Method and device for evaluating reservoir fracture effectiveness

Abstract

The application discloses a reservoir fracture effectiveness evaluation method and device, and the method comprises the following steps: obtaining effective stress, acoustic velocity and fracture parameters measured under variable pressure conditions of artificial cores; performing data fitting on the effective stress, acoustic velocity and fracture parameters measured under variable pressure conditions, and matrix porosity and interstitial material content of the artificial cores, to obtain a fitting relationship between the effective stress, acoustic velocity, matrix porosity, interstitial material content and fracture parameters of a reservoir to be evaluated; determining the effective stress, matrix porosity, interstitial material content and acoustic velocity of different depth positions of the reservoir to be evaluated according to logging data; and predicting the fracture parameters of different depth positions of the reservoir to be evaluated according to the effective stress, matrix porosity, interstitial material content, acoustic velocity of different depth positions of the reservoir to be evaluated and the fitting relationship, to obtain a fracture effectiveness evaluation result. The application can improve the accuracy and universality of reservoir fracture effectiveness evaluation.

Description

Technical Field

[0001] This invention relates to the field of oil and gas geology and exploration and development technology, and in particular to a method and apparatus for evaluating the effectiveness of reservoir fractures. Background Technology

[0002] Traditional fracture effectiveness assessment relies heavily on core sampling, conventional logging response characteristic analysis, and electrical imaging qualitative analysis. This approach suffers from limitations in sample representativeness, single observation scale, and insufficient continuity in vertical evaluation. Consequently, it is difficult to accurately identify and finely characterize fracture effectiveness in reservoirs with dual-porosity media structures, leading to issues such as weak targeting of oil testing and selection, and missed or misjudged effective reservoirs.

[0003] To address the aforementioned issues, existing research has attempted to evaluate the effectiveness of formation fractures using different technical approaches. For example, core CT scans are used to evaluate formation fracture effectiveness, but this method cannot achieve continuous quantitative characterization in the longitudinal direction. The image-based fracture segmentation and selection-based electro-imaging fracture aperture correction method can only acquire fracture information at the millimeter level or above around the wellbore, and cannot accurately identify micro-fractures with smaller apertures or peri-well fractures. Fracture identification methods based on sonic transit time and density logging are only applicable to formations under normal pressure and lack versatility. Summary of the Invention

[0004] This invention provides a method for evaluating the effectiveness of reservoir fractures, used to continuously evaluate the effectiveness of reservoir fractures vertically, and to improve the accuracy and versatility of reservoir fracture effectiveness evaluation. The method includes: Effective stress, P-wave velocity, S-wave velocity, and fracture parameters were obtained from artificial cores under varying pressure conditions; the artificial cores were used to simulate the rock characteristics and pore medium structure of the reservoir to be evaluated. Data fitting was performed on the effective stress, P-wave velocity, S-wave velocity, and fracture parameters measured under variable pressure conditions, as well as the matrix porosity and interstitial content of the artificial core, to obtain the fitting relationship between the effective stress, P-wave velocity, S-wave velocity, matrix porosity, interstitial content and fracture parameters of the reservoir to be evaluated. Based on well logging data, the effective stress, matrix porosity, interstitial material content, P-wave velocity, and S-wave velocity at different depths of the reservoir to be evaluated are determined. Based on the effective stress, matrix porosity, interstitial material content, P-wave velocity, S-wave velocity, and the fitted relationship at different depths of the reservoir to be evaluated, the fracture parameters at different depths of the reservoir to be evaluated are predicted, and the predicted fracture parameters are determined as the fracture effectiveness evaluation results.

[0005] This invention also provides a reservoir fracture effectiveness evaluation device for continuously evaluating reservoir fracture effectiveness in a longitudinal direction, thereby improving the accuracy and versatility of reservoir fracture effectiveness evaluation. The device includes: The experimental data acquisition module is used to: acquire the effective stress, P-wave velocity, S-wave velocity, and fracture parameters of the artificial core under varying pressure conditions; the artificial core is used to simulate the rock characteristics and pore medium structure of the reservoir to be evaluated. The experimental data analysis module is used to: perform data fitting on the effective stress, P-wave velocity, S-wave velocity, and fracture parameters measured under variable pressure conditions, as well as the matrix porosity and interstitial content of artificial cores, to obtain the fitting relationship between effective stress, P-wave velocity, S-wave velocity, matrix porosity, interstitial content and fracture parameters of the reservoir to be evaluated. The reservoir analysis module is used to determine the effective stress, matrix porosity, interstitial material content, P-wave velocity, and S-wave velocity at different depths of the reservoir to be evaluated, based on well logging data. The fracture effectiveness evaluation module is used to: predict fracture parameters at different depths of the reservoir to be evaluated based on the effective stress, matrix porosity, interstitial material content, P-wave velocity, S-wave velocity, and the fitted relationship at different depths of the reservoir to be evaluated, and determine the fracture parameter prediction results as the fracture effectiveness evaluation results.

[0006] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described reservoir fracture effectiveness evaluation method.

[0007] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described reservoir fracture effectiveness evaluation method.

[0008] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described reservoir fracture effectiveness evaluation method.

[0009] Compared to existing technologies that cannot achieve continuous quantitative characterization in the longitudinal direction or have limited accuracy in evaluating fracture effectiveness, this invention obtains effective stress, P-wave velocity, S-wave velocity, and fracture parameters from artificial cores simulating the rock characteristics and porous media structure of the reservoir to be evaluated under varying pressure conditions. Data fitting is performed on the above measurements, along with the matrix porosity and interstitial material content of the artificial cores, to obtain a quantitative fitting relationship between effective stress, P-wave velocity, S-wave velocity, matrix porosity, interstitial material content, and fracture parameters. The inventors discovered that changes in acoustic velocity are sensitive to fractures, and the aforementioned fitting relationship can overcome the limitation of electrical imaging in identifying small-aperture micro-fractures and peri-well fractures. Furthermore, based on well logging data, the above parameters at different depths of the reservoir to be evaluated are determined, and the fitting relationship is used to predict fracture parameters at different depths, overcoming the problems of limited representativeness of core sampling and insufficient continuity in longitudinal evaluation. In addition, the effective stress parameter is directly introduced into the fitting relationship, fully considering the pressure coupling effect. Therefore, this method is particularly suitable for ultra-deep and ultra-high-pressure formations, overcoming the shortcomings of existing acoustic transit-time methods that are not applicable to ultra-deep and ultra-high-pressure formations. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 This is a flowchart of the reservoir fracture effectiveness evaluation method in an embodiment of the present invention; Figure 2 This is a flowchart illustrating the preparation process of the artificial rock core in an embodiment of the present invention; Figure 3 This is an example diagram showing the mineral composition analysis results of the artificial rock core in an embodiment of the present invention; Figure 4 This is an example diagram of the micropore structure of a calcium-cemented matrix pore sample in an embodiment of the present invention; Figure 5 This is an example diagram of the micropore structure of a silica cement matrix pore sample in an embodiment of the present invention; Figure 6 This is an example diagram of the micropore structure of a silica cement crack sample in an embodiment of the present invention; Figure 7 This is an example diagram of the micropore structure of another silica cement crack sample in an embodiment of the present invention; Figure 8 This is an example diagram showing the correlation analysis results between the longitudinal and transverse wave velocity ratios and the effective stress in an embodiment of the present invention. Figure 9 This is an example diagram showing the correlation analysis results between the longitudinal and transverse wave velocity ratio and the crack longitudinal and transverse wave ratio in an embodiment of the present invention; Figure 10 This is a comparison chart of the predicted and measured sound velocity ratios of matrix porous sandstone in this embodiment of the invention. Figure 11 This is a comparison chart of the predicted and measured results of the crack aspect ratio in an embodiment of the present invention. Figure 12 This is an example diagram showing the effectiveness evaluation results of fractures in well A in an embodiment of the present invention; Figure 13 This is an example diagram showing the effectiveness evaluation results of fractures in well B in an embodiment of the present invention; Figure 14 This is a schematic diagram of the reservoir fracture effectiveness evaluation device in an embodiment of the present invention. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0012] Existing methods for evaluating the effectiveness of fractures have limitations in terms of adaptability, continuity, accuracy, and large-scale application. In order to achieve high precision, quantification, detailed reservoir characterization across the entire well section, and continuous vertical analysis, this invention proposes a method for evaluating the effectiveness of reservoir fractures.

[0013] Figure 1 This is a flowchart of the reservoir fracture effectiveness evaluation method in an embodiment of the present invention. Figure 1 As shown, this method can be implemented in the following steps: Step 101: Obtain the effective stress, P-wave velocity, S-wave velocity, and fracture parameters of the artificial core under varying pressure conditions; the artificial core is used to simulate the rock characteristics and pore medium structure of the reservoir to be evaluated. Step 102: Perform data fitting on the effective stress, P-wave velocity, S-wave velocity, and fracture parameters measured under variable pressure conditions, as well as the matrix porosity and interstitial content of the artificial core, to obtain the fitting relationship between the effective stress, P-wave velocity, S-wave velocity, matrix porosity, interstitial content, and fracture parameters of the reservoir to be evaluated. Step 103: Based on the logging data, determine the effective stress, matrix porosity, interstitial material content, P-wave velocity, and S-wave velocity at different depths of the reservoir to be evaluated; Step 104: Based on the effective stress, matrix porosity, interstitial material content, P-wave velocity, S-wave velocity, and the fitted relationship at different depths of the reservoir to be evaluated, predict the fracture parameters at different depths of the reservoir to be evaluated, and determine the fracture parameter prediction results as the fracture effectiveness evaluation results.

[0014] Compared with existing technologies that cannot achieve continuous quantitative characterization in the longitudinal direction or have limited accuracy in evaluating fracture effectiveness, this invention obtains effective stress, P-wave velocity, S-wave velocity, and fracture parameters from artificial cores simulating the rock characteristics and porous media structure of the reservoir to be evaluated under varying pressure conditions. Data fitting is performed on the above measurement data, as well as the matrix porosity and interstitial material content of the artificial cores, to obtain a quantitative fitting relationship between effective stress, P-wave velocity, S-wave velocity, matrix porosity, interstitial material content, and fracture parameters. The inventors discovered that changes in acoustic velocity are sensitive to fractures, and the aforementioned fitting relationship can overcome the limitation of electrical imaging in identifying small-aperture micro-fractures and peri-well fractures. Furthermore, based on well logging data, the above parameters at different depths of the reservoir to be evaluated are determined, and the fracture parameters at different depths are predicted through the fitting relationship, overcoming the problems of limited representativeness of core sampling and insufficient continuity of longitudinal evaluation. Furthermore, existing fracture identification methods based on sonic transit time and density logging are only applicable to atmospheric pressure formations and have poor applicability in ultra-deep and ultra-high pressure formations. This method directly incorporates effective stress parameters into the fitting relationship and fully considers the pressure coupling effect. Therefore, this method is particularly suitable for ultra-deep and ultra-high pressure formations and can overcome the shortcomings of existing sonic transit time methods that are not applicable to ultra-deep and ultra-high pressure formations.

[0015] Traditional quantitative studies of microfractures are mostly based on fracture parameters measured from core samples under surface conditions, which do not match actual formation conditions, leading to poor application results. However, this invention considers the influence of pressure coupling on fracture parameters, further improving the accuracy of fracture effectiveness evaluation and solving the problem of inaccurate fracture effectiveness assessment in ultra-deep and ultra-high-pressure reservoirs.

[0016] In this embodiment of the invention, effective stress, P-wave velocity, S-wave velocity, and fracture parameters are obtained by measuring artificial cores under varying pressure conditions; the artificial cores are used to simulate the rock characteristics and pore medium structure of the reservoir to be evaluated.

[0017] For example, the effective stress, longitudinal wave velocity, transverse wave velocity, and crack parameters measured in artificial rock cores under varying pressure conditions can be obtained using a CT-sound velocity measurement device.

[0018] In one embodiment, the artificial core is prepared as follows: Based on the analysis data of natural cores or rock fragments of the reservoir to be evaluated, the mineral composition, grain size, and physical properties of the reservoir to be evaluated are obtained; based on the mineral composition, grain size, and physical properties of the reservoir to be evaluated, the artificial core preparation parameters are determined, including: matrix porosity, composition and proportion of framework minerals, cement type, interstitial material type, and fracture laying parameters; based on the matrix porosity in the artificial core preparation parameters, the pressing thickness of the artificial core is calculated; based on the artificial core preparation parameters and the pressing thickness, the artificial core is prepared by chemical cementation reaction followed by water bath demolding at room temperature.

[0019] Figure 2 This is a flowchart illustrating the preparation process of the artificial rock core in an embodiment of the present invention. Figure 2 As shown, the preparation process of artificial rock cores is as follows: 1. Based on the analysis data of natural cores or rock fragments in the study area (i.e., core, rock fragment and thin section analysis results), mineral composition information, grain size information and physical property information of the block where the reservoir to be evaluated is located can be obtained.

[0020] For example, mineral composition, grain size, and physical property information were obtained from multiple rock samples of the reservoir to be evaluated, as shown in Table 1. The mineral composition information includes the types and contents of framework minerals, the types and contents of cements, and the types and contents of clays; the physical property information includes, but is not limited to, porosity.

[0021] Table 1

[0022] 2. Based on the obtained mineral composition information, design artificial core sample preparation parameters and determine the main framework minerals of the block where the reservoir to be evaluated is located.

[0023] If the determined mineral composition of the artificial core contains quartz and feldspar, acid washing is required when preparing the main framework minerals to remove any carbonate rock minerals that may be mixed in, ensuring the purity of the minerals. Then, the acid-washed aggregates are mixed in proportion to obtain the rock framework material of the artificial core.

[0024] As shown in Table 1, the selected natural rock cores are mainly composed of feldspar and quartz, with a content of over 95%, and contain only trace amounts of calcite (less than 1%). Therefore, in order to avoid the influence of carbonate minerals mixed in the quartz sand and feldspar sand on the sample preparation results as much as possible, the quartz sand and feldspar sand need to be acid-washed with 1% dilute hydrochloric acid first, and the acid-washed minerals need to be rinsed multiple times with pure water until the pH test paper shows neutrality, and the raw material processing is completed.

[0025] 3. If structural clay is present in the study area, add the corresponding clay minerals to the framework minerals and mix them to obtain the framework material. Determine whether structural clay is present based on the aforementioned thin section analysis results. If structural clay is present, mix it into the rock aggregate according to the determined structural clay content and stir evenly to obtain the framework material.

[0026] In the embodiments shown in Table 1, the skeleton particles are mainly quartz and feldspar, without structural clay minerals. Quartz and feldspar are selected and proportioned according to the particle size information shown in Table 1 to obtain the skeleton material.

[0027] 4. Then, based on the obtained information, determine the cementing type and select the appropriate cementing material. Mix the cementing material with the skeleton material and sieve to obtain a uniform mixture of skeleton and cementing material. Cementing types can be divided into siliceous cementing, calcareous cementing, and composite cementing. Siliceous cementing requires the addition of sodium silicate as a cementing agent, calcareous cementing requires the addition of calcium hydroxide as a cementing agent, and composite cementing requires the addition of both sodium silicate and calcium hydroxide as cementing agents.

[0028] In the embodiments shown in Table 1, the type of binder is siliceous binder. Therefore, sodium silicate solution was selected as the reaction solution. The sodium silicate solution was stirred and mixed with the skeleton material and then sieved to obtain a uniform skeleton and binder mixture.

[0029] 5. Based on the analysis data of natural cores or rock fragments in the study area, determine whether there is dispersed clay and clay mineral types. If so, add clay minerals (interstitial material) to the skeleton and cement mixture, and mix them to obtain a mixture of artificial core samples that conforms to the block where the reservoir to be evaluated is located.

[0030] In the embodiments shown in Table 1, the mineral composition contains 3.5% clay and 1% calcite, mainly existing in the pores as dispersed clay. Therefore, calcite powder and illite powder were selected as interstitial materials and added to the skeleton and cement mixture. After being stirred evenly, the mixture was sieved to obtain a mixture suitable for artificial core preparation of the block where the reservoir to be evaluated is located.

[0031] 6. Based on the sample preparation requirements of the artificial core, prepare either a fractured sample with cracks or a matrix pore sample without cracks. If preparing a matrix pore sample, add the mixture directly into the cleaned mold; if preparing a fractured sample, use a high-molecular-weight material that is pressure-resistant, chemically stable, but easily soluble in water as the fracture material. Distribute the mixture and fracture material evenly according to the preset fracture parameters, and fill the mold with a layer of fracture material and a layer of mixture.

[0032] In the embodiments shown in Table 1, the selected natural core samples were of a blocky structure. Two sets of control experiments were designed: a matrix-pore sample containing only matrix pores and a dual-porosity medium sample containing both pores and fractures (i.e., the aforementioned fractured sample). In this example, the dual-porosity medium sample was designed with 100 fractures and a fracture aspect ratio of 0.02.

[0033] It should be noted that traditional artificial core preparation methods utilize pre-formed cracks in materials such as thin sheets and metal foils. During compaction and cementation, these cracks are prone to deformation, displacement, bending, or breakage, leading to significant deviations in the final crack geometry from the pre-set values ​​and poor repeatability. Furthermore, traditional artificial core preparation methods often employ high-temperature sintering. When sintering ceramics, glass, or other artificial sandstone, high temperatures must be applied to solidify loose particles into dense rock. This can cause problems such as mineral phase transformation, alterations in cement properties, and distortion of the pore structure.

[0034] The aforementioned crack material ensures that the predetermined parameters of the crack material laid during mechanical compaction and chemical bonding will not deform or break due to pressure, and also ensures that the crack material can be completely dissolved by a simple water bath treatment after sample preparation, so as to obtain an artificial sample with cracks containing the predetermined crack parameters.

[0035] 7. Calculate the compaction degree of the artificial core based on the porosity in the artificial core preparation parameters, the mass of the artificial core after preparation, and the size of the mold.

[0036] In one embodiment, the pressing thickness of the artificial core is calculated according to the following formula: ; In the above formula, H The thickness of the artificial rock core, in units of ; m The mass of the artificial rock core, in units ; Density of the rock skeleton in the artificial core, in units ; The fluid density in the artificial rock core, in units ; denoted as matrix porosity in the artificial core preparation parameters, dimensionless; S represents the base area corresponding to the inner diameter of the mold used to prepare the artificial core, in units of... .

[0037] 8. Place the mold containing the filling material on the press and mechanically compact it according to the calculated compaction degree. The method of mechanical compaction should be selected according to the sample preparation requirements: if it is necessary to make an artificial sample with well-developed stratification and cracks, then mechanically compact it according to the preset pressure after each layer of crack material and mixture is laid; if it is necessary to make a blocky artificial sample with cracks, then compact it once after all the crack material and mixture have been laid in sequence.

[0038] In the embodiment shown in Table 1, after all the crack material and mixture were laid, a single compaction method was adopted. Specifically, compaction was carried out at a pressure rate of 5 MPa / 5 min, with a stabilization period of 5 MPa for 2 hours to ensure that the sample was fully compacted, until the sample was pressurized to the preset height, thus completing the sample blank preparation.

[0039] 9. Place the compacted sample embryo into the reaction vessel and introduce reaction gas to carry out the reaction. After the sample mass no longer changes, take the sample out of the reaction vessel and place it in a room temperature water bath to obtain an artificial core that conforms to the block where the reservoir to be evaluated is located.

[0040] Figure 3 This is an example diagram showing the mineral composition analysis results of an artificial rock core in an embodiment of the present invention. Figure 3 The mineral composition and content of 18 artificial rock cores prepared by the above method are presented. Among them, No. 1 is the control group sample, whose mineral composition is designed and prepared based on the main mineral composition of natural rock cores in the study area. It is basically consistent with the mineral composition information of natural rock cores in Table 1 and is mainly used to verify the reliability of the constructed fitting relationship. Nos. 2 to 18 are experimental group samples, which are used to study the effects of changes in mineral composition, porosity, cement type, fracture aspect ratio and other factors on the acoustic properties of rocks, thereby providing data support for the construction of the fitting relationship.

[0041] Figure 4 This is an example diagram of the micropore structure of a calcium-cemented matrix pore sample in an embodiment of the present invention. Figure 5 This is an example diagram of the micropore structure of a silica-cemented matrix pore sample in an embodiment of the present invention. Figure 4 and Figure 5 As shown, the matrix pore samples prepared by the two cementation methods have clear design targets, and their specific characteristics are as follows: (1) Maintaining consistent matrix porosity and only changing the type and content of cementing materials, the core purpose of which is to explore the influence of changes in cementing material type and content on the P-wave / S-wave velocity ratio of rocks; (2) Fixing the type and content of cementing materials and only adjusting the matrix porosity, aiming to analyze the intrinsic relationship between matrix porosity and the P-wave / S-wave velocity ratio of rocks. Based on clarifying the laws governing the P-wave / S-wave response of rocks by cementing material content, cementing material type, and matrix porosity, the relationship between the matrix response and the P-wave / S-wave velocity ratio can be constructed.

[0042] Figure 6 This is an example diagram of the micropore structure of a silica cemented crack sample in an embodiment of the present invention. Figure 6 The sample with silica cemented cracks with 100 crack parameters and a crack aspect ratio of 0.008 is shown.

[0043] Figure 7 This is an example diagram of the micropore structure of another silica cemented crack sample in an embodiment of the present invention. Figure 7 The sample with silica cemented cracks with 100 crack parameters and a crack aspect ratio of 0.02 is shown.

[0044] Figure 4 , Figure 5 , Figure 6 , Figure 7 The bottom right corner shows the scale.

[0045] The samples shown in Figures 6 and 7 have consistent matrix porosity, mineral composition, cement type, cement content, and number of fractures, with differences only in fracture aspect ratio. Two sets of comparative samples were set up primarily to investigate the influence of fracture aspect ratio on P-wave and S-wave velocities under the condition of the same number of fractures. Based on clarifying the influence of fracture aspect ratio on P-wave and S-wave velocities, a quantitative relationship between fracture response characteristics and the P-wave and S-wave velocity ratio was further established.

[0046] In this embodiment of the invention, the effective stress, P-wave velocity, S-wave velocity, and fracture parameters measured under variable pressure conditions, as well as the matrix porosity and interstitial content of the artificial core, are fitted to obtain the fitting relationship between the effective stress, P-wave velocity, S-wave velocity, matrix porosity, interstitial content, and fracture parameters of the reservoir to be evaluated.

[0047] Deep and ultra-deep oil and gas resources have become a core potential area for alleviating the contradiction between oil and gas supply and demand and ensuring the self-sufficiency and controllability of energy supply. However, these reservoirs are generally located in zones of intense tectonic activity, having undergone the superposition of multiple phases and types of tectonic movements, accompanied by intense compressive-torsional deformation, and are characterized by deep burial, high pressure, complex lithology, and intense diagenesis. In terms of reservoir space, the dual modification by tectonic stress and diagenesis has formed a reservoir structure dominated by fracture-pore composite reservoir systems, with widespread development of dual-porosity media systems where fractures and pores coexist.

[0048] Controlled by complex tectonic evolution, multiple phases of diagenesis, and current stress field modification, this type of reservoir is highly heterogeneous and has complex oil and gas seepage patterns. Among them, the development characteristics (density, occurrence, aperture, connectivity) and effectiveness (such as permeability and oil control) of fractures are the core key factors controlling reservoir properties, smooth oil and gas migration paths, and production capacity.

[0049] In this embodiment of the invention, a quantitative relationship between fracture parameters and multiple measurable physical quantities was established through a controllable artificial core experiment. This quantitative relationship can be extended to the evaluation of complex fractured reservoirs in deep and ultra-deep layers.

[0050] In one embodiment, the crack parameters include: crack aspect ratio.

[0051] In one embodiment, data fitting is performed on the effective stress, P-wave velocity, S-wave velocity, and fracture aspect ratio measured under varying pressure conditions to obtain a first fitting relationship between the fracture aspect ratio and the effective stress, P-wave velocity, and S-wave velocity; wherein the first fitting relationship includes regression coefficients reflecting the influence of the matrix background; the regression coefficients are then fitted with the matrix porosity and interstitial material content of the artificial core to obtain a second fitting relationship between the regression coefficients and the matrix porosity and interstitial material content; based on the first and second fitting relationships, the fitting relationship between the effective stress, P-wave velocity, S-wave velocity, matrix porosity, interstitial material content, and fracture parameters of the reservoir to be evaluated is obtained.

[0052] In one embodiment, the artificial core comprises: a fractured sample with cracks and a matrix pore sample without cracks. By setting up two types of artificial cores, the influence of the matrix background can be peeled off layer by layer during the fitting step, allowing for the separate extraction of the acoustic and mechanical response characteristics of the cracks.

[0053] Figure 8 This is an example graph showing the correlation analysis results between the P-wave and S-wave velocity ratio and the effective stress in an embodiment of the present invention. Figure 8 As shown, it can be seen that the P-wave velocity ratio of the fractured sample decreases exponentially with the increase of effective pressure (the difference between overburden pressure and pore pressure); at the same time, it was also found that the P-wave velocity ratio of the matrix pore sample remains basically unchanged with the increase of effective pressure, and there is a universal relationship between matrix response and matrix pores and interstitial material.

[0054] In one embodiment, data fitting is performed on the effective stress, P-wave velocity, S-wave velocity, and crack aspect ratio measured under varying pressure conditions on the crack sample to obtain a first fitting relationship between the crack aspect ratio and the effective stress, P-wave velocity, and S-wave velocity. Based on the first fitting relationship and the effective stress, P-wave velocity, and S-wave velocity measured on the matrix porous sample under varying pressure conditions, the regression coefficient value of the matrix porous sample under varying pressure conditions is calculated. The regression coefficient value is then fitted with the matrix porosity and interstitial material content of the matrix porous sample to obtain a second fitting relationship between the regression coefficient and the matrix porosity and interstitial material content.

[0055] Figure 9 This is an example diagram showing the correlation analysis results between the longitudinal and transverse wave velocity ratio and the crack longitudinal and transverse wave ratio in an embodiment of the present invention. Figure 9As shown, the ratio of longitudinal and transverse wave velocities in the cracked sample is positively correlated with the longitudinal-to-transverse wave ratio exponent.

[0056] Finally, by analyzing the crack aspect ratio coefficients obtained under different pressure conditions... Analysis of experimental data revealed that the crack aspect ratio It is a parameter related to longitudinal wave velocity, slow acoustic wave velocity (SV wave velocity), and effective stress.

[0057] In one embodiment, the shear wave velocity includes: SV wave velocity; the first fitting relationship is obtained according to the following formula: ; In the above formula, The aspect ratio of the crack is dimensionless. For longitudinal wave velocity, in units ; SV wave velocity, in units ; For effective stress, unit a. b The fitting coefficients are dimensionless. C The regression coefficient is related to matrix porosity and interstitial material content, and is dimensionless. e It is a natural constant.

[0058] In one embodiment, a It is 0.116. b It is 0.042.

[0059] The foregoing analysis revealed a universal relationship between matrix response and matrix pore size and interstitial material. Therefore, regression coefficients can be determined based on different types of matrix pore samples. C .

[0060] In one embodiment, the second fitting relationship is obtained according to the following formula: ; In the above formula, C These are regression coefficients, dimensionless; The matrix porosity is dimensionless. The content of interstitial material is dimensionless. c , d and e The fitting coefficients are dimensionless.

[0061] In one embodiment, c 0.345 d It is 0.564. e It is 1.528.

[0062] Figure 10This is a comparison chart of the predicted and measured sound velocity ratios of the matrix porous sandstone in this embodiment of the invention. Figure 10 Demonstrates the use of regression coefficients C The correlation analysis between the calculated P-wave and S-wave velocity ratios and the laboratory measured P-wave and S-wave velocity ratios shows that the matrix porous sandstone includes pure sandstone, argillaceous sandstone, and calcareous sandstone. This indicates that the predicted results and the measured results for different lithological reservoirs have a good correlation.

[0063] Figure 11 This is a comparison chart of the predicted and measured results of the crack aspect ratio in an embodiment of the present invention. Figure 11 As shown, the predicted results of the crack aspect ratio have a good correlation with the measured results of the artificial core, with a correlation coefficient as high as 0.9851.

[0064] In this embodiment of the invention, based on well logging data, the effective stress, matrix porosity, interstitial material content, P-wave velocity, and S-wave velocity at different depths of the reservoir to be evaluated are determined.

[0065] For example, effective stress ,in: The overlying formation pressure can be calculated using the integration method based on density logging data. Pore ​​pressure, which can be determined based on density logging data and sonic logging or drilling test data. Matrix porosity. The interstitial material content can be determined based on neutron density logging or nuclear magnetic resonance logging data. This refers to the content of clay minerals or other cementing materials. The clay mineral content can be determined based on neutron-density logging data or lithology scanning logging data.

[0066] In this embodiment of the invention, based on the effective stress, matrix porosity, interstitial material content, P-wave velocity, S-wave velocity, and the fitting relationship at different depths of the reservoir to be evaluated, fracture parameters at different depths of the reservoir to be evaluated are predicted, and the predicted fracture parameters are determined as the fracture effectiveness evaluation results.

[0067] For example, based on well logging data, the effective stress, porosity, and interstitial material content varying with depth within the well section are calculated. The calculated effective stress, porosity, and interstitial material content are then substituted into the aforementioned fitting relationship, and combined with the regression coefficient C determined based on experimental data from matrix pore samples, the fracture aspect ratio is calculated. The fracture aspect ratio describes the ratio of fracture aperture to fracture length. The larger the ratio, the larger the fracture aperture, the more open the fracture surface, and the stronger the fluid flow capacity, i.e., the higher the fracture effectiveness. Based on this, a continuous and quantitative longitudinal characterization of fracture effectiveness is achieved.

[0068] Figure 12 This is an example diagram showing the effectiveness evaluation results of fractures in well A in an embodiment of the present invention. Figure 13 This is an example diagram showing the effectiveness evaluation results of fractures in well B in an embodiment of the present invention. Figure 12 and Figure 13 This displays the fracture effectiveness evaluation results of two adjacent wells in the same structural region of a certain block. (Comparison) Figure 12 and Figure 13 It can be seen that the fracture effectiveness of the reservoir under evaluation in Well A is higher than that in Well B, and the fracture opening is better. Well A achieved high production immediately after perforation failure, while Well B had no natural production capacity after perforation failure, but achieved industrial oil flow after fracturing. The oil test results confirm that the fracture effectiveness evaluation results of this method are accurate and reliable, and can be used to identify vertical high-yield layers.

[0069] This invention also provides a reservoir fracture effectiveness evaluation device, as described in the following embodiments. Since the principle behind this device is similar to that of the reservoir fracture effectiveness evaluation method, its implementation can be referenced from the implementation of the reservoir fracture effectiveness evaluation method; repeated details will not be elaborated further.

[0070] Figure 14 This is a schematic diagram of a reservoir fracture effectiveness evaluation device in an embodiment of the present invention. Figure 14 As shown, the device includes: The experimental data acquisition module 1401 is used to: acquire the effective stress, P-wave velocity, S-wave velocity and fracture parameters of the artificial core under variable pressure conditions; the artificial core is used to simulate the rock characteristics and pore medium structure of the reservoir to be evaluated; The experimental data analysis module 1402 is used to: perform data fitting on the effective stress, P-wave velocity, S-wave velocity and fracture parameters measured under variable pressure conditions, as well as the matrix porosity and interstitial content of artificial cores, to obtain the fitting relationship between effective stress, P-wave velocity, S-wave velocity, matrix porosity, interstitial content and fracture parameters of the reservoir to be evaluated. The reservoir analysis module 1403 is used to: determine the effective stress, matrix porosity, interstitial material content, P-wave velocity, and S-wave velocity at different depths of the reservoir to be evaluated based on well logging data. The fracture effectiveness evaluation module 1404 is used to: predict fracture parameters at different depths of the reservoir to be evaluated based on the effective stress, matrix porosity, interstitial material content, P-wave velocity, S-wave velocity and the fitted relationship at different depths of the reservoir to be evaluated, and determine the fracture parameter prediction results as the fracture effectiveness evaluation results.

[0071] In one embodiment, the artificial rock core is prepared in the following manner: Based on the analysis data of natural core or rock cuttings of the reservoir to be evaluated, obtain the mineral composition information, grain size information and physical property information of the reservoir to be evaluated. Based on the mineral composition, grain size, and physical property information of the reservoir to be evaluated, artificial core preparation parameters are determined. These artificial core preparation parameters include: matrix porosity, composition and proportion of framework minerals, cement type, interstitial material type, and fracture laying parameters. The pressing thickness of the artificial core is calculated based on the matrix porosity in the artificial core preparation parameters. Based on the artificial core preparation parameters and the pressing thickness, the artificial core is prepared by chemical bonding reaction and then demolded in a water bath at room temperature.

[0072] In one embodiment, the pressing thickness of the artificial core is calculated according to the following formula: ; In the above formula, H The thickness of the artificial rock core, in units of ; m The mass of the artificial rock core, in units ; Density of the rock skeleton in the artificial core, in units ; The fluid density in the artificial rock core, in units ; denoted as matrix porosity in the artificial core preparation parameters, dimensionless; S represents the base area corresponding to the inner diameter of the mold used to prepare the artificial core, in units of... .

[0073] In one embodiment, the crack parameters include: crack aspect ratio.

[0074] In one embodiment, the experimental data analysis module 1402 is specifically used for: Data fitting was performed on the effective stress, P-wave velocity, S-wave velocity, and crack aspect ratio measured under variable pressure conditions to obtain the first fitting relationship between crack aspect ratio and effective stress, P-wave velocity, and S-wave velocity; wherein the first fitting relationship includes regression coefficients reflecting the influence of matrix background. The regression coefficients were fitted with the matrix porosity and interstitial content of the artificial rock core to obtain a second fitting relationship between the regression coefficients and the matrix porosity and interstitial content. Based on the first and second fitting relationships, the fitting relationships between effective stress, P-wave velocity, S-wave velocity, matrix porosity, interstitial material content and fracture parameters of the reservoir to be evaluated are obtained.

[0075] In one embodiment, the artificial core comprises: a fractured sample with cracks and a matrix pore sample without cracks; Experimental data analysis module 1402 is specifically used for: Data fitting was performed on the effective stress, P-wave velocity, S-wave velocity, and crack aspect ratio measured under variable pressure conditions to obtain the first fitting relationship between crack aspect ratio and effective stress, P-wave velocity, and S-wave velocity. Based on the first fitting relationship and the effective stress, longitudinal wave velocity, and transverse wave velocity of the matrix pore sample measured under varying pressure conditions, the regression coefficient values ​​of the matrix pore sample under varying pressure conditions are calculated. The regression coefficient values ​​were fitted with the matrix porosity and interstitial content of the matrix pore sample to obtain a second fitting relationship between the regression coefficients and the matrix porosity and interstitial content.

[0076] In one embodiment, the shear wave velocity includes: SV wave velocity; Experimental data analysis module 1402 is specifically used for: The first fitting relationship is obtained using the following formula: ; In the above formula, The aspect ratio of the crack is dimensionless. For longitudinal wave velocity, in units ; SV wave velocity, in units ; For effective stress, unit a. b The fitting coefficients are dimensionless. C The regression coefficient is related to matrix porosity and interstitial material content, and is dimensionless. e It is a natural constant.

[0077] In one embodiment, the experimental data analysis module 1402 is specifically used for: The second fitting relationship is obtained using the following formula: ; In the above formula, C These are regression coefficients, dimensionless; The matrix porosity is dimensionless. The content of interstitial material is dimensionless. c , d and e The fitting coefficients are dimensionless.

[0078] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described reservoir fracture effectiveness evaluation method.

[0079] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described reservoir fracture effectiveness evaluation method.

[0080] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described reservoir fracture effectiveness evaluation method.

[0081] Compared to existing technologies that cannot achieve continuous quantitative characterization in the longitudinal direction or have limited accuracy in evaluating fracture effectiveness, this invention obtains effective stress, P-wave velocity, S-wave velocity, and fracture parameters from artificial cores simulating the rock characteristics and porous media structure of the reservoir to be evaluated under varying pressure conditions. Data fitting is performed on the above measurements, along with the matrix porosity and interstitial material content of the artificial cores, to obtain a quantitative fitting relationship between effective stress, P-wave velocity, S-wave velocity, matrix porosity, interstitial material content, and fracture parameters. The inventors discovered that changes in acoustic velocity are sensitive to fractures, and the aforementioned fitting relationship can overcome the limitation of electrical imaging in identifying small-aperture micro-fractures and peri-well fractures. Furthermore, based on well logging data, the above parameters at different depths of the reservoir to be evaluated are determined, and the fitting relationship is used to predict fracture parameters at different depths, overcoming the problems of limited representativeness of core sampling and insufficient continuity in longitudinal evaluation. In addition, the effective stress parameter is directly introduced into the fitting relationship, fully considering the pressure coupling effect. Therefore, this method is particularly suitable for ultra-deep and ultra-high-pressure formations, overcoming the shortcomings of existing acoustic transit-time methods that are not applicable to ultra-deep and ultra-high-pressure formations.

[0082] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0083] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0084] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0085] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0086] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for evaluating the effectiveness of reservoir fractures, characterized in that, include: The effective stress, P-wave velocity, S-wave velocity, and crack parameters of the artificial rock core were obtained under varying pressure conditions. The artificial core is used to simulate the rock characteristics and pore medium structure of the reservoir to be evaluated. Data fitting was performed on the effective stress, P-wave velocity, S-wave velocity, and fracture parameters measured under variable pressure conditions, as well as the matrix porosity and interstitial content of the artificial core, to obtain the fitting relationship between the effective stress, P-wave velocity, S-wave velocity, matrix porosity, interstitial content and fracture parameters of the reservoir to be evaluated. Based on well logging data, the effective stress, matrix porosity, interstitial material content, P-wave velocity, and S-wave velocity at different depths of the reservoir to be evaluated are determined. Based on the effective stress, matrix porosity, interstitial material content, P-wave velocity, S-wave velocity, and the fitted relationship at different depths of the reservoir to be evaluated, the fracture parameters at different depths of the reservoir to be evaluated are predicted, and the predicted fracture parameters are determined as the fracture effectiveness evaluation results. The crack parameters include: crack aspect ratio; Data fitting was performed on the effective stress, P-wave velocity, S-wave velocity, and fracture parameters measured under varying pressure conditions, as well as the matrix porosity and interstitial material content of the artificial core. The fitting relationships between the effective stress, P-wave velocity, S-wave velocity, matrix porosity, interstitial material content, and fracture parameters of the reservoir being evaluated were obtained, including: Data fitting was performed on the effective stress, P-wave velocity, S-wave velocity, and crack aspect ratio measured under variable pressure conditions to obtain the first fitting relationship between crack aspect ratio and effective stress, P-wave velocity, and S-wave velocity; wherein the first fitting relationship includes regression coefficients reflecting the influence of matrix background. The regression coefficients were fitted with the matrix porosity and interstitial content of the artificial rock core to obtain a second fitting relationship between the regression coefficients and the matrix porosity and interstitial content. Based on the first and second fitting relationships, the fitting relationships between effective stress, P-wave velocity, S-wave velocity, matrix porosity, interstitial material content and fracture parameters of the reservoir to be evaluated are obtained.

2. The method as described in claim 1, characterized in that, The artificial rock core is prepared in the following manner: Based on the analysis data of natural core or rock cuttings of the reservoir to be evaluated, obtain the mineral composition information, grain size information and physical property information of the reservoir to be evaluated. Based on the mineral composition, grain size, and physical property information of the reservoir to be evaluated, artificial core preparation parameters are determined. These artificial core preparation parameters include: matrix porosity, composition and proportion of framework minerals, cement type, interstitial material type, and fracture laying parameters. The pressing thickness of the artificial core is calculated based on the matrix porosity in the artificial core preparation parameters. Based on the artificial core preparation parameters and the pressing thickness, the artificial core is prepared by chemical bonding reaction and then demolded in a water bath at room temperature.

3. The method as described in claim 2, characterized in that, Calculate the compaction thickness of the artificial core using the following formula: ； In the above formula, H The thickness of the artificial rock core, in units of ; m The mass of the artificial rock core, in units ; Density of the rock skeleton in the artificial core, in units ; The fluid density in the artificial rock core, in units ; Matrix porosity, a dimensionless parameter in artificial core preparation parameters; S represents the base area corresponding to the inner diameter of the mold used to prepare the artificial rock core, in units of... .

4. The method as described in claim 1, characterized in that, The artificial core includes: a fractured sample with cracks and a matrix pore sample without cracks; Data fitting was performed on the effective stress, P-wave velocity, S-wave velocity, and crack aspect ratio measured under varying pressure conditions to obtain the first fitting relationship between the crack aspect ratio and the effective stress, P-wave velocity, and S-wave velocity, including: Data fitting was performed on the effective stress, P-wave velocity, S-wave velocity, and crack aspect ratio measured under variable pressure conditions to obtain the first fitting relationship between crack aspect ratio and effective stress, P-wave velocity, and S-wave velocity. The regression coefficients were fitted with the matrix porosity and interstitial material content of the artificial rock core to obtain a second fitting relationship between the regression coefficients and the matrix porosity and interstitial material content, including: Based on the first fitting relationship and the effective stress, longitudinal wave velocity, and transverse wave velocity of the matrix pore sample measured under varying pressure conditions, the regression coefficient values ​​of the matrix pore sample under varying pressure conditions are calculated. The regression coefficient values ​​were fitted with the matrix porosity and interstitial content of the matrix pore sample to obtain a second fitting relationship between the regression coefficients and the matrix porosity and interstitial content.

5. The method as described in claim 4, characterized in that, The transverse wave velocity includes: SV wave velocity; The first fitting relationship is obtained using the following formula: ； In the above formula, The aspect ratio of the crack is dimensionless. For longitudinal wave velocity, in units ; SV wave velocity, in units ; For effective stress, unit a. b The fitting coefficients are dimensionless. C The regression coefficient is related to matrix porosity and interstitial material content, and is dimensionless. e It is a natural constant.

6. The method as described in claim 5, characterized in that, The second fitting relationship is obtained using the following formula: ； In the above formula, C These are regression coefficients, dimensionless; The matrix porosity is dimensionless. The content of interstitial material is dimensionless. c , d and e The fitting coefficients are dimensionless.

7. A reservoir fracture effectiveness evaluation device, characterized in that, include: The experimental data acquisition module is used to: acquire the effective stress, P-wave velocity, S-wave velocity, and fracture parameters of the artificial core under varying pressure conditions; the artificial core is used to simulate the rock characteristics and pore medium structure of the reservoir to be evaluated. The experimental data analysis module is used to: perform data fitting on the effective stress, P-wave velocity, S-wave velocity, and fracture parameters measured under variable pressure conditions, as well as the matrix porosity and interstitial content of artificial cores, to obtain the fitting relationship between effective stress, P-wave velocity, S-wave velocity, matrix porosity, interstitial content and fracture parameters of the reservoir to be evaluated. The reservoir analysis module is used to determine the effective stress, matrix porosity, interstitial material content, P-wave velocity, and S-wave velocity at different depths of the reservoir to be evaluated, based on well logging data. The fracture effectiveness evaluation module is used to: predict fracture parameters at different depths of the reservoir to be evaluated based on the effective stress, matrix porosity, interstitial material content, P-wave velocity, S-wave velocity and the fitted relationship at different depths of the reservoir to be evaluated, and determine the fracture parameter prediction results as the fracture effectiveness evaluation results. The crack parameters include: crack aspect ratio; The experimental data analysis module is specifically used for: Data fitting was performed on the effective stress, P-wave velocity, S-wave velocity, and crack aspect ratio measured under variable pressure conditions to obtain the first fitting relationship between crack aspect ratio and effective stress, P-wave velocity, and S-wave velocity; wherein the first fitting relationship includes regression coefficients reflecting the influence of matrix background. The regression coefficients were fitted with the matrix porosity and interstitial content of the artificial rock core to obtain a second fitting relationship between the regression coefficients and the matrix porosity and interstitial content. Based on the first and second fitting relationships, the fitting relationships between effective stress, P-wave velocity, S-wave velocity, matrix porosity, interstitial material content and fracture parameters of the reservoir to be evaluated are obtained.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 6.

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