Deepwater gravity flow sandstone reservoir three-dimensional streamline body construction method, related method and device

Abstract

The invention discloses a deepwater gravity flow sandstone reservoir three-dimensional streamline body construction method, a related method and a device. The method for constructing the three-dimensional streamline body of the deepwater gravity flow sandstone reservoir comprises the following steps: constructing a reservoir geologic model of a target interval according to logging data and seismic data of a research area, determined seismic horizon interpretation results of the top surface and the bottom surface of the target deepwater sedimentary reservoir interval and obtained quantitative parameters of single-phase deepwater gravity flow channel development, grading seismic slicing is carried out; analyzing and depicting each slice layer of the reservoir geologic model of the target interval after slicing according to sensitive seismic attributes determined by seismic attribute analysis, and tracking to obtain a main line of a deep water gravity flow channel in each slice layer; determining azimuth information of the main wire penetrating through a plurality of plane grid nodes of the corresponding slice layer; and performing three-dimensional space simulation on the reservoir geologic model of the sliced target interval according to the azimuth information of the plurality of plane grid nodes of the main line to obtain a three-dimensional streamline body of the deepwater gravity flow sandstone reservoir.

Description

TECHNICAL FIELD

[0001] The present application relates to a kind of deepwater gravity flow sandstone reservoir three-dimensional flow line body construction method, related method and device. BACKGROUND

[0002] Global deepwater oil and gas resources are abundant, and in recent years, new recoverable reserves have exceeded 50%, becoming the main battlefield of resource allocation for major oil companies. The proportion of global oil and gas discoveries originating from deepwater gravity flow sandstone reservoirs is extremely high, and the cumulative recoverable reserves of deepwater gravity flow sandstone reservoirs account for 60% to 70%, which has become an important field of global oil and gas exploration and development, with very broad prospects. In recent years, major oil and gas exploration breakthroughs have been made in the waters around Africa, the waters along the South Atlantic coast, the Guyana-Suriname waters and the Bay of Bengal, with good oil and gas exploration prospects.

[0003] The tectonic setting, fluid type, formation mechanism, sedimentary pattern, evolution and internal structure of deepwater gravity flow sandstone reservoirs are significantly different from those of continental fluvial facies reservoirs. The channel system reservoirs developed under the control of gravity flow not only change rapidly laterally, but also generally have vertical incised aggradation. The combined underflow can produce complex sand body migration and superimposed patterns, and the internal structure of the reservoir is diverse. Moreover, the lithology is more complex, the particle size varies greatly, and the reservoir has strong heterogeneity. SUMMARY

[0004] To better realize the three-dimensional reservoir geological modeling of deepwater gravity flow sandstone reservoirs, a deepwater gravity flow sandstone reservoir three-dimensional flow line body construction method, related method and device are provided in the embodiments of the present application.

[0005] In a first aspect, the embodiments of the present application provide a deepwater gravity flow sandstone reservoir three-dimensional flow line body construction method, comprising:

[0006] According to the logging data and seismic data of the study area, the seismic horizon interpretation results of the top surface and the bottom surface of the target deepwater sedimentary reservoir section are determined;

[0007] According to the seismic horizon interpretation results of the top surface and the bottom surface of the target deepwater sedimentary reservoir section and the obtained quantitative parameters of single-stage deepwater gravity flow channel development, grid processing is performed, the reservoir geological model of the target layer section is constructed, and hierarchical seismic slices are performed to obtain the reservoir geological model of the target layer section after slicing;

[0008] According to the sensitive seismic attributes determined by seismic attribute analysis, each slice layer of the reservoir geological model of the target layer section after slicing is analyzed to obtain an analysis result, and the planar distribution characteristics of the deepwater gravity flow channel are depicted according to the analysis result, and the main thread of the deepwater gravity flow channel in each slice layer is tracked;

[0009] For each main channel line, determine the orientation information of multiple planar grid nodes of the corresponding slice layer of the reservoir geological model of the target segment after the main channel line passes through the slice;

[0010] Based on the orientation information of multiple planar grid nodes of the main channel, a preset interpolation algorithm is used to perform a three-dimensional spatial simulation of the geological model of the target layer reservoir after slicing, and obtain the three-dimensional streamline of the deep-water gravity flow sandstone reservoir.

[0011] In one or more optional embodiments, the process involves gridding based on the seismic horizon interpretation results of the top and bottom surfaces of the target deep-water sedimentary reservoir section and the obtained quantitative parameters of single-stage deep-water gravity flow channel development to construct a reservoir geological model of the target section. This model is then subjected to graded seismic slicing to obtain a sliced ​​reservoir geological model of the target section, including:

[0012] Based on the seismic stratigraphic interpretation results of the top and bottom surfaces of the target deep-water sedimentary reservoir section, the top and bottom boundaries of the target deep-water gravity flow reservoir section are determined.

[0013] Guided by the deep-water sedimentary geological model and based on the seismic-geological interpretation results, the top and bottom boundaries of each deep-water gravity flow channel within the target deep-water gravity flow reservoir section are identified.

[0014] Based on the set planar grid and vertical grid step size, the target deep-water gravity flow reservoir section is gridded to obtain the reservoir geological model of the target section.

[0015] The slice division time interval is determined based on the average layer velocity of the target deep-water sedimentary reservoir segment and the vertical grid step size of the geological model of the target segment reservoir.

[0016] Based on the time interval of the slice division, the deep-water gravity flow channels within the target layer reservoir geological model are divided to determine the top and bottom boundaries of the single-phase deep-water gravity flow channels, thereby obtaining the target layer reservoir geological model after slicing.

[0017] In one or more optional embodiments, the step of meshing the target deepwater gravity flow reservoir section according to a set planar grid and vertical grid step size to obtain the reservoir geological model of the target section includes:

[0018] The planar grid step size and multiple vertical grid step sizes of different sizes are obtained for geological modeling, and multiple initial reservoir geological models are constructed.

[0019] Based on the logging data of the study area, determine the stratification coefficient information corresponding to the logging interpretation results of the target deep-water sedimentary reservoir section;

[0020] Determine the stratification coefficient information corresponding to each initial reservoir geological model; the stratification coefficient information includes the maximum value, minimum value, average value, median, mode, and number of model grids;

[0021] Determine the correlation coefficient between the well logging interpretation results and each of the initial reservoir geological models;

[0022] Based on the stratification coefficient information corresponding to the well logging interpretation results, the stratification coefficient information corresponding to each initial reservoir geological model, and the correlation coefficient between the well logging interpretation results and each initial reservoir geological model, the target reservoir geological model is selected.

[0023] In one or more optional embodiments, before meshing the target deepwater gravity flow reservoir segment according to a set planar grid and vertical grid step size to obtain the reservoir geological model of the target segment, the method further includes:

[0024] Based on a pre-set deep-water sedimentary geology knowledge base, quantitative parameters of single-stage deep-water gravity flow channel development corresponding to the geological conditions of the target deep-water sedimentary reservoir section are obtained; the deep-water sedimentary geology knowledge base includes quantitative parameters of deep-water gravity flow channel development under multiple different geological conditions; the quantitative parameters include the width, thickness, and width-to-thickness ratio of single-stage deep-water gravity flow channel development.

[0025] Obtain the average layer velocity of the target deep-water sedimentary reservoir segment;

[0026] Based on the quantitative parameters of the development of a single-stage deep-water gravity channel, the median thickness, median width, and median width-to-thickness ratio of the single-stage deep-water gravity channel were determined.

[0027] Based on the average layer velocity of the target deep-water sedimentary reservoir segment and the median thickness, median width, and median width-to-thickness ratio of a single-stage deep-water gravity flow channel, the time interval corresponding to the thickness of the single-stage deep-water gravity flow channel is determined.

[0028] In one or more optional embodiments, the quantitative parameters of the single-stage deep-water gravity flow channel further include curvature, wavelength, and amplitude;

[0029] Before performing a three-dimensional spatial simulation of the reservoir geological model of the target segment after slicing, using the ordinary kriging algorithm with the following formula based on the azimuth information of multiple planar grid nodes of the main channel line, to obtain the three-dimensional streamline of the deep-water gravity flow sandstone reservoir, the following steps are also included:

[0030] Guided by the deep-water sedimentary geological model, and using the quantitative parameters of the single-stage deep-water gravity flow channel as constraints, outlier processing is performed on the azimuth information of multiple planar grid nodes of each main channel.

[0031] In one or more optional embodiments, based on the azimuth information of multiple planar grid nodes of the main channel, a preset interpolation algorithm is used to perform a three-dimensional spatial simulation of the reservoir geological model of the target segment after slicing, to obtain a three-dimensional streamline body of the deep-water gravity flow sandstone reservoir, including:

[0032] Based on the azimuth information of multiple planar grid nodes of the main channel, the ordinary kriging algorithm with the following formula is used to perform a three-dimensional spatial simulation of the reservoir geological model of the target segment after slicing, resulting in a three-dimensional streamline body of the deep-water gravity flow sandstone reservoir:

[0033]

[0034] Where, γ E (h) are two regionalized random variables with a distance interval of h in any direction. and The variance of the increment, N(h), is the total number of regionalized sample point pairs when the distance interval variable is h. This represents the orientation information of the planar grid node at spatial location xi. This provides the orientation information of the planar grid node at spatial location xi+h.

[0035] In one or more optional embodiments, the analysis of each slice of the reservoir geological model of the target segment after slicing, based on the sensitive seismic attributes determined by seismic attribute analysis, yields analysis results. Based on these results, the planar distribution characteristics of the deep-water gravity flow channel are characterized, and the main channel line of the single-stage deep-water gravity flow channel in each slice is traced, including:

[0036] Based on the sensitive seismic attributes determined by seismic attribute analysis, each slice of the reservoir geological model of the target segment after slicing is analyzed to obtain the analysis results; the sensitive seismic attributes include at least one single seismic attribute and / or at least one fused seismic attribute.

[0037] Based on the analysis results and the development of deep-water gravity channels on the seismic profile after well-seismic calibration, the planar distribution characteristics of deep-water gravity channels are characterized.

[0038] Based on the planar distribution characteristics of the deep-water gravity channel, and using the quantitative parameters of the single-stage deep-water gravity channel as constraints, the distribution range of the main channel line of the single-stage deep-water gravity channel in the deep-water gravity channel is tracked and characterized, and the main channel line of the single-stage deep-water gravity channel in each slice layer is obtained.

[0039] In one or more optional embodiments, determining the azimuth information of multiple planar grid nodes of the corresponding slice layer of the reservoir geological model of the target segment after the main channel line passes through each main channel line includes:

[0040] For each of the main thallies, mark multiple planar grid nodes of the corresponding slice layer of the reservoir geological model of the target segment after the main thallies pass through the slice;

[0041] For each planar grid node, calculate the angle between the segment of the main thalli passing through the planar grid node and the due north direction to obtain the azimuth information of the planar grid node.

[0042] Secondly, embodiments of the present invention provide a method for constructing a three-dimensional property parameter model of a deep-water gravity flow sandstone reservoir, including:

[0043] Obtain the three-dimensional streamline of the deep-water gravity flow sandstone reservoir obtained by the three-dimensional streamline construction method of the deep-water gravity flow sandstone reservoir described in the first aspect above;

[0044] Using the three-dimensional streamlines of the deep-water gravity flow sandstone reservoir as a constraint, a three-dimensional attribute parameter model of the deep-water gravity flow sandstone reservoir is constructed; wherein, the three-dimensional attribute parameter model of the deep-water gravity flow sandstone reservoir is a three-dimensional lithofacies parameter model and / or a three-dimensional reservoir physical property parameter model.

[0045] Thirdly, embodiments of the present invention provide a device for constructing three-dimensional streamlines in deep-water gravity flow sandstone reservoirs, comprising:

[0046] The seismic interpretation module is used to determine the seismic horizon interpretation results of the top and bottom surfaces of the target deep-water sedimentary reservoir section based on well logging data and seismic data of the study area.

[0047] The graded slicing module is used to perform gridding based on the seismic horizon interpretation results of the top and bottom surfaces of the target deep-water sedimentary reservoir section and the quantitative parameters of the development of single-stage deep-water gravity flow channels, to construct a reservoir geological model of the target section, and to perform graded seismic slicing to obtain the sliced ​​reservoir geological model of the target section.

[0048] The main channel line determination module is used to analyze each slice of the reservoir geological model of the target segment after slicing based on the sensitive seismic attributes determined by seismic attribute analysis, obtain analysis results, characterize the planar distribution characteristics of deep water gravity flow channels based on the analysis results, and trace the main channel line of deep water gravity flow channels in each slice layer.

[0049] The node orientation determination module is used to determine the orientation information of multiple planar grid nodes of the corresponding slice layer of the reservoir geological model of the target segment after the main channel line passes through the slice for each main channel line.

[0050] The streamline model construction module is used to perform three-dimensional spatial simulation of the reservoir geological model of the target segment after slicing, based on the orientation information of multiple planar grid nodes of the main channel line and using a preset interpolation algorithm, to obtain the three-dimensional streamline of the deep-water gravity flow sandstone reservoir.

[0051] Fourthly, embodiments of the present invention provide a device for constructing a three-dimensional property parameter model of a deep-water gravity flow sandstone reservoir, comprising:

[0052] The seismic interpretation module is used to determine the seismic horizon interpretation results of the top and bottom surfaces of the target deep-water sedimentary reservoir section based on well logging data and seismic data of the study area.

[0053] The graded slicing module is used to perform gridding based on the seismic horizon interpretation results of the top and bottom surfaces of the target deep-water sedimentary reservoir section and the quantitative parameters of the development of single-stage deep-water gravity flow channels, to construct a reservoir geological model of the target section, and to perform graded seismic slicing to obtain the sliced ​​reservoir geological model of the target section.

[0054] The main channel line determination module is used to analyze each slice of the reservoir geological model of the target segment after slicing based on the sensitive seismic attributes determined by seismic attribute analysis, obtain analysis results, characterize the planar distribution characteristics of deep water gravity flow channels based on the analysis results, and trace the main channel line of deep water gravity flow channels in each slice layer.

[0055] The node orientation determination module is used to determine the orientation information of multiple planar grid nodes of the corresponding slice layer of the reservoir geological model of the target segment after the main channel line passes through the slice for each main channel line.

[0056] The streamline model construction module is used to perform three-dimensional spatial simulation of the reservoir geological model of the target segment after slicing, based on the orientation information of multiple planar grid nodes of the main channel line and using a preset interpolation algorithm, to obtain the three-dimensional streamline of the deep-water gravity flow sandstone reservoir.

[0057] The attribute parameter model construction module is used to construct a three-dimensional attribute parameter model of the deep-water gravity flow sandstone reservoir using the three-dimensional streamlines of the deep-water gravity flow sandstone reservoir as a constraint condition; wherein, the three-dimensional attribute parameter model of the deep-water gravity flow sandstone reservoir is a three-dimensional lithofacies parameter model and / or a three-dimensional reservoir physical property parameter model.

[0058] Fifthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for constructing a three-dimensional streamline of a deep-water gravity flow sandstone reservoir as described in the first aspect or the method for constructing a three-dimensional attribute parameter model of a deep-water gravity flow sandstone reservoir as described in the second aspect.

[0059] In a sixth aspect, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method for constructing a three-dimensional streamline of a deep-water gravity flow sandstone reservoir as described in the first aspect or the method for constructing a three-dimensional attribute parameter model of a deep-water gravity flow sandstone reservoir as described in the second aspect.

[0060] In a seventh aspect, embodiments of the present invention provide a computer program product containing instructions that, when the computer program product is run on a computer device, cause the computer device to execute the method for constructing a three-dimensional streamline of a deep-water gravity flow sandstone reservoir as described in the first aspect or the method for constructing a three-dimensional attribute parameter model of a deep-water gravity flow sandstone reservoir as described in the second aspect.

[0061] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0062] This invention provides a method, related methods, and apparatus for constructing a three-dimensional streamline body of a deep-water gravity flow sandstone reservoir. This method, tailored to the characteristics of "sparse well network and large well spacing" data in deep-water sedimentary oil and gas fields, utilizes geological models and integrates well logging and seismic data. It fully leverages and extracts geological information such as lithofacies, physical property development characteristics, and spatial distribution from seismic data. From the perspective of serving three-dimensional reservoir geological modeling, it systematically characterizes the distribution characteristics of the main channel of the deep-water gravity flow through fine slicing, seismic multi-attribute analysis, and channel distribution sculpting. It calculates the azimuth information of nodes on the main channel, constructs azimuth constraints based on the geological model, and integrates these to form a three-dimensional streamline body of the deep-water gravity flow sandstone reservoir carrying azimuth information. This achieves a reasonable characterization of deep-water sedimentary reservoir attribute parameters under the limited dynamic and static data conditions of deep-water sedimentary oil and gas reservoirs, enabling its application in production practice and minimizing the uncertainty of reservoir geological models.

[0063] The method for constructing three-dimensional streamlines in deep-water gravity flow sandstone reservoirs provided in this invention can systematically and comprehensively restore the spatial sedimentary evolution characteristics of the main channel of deep-water gravity flow. It also provides good constraint on the azimuthal distribution of lithofacies, reservoir properties, and other attribute parameters in three-dimensional space. By constraining three-dimensional lithofacies modeling and reservoir property parameter modeling under lithofacies control, this method, compared with traditional methods that use a single fixed azimuth or only a single azimuth trend surface to constrain the entire thick target layer, is particularly effective in deep-water gravity flow sandstone reservoirs in marine areas with "sparse well networks and large well spacing." In terms of inter-well attribute parameter prediction, it can provide results with a more reasonable spatial distribution of attribute parameters and minimize the uncertainty of inter-well prediction under the current data level. It can construct a three-dimensional lithofacies model and a three-dimensional reservoir physical property parameter model that can approximate the geological reality as closely as possible and meet the accuracy requirements of reservoir prediction in the development stage. Ultimately, it can achieve the goal of improving the accuracy of reservoir geological modeling under the condition of few wells in the sea area, and strongly support the prediction of favorable reservoirs, the optimization of well location design, the accurate calculation of oil and gas reserves, the preparation of development plans, the deployment of large-scale well networks, and the improvement of dynamic model conformity rate in deep-water gravity flow sandstone oil and gas reservoirs in the sea area.

[0064] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0065] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0066] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0067] Figure 1 This is a flowchart of a method for constructing three-dimensional streamlines in a deep-water gravity flow sandstone reservoir according to an embodiment of the present invention;

[0068] Figure 2 This is a schematic diagram illustrating the difference between the migration and superposition modes of deep-water gravity flow channels and terrestrial river meandering channels in one embodiment of the present invention;

[0069] Figure 3 This is a profile of the seismic calibration results of a deep-water gravity flow channel reservoir well in one embodiment of the present invention;

[0070] Figure 4 This is a schematic diagram illustrating the principle of a three-level seismic slicing method in one embodiment of the present invention;

[0071] Figure 5 This is a schematic diagram of a three-level seismic slicing implementation scheme in one embodiment of the present invention;

[0072] Figure 6 This is a plan view of the seismically sensitive properties of a deep-water sedimentary reservoir in one embodiment of the present invention;

[0073] Figure 7 This is a diagram showing the results of multi-attribute identification of the main channel of a deep-water gravity flow channel based on a three-level seismic slicing scheme in one embodiment of the present invention;

[0074] Figure 8 This is a schematic diagram of a three-dimensional streamline model of a deep-water gravity flow sandstone reservoir in one embodiment of the present invention;

[0075] Figure 9 This is a flowchart of a method for constructing a three-dimensional property parameter model of a deep-water gravity flow sandstone reservoir according to an embodiment of the present invention;

[0076] Figure 10 This is a schematic diagram of the structure of a three-dimensional streamline construction device for deep-water gravity flow sandstone reservoirs according to an embodiment of the present invention;

[0077] Figure 11 This is a schematic diagram of the structure of a device for constructing a three-dimensional property parameter model of a deep-water gravity flow sandstone reservoir according to an embodiment of the present invention. Detailed Implementation

[0078] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0079] Accurate and detailed characterization of deep-water gravity flow sandstone reservoirs and the establishment of three-dimensional geological models that closely approximate geological realities are of great significance for efficiently supporting the prediction of favorable reservoir distribution in deep-water sedimentary oil and gas reservoirs, optimizing well location design at different stages of the entire life cycle, formulating and adjusting development plans, and deploying the potential tapping of remaining resources.

[0080] The inventors discovered that the accurate and detailed characterization and modeling of deep-water reservoirs in offshore areas currently faces the challenge of "few wells and insufficient data." Three-dimensional geological modeling of deep-water gravity flow sandstone reservoirs cannot follow the onshore oil and gas field modeling approach of "primarily relying on dense well network data and supplementing it with seismic data." In the process of reservoir geological modeling, sedimentary facies or lithofacies modeling is a crucial and challenging aspect, and its model quality has a profound impact on the accuracy of the geological model. For fluvial sedimentary reservoirs in mature onshore development blocks, lithofacies simulation can be conducted using abundant well data from dense well networks (average well spacing of approximately 100m). Due to the high degree of well control and abundant dynamic and static data, data analysis based on multi-well geological understanding can be carried out according to the lithofacies classification results. Combined with the main source direction, simulation parameters such as primary and secondary range and azimuth information are artificially set to achieve spatial distribution characterization of lithofacies attributes, thus meeting the requirements for detailed characterization and development deployment of fluvial reservoirs in dense well network areas. For deep-water sedimentary oil and gas fields in the ocean, due to marine engineering conditions, development is usually limited to a "sparse well network, large well spacing" model. Therefore, it is difficult to apply the approach of dense well network fluvial oil and gas reservoirs on land to lithofacies modeling. Given the characteristics of "sparse well network, large well spacing" in deep-water sedimentary oil and gas fields, accurately characterizing the spatial distribution of lithofacies parameters in deep-water gravity flow sandstone reservoirs using limited dynamic and static data is a pressing technical problem that needs to be solved to achieve three-dimensional reservoir geological modeling.

[0081] In view of the above problems, the present invention is proposed to provide a method, related methods and apparatus for constructing three-dimensional streamlines in deep-water gravity flow sandstone reservoirs that overcomes or at least partially solves the above problems.

[0082] Example 1

[0083] Embodiment 1 of this invention provides a method for constructing three-dimensional streamlines in deep-water gravity flow sandstone reservoirs, referring to... Figure 1 As shown, the method may include the following steps:

[0084] S101: Based on well logging and seismic data of the study area, determine the seismic horizon interpretation results of the top and bottom surfaces of the target deep-water sedimentary reservoir section;

[0085] S102: Based on the seismic stratigraphic interpretation results of the top and bottom surfaces of the target deep-water sedimentary reservoir section and the quantitative parameters of the development of single-stage deep-water gravity flow channels, the target section reservoir geological model is constructed by gridding and graded seismic slicing to obtain the sliced ​​target section reservoir geological model.

[0086] S103: Based on the sensitive seismic attributes determined by the seismic attribute analysis, analyze each slice of the reservoir geological model of the target segment after slicing, obtain the analysis results, and characterize the planar distribution characteristics of the deep-water gravity flow channel based on the analysis results, and trace the main channel line of the deep-water gravity flow channel in each slice.

[0087] S104: For each main channel line, determine the orientation information of multiple planar grid nodes of the corresponding slice layer of the reservoir geological model of the target segment after the main channel line passes through the slice;

[0088] S105: Based on the orientation information of multiple planar grid nodes of the main channel line, a preset interpolation algorithm is used to perform a three-dimensional spatial simulation of the geological model of the target layer reservoir after slicing, so as to obtain the three-dimensional streamline of the deep-water gravity flow sandstone reservoir.

[0089] The method for constructing three-dimensional streamlines of deep-water gravity flow sandstone reservoirs provided in this invention addresses the characteristics of "sparse well network and large well spacing" in deep-water sedimentary oil and gas fields. Guided by geological models, it integrates well logging and seismic data, fully utilizing and mining geological information such as lithofacies, physical property development characteristics, and spatial distribution from seismic data. From the perspective of serving three-dimensional reservoir geological modeling, it systematically depicts the distribution characteristics of the main channel of the deep-water gravity flow through fine slicing, seismic multi-attribute analysis, and channel distribution sculpting. It calculates the azimuth information of nodes on the main channel, constructs azimuth constraints based on geological models, and integrates them to form a three-dimensional streamline of the deep-water gravity flow sandstone reservoir carrying azimuth information. This method achieves reasonable characterization of deep-water sedimentary reservoir attribute parameters under the limited dynamic and static data conditions of deep-water sedimentary oil and gas reservoirs, enabling its application in production practice and minimizing the uncertainty of reservoir geological models.

[0090] The method for constructing three-dimensional streamlines in deep-water gravity flow sandstone reservoirs provided in this invention can systematically and comprehensively restore the spatial sedimentary evolution characteristics of the main channel of deep-water gravity flow. It also provides good constraint on the azimuthal distribution of lithofacies, reservoir properties, and other attribute parameters in three-dimensional space. By constraining three-dimensional lithofacies modeling and reservoir property parameter modeling under lithofacies control, this method, compared with traditional methods that use a single fixed azimuth or only a single azimuth trend surface to constrain the entire thick target layer, is particularly effective in deep-water gravity flow sandstone reservoirs in marine areas with "sparse well networks and large well spacing." In terms of inter-well attribute parameter prediction, it can provide results with a more reasonable spatial distribution of attribute parameters and minimize the uncertainty of inter-well prediction under the current data level. It can construct a three-dimensional lithofacies model and a three-dimensional reservoir physical property parameter model that can approximate the geological reality as closely as possible and meet the accuracy requirements of reservoir prediction in the development stage. Ultimately, it can achieve the goal of improving the accuracy of reservoir geological modeling under the condition of few wells in the sea area, and strongly support the prediction of favorable reservoirs, the optimization of well location design, the accurate calculation of oil and gas reserves, the preparation of development plans, the deployment of large-scale well networks, and the improvement of dynamic model conformity rate in deep-water gravity flow sandstone oil and gas reservoirs in the sea area.

[0091] The inventors' research on the spatial orientation distribution of reservoir facies and other attribute parameters revealed that Chinese patent application CN 115588103A discloses a device and method for predicting the azimuth angle of the main channel in narrow-channel reservoirs. This method characterizes and extracts the boundaries of narrow-channel reservoirs in marine meandering river sediments, and calculates the azimuth angle of the boundary nodes within the reservoir segment to guide the geological modeling of fluvial reservoirs. Similarly, Chinese patent application CN 113031065A discloses a method for geological modeling of oilfield sand bodies. For marine meandering river sedimentary reservoirs, in the facies modeling section, it also compiles a directional map of points on the boundary of the channel sand body to represent the direction of channel undulation. The paper, "Research on Geological Modeling of Sapu Oil Reservoir in Xingshugang Oilfield," focuses on the meandering river sedimentary reservoir in a dense well network area. It argues that using a fixed single source direction cannot fully characterize the three-dimensional spatial distribution characteristics of reservoir attributes. The paper proposes determining the main range direction based on the source direction and establishing azimuth trend surfaces for the main channel and point bars to constrain the spatial distribution of reservoir attribute parameters.

[0092] Based on the above-mentioned existing technology survey results, applying the above technology to characterize the spatial orientation distribution of lithofacies and other property parameters in deep-water gravity flow sandstone reservoirs presents at least the following three problems:

[0093] (1) The sedimentary characteristics and formation mechanisms of deep-water sedimentary reservoirs differ from those of meandering river reservoirs. However, the geological models guide lithofacies modeling in the same way. Meandering river reservoirs are usually characterized by point bar sedimentary bodies that are continuously distributed in the plane and are characterized by lateral accretion. They exhibit single seismic facies characteristics, relatively small thickness, and flat reflection characteristics at the top. The meandering annular channel in the plane shows strong amplitude patchy filling characteristics. In contrast, the formation mechanism of deep-water gravity flow sandstone reservoirs is relatively complex. Due to relatively continuous erosion, occasional straightening of bends, and more sediments on the inner bank (or convex bank) than on the outer bank (or concave bank), deep-water gravity flow channel units exhibit both lateral migration and vertical accretion characteristics. Seismically, they may have single or multiple seismic facies characteristics, and their thickness is usually very large.

[0094] (2) Regarding the constraint of azimuth information on meandering rivers and deep-water gravity flow sandstone reservoirs, the former is a reservoir with lateral accretion characteristics and a small thickness, while the latter is a reservoir with both lateral migration and vertical accretion characteristics and a large thickness. However, only a single-layer azimuth information trend surface is used to characterize the distribution of the main sedimentary units, thereby constraining the spatial distribution of attribute parameters. This method of using only one azimuth information trend surface to constrain attribute parameters for modeling the entire target layer has strong uncertainty and cannot reflect the sedimentary spatial evolution characteristics of deep-water gravity flow channels.

[0095] (3) While using the delineation of channel boundaries and the calculation of the azimuth of boundary nodes as a means of trend constraint can improve the accuracy of the lithological model of meandering river reservoirs to a certain extent, the azimuth information provided by the boundaries is relatively limited for deep-water gravity flow sandstone reservoirs, which have more complex formation mechanisms and development characteristics. At the same time, compared with river facies deposits such as meandering rivers, the boundaries of deep-water gravity flow channels are often affected by the superposition of multiple phases of channel erosion, which leads to uncertainty in delineating the boundaries of deep-water gravity flow channels based on seismic attributes. Moreover, the boundaries of deep-water gravity flow channels cannot well reflect the trend of the main channel and the quantitative scale of channel development (such as channel width and thickness). Therefore, using the identified deep-water gravity flow channel boundaries as the spatial distribution direction of trend constraint attribute parameters carries certain risks and may reduce the accuracy of the model.

[0096] In view of the above-mentioned problems in the application of existing technologies to characterize the spatial orientation distribution of lithofacies and other property parameters in deep-water gravity flow sandstone reservoirs, the inventors propose this solution. Addressing the characteristics of "sparse well networks and large well spacing" in deep-water sedimentary oil and gas fields, this solution adopts a "well-seismic integration, with seismic as the primary approach" strategy. Guided by a deep-water sedimentary geological model, it integrates seismic data, fully utilizing the high lateral resolution of seismic data. The well-seismic depth fusion extracts geological information such as lithofacies, physical property development characteristics, and spatial distribution from the seismic data, constructing geological constraints that can control the spatial orientation changes of lithofacies, physical properties, and other property parameters, and establishing a three-dimensional streamline body for deep-water gravity flow sandstone reservoirs. Since the azimuth information on the main channel of the deep-water gravity flow channel better reflects the guidance of the deep-water sedimentary geological model, it can provide effective azimuth information and constraints for the spatial distribution characterization of property parameters, thus improving model accuracy.

[0097] Based on this, those skilled in the art, by combining attribute parameter scale information (primary and secondary range magnitudes, channel width and thickness, etc.) summarized from well-seismic integrated interpretation and geological knowledge bases, can complete the spatial characterization of lithofacies and other attribute parameters through hierarchical facies control simulation. This compensates for the difficulty in accurately characterizing the spatial distribution of lithofacies and physical properties under low well control conditions at sea. This can minimize the uncertainty in the characterization and 3D geological modeling of deepwater gravity flow sandstone reservoirs in offshore blocks, strongly supporting the optimization of well location design, accurate calculation of oil and gas reserves, development plan preparation, large-scale well network deployment, and improvement of dynamic model conformity in deepwater gravity flow sandstone oil and gas reservoirs in offshore blocks.

[0098] The study area described in step S101 above can be any offshore deep-water gravity flow sandstone reservoir study block. By combining well logging and seismic analysis, the main deep-water sedimentary reservoir section within the study area is determined, i.e., the target deep-water sedimentary reservoir section, and then the seismic interpretation of the top and bottom surfaces of the target deep-water sedimentary reservoir section is completed. Specifically, step S101 above, based on well logging data and seismic data of the study area, determines the seismic stratigraphic interpretation results of the top and bottom surfaces of the target deep-water sedimentary reservoir section, which may include the following steps:

[0099] (1) Conduct synthetic seismic recordings to determine the seismic top and bottom layers corresponding to the main deep-water sedimentary reservoir sections; for each well in the study area, select the sonic transit time and density curves in the logging data as input parameters for synthetic seismic recordings, calculate the reflection coefficients of various lithological interfaces in the underground strata, analyze the seismic data of the study area (such as the dominant frequency and waveform characteristics of seismic waves), select wavelet frequencies in the frequency range of the seismic data, select convolution wavelets according to the waveform characteristics of different ages and layers of the seismic data, perform convolution operations based on the above wavelet frequencies, convolution wavelets and reflection coefficients, generate synthetic seismic traces, establish time-depth relationships for single wells by comparing synthetic seismic traces with actual seismic data, and calibrate the synthetic records of single wells according to the established time-depth relationships to ensure that they correspond accurately to the layers on the seismic profile.

[0100] (2) After completing the calibration of the synthetic seismic records of each well and obtaining the corresponding time-depth relationship, planar seismic tracking interpretation of the target deep-water sedimentary reservoir section is carried out. Using the calibrated single-well synthetic seismic records as control points, the top and bottom reflection horizons of the main deep-water sedimentary reservoir section are tracked on the seismic profile. The horizon information on adjacent seismic profiles is connected and closed to form a distribution map of the main deep-water sedimentary reservoir section on the plane, and the seismic horizon interpretation results of the top and bottom surfaces of the deep-water gravity flow reservoir section are obtained.

[0101] In this embodiment of the invention, the main deep-water sedimentary reservoir sections in the study area are determined by combining well logging data and seismic data in the above-described manner. Furthermore, seismic interpretation of the top and bottom surfaces of the reservoirs is completed to accurately define the location and morphology of the deep-water sedimentary reservoir sections. By synthesizing seismic records, well logging data (sonic transit time and density curves) are combined with seismic data to determine the specific location of the reservoir on the seismic profile. After completing the calibration of the synthetic seismic records for each well and obtaining the corresponding time-depth relationships, planar seismic tracing interpretation is performed on the deep-water sedimentary reservoir sections of the study block to complete the seismic horizon interpretation of the top and bottom surfaces of the deep-water gravity flow reservoir sections throughout the area.

[0102] In step S102 above, based on the seismic stratigraphic interpretation results of the top and bottom surfaces of the target deep-water sedimentary reservoir section and the obtained quantitative parameters of single-stage deep-water gravity flow channel development, gridding is performed to construct a reservoir geological model of the target section, and graded seismic slicing is performed to obtain a sliced ​​reservoir geological model of the target section. Specifically, this may include the following steps:

[0103] Based on the seismic stratigraphic interpretation results of the top and bottom surfaces of the target deep-water sedimentary reservoir section, the top and bottom boundaries of the target deep-water gravity flow reservoir section are determined.

[0104] Guided by the deep-water sedimentary geological model, and based on the seismic-geological interpretation results, the top and bottom boundaries of each deep-water gravity flow channel within the target deep-water gravity flow reservoir section are identified.

[0105] Based on the set planar grid and vertical grid step size, the target deep-water gravity flow reservoir section is gridded to obtain the reservoir geological model of the target section.

[0106] The slice division time interval is determined based on the average layer velocity of the target deep-water sedimentary reservoir segment and the vertical grid step size of the geological model of the target segment reservoir.

[0107] Based on the time interval of the slice division, the deep-water gravity flow channels within the target layer reservoir geological model are divided to determine the top and bottom boundaries of the single-phase deep-water gravity flow channels, thereby obtaining the target layer reservoir geological model after slicing.

[0108] In one specific embodiment, determining the top and bottom boundaries of the target deep-water gravity flow reservoir section based on the seismic horizon interpretation results of the top and bottom surfaces specifically includes: on the seismic profile, clearly identifying the top and bottom reflection horizons of the target deep-water gravity flow reservoir section based on the seismic horizon interpretation results. On the seismic profile, the top boundary corresponds to the top of the top reflection horizon of the reservoir section. By connecting the top points of this horizon on adjacent seismic profiles, a two-dimensional plan view of the top surface of the reservoir section can be drawn, thereby determining its top boundary. The bottom boundary corresponds to the bottom of the bottom reflection horizon of the reservoir section. By connecting the bottom points of this horizon on adjacent seismic profiles, a two-dimensional plan view of the bottom surface of the reservoir section can be drawn, thereby determining its bottom boundary.

[0109] In one specific embodiment, the identification of the top and bottom boundaries of each deep-water gravity flow channel within the target deep-water gravity flow reservoir section, guided by a deep-water sedimentary geological model and based on seismic-geological interpretation results, can specifically be the identification of the top and bottom boundaries of deep-water gravity flow channels of different types (e.g., restricted channels, unrestricted channels) and different vertical development patterns (thick-to-very-thick deep-water gravity flow channels with strong vertical accretion, and medium-thick-to-thin deep-water gravity flow channels with weak vertical accretion) identified after seismic-geological interpretation, guided by a deep-water sedimentary geological model.

[0110] In practical implementation, the first step is to gain a thorough understanding of deep-water sedimentary geological models, particularly regarding different types of channels (such as restricted and unrestricted channels) and their longitudinal development patterns (e.g., types with varying degrees of vertical accretion). Simultaneously, high signal-to-noise ratio seismic data is prepared, and seismic attribute images reflecting the structure, morphology, and physical properties of subsurface geological bodies are extracted. Seismic attribute analysis techniques, such as amplitude, frequency, phase, and coherence, are used to enhance the identification features of channels in the seismic attribute images. These attributes can help identify channel direction, internal structure, and sedimentary characteristics. Guided by the geological model and combined with the seismic attribute images, channel identification begins. For example, morphological features of channels in seismic images are observed, such as the location, curvature, and branching of the main channel. Based on changes in seismic attributes, sedimentary characteristics of the channels are analyzed, such as frequency and reflection intensity, to infer the sedimentary environment and processes of the channels. By comparing the channel morphology and sedimentary characteristics of different stratigraphic slices, the longitudinal and transverse development characteristics of the channels are determined. Different types of channels (restricted and unrestricted channels) are identified and distinguished based on differences in longitudinal and transverse overlay patterns. After identifying different types of channels, the main channels of individual channels are traced sequentially on stratigraphic slices. By analogy with the global quantitative database of deep-water sedimentary reservoirs, parameters such as width, depth, and width-to-depth ratio of individual channels in different channel systems are compared to characterize the planar distribution of the main channels of each channel. Through these steps, guided by deep-water sedimentary geological models, the superposition patterns of channels within different types of channel systems can be accurately identified based on seismic-geological interpretation, and the distribution of internal channel bodies can be determined.

[0111] In one specific embodiment, the above-mentioned meshing of the target deep-water gravity flow reservoir section according to the set planar grid and vertical grid step size to obtain the reservoir geological model of the target section includes:

[0112] The planar grid step size and multiple vertical grid step sizes of different sizes are obtained for geological modeling, and multiple initial reservoir geological models are constructed.

[0113] Based on the logging data of the study area, determine the stratification coefficient information corresponding to the logging interpretation results of the target deep-water sedimentary reservoir section;

[0114] Determine the stratification coefficient information corresponding to each initial reservoir geological model; the stratification coefficient information includes the maximum value, minimum value, average value, median, mode, and number of model grids;

[0115] Determine the correlation coefficient between the well logging interpretation results and each of the initial reservoir geological models;

[0116] Based on the stratification coefficient information corresponding to the well logging interpretation results, the stratification coefficient information corresponding to each initial reservoir geological model, and the correlation coefficient between the well logging interpretation results and each initial reservoir geological model, the target reservoir geological model is selected.

[0117] In this embodiment of the invention, before performing step S102, the following may also be included:

[0118] Based on a pre-set deep-water sedimentary geology knowledge base, quantitative parameters of single-stage deep-water gravity flow channel development corresponding to the geological conditions of the target deep-water sedimentary reservoir section are obtained; the deep-water sedimentary geology knowledge base includes quantitative parameters of deep-water gravity flow channel development under multiple different geological conditions; the quantitative parameters include the width, thickness, and width-to-thickness ratio of single-stage deep-water gravity flow channel development.

[0119] Obtain the average layer velocity of the target deep-water sedimentary reservoir segment;

[0120] Based on the quantitative parameters of the development of a single-stage deep-water gravity channel, the median thickness, median width, and median width-to-thickness ratio of the single-stage deep-water gravity channel were determined.

[0121] Based on the average layer velocity of the target deep-water sedimentary reservoir segment and the median thickness, median width, and median width-to-thickness ratio of a single-stage deep-water gravity flow channel, the time interval corresponding to the thickness of the single-stage deep-water gravity flow channel is determined.

[0122] In this embodiment of the invention, the preset deep-water sedimentary geology knowledge base can be a pre-constructed global deep-water sedimentary geology knowledge base. Based on the information obtained from the global deep-water sedimentary geology knowledge base, specifically, it includes quantitative information on the development characteristics of deep-water sedimentary units obtained from statistical analysis of deep-water sedimentary oil and gas reservoir examples worldwide based on field outcrops, earthquakes, core samples, well logging, and similar structural backgrounds, similar sedimentary environments, and the same age. It also includes statistical information on deep-water sedimentary oil and gas reservoir examples from different ages, such as quantitative information on deep gravity channel reservoirs corresponding to deep-water gravity channels developed in shallow layers and under relatively weak tectonic influence.

[0123] The planar grid step size for geological modeling can be set based on the well spacing within the offshore well area of ​​the study region. When setting this step size, the inter-well grid step size should not be too sparse; for example, the number of inter-well planar grid steps should be no less than 5. The size of each vertical grid step size can be set according to the actual situation, for example, to 0.2, 0.4, 0.6…1.6 meters. It should be noted that when setting the vertical grid step size, it should be smaller than the thickness of a single-phase deep-water gravity flow channel to allow for further subdivision and slicing within the time interval corresponding to the thickness of the single-phase deep-water gravity flow channel.

[0124] The aforementioned determination of the stratification coefficient information corresponding to the well logging interpretation results of the target deep-water sedimentary reservoir section based on the well logging data of the study area may specifically include: acquiring well logging data of the study area (such as natural gamma logging, resistivity logging, sonic logging, density logging, etc.), processing and interpreting the well logging data to obtain the effective thickness of the reservoir in the target deep-water sedimentary reservoir section, calculating the stratification coefficient, and statistically analyzing the maximum, minimum, average, median, and mode of the effective reservoir thickness. The stratification coefficient can be calculated using various methods, such as the coefficient of variation, the ratio of standard deviation to mean, etc., and specific details can be found in the detailed descriptions of existing technologies.

[0125] To evaluate stratification coefficients based on various vertical grid partitioning schemes, initial reservoir geological models were constructed according to different schemes. Each initial reservoir geological model divided the strata into grids of varying numbers and sizes, assigned an effective reservoir thickness to each grid, calculated the stratification coefficient, and statistically analyzed the maximum, minimum, average, median, mode, and number of grids in the effective reservoir thickness. The number of grids in the model refers to the total number of grids in the initial reservoir geological model under each partitioning scheme.

[0126] In this embodiment of the invention, the step of determining the correlation coefficient between the well logging interpretation results and each initial reservoir geological model can be achieved through cross-plot analysis of the effective reservoir thickness determined by well logging interpretation and the effective reservoir thickness of the initial reservoir geological model. The correlation coefficient characterizes the agreement rate between the geological model and the effective reservoir thickness at the well point.

[0127] Based on the stratification coefficient information corresponding to the well logging interpretation results, the stratification coefficient information corresponding to each initial reservoir geological model, and the correlation coefficient between the well logging interpretation results and each initial reservoir geological model, the target reservoir geological model is selected. Specifically, this can involve comparing the key parameters (including median and mode) in the stratification coefficients of the well logging interpretation results and the stratification coefficients of the initial reservoir geological models under different vertical grid division schemes, and comparing the correlation coefficient between the effective thickness of the well logging interpretation and the effective thickness of the geological model corresponding to the initial reservoir geological models under different vertical grid division schemes. This process fully considers the number of model grids in each initial reservoir geological model and the impact of the number of model grids on the computer's processing speed. Finally, a geological model is selected from all the initial reservoir geological models as the target reservoir geological model.

[0128] In this embodiment of the invention, the above-mentioned determination of the slice division time interval based on the average layer velocity of the target deep-water sedimentary reservoir segment and the vertical grid step size Vm of the target segment reservoir geological model is to divide the vertical grid step size Vm of the target segment reservoir geological model by the average layer velocity of the target deep-water sedimentary reservoir segment to obtain the time interval corresponding to the vertical grid step size of the target segment reservoir geological model, which is the slice division time interval Vc.

[0129] In this embodiment of the invention, when performing the above-described steps of dividing the deep-water gravity flow channels within the target reservoir geological model according to the slice division time interval Vc, determining the top and bottom boundaries of the single-stage deep-water gravity flow channels, and obtaining the sliced ​​target reservoir geological model, it should be noted that this step requires distinguishing between different types and vertical development patterns of deep-water gravity flow channels, namely, thick-to-very-thick deep-water gravity flow channels with strong vertical accretion and medium-thick-to-thin deep-water gravity flow channels with weak vertical accretion. Combining the time interval corresponding to the vertical grid step size and the seismic sampling rate, the former requires subdivision and slicing of no less than 5 Vc within the single-stage deep-water gravity flow channel, while the latter only requires subdivision and slicing of 1-2 Vc within the single-stage deep-water gravity flow channel.

[0130] In this embodiment of the invention, through step S102, a first-level slicing is performed to determine the top and bottom boundaries of the target deep-water gravity flow reservoir section. Next, a second-level slicing is performed to determine the top and bottom boundaries of each deep-water gravity flow channel within the target deep-water gravity flow reservoir section. Finally, a third-level slicing is performed, based on the top and bottom boundaries of the deep-water gravity flow channels of different types and vertical development patterns within the target deep-water gravity flow reservoir section, to further subdivide and slice the single-stage deep-water gravity flow channels, determining the top and bottom boundaries of each single-stage deep-water gravity flow channel. This achieves a differentiated three-level seismic slicing scheme guided by a deep-water sedimentary geological model.

[0131] The aforementioned three-level seismic slicing scheme for deep-water sedimentary reservoirs fully considers theoretical guidance from deep-water gravity flow depositional models and seismic sedimentology, the migration and superposition patterns of different types of deep-water gravity flow channels, the constraints of the deep-water sedimentary geological knowledge base, and the principle of rationality in the vertical grid division of three-dimensional geological modeling of reservoirs. Compared with the traditional method of opening fixed time windows for slicing, it can propose personalized and detailed slicing schemes for specific deep-water sedimentary geological bodies. Under the premise of ensuring the accuracy of the vertical grid of geological modeling, it can restore the sedimentary evolution pattern of deep-water gravity flow channels to the greatest extent possible, laying the foundation for accurate and detailed characterization of the main channel of deep-water gravity flow channels.

[0132] In step S103 above, based on the sensitive seismic attributes determined by seismic attribute analysis, each slice of the reservoir geological model of the target segment after slicing is analyzed to obtain analysis results. Based on the analysis results, the planar distribution characteristics of the deep-water gravity flow channel are characterized, and the main channel line of the single-stage deep-water gravity flow channel in each slice is traced. Specifically, this may include the following steps:

[0133] Based on the sensitive seismic attributes determined by seismic attribute analysis, each slice of the reservoir geological model of the target segment after slicing is analyzed to obtain the analysis results; the sensitive seismic attributes include at least one single seismic attribute and / or at least one fused seismic attribute.

[0134] Based on the analysis results and the development of deep-water gravity channels on the seismic profile after well-seismic calibration, the planar distribution characteristics of deep-water gravity channels are characterized.

[0135] Based on the planar distribution characteristics of the deep-water gravity channel, and using the quantitative parameters of the single-stage deep-water gravity channel as constraints, the distribution range of the main channel line of the single-stage deep-water gravity channel in the deep-water gravity channel is tracked and characterized, and the main channel line of the single-stage deep-water gravity channel in each slice layer is obtained.

[0136] In this embodiment of the invention, the aforementioned sensitive seismic attribute can be selected after slicing by conducting seismic multi-attribute analysis, choosing one or more single seismic attributes or fused seismic attribute types that are most sensitive to characterizing deep-water gravity flow channels. The selected single seismic attribute or fused seismic attribute is required to have the best identification effect on the planar distribution characteristics of deep-water gravity flow channels in the study area based on existing data, so as to characterize the development stages of deep-water gravity flow channels and track the main channel lines of each channel.

[0137] Specifically, this can be achieved by subdividing and slicing the geological bodies of the target study area based on a three-level seismic slicing scheme. After well-seismic calibration clarifies the seismic facies characteristics of deep-water gravity flow channels near the well, sensitive seismic attribute analysis is conducted on each slice of deep-water sedimentary unit to identify the sensitive seismic attributes or fused seismic attribute types of deep-water gravity flow channels. Sensitive seismic attributes of deep-water gravity flow channels are typically amplitude attributes, root mean square attributes, coherence attributes, and angle gather seismic attributes. Fused seismic attributes are attributes obtained by fusing multiple sensitive seismic attributes using machine learning, neural networks, or other artificial intelligence algorithms. Specific implementation methods can be found in the detailed descriptions of existing technologies and will not be elaborated upon here. Alternatively, fused seismic attributes can be attributes obtained by fusing RGB attributes from different angle gathers (near-middle-far) or attributes obtained by fusing amplitude-frequency-coherence attributes.

[0138] It should be noted that when the target stratigraphic unit in deep-water sediments is thick (its thickness exceeds the resolution of one seismic axis) or when the stratigraphic level coincides with the target sedimentary body and there are challenges in directly extracting attributes along the stratigraphic line for mapping, it is a very effective technique to extract various seismic attributes for analysis by opening appropriate time windows.

[0139] In this embodiment of the invention, based on step S103 above, and guided by the deep-water sedimentary geological model, the reservoir geological model of the target layer obtained in step S102 according to the three-level seismic slicing scheme is analyzed based on the sensitive seismic attributes of the deep-water gravity flow channels in the study area. Planar seismic multi-attribute analysis of the deep-water gravity flow channels in each slice layer is conducted. Simultaneously, the development of deep-water gravity flow channels, such as migration, cutting, and superposition, on the seismic profile after well-seismic calibration is combined with the planar and profile analysis to characterize the planar distribution characteristics of the deep-water gravity flow channels. Furthermore, under the constraint of quantitative parameters such as the width, thickness, and width-to-thickness ratio of single-stage deep-water gravity flow channels obtained from a pre-set deep-water sedimentary geological knowledge base, the distribution range of the main channel lines of single-stage deep-water gravity flow channels is tracked and characterized. The characterized main channel line data is stored in boundary data format and numbered (e.g., MTP-1, MTP-2…).

[0140] It should be noted that for single-phase deep-water gravity flow channels whose width is within the range of single-phase deep-water gravity flow channel widths obtained from the preset deep-water sedimentary geology knowledge base, the distribution range of the main channel line can be approximately taken as the center position between the envelopes of sensitive seismic attributes or fused seismic attributes.

[0141] In this embodiment of the invention, through the above step S103, a fine engraving combining well and seismic techniques is performed on the main channel line of the deep-water gravity flow channel. The engraved main channel line can reflect the overall streamline distribution characteristics of the deep-water gravity flow channel. At the same time, the distribution characteristics of the main channel line are constrained by the quantitative parameters of the deep-water sedimentary channel in the deep-water sedimentary geology knowledge base, which can achieve a fine engraving of the spatial distribution of the deep-water gravity flow channel.

[0142] In step S104 above, for each main channel line, the azimuth information of multiple planar grid nodes of the corresponding slice layer of the reservoir geological model of the target segment after the main channel line passes through the slice is determined. Specifically, this may include the following steps:

[0143] For each of the main thallies, mark multiple planar grid nodes of the corresponding slice layer of the reservoir geological model of the target segment after the main thallies pass through the slice;

[0144] For each planar grid node, calculate the angle between the segment of the main thalli passing through the planar grid node and the due north direction to obtain the azimuth information of the planar grid node.

[0145] In this embodiment of the invention, the main channel lines of the single-stage deep-water gravity flow channel depicted in each slice layer of the reservoir geological model of the target segment after slicing are marked. Specifically, the planar grid nodes of the slice layers through which the main channel lines of the single-stage deep-water gravity flow channel pass are marked based on the main channel line number (e.g., MTP-1_1, MTP-1_2, etc.).

[0146] In this embodiment of the invention, the method for calculating the azimuth data information of the main channel lines at the planar grid nodes can specifically involve using the angle between the line segment portion of each main channel line passing through the planar grid node of each slice layer in the target layer reservoir geological model after slicing and the due north direction as the azimuth information of the corresponding planar grid node. This angle can also be called the instantaneous azimuth angle, expressed in degrees (°), and its numerical range is 0–360°. In this embodiment of the invention, the azimuth information of each planar grid node can be stored in the corresponding grid of the target layer reservoir geological model after slicing.

[0147] In step S105 above, based on the azimuth information of multiple planar grid nodes of the main channel line, a preset interpolation algorithm is used to perform a three-dimensional spatial simulation of the reservoir geological model of the target segment after slicing, to obtain the three-dimensional streamline of the deep-water gravity flow sandstone reservoir. Specifically, this may include the following steps:

[0148] Based on the azimuth information of multiple planar grid nodes of the main channel, the ordinary kriging algorithm with the following formula is used to perform a three-dimensional spatial simulation of the reservoir geological model of the target segment after slicing, resulting in a three-dimensional streamline body of the deep-water gravity flow sandstone reservoir:

[0149]

[0150] Where, γ E (h) are two regionalized random variables with a distance interval of h in any direction. and The variance of the increment, N(h), is the total number of regionalized sample point pairs when the distance interval variable is h. This represents the orientation information of the planar grid node at spatial location xi. This provides the orientation information of the planar grid node at spatial location xi+h.

[0151] In this embodiment of the invention, after obtaining the azimuth information of the main channel streamlines (sets) in the corresponding grid of the reservoir geological model of the target segment after slicing, a conventional kriging algorithm is used to perform three-dimensional spatial simulation and prediction of the azimuth information of the main channel streamlines (sets). This completes the spatial distribution prediction of the azimuth angle of the main channel in deep-water gravity flow channels, realizing the construction of the three-dimensional streamline body of deep-water gravity flow sandstone reservoirs. Therefore, the positional value of the point to be predicted can be estimated by considering the spatial correlation between the point to be predicted and the sampling point, while ensuring that the variance of the estimation error is minimized.

[0152] In one specific embodiment, before performing a three-dimensional spatial simulation of the reservoir geological model of the sliced ​​target section using the ordinary kriging algorithm with the following formula based on the azimuth information of multiple planar grid nodes of the main channel line, and obtaining the three-dimensional streamline of the deep-water gravity flow sandstone reservoir, the method may further include:

[0153] Guided by the deep-water sedimentary geological model, and using the quantitative parameters of the single-stage deep-water gravity flow channel as constraints, outlier processing is performed on the azimuth information of multiple planar grid nodes of each main channel.

[0154] In this embodiment of the invention, through the above-mentioned outlier processing method, guided by the deep-water sedimentary geological model, and by comprehensively controlling the quantitative parameters of curvature, wavelength, and amplitude of single-period deep-water gravity flow channels in the preset deep-water sedimentary geological knowledge base, data analysis is carried out on the azimuth information of each main channel streamline (set) of each cross section sampled and stored in the geological model grid. Azimuth information that is obviously inconsistent with the deep-water sedimentary geological model and geological understanding, as well as outlier point values ​​generated during the main channel tracking process due to seismic attribute noise, are removed.

[0155] To provide a clearer and more complete explanation of the embodiments of the present invention, the following section uses a case study block of a deep-water gravity flow sandstone reservoir as an example to detail the implementation process of the three-dimensional streamline construction method for deep-water gravity flow sandstone reservoirs provided by the embodiments of the present invention:

[0156] The study block provided in this embodiment of the invention is a typical deep-water sedimentary reservoir located on a relatively narrow continental shelf on a passive continental margin. It is characterized by Eocene sandy deep-water gravity channel deposits, forming superimposed thick and very thick layers, coarse to medium-coarse sandstone and gravelly sandstone reservoirs. Later, due to the influence of bottom currents, fine-grained sediments were suspended and modified and migrated, resulting in poor sorting and rounding, and good reservoir properties.

[0157] Reference Figure 2As shown, the differences between the deep-water gravity flow channels and conventional meandering channels of terrestrial fluvial facies in the aforementioned study block are evident. Clearly, the deep-water gravity flow channels in this study block exhibit both lateral migration and strong vertical accretion characteristics, leading to the formation of thicker sandstone reservoirs. In contrast, conventional meandering channels of terrestrial fluvial facies are primarily characterized by lateral accretion with weaker vertical downcutting, resulting in thinner reservoirs. These differences will provide important guidance for identifying the main channel lines of deep-water gravity flow channels using level 3 seismic slices and well-seismic combined methods in the reservoir geological model of the study block.

[0158] First, based on step S101 above, the seismic interpretation results of the top and bottom surfaces of the target deep-water sedimentary reservoir section are determined according to the well logging and seismic data of the study area. In this step, the establishment of time-depth relationships through single-well synthetic record calibration using a combination of well and seismic data is the basis for stratigraphic tracing and identification of the main channel of deep-water gravity flow in this case study area. Figure 3 The well seismic calibration profile of the deepwater gravity flow channel reservoir in Well X1 in the study area is shown below. The detailed implementation process is as follows: First, the acoustic transit time curve (AC) and density curve (DEN) of Well X1 are used to generate the acoustic impedance curve, and the reflection coefficient of each lithological interface is calculated from the acoustic impedance curve. Through in-depth analysis of the seismic data in the study area, a Rick wavelet with a frequency of 25Hz and a negative phase of 180° is selected. The selected frequency, wavelet and reflection coefficient are convolved to generate a synthetic seismic trace.

[0159] In practice, the inventors discovered that because the frequency of near-channel stacked seismic data in this case study area is relatively high, it can reflect relatively rich seismic interface information and is closest to self-excited and self-receiving seismic reflections. Therefore, in this case study area, near-channel stacked seismic traces and synthetic seismic traces were compared and fine-tuned to achieve the best match between the two. Using the above operations, synthetic seismic records of other wells in the study area were calibrated to obtain the time-depth relationship of each well. Based on this, the top and bottom surfaces of the target deep-water sedimentary reservoir section were interpreted using planar tracking. Finally, the planar tracking interpretation of six bedding interfaces (S1 to S6) in five layers (named Zone 1 to Zone 5) of the reservoir section in the study area was completed.

[0160] Next, based on step S102 above, a reservoir geological model of the target section is constructed, and three-level seismic slicing is performed to obtain the sliced ​​reservoir geological model of the target section (hereinafter referred to as the reservoir geological model). (Refer to...) Figure 4The diagram illustrates the principle of the three-level seismic slicing method in this study area. As can be seen from the diagram, the seismic slicing in the study area is mainly divided into three levels: the first level is the top and bottom boundaries of deep-water gravity flow reservoir segments; the second level is the top and bottom boundaries of thick-to-very-thick deep-water gravity flow channels with strong vertical accretion and medium-thick-thin deep-water gravity flow channels with weak vertical accretion, identified after seismic-geological interpretation under the guidance of deep-water sedimentary geological models, i.e., the top and bottom boundaries of the channel complex; the third level involves the identification and division of the top and bottom boundaries of single-stage channels within the channel complex. This process requires full reference to the single-stage channel scale information in the global deep-water sedimentary geological knowledge base and a rational vertical grid division scheme based on the geological body model of the case study area.

[0161] Reference Figure 5 The diagram shows a schematic of the three-level seismic slicing implementation scheme for the reservoir geological model in this case study area. As can be seen from the diagram, the first level represents the top and bottom boundaries of the deep-water gravity flow reservoir segment, as indicated by the dashed lines in the diagram. Before performing the second and third level slicing, the average layer velocity of the reservoir segment in this case study area is determined to be 3300 m / s. Based on the analogy information obtained from the global deep-water sedimentary geology knowledge base regarding the development scale of single-stage deep-water gravity flow channels in the target study area, the median thickness of a single-stage deep-water gravity flow channel is 10 m, the median width is 248 m, and the median width-to-thickness ratio is 22. Therefore, the time interval corresponding to the thickness of a single-stage deep-water gravity flow channel can be calculated to be approximately 3 ms.

[0162] Conduct a feasibility study on a rationalized vertical grid partitioning scheme for the reservoir geological model of the target study area:

[0163] i) The average well spacing in the target study area is greater than 2km. To ensure the accuracy of the simulation, the grid step size between wells is selected as 100m×100m.

[0164] ii) Design various vertical grid division schemes (vertical grid step lengths of 0.2m, 0.4m, 0.6m, 0.8m, 1.0m, 1.2m, 1.4m and 1.6m), conduct stratification coefficient evaluation, and refer to Table 1 to statistically analyze the maximum, minimum, average, median and mode information of stratification coefficients of well logging interpretation results (effective reservoir thickness) and reservoir geological models (effective reservoir thickness) under different vertical grid division schemes;

[0165] iii) Conduct a correlation analysis between the reservoir geological model and the effective thickness at well points, which is achieved through cross-plot analysis of the effective thickness interpreted from well logging and the effective thickness of the reservoir geological model;

[0166] iv) By comparing the key parameters (mainly median and mode) of the stratification coefficients under different vertical grid division schemes with the cross plot analysis of the effective thickness of the well logging interpretation and the effective thickness of the geological model, and taking into full account the number of geological model grids and the computer processing speed, the vertical grid step size of the reservoir geological model in the target study area was finally determined to be 1.0m, and the time interval Vc corresponding to the vertical grid step size was calculated to be approximately 0.3ms.

[0167] Table 1. Vertical grid rationalization scheme for reservoir geological model of target study area.

[0168]

[0169] The second step is to determine the top and bottom boundaries of the channel complex. Based on the deep-water sedimentary model, and after identifying the mudstone strata through a combination of well and seismic analysis, three groups of deep-water gravity flow channel complexes with different types and longitudinal development patterns were identified in this case study area, located in Zone 2, Zone 3 and Zone 4, respectively.

[0170] The third level is the subdivision of single-stage deep-water gravity flow channels. Based on the lateral variations of seismic reflection waveforms and amplitudes, and guided by the global deep-water sedimentary geology knowledge base, 15 single-stage deep-water gravity flow channels were identified within the deep-water gravity flow channel complex, distributed in Zones 1 to 5. Among them, thick-to-very-thick layered deep-water gravity flow channels with strong vertical accretion are mainly developed in Zones 2, 3, and 4 (upper part), and each single-stage deep-water gravity flow channel can be subdivided and sliced ​​according to at least 5 Vc time intervals; medium-thick-to-thin layered deep-water gravity flow channels with weak vertical accretion are mainly developed in Zones 1, 4 (upper part), and 5, and each single-stage deep-water gravity flow channel can be subdivided and sliced ​​according to 1-2 Vc time intervals. Finally, the subdivision and slicing of a total of 81 layers in the deep-water gravity flow sandstone reservoir section of this case study area were completed.

[0171] Next, based on the above step S103: the main channel lines of deep-water gravity flow channels in each slice of the reservoir geological model are traced. Specifically, this can be achieved by combining seismic analysis with multi-attribute analysis of deep-water gravity flow channels in each slice, selecting the sensitive seismic attributes that characterize deep-water sedimentary units, characterizing the development stages of deep-water gravity flow channels, and tracing the main channel lines of each deep-water gravity flow channel.

[0172] For example, refer to Figure 6The image shows a planar diagram of amplitude attributes, one of the sensitive seismic attributes of deep-water sedimentary reservoirs. Deep-water gravity-flow channel seismic characteristics exhibit semi-continuous, low-frequency, and strong amplitude reflection features. The attribute plane often shows strip-shaped strong amplitude reflection units, and the channel exhibits strong downcutting characteristics on the seismic profile. Drilling / logging characteristics show box-shaped or bell-shaped curves in the natural gamma curve. The core shows multiple phases of positive rhythmic sand bodies superimposed, and erosion-faced conglomerate is visible at the bottom.

[0173] For example, refer to Figure 7 As shown, this is a display of the results of multi-attribute identification of deep-water sedimentary main channel lines based on a three-level seismic slicing scheme. The three sensitive seismic attributes on the left are the far-angle gather seismic attribute, the near-middle-far channel RGB attribute fusion, and the amplitude attribute and coherence attribute superposition. Comprehensive analysis of seismic multi-attribute fusion, coherence slicing, and seismic profiles reveals that the above-mentioned sensitive attributes and fused seismic attributes for deep-water gravity channels mainly exhibit characteristics such as striped weak amplitude display, relatively poor coherence, and waveform bifurcation and separation. This achieves the characterization of 15 phases of single-channel sedimentary main channels within the three groups of deep-water gravity channel complexes. The target of the deep-water gravity channel was determined, and the median thickness (10m), median width (248m), and median width-to-thickness ratio (22) of the single-stage deep-water gravity channel were obtained based on the pre-set deep-water sedimentary geology knowledge base to constrain the distribution range of the main channel lines of the single-stage deep-water gravity channel. Finally, a total of 286 main channels were delineated in 81 slices within Zone 1 to Zone 5 of the reservoir section of the study area, which were marked as (MTP-1, MTP-2, ..., MTP-286), and the distribution range of the main channels in the 81 slices was clarified.

[0174] Next, based on step S104 above: for each main channel line, determine the azimuth information of multiple planar grid nodes of the corresponding slice layer of the reservoir geological model through which the main channel line passes. After completing the planar and vertical gridding of the reservoir segment of the geological body in this case study area, the resulting reservoir geological model (with a total of approximately 37.66 million grids) is characterized by 286 main channel lines. The planar grid nodes of these main channel lines that pass through the slice layer are numbered (Main channel line 1: MTP-1_1, MTP-1_2, ..., MTP-1_366). The angle between the line segment of the main channel line passing through the planar grid node of the slice layer of the reservoir geological model and the due north direction is calculated as the azimuth information and stored in the corresponding grid of the reservoir geological model.

[0175] Next, outlier processing is performed, removing outlier planar mesh nodes and their corresponding orientation information.

[0176] Finally, based on step S105, a three-dimensional spatial simulation was performed using the ordinary kriging algorithm, and the three-dimensional streamline model of the deep-water gravity flow sandstone reservoir was finally constructed.

[0177] Reference Figure 8 The figure shows the three-dimensional streamline model of the deep-water gravity flow sandstone reservoir in the study area. The three-dimensional streamline model of the deep-water gravity flow sandstone reservoir obtained through the above steps can provide a more reasonable spatial distribution of attribute parameters, and maximize the approximation of the geological reality of the reservoir under the data level of "sparse well network and large well spacing".

[0178] Example 2

[0179] Based on the same inventive concept, this invention also provides a method for constructing a three-dimensional attribute parameter model of a deep-water gravity flow sandstone reservoir, referring to... Figure 9 As shown, the method includes:

[0180] S101: Based on well logging and seismic data of the study area, determine the seismic horizon interpretation results of the top and bottom surfaces of the target deep-water sedimentary reservoir section;

[0181] S102: Based on the seismic stratigraphic interpretation results of the top and bottom surfaces of the target deep-water sedimentary reservoir section and the quantitative parameters of the development of single-stage deep-water gravity flow channels, the target section reservoir geological model is constructed by gridding and graded seismic slicing to obtain the sliced ​​target section reservoir geological model.

[0182] S103: Based on the sensitive seismic attributes determined by the seismic attribute analysis, analyze each slice of the reservoir geological model of the target segment after slicing, obtain the analysis results, and characterize the planar distribution characteristics of the deep-water gravity flow channel based on the analysis results, and trace the main channel line of the deep-water gravity flow channel in each slice.

[0183] S104: For each main channel line, determine the orientation information of multiple planar grid nodes of the corresponding slice layer of the reservoir geological model of the target segment after the main channel line passes through the slice;

[0184] S105: Based on the orientation information of multiple planar grid nodes of the main channel line, a preset interpolation algorithm is used to perform a three-dimensional spatial simulation of the reservoir geological model of the target segment after slicing, so as to obtain the three-dimensional streamline of the deep-water gravity flow sandstone reservoir.

[0185] S106: Using the three-dimensional streamlines of the deep-water gravity flow sandstone reservoir as a constraint, construct a three-dimensional attribute parameter model of the deep-water gravity flow sandstone reservoir; wherein, the three-dimensional attribute parameter model of the deep-water gravity flow sandstone reservoir is a three-dimensional lithofacies parameter model and / or a three-dimensional reservoir physical property parameter model.

[0186] In this embodiment of the invention, by constructing a three-dimensional streamline body of a deep-water gravity flow sandstone reservoir, the geological information such as lithofacies, physical property development characteristics, and spatial distribution contained in seismic data is deeply mined. Constraints are constructed to control the spatial distribution and orientation changes of lithofacies, physical property, and other attribute parameters, thereby constraining three-dimensional lithofacies modeling and reservoir physical property parameter modeling under lithofacies control. Simultaneously, in conjunction with the attribute parameter scale information (primary and secondary range sizes, channel width and thickness, etc.) of deep-water gravity flow channels summarized based on well-seismic integrated interpretation and a pre-set deep-water sedimentary geological knowledge base, spatial characterization of lithofacies and other attribute parameters can be completed through hierarchical facies control simulation. This constructs a three-dimensional lithofacies model and a three-dimensional reservoir physical property parameter model that can approximate geological reality to the greatest extent and meet the reservoir prediction accuracy requirements during the development stage. This can compensate for the insufficiency of accurately characterizing the spatial distribution of lithofacies and physical property parameters under low well control conditions at sea, and guide research work such as inter-well favorable reservoir prediction, well location design optimization, accurate calculation of oil and gas reserves, development plan preparation, large-scale well network deployment, and improvement of dynamic model conformity rate.

[0187] The method for constructing a three-dimensional attribute parameter model of deep-water gravity flow sandstone reservoirs provided in this invention constructs a three-dimensional streamline body of the deep-water gravity flow sandstone reservoir. This streamline body constrains three-dimensional lithofacies modeling and reservoir property parameter modeling under lithofacies control. Compared with traditional methods that use a single fixed azimuth or only a single azimuth trend surface to constrain the entire thick target layer, this method constrains the azimuthal distribution of lithofacies, reservoir properties, and other attribute parameters in three-dimensional space, particularly for deep-water gravity flow sandstone reservoirs in marine areas with "sparse well networks and large well spacing" conditions. This is especially beneficial for reservoirs characterized by "vertical downward incision, strong accretion, and thick to very thick layers." For "relatively developed" deep-water gravity flow sandstone reservoirs, this method can provide more reasonable results regarding the spatial distribution of attribute parameters, and minimize the uncertainty of inter-well predictions under the current data level. It can construct three-dimensional lithofacies models and three-dimensional reservoir physical property parameter models that can approximate geological reality to the greatest extent and meet the accuracy requirements of reservoir prediction during the development stage. Ultimately, it can achieve the goal of improving the accuracy of reservoir geological modeling under the condition of few wells in the marine area, and strongly support the inter-well prediction of favorable reservoirs, the optimization of well location design, the accurate calculation of oil and gas reserves, the preparation of development plans, the deployment of large-scale well networks, and the improvement of dynamic model conformity rate in deep-water gravity flow sandstone oil and gas reservoirs in marine blocks.

[0188] Example 3

[0189] Based on the same inventive concept, this invention also provides a device for constructing three-dimensional streamlines in deep-water gravity flow sandstone reservoirs, referring to... Figure 10 As shown, the device includes:

[0190] The seismic interpretation module 101 is used to determine the seismic horizon interpretation results of the top and bottom surfaces of the target deep-water sedimentary reservoir section based on well logging data and seismic data of the study area.

[0191] The graded slicing module 102 is used to perform gridding based on the seismic stratigraphic interpretation results of the top and bottom surfaces of the target deep-water sedimentary reservoir section and the quantitative parameters of the development of single-stage deep-water gravity flow channels, to construct a reservoir geological model of the target section, and to perform graded seismic slicing to obtain the sliced ​​reservoir geological model of the target section.

[0192] The main channel line determination module 103 is used to analyze each slice of the reservoir geological model of the target segment after slicing based on the sensitive seismic attributes determined by seismic attribute analysis, obtain analysis results, characterize the planar distribution characteristics of the deep water gravity flow channel based on the analysis results, and trace the main channel line of the deep water gravity flow channel in each slice layer.

[0193] The node orientation determination module 104 is used to determine the orientation information of multiple planar grid nodes of the corresponding slice layer of the reservoir geological model of the target segment after the main channel line passes through the slice for each main channel line.

[0194] The streamline model construction module 105 is used to perform three-dimensional spatial simulation of the reservoir geological model of the target layer after slicing, based on the orientation information of multiple planar grid nodes of the main channel line and using a preset interpolation algorithm, to obtain the three-dimensional streamline of the deep-water gravity flow sandstone reservoir.

[0195] Example 4

[0196] Based on the same inventive concept, this invention also provides a device for constructing a three-dimensional property parameter model of a deep-water gravity flow sandstone reservoir, referring to... Figure 11 As shown, the device includes:

[0197] The seismic interpretation module 101 is used to determine the seismic horizon interpretation results of the top and bottom surfaces of the target deep-water sedimentary reservoir section based on well logging data and seismic data of the study area.

[0198] The graded slicing module 102 is used to perform gridding based on the seismic stratigraphic interpretation results of the top and bottom surfaces of the target deep-water sedimentary reservoir section and the quantitative parameters of the development of single-stage deep-water gravity flow channels, to construct a reservoir geological model of the target section, and to perform graded seismic slicing to obtain the sliced ​​reservoir geological model of the target section.

[0199] The main channel line determination module 103 is used to analyze each slice of the reservoir geological model of the target segment after slicing based on the sensitive seismic attributes determined by seismic attribute analysis, obtain analysis results, characterize the planar distribution characteristics of the deep water gravity flow channel based on the analysis results, and trace the main channel line of the deep water gravity flow channel in each slice layer.

[0200] The node orientation determination module 104 is used to determine the orientation information of multiple planar grid nodes of the corresponding slice layer of the reservoir geological model of the target segment after the main channel line passes through the slice for each main channel line.

[0201] The streamline model construction module 105 is used to perform three-dimensional spatial simulation of the reservoir geological model of the target layer after slicing, based on the orientation information of multiple planar grid nodes of the main channel line and using a preset interpolation algorithm, to obtain the three-dimensional streamline of the deep-water gravity flow sandstone reservoir.

[0202] The attribute parameter model construction module 106 is used to construct a three-dimensional attribute parameter model of the deep-water gravity flow sandstone reservoir using the three-dimensional streamline of the deep-water gravity flow sandstone reservoir as a constraint condition; wherein, the three-dimensional attribute parameter model of the deep-water gravity flow sandstone reservoir is a three-dimensional lithofacies parameter model and / or a three-dimensional reservoir physical property parameter model.

[0203] Example 5

[0204] Based on the same inventive concept, this embodiment of the invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for constructing three-dimensional streamlines of deep-water gravity flow sandstone reservoirs or the above-described method for constructing three-dimensional attribute parameter models of deep-water gravity flow sandstone reservoirs.

[0205] Example 6

[0206] Based on the same inventive concept, this embodiment of the invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the above-described method for constructing three-dimensional streamlines of deep-water gravity flow sandstone reservoirs or the above-described method for constructing three-dimensional attribute parameter models of deep-water gravity flow sandstone reservoirs.

[0207] Example 7

[0208] Based on the same inventive concept, this embodiment of the invention also provides a computer program product containing instructions. When the computer program product is run on a computer device, it causes the computer device to execute the above-described method for constructing a three-dimensional streamline of a deep-water gravity flow sandstone reservoir or the above-described method for constructing a three-dimensional attribute parameter model of a deep-water gravity flow sandstone reservoir.

[0209] The principles by which the above-described apparatus, computer-readable storage medium, computer device, and computer program product containing instructions solve the problem in the embodiments of the present invention are similar to those of the aforementioned methods. Therefore, their implementation can refer to the implementation of the aforementioned methods, and repeated details will not be described again.

[0210] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0211] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0212] 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.

[0213] 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.

[0214] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for constructing three-dimensional streamlines in deep-water gravity flow sandstone reservoirs, characterized in that, The method comprises the following steps: determining the seismic horizon interpretation results of the top surface and the bottom surface of the target deep-water sedimentary reservoir section according to the logging data and the seismic data of the research area; performing grid processing according to the seismic horizon interpretation results of the top surface and the bottom surface of the target deep-water sedimentary reservoir section and the quantitative parameters of the single-stage deep-water gravity flow channel development, constructing a reservoir geological model of the target section, and performing hierarchical seismic slicing to obtain the reservoir geological model of the target section after slicing; analyzing each sliced layer of the reservoir geological model of the target section after slicing according to the sensitive seismic attributes determined through seismic attribute analysis, obtaining an analysis result, and depicting the planar distribution characteristics of the deep-water gravity flow channel according to the analysis result to track the main thread of the deep-water gravity flow channel in each sliced layer; for each main thread, determining the azimuth information of a plurality of planar grid nodes through which the main thread passes through the corresponding sliced layer of the reservoir geological model of the target section after slicing; performing three-dimensional space simulation on the reservoir geological model of the target section after slicing according to the azimuth information of the plurality of planar grid nodes of the main thread by using a preset interpolation algorithm to obtain a three-dimensional flow line body of the deep-water gravity flow sandstone reservoir.

2. The method of claim 1, wherein, The grid processing, the construction of the reservoir geological model of the target section, and the hierarchical seismic slicing to obtain the reservoir geological model of the target section after slicing according to the seismic horizon interpretation results of the top surface and the bottom surface of the target deep-water sedimentary reservoir section and the quantitative parameters of the single-stage deep-water gravity flow channel development comprise the following steps: determining the top surface boundary and the bottom surface boundary of the target deep-water gravity flow reservoir section according to the seismic horizon interpretation results of the top surface and the bottom surface of the target deep-water sedimentary reservoir section; identifying the top surface boundary and the bottom surface boundary of each deep-water gravity flow channel in the target deep-water gravity flow reservoir section under the guidance of the deep-water sedimentary geological model and based on the seismic-geological interpretation results; performing grid processing on the target deep-water gravity flow reservoir section according to the set planar grid and vertical grid step to obtain the reservoir geological model of the target section; determining the slicing time interval according to the average interval velocity of the target deep-water sedimentary reservoir section and the vertical grid step of the reservoir geological model of the target section; dividing each deep-water gravity flow channel in the reservoir geological model of the target section according to the slicing time interval to determine the top surface boundary and the bottom surface boundary of the single-stage deep-water gravity flow channel, and obtaining the reservoir geological model of the target section after slicing.

3. The method of claim 2, wherein, The grid processing on the target deep-water gravity flow reservoir section according to the set planar grid and vertical grid step to obtain the reservoir geological model of the target section comprises the following steps: obtaining a geological modeling planar grid step and a plurality of vertical grid steps of different sizes to construct a plurality of initial reservoir geological models; determining the stratification coefficient information corresponding to the logging interpretation results of the target deep-water sedimentary reservoir section according to the logging data of the research area; determining the stratification coefficient information corresponding to each initial reservoir geological model; the stratification coefficient information comprises a maximum value, a minimum value, an average value, a median value, a mode value, and a model grid number; determining a correlation coefficient corresponding to each of the initial reservoir geological models and the well interpretation result; screening the target interval reservoir geological model according to the layering coefficient information corresponding to the well interpretation result, the layering coefficient information corresponding to each of the initial reservoir geological models and the correlation coefficient corresponding to each of the initial reservoir geological models and the well interpretation result.

4. The method of claim 2, wherein, Before the target deepwater gravity flow reservoir interval is gridded according to the set plane grid and vertical grid step, the method further comprises: obtaining quantitative parameters of single-stage deepwater gravity flow channel development corresponding to the geological conditions of the target deepwater sedimentary reservoir interval according to a preset deepwater sedimentary geological knowledge base; the deepwater sedimentary geological knowledge base comprises quantitative parameters of deepwater gravity flow channel development under different geological conditions; the quantitative parameters comprise width, thickness and width-thickness ratio of single-stage deepwater gravity flow channel development; obtaining average interval velocity of the target deepwater sedimentary reservoir interval; determining median values of thickness, width and width-thickness ratio of single-stage deepwater gravity flow channel development according to the quantitative parameters of single-stage deepwater gravity flow channel development; determining a time interval corresponding to single-stage deepwater gravity flow channel thickness according to the average interval velocity of the target deepwater sedimentary reservoir interval and the median values of thickness, width and width-thickness ratio of single-stage deepwater gravity flow channel development.

5. The method of claim 4, wherein, The quantitative parameters of the single-stage deepwater gravity flow channel further comprise curvature, wavelength and amplitude. Before the three-dimensional space simulation of the sliced target interval reservoir geological model is performed to obtain the three-dimensional flow line body of the deepwater gravity flow sandstone reservoir according to the azimuth information of the plurality of plane grid nodes of the main thread line and using the ordinary Kriging algorithm of the following formula, the method further comprises: performing outlier processing on the azimuth information of the plurality of plane grid nodes of each of the main thread lines based on the deepwater sedimentary geological model and taking the quantitative parameters of the single-stage deepwater gravity flow channel as constraint conditions.

6. The method according to any one of claims 1 to 5, characterized in that, The three-dimensional space simulation of the sliced target interval reservoir geological model is performed according to the azimuth information of the plurality of plane grid nodes of the main thread line and using a preset interpolation algorithm to obtain the three-dimensional flow line body of the deepwater gravity flow sandstone reservoir, comprising: The three-dimensional space simulation of the sliced target interval reservoir geological model is performed according to the azimuth information of the plurality of plane grid nodes of the main thread line and using the ordinary Kriging algorithm of the following formula to obtain the three-dimensional flow line body of the deepwater gravity flow sandstone reservoir: Where, γ E (h) represents two regionalized random variables z with a distance interval of h in any direction. xi and The variance of the increment, N(h), is the total number of regionalized sample point pairs when the distance interval variable is h. For spatial position x i The orientation information of the planar grid nodes at that location. For spatial position x i Orientation information of the planar mesh node at +h.

7. The method of claim 1, wherein, The sensitive seismic attribute determined according to the seismic attribute analysis is used to analyze each slice layer of the sliced target interval reservoir geological model to obtain an analysis result, and the planar distribution characteristics of the deepwater gravity flow channel are depicted according to the analysis result, and the main thread line of the single-stage deepwater gravity flow channel in each slice layer is tracked, comprising: The sensitive seismic attribute determined according to the seismic attribute analysis is used to analyze each slice layer of the sliced target interval reservoir geological model to obtain an analysis result; the sensitive seismic attribute comprises at least one single seismic attribute and / or at least one fused seismic attribute. According to the analysis result and the development of the deep-water gravity flow channel on the calibrated seismic profile, the planar distribution characteristics of the deep-water gravity flow channel are described; According to the planar distribution characteristics of the deep-water gravity flow channel, the main thread distribution range of the single-stage deep-water gravity flow channel in the deep-water gravity flow channel is tracked and described by taking the quantitative parameters of the single-stage deep-water gravity flow channel as a constraint condition, and the main thread of the single-stage deep-water gravity flow channel in each slice layer is obtained.

8. The method of claim 1, wherein, For each main thread, the azimuth information of a plurality of plane grid nodes of the corresponding slice layer of the post-slice target reservoir geological model through by the main thread is determined, including: For each main thread, the plurality of plane grid nodes of the corresponding slice layer of the post-slice target reservoir geological model through by the main thread are marked; For each plane grid node, the angle degree between the line segment part in the plane grid node through by the main thread and the north direction is calculated to obtain the azimuth information of the plane grid node.

9. A method for constructing a three-dimensional attribute parameter model of a deepwater gravity flow sandstone reservoir, characterized in that, It comprises: The deep-water gravity flow sandstone reservoir three-dimensional streamline body obtained by the deep-water gravity flow sandstone reservoir three-dimensional streamline body construction method according to any one of claims 1-8; The deep-water gravity flow sandstone reservoir three-dimensional attribute parameter model is constructed by taking the deep-water gravity flow sandstone reservoir three-dimensional streamline body as a constraint condition, wherein the deep-water gravity flow sandstone reservoir three-dimensional attribute parameter model is a three-dimensional lithofacies parameter model and / or a three-dimensional reservoir physical property parameter model.

10. A three-dimensional streamline body construction device for deepwater gravity flow sandstone reservoirs, characterized by, It comprises: The seismic interpretation module is configured to determine seismic horizon interpretation results of top and bottom surfaces of a target deep-water sedimentary reservoir section according to well logging data and seismic data of a study area; The hierarchical slicing module is configured to perform gridding processing according to the seismic horizon interpretation results of the top and bottom surfaces of the target deep-water sedimentary reservoir section and the obtained quantitative parameters of single-stage deep-water gravity flow channel development, construct a target reservoir geological model, and perform hierarchical seismic slicing to obtain a post-slice target reservoir geological model; The main thread determination module is configured to analyze each slice layer of the post-slice target reservoir geological model according to sensitive seismic attributes determined by seismic attribute analysis, obtain an analysis result, and describe planar distribution characteristics of a deep-water gravity flow channel according to the analysis result to track main threads of the deep-water gravity flow channel in each slice layer. The node azimuth determination module is configured to determine, for each main thread, azimuth information of a plurality of plane grid nodes of a corresponding slice layer of the post-slice target reservoir geological model through by the main thread. The streamline body model construction module is configured to perform three-dimensional space simulation on the post-slice target reservoir geological model by using a preset interpolation algorithm according to the azimuth information of the plurality of plane grid nodes of the main thread to obtain a deep-water gravity flow sandstone reservoir three-dimensional streamline body.

11. A device for constructing a three-dimensional attribute parameter model of a deepwater gravity flow sandstone reservoir, characterized in that, The seismic interpretation module is configured to determine seismic horizon interpretation results of top and bottom surfaces of a target deep-water sedimentary reservoir section according to well logging data and seismic data of a study area; ​ The hierarchical slicing module is configured to perform gridding processing according to the seismic horizon interpretation results of the top surface and the bottom surface of the target deep-water sedimentary reservoir section and the obtained quantitative parameters of the single-stage deep-water gravity flow channel development, construct a reservoir geological model of the target section, and perform hierarchical seismic slicing to obtain the reservoir geological model of the target section after slicing; The main thread line determination module is configured to analyze each slice layer of the reservoir geological model of the target section after slicing according to sensitive seismic attributes determined through seismic attribute analysis, obtain an analysis result, and depict the planar distribution characteristics of the deep-water gravity flow channel and track the main thread line of the deep-water gravity flow channel in each slice layer according to the analysis result; The node direction determination module is configured to determine the direction information of a plurality of planar grid nodes through which the main thread line passes the corresponding slice layer of the reservoir geological model of the target section after slicing for each main thread line; The streamline body model construction module is configured to perform three-dimensional space simulation on the reservoir geological model of the target section after slicing according to the direction information of the plurality of planar grid nodes of the main thread line by using a preset interpolation algorithm to obtain a deep-water gravity flow sandstone reservoir three-dimensional streamline body. The attribute parameter model construction module is configured to construct a deep-water gravity flow sandstone reservoir three-dimensional attribute parameter model by taking the deep-water gravity flow sandstone reservoir three-dimensional streamline body as a constraint condition; and the deep-water gravity flow sandstone reservoir three-dimensional attribute parameter model is a three-dimensional lithofacies parameter model and / or a three-dimensional reservoir physical property parameter model.

12. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the deep-water gravity flow sandstone reservoir three-dimensional streamline body construction method of any one of claims 1-8 or the deep-water gravity flow sandstone reservoir three-dimensional attribute parameter model construction method of claim 9.

13. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that, The program is executed by the processor to implement the deep-water gravity flow sandstone reservoir three-dimensional streamline body construction method of any one of claims 1-8 or the deep-water gravity flow sandstone reservoir three-dimensional attribute parameter model construction method of claim 9.

14. A computer program product comprising instructions which, when the computer program product is run on computer equipment, cause the computer equipment to perform the deep-water gravity flow sandstone reservoir three-dimensional streamline body construction method of any one of claims 1-8 or the deep-water gravity flow sandstone reservoir three-dimensional attribute parameter model construction method of claim 9.

Images