according to the physical property data arranged in the step 1), and establishing a physical property model of the coal containing or water-resisting layer on the basis of a phase control technology and theory.
2. The three-dimensional visualization modeling method for the coal-based or water-resisting layer according to claim 1, wherein the fault model, the structural level model, the sedimentary micro-phase model and the coal-based or water-resisting layer physical property model which are established in the steps 3) -7) are all established by using Petrel software.
3. the coal-based or water-resisting layer three-dimensional visual modeling method of claim 1, wherein the phase control technology and theory in the step 7) are based on the deposition microphase as a constraint condition, and the physical characteristics of the coal-based strata are further simulated in a phase-splitting manner to establish a relevant model.
4. the three-dimensional visual modeling method for the coal-based aquifer or water-resisting layer according to claim 1, wherein the lithology data of the drill hole in the step 1) is mainly analyzed according to a drill hole histogram, or the drill hole core is directly observed, and the lithology of the core is divided to distinguish the type of the aquifer rock from the type of the water-resisting layer rock.
5. the three-dimensional visual modeling method for the coal measure containing or water-resisting layer according to claim 1, wherein a fault cross line generated by fault point data exists in the digital construction diagram in the step 3), and the fault cross line of each small layer displays an ascending tray and a descending tray of the fault at the position; if the fault cross line does not exist, controlling the fault according to the fault point data, combining the fault points of the same fault on a plane, and drawing the fault cross lines of different layers;
judging the nature of the fault according to the difference of the construction lines of the construction diagram; and judging the fault property according to the condition of the regional stress, wherein the reverse fault does not appear under the condition of the regional tensile stress, and the reverse fault is mainly used under the condition of the compressive stress.
6. The coal measure aquifer or water-resisting layer three-dimensional visual modeling method of claim 1, wherein the gridding of the model in the step 4) is the cornerstone of the geological model, and the resolution of the grid directly influences the accuracy of the model generation.
7. The method of claim 1, wherein the kriging estimate in step 5) is an optimal unbiased estimation method for stochastic simulation, based on known variables, using a variogram to make optimal unbiased estimates of unknown values for points to be estimated, and the regionalized variable Z (x) is represented by a linear combination of the random variables Z (x) at x: wherein Z x (x) -the Krigin estimated value of the point to be estimated; z (xi) -the observed value at a point xi around the point to be estimated, i ═ 1,2,3 …, n; n is a natural number; the weighting factor at λ i-xi represents the magnitude of the influence of xi on the estimate Z x (x).
8. The coal measure or water-resisting layer three-dimensional visualization modeling method according to claim 1, wherein in the step 5), the initial structure layer model obtained through the kriging interpolation is inconsistent with the actual geological conditions due to the mechanical operation problem, and the layer models are overlapped due to the lack of control of the layered data in some parts, so that the generated structure model has a leak; the layer model needs to be smoothed to fit the actual geological situation.
9. the coal measure water-bearing or water-resisting layer three-dimensional visualization modeling method of claim 1, wherein in the step 6), the sequential simulation method comprises the following specific steps:
the mining area is gridded into N grid nodes, wherein the conditional joint probability models of N random variables Zi are as follows:
F[Z,Z,…,Z/(n)]＝Prob{Z≤z,i＝1,2,…,N/(n)}
the conditional cumulative distribution function is known from the above equation:
Z-Prob{Z≤z/(n)}；
Z-Prob{Z≤z/(n+1)}
…
Z-Prob{Z≤z/(n+N-1)}；
wherein, i is 1,2, …, N is a natural number, and N is a positive integer;
According to the conditional probability cumulative distribution function of the grid variables, the sequential simulation algorithm is realized by the following steps:
(1) extracting a sample from a condition accumulation distribution function of the variable under the condition of knowing n original data to obtain a first sample which is set as z 1;
(2) Adding Z1 into an original data set, changing the current original data into (n +1) ═ n ═ u { Z1 ═ Z1}, extracting a sample from a condition accumulation distribution function under a new condition, and obtaining a second sample which is set as Z2;
(3) repeating the step (2) to obtain samples z3, …, zN, wherein the group of samples is a simulation result;
(4) repeating the steps (1) to (3) for n times to obtain n simulation results.
10. the coal measure water-containing or water-resisting layer three-dimensional visual modeling method of claim 1, wherein in the step 6),
The standard spherical variation function model is as follows:
wherein u is an independent variable of the function, a is a variable range, the influence of the variable is represented, and the variable range is smaller than the original data, so that the continuity is good and the randomness is small; if the data is larger than the original data, the randomness is high;
The index variation content model is as follows:
wherein, C0 is a gold constant representing the size of spatial variability, and C is an arch height representing the size of variable difference; setting the direction of a main object source to be C0 to be 0, wherein the variable range a is smaller than the original data; setting the direction of the secondary object source to be C0 greater than the direction of the primary object source, and setting the variable range a greater than the original data;
the discontinuous variation function block gold effect model is as follows:
A larger arch height C indicates a larger difference; c + C0, called the base station value, characterizes the overall variability in the variables over space.
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