Natural fracture distribution prediction method and system

Abstract

The invention discloses a natural fracture distribution prediction method and system, and the method comprises the steps: S1, predicting the natural fracture development of a target region through a geological analysis method, and obtaining a first prediction result; s2, predicting natural fracture development of the target area by using a fractal theory to obtain a second prediction result; s3, predicting natural fracture development of the target area by using a rock fracture degree theory to obtain a third prediction result; s4, predicting natural fracture development of the target area by using an earthquake prediction method to obtain a fourth prediction result; and S5, performing weight analysis according to the first prediction result, the second prediction result, the third prediction result and the fourth prediction result to realize quantitative prediction of natural fracture distribution.

Description

Technical Field

[0001] The invention belongs to the technical field of geological exploration, and in particular relates to a natural fracture distribution prediction method and system. Background Art

[0002] With the increasing depletion of conventional oil and gas resources in the world, unconventional oil and gas resources have become the focus and hotspot of exploration and development. Tight sandstone gas reservoirs obey the theory of continuous gas accumulation. Low porosity, low permeability, strong heterogeneity, wide distribution range, low reservoir abundance, and large production capacity gap are its typical characteristics. The development of natural fractures is of great significance to the transformation of tight sandstone reservoirs. Natural fractures are the storage place of tight sandstone oil and gas, and also control the seepage system of such reservoirs; the development degree of natural fractures is the key to whether tight sandstone gas can obtain high and stable production; if the vertical extension distance of the open fracture is large, it is not conducive to the preservation of tight gas reservoirs, and natural gas is easy to lose. At the same time, tight sandstone reservoirs often need to be transformed by fracturing to increase production capacity, and the occurrence, development degree, scale, opening size, filling condition, direction and angle between the current maximum principal stress of natural fractures will also affect the effect of fracturing. Obviously, the static study of fractures plays a vital role in the exploration and development of tight sandstone oil and gas. However, it is currently difficult to accurately predict various types of cracks. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method and system for predicting the distribution of natural fractures.

[0004] To achieve the above object, the present invention adopts the following technical solution:

[0005] A natural fracture distribution prediction method, comprising:

[0006] Step S1, predicting the development of natural fractures in the target area using a geological analysis method to obtain a first prediction result;

[0007] Step S2, predicting the development of natural fractures in the target area using fractal theory to obtain a second prediction result;

[0008] Step S3, predicting the development of natural fractures in the target area using the rock fracture degree theory to obtain a third prediction result;

[0009] Step S4, predicting the development of natural fractures in the target area using an earthquake prediction method to obtain a fourth prediction result;

[0010] Step S5: Perform weight analysis based on the first prediction result, the second prediction result, the third prediction result and the fourth prediction result to achieve quantitative prediction of natural fracture distribution.

[0011] Preferably, in step S5, the weight coefficients of the four prediction results are determined by using grey correlation analysis method to obtain the grey correlation degree between the parent sequence and the subsequence; wherein the four prediction results are taken as subsequences and the core fracture density is taken as the parent sequence.

[0012] Preferably, in step S5, a comprehensive fracture development index is obtained by using a weight coefficient of the prediction result.

[0013] The present invention also provides a natural fracture distribution prediction system, comprising:

[0014] A first processing module is used to predict the development of natural fractures in the target area by using a geological analysis method to obtain a first prediction result;

[0015] The second processing module is used to predict the development of natural fractures in the target area by using fractal theory to obtain a second prediction result;

[0016] The third processing module is used to predict the development of natural fractures in the target area by using the rock fracture degree theory to obtain a third prediction result;

[0017] A fourth processing module is used to predict the development of natural fractures in the target area by using an earthquake prediction method to obtain a fourth prediction result;

[0018] The fifth processing module is used to perform weight analysis according to the first prediction result, the second prediction result, the third prediction result and the fourth prediction result to achieve quantitative prediction of natural fracture distribution.

[0019] Preferably, the fifth processing module uses grey correlation analysis to determine weight coefficients of the four prediction results, and obtains the grey correlation between the parent sequence and the subsequence; wherein the four prediction results are taken as subsequences, and the core fracture density is taken as the parent sequence.

[0020] Preferably, the fifth processing module obtains a comprehensive fracture development index by using a weight coefficient of the prediction result.

[0021] The present invention adopts weight evaluation theory to perform weight analysis on the results obtained by different fracture prediction technologies, and finally realizes quantitative prediction of natural fracture distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0023] Figure 1This is a flow chart of a method for predicting natural fracture distribution according to an embodiment of the present invention.

[0024] Figure 2 The fracture prediction result based on geological analysis in an embodiment of the present invention;

[0025] Figure 3 The crack prediction result based on the fractal method in the embodiment of the present invention;

[0026] Figure 4 The fracture prediction result based on the rock fracture degree theory in the embodiment of the present invention;

[0027] Figure 5 The fracture prediction result based on seismic data in an embodiment of the present invention;

[0028] Figure 6 This is the comprehensive prediction result of cracks in the embodiment of the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0031] Embodiment 1:

[0032] like Figure 1-6 As shown, an embodiment of the present invention provides a natural fracture distribution prediction method, comprising:

[0033] Step S1, predicting the development of natural fractures in the target area using a geological analysis method to obtain a first prediction result;

[0034] Step S2, predicting the development of natural fractures in the target area using fractal theory to obtain a second prediction result;

[0035] Step S3, predicting the development of natural fractures in the target area using the rock fracture degree theory to obtain a third prediction result;

[0036] Step S4, predicting the development of natural fractures in the target area using an earthquake prediction method to obtain a fourth prediction result;

[0037] Step S5: Perform weight analysis based on the first prediction result, the second prediction result, the third prediction result and the fourth prediction result to achieve quantitative prediction of natural fracture distribution.

[0038] As an implementation of the present invention, in step S2, for the fracture structure distributed in the two-dimensional plane, a square with a side length of r is used to divide the research area into several parts, denoted as N(r). When the probability of the fractal set element appearing in the i-th unit is P i (r), the total amount of information at this time can be calculated as:

[0039]

[0040] If we change the side length r of the square, we have

[0041] I(r)=I0-D1lnr

[0042] At this time, the information dimension is calculated using the following formula:

[0043]

[0044] Among them, P i (r) is the probability that the fracture is located in the box with the i-th square side length r, that is, the fracture strength. The total number of fracture sets in the box with the i-th side length r is n i , the total number of boxes is N(r), then we have

[0045]

[0046] In fact, by changing the side length r of the box during the calculation process, if I(r) and ln(r) have the following linear relationship:

[0047] I(r)=-D1lnr+I0

[0048] At this time, the information dimension D1 is the slope of the straight line.

[0049] As an implementation method of an embodiment of the present invention, in step S3, the rock fracture degree theory uses the finite element method. The finite element method is a numerical method for approximately solving general continuous problems, and is widely used in calculating stress distribution and structural deformation. The finite element method is used for crack prediction. Its basic idea is to discretize a continuous geological body (research object) into a finite number of continuous units. Different units are connected by nodes, and rock mechanics parameters are assigned to each unit. Combined with the node equilibrium condition and the boundary force condition, the displacement of the node and the stress in the unit are obtained. The joint equation group with the overall stiffness matrix as the coefficient is used to construct an interpolation function to obtain the displacement on each node, and finally calculate the approximate value of the stress and strain in each unit. In fact, in the application process, it is necessary to assume that the unit is homogeneous. By continuously refining the geological unit, the model can be made closer to the geological body.

[0050] After the elastic body is loaded, the displacement vector [u] of different points in the geological body is:

[0051] [u]=(uvw) T

[0052] u=u(x,y,z)

[0053] v=v(x,y,z)

[0054] w=w(x,y,z)

[0055] Among them, u, v, and w represent the displacement components of the geological body in three directions, which are functions of x, y, and z. At this time, its stress state can be expressed by the stress vector [σ] at different points inside:

[0056] [σ]=(σ x σ y σ z τ xy τ yz τ zx ) T

[0057] Its strain state can be expressed by the strain vector [ε] at different points inside:

[0058] [ε]=(ε x ε y ε z γ xy γ yz γ zx ) T

[0059] When a loaded elastic object reaches a state of equilibrium, its stress and external force, strain and displacement conform to the basic equations of elasticity. Combined with the set boundary conditions, the equations of elasticity can be solved. In the geomechanics model, the stress value and direction (including the maximum principal stress, intermediate principal stress and minimum principal stress) corresponding to each finite element can be obtained by solving the equations.

[0060] The following is the method for calculating stress for each element:

[0061]

[0062] The above formula can be simplified into a diagonal matrix by performing an orthogonal similarity transformation, and three eigenvalues ​​can be obtained.

[0063]

[0064] The corresponding three eigenvalues ​​are the corresponding three principal stress values, and the eigenvalue vectors are the cosines of the corresponding principal stress directions. After obtaining the distribution law of the three principal stresses, the natural cracks can be predicted in combination with the rock fracture criterion.

[0065] As an implementation method of the present invention, in step S5, the linear weighting of weight analysis is a quantitative evaluation method, that is, a superordinate indicator is a linear function of its subordinate indicators. Let Y be an evaluation indicator, and its subordinate indicator set is {y1, y2, ..., y n}, then the evaluation value of Y can be obtained by {y1,y2,...,y n} is calculated by the linear function, that is,

[0066] With {y1,y2,…,y n}When calculating the evaluation value of Y using the linear function of

[0067] Y=β1y1+β2y2+…+β n y n

[0068] For any linear comprehensive evaluation model, if {y1,y2,…,y n} are mutually exclusive and have no causal relationship. Y has no effect on {y1,y2,…,y n}There is no direct corresponding functional relationship, so the coefficients β1, β2, ..., β n It only reflects the subjective preference of the value subject. The linear comprehensive evaluation model at this time can be defined as a linear weighted evaluation model. Then the indicator y j The weight is β j .

[0069] Further, in step S5, the grey correlation analysis method is used to determine the weight coefficients of the four prediction results, and the grey correlation degree between the parent sequence and the subsequence is obtained; wherein the four prediction results are taken as subsequences, and the core fracture density is taken as the parent sequence; and at the same time, the comprehensive fracture development index is obtained through the weight coefficients of the prediction results.

[0070] Define the parent sequence X i and subsequence Y j They are:

[0071] X i ={x i (1),x i (2),...,x i (k),...}

[0072] Y j ={y j (1),y j (2),...,y j (k),...}

[0073] The calculation formula for determining the grey correlation coefficient is:

[0074]

[0075] Among them, δ is the resolution coefficient, and its value is within 0-1; △ mink and maxk They represent the minimum and maximum level differences respectively, and the calculation formulas are:

[0076] △ mink =min j min i |X k (i)-Y j (i)|

[0077] △ maxk =max j max i |X k (i)-Y j (i)|

[0078] Calculate the parent sequence X i and subsequence Y j The correlation formula is:

[0079]

[0080] The weight coefficient calculation formula is:

[0081]

[0082] According to the main control level of the cracks, each prediction result is assigned a corresponding value. According to the level I, II, and III crack development zones determined by the four methods (earthquake prediction has only two levels), the level I crack development zone has the highest main control level, followed by the level II crack development zone. For the crack prediction results determined by the geological analysis method, fractal theory, and rock fracture degree theory, the level I, II, and III crack development zones are assigned values ​​of 3, 2, and 1; for the crack development zones determined by earthquake prediction technology, the crack development zones are assigned a value of 3, and other areas are assigned a value of 1.5, as shown in Table 1.

[0083] Table 1

[0084]

[0085] The grey correlation method is used to determine the weight coefficients of the four methods. First, the original data is normalized, and then the grey correlation coefficient, minimum difference, maximum difference, correlation degree and weight coefficient are calculated. Finally, the grey correlation degree between the parent sequence and the child sequence is determined, and the comprehensive crack development index I is introduced. F Characterizes the comprehensive development degree of cracks, that is,

[0086] I F =a1X1+a2X2+a3X3+a4X4

[0087] Among them, a1, a2, a3, and a4 are the weight coefficients of the four methods: geological analysis method, fractal theory, rock fracture degree theory, and earthquake prediction method, respectively, and they are dimensionless; X1, X2, X3, and X4 are the weight values ​​corresponding to these four methods, and they are dimensionless.

[0088] The cracks in the target area are divided into three development levels, as shown in Table 2.

[0089] Table 2

[0090] Crack development level Level I Level II Grade III <![CDATA[Comprehensive fracture development index (I F )]]> <![CDATA[I F ≥2.40]]> <![CDATA[1.80≤I F <2.40]]> <![CDATA[I F <1.80]]> Description of fracture development Very developed More developed Underdevelopment

[0091] Embodiment 2:

[0092] The embodiment of the present invention further provides a natural fracture distribution prediction system, comprising:

[0093] A first processing module is used to predict the development of natural fractures in the target area by using a geological analysis method to obtain a first prediction result;

[0094] The second processing module is used to predict the development of natural fractures in the target area by using fractal theory to obtain a second prediction result;

[0095] The third processing module is used to predict the development of natural fractures in the target area by using the rock fracture degree theory to obtain a third prediction result;

[0096] A fourth processing module is used to predict the development of natural fractures in the target area by using an earthquake prediction method to obtain a fourth prediction result;

[0097] The fifth processing module is used to perform weight analysis according to the first prediction result, the second prediction result, the third prediction result and the fourth prediction result to achieve quantitative prediction of natural fracture distribution.

[0098] As an implementation method of an embodiment of the present invention, the fifth processing module uses a grey correlation analysis method to determine the weight coefficients of the four prediction results, and obtains the grey correlation degree between the parent sequence and the subsequence; wherein the four prediction results are taken as subsequences, and the core fracture density is taken as the parent sequence.

[0099] As an implementation of the embodiment of the present invention, the fifth processing module obtains a comprehensive fracture development index through a weight coefficient of the prediction result.

[0100] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for predicting natural fracture distribution, characterized in that: include: Step S1, predicting the development of natural fractures in the target area using a geological analysis method to obtain a first prediction result; Step S2, predicting the development of natural fractures in the target area using fractal theory to obtain a second prediction result; Step S3, predicting the development of natural fractures in the target area using the rock fracture degree theory to obtain a third prediction result; Step S4, predicting the development of natural fractures in the target area using an earthquake prediction method to obtain a fourth prediction result; Step S5: Perform weight analysis based on the first prediction result, the second prediction result, the third prediction result and the fourth prediction result to achieve quantitative prediction of natural fracture distribution.

2. The natural fracture distribution prediction method according to claim 1, characterized in that: In step S5, the weight coefficients of the four prediction results are determined by using the grey correlation analysis method to obtain the grey correlation degree between the parent sequence and the subsequence; wherein the four prediction results are taken as the subsequences and the core fracture density is taken as the parent sequence.

3. The natural fracture distribution prediction method according to claim 2, characterized in that: In step S5, the comprehensive fracture development index is obtained by using the weight coefficient of the prediction result.

4. A natural fracture distribution prediction system, characterized in that: include: A first processing module is used to predict the development of natural fractures in the target area by using a geological analysis method to obtain a first prediction result; The second processing module is used to predict the development of natural fractures in the target area by using fractal theory to obtain a second prediction result; The third processing module is used to predict the development of natural fractures in the target area by using the rock fracture degree theory to obtain a third prediction result; A fourth processing module is used to predict the development of natural fractures in the target area by using an earthquake prediction method to obtain a fourth prediction result; The fifth processing module is used to perform weight analysis according to the first prediction result, the second prediction result, the third prediction result and the fourth prediction result to achieve quantitative prediction of natural fracture distribution.

5. The natural fracture distribution prediction device according to claim 4, characterized in that: The fifth processing module uses the grey correlation analysis method to determine the weight coefficients of the four prediction results and obtain the grey correlation degree between the parent sequence and the subsequence; wherein the four prediction results are taken as the subsequences and the core fracture density is taken as the parent sequence.

6. The natural fracture distribution prediction device according to claim 5, characterized in that: The fifth processing module obtains the comprehensive fracture development index through the weight coefficient of the prediction result.

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