Method and system for multi-dimensionally representing bioclastic limestone high-permeability reservoir

Abstract

The invention discloses a multi-dimensional characterization method and system for a bioclastic limestone high-permeability reservoir, and relates to the technical field of carbonate reservoir characterization. According to the method, conventional geological data and rock electricity experiment data are comprehensively utilized, the well logging identification standard of the high-permeability reservoir is established, one-dimensional identification of the high-permeability reservoir is achieved, research is developed on the basis, two-dimensional description of the high-permeability reservoir is carried out on the basis of a framework section map of a rock stratum in a research area and a planar graph of a seismic slice, and the high-permeability reservoir is identified. According to the method, the three-dimensional geologic modeling is guided to be established according to the geologic characteristic parameters of the high-permeability reservoir, in addition, the high-permeability reservoir in the three-dimensional geologic model is corrected, and the permeability model is corrected by using production data, so that the permeability model better conforms to the actual oil reservoir characteristics, the characterization result is more effective, and the applicability is higher. According to the method and system, effective characterization of the high-permeability reservoir in the bioclastic limestone reservoir can be achieved, and a geological basis is provided for efficient development of the reservoir and scheme optimization and adjustment.

Description

Technical Field

[0001] This invention belongs to the field of carbonate reservoir characterization technology, and specifically relates to a method and system for multidimensional characterization of high-permeability bioclastic limestone reservoirs. Background Technology

[0002] High-permeability reservoirs are thin reservoirs sandwiched within existing reservoirs with significantly higher permeability than adjacent reservoirs, accounting for a high proportion of flow in oil and gas production. Foreign scholars have used terms such as "thief layer," "ultra-high permeability reservoir," and "high-permeability strip" to refer to this type of flow unit in carbonate reservoirs. High-permeability reservoirs have a significant impact on oil reservoir development. In the early stages of oilfield development, high-permeability reservoirs are the target layers for rapid large-scale production, resulting in high single-well production. However, because high-permeability reservoirs constitute a relatively small proportion of the reservoir's thickness, their reserve size is limited, leading to a rapid decline in single-well production. After water injection development, the injected water rapidly surges along the high-permeability reservoir, causing premature water breakthrough and water flooding in production wells. Meanwhile, the oil and gas in the surrounding rock reservoirs are difficult to extract due to the significant permeability difference, resulting in an uneven utilization of reserves in thick oil reservoirs. Effective characterization of high-permeability reservoirs is fundamental to thick bioclastic limestone oil reservoirs. However, the geological origins of high-permeability reservoirs are complex and diverse, with weak distribution patterns, and the geological identification results have poor matching with development dynamics, severely restricting the efficient development of oil reservoirs.

[0003] Currently, the main methods used domestically and internationally for characterizing high-permeability reservoirs include geological methods, drilling methods, logging methods, testing methods, and reservoir engineering methods, utilizing data covering core data, logging data, and production dynamics. Among these, core and analytical testing data are the most intuitive and reliable basic data for characterizing high-permeability reservoirs. Based on core permeability, the permeability cutoff value for high-permeability reservoirs is determined, enabling the identification and prediction of high-permeability reservoirs. However, this method has the following problems: when the number of cored wells is small and data is scarce, it will restrict the identification of high-permeability reservoirs; furthermore, the permeability cutoff value for high-permeability reservoirs varies in different oilfields, and the average reservoir permeability and gravity effects can all affect the development of high-permeability reservoirs. In a high-quality reservoir context, only thin reservoirs with permeabilities of several hundred or even Darcy levels can form high-permeability reservoirs, while in a poor reservoir context, thin reservoirs with permeabilities of tens of millidarcy levels can form high-permeability reservoirs. In addition, there is a difference between static and dynamic permeability in cores; dynamic permeability can be 2 to 3 orders of magnitude higher than static permeability.

[0004] Conventional logging data is relatively inexpensive, abundant, and easily accessible in mining operations, making it a widely used static data source for characterizing high-permeability reservoirs. However, the application of these conventional logging data has certain applicability and limitations in practical applications. The accuracy of logging methods in characterizing high-permeability reservoirs can be affected by factors such as fractures, high-level diagenetic alteration, and long-term water injection development, leading to poor characterization accuracy.

[0005] Among these methods, dynamic monitoring has the advantage of simple application and is widely used in oilfields with long water injection development periods and abundant production data. Methods for characterizing high-permeability reservoirs through production dynamics mainly include tracer methods, well testing methods, and drilling methods. The tracer method was the earliest used to determine the connectivity between water injection wells and production wells, using tracer indicator curves to identify high-permeability bands between them. Well testing methods include pulse testing, interference testing, and pressure instability testing. However, both tracer and well testing methods are time-consuming and costly. Drilling methods can only characterize reservoirs with large caverns or fractures, and only provide a rough depth estimate of the high-permeability reservoir section, failing to obtain detailed information on high-permeability reservoirs. Therefore, the accuracy and reliability of the characterization are poor, and the results can only be used as a reference.

[0006] In summary, current methods for characterizing high-permeability reservoirs all have certain applicability and limitations. The unique geological characteristics of high-permeability reservoirs and their dynamic production effects during development make it difficult to effectively characterize them using a single method. Although there are many methods for studying high-permeability reservoirs in carbonate rocks, there is still no systematic, widely applicable, and highly accurate method for characterizing high-permeability reservoirs. Therefore, this invention needs to provide a method and system for multi-dimensional characterization of high-permeability reservoirs in bioclastic limestone to address the aforementioned problems. Summary of the Invention

[0007] To address the aforementioned issues, this invention provides a method and system for multidimensional characterization of high-permeability bioclastic limestone reservoirs. This method employs a multidimensional characterization approach, utilizing a technical route of one-dimensional identification, two-dimensional characterization, three-dimensional prediction, and four-dimensional development evaluation. The approach is gradual, simple, and feasible, comprehensively utilizing multiple data sources, combining macroscopic and microscopic perspectives, and verifying static and dynamic data to ensure the accuracy and effectiveness of the characterization results.

[0008] To achieve the above objectives, the present invention provides a method for multidimensional characterization of high-permeability bioclastic limestone reservoirs, comprising the following steps:

[0009] Step S1: Based on core data from the core well, analytical test data, and logging curve data, geological standards for high-permeability reservoirs are obtained through geostatistics; combined with fine calibration of rock electrical properties, logging identification standards for high-permeability reservoirs are obtained, thus achieving one-dimensional identification of high-permeability reservoirs.

[0010] Step S2: Based on the high-permeability reservoir logging identification standard, identify high-permeability reservoirs in the bioclastic limestone of the study area. Select a framework profile in the study area, and through well-to-well comparison, statistically analyze the development frequency and stacking pattern of the high-permeability reservoirs. Based on the seismic and logging data of the study area, select seismic attributes that are highly correlated with the high-permeability reservoirs. Through seismic slices, statistically analyze the distribution pattern and area of ​​the high-permeability reservoirs to achieve a two-dimensional characterization of the high-permeability reservoirs.

[0011] Step S3: Obtain the geological characteristic parameters of the high-permeability reservoir, and establish a three-dimensional geological model using the sequential indicator simulation method and seismic attributes as constraints. Based on production data and numerical simulation methods, correct the high-permeability reservoir in the three-dimensional geological model to make it consistent with the development and production dynamics, obtain the reserve scale and distribution pattern of the high-permeability reservoir, and realize the three-dimensional prediction of the high-permeability reservoir.

[0012] Furthermore, in step S1, the geological statistics parameters include the following: lithology of the high-permeability reservoir, permeability of the high-permeability reservoir, permeability difference between the high-permeability reservoir and the surrounding rock, thickness of the high-permeability reservoir, thickness ratio of the high-permeability reservoir to the surrounding rock, single-well productivity of the high-permeability reservoir, and time to water breakthrough.

[0013] Furthermore, in step S1, the parameters of the high-permeability reservoir logging identification standard include the following: resistivity, gamma, sonic transit time, density, and neutrons.

[0014] Further, in step S3, obtaining the geological characteristic parameters of the high-permeability reservoir includes the following steps: classifying the high-permeability reservoir into types based on geological genesis, establishing a development model of the high-permeability reservoir, and obtaining the geological characteristic parameters of the high-permeability reservoir by fitting a variogram under the guidance of the development model.

[0015] Furthermore, the development patterns of the high-permeability reservoirs include sedimentary development type, diagenetic development type, biogenic type, and composite type. Among them, the composite type is a combination of sedimentary, diagenetic, and tectonic processes.

[0016] Further, in step S3, the high-permeability reservoir in the three-dimensional geological model is corrected, including: taking the ratio of the dynamic permeability obtained from production data testing to the permeability calculated from the permeability model in the three-dimensional geological model; determining the ratio as a multiplier, and then multiplying the permeability model in the three-dimensional geological model by the multiplier to obtain the dynamic permeability model, thus completing the correction of the high-permeability reservoir in the three-dimensional geological model.

[0017] Furthermore, the method also includes a development evaluation process for the identified high-permeability reservoir: for the high-permeability reservoir, determining the perforation and refilling intervals; and optimizing the layout of the development layer system and well type and well network based on the characterization characteristics of the distribution pattern and spread law of the high-permeability reservoir obtained in steps S2 and S3.

[0018] Furthermore, development layers are divided according to the characteristics of the high-permeability reservoirs in the oil reservoir, and different development well networks are deployed: when the planar thickness of the high-permeability reservoir in the layer is less than a first predetermined value, the lateral distribution is discontinuous, and a small-spacing reverse nine-point injection-production well network is adopted; when the lateral stacking scale of the high-permeability reservoir in the layer is greater than a second predetermined value, a large-spacing vertical well reverse nine-point injection-production well network is adopted; when the high-permeability reservoir in the layer is distributed in a network shape, and the reservoir thickness and planar distribution are relatively regular, an irregular five-point injection-production well network is adopted; when the high-permeability reservoir in the layer has a certain thickness and the planar distribution is continuous, large-spacing horizontal oil production wells are deployed, and no water injection wells are deployed; and in all injection-production well networks, oil production wells should be deployed in high-permeability reservoirs, while water injection wells should be deployed in the surrounding rock layer.

[0019] Another aspect of the present invention provides a system for multidimensional characterization of high-permeability reservoirs in bioclastic limestone, comprising:

[0020] The identification module is used to obtain the geological standards of high-permeability reservoirs through geostatistics based on core data from core wells, analytical test data, and logging curve data; and to obtain the logging identification standards of high-permeability reservoirs by combining rock electrical fine calibration, so as to realize one-dimensional identification of high-permeability reservoirs.

[0021] The two-dimensional characterization module is used to identify high-permeability reservoirs in bioclastic limestone in the study area based on the high-permeability reservoir logging identification standard; to select a framework profile in the study area; to statistically analyze the development frequency and stacking pattern of the high-permeability reservoirs through well-to-well comparison; and to select seismic attributes highly correlated with the high-permeability reservoirs based on the seismic and logging data of the study area; and to statistically analyze the distribution pattern and area of ​​the high-permeability reservoirs through seismic slices, thereby achieving two-dimensional characterization of the high-permeability reservoirs.

[0022] The three-dimensional prediction module is used to obtain the geological characteristic parameters of the high-permeability reservoir, establish a three-dimensional geological model using a sequential indicator simulation method and seismic attributes as constraints; and to correct the high-permeability reservoir in the three-dimensional geological model based on production data and numerical simulation methods so that it matches the development and production dynamics, obtain the reserve scale and distribution pattern of the high-permeability reservoir, and realize the three-dimensional prediction of the high-permeability reservoir.

[0023] Furthermore, it also includes a development evaluation module for determining perforation and filler perforation intervals for the high-permeability reservoir; and for optimizing the layout of development layers and well types and well networks based on the characterization characteristics of the distribution patterns and spread of the high-permeability reservoir obtained from the two-dimensional characterization module and the three-dimensional prediction module.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] (1) This invention conducts multi-dimensional characterization of high-permeability reservoirs. It adopts a technical route of one-dimensional identification, two-dimensional characterization, three-dimensional prediction and four-dimensional development evaluation. It is gradual, simple and feasible to operate, and makes comprehensive use of multiple data. It combines macroscopic and microscopic data and verifies static and dynamic data to ensure the accuracy and effectiveness of the characterization results.

[0026] (2) This invention classifies high-permeability reservoirs by geological genesis and establishes development models for different high-permeability reservoirs. In geological modeling, geological characteristic parameters are obtained by fitting the variation function through the development model. The simulation results are consistent with geological laws, which improves the accuracy of high-permeability reservoir prediction.

[0027] (3) This invention uses production data (PLT) to correct the three-dimensional geological model. The distribution pattern and attribute characteristics of the corrected high-permeability reservoir are more consistent with the actual geological characteristics of the oil reservoir, which improves the guiding significance of the three-dimensional geological model for development and production.

[0028] (4) Based on the characterization results of the distribution pattern and distribution of high-permeability reservoirs, this invention optimizes the development layer system and adjusts the layout of well type and well network, which greatly improves the efficiency and success rate of reservoir development. Attached Figure Description

[0029] Figure 1 A schematic flowchart of a method for multidimensional characterization of high-permeability bioclastic limestone reservoirs according to one embodiment of the present invention is shown.

[0030] Figure 2 The following diagram illustrates the geological parameter distribution of the high-permeability reservoir above the core well in an embodiment of the present invention; wherein, a is the permeability distribution of the high-permeability reservoir above the core well; b is the permeability ratio distribution of the high-permeability reservoir above the core well to the surrounding rock; c is the thickness distribution of the high-permeability reservoir above the core well; and d is the thickness ratio distribution of the high-permeability reservoir above the core well to the surrounding rock.

[0031] Figure 3 The following is an example of a plan view combined with a section view to depict the distribution of high-permeability reservoirs in an embodiment of the present invention; wherein, a is a well-connected section view, b is a plan view of the high-permeability reservoirs in the MA layer system, c is a plan view of the high-permeability reservoirs in the MB1 layer system, and d is a plan view of the high-permeability reservoirs in the MB2 layer system.

[0032] Figure 4 This diagram illustrates the sedimentary pattern of the M-group thick bioclastic limestone study area in an embodiment of the present invention.

[0033] Figure 5 The following are core thin section feature images of three types of high-permeability reservoirs in embodiments of the present invention; wherein, a is a core thin section feature image of a platform margin shoal type high-permeability reservoir, b is a core thin section feature image of a tidal channel type high-permeability reservoir, and c is a core thin section feature image of an inner platform shoal type high-permeability reservoir.

[0034] Figure 6 The seismic impedance response diagram of a tidal channel-type high-permeability reservoir in an embodiment of the present invention is shown.

[0035] Figure 7 The following is a three-dimensional geological model of the high-permeability reservoir in the study area in an embodiment of the present invention; wherein, a is a three-dimensional geological model of the high-permeability reservoir of the MA layer, b is a three-dimensional geological model of the high-permeability reservoir of the MB1 layer, and c is a three-dimensional geological model of the high-permeability reservoir of the MB2 layer.

[0036] Figure 8 The diagram shows the distribution of a three-dimensional well network for the development of a high-permeability reservoir in an embodiment of the present invention. Detailed Implementation

[0037] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present invention will be described in detail below with reference to the embodiments.

[0038] To address the aforementioned problems, one aspect of the present invention provides a method for multidimensional characterization of high-permeability reservoirs in bioclastic limestone, such as... Figure 1 As shown, the process includes the following steps: Step S1, based on core data from the core well, analytical test data, and logging curve data, the geological standards for high-permeability reservoirs are obtained through geostatistics; combined with fine calibration of rock electrical properties, the logging identification standards for high-permeability reservoirs are obtained, thereby achieving one-dimensional identification of high-permeability reservoirs;

[0039] Step S2: Based on the high-permeability reservoir logging identification standard, high-permeability reservoirs are identified in the bioclastic limestone of the study area. A skeleton profile is selected in the study area. Through well-to-well comparison, the development frequency and stacking pattern of high-permeability reservoirs are statistically analyzed. Based on the seismic and logging data of the study area, seismic attributes with high correlation to high-permeability reservoirs are selected. Through seismic slices, the distribution pattern and area of ​​high-permeability reservoirs are statistically analyzed to achieve a two-dimensional characterization of high-permeability reservoirs.

[0040] Step S3: Obtain the geological characteristic parameters of the high-permeability reservoir. Using the sequential indicator simulation method and seismic attributes as constraints, establish a three-dimensional geological model. Based on production data and numerical simulation methods, correct the high-permeability reservoir in the three-dimensional geological model to make it consistent with the development and production dynamics, obtain the reserve scale and distribution pattern of the high-permeability reservoir, and realize the three-dimensional prediction of the high-permeability reservoir.

[0041] The principle underlying this invention is that the permeability of a high-permeability reservoir itself has no clear boundary, exhibiting a significant permeability difference from the surrounding rock. The permeability difference between the high-permeability layer and the surrounding rock is at least 5 times, and can be as high as 3 orders of magnitude, with a minimum of [missing value]. This invention comprehensively utilizes conventional geological data from core samples, analytical testing data, and well logging curves, as well as data from rock electrical experiments, to statistically identify commonalities in existing geological data of high-permeability reservoirs. It establishes well logging identification standards for high-permeability reservoirs, achieving one-dimensional identification. Based on this, further research is conducted, using skeletal profile maps of the strata in the study area and planar maps of seismic slices to perform two-dimensional characterization of high-permeability reservoirs. This clarifies the development frequency, stacking patterns, distribution patterns, and area scale of high-permeability reservoirs, providing guidance for the deployment of well types and well networks during subsequent reservoir development.

[0042] Meanwhile, this invention guides the establishment of three-dimensional geological models based on the geological characteristic parameters of high-permeability reservoirs. This is because high-permeability reservoirs of different geological origins have different distribution patterns and attribute distributions. By obtaining the geological characteristic parameters of high-permeability reservoirs and using a sequential indicator simulation method with seismic attributes as constraints, the established three-dimensional geological model better conforms to geological laws, resulting in higher accuracy in characterization. Furthermore, this invention also calibrates the high-permeability reservoirs in the three-dimensional geological model, using production data to correct the static permeability model, making it better reflect actual reservoir characteristics and resulting in more effective and applicable characterization results. The method of this invention can effectively characterize high-permeability reservoirs in thick bioclastic limestone (up to 400m thick) reservoirs, providing a geological basis for efficient reservoir development and scheme optimization.

[0043] In a specific embodiment of the present invention, in step S1, the geological statistics parameters include the following: lithology of the high-permeability reservoir, permeability of the high-permeability reservoir, permeability difference between the high-permeability reservoir and the surrounding rock, thickness of the high-permeability reservoir, thickness ratio, single-well productivity of the high-permeability reservoir, and time to water breakthrough.

[0044] In a specific embodiment of the present invention, in step S1, the parameters of the high-permeability reservoir logging identification standard include the following: resistivity, gamma, sonic transit time, density, and neutrons.

[0045] Among these factors, resistivity reflects the oil-bearing potential of the reservoir. Since high-permeability reservoirs typically have high oil content, their resistivity logging values ​​are higher than those of the surrounding rock. Gamma ray reflects lithology; high-permeability reservoirs are often composed of grainy limestone with low clay content, resulting in lower gamma ray values ​​compared to the surrounding rock. Acoustic transit time, density, and neutron counts reflect porosity. Because high-permeability reservoirs usually have high porosity, their acoustic transit time is higher than that of the surrounding rock, density logging values ​​are lower, and neutron counts are higher. By comprehensively considering these factors, high-permeability reservoirs can be effectively identified.

[0046] In a specific embodiment of the present invention, step S3, obtaining the geological characteristic parameters of high-permeability reservoirs, includes the following steps: classifying high-permeability reservoirs according to their geological origins, establishing a development model for high-permeability reservoirs, and obtaining the geological characteristic parameters of high-permeability reservoirs by fitting a variogram under the guidance of the development model.

[0047] This invention classifies high-permeability reservoirs based on geological factors such as tectonic activity, sedimentation, diagenesis, and biogenic processes, and establishes development models for high-permeability reservoirs, including sedimentary development type, diagenetic development type, biogenic type, and composite type. Among them, the composite type is a combination of sedimentary, diagenetic, and tectonic processes.

[0048] In a specific embodiment of the present invention, the high-permeability reservoir in the three-dimensional geological model is corrected by: taking the ratio of the dynamic permeability obtained from production data testing to the permeability calculated from the permeability model in the three-dimensional geological model; determining the ratio as a multiplier; and then multiplying the permeability model in the three-dimensional geological model by the multiplier to obtain the dynamic permeability model, thereby completing the correction of the high-permeability reservoir in the three-dimensional geological model.

[0049] During oilfield development, pressure recovery well tests are conducted, directly obtaining dynamic permeability data. However, the dynamic permeability differs from the permeability in the 3D geological model, requiring correction. The correction method involves comparing the two permeability values, determining the difference, establishing an average multiplier, and then multiplying the entire model by the multiplier to complete the correction. The corrected permeability model can be called a dynamic permeability model, which has better matching characteristics to actual development and is closer to reality. Through the above steps, this invention can further screen and verify the previously identified high-permeability reservoirs, eliminating reservoirs that do not conform to development and production dynamics and are mistakenly identified as high-permeability reservoirs, thereby further improving the accuracy of characterization and prediction.

[0050] In a specific embodiment of the present invention, the method further includes a development evaluation process for the identified high-permeability reservoirs: for the high-permeability reservoirs, determining the perforation and refill intervals; based on the characterization characteristics of the distribution pattern and spread of the high-permeability reservoirs obtained in steps S2 and S3, optimizing the layout of the development layer system and well pattern. Based on the aforementioned high-permeability reservoirs identified in the study area, the present invention determines the perforation intervals and refill intervals for these high-permeability reservoirs, clarifies development sites, and then, combined with the aforementioned characterization characteristics of the distribution pattern and spread of the high-permeability reservoirs, guides the deployment of well patterns and well networks.

[0051] In a specific embodiment of the present invention, development layers are divided according to the characteristics of high-permeability reservoirs in the oil reservoir, and different development well networks are deployed: when the planar scale of the high-permeability reservoir thickness in the layer is less than a first predetermined value, the lateral distribution is discontinuous, and a small-spacing reverse nine-point injection-production well network is adopted; when the lateral stacking scale of the high-permeability reservoir in the layer is greater than a second predetermined value, a large-spacing vertical well reverse nine-point injection-production well network is adopted; when the high-permeability reservoir in the layer is distributed in a network pattern, and the reservoir thickness and planar distribution regularity are weak, an irregular five-point injection-production well network is adopted; when the high-permeability reservoir in the layer has a certain thickness and the planar distribution is continuous, large-spacing horizontal oil production wells are deployed, and no water injection wells are deployed; and in all injection-production well networks, oil production wells should be deployed in high-permeability reservoirs, while water injection wells should be deployed in the surrounding rock layer. The first predetermined value refers to the smaller value when comparing the planar scale of all high-permeability reservoirs; the second predetermined value refers to the larger value when comparing the stacking scale of all high-permeability reservoirs.

[0052] Example

[0053] This invention takes the M-formation of the Middle East X oilfield as the study area, which is a thick bioclastic limestone formation (thickness of 350m), and uses the method of this invention to characterize high-permeability reservoirs (thickness of 0.5-7m).

[0054] Step S101: One-dimensional identification of high-permeability reservoirs: Based on core data from cored wells, analytical test data, and logging curve data, the permeability of the high-permeability reservoir, the permeability difference between the high-permeability reservoir and the surrounding rock, the thickness of the high-permeability reservoir, and the thickness ratio are calculated. The above data are shown in [reference needed]. Figure 2 The high-permeability reservoir exhibits significantly higher single-well productivity than the surrounding rock, and its water cut shows a rapid, convex increase over time. The geological standards for high-permeability reservoirs are established as follows: permeability > 45 mD, permeability difference between the high-permeability reservoir and the surrounding rock > 5, reservoir thickness < 7 m, and thickness ratio of the high-permeability reservoir to the surrounding rock < 10%. Combined with fine-tuned rock electrical calibration, the logging identification standards for high-permeability reservoirs are determined as follows: resistivity (RT) ≥ 50 ohm / mm², gamma ray (GR) ≤ 25 API, sonic transit time ≥ 65 m / ms, and density ≤ 2.45 g / cm³. 3 Neutrons ≥ 20 V / V.

[0055] Step S102: Two-dimensional characterization of high-permeability reservoirs using a combination of planar and profile analysis: High-permeability reservoirs are identified in non-cored wells according to logging identification standards. A framework profile is selected in the study area, and through well-to-well comparison, the development frequency and stacking patterns of high-permeability reservoirs are statistically analyzed (e.g., ...). Figure 3As shown, the high-permeability reservoirs in the MA layer have a development frequency of 3–8 per 100m, distributed in an isolated manner; the high-permeability reservoirs in the MB1 layer have a development frequency of 2–8 per 100m, distributed in a network pattern; the high-permeability reservoirs in the MB2 layer are concentrated at the top (1 per 80m, developing in contiguous areas throughout the region). Based on well-seismic analysis, seismic attributes (seismic impedance response values) with high correlation to high-permeability reservoirs are selected. Through seismic slices, the distribution pattern and scale of high-permeability reservoirs are statistically analyzed (e.g., Figure 3 (As shown).

[0056] from Figure 3 As can be seen, due to the significant differences in the thickness and scale of high-permeability layers formed by different factors, ranging from a few meters to tens of meters, and their planar distribution ranging from hundreds of meters to tens of kilometers, the planar distribution is mainly patchy (e.g. Figure 3 As shown in b), mesh (as shown in b) Figure 3 (as shown in c) and mat-like (as shown in c) Figure 3 The distribution patterns are shown in Figure d. Patchy distribution refers to high-permeability reservoirs with a limited spatial extent, appearing isolated within the surrounding rock in a planar manner. Network distribution refers to high-permeability layers distributed in strips within the oilfield, with multiple sets of high-permeability layers spatially connected and exhibiting significant variations in scale. These layers extend over long distances along the strip direction, exhibiting good inter-well connectivity, while rapidly pinching out perpendicular to the strip direction. Sheet distribution refers to high-permeability layers distributed almost throughout the entire oilfield, with only localized areas lacking development.

[0057] Step S103: The study area is a carbonate rock gentle slope environment (its sedimentary pattern is as follows). Figure 4 As shown in the figure, due to the different development scales, distribution segments, stacking patterns, and genetic mechanisms of different high-permeability reservoirs, based on the development environment of high-permeability reservoirs in the study area, the development patterns of high-permeability reservoirs are divided into platform margin shoal type, tidal channel type, and intraplatform shoal type (core thin section characteristic diagrams of these three types of high-permeability reservoirs are shown in the figure). Figure 5 As shown in the figure, its platform edge shoal type and tidal channel type are sedimentary development patterns, while its inner platform shoal type belongs to a composite development pattern.

[0058] Step S104: Based on the development patterns and characteristics of different types of high-permeability reservoirs, geological characteristic parameters of high-permeability reservoirs are obtained through variogram fitting. In this embodiment, for the platform margin shoal type development pattern, the principal range of the variogram is 1000–2000 m, the secondary range is 500–800 m, and the vertical range is 1–2 m; for the tidal channel type development pattern, the principal range of the variogram is 3000–5000 m, the secondary range is 200–500 m, and the vertical range is 1–5 m; for the platform inner shoal type development pattern, the principal range of the variogram is 5000–10000 m, the secondary range is 2000–3000 m, and the vertical range is 2–10 m. Then, through sequential indicator simulation, seismic attributes (such as…) are used… Figure 6Using these as constraints, a three-dimensional geological model is established to achieve three-dimensional prediction of high-permeability reservoirs and clarify the reserve scale and distribution patterns in high-permeability reservoirs.

[0059] Its three-dimensional geological model is shown below. Figure 7 (Figure a shows a local 3D geological model of the MA stratum, Figure b shows a local 3D geological model of the MB1 stratum, and Figure c shows a local 3D geological model of the MB2 stratum.) Based on this model, predictions of the thick bioclastic limestone formation in Group M revealed that high-permeability reservoirs account for only 4% of the entire Group M, but their reserves represent 15% of the total Group M's reserves. These high-permeability reservoirs are mainly developed at the top of MA, MB1, and MB2. The high-permeability layers in the MA stratum are spotted and small in planar scale, but locally overlap with the platform shoals, resulting in a larger planar distribution. The high-permeability reservoirs in the MB1 stratum are strip-shaped and network-like in planar view. The high-permeability reservoirs in the MB2 stratum are multi-stage superimposed and developed throughout the entire work area, appearing as continuous sheets in planar view.

[0060] Step S105: Based on a large amount of production data (PLT) from the oilfield and numerical simulation methods, the high-permeability reservoirs in the geological model are corrected. Generally, the permeability of high-permeability reservoirs obtained from geological data is lower than the permeability in the actual oil reservoir. According to dynamic correction, in well logging with production data, the dynamic permeability obtained from pressure recovery well testing is compared with the permeability calculated in the permeability model. The ratio is determined as the multiplier. The permeability model is then multiplied by the multiplier to obtain the dynamic permeability model, making it consistent with the development and production dynamics, thus completing the correction of high-permeability reservoirs in the three-dimensional geological model.

[0061] Step S106: Based on the distribution patterns and characteristics of high-permeability layers, adhere to stratified development and deploy different development well networks. See the detailed well network deployment map below. Figure 8 The MA series features high-permeability reservoirs within the platform shoal, exhibiting a spotted pattern but laterally overlapping with the platform shoal. These reservoirs have a large planar distribution and good connectivity, necessitating a wide-spaced vertical well inverted nine-point injection-production well network. The MB1 series exhibits high-permeability reservoirs in the tidal channel with strong directional distribution but weak regularity in reservoir thickness and planar distribution, requiring an irregular five-point injection-production well network. The MB2+MC series boasts significant formation thickness and production volume; to rapidly achieve large-scale production, horizontal wells are employed for development. The MB2 top platform shoal high-permeability reservoir is relatively thin but possesses good physical properties, stable distribution, and long pressure propagation. Wide-spaced horizontal wells are used for development, deploying only production wells. The underlying reservoir has poorer physical properties and shorter pressure propagation, requiring a narrow-spaced injection-production well group. Water injection wells are deployed in the lower part of MC, while production wells are deployed in the lower MB2 section. Once the marginal production wells in the lower MB2 section become water-bearing, they are converted to water injection wells. Production wells are deployed in the high-permeability reservoir, while water injection wells are deployed in the surrounding rock layer.

[0062] The above method was tested using a multi-dimensional system for characterizing high-permeability bioclastic limestone reservoirs, which includes:

[0063] The identification module is used to obtain the geological standards of high-permeability reservoirs through geostatistics based on core data from core wells, analytical test data, and logging curve data; and to obtain the logging identification standards of high-permeability reservoirs by combining rock electrical fine calibration, so as to realize one-dimensional identification of high-permeability reservoirs.

[0064] The two-dimensional characterization module is used to identify high-permeability reservoirs in bioclastic limestone in the study area based on high-permeability reservoir logging identification standards. It is also used to select skeleton profiles in the study area, statistically analyze the development frequency and stacking pattern of high-permeability reservoirs through well-to-well comparison, and select seismic attributes with high correlation to high-permeability reservoirs based on seismic and logging data in the study area. Through seismic slicing, it statistically analyzes the distribution pattern and area of ​​high-permeability reservoirs to achieve two-dimensional characterization of high-permeability reservoirs.

[0065] The three-dimensional prediction module is used to obtain the geological characteristic parameters of high-permeability reservoirs, and to establish a three-dimensional geological model by using seismic attributes as constraints through a sequential indicator simulation method. It is also used to correct the high-permeability reservoirs in the three-dimensional geological model based on production data and numerical simulation methods so that they are consistent with the development and production dynamics, and to obtain the reserve scale and distribution pattern of high-permeability reservoirs, so as to realize the three-dimensional prediction of high-permeability reservoirs.

[0066] The development evaluation module is used to determine the perforation and refill intervals for high-permeability reservoirs; and to optimize the layout of development layers and well types and well networks based on the characterization characteristics of the distribution patterns and distribution laws of high-permeability reservoirs obtained from the two-dimensional characterization module and the three-dimensional prediction module.

[0067] The above embodiments are merely illustrative of implementation methods of the present invention, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the present invention. The present invention can also be implemented in other specific ways or forms without departing from the spirit or essential characteristics of the present invention. Therefore, the described embodiments should be considered illustrative rather than limiting in any respect. The scope of the present invention should be defined by the appended claims, and any variations equivalent to the intent and scope of the claims should also be included within the scope of the present invention.

Claims

1. A method for multidimensional characterization of high-permeability reservoirs in bioclastic limestone, characterized in that, Includes the following steps: Step S1: Based on core data from cored wells, analytical test data, and well logging data, geological standards for high-permeability reservoirs are obtained through geostatistics; combined with fine calibration of rock electrical properties, well logging identification standards for high-permeability reservoirs are obtained. Achieve one-dimensional identification of high-permeability reservoirs; Step S2: Based on the high-permeability reservoir logging identification standard, identify high-permeability reservoirs in the bioclastic limestone of the study area. Select a framework profile in the study area, and through well-to-well comparison, statistically analyze the development frequency and stacking pattern of the high-permeability reservoirs. Based on the seismic and logging data of the study area, select seismic attributes that are highly correlated with the high-permeability reservoirs. Through seismic slices, statistically analyze the distribution pattern and area of ​​the high-permeability reservoirs to achieve a two-dimensional characterization of the high-permeability reservoirs. Step S3: Obtain the geological characteristic parameters of the high-permeability reservoir, and establish a three-dimensional geological model using the sequential indicator simulation method and seismic attributes as constraints. Based on production data and numerical simulation methods, correct the high-permeability reservoir in the three-dimensional geological model to make it consistent with the development and production dynamics, obtain the reserve scale and distribution pattern of the high-permeability reservoir, and realize the three-dimensional prediction of the high-permeability reservoir.

2. The method for multi-dimensional characterization of high-permeability bioclastic limestone reservoirs according to claim 1, characterized in that, In step S1, the geological statistics parameters include the following: lithology of the high-permeability reservoir, permeability of the high-permeability reservoir, permeability difference between the high-permeability reservoir and the surrounding rock, thickness of the high-permeability reservoir, thickness ratio of the high-permeability reservoir to the surrounding rock, single-well productivity of the high-permeability reservoir, and time to water breakthrough.

3. The method for multi-dimensional characterization of high-permeability bioclastic limestone reservoirs according to claim 1, characterized in that, In step S1, the parameters of the high-permeability reservoir logging identification standard include the following: resistivity, gamma, sonic transit time, density, and neutrons.

4. The method for multi-dimensional characterization of high-permeability bioclastic limestone reservoirs according to claim 1, characterized in that, In step S3, the geological characteristic parameters of the high-permeability reservoir are obtained, including the following steps: Based on the geological genesis, the high-permeability reservoirs are classified into different types, and a development model for the high-permeability reservoirs is established. Under the guidance of the development model, the geological characteristic parameters of the high-permeability reservoirs are obtained by fitting a variogram function.

5. The method for multi-dimensional characterization of high-permeability bioclastic limestone reservoirs according to claim 4, characterized in that, The development patterns of the high-permeability reservoirs include sedimentary development type, diagenetic development type, biogenic type, and composite type.

6. The method for multi-dimensional characterization of high-permeability bioclastic limestone reservoirs according to claim 1, characterized in that, In step S3, the high-permeability reservoirs in the three-dimensional geological model are corrected, including: The ratio of the dynamic permeability obtained from production data testing to the permeability calculated from the permeability model in the three-dimensional geological model is taken. The ratio is determined as a multiplier, and the permeability model in the three-dimensional geological model is multiplied by the multiplier to obtain a dynamic permeability model, thus completing the correction of high-permeability reservoirs in the three-dimensional geological model.

7. The method for multidimensional characterization of high-permeability bioclastic limestone reservoirs according to any one of claims 1 to 6, characterized in that, The method also includes a development evaluation process for the identified high-permeability reservoirs: For the high-permeability reservoir, the perforation and refilling intervals are determined; based on the characterization characteristics of the distribution pattern and spread law of the high-permeability reservoir obtained in steps S2 and S3, the layout of the development layer system and well type and well network is optimized.

8. The method for multidimensional characterization of high-permeability bioclastic limestone reservoirs according to claim 7, characterized in that, Based on the characteristics of the high-permeability reservoirs described in the oil reservoir, development layers are divided, and different development well patterns are deployed: When the planar thickness of the high-permeability reservoir in a stratum is less than the first predetermined value, and the lateral distribution is discontinuous, a small-spacing reverse nine-point injection-production well network is adopted; when the lateral stacking of the high-permeability reservoir in a stratum is greater than the second predetermined value, a large-spacing vertical well reverse nine-point injection-production well network is adopted; when the high-permeability reservoir in a stratum is distributed in a network pattern, and the reservoir thickness and planar distribution are relatively irregular, an irregular five-point injection-production well network is adopted; when the high-permeability reservoir in a stratum has a certain thickness and the planar distribution is continuous, large-spacing horizontal oil production wells are deployed, and no water injection wells are deployed; and in all injection-production well networks, oil production wells should be deployed in high-permeability reservoirs, while water injection wells should be deployed in surrounding rock layers.

9. A system for multidimensional characterization of high-permeability reservoirs in bioclastic limestone, characterized in that, include: The identification module is used to obtain the geological standards of high-permeability reservoirs through geological statistics based on core data from core wells, analytical test data, and logging curve data. And it is used to combine rock electrical fine calibration to obtain high-permeability reservoir logging identification standards, which are used to achieve one-dimensional identification of high-permeability reservoirs; The two-dimensional characterization module is used to identify high-permeability reservoirs in bioclastic limestone in the study area based on the high-permeability reservoir logging identification standard; to select a framework profile in the study area; to statistically analyze the development frequency and stacking pattern of the high-permeability reservoirs through well-to-well comparison; and to select seismic attributes highly correlated with the high-permeability reservoirs based on the seismic and logging data of the study area; and to statistically analyze the distribution pattern and area of ​​the high-permeability reservoirs through seismic slices, thereby achieving two-dimensional characterization of the high-permeability reservoirs. The three-dimensional prediction module is used to obtain the geological characteristic parameters of the high-permeability reservoir, establish a three-dimensional geological model using a sequential indicator simulation method and seismic attributes as constraints; and to correct the high-permeability reservoir in the three-dimensional geological model based on production data and numerical simulation methods so that it matches the development and production dynamics, obtain the reserve scale and distribution pattern of the high-permeability reservoir, and realize the three-dimensional prediction of the high-permeability reservoir.

10. The system for multidimensional characterization of high-permeability bioclastic limestone reservoirs according to claim 9, characterized in that, It also includes a development evaluation module for determining perforation and filler perforation intervals for the high-permeability reservoir; and a module for optimizing the layout of development layers and well types and networks based on the characterization characteristics of the distribution patterns and spread of the high-permeability reservoir obtained from the two-dimensional characterization module and the three-dimensional prediction module.

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