A method and system for permeability evaluation of thick sand-shale reservoirs
By drilling full-diameter cores for multi-scale pore structure analysis and imaging logging to divide flow units, a permeability interpretation model for dual-pore medium parameters was established. This model was then corrected using dynamic production data, solving the problem of low accuracy in permeability evaluation of thick sandstone and conglomerate reservoirs and achieving more precise permeability evaluation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINESE ACAD OF GEOLOGICAL SCI
- Filing Date
- 2026-04-08
- Publication Date
- 2026-06-12
AI Technical Summary
Existing technologies have low accuracy in evaluating the permeability of thick sandstone and conglomerate reservoirs, making it difficult to fully reflect the influence of different pore structures on seepage characteristics.
By drilling full-diameter cores from thick sandstone and conglomerate reservoirs, multi-scale pore structure characterization analysis was conducted to determine the dominant seepage components. Based on imaging logging, flow units were divided, and a permeability interpretation model for dual-pore medium parameters was established. Dynamic production data were used for verification and correction.
The accuracy of permeability assessment for thick sandstone and conglomerate reservoirs has been improved. By fusing multi-scale data from full-diameter core samples and imaging logging, the permeability interpretation model is dynamically corrected, thereby enhancing the precision of the assessment results.
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Figure CN122193050A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of reservoir evaluation technology, specifically to a method and system for permeability evaluation of thick sandstone and conglomerate reservoirs. Background Technology
[0002] Thick sandstone and conglomerate reservoirs are generally characterized by large gravel size and complex pore structure, and their permeability behavior is controlled by multiple types of pores. Commonly used permeability assessment methods are mainly based on small-size core test results or empirical interpretation of well logging parameters. However, small-size cores are insufficient to cover the overall structural characteristics of gravel and pores in sandstone and conglomerate reservoirs. On the other hand, relying solely on a single porosity parameter cannot fully reflect the influence of different pore structures on permeability characteristics, leading to a discrepancy between the assessment results and the actual permeability capacity of the reservoir, thus limiting the accuracy of permeability assessment. Summary of the Invention
[0003] This application provides a method and system for permeability evaluation of thick sandstone and conglomerate reservoirs, which addresses the technical problem of low accuracy in permeability evaluation results of thick sandstone and conglomerate reservoirs in the prior art.
[0004] In view of the above problems, this application provides a method and system for permeability evaluation of thick sandstone and conglomerate reservoirs.
[0005] A first aspect of this application provides a method for evaluating the permeability of thick sandstone and conglomerate reservoirs, the method comprising:
[0006] Full-diameter core samples were drilled from thick sandstone and conglomerate reservoirs, and multi-scale pore structure characterization analysis was performed on the full-diameter core samples to determine the dominant seepage components. Based on the dominant seepage components, the thick sandstone and conglomerate reservoirs were divided into flow units using coupled imaging logging to determine multiple flow units. For each flow unit, a permeability interpretation model based on dual pore medium parameters was established. The permeability interpretation model was verified and corrected using dynamic production data.
[0007] A second aspect of this application provides a permeability evaluation system for thick sandstone and conglomerate reservoirs, the system comprising:
[0008] The analysis module is used to drill full-diameter cores from thick sandstone and conglomerate reservoirs and perform multi-scale pore structure characterization analysis on the full-diameter cores to determine the dominant seepage components; the partitioning module is used to partition the thick sandstone and conglomerate reservoirs into flow units based on the dominant seepage components using coupled imaging logging to determine multiple flow units; the model building module is used to build a permeability interpretation model based on dual pore medium parameters for each flow unit; and the verification and calibration module is used to verify and calibrate the permeability interpretation model using dynamic production data.
[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:
[0010] This application involves drilling full-diameter core samples from thick sandstone and conglomerate reservoirs and performing multi-scale pore structure characterization analysis on these core samples to determine the dominant flow components. Based on these dominant flow components, the thick sandstone and conglomerate reservoirs are divided into flow units using coupled imaging logging, identifying multiple flow units. For each flow unit, a permeability interpretation model based on dual pore medium parameters is established. The permeability interpretation model is then verified and corrected using dynamic production data. This invention addresses the technical problem of low accuracy in permeability evaluation results for thick sandstone and conglomerate reservoirs in existing technologies. By establishing a permeability interpretation model based on multi-scale data fusion from full-diameter core samples and imaging logging, and performing dynamic correction, the accuracy of permeability evaluation for thick sandstone and conglomerate reservoirs is improved. Attached Figure Description
[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.
[0012] Figure 1 A schematic flowchart of a method for permeability evaluation of thick sandstone and conglomerate reservoirs provided in this application embodiment;
[0013] Figure 2 This is a schematic diagram of a permeability evaluation system for thick sandstone and conglomerate reservoirs provided in an embodiment of this application.
[0014] Figure labeling: Analysis module 11, partitioning module 12, model building module 13, verification and calibration module 14. Detailed Implementation
[0015] This application provides a method and system for permeability evaluation of thick sandstone and conglomerate reservoirs. It addresses the technical problem of low accuracy in permeability evaluation results of thick sandstone and conglomerate reservoirs in existing technologies by establishing a permeability interpretation model based on multi-scale data fusion of full-diameter core samples and imaging logging, and performing dynamic correction, thereby improving the accuracy of permeability evaluation of thick sandstone and conglomerate reservoirs.
[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.
[0017] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.
[0018] Example 1, as Figure 1 As shown, this application provides a method for permeability evaluation of thick sandstone and conglomerate reservoirs, the method comprising:
[0019] Step S100: Drill full-diameter cores from thick sandstone and conglomerate reservoirs and perform multi-scale pore structure characterization analysis on the full-diameter cores to determine the dominant seepage components.
[0020] In this embodiment of the application, coring operations are first carried out in the target well section. By selecting coring tools suitable for large-diameter gravel formations, full-diameter cores are drilled from thick sandstone and conglomerate reservoirs.
[0021] Subsequently, multi-scale pore structure characterization analysis was performed on the full-diameter core. This process involved first performing CT scanning to segment the full-diameter core to obtain multi-scale pore structure segmentation results, and then performing horizontal permeability measurements on the full-diameter core to obtain corresponding permeability benchmark values. Finally, the multi-scale pore structure segmentation results were compared with the permeability benchmark values to generate dominant seepage components.
[0022] Furthermore, the method provided in the application embodiment, which involves performing multi-scale pore structure characterization analysis on the full-diameter core to determine the dominant seepage components, also includes:
[0023] The full-diameter core was segmented by CT scanning to obtain multi-scale pore structure segmentation results; horizontal permeability was measured on the full-diameter core to obtain a permeability benchmark value; the multi-scale pore structure segmentation results were compared with the permeability benchmark value to generate the dominant seepage component.
[0024] In this embodiment of the application, when performing CT scan segmentation on a full-diameter core, the core scan image is first obtained through CT scan, and multi-scale pore structure indicators are extracted. The multi-scale pore structure indicators include macropores, micropores and cracks, and macropores include inter-gravel pores and dissolution pores. Then, the core scan image is classified and segmented according to the multi-scale pore structure indicators to generate the corresponding multi-scale pore structure segmentation results.
[0025] Next, a baseline permeability value is obtained through horizontal permeability measurement. Specifically, the end faces of a full-diameter core are trimmed, and the core length and cross-sectional area are measured. The full-diameter core is then covered and sealed to create stable seepage boundary conditions. The treated full-diameter core is installed in a core permeability measuring device, with fluid inlets and outlets at both ends of the core. Test fluid is injected into the device, and gas is expelled. A stable pressure difference is applied across the core, allowing the test fluid to flow horizontally through the full-diameter core. After the flow stabilizes, the volume of fluid passing through the core per unit time, the pressure difference between the core inlet and outlet, and the viscosity parameters of the test fluid are collected. Based on these parameters, according to... The permeability is calculated, where K is the permeability, μ is the viscosity of the test fluid, Q is the volumetric flow rate through the core per unit time, L is the core length, and A is the core cross-sectional area. The pressure difference between the core inlet and outlet is given, and the calculated K is used as the permeability benchmark value.
[0026] After obtaining the multi-scale pore structure segmentation results and permeability benchmark values, the spatial distribution of different pore types in full-diameter core samples was statistically analyzed. The volume distribution ratio of each pore type in the total core volume was calculated. Based on the correspondence between the volume distribution ratio of pore types and the corresponding permeability benchmark values in multiple sets of full-diameter core samples, the influence of different pore types on permeability formation was analyzed. When the permeability influence ratio corresponding to a certain pore type exceeds a preset threshold, that pore type is determined to be the dominant seepage component, where the preset threshold is 70%.
[0027] Furthermore, the method provided in the application embodiment, which involves performing CT scanning segmentation on the full-diameter core to obtain multi-scale pore structure segmentation results, also includes:
[0028] The full-diameter core was subjected to CT scanning to obtain core scan images; multi-scale pore structure indices were obtained, including macropores, micropores and fractures, with macropores including intergravel pores and dissolution pores; the core scan images were segmented according to the multi-scale pore structure indices to generate multi-scale pore structure segmentation results.
[0029] In this embodiment of the application, X-ray computed tomography is first used to perform CT scanning on the full-diameter core. By performing continuous tomographic imaging of the core along the axial direction under the set scanning parameters, tomographic scanning data covering the entire volume of the full-diameter core is obtained. The core scanning image is then reconstructed based on the tomographic scanning data. The core scanning image is composed of voxel units, and the voxel gray value is used to reflect the density differences of different media inside the core.
[0030] After obtaining core scan images, these images are analyzed to obtain multi-scale pore structure indices. In this process, pore spaces are identified based on voxel grayscale differences in the core scan images, and spatial connectivity analysis is performed on the pore volumes to extract the volume, equivalent diameter, and geometric morphology parameters of each connected pore volume. Based on this, pores are classified according to their scale and morphological characteristics. For example, pore volumes with a volume and equivalent diameter within a preset scale range are identified as macropores, pore volumes with a smaller scale range are identified as micropores, and pore volumes with a high aspect ratio, a sheet-like or linear distribution, and continuous spatial extension are identified as fractures. When further distinguishing large pores, they are identified based on their spatial relationship with gravel particles and the morphological characteristics of pore boundaries. Large pores that form gaps between gravel particles are identified as inter-gravel pores, and large pores that exhibit dissolution morphological characteristics are identified as dissolution pores. This results in a multi-scale pore structure index that includes large pores, micropores, cracks, inter-gravel pores, and dissolution pores.
[0031] Finally, the core scan images were segmented based on multi-scale pore structure indices. In this process, voxels in the core scan images were classified and labeled according to the scale, morphological, and spatial distribution characteristics of different pore types. Voxels belonging to the same pore type were spatially grouped, thus distinguishing pore structures of different scales and types in the core scan images. Ultimately, a multi-scale pore structure segmentation result reflecting the spatial distribution of various pore types in the full-diameter core was generated.
[0032] Furthermore, the method provided in the application embodiments also includes:
[0033] The dominant permeation component refers to the pore type that contributes more than a preset threshold to the permeability benchmark value.
[0034] In this embodiment of the application, the dominant seepage component is determined by analyzing the relationship between different pore types and the permeability benchmark value. Specifically, after statistically analyzing various pore types in the full-diameter core, the influence of each pore type on the permeability benchmark value is determined. When the contribution of a certain pore type to the permeability benchmark value exceeds a preset threshold, that pore type is determined as the dominant seepage component.
[0035] Step S200: Based on the dominant seepage components, perform coupled imaging logging to divide the thick sandstone and conglomerate reservoir into flow units and determine multiple flow units.
[0036] Furthermore, in the method provided in the application embodiments, based on the dominant seepage components, the thick sandstone and conglomerate reservoir is divided into flow units by coupled imaging logging to determine multiple flow units, and the method further includes:
[0037] Obtain imaging logging data of the thick sandstone and conglomerate reservoir; perform data cleaning and feature recognition on the imaging logging data to obtain imaging logging feature data; based on the dominant seepage components, divide the thick sandstone and conglomerate reservoir into flow units according to the imaging logging feature data to obtain the multiple flow units.
[0038] In this embodiment of the application, when dividing the flow units of thick sandstone and conglomerate reservoirs into coupled imaging logging based on the dominant seepage components, imaging logging data of the corresponding well section of the thick sandstone and conglomerate reservoir is first obtained. The imaging logging data is obtained by micro-resistivity imaging logging or sonic imaging logging. High-resolution image data is continuously acquired along the well wall by downhole imaging instruments to reflect the rock structure, pore development and fracture distribution characteristics of the well wall. The imaging logging data records the reservoir microstructure information in the form of two-dimensional or unfolded diagrams.
[0039] Next, the imaging logging data undergoes data cleaning and feature recognition processing. First, the imaging logging data is cleaned to remove noise and artifacts caused by abnormal instrument response, wellbore variations, or mud effects, obtaining continuous and comparable imaging logging images. Then, feature recognition processing is performed on the cleaned imaging logging images. Based on the differences in brightness contrast, geometric morphology, and texture features of different structures in the imaging logging images, pore and fracture structures are identified and labeled. This extracts imaging logging feature data that characterizes the degree of pore development, pore size characteristics, fracture development degree, and their distribution along well depth. This imaging logging feature data serves as a direct basis for determining flow unit division.
[0040] Finally, based on the dominant flow components, the thick sandstone and conglomerate reservoirs were divided into flow units according to imaging logging characteristic data. In this process, the dominant flow components determined at the core scale were used as the basis for pore type identification, and were compared and analyzed segment by segment with the downhole pore structure characteristics reflected by the imaging logging characteristic data. When the imaging logging characteristic data showed that the pore structure within the well section was well-developed and continuously distributed, and the identified pore type was consistent with the dominant flow components, the well section was classified as a pore-dominated flow unit based on this correspondence. When the imaging logging characteristic data showed that pore and fracture structures coexisted, and the pores and fractures were spatially connected, with the corresponding flow channels composed of both, the well section was classified as a pore-fracture composite flow unit based on this structural combination characteristic. When the imaging logging characteristic data showed that the degree of pore and fracture development was low, and the wellbore structure mainly exhibited a dense background with corresponding matrix micropore characteristics, the well section was classified as a matrix micropore type flow unit based on this characteristic. Through the above-mentioned step-by-step processing from imaging logging data to imaging logging characteristic data, and then to flow unit type, multiple flow units of thick sandstone and conglomerate reservoirs are finally obtained.
[0041] Step S300: For each flow unit, establish a permeability interpretation model based on dual-pore medium parameters.
[0042] Furthermore, in the method provided in the application embodiments, the penetration rate interpretation model is:
[0043] K = a*(Φ_vug)^b*(Φ_total)^c+d; where K is the permeability, Φ_vug is the macropore porosity, Φ_total is the total porosity, and a, b, c, and d are model coefficients related to the type of flow unit.
[0044] In this embodiment, a permeability interpretation model based on dual-porosity media parameters is established for each flow unit. Specifically, the flow unit is first used as the basic object for permeability interpretation. The flow units divided within the well depth range are mapped to logging depth intervals to clarify the distribution location of each flow unit in the well depth direction and its corresponding pore structure type. Based on this, dual-porosity media parameters for permeability interpretation are obtained for each flow unit. These parameters include macropore porosity Φ_vug, which characterizes the large-scale pore system, and total porosity Φ_total, which characterizes the overall pore characteristics of the reservoir. Macropore porosity Φ_vug is obtained through interpretation using imaging logging data or nuclear magnetic resonance logging data, while total porosity Φ_total is calculated using conventional porosity logging data such as density logging and neutron logging.
[0045] After obtaining the dual-porosity medium parameters corresponding to each flow unit, a permeability interpretation relationship is established separately for each flow unit. Its mathematical expression is K = a*(Φ_vug)^b*(Φ_total)^c+d, where K is the permeability, and a, b, c, and d are model coefficients related to the flow unit type. To determine the model coefficients, measured permeability data from core samples within the same flow unit are selected as calibration data, and the measured permeability data are matched with the macropore porosity Φ_vug and total porosity Φ_total at the corresponding depth. With permeability K as the dependent variable and Φ_vug and Φ_total as independent variables, regression analysis is performed on the calibration data. The model coefficients a, b, c, and d are determined through numerical fitting to minimize the deviation between the calculated results and the measured permeability.
[0046] Through the above process, a permeability interpretation model is finally obtained for each flow unit, using macropore porosity and total porosity as dual porosity medium parameters.
[0047] Step S400: Validate and correct the penetration rate interpretation model using dynamic production data.
[0048] Furthermore, the method provided in the application embodiments, which uses dynamic production data to verify and correct the penetration rate interpretation model, further includes:
[0049] The permeability of the thick sandstone and conglomerate reservoir is evaluated according to the permeability interpretation model to obtain a permeability profile; the permeability of the thick sandstone and conglomerate reservoir is fitted and inverted according to the dynamic production data to obtain an inverted permeability profile; the permeability profile is dynamically verified according to the inverted permeability profile to obtain a permeability verification result; and the permeability interpretation model is corrected according to the permeability verification result.
[0050] In this embodiment of the application, when verifying and correcting the permeability interpretation model using dynamic production data, the permeability of the thick sandstone and conglomerate reservoir is first evaluated based on the permeability interpretation model. In this process, the permeability interpretation model for the corresponding flow unit is applied to the logging data at each depth. The porosity of large pores Φ_vug and the total porosity Φ_total at the same depth are input, and the permeability values at each depth point are calculated according to the permeability interpretation model. The calculation results are then continuously arranged along the well depth direction, thereby forming a permeability profile characterizing the static permeability distribution of the thick sandstone and conglomerate reservoir.
[0051] Next, permeability fitting and inversion are performed on the thick sandstone and conglomerate reservoir based on dynamic production data. In this process, dynamic production data corresponding to the thick sandstone and conglomerate reservoir are selected, and the dynamic production data is used as a constraint condition. Permeability is introduced as the parameter to be inverted. By adjusting the permeability values of different layers or depth ranges, the calculated dynamic production response is matched with the actual dynamic production data, thereby obtaining an inverted permeability profile that reflects the actual seepage behavior of the reservoir.
[0052] Subsequently, a profile comparison verification method was used to dynamically verify the permeability profile. In this process, within the same well section or layer, the permeability profile obtained from well logging interpretation was compared layer by layer or segment by segment with the inverted permeability profile. The numerical relationship between the two permeability profiles at each corresponding position was compared, and the distribution of the deviation between the two was statistically analyzed. Simultaneously, the high-value segments, low-value segments, and inflection points of the two permeability profiles in the well depth direction were analyzed to determine the degree of consistency between the two profiles in terms of overall distribution and vertical variation trend. This resulted in permeability verification results used to characterize the degree of conformity between the static interpretation results and the dynamic production response.
[0053] After obtaining the permeability verification results, the permeability interpretation model is corrected using a model parameter correction method. Specifically, based on the deviation characteristics reflected in the permeability verification results, systematic overestimation or underestimation issues in the permeability interpretation model within different flow units or depth ranges are identified. Accordingly, the model coefficients a, b, c, and d for the corresponding flow units are adjusted accordingly. During the adjustment process, the goal is to reduce the numerical deviation and distribution differences between the well logging-interpreted permeability profile and the inverted permeability profile. The model coefficients are refitted so that the permeability profile recalculated by the adjusted permeability interpretation model is consistent with the inverted permeability profile in terms of numerical magnitude and trend, thus completing the verification and correction of the permeability interpretation model.
[0054] In summary, the embodiments of this application have at least the following technical effects:
[0055] This application involves drilling full-diameter core samples from thick sandstone and conglomerate reservoirs and performing multi-scale pore structure characterization analysis on these core samples to determine the dominant flow components. Based on these dominant flow components, the thick sandstone and conglomerate reservoirs are divided into flow units using coupled imaging logging, identifying multiple flow units. For each flow unit, a permeability interpretation model based on dual pore medium parameters is established. The permeability interpretation model is then verified and corrected using dynamic production data. This invention addresses the technical problem of low accuracy in permeability evaluation results for thick sandstone and conglomerate reservoirs in existing technologies. By establishing a permeability interpretation model based on multi-scale data fusion from full-diameter core samples and imaging logging, and performing dynamic correction, the accuracy of permeability evaluation for thick sandstone and conglomerate reservoirs is improved.
[0056] Example 2, based on the same inventive concept as the permeability evaluation method for thick sandstone and conglomerate reservoirs in the foregoing examples, such as... Figure 2 As shown, this application provides a permeability evaluation system for thick sandstone and conglomerate reservoirs. The system and method embodiments in this application are based on the same inventive concept. The system includes:
[0057] Analysis module 11 is used to drill full-diameter cores from thick sandstone and conglomerate reservoirs and perform multi-scale pore structure characterization analysis on the full-diameter cores to determine the dominant seepage components; partitioning module 12 is used to partition the thick sandstone and conglomerate reservoirs into flow units based on the dominant seepage components using coupled imaging logging to determine multiple flow units; model building module 13 is used to build a permeability interpretation model based on dual pore medium parameters for each flow unit; verification and correction module 14 is used to verify and correct the permeability interpretation model using dynamic production data.
[0058] Furthermore, the system is also used to implement the following functions:
[0059] The full-diameter core was segmented by CT scanning to obtain multi-scale pore structure segmentation results; horizontal permeability was measured on the full-diameter core to obtain a permeability benchmark value; the multi-scale pore structure segmentation results were compared with the permeability benchmark value to generate the dominant seepage component.
[0060] Furthermore, the system is also used to implement the following functions:
[0061] The full-diameter core was subjected to CT scanning to obtain core scan images; multi-scale pore structure indices were obtained, including macropores, micropores and fractures, with macropores including intergravel pores and dissolution pores; the core scan images were segmented according to the multi-scale pore structure indices to generate multi-scale pore structure segmentation results.
[0062] Furthermore, the system is also used to implement the following functions:
[0063] The dominant permeation component refers to the pore type that contributes more than a preset threshold to the permeability benchmark value.
[0064] Furthermore, the system is also used to implement the following functions:
[0065] Obtain imaging logging data of the thick sandstone and conglomerate reservoir; perform data cleaning and feature recognition on the imaging logging data to obtain imaging logging feature data; based on the dominant seepage components, divide the thick sandstone and conglomerate reservoir into flow units according to the imaging logging feature data to obtain the multiple flow units.
[0066] Furthermore, the system is also used to implement the following functions:
[0067] The permeability interpretation model is: K=a*(Φ_vug)^b*(Φ_total)^c+d; where K is the permeability, Φ_vug is the macropore porosity, Φ_total is the total porosity, and a, b, c, and d are model coefficients related to the type of flow unit.
[0068] Furthermore, the system is also used to implement the following functions:
[0069] The permeability of the thick sandstone and conglomerate reservoir is evaluated according to the permeability interpretation model to obtain a permeability profile; the permeability of the thick sandstone and conglomerate reservoir is fitted and inverted according to the dynamic production data to obtain an inverted permeability profile; the permeability profile is dynamically verified according to the inverted permeability profile to obtain a permeability verification result; and the permeability interpretation model is corrected according to the permeability verification result.
[0070] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.
[0071] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.
Claims
1. A method for evaluating the permeability of thick sandstone and conglomerate reservoirs, characterized in that, The method includes: Full-diameter cores were drilled from thick sandstone and conglomerate reservoirs, and multi-scale pore structure characterization analysis was performed on the full-diameter cores to determine the dominant seepage components. Based on the dominant seepage components, the flow units of the thick sandstone and conglomerate reservoir are divided by coupled imaging logging to determine multiple flow units; For each flow unit, a permeability interpretation model based on dual-pore medium parameters is established. The penetration rate interpretation model was validated and corrected using dynamic production data.
2. The permeability evaluation method for thick sandstone and conglomerate reservoirs as described in claim 1, characterized in that, Multi-scale pore structure characterization analysis was performed on the full-diameter core to determine the dominant seepage components, including: The full-diameter core was segmented by CT scanning to obtain multi-scale pore structure segmentation results; Horizontal permeability measurements were performed on the full-diameter core to obtain a permeability baseline value; By comparing the multi-scale pore structure segmentation results with the permeability benchmark value, the dominant seepage component is generated.
3. The permeability evaluation method for thick sandstone and conglomerate reservoirs as described in claim 2, characterized in that, The full-diameter core was segmented using CT scanning to obtain multi-scale pore structure segmentation results, including: The full-diameter core was subjected to CT scanning to obtain core scan images; Obtain multi-scale pore structure indices, which include macropores, micropores, and cracks; the macropores include intergravel pores and dissolution pores. The core scan image is segmented based on the multi-scale pore structure index to generate the multi-scale pore structure segmentation result.
4. The permeability evaluation method for thick sandstone and conglomerate reservoirs as described in claim 2, characterized in that, The dominant permeation component refers to the pore type that contributes more than a preset threshold to the permeability benchmark value.
5. The permeability evaluation method for thick sandstone and conglomerate reservoirs as described in claim 1, characterized in that, Based on the aforementioned dominant seepage components, the thick sandstone and conglomerate reservoir is divided into flow units using coupled imaging logging, identifying multiple flow units, including: Obtain imaging logging data of the thick sandstone and conglomerate reservoir; The imaging logging data is cleaned and feature identified to obtain imaging logging feature data; Based on the dominant seepage components, the thick sandstone and conglomerate reservoir is divided into flow units according to the imaging logging characteristic data, and the multiple flow units are obtained.
6. The permeability evaluation method for thick sandstone and conglomerate reservoirs as described in claim 1, characterized in that, The penetration rate interpretation model is as follows: K=a*(Φ_vug)^b*(Φ_total)^c+d; Where K is the permeability, Φ_vug is the macropore porosity, Φ_total is the total porosity, and a, b, c, and d are model coefficients related to the type of flow unit.
7. The permeability evaluation method for thick sandstone and conglomerate reservoirs as described in claim 1, characterized in that, The penetration rate interpretation model is validated and corrected using dynamic production data, including: The permeability of the thick sandstone and conglomerate reservoir was evaluated based on the permeability interpretation model, and a permeability profile was obtained. Based on the dynamic production data, the permeability of the thick sandstone and conglomerate reservoir was fitted and inverted to obtain the inverted permeability profile. The permeability profile is dynamically verified based on the inverted permeability profile to obtain the permeability verification results. The permeability interpretation model is corrected based on the permeability verification results.
8. A permeability evaluation system for thick sandstone and conglomerate reservoirs, characterized in that, The system is used to perform a permeability evaluation method for thick sandstone and conglomerate reservoirs as described in any one of claims 1-7, the system comprising: The analysis module is used to drill full-diameter cores from thick sandstone and conglomerate reservoirs and perform multi-scale pore structure characterization analysis on the full-diameter cores to determine the dominant seepage components. The segmentation module is used to segment the flow units of the thick sandstone and conglomerate reservoir based on the dominant seepage components, and to determine multiple flow units. The model building module is used to build a permeability interpretation model based on dual-pore medium parameters for each flow unit. The verification and calibration module is used to verify and calibrate the penetration rate interpretation model using dynamic production data.