A lithology-constrained brittle logging evaluation method based on rock physics experiments

Abstract

The present application belongs to the technical field of well logging evaluation, and discloses a lithology-constrained brittleness logging evaluation method based on rock physical experiment. The method comprises the following steps: collecting rock experiment data of a block, collating and analyzing well logging data of a target layer of a drilled well of the block, obtaining static Young's modulus and static Poisson's ratio of the well logging method through lithology-constrained rock elastic parameter correlation analysis, performing lithology-constrained brittleness logging evaluation, calculating a static brittleness index through multi-lithology fusion, and saving and drawing output of the results. The present application maximizes fracturing utilization of a geological target layer, reduces exploration and development production cost, and improves exploration and development efficiency of tight oil and gas and shale gas.

Description

Technical Field

[0001] This invention belongs to the field of well logging evaluation technology, specifically relating to a lithologically constrained brittle well logging evaluation method based on rock physics experiments. Background Technology

[0002] The brittleness index indicates the ease of fracturing and reflects the complexity of fractures formed after reservoir fracturing. Reservoir brittleness assessment is becoming an important reference indicator for oilfield exploration and development, determining the optimal fracturing zones and the effectiveness of fracturing operations. Brittleness assessment technology has evolved from experience to science, and from qualitative to quantitative methods. Currently, the rapid development of rock physics experimental techniques provides a foundation for geologists to shift from qualitative to lithologically constrained quantitative assessment of rock brittleness.

[0003] The patent "A Shale Oil and Gas Reservoir Evaluation Method Based on Digital Core Simulation" (CN114034619A) focuses solely on shale using digital rock physics experiments. It employs single-factor analysis of the influence of porosity, fluid characteristics, fracture occurrence, and minerals on rock brittleness to obtain a brittleness index evaluation method. The steps include: Step 1: Constructing a three-dimensional digital core model of shale based on two-dimensional core images; Step 2: Constructing three-dimensional digital core models of shale with different micropore development, fluid characteristics, fracture occurrence, and mineral composition characteristics; Step 3: Using digital rock physics experiments, performing single-factor analysis of the influence of porosity, fluid characteristics, fracture occurrence, and minerals on rock brittleness; Step 4: Establishing a brittleness index model that comprehensively reflects mineral composition, fracture occurrence, porosity, and fluid characteristics. This method can replace traditional physical experiments, which are very difficult to perform in practice, providing new methods and ideas for shale rock physics and rock mechanics research. Furthermore, the use of digital rock physics experiments based on digital cores provides fundamental theoretical support for shale brittleness logging evaluation and even shale reservoir fracturing research.

[0004] However, this patent is based on two-dimensional core images, which suffer from limited information and poor contrast, resulting in unclear details and affecting image quality, usability, and stability. This leads to poor accuracy in the constructed three-dimensional digital core model of shale, consequently impacting subsequent shale brittleness assessment results. Against this backdrop, this invention researches a lithology-constrained brittleness logging calculation method based on rock physics experiments. Firstly, it innovatively introduces logging technology to guide the brittleness assessment of target formations, integrating triaxial rock mechanics experiments and rock acoustic property experiments. The brittleness degree of target formations is evaluated separately for each lithology, forming a lithology-controlled brittleness logging calculation method. This new technology calculates the brittleness index of the target formation, which can more effectively guide precise fracturing design compared to methods without considering lithology constraints. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, this invention provides a lithology-constrained brittleness logging evaluation method based on rock physics experiments. On the basis of rock physics experiments, it introduces the combination of rock physics experiments and logging technology to guide the fracturing design of multiple target segments with different lithologies or when the lithology combination of target segments is complex. It evaluates the brittleness degree of the target segment by lithology, thus forming a lithology-controlled brittleness logging evaluation method.

[0006] The above-mentioned objective of this invention is achieved through the following technical solution: a method for evaluating lithologically constrained brittle well logging based on rock physics experiments, comprising the following steps:

[0007] 1. Collect experimental data on the blocks of rocks;

[0008] 2. Organize and analyze well logging data of the target formations already drilled in the block;

[0009] 3. Correlation analysis of lithology-constrained rock elastic parameters yielded the static Young's modulus and static Poisson's ratio from well logging.

[0010] 4. Evaluation of lithology-constrained brittleness in logging: The static Young's modulus and static Poisson's ratio obtained from the logging method are used to calculate the static brittleness index of lithology i.

[0011] 5. Based on the number of lithological categories in the target layer, repeat steps 3 and 4 to regress each lithology one by one until all lithologies in the target layer have been regressed. Calculate the static brittleness index by integrating multiple lithologies, and save and output the results as plots.

[0012] Furthermore, the formula for calculating the brittleness index in step 4 is as follows:

[0013]

[0014] In the formula:

[0015] C i —Static brittleness index of lithology i, dimensionless;

[0016] E i —Static Young's modulus of lithology i obtained by well logging, 10 4 MP a ;

[0017] μ i —Static Poisson's ratio for lithology i using well logging, dimensionless.

[0018] Furthermore, the data collected in step 1 includes: triaxial mechanical compression test data of rocks, including data on the lithology, depth, density, confining pressure, and compressive strength of the test samples; and acoustic property test data of rocks, including data on the density, confining pressure, longitudinal wave velocity, and transverse wave velocity of the test samples.

[0019] Furthermore, the data collected and analyzed in step 2 includes well depth, natural gamma, spontaneous potential, neutrons, density, sonic transit time, shallow and deep lateral resistance, and lithological profile data.

[0020] Furthermore, step 3 specifically involves: taking core samples from the target layer and analyzing the rock mechanics and physics test data, classifying them into lithology 1, lithology 2, ... lithology i... lithology n, determining the longitudinal and transverse deformation of regularly shaped rock specimens under confining pressure, and obtaining the static Young's modulus, static Poisson's ratio, and triaxial compressive strength of the rock through triaxial mechanical compression tests; and using the ultrasonic pulse transmission method to measure the propagation time of longitudinal or transverse waves along the length of the specimen, calculating the longitudinal and transverse wave velocities of the specimen, thereby obtaining the dynamic Young's modulus and dynamic Poisson's ratio of the rock.

[0021] The specific calculation steps for rock i in step 3 are as follows:

[0022] 3.1. Calculation of static Young's modulus and static Poisson's ratio using the triaxial compression test method for rocks;

[0023] 3.2. Dynamic Young's modulus and dynamic Poisson's ratio were calculated using an ultrasonic pulse transmission method to conduct dynamic experiments on the acoustic properties of rocks.

[0024] 3.3. By fusing triaxial mechanical experimental data and acoustic property experimental data of rocks, and performing linear regression analysis, the dynamic and static Young's modulus and dynamic and static Poisson's ratio regression equations of lithology i were obtained.

[0025] 3.4. Array acoustic logging utilizes the time difference of longitudinal and transverse waves, combined with density logging, to determine the dynamic Young's modulus and dynamic Poisson's ratio of the logging method. The linear regression results of rock triaxial mechanical experimental data and rock acoustic property experimental data are then fused with the logging data.

[0026] Furthermore, in step 3.1, the static Young's modulus and static Poisson's ratio of the rock are calculated using the following formulas through a triaxial mechanical compression test:

[0027]

[0028] In the formula:

[0029] E a —Static Young's modulus of rock obtained by triaxial mechanical testing, N / m 2 ;

[0030] μ a —Static Poisson's ratio in the triaxial mechanics test of rocks, dimensionless;

[0031] σ1—Axial pressure of the rock sample, MPa;

[0032] σ3—Containing pressure of the rock sample, MPa;

[0033] (σ1-σ3)( 50) —50% of the maximum principal stress difference, MPa;

[0034] —50% of the axial pressure, MPa;

[0035] —50% of the confining pressure, MPa;

[0036] — Axial compressive strain, dimensionless;

[0037] — Radial compressive strain, dimensionless.

[0038] Furthermore, in step 3.2, the dynamic Young's modulus and dynamic Poisson's ratio are calculated using the ultrasonic pulse transmission method through the following formulas to perform rock acoustic characteristic dynamic experiments:

[0039]

[0040] In the formula:

[0041] E b —Dynamic Young's modulus of rock acoustic properties, N / m 2 ;

[0042] μ b —Dynamic Poisson's ratio, dimensionless, experimental method for rock acoustic characteristics;

[0043] v p —P-wave velocity of the rock sample, m / s;

[0044] v s —Shear wave velocity of the rock sample, m / s;

[0045] ρ d —Bulk density of the rock sample, kg / m³ 3 .

[0046] Furthermore, in step 3.3, by fusing triaxial mechanical experimental data and acoustic property experimental data of rocks, linear regression analysis is performed to derive the regression equations for the dynamic and static Young's modulus and dynamic and static Poisson's ratio of lithology i, as follows:

[0047] E a =m×E b +n (6)

[0048] μ a =e×μ b +f (7)

[0049] In the formula: m, n, e, and f are coefficients.

[0050] Furthermore, when the logging data includes dynamic Young's modulus and dynamic Poisson's ratio data, the dynamic Young's modulus and dynamic Poisson's ratio are substituted into Formulas 6 and 7 respectively to calculate the static Young's modulus and static Poisson's ratio of the logging method.

[0051] Furthermore, when the logging data includes P-wave and S-wave travel times but lacks dynamic Young's modulus and dynamic Poisson's ratio data, the dynamic Young's modulus and dynamic Poisson's ratio of the logging method are first calculated using the P-wave and S-wave travel times, i.e., formulas 8 and 9. Then, the dynamic Young's modulus and dynamic Poisson's ratio of the logging method are substituted into formulas 6 and 7 to calculate the static Young's modulus and static Poisson's ratio of the logging method.

[0052]

[0053] In the formula:

[0054] E c —Dynamic Young's modulus from well logging, N / m 2 ;

[0055] μ c —Dynamic Poisson's ratio from well logging methods, dimensionless;

[0056] Δt s —Logging shear wave transit time, μs / m;

[0057] Δt p —Log P-wave transit time, μs / m;

[0058] ρ h —Well logging density value, g / cm³ 3 .

[0059] Furthermore, when the logging data has no shear wave transit time, only P-wave transit time, and no logging dynamic Young's modulus and dynamic Poisson's ratio, the shear wave transit time is first calculated using the logging density and P-wave transit time (Formula 10). Then, the steps are repeated when the logging data has both P-wave and shear wave transit times, but no logging dynamic Young's modulus and dynamic Poisson's ratio data, to calculate the logging static Young's modulus and static Poisson's ratio.

[0060] Δt s =1.314×Δt p -19.343×ρ h +80.964 (10).

[0061] The advantages of this invention compared to existing technologies are: it provides a new brittleness evaluation method for complex lithologies in oil and gas exploration and development; it integrates rock physics experiments and well logging technology for accurate brittleness evaluation; it achieves a unified approach to geology and engineering; and, while meeting practical production needs, it simplifies rock types as much as possible to obtain simpler lithological profiles, striving for fewer but more precise lithologies to achieve accurate, comprehensive, and efficient brittleness evaluation. It also maximizes the fracturing utilization of target geological formations, reduces exploration and development costs, and improves the efficiency of tight oil and gas and shale gas exploration and development. Attached Figure Description

[0062] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0063] Figure 1 This is a flowchart of lithology-constrained brittle logging calculations;

[0064] Figure 2 This is a graph showing the relationship between the triaxial mechanical experiments and acoustic property experiments of argillaceous tuff;

[0065] Figure 3 This is a diagram showing the Poisson's ratio relationship between triaxial mechanical experiments and acoustic characteristic experiments on argillaceous tuff;

[0066] Figure 4 This is a diagram of the lithology-constrained brittle logging results from Example 5. Detailed Implementation

[0067] The present invention is described in detail below through specific embodiments, but this does not limit the scope of protection of the present invention. Unless otherwise specified, the experimental methods used in the present invention are all conventional methods, and the experimental equipment, materials, reagents, etc. used can all be obtained commercially.

[0068] This invention develops a lithology-constrained logging evaluation method based on rock physics experiments, such as... Figure 1 As shown, it specifically includes:

[0069] Step 110: Input basic data for the target area, and import all experimental data, well logging data, and geological data into the computer;

[0070] Step 120: Analyze and interpret the lithology of the target stratum. While meeting the actual production requirements, the lithology should be as concise and precise as possible.

[0071] Step 210: Calculation of static Young's modulus and static Poisson's ratio using the triaxial mechanics test method for rocks;

[0072] Step 220: Calculation of dynamic Young's modulus and dynamic Poisson's ratio using rock acoustic property test method;

[0073] Step 230: Linear regression of rock triaxial mechanical test method and rock acoustic property test method, mainly to establish the correlation model of dynamic and static Young's modulus and dynamic and static Poisson's ratio;

[0074] Step 310: Calculate the transverse wave time difference using the longitudinal wave time difference and density;

[0075] Step 320: Calculation of dynamic Young's modulus and dynamic Poisson's ratio using well logging method;

[0076] Step 130: Correcting the logging method using regression equations. Based on the correlation model of dynamic and static Young's modulus and dynamic and static Poisson's ratio, correct the dynamic Young's modulus and dynamic Poisson's ratio of the logging method to obtain the static Young's modulus and static Poisson's ratio of the logging method.

[0077] Step 140: Calculation of the brittleness index;

[0078] Step 150: Save and output the calculation results of the multi-lithological brittleness index. After fusing all the static brittleness indices of the target layer, the results are output using a computer software system.

[0079] Example 1: Collecting rock physics experimental data from the target block

[0080] The L-type rift basin contains three types of reservoirs: clastic rocks, volcanic rocks, and submerged volcanic clastic rocks. The Ying-2 member volcanic clastic rock reservoir is the primary target layer, characterized by its large thickness (400m–1500m), numerous gas-bearing units (12 sandstone groups), and thin individual gas layers (generally 1m–5m). The reservoir exhibits poor physical properties and strong heterogeneity. Therefore, selecting reservoirs with high brittleness to guide the selection of optimal stimulation techniques and fluid systems is crucial.

[0081] The L-type fault depression contains two wells and a total of nine samples, all of which have data from triaxial compression tests and acoustic property tests. The target layer consists of submerged breccia tuff and argillaceous tuff. Six samples of argillaceous tuff were tested: three from well L2 at a depth of 3612.9 m and three from well L1 at a depth of 3836 m. Three samples of submerged breccia tuff were tested from well L2 at a depth of 3615.8 m. Well L2, in the target section of the Ying-2 Member (3606.8 m–3642.2 m), consists of alternating layers of gray argillaceous tuff and gray submerged breccia tuff.

[0082] Example 2: Organizing and analyzing logging data of drilled wells in the target formation of the block

[0083] We compiled logging data from well L2, including depth curves, drilling time curves, mud density, mud viscosity, spontaneous potential, spontaneous gamma, wellbore curves, lithological profiles, deep and shallow lateral resistance curves, neutron curves, density curves, sonic transit time curves, total hydrocarbon content curves, and comprehensive logging interpretation results.

[0084] Example 3: Correlation Analysis of Elastic Parameters of Lithology-Constrained Rocks

[0085] First, for the argillaceous tuff of well L2, triaxial mechanical compression test data and elastic parameters were calculated based on the rock's acoustic properties. The specific calculation steps are as follows:

[0086] (1) Calculation of static Young's modulus and static Poisson's ratio using the triaxial compression test method for rocks.

[0087] The purpose of triaxial mechanical compression tests on rocks is to understand the deformation characteristics and strength properties of rocks under complex stress states. The longitudinal and transverse deformations of rock specimens from Well L2 under confining pressure were measured, and the static Young's modulus and static Poisson's ratio of the rocks were obtained (see Table 1).

[0088]

[0089] (2) Calculation of dynamic Young's modulus and dynamic Poisson's ratio by rock acoustic property test method

[0090] The propagation time of longitudinal or transverse waves along the length of the L2 well sample was measured using the ultrasonic pulse transmission method, and the dynamic Young's modulus and dynamic Poisson's ratio of the sample were calculated (see Table 2).

[0091]

[0092] (3) Linear regression analysis of data from the triaxial compression test and the acoustic property test of rock (see...) Figure 2 , Figure 3 The regression equations for the dynamic and static Young's modulus and dynamic and static Poisson's ratio of the argillaceous tuff in well L2 were obtained (see Formula 11 and Formula 12).

[0093] E a =1.6134×E b -25.815 (11)

[0094] μ a =0.2192×μ b +0.2077 (12)

[0095] (4) The linear regression results of the rock triaxial mechanical compression test and the rock acoustic characteristic test are integrated with the logging data. The logging data of L2 well only has acoustic transit time. First, the transverse wave transit time is calculated using formula 10. Then, the dynamic Young's modulus and dynamic Poisson's ratio of the logging method are calculated using formulas 8 and 9. Finally, according to formulas 11 and 12, the dynamic Young's modulus and dynamic Poisson's ratio of the logging method are integrated with the linear regression results of the rock triaxial mechanical compression test and the rock acoustic characteristic test to obtain the static Young's modulus and static Poisson's ratio of the logging method of argillaceous tuff.

[0096] Example 4: Evaluation of Lithology-Constrained Brittle Logging

[0097] Substitute the static Young's modulus and static Poisson's ratio obtained from Equations 11 and 12 into Formula 1 for calculating the brittleness index to obtain the static brittleness index of argillaceous tuff.

[0098] The target layer of well L2 is 3606.8m to 3642.2m, which contains two types of lithology: breccia tuff and argillaceous tuff. Steps three and four are repeated for the breccia tuff until all lithologies of the target layer are regressed.

[0099] Example 5: Saving Results and Outputting Drawings

[0100] The calculation results of lithology-constrained brittle well logging based on rock physics experiments are saved and output using computer software systems, such as... Figure 4 .

[0101] The embodiments described above are merely preferred embodiments of the present invention, and not all feasible embodiments of the present invention. Any obvious modifications made by those skilled in the art without departing from the principles and spirit of the present invention should be considered to be included within the scope of protection of the claims of the present invention.

Claims

1. A method for evaluating lithologically constrained brittle well logging based on rock physics experiments, characterized in that, The steps are as follows: S1. Collect experimental data on the block rocks; S2. Organize and analyze well logging data of the target formations already drilled in the block; S3. Correlation analysis of lithologically constrained rock elastic parameters yields the static Young's modulus and static Poisson's ratio from well logging. S4. Evaluation of lithology-constrained brittleness logging: The static Young's modulus and static Poisson's ratio obtained from the logging method are used to calculate the static brittleness index of lithology i. S5. Based on the number of lithological categories in the target layer, repeat steps S3 and S4 to regress each lithology one by one until all lithological regressions of the target layer are completed. Calculate the static brittleness index by fusing multiple lithologies, and save and output the results as plots.

2. The lithology-constrained brittleness logging evaluation method based on rock physics experiments according to claim 1, characterized in that, The formula for calculating the brittleness index in step S4 is as follows: In the formula: C i —Static brittleness index of lithology i, dimensionless; E i —Static Young's modulus of lithology i obtained by well logging, 10 4 MP a ; μ i —Static Poisson's ratio for lithology i using well logging, dimensionless.

3. The lithology-constrained brittleness logging evaluation method based on rock physics experiments according to claim 1, characterized in that, The data collected in step S1 includes: triaxial mechanical compression test data of rocks, including lithology, depth, density, confining pressure, and compressive strength data of the test samples; acoustic property test data of rocks, including density, confining pressure, P-wave velocity, and S-wave velocity data of the test samples; and the data processed and analyzed in step S2 includes well depth, natural gamma, spontaneous potential, neutrons, density, acoustic transit time, shallow and deep lateral resistance, and lithological profile data.

4. The lithology-constrained brittleness logging evaluation method based on rock physics experiments according to claim 1, characterized in that, Step S3 specifically involves: taking core samples from the target layer and analyzing the rock mechanics and physics test data, classifying them into lithology 1, lithology 2, ... lithology i... lithology n, determining the longitudinal and transverse deformation of regularly shaped rock specimens under confining pressure, and obtaining the static Young's modulus, static Poisson's ratio, and triaxial compressive strength of the rock through triaxial mechanical compression tests; and using the ultrasonic pulse transmission method to measure the propagation time of longitudinal or transverse waves along the length of the specimen, calculating the longitudinal and transverse wave velocities of the specimen, thereby obtaining the dynamic Young's modulus and dynamic Poisson's ratio of the rock. The specific calculation steps for rock i in step S3 are as follows: S3.

1. Calculation of static Young's modulus and static Poisson's ratio using the triaxial compression test method for rocks; S3.

2. Dynamic Young's modulus and dynamic Poisson's ratio were calculated by performing a dynamic experiment on the acoustic properties of rocks using the ultrasonic pulse transmission method; S3.

3. By fusing triaxial mechanical experimental data and acoustic property experimental data of rocks, and performing linear regression analysis, the dynamic and static Young's modulus and dynamic and static Poisson's ratio regression equations of lithology i were obtained. S3.

4. Array acoustic logging utilizes the time difference of longitudinal and transverse waves, combined with density logging, to determine the dynamic Young's modulus and dynamic Poisson's ratio of the logging method. The linear regression results of rock triaxial mechanical experimental data and rock acoustic property experimental data are then fused with the logging data.

5. The lithology-constrained brittleness logging evaluation method based on rock physics experiments according to claim 4, characterized in that, In step S3.1, the static Young's modulus, static Poisson's ratio, and triaxial compressive strength of the rock are calculated using the following formulas through a triaxial mechanical compression test: In the formula: E a —Static Young's modulus of rock obtained by triaxial mechanical testing, N / m 2 ; μ a —Static Poisson's ratio in the triaxial mechanics test of rocks, dimensionless; σ1—Axial pressure of the rock sample, MPa; σ3—Containing pressure of the rock sample, MPa; (σ1-σ3) (50) —50% of the maximum principal stress difference, MPa; —50% of the axial pressure, MPa; —50% of the confining pressure, MPa; — Axial compressive strain, dimensionless; — Radial compressive strain, dimensionless.

6. The lithology-constrained brittleness logging evaluation method based on rock physics experiments according to claim 4, characterized in that, In step S3.2, the dynamic Young's modulus and dynamic Poisson's ratio are calculated using the ultrasonic pulse transmission method in the following formulas: In the formula: E b —Dynamic Young's modulus of rock acoustic properties, N / m 2 ; μ b —Dynamic Poisson's ratio, dimensionless, experimental method for rock acoustic characteristics; v p —P-wave velocity of the rock sample, m / s; v s —Shear wave velocity of the rock sample, m / s; ρ d —Bulk density of the rock sample, kg / m³ 3 .

7. The lithology-constrained brittleness logging evaluation method based on rock physics experiments according to claim 4, characterized in that, Step S3.3 involves fusing triaxial mechanical experimental data and acoustic property experimental data of rocks, and performing linear regression analysis to derive the regression equations for the dynamic and static Young's modulus and dynamic and static Poisson's ratio of lithology i. The formulas are as follows: E a =m×E b +n (6) m a =e×μ b +f (7) In the formula: m, n, e, and f are coefficients.

8. The lithology-constrained brittleness logging evaluation method based on rock physics experiments according to claim 7, characterized in that, When well logging data includes dynamic Young's modulus and dynamic Poisson's ratio, the dynamic Young's modulus and dynamic Poisson's ratio are substituted into Formulas 6 and 7 respectively to calculate the static Young's modulus and static Poisson's ratio using the well logging method.

9. The lithology-constrained brittleness logging evaluation method based on rock physics experiments according to claim 8, characterized in that, When logging data includes P-wave and S-wave travel time but lacks dynamic Young's modulus and dynamic Poisson's ratio data, the dynamic Young's modulus and dynamic Poisson's ratio of the logging method are first calculated using the P-wave and S-wave travel time (Equations 8 and 9). Then, the dynamic Young's modulus and dynamic Poisson's ratio of the logging method are substituted into Equations 6 and 7 to calculate the static Young's modulus and static Poisson's ratio of the logging method. In the formula: E c —Dynamic Young's modulus from well logging, N / m 2 ; μ c —Dynamic Poisson's ratio from well logging methods, dimensionless; Δt s —Logging shear wave transit time, μs / m; Δt p —Loop logging P-wave transit time, μs / m; ρ h —Well logging density value, g / cm³ 3 .

10. The lithology-constrained brittleness logging evaluation method based on rock physics experiments according to claim 9, characterized in that, When the logging data has no shear wave transit time, only P-wave transit time, and no logging dynamic Young's modulus and dynamic Poisson's ratio, first use the logging density and P-wave transit time to calculate the shear wave transit time (Formula 10). Then repeat the steps where the logging data has P-wave and shear wave transit times but no logging dynamic Young's modulus and dynamic Poisson's ratio data to calculate the logging static Young's modulus and static Poisson's ratio. Δt s =1.314×Δt p -19.343×ρ h +80.964 (10)。

Images