Horizontal stress logging evaluation method based on variable tectonic stress coefficient

Abstract

The invention discloses a horizontal stress logging evaluation method based on a variable tectonic stress coefficient, and relates to the technical field of exploration geophysics, and the method comprises the steps: S1, defining the separation degree of different porosity curves; s2, performing dimensionality reduction on the porosity curve separation degree by applying a principal component analysis technology, obtaining response characteristics among principal components of the experimental sample, and establishing a reservoir type identification chart according to the response characteristics among the principal components; s3, determining a tectonic stress coefficient prediction model in which the minimum horizontal principal stress continuously changes according to the change rule of the tectonic stress coefficients of different reservoirs along with the principal component; and S4, determining a prediction method of the maximum horizontal principal stress according to the response law of the two-dimensional stress difference ratio data and the principal component characteristic value. According to the method, the reservoir classification precision can be improved by processing the logging data through the principal component analysis technology, then the variable tectonic stress coefficient determination method is established according to the types of the reservoirs, and therefore the prediction precision of complex mineral component stratum horizontal stress logging evaluation is improved.

Description

Technical Field

[0001] The invention relates to the field of exploration geophysical technology, and in particular to a horizontal stress logging evaluation method based on variable tectonic stress coefficient. Background Art

[0002] Geostress evaluation is an essential research topic for the efficient development of low-permeability oil and gas reservoirs and for increasing reserves and production. In particular, influenced by the successful commercial development of shale oil and gas in North America, the importance of geostress evaluation has attracted wider attention. Horizontal well development and multi-stage hydraulic fracturing are the core technologies for breakthroughs in the development of shale oil and gas sweet spots. The magnitude and direction of geostress directly affect the design of horizontal well drilling trajectories, the formulation of hydraulic fracturing plans, and the adjustment and improvement of subsequent development plans. It is not limited to shale oil and gas reservoirs. Geostress evaluation has become one of the core elements for characterizing the "engineering sweet spots" of low-permeability tight oil reservoirs.

[0003] Geostress is divided into original rock stress and pore fluid pressure, and original rock stress includes three different directions of geostress, namely vertical stress and maximum and minimum principal stress in the horizontal direction. The quantitative evaluation of geostress is divided into two categories: experimental test and numerical calculation. Experimental test mainly includes hydraulic fracturing, acoustic emission, differential strain, etc. Numerical calculation is mainly based on well logging interpretation, drilling, numerical simulation and other methods. Among them, well logging interpretation method is widely used by oil and gas field researchers due to its advantages of high resolution and strong continuity. Huang Rongzun model is often used in well logging interpretation of horizontal stress, and its expression is: In the formula, σ H , σ h are the maximum and minimum horizontal principal stresses, MPa; ν is the static Poisson's ratio; ξ H and h are the tectonic stress coefficients corresponding to the maximum and minimum horizontal stresses respectively; σ V is the vertical stress, MPa; α is the Biot coefficient, dimensionless; P p is the formation pressure, MPA; however, the Huang Rongzun model uses a fixed single tectonic stress coefficient. This model is more suitable for horizontal stress evaluation of isotropic formations, and has certain limitations in its applicability to horizontal stress logging evaluation of complex lithology reservoirs.

[0004] Therefore, it is urgent to study a horizontal stress logging evaluation method. Summary of the invention

[0005] In order to solve the above technical problems, the present invention discloses a horizontal stress logging evaluation method and device based on variable tectonic stress coefficient. The method reflects the difference in mineral components by the degree of separation of porosity logging curves, and extracts the principal components of different separation parameters through principal component analysis technology, thereby forming a method for using principal components to distinguish reservoir types and then predicting continuously changing tectonic stress coefficients.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A horizontal stress logging evaluation method based on variable tectonic stress coefficient comprises the following steps:

[0008] S1. Define the separation of different porosity curves;

[0009] S2. Perform dimensionality reduction processing on the porosity curve separation, and establish a reservoir type identification chart based on the principal component eigenvalues ​​based on the petrological analysis results;

[0010] S3. After obtaining different lithology identification standards based on the identification plate, determine the tectonic stress coefficient prediction model for the continuous change of the minimum horizontal principal stress of the target layer according to the variation law of the tectonic stress coefficient of different lithologies obtained based on experiments with the main components;

[0011] S4. Determine the prediction method of the maximum horizontal principal stress based on the response law of the two-way stress difference ratio data and the principal component eigenvalue.

[0012] Optionally, in step S1, different porosity curves include density neutron separation RP, acoustic wave density separation DR, acoustic wave neutron separation DP, and the difference DRP between the acoustic wave density separation DR and the density neutron separation RP, specifically:

[0013]

[0014] DRP = DR-RP;

[0015] Where RHOB is the density, g / cm 3 , PHIN is the neutron logging value, v / v; RHOBL and RHOBR are 1.95 g / cm 3 and 2.95g / cm 3 , i.e. the left and right scale boundaries of the density logging curve; PHINL and PHINR are 0.45 and -0.15 respectively, i.e. the scale boundaries of the neutron logging curve; DT is the acoustic time difference, μs / ft, DTL and DTR are 0μs / ft and 100μs / ft respectively, i.e. the left and right scale boundaries of the acoustic time difference; DRP is dimensionless.

[0016] Optionally, in step S2, four porosity curve separations of a well section with better target layer quality (not affected by expansion and other factors causing abnormal logging data) are selected as input data for principal component analysis, and the principal component eigenvalue greater than 1 or the extraction interpretation rate of 85% or above is used as the principal component analysis standard.

[0017] Optionally, in step S3, the minimum horizontal principal stress σ is obtained according to the acoustic emission experiment. h, according to the Huang Rongzun model, the minimum horizontal principal stress tectonic stress coefficient ξ is calculated h , get the principal component eigenvalue F 1 or F 2 With ξ h The relationship is obtained by linear fitting, and the expression is:

[0018] ξ h =aF 1 +b;

[0019] ξ h =aF 2 +b;

[0020] Where a and b are the minimum horizontal principal stress coefficient and the principal component eigenvalue F 1 or F 2 The linear fitting coefficients of .

[0021] Optionally, in step S4, the biaxial stress difference ratio is the ratio of the difference between the maximum horizontal principal stress and the minimum horizontal principal stress to the minimum horizontal principal stress. Acquired from acoustic emission experiments According to the principal component eigenvalue F 1 or F 2 and The linear fitting expression is:

[0022]

[0023] In the formula, c and d are and the principal component eigenvalue F 1 or F 2 The linear fitting coefficient of

[0024] After obtaining the linear fitting coefficients c and d, the maximum horizontal principal stress is calculated according to the following formula:

[0025] σ H =σ h (cF 1 +d)+σ h ;

[0026] σ H =σ h (cF 2 +d)+σ h .

[0027] The beneficial effects of the present invention are as follows: compared with the application effects of the traditional horizontal stress prediction method, the present invention has the following effects:

[0028] (1) For strata with complex rock and mineral components, the fixed tectonic stress coefficient cannot reflect the heterogeneity differences between reservoirs, which makes the Huang Rongzun model less applicable and the accuracy of horizontal stress prediction results is low. The present invention adopts a variable tectonic stress coefficient that is more in line with the actual characteristics of horizontal stress in heterogeneous reservoirs.

[0029] (2) The present invention uses principal component analysis technology to process logging data to improve the accuracy of reservoir classification, and then establishes a method for determining variable tectonic stress coefficients based on reservoir types, thereby improving the prediction accuracy of horizontal stress logging evaluation of formations with complex mineral components. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic diagram of the separation degree of different porosity curves;

[0031] Figure 2 The principal component eigenvalue F of the sample for the acoustic emission experiment 1 With F 2 Response characteristics;

[0032] Figure 3 A reservoir type identification chart established based on the response characteristics between the principal components;

[0033] Figure 4 is the minimum horizontal principal stress structural stress coefficient and the principal component eigenvalue F 2 relation;

[0034] Figure 5 is based on the ratio of the two-way stress difference and the principal component eigenvalue F 2 Response characteristics;

[0035] Figure 6 Result diagram for determining stress prediction and measured values ​​using the fixed structural stress coefficient method;

[0036] Figure 7 Result diagram of stress prediction and measured value determined by variable tectonic stress coefficient method;

[0037] Figure 8 This is a diagram showing the effect of using the method of the present invention to treat the MHX3 well. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the invention claimed for protection, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0039] A horizontal stress logging evaluation method based on variable tectonic stress coefficient comprises the following steps:

[0040] S1. Define the separation of different porosity curves

[0041] like Figure 1 As shown in FIG. 1 , different porosity curves include density neutron separation RP, acoustic wave density separation DR, acoustic wave neutron separation DP, and the difference DRP between acoustic wave density separation DR and density neutron separation RP, specifically:

[0042]

[0043] DRP = DR-RP;

[0044] Where RHOB is the density, g / cm 3 , PHIN is the neutron logging value, v / v; RHOBL and RHOBR are 1.95 g / cm 3 and 2.95g / cm 3 , i.e. the left and right scale boundaries of the density logging curve; PHINL and PHINR are 0.45 and -0.15 respectively, i.e. the scale boundaries of the neutron logging curve; DT is the acoustic time difference, μs / ft, DTL and DTR are 0μs / ft and 100μs / ft respectively, i.e. the left and right scale boundaries of the acoustic time difference; DRP is dimensionless.

[0045] S2. Perform dimensionality reduction processing on the porosity curve separation, and establish a reservoir type identification chart based on the principal component eigenvalues ​​based on the petrological analysis results;

[0046] Figure 2 The principal component eigenvalue F of the sample for the acoustic emission experiment 1 With F 2 Response characteristics, F 1 and F 2It is the response and characterization of the mineral component type along the direction of maximum differentiation. There is also an obvious trend line inflection point between the two. The slope of the trend line decreases after the inflection point, indicating that the associated characteristics or combination types of the mineral components have changed before and after the inflection point. Figure 3 It is a reservoir type identification chart established based on the response characteristics between the main components. Compared with the type I rock formation, the quartz content of the type II rock formation is significantly lower, but the calcite content is higher, the feldspar content is lower, and especially the clay mineral content is 10.3% higher, reaching 52.7%, and the overall component characteristics of the mudstone formation are biased. The average value of the minimum horizontal principal stress tectonic stress coefficient of the type II rock formation is significantly higher than that of the type I, as shown in Table 1.

[0047] Table 1 Statistical table of average mineral content and tectonic stress coefficient of two types of strata

[0048]

[0049] S3. After obtaining different lithology identification standards based on the identification plate, determine the tectonic stress coefficient prediction model for the continuous change of the minimum horizontal principal stress of the target layer according to the variation law of the tectonic stress coefficient of different lithologies obtained based on experiments with the main components;

[0050] Figure 4 is the minimum horizontal principal stress structural stress coefficient and the principal component eigenvalue F 2 The minimum horizontal principal stress coefficient of type I and type II strata increases with F 2 However, due to the large difference in mineral content between the two types of strata, the two types of strata increase with F 2 The trend of change is obviously different, and its linear trend line basically maintains a parallel relationship. According to the relationship between the minimum horizontal principal stress coefficient and the principal component F of different types of strata, 2 The changing law of can be used to obtain the continuously changing tectonic stress coefficient.

[0051] S4. Determine the prediction method of the maximum horizontal principal stress according to the response law of the two-way stress difference ratio data and the principal component eigenvalue;

[0052] like Figure 5 As shown in the figure, the ratio of the two-way stress difference increases with F 2 The difference in mineral content also leads to different change trends in the two types of strata. Therefore, based on the determination of the continuous minimum horizontal principal stress, the principal component eigenvalue F 2 The corresponding maximum horizontal principal stress is obtained according to different formulas.

[0053] Application Examples

[0054] This application example takes the tight conglomerate reservoir of the MHX3 well in a work area of ​​the Junggar Basin with acoustic emission experiments as an example to show the difference in horizontal stress prediction results between fixed tectonic stress coefficients and variable tectonic stress coefficients. Table 2 shows the X-ray diffraction analysis results of the joint measurement of 13 groups of acoustic emission samples in the well. It should be pointed out that since 3 core plug samples were taken every 45° in the horizontal direction for each group of acoustic emission samples, the mineral content of each group of samples was averaged. According to Table 1, the mineral composition of the tight conglomerate reservoir is extremely complex. Although the main mineral components are basically the same, the content is significantly different. Among them, the content of quartz and feldspar is 24.3% to 68.1%, with an average content of 47.4%; the content of authigenic carbonate is mainly calcite, with a content of 0% to 15.4%, and an average content of 4.7%; the content of clay minerals is generally high, with a content of 22.9% to 69.5%, and an average content of 45.6%. In addition, a small amount of hematite and lauzenite minerals are also developed.

[0055] Table 2 Statistics of tight conglomerate X-ray diffraction analysis results (MHX3 well)

[0056]

[0057] Table 3 Acoustic emission test data of 13 groups and 39 core samples obtained from the Urho Formation in Well MHX3 in the Mahu Sag, including the maximum and minimum horizontal principal stresses, and the maximum and minimum horizontal tectonic stress coefficients. According to the experimental results, in the depth range of XX 81.17m to XX 35.87m, the maximum horizontal principal stress of Well MHX3 is distributed between 65.03 and 79.74MPa, with an average value of 73.71MPa; the minimum horizontal stress is distributed between 51.28 and 68.98MPa, with an average value of 57.08MPa; and the biaxial stress difference is between 6.81 and 25.49MPa, with an average value of 16.63MPa. The tectonic stress coefficient required for the prediction of the maximum and minimum horizontal principal stresses obtained by inversion is, where ξ H The distribution range is between 0.264394 and 0.548724, and the average value is 0.388867. h Between 0.010913 and 0.250416, with an average value of 0.101505.

[0058] Table 3 Statistics of acoustic emission test results of the conglomerate of the Wuerhe Formation (MHX3 well)

[0059]

[0060] Table 4 is a statistical table of horizontal stress prediction errors. The first column is the horizontal stress prediction method, where the first line is the horizontal stress prediction method using a fixed tectonic stress coefficient, and the second line is the minimum and maximum horizontal principal stresses obtained based on the variable tectonic stress coefficient. According to the average relative error, the average relative error of the minimum horizontal principal stress dropped from 4.29% of the fixed tectonic stress coefficient method to 1.94%, and the maximum horizontal principal stress value decreased from 4.79% to 3.93%, and the corresponding root mean square error decreased to a certain extent. From the perspective of correlation, the correlation coefficient of the minimum horizontal principal stress increased to 0.95, and the correlation coefficient of the maximum horizontal principal stress prediction increased from 0.11 of the fixed tectonic stress coefficient to 0.48.

[0061] Table 4 Statistics of horizontal stress prediction errors

[0062]

[0063] Figure 6 and Figure 7 The comparison between the predicted stress values ​​and measured values ​​determined by two horizontal stress prediction methods is shown. The predicted horizontal stress values ​​obtained by the fixed average tectonic stress coefficient are concentrated near a certain value, which is difficult to reflect the large difference in the actual horizontal stress changes. The horizontal stress accuracy based on the variable tectonic stress coefficient is significantly higher and more convergent on the 45° diagonal line, indicating that the variable fixed tectonic stress coefficient is more in line with the horizontal stress logging evaluation of complex mineral composition reservoirs.

[0064] The present invention was applied to the MHX3 well, and the application effect between the predicted value of horizontal stress and the measured value was compared. Figure 8 As shown in the figure, the first track is the natural gamma (GR), spontaneous potential (SP) and wellbore curve (CALI); the second track shows the depth track; the third track is the principal component eigenvalue F 1 and F 2 ; The fourth track is the resistivity of the original formation (RT), the invasion zone (RI) and the flushing zone (RXO); the fifth and sixth tracks are the predicted minimum and maximum horizontal principal stress values ​​(σ h and σ H ) and measured values ​​(σ h_L and σ H_L ), among which the prediction results of the minimum horizontal principal stress are better and more reliable, and the prediction results of the maximum horizontal principal stress are consistent with the overall trend of the measured values.

[0065] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by technicians in this technical field within the essential scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A horizontal stress logging evaluation method based on variable tectonic stress coefficient, characterized in that: The steps include: S1. Define the separation of different porosity curves; S2. Perform dimensionality reduction processing on the porosity curve separation, and establish a reservoir type identification chart based on the principal component eigenvalues ​​based on the petrological analysis results; S3, a tectonic stress coefficient prediction model for determining the continuous change of the minimum horizontal principal stress of the target layer; S4. Determine the maximum horizontal principal stress based on the response law of the ratio of the two-way stress difference and the principal component eigenvalue.

2. A horizontal stress logging evaluation method based on variable tectonic stress coefficient according to claim 1, characterized in that: In step S1, the separations of different porosity curves include density neutron separation RP, acoustic wave density separation DR, acoustic wave neutron separation DP, and the difference DRP between the acoustic wave density separation DR and the density neutron separation RP, specifically: DRP = DR-RP; Where RHOB is the density, g / cm 3 , PHIN is the neutron logging value, v / v; RHOBL and RHOBR are 1.95 g / cm 3 and 2.95g / cm 3 , i.e. the left and right scale boundaries of the density logging curve; PHINL and PHINR are 0.45 and -0.15 respectively, i.e. the scale boundaries of the neutron logging curve; DT is the acoustic time difference, μs / ft, DTL and DTR are 0μs / ft and 100μs / ft respectively, i.e. the left and right scale boundaries of the acoustic time difference; DRP is dimensionless.

3. The horizontal stress logging evaluation method based on variable tectonic stress coefficient according to claim 1, characterized in that: In step S2, the principal component analysis standard is that the principal component eigenvalue is greater than 1 or the extraction explanation rate is 85% or above.

4. The horizontal stress logging evaluation method based on variable tectonic stress coefficient according to claim 1, characterized in that: In step S3, the minimum horizontal principal stress σ is obtained according to the acoustic emission experiment. h , according to the Huang Rongzun model, the minimum horizontal principal stress tectonic stress coefficient ξ is calculated h , get the principal component eigenvalue F1 or F2 and ξ h The relationship is obtained by linear fitting, and the expression is: ξ h =aF1+b; x h =aF2+b; Where a and b are the linear fitting coefficients of the minimum horizontal principal stress tectonic stress coefficient and the principal component eigenvalue F1 or F2.

5. The horizontal stress logging evaluation method based on variable tectonic stress coefficient according to claim 1, characterized in that: In step S4, the biaxial stress difference ratio is the ratio of the difference between the maximum horizontal principal stress and the minimum horizontal principal stress to the minimum horizontal principal stress. Acquired from acoustic emission experiments According to the principal component eigenvalue F1 or F2 and The linear fitting expression is: In the formula, c and d are Linear fitting coefficient with the principal component eigenvalue F1 or F2; After obtaining the linear fitting coefficients c and d, the maximum horizontal principal stress is calculated according to the following formula: s H =s h (cF1+d)+σ h ; s H =s h (cF2+d)+σ h 。

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