A method, device, equipment and medium for evaluating and predicting a low-permeability reservoir dessert

Abstract

This invention discloses a method, apparatus, equipment, and medium for evaluating and predicting sweet spots in low-permeability reservoirs. The method includes: acquiring parameters reflecting reservoir physical properties, microstructure, and petrological characteristics; using these parameters and rock permeability to create interactive graphs to form various reservoir classification evaluation charts, identifying the classification boundaries of different reservoir types, thereby completing reservoir classification; determining sweet spot reservoirs based on different reservoir types and identifying the main controlling factors for sweet spot reservoir development; constructing petrological parameters of the main controlling factors for low-permeability sweet spots based on these main controlling factors, and generating well logging curves for these main controlling factors; selecting the seismic attribute with the best correlation to the main controlling factors based on the well logging curves of the main controlling factors; and performing geological modeling using the well logging curves of the main controlling factors as input and the optimal seismic attribute as a constraint to achieve precise prediction of sweet spot reservoirs. Therefore, this invention can effectively improve the accuracy of identifying and predicting low-permeability sweet spot reservoirs.

Description

Technical Field

[0001] This invention relates to the field of oil and gas reservoir development technology, and in particular to a method, apparatus, equipment and medium for evaluating and predicting sweet spots in low-permeability reservoirs. Background Technology

[0002] In recent years, oil and gas exploration and development, both onshore and offshore, has gradually shifted towards medium-deep reservoirs, which represent the future direction of oil and gas development. However, with increasing depth, the quality of oil and gas reservoirs inevitably deteriorates, thus medium-deep oil and gas reservoirs are mostly low-permeability or even tight reservoirs.

[0003] Low-permeability oil and gas reservoirs suffer from poor reservoir properties, resulting in less effective development compared to conventional reservoirs. Economical and efficient development is currently a key challenge for low-permeability oil and gas reservoirs. High production with fewer wells is a crucial path for the economical and efficient development of low-permeability reservoirs, and the key to achieving high production with fewer wells lies in the identification and prediction of sweet spots. Historically, the identification and evaluation of sweet spots in low-permeability reservoirs has been limited to core experiments, failing to establish an effective pathway from microscopic to macroscopic analysis and thus unable to provide guidance for well placement in sweet spot areas. Summary of the Invention

[0004] The present invention aims to at least solve one of the technical problems existing in the prior art. Therefore, in response to the above-mentioned problems, the object of the present invention is to provide a method, apparatus, device, and medium for evaluating and predicting sweet spots in low-permeability reservoirs, capable of finely characterizing sweet spot reservoirs and improving the accuracy of identification and prediction of low-permeability sweet spot reservoirs.

[0005] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows:

[0006] In a first aspect, the present invention provides a method for evaluating and predicting sweet spots in low-permeability reservoirs, comprising: The parameters reflecting reservoir physical properties, micropore structure, and reservoir petrological characteristics are obtained. These parameters are then used to create interactive diagrams with rock permeability to form various reservoir classification and evaluation charts. The classification boundaries of different types of reservoirs are identified, thereby completing the reservoir classification. Based on different reservoir types, sweet spot reservoirs are identified, and the main controlling factors for sweet spot reservoir development are sought. Based on the main controlling factors of sweet spot reservoir development, the petrophysical parameters of the main controlling factors of low-permeability sweet spots were constructed, and the well logging curves of the main controlling factors were generated. Based on the logging curves of the main controlling factors, the seismic attribute with the best correlation to the main controlling factors of the sweet spot is selected. Geological modeling is performed using well logging curves of the main controlling factors as input and optimal seismic attributes as constraints to achieve precise prediction of sweet spot reservoirs.

[0007] In some possible implementations, parameters reflecting reservoir properties and microstructure include reservoir property parameters such as porosity, permeability, and microstructure parameters such as average pore throat radius, median pore throat radius, pore throat sorting coefficient, and pore content of different pore sizes; reservoir petrological parameters include reservoir rock grain size, rock grain sorting, rock pore type, pore filling material content, and mineral composition.

[0008] In some possible implementations, the above parameters and rock permeability are used to create interactive maps to form various reservoir classification and evaluation charts, identify the classification boundaries of different types of reservoirs, and thus complete the reservoir classification. Specifically, the obtained rock permeability is used to draw an intersection map with other reservoir parameters. Reservoir classification is carried out by the clustering and interval distribution of points or lines in the intersection map. Samples represented by points or lines that cluster together or have a high degree of overlap are classified into the same category, or samples with relatively consistent distribution intervals of pore content of different pore sizes are classified into the same category. By summarizing the reservoir parameter characteristics of samples of the same category, the reservoir classification boundaries are identified, thereby classifying the reservoirs into 3-5 categories. The best reservoirs are classified as Category I, and the worst reservoirs are classified as Category III, Category IV, or Category V. Category I or Category I and Category II are regarded as sweet spot reservoirs.

[0009] In some possible implementations, sweet spot reservoirs are identified based on different reservoir types, and the main controlling factors for sweet spot reservoir development are identified. This includes: identifying the main controlling factors through cross-plots, using permeability and average grain size, median grain size, cement content, interstitial material content, and well logging interpretation of clay content to cross-plot, and determining parameters that have a good correlation with permeability as one of the main controlling factors affecting reservoir quality.

[0010] In some possible implementations, the key feature is the construction of petrophysical parameters of the main controlling factors of low-permeability sweet spots based on the main controlling factors of sweet spot reservoir development, and the generation of well logging curves of the main controlling factors. The process is as follows: By identifying the logging response characteristics at corresponding depth locations of all core or wall core samples for which the main control factors have been measured, a logging curve sequence is obtained. Well logging curves that reflect the magnitude of the main controlling factors are generated from the well logging curve sequence.

[0011] In some possible implementations, generating well logging curves reflecting the magnitude of the controlling factors through well logging curve sequences can employ either of the following two methods: Method 1: Identifying and selecting well logging curves with good correlation to the controlling factors through the intersection plots of the controlling factors and different well logging curve sequences, and performing multiple linear fitting with the controlling factors as the dependent variable and the selected well logging curve values ​​as the independent variables; Method 2: Generating well logging curves for the controlling factor values ​​using machine learning or neural network learning methods: Inputting the values ​​of all controlling factors and the values ​​of different series of well logging curves corresponding to the values, as well as the values ​​of different sequences of well logging curves in the target segment, and using neural networks or machine learning tools for deep learning to finally generate well logging curves for the controlling factors.

[0012] In some possible implementations, when the controlling factor is selected as the maximum grain size, based on the logging curve of the maximum grain size, the seismic attribute with the best correlation to the maximum grain size is preferred, including: The reservoir particle size is divided into coarse and fine particles, with a maximum particle size of 3 mm as the boundary. Particles with a maximum particle size greater than 3 mm are coarse particles, and those with a maximum particle size less than 3 mm are fine particles. By intersecting the seismic attributes of multiple rounds with the logging curves of the maximum grain size, lithological indicator factors can be found, which can effectively distinguish between coarse-grained and fine-grained reservoirs. By verifying the geological profile of the well through lithological indicator factors, it was found that lithological indicator factors can effectively indicate the development location of relatively coarse-grained reservoirs. Therefore, the seismic attribute with the best correlation to the maximum grain size was determined to be the lithological indicator factor.

[0013] Some possible implementations involve geological modeling using well logging curves of the controlling factors as input and optimal seismic attributes as constraints to achieve precise prediction of sweet spot reservoirs, including: Using the logging curve of the largest grain size as input data and lithological indicator factors as constraints, Petrel was used for geological modeling to obtain the grain size attribute volume; A spatial data volume for porosity and permeability is established using particle size attribute volume as a constraint. Based on spatial data volumes of porosity and permeability and permeability classification boundaries, the spatial distribution of sweet spot reservoirs is identified.

[0014] Secondly, the present invention also provides a device for evaluating and predicting sweet spots in low-permeability reservoirs, comprising: The reservoir classification unit is configured to acquire parameters reflecting reservoir physical properties, micropore structure, and reservoir petrological characteristics, and to use these parameters and rock permeability to draw interactive diagrams to form various reservoir classification evaluation charts, identify the classification boundaries of different types of reservoirs, and thus complete the reservoir classification. The factor determination unit is configured to identify sweet spot reservoirs based on different reservoir types and to find the main controlling factors for sweet spot reservoir development. The parameter construction unit is configured to construct the petrophysical parameters of the main controlling factors of low-permeability sweet spots based on the main controlling factors of sweet spot reservoir development, and generate the logging curves of the main controlling factors; The attribute optimization unit is configured to optimize the seismic attribute with the best correlation to the sweet spot main control factor based on the logging curve of the main control factor; Sweet spot reservoir identification units are configured to perform geological modeling with optimal seismic properties as constraints, and obtain spatial numerical volumes of porosity and permeability to identify the spatial distribution of sweet spot reservoirs.

[0015] Thirdly, the present invention also provides an electronic device, characterized in that it includes: at least one processor; and a memory communicatively connected to the processor; wherein the memory stores instructions executable by the processor, the instructions being executed by the processor to enable the processor to perform the method described thereon.

[0016] Fourthly, the present invention also provides a computer-readable storage medium for storing one or more programs, characterized in that the one or more programs include computer instructions for causing a computer to perform the method described therein.

[0017] This invention, by adopting the above technical solutions, has the following characteristics: It can conduct graded evaluation of low-permeability reservoirs, identify the main controlling factors for the development of sweet spots in low-permeability reservoirs, and simultaneously predict sweet spot reservoirs by optimizing geophysical properties closely related to the main controlling factors. Finally, it conducts three-dimensional geological modeling through seismic attribute constraints to achieve refined characterization of sweet spot reservoirs, effectively improving the accuracy of identifying and predicting low-permeability sweet spot reservoirs, and providing support for high-yield oil and gas development with fewer wells. In summary, this invention, starting from the main controlling factors of sweet spot reservoirs, proposes a method for evaluating, characterizing, and predicting sweet spots in low-permeability reservoirs, providing support for high-yield oil and gas reservoirs with fewer wells, and can be widely applied in the process of oil and gas reservoir development. Attached Figure Description

[0018] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. In the drawings: Figure 1 This is a flowchart illustrating the method for evaluating and predicting sweet spots in low-permeability reservoirs in real time, as described in this invention.

[0019] Figure 2 This is a classification boundary diagram for identifying the average pore throat radius and permeability of different types of reservoirs in an embodiment of the present invention.

[0020] Figure 3 This is a schematic diagram of the mercury ingress curve according to an embodiment of the present invention.

[0021] Figure 4 This is a classification diagram of different pore sizes and pore contents according to embodiments of the present invention.

[0022] Figure 5 This is a classification diagram of superimposed mercury intrusion curves according to an embodiment of the present invention.

[0023] Figure 6 This is a permeability-median pore throat radius intersection classification diagram for an embodiment of the present invention.

[0024] Figure 7 This is a classification diagram of the intersection of permeability and mainstream throat radius in an embodiment of the present invention.

[0025] Figure 8 This is a permeability-average pore throat radius intersection classification diagram of an embodiment of the present invention.

[0026] Figure 9 This is an interaction diagram of permeability and pore content in an embodiment of the present invention.

[0027] Figure 10 This is an interaction diagram of permeability and maximum particle size in an embodiment of the present invention.

[0028] Figure 11 This is a graph showing the intersection of the maximum particle size measured by core or wall core experiments in an embodiment of the present invention with the GR logging curve value.

[0029] Figure 12 This is a graph showing the intersection of the maximum particle size measured by core or wall core experiments in an embodiment of the present invention with the NDS curve value.

[0030] Figure 13 This is a maximum particle size curve obtained by neural network learning calculation in an embodiment of the present invention.

[0031] Figure 14 The lithological indicator factor in this embodiment of the invention can effectively distinguish between coarse-grained and fine-grained reservoir maps.

[0032] Figure 15 This is a seismic profile of the lithology indicator factor in an embodiment of the present invention.

[0033] Figure 16 This is a schematic diagram illustrating the distribution of different types of reservoirs identified based on permeability in an embodiment of the present invention.

[0034] Figure 17 This is a structural diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0035] It should be understood that the terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “described” as used herein may also include the plural forms. The terms “comprising,” “including,” “containing,” and “having” are inclusive and therefore indicate the presence of the stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not construed as requiring them to be performed in a particular order described or illustrated unless the order of performance is explicitly indicated. It should also be understood that additional or alternative steps may be used.

[0036] Although terms such as first, second, third, etc., may be used in this document to describe multiple elements, components, regions, layers, and / or segments, these elements, components, regions, layers, and / or segments should not be limited by these terms. These terms may be used only to distinguish one element, component, region, layer, or segment from another. Unless the context clearly indicates otherwise, terms such as "first," "second," and other numerical terms used herein do not imply order or sequence. Therefore, the first element, component, region, layer, or segment discussed below may be referred to as the second element, component, region, layer, or segment without departing from the teachings of the exemplary embodiments.

[0037] For ease of description, spatial relative terms may be used in the text to describe the relationship of one element or feature relative to another element or feature as shown in the figure. These relative terms include, for example, "inside," "outside," "middle," "outer," "below," "above," etc. Such spatial relative terms are intended to include different orientations of the device in use or operation, other than those depicted in the figure.

[0038] Because the identification and evaluation of sweet spots in low-permeability reservoirs are based solely on core experiments and do not follow an effective path from microscopic to macroscopic analysis, they fail to provide guidance for well placement in sweet spot areas. This invention provides a method, apparatus, equipment, and medium for evaluating and predicting sweet spots in low-permeability reservoirs. The method includes: acquiring parameters reflecting reservoir physical properties, microscopic pore structure, and reservoir petrological characteristics; using these parameters and rock permeability to create interactive graphs to form various reservoir classification evaluation charts, identifying the classification boundaries of different reservoir types, thereby completing reservoir classification; determining sweet spot reservoirs based on different reservoir types and identifying the main controlling factors for sweet spot reservoir development; constructing petrological parameters of the main controlling factors for low-permeability sweet spot reservoir development based on these main controlling factors, and generating well logging curves for these main controlling factors; selecting the seismic attribute with the best correlation to the main controlling factors based on the well logging curves of the main controlling factors; and performing geological modeling using the well logging curves of the main controlling factors as input and the optimal seismic attribute as a constraint to achieve precise prediction of sweet spot reservoirs. Therefore, this invention can effectively improve the accuracy of identifying and predicting low-permeability sweet spot reservoirs.

[0039] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the invention and to fully convey the scope of the invention to those skilled in the art.

[0040] Example 1: As Figure 1 As shown, the method for evaluating and predicting sweet spots in low-permeability reservoirs provided in this embodiment includes: S1, evaluation of sweet spot classification in low-permeability reservoirs.

[0041] In this embodiment, the sweet spot classification evaluation of low-permeability reservoirs includes: S11. Various parameters reflecting reservoir properties and micropore structure can be obtained through experiments on reservoir micropore structure and petrology. At the same time, reservoir petrological characteristic parameters can also be obtained.

[0042] Furthermore, various parameters reflecting reservoir properties and microstructure include reservoir property parameters such as porosity and permeability, as well as microstructure parameters such as average pore throat radius, median pore throat radius, pore throat sorting coefficient, and pore content of different pore sizes.

[0043] Furthermore, reservoir petrological characteristics include reservoir rock grain size, rock grain sorting, rock pore type, pore-filling material (cement and matrix) content, and mineral composition.

[0044] S12. Using the above data and rock permeability, an interactive diagram is drawn to form various reservoir classification and evaluation charts, thereby identifying the porosity and permeability limits of different types of reservoirs.

[0045] In this embodiment, a cross-plot of permeability and average pore throat radius is used to identify the classification boundaries of average pore throat radius and permeability for different types of reservoirs by judging the clustering of points on the plot. For example, first, a cross-plot of permeability and average pore throat radius is drawn. Based on the clustering of points on the cross-plot and the development experience of this oilfield or surrounding oilfields, clustered points are classified into the same category, such as... Figure 2 As shown, they can be divided into three categories.

[0046] Furthermore, after identifying the permeability classification boundaries, the corresponding porosity classification boundaries are identified using a porosity-permeability cross-plot combined with the permeability classification boundaries (based on the porosity-permeability cross-plot and the previously obtained permeability classification boundaries, the corresponding porosity boundaries are found, such as...). Figure 3 (As shown). Porosity can also be statistically analyzed across different pore size ranges to create reservoir classification charts. For example, using high-pressure mercury intrusion porosimetry results of samples with permeability distributions in different ranges, the porosity components of each sample within the following pore throat radius ranges can be statistically analyzed: <0.01μm, 0.01-0.1μm, 0.1-0.5μm, 0.5-1μm, 1-2μm, 2-5μm, 5-10μm, 10-20μm, 20-50μm, 50-100μm, >100μm. Then, a stacked bar chart of porosity content for all samples with different pore sizes can be plotted, arranging all samples from lowest to highest permeability on the chart. By observing the changes in porosity content across different pore sizes, the classification boundaries of reservoir permeability can be identified, such as... Figure 4 As shown, the largest pore size to the left of the first dashed line is only 0.5-1 μm, while the largest to the right of the first dashed line is 1-2 μm. The largest pore size to the right of the second dashed line is greater than 2 μm. Alternatively, by plotting the mercury intrusion curves of all samples on the same coordinate axis and observing the changes in the shape of the mercury intrusion curves, samples with similar shapes and high curve overlap can be grouped together. For example, ... Figure 5 As shown, blacks overlap highly with blacks, and grays overlap highly with grays.

[0047] Furthermore, by reading the permeability distribution of each type of sample, the permeability classification boundaries can be identified. The shape of the mercury injection curve includes the level of displacement pressure and the change in the slope of the mercury injection curve. Therefore, the classification boundaries of different reservoir types can be identified using the above charts. Note that the boundaries identified by different charts for the same parameter may not be completely consistent and may have some deviation. In this case, it is necessary to integrate multiple information, such as oilfield development experience, to determine the final classification boundary. However, the final classification boundary for each reservoir type will definitely be near one or more of the identified boundaries. Ultimately, reservoirs can be divided into 3-5 categories, with the best reservoirs being Category I and the worst reservoirs being Category III, IV, or V.

[0048] S2. Identification of key controlling factors for sweet spots in low-permeability reservoirs.

[0049] In this embodiment, different types of reservoirs have been identified through S1. Generally, Class I reservoirs or Class I and Class II reservoirs are considered sweet spot reservoirs (different oilfields can define this based on development experience, which is not limited here). For subsequent spatial prediction of sweet spot reservoirs, it is necessary to identify the main controlling factors for sweet spot reservoir development. Reservoir quality is influenced by both sedimentation and diagenesis. In sedimentation, sediment grain size and matrix content have a significant impact on reservoir quality, while in diagenesis, compaction and cementation have a more pronounced effect. Compaction is mainly related to burial depth. The strength of cementation and its impact on reservoir quality need to be evaluated. Furthermore, in some cases, cement and matrix may be included as interstitial material, and the impact of interstitial material on reservoir quality needs to be evaluated. Therefore, the identification of main controlling factors also relies primarily on cross-plots. Cross-plots are generally performed using permeability with average grain size, median grain size, cement content, interstitial material content, and well logging interpretation of clay content, etc., which have a good correlation with permeability (e.g., correlation coefficient R). 2 Parameters greater than 0.3 are considered one of the main controlling factors affecting reservoir quality. Generally, there are 1-3 main controlling factors for reservoirs.

[0050] S3, construction of rock physical parameters as the main controlling factor of low-permeability sweet spots.

[0051] In this embodiment, since the controlling factors obtained based on S2 rely entirely on core or wall core experiments, and the number of core or wall core experiments for each oilfield is limited, it cannot be effectively extended to the entire oilfield. To better identify the distribution of controlling factors across the entire oilfield, a two-step method is used to predict the spatial development of controlling factors, specifically: Step 1: Obtain the logging curve sequence by identifying the logging response characteristics (logging response characteristics are the values ​​of logging curves) at the corresponding depth locations of all core or wall core samples for which the main control factors have been measured.

[0052] Step 2: Generate logging curves that reflect the magnitude of the main controlling factors through logging curve sequences.

[0053] Furthermore, this embodiment provides two methods for generating logging curves of the main controlling factors, including: Method 1: When a large number of core samples or wall cores have been measured for the main controlling factors, corresponding logging curves reflecting the main controlling factors can be generated through multiple linear fitting. First, by analyzing the cross-plots of the main controlling factors and different logging curve sequences, one or more logging curves with good correlation (e.g., correlation coefficient greater than 0.3) to the main controlling factors are identified and selected. Then, using the main controlling factors as the dependent variable and the selected logging curve values ​​as independent variables, multiple linear fitting is performed. ; in, , z represents the coefficients of the equation that need to be obtained through fitting, which are obtained using multivariate fitting. , After obtaining the z coefficient value, the calculation of the main controlling factors of the target layer can be carried out.

[0054] Method 2: Generate logging curves for controlling factor values ​​using machine learning or neural network learning methods: By inputting the values ​​of all controlling factors and the values ​​of different series of logging curves corresponding to the values, as well as the values ​​of different sequences of logging curves for the target segment, deep learning is carried out using neural network or machine learning tools, and finally the logging curves for the target segment of the controlling factors are generated.

[0055] S4. The geophysical attribute with the best correlation to the main controlling factor of dessert is selected.

[0056] In this embodiment, the well logging curves of the target controlling factors are obtained based on S3. In order to better carry out spatial prediction, it is necessary to combine them with three-dimensional seismic data. By intersecting the well logging curves of the corresponding controlling factors with different seismic attributes (such as root mean square amplitude attribute, minimum amplitude attribute, etc.), the seismic attribute with the best correlation with the controlling factors is selected, thereby completing the conversion from well to three-dimensional spatial prediction.

[0057] S5. Geological modeling and prediction based on seismic attribute constraints.

[0058] In this embodiment, based on step S4, three-dimensional volume data (the three-dimensional volume data is seismic attribute data) that can preliminarily indicate the spatial development of the main controlling factors is obtained. However, due to the limited resolution of seismic data, in order to further improve the accuracy of the sweet spot spatial prediction, the interpolation function of Petrel geological modeling can be used, with seismic attributes as constraints, to further improve the accuracy of the sweet spot prediction. The specific process is as follows: First, the seismic attribute volume in the time domain is converted into the seismic attribute volume in the depth domain.

[0059] Subsequently, geological modeling work was carried out. During the geological modeling process, seismic attribute volumes, which indicate the development of the main controlling factors, were used as constraints to model attribute volumes such as porosity and permeability, ultimately obtaining spatial numerical volumes of porosity and permeability. Due to the constraints of these attributes, the modeling accuracy of reservoir properties was significantly improved, effectively enabling the spatial characterization and prediction of sweet spot reservoirs, and providing strong support for the deployment of well locations in oilfield development.

[0060] The following uses a low-permeability oilfield A as a specific example to illustrate the detailed implementation process of the low-permeability reservoir sweet spot evaluation and prediction method of the present invention.

[0061] S1. Reservoir classification and evaluation.

[0062] In this embodiment, reservoir classification and evaluation includes: First, data such as porosity, permeability, average pore throat radius, median pore throat radius, mainstream throat radius, pore size distribution curve, and mercury intrusion curve are obtained for each sample through experiments such as pore permeability measurement of oilfield cores or wall cores and high-pressure mercury intrusion.

[0063] Secondly, by plotting permeability versus median pore throat radius ( Figure 6 ), mainstream larynx radius ( Figure 7 Intersection of average pore throat radius ( Figure 8 ), and the overlay plate of mercury ingress curves ( Figure 5 Identification chart for pore content of different pore sizes ( Figure 4 Maps such as [list of maps] are used to classify reservoirs. The intersection map of permeability and median pore throat radius, main channel throat radius, and average pore throat radius primarily uses the clustering of scatter points in the intersection map to determine the classification boundaries. For the superimposed mercury intrusion porosimetry (MIP) curve classification map, classification is mainly based on the morphological similarity and degree of overlap of the curves. The classification map for different pore sizes and their contents is based on the distribution characteristics of pore contents of different sizes to divide them into different types, for example... Figure 4 When the sample permeability is less than 5 mD, the maximum pore throat radius does not exceed 1 μm. When the sample permeability is between 5 and 70 mD, the maximum pore throat radius does not exceed 2 μm. When the sample permeability is greater than 70 mD, pores with a pore throat radius greater than 2 μm are more developed.

[0064] Finally, the boundaries of various parameters for each reservoir type identified by the above charts may have some deviation. Ultimately, a more reasonable classification boundary for each parameter needs to be determined based on the boundaries of each chart. The reservoir classification boundaries for oilfield A, determined based on the above charts, are shown in Table 1.

[0065] Table 1 Reservoir Classification and Evaluation Table

[0066] S2, Identification of key factors controlling dessert flavor.

[0067] In this embodiment, the main sedimentary and diagenetic controlling factors for the development of sweet spot reservoirs (high-permeability reservoirs) are identified, utilizing permeability and dissolution porosity content (divided into intergranular dissolution porosity and intragranular dissolution porosity, etc.). Figure 9 ), Particle size parameter (which can be the maximum particle size, such as Figure 10 Interactive graphs can be created using parameters such as median particle size, cement content, clay content, and interstitial material content to determine the relationship between these parameters and permeability. Parameters showing a strong correlation with permeability can be considered as key control factors for reservoir quality. In this example, the interactive graph of permeability and dissolution porosity shows a poor correlation between permeability and dissolution porosity content, indicating that dissolution is not a key control factor for reservoir quality in oilfield A. However, the strong correlation between maximum particle size and permeability demonstrates that maximum particle size is a key control factor for reservoir quality.

[0068] S3, construction of rock physical parameters as the main controlling factor of low-permeability sweet spots.

[0069] In this embodiment, after identifying the main controlling factors, it is necessary to generate logging curves for the main controlling factors. This embodiment uses a neural network learning method: First, the logging curves are optimized by intersecting the maximum particle size (i.e., the main controlling factor) measured by core or wall core experiments with the logging curve values ​​(such as GR curve, DEN curve, NDS curve, etc.) at the corresponding depth points, and selecting the curves with better correlation with the main controlling factors.

[0070] Specifically, the intersection of the maximum grain size measured by core or wall core experiments in oilfield A with multiple well logging curves revealed a good correlation between the maximum grain size of reservoir A and the natural gamma ray logging (GR) curve (correlation coefficient greater than 0.3). It also showed a good correlation with NDS (density-neutron difference) (correlation coefficient greater than 0.3), where NDS is calculated using the following formula:

[0071] In the formula, DEN This refers to the density logging curve value. CNCF This represents the neutron logging curve value.

[0072] By inputting the GR curve, the calculated NDS curve, and the maximum grain size measured by core or wall core experiments into a neural network learning tool, the maximum grain size curve can be calculated. Figure 13 As shown.

[0073] S4. The geophysical attribute with the best correlation to the main controlling factor of dessert is selected.

[0074] In this embodiment, directly interpolating the maximum grain size measured by core or wall core experiments with seismic attributes presents two problems. First, since the amount of maximum grain size data measured by core or wall core experiments is relatively small, the interpolation may not reveal any patterns. Second, because core or wall core experiments provide data from a specific depth point, while seismic data has a lower vertical resolution, the seismic attribute value at a certain depth point may reflect the overall values ​​within a 10m vertical range. Therefore, the interaction between the two is not very meaningful. Well logging curves, on the other hand, provide relatively continuous measurements throughout the entire well section, with a large data volume, effectively reducing the impact of resolution and data volume.

[0075] After calculating the maximum grain size curves for all wells within the oilfield study area, seismic attributes with good correlation to the maximum grain size can be found by intersecting the well data with seismic attributes. When the seismic resolution is good (e.g., the target layer is shallow), the curve value can be directly intersected with the generated maximum grain size curve to find seismic attributes with good correlation. When the seismic resolution is limited and it is impossible to finely distinguish different grain size values, the maximum grain size can be coarsened, and the grain size can be divided into coarse and fine grains based on a certain threshold value. Then, it can be intersected with seismic attributes to find seismic attributes that can distinguish between coarse and fine grains, with coarse grains corresponding to better reservoir quality. For oilfield A, based on the relationship between maximum grain size and permeability and development experience, the reservoir grain size is divided into coarse and fine grains with a maximum grain size of 3 mm as the boundary. Grain sizes greater than 3 mm are considered coarse grains, and grain sizes less than 3 mm are considered fine grains.

[0076] By intersecting the seismic attributes from multiple rounds with the logging curves of the maximum grain size, lithological indicator factors can be found that effectively distinguish between coarse-grained and fine-grained reservoirs. Figure 14 As shown. The lithological indicator factor is calculated using the wave impedance and density properties of seismic data:

[0077] In the formula, DEN s For density attribute values, IP This represents the wave impedance attribute value.

[0078] Finally, the use of well-connected geological profiles based on lithological indicator factors further validated that lithological indicator factors can effectively indicate the development location of relatively coarse-grained reservoirs (relatively high-quality reservoirs, i.e., sweet spot reservoirs), such as... Figure 15 As shown in the figure, the location of coarse grains is displayed on the grain size logging curve, and the lithology indicator factor is also relatively high, showing a more obvious response.

[0079] S5. Geological modeling and prediction constrained by earthquake attributes.

[0080] In this embodiment, due to the limited resolution of seismic data, it is difficult to further guide the deployment of development wells. However, by using seismic attributes as constraints and leveraging the powerful interpolation capabilities of Petrel modeling, precise prediction of sweet spot reservoirs can be achieved. The specific process is as follows: First, in the attribute modeling process of geological modeling, the logging curve of the largest grain size in the well is used as input data and lithological indicator factors are used as constraints to establish a grain size attribute body; Subsequently, when establishing porosity and permeability attribute volumes, particle size attribute volumes are used as constraints, thereby effectively improving the prediction accuracy of reservoir properties.

[0081] Finally, by analyzing the permeability volume and combining it with the permeability classification boundaries in Table 1, the spatial distribution of sweet spot reservoirs (Class I and Class II reservoirs) can be identified, such as... Figure 16 As shown.

[0082] Example 2: Following the method for evaluating and predicting sweet spots in low-permeability reservoirs provided in Example 1, this example provides a device for evaluating and predicting sweet spots in low-permeability reservoirs. The device provided in this example can implement the method for evaluating and predicting sweet spots in low-permeability reservoirs in Example 1. This device can be implemented through software, hardware, or a combination of both. For ease of description, this example is described by dividing the functions into various units. Of course, in implementation, the functions of each unit can be implemented in one or more software and / or hardware components. For example, the device may include integrated or separate functional modules or units to perform the corresponding steps in the methods of Example 1. Since the device in this example is basically similar to the method example, the description process of this example is relatively simple. For relevant details, please refer to the description in Example 1. The example of the device for evaluating and predicting sweet spots in low-permeability reservoirs provided by this invention is merely illustrative.

[0083] Specifically, the present invention also provides a device for evaluating and predicting sweet spots in low-permeability reservoirs, comprising: The reservoir classification unit is configured to acquire parameters reflecting reservoir physical properties, micropore structure, and reservoir petrological characteristics, and to use these parameters and rock permeability to draw interactive diagrams to form various reservoir classification evaluation charts, identify the classification boundaries of different types of reservoirs, and thus complete the reservoir classification. The factor determination unit is configured to identify sweet spot reservoirs based on different reservoir types and to find the main controlling factors for sweet spot reservoir development. The parameter construction unit is configured to construct the petrophysical parameters of the main controlling factors of low-permeability sweet spots based on the main controlling factors of sweet spot reservoir development, and generate the logging curves of the main controlling factors; The attribute optimization unit is configured to optimize the seismic attribute with the best correlation to the sweet spot main control factor based on the logging curve of the main control factor; Sweet spot reservoir identification units are configured to perform geological modeling with optimal seismic properties as constraints, and obtain spatial numerical volumes of porosity and permeability to identify the spatial distribution of sweet spot reservoirs.

[0084] Example 3: This example provides an electronic device corresponding to the low-permeability reservoir sweet spot evaluation and prediction method provided in Example 1. The electronic device can be an electronic device for the client, such as a mobile phone, laptop, tablet computer, desktop computer, etc., to execute the method of Example 1.

[0085] like Figure 17 As shown, the electronic device includes a processor, a memory, a communication interface, and a bus. The processor, memory, and communication interface are connected via the bus to enable communication between them. The memory stores a computer program that can run on the processor. When the processor runs the computer program, it executes the method of Embodiment 1. The implementation principle and technical effects are similar to those of Embodiment 1, and will not be repeated here. Those skilled in the art will understand that... Figure 17 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computing device on which the present application is applied. The specific computing device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0086] In a preferred embodiment, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), and optical discs.

[0087] In a preferred embodiment, the processor can be any type of general-purpose processor such as a central processing unit (CPU) or a digital signal processor (DSP), and is not limited thereto.

[0088] Example 4: This example provides a computer-readable storage medium for storing one or more programs, the one or more programs including computer instructions, which, when executed by a computer, cause the computer to perform the method provided in Example 1 above.

[0089] In a preferred embodiment, the computer-readable storage medium may be a tangible device for holding and storing instructions executable, such as, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. The computer-readable storage medium stores computer program instructions that cause a computer to perform the method provided in Embodiment 1 above.

[0090] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0091] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0092] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0093] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In the description of this specification, the terms "a preferred embodiment," "furthermore," "specifically," "in this embodiment," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the embodiments in this specification. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating and predicting sweet spots in low-permeability reservoirs, characterized in that, include: The parameters reflecting reservoir physical properties, micropore structure, and reservoir petrological characteristics are obtained. These parameters are then used to create interactive diagrams with rock permeability to form various reservoir classification and evaluation charts. The classification boundaries of different types of reservoirs are identified, thereby completing the reservoir classification. Based on different reservoir types, sweet spot reservoirs are identified, and the main controlling factors for sweet spot reservoir development are sought. Based on the main controlling factors of sweet spot reservoir development, the petrophysical parameters of the main controlling factors of low-permeability sweet spots were constructed, and the well logging curves of the main controlling factors were generated. Based on the logging curves of the main controlling factors, the seismic attribute with the best correlation to the main controlling factors of the sweet spot is selected. Geological modeling is performed using well logging curves of the main controlling factors as input and optimal seismic attributes as constraints to achieve precise prediction of sweet spot reservoirs.

2. The method for evaluating and predicting sweet spots in low-permeability reservoirs according to claim 1, characterized in that, Parameters reflecting reservoir properties and microstructure include reservoir properties such as porosity and permeability, and microstructure parameters such as average pore throat radius, median pore throat radius, pore throat sorting coefficient, and pore content of different pore sizes. Reservoir petrological parameters include reservoir rock grain size, rock grain sorting, rock pore type, pore filling material content, and mineral composition.

3. The method for evaluating and predicting sweet spots in low-permeability reservoirs according to claim 2, characterized in that, By using the above parameters and rock permeability to create interactive diagrams, various reservoir classification and evaluation charts are generated to identify the classification boundaries of different reservoir types, thereby completing the reservoir classification. Specifically: Cross-plots are drawn using the obtained rock permeability and other reservoir parameters. Reservoirs are classified based on the clustering and distribution of points or lines in the cross-plots. Samples represented by clustered or highly overlapping points or lines are classified into the same category, or samples with relatively consistent distribution ranges of pore content of different pore sizes are classified into the same category. By summarizing the reservoir parameter characteristics of samples in the same category, the reservoir classification boundaries are identified, thereby classifying reservoirs into 3-5 categories. The best reservoirs are classified as Category I, and the worst reservoirs are classified as Category III, IV, or V. Category I or both Category I and II are considered sweet spot reservoirs.

4. The method for evaluating and predicting sweet spots in low-permeability reservoirs according to claim 2, characterized in that, Based on different reservoir types, sweet spot reservoirs are identified, and the main controlling factors for sweet spot reservoir development are sought. This includes: identifying the main controlling factors through cross-plots, using permeability and average grain size, median grain size, cement content, interstitial material content, and well logging interpretation of clay content to conduct cross-plots, and determining parameters that have a good correlation with permeability as one of the main controlling factors affecting reservoir quality.

5. The method for evaluating and predicting sweet spots in low-permeability reservoirs according to claim 4, characterized in that, Based on the controlling factors of sweet spot reservoir development, the rock physical parameters of the controlling factors of low-permeability sweet spots are constructed, and the well logging curves of the controlling factors are generated. The process is as follows: By identifying the logging response characteristics at corresponding depth locations of all core or wall core samples for which the main control factors have been measured, a logging curve sequence is obtained. Well logging curves that reflect the magnitude of the main controlling factors are generated from the well logging curve sequence.

6. The method for evaluating and predicting sweet spots in low-permeability reservoirs according to claim 1, characterized in that, Generating well logging curves that reflect the magnitude of the main controlling factors from a well logging curve sequence can be achieved using either of the following two methods: Method 1: By using the intersection plots of the main control factors and different logging curve sequences, identify and select one or more logging curves that have a good correlation with the main control factors, and use the main control factors as the dependent variable and the selected one or more logging curve values ​​as independent variables for multiple linear fitting. Method 2: Generate logging curves for controlling factors using machine learning or neural network learning methods: Input the values ​​of all controlling factors and the values ​​of different series of logging curves corresponding to the values, as well as the values ​​of different sequences of logging curves for the target section. Use neural networks or machine learning tools for deep learning to finally generate logging curves for the controlling factors.

7. The method for evaluating and predicting sweet spots in low-permeability reservoirs according to claim 1, characterized in that, When the main controlling factor is the maximum grain size, based on the logging curve of the maximum grain size, the seismic attribute with the best correlation to the maximum grain size is selected, including: The reservoir particle size is divided into coarse and fine particles, with a maximum particle size of 3 mm as the boundary. Particles with a maximum particle size greater than 3 mm are coarse particles, and those with a maximum particle size less than 3 mm are fine particles. By intersecting the seismic attributes of multiple rounds with the logging curves of the maximum grain size, lithological indicator factors can be found, which can effectively distinguish between coarse-grained and fine-grained reservoirs. By verifying the geological profile of the well through lithological indicator factors, it was found that lithological indicator factors can effectively indicate the development location of relatively coarse-grained reservoirs. Therefore, the seismic attribute with the best correlation to the maximum grain size was determined to be the lithological indicator factor.

8. The method for evaluating and predicting sweet spots in low-permeability reservoirs according to claim 7, characterized in that, Geological modeling is performed using well logging curves of the main controlling factors as input and optimal seismic attributes as constraints to achieve precise prediction of sweet spot reservoirs, including: Using the logging curve of the largest grain size as input data and lithological indicator factors as constraints, Petrel was used for geological modeling to obtain the grain size attribute volume; A spatial data volume for porosity and permeability is established using particle size attribute volume as a constraint. Based on spatial data volumes of porosity and permeability and permeability classification boundaries, the spatial distribution of sweet spot reservoirs is identified.

9. A device for evaluating and predicting sweet spots in low-permeability reservoirs, characterized in that, include: The reservoir classification unit is configured to acquire parameters reflecting reservoir physical properties, micropore structure, and reservoir petrological characteristics, and to use these parameters and rock permeability to draw interactive diagrams to form various reservoir classification evaluation charts, identify the classification boundaries of different types of reservoirs, and thus complete the reservoir classification. The factor determination unit is configured to identify sweet spot reservoirs based on different reservoir types and to find the main controlling factors for sweet spot reservoir development. The parameter construction unit is configured to construct the petrophysical parameters of the main controlling factors of low-permeability sweet spots based on the main controlling factors of sweet spot reservoir development, and generate the logging curves of the main controlling factors; The attribute optimization unit is configured to optimize the seismic attribute with the best correlation to the sweet spot main control factor based on the logging curve of the main control factor; Sweet spot reservoir identification units are configured to perform geological modeling with optimal seismic properties as constraints, and obtain spatial numerical volumes of porosity and permeability to identify the spatial distribution of sweet spot reservoirs.

10. An electronic device, characterized in that, include: At least one processor; and a memory communicatively connected to the processor; wherein the memory stores instructions executable by the processor, the instructions being executed by the processor to enable the processor to perform the method according to any one of claims 1-8.

11. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include computer instructions for causing a computer to perform the method according to any one of claims 1-8.

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