Method and device for determining connectivity of fractured-vuggy reservoir

Abstract

The invention provides a method and device for determining the connectivity of a fracture-vug reservoir, and the method comprises the steps: carrying out the sample point extraction of a target block of the fracture-vug reservoir, and obtaining a target point cloud; constructing a plurality of connection units according to the influence domain radius of each node in the target point cloud, and performing flow simulation on the plurality of connection units to form a connection unit system of the fractured-vuggy reservoir; characteristic parameters of each connection unit in the connection unit system are calculated, and the characteristic parameters comprise conductivity and communication volume; inversion is carried out through the characteristic parameters of the fracture-vug type oil reservoir connection unit, and a data driving model of the fracture-vug type oil reservoir is obtained; and performing path tracking on the data-driven model to determine the connectivity of the inter-well communication path in the target block. According to the scheme, the problem that only the connectivity of the fracture-vug type reservoir can be evaluated qualitatively in the prior art is solved, the technical effect of quantitatively evaluating the connectivity of the fracture-vug type reservoir is achieved, and oil and gas development is guided.

Description

Technical Field

[0001] The present application belongs to the field of geological exploration technology, and in particular, relates to a method and device for determining the connectivity of fracture-cavity oil reservoirs. Background Art

[0002] The existing determination of the connectivity of fracture-cavity oil reservoirs is generally based on the sliding window traversal to calculate the maximum fluctuation change amplitude characteristic parameters of the production data of the production wells within a period of time after the water injection of the fracture-cavity oil reservoir; then, the multi-fractal spectrum is used to quantify the characteristic parameters of the degree of change of the water production and oil production production indicators of each production well after the water injection of the fracture-cavity oil reservoir; then, based on the calculated maximum fluctuation change amplitude characteristic parameters of the production data, and the characteristic parameters of the degree of change of oil production and water production, and compared with the preset threshold range of each parameter, the inter-well connectivity mode of the fracture-cavity oil reservoir is automatically determined as one of the inter-well fracture connectivity, inter-well cave connectivity and inter-well fracture-cavity composite connectivity. Alternatively, by reading the oil well production time series data, the oil well production time series data is analyzed for similarity to output the inter-well connectivity relationship.

[0003] However, the existing methods for determining the connectivity of fracture-vuggy reservoirs all have problems such as long calculation time, many required parameters, and difficulty in obtaining them. They cannot guarantee the accuracy of the prediction results and can only qualitatively evaluate the connectivity of fracture-vuggy reservoirs, but cannot quantitatively evaluate the degree of connectivity of fracture-vuggy reservoirs. Summary of the invention

[0004] The purpose of the present application is to provide a method and device for determining the connectivity of a fracture-vuggy oil reservoir, which can achieve the purpose of quantitatively evaluating the connectivity of a fracture-vuggy reservoir.

[0005] The present application provides a method and device for determining the connectivity of a fracture-cavity reservoir, which is implemented as follows:

[0006] A method for determining connectivity of a fracture-vuggy reservoir, the method comprising:

[0007] Extracting sample points from a target block of a fracture-cavity oil reservoir to restore the fracture-cavity body morphology of the fracture-cavity oil reservoir and obtain a target point cloud;

[0008] Constructing a plurality of connection units based on the influence domain radius of each node in the target point cloud, and performing flow simulation on the plurality of connection units to form a connection unit system of the fracture-vuggy oil reservoir;

[0009] Calculating characteristic parameters of each connection unit in the connection unit system, wherein the characteristic parameters include: conductivity characterizing the fluid permeability of the connection unit, and connected volume characterizing the geometric properties of the connection unit;

[0010] Inverting the characteristic parameters of the fracture-cavity oil reservoir connection unit to obtain a data-driven model of the fracture-cavity oil reservoir;

[0011] Path tracing is performed on the data driven model to determine the connectivity of the inter-well communication paths in the target block.

[0012] In one embodiment, calculating the characteristic parameters of each linking unit in the linking unit system includes:

[0013] The conductivity between the first node and the second node is calculated according to the following formula as the conductivity corresponding to the connection unit formed by the first node and the second node:

[0014]

[0015]

[0016] Among them, T ij represents the conductivity between nodes i and j when i is the central node and first node j is the second node, k ij represents the permeability between node i and node j, λ ij represents the flow coefficient between node i and node j, α is the unit conversion coefficient, V i represents the control volume of node i, represents the generalized difference coefficient of the mth row of node j in the influence domain of node i, m represents the row number of the generalized difference coefficient matrix, k ro represents the relative permeability of the oil phase between nodes i and j, k rw represents the relative permeability of the water phase between nodes i and j, μ o represents the oil phase viscosity between nodes i and j, μ w represents the viscosity of the water phase between nodes i and j.

[0017] In one embodiment, calculating the characteristic parameters of each linking unit in the linking unit system includes:

[0018] The connected volume of each connected unit and the connected pore volume of each connected unit are calculated according to the following formula:

[0019]

[0020]

[0021] Among them, V p,ij represents the connected pore volume of the connected unit formed by nodes i and j, V ij represents the connected volume of the connection unit formed by node i and node j, represents the average porosity of the connected unit formed by nodes i and j, represents the average thickness of the connection unit formed by nodes i and j, L ij represents the distance between node i and node j, G ij represents the geometric factor of the connection unit formed by nodes i and j, V R represents the total volume of the reservoir, and p represents the pore volume;

[0022] The volume of connected pores in the control area of ​​each node is calculated according to the following formula:

[0023]

[0024]

[0025] Among them, V p,i represents the volume of connected pores in the control area of ​​node i, represents the average porosity in the control area of ​​node i, represents the average thickness in the control area of ​​node i, V i represents the control volume of node i, V p,j represents the volume of connected pores in the control region of node j, represents the average porosity in the control region of node j, represents the average thickness in the control area of ​​node j, V j represents the control volume of node j, Indicates the average thickness of the current oil layer.

[0026] In one embodiment, performing path tracing on the data driven model to determine the connectivity of the well-to-well communication paths in the target block includes:

[0027] The splitting coefficient of the connection unit is calculated according to the following formula:

[0028]

[0029] in, represents the splitting coefficient of the connection unit formed by node i and node j, node j represents the injection well, n c ′ represents the number of well points connected to node j and with lower pressure than node j, represents the total outflow of liquid from node j, n represents the nth time step, k represents the kth path, and q represents the liquid flow of the connected unit;

[0030] The splitting coefficient and connected volume of the connection unit are taken as the connectivity of the inter-well connection path formed by the connection unit.

[0031] In one embodiment, sample points are extracted from a target block of a fracture-vuggy oil reservoir to restore the fracture-vuggy body morphology of the fracture-vuggy oil reservoir and obtain a target point cloud, including:

[0032] Acquire data from the external environment of the target block through a variety of preset sensors;

[0033] Classify and preprocess the data according to preset rules to reduce the noise in the data;

[0034] Based on the denoised data, image segmentation and edge detection are performed to extract the feature information of each sample point, where the feature information includes: shape, size, and depth;

[0035] According to the characteristic information of each sample point, the target point cloud with multiple resolutions and densities is dynamically generated.

[0036] In one embodiment, after inverting the characteristic parameters of the fracture-cavity reservoir connection unit to obtain the data-driven model of the fracture-cavity reservoir, the method further includes:

[0037] Establishing a history matching objective function for the data driven model;

[0038] Based on the optimal principle, the data-driven model of the fracture-vuggy reservoir is historically matched, and iteratively obtained to determine the local minimum value of the historical matching objective function;

[0039] Determine the model parameters of the data-driven model when the history matching objective function takes a local minimum value to obtain an optimized data-driven model.

[0040] In one embodiment, the history matching objective function is:

[0041]

[0042] m low ≤m≤m up

[0043]

[0044] Among them, O(m) is the historical matching objective function, m is the vector composed of connected volume, conductivity and phase permeability parameters, G(m) is the vector composed of production dynamic data generated by data-driven model calculation, d obs represents the actual observed value, C d represents the dynamic covariance matrix, m low The vector representing the upper limit of the characteristic parameters, m up The vector representing the lower limit of the characteristic parameters, V R represents the total effective pore volume of the data-driven model, VR,low represents the lower limit of the total effective pore volume of the data-driven model, V R,up represents the upper limit of the total effective pore volume of the data-driven model, R represents the reservoir, and N w represents the number of well nodes, Represents the initial connection volume of the connection unit.

[0045] A device for determining connectivity of a fracture-cavity reservoir, comprising:

[0046] An extraction module is used to extract sample points from a target block of a fracture-cavity oil reservoir to restore the fracture-cavity body morphology of the fracture-cavity oil reservoir and obtain a target point cloud;

[0047] A construction module, used to construct a plurality of connection units according to the influence domain radius of each node in the target point cloud, and perform flow simulation on the plurality of connection units to form a connection unit system of the fracture-vuggy oil reservoir;

[0048] A calculation module, used to calculate characteristic parameters of each connection unit in the connection unit system, wherein the characteristic parameters include: conductivity characterizing the fluid permeability of the connection unit, and a connected volume characterizing the geometric properties of the connection unit;

[0049] An inversion module, used for inverting characteristic parameters of the fracture-cavity oil reservoir connection unit to obtain a data-driven model of the fracture-cavity oil reservoir;

[0050] The determination module is used to perform path tracing on the data-driven model to determine the connectivity of the inter-well connection paths in the target block.

[0051] An electronic device comprises a processor and a memory for storing instructions executable by the processor, wherein the steps of the above method are implemented when the processor executes the instructions.

[0052] A computer-readable storage medium stores a computer program / instruction, which implements the steps of the above method when executed by a processor.

[0053] The method for determining the connectivity of fracture-cavity reservoirs provided in this application discretizes the target block of the fracture-cavity reservoir into a series of spatial nodes, then constructs a fracture-cavity reservoir connection unit system in combination with the node influence domain, and then applies the path tracking algorithm to the established connection unit system to find the well-to-well connection path, so that the dynamic connectivity between any wells and at any time can be quantitatively calculated to accurately determine the spatial development changes of the fracture-cavity reservoir. The above scheme solves the problem that the existing connectivity of fracture-cavity reservoirs can only be qualitatively evaluated, and achieves the technical effect of quantitatively evaluating the connectivity of fracture-cavity reservoirs to guide oil and gas development. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0055] Figure 1 is a schematic diagram of a point cloud provided by this application;

[0056] Figure 2 It is a logical schematic diagram of the processing flow of the connection unit system provided by the present application;

[0057] Figure 3 It is a schematic diagram of the connection unit system provided by the present application without encryption;

[0058] Figure 4 It is a schematic diagram of an encrypted connection unit system provided by the present application;

[0059] Figure 5 It is a schematic diagram of each connection unit in the connection unit system provided by this application and a schematic diagram of model characterization parameters;

[0060] Figure 6 is a method flow chart of an embodiment of a method for determining connectivity of fracture-cavity reservoirs provided by the present application;

[0061] Figure 7 It is a hardware structure block diagram of an electronic device for a method for determining connectivity of a fracture-cavity reservoir provided in the present application;

[0062] Figure 8 It is a schematic diagram of the module structure of an embodiment of a device for determining connectivity of fracture-vuggy oil reservoirs provided in the present application. DETAILED DESCRIPTION

[0063] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0064] In this case, a quantitative identification method of three-dimensional connectivity of fracture-cavity reservoirs based on point cloud technology is provided. The grid-based fracture-cavity reservoir geological model is discretized into a series of spatial nodes, and then the fracture-cavity reservoir connection unit system is constructed by combining the node influence domain and angle. Then, the path tracing algorithm is applied in the established network system to find the well-to-well connection path, so as to quantitatively calculate the dynamic connectivity between any wells and at any time, so as to achieve a clear understanding of the development and change law of the reservoir space of the fracture-cavity reservoir. Furthermore, the reservoir geological data and production dynamic data can be used to fit the production capacity prediction equation, so as to plan development measures and adjust the preparation of the plan to effectively guide the development of the oil field.

[0065] Specifically, a method for quantitatively identifying the connectivity of a fracture-vuggy reservoir in three dimensions is provided, which may include the following steps:

[0066] S1: Adaptive point cloud technology is used to extract points from the target block, reproduce and restore the fracture body shape, and select feature data.

[0067] Specifically, it may include:

[0068] Data collection: using various sensors (e.g., cameras, lidar, CT scanners, etc.) to obtain data from the external environment;

[0069] Data preprocessing: Based on data collection, point cloud data is classified according to certain rules, and data preprocessing is performed using noise reduction, filtering, registration, and simplification to effectively reduce noise and harmful information in the data;

[0070] Feature extraction: extract the shape, size, depth and other feature information of the object from the data through methods such as image segmentation and edge detection;

[0071] Point cloud generation: Based on the extracted feature information, dynamically generate Figure 1 Point clouds of different resolutions and densities are shown, and the quality of the generated point clouds is evaluated and optimized.

[0072] S2: The point cloud is simplified by CEM (Cross Entropy Method), and a series of one-dimensional connection units are constructed based on the influence domain radius of the node. The flow simulation analysis of these connection units is performed to obtain the connection unit system.

[0073] Specifically, Figure 2As shown, in the connection unit system, the calculation domain formed by the node system and the node influence domain can be used to construct the connection unit between the nodes (e1, e2, e3 and e4), and then the characteristic parameters of the connection unit are characterized. For example, e1 and e2 form a connection unit. The characteristic parameters of the connection unit may include conductivity and connection volume, where conductivity is the flow rate per unit pressure difference and connection volume is the material basis for connectivity.

[0074] S3: Seam mesh encryption:

[0075] Furthermore, after the connection unit system is constructed, based on the well nodes, auxiliary points are manually added for irregular well networks, water bodies, etc., or some well network encryption methods (for example, setting virtual well points, directly connected fracture-hole encryption) are adopted to encrypt the connection unit system, so that the obtained connection unit system is more in line with the actual production needs of the reservoir model. Specifically, Figure 3 As shown in FIG. 1 , it is a connection unit system that is not encrypted, including nodes: I1, P1, P2, P3 and P4, such as Figure 4 As shown, the encrypted connection unit system is formed after adding nodes.

[0076] S4: Model characteristic parameter solution:

[0077] In order to calculate the seepage transport equation of the connected unit system, it is necessary to calculate the characteristic parameters that characterize the connected unit. Specifically, the characteristic parameter values ​​can be calculated and solved. First, the conductivity T ij The fluid permeability of the connected unit is characterized by the connected volume V p,ij Characterize the geometric properties of the connection unit, defined as Figure 5 The model characterization parameters shown in the figure, where the small circles (1, 2, 3, 4, i) represent well nodes, the dotted circles represent the connected volume within the range centered on the well point, and the elliptical circles represent the connected volume between wells. The conductivity is the volume flow rate of the fluid in the seepage channel under unit pressure difference, and the unit is m 3 / d / MPa, the connected volume is the capacity of the unit between two nodes to store fluid, the unit is m 3 .

[0078] 1) Find the conductivity:

[0079] Assuming that node i and node j are the central nodes, the conductivity between nodes (i, j) is calculated as follows:

[0080]

[0081] Among them, k ij represents the permeability, λ ij represents the conduction coefficient, k ijIt represents the permeability in mD, α is the unit conversion coefficient, and its value is 0.0864; V i represents the control volume of node i; T ij It represents the connectivity conductivity between nodes (i, j) when i is the central node, T ji It is expressed as the connectivity conductivity between nodes (i, j) with j as the central node.

[0082] Considering that in multiphase flow or multicomponent models, the corresponding conductivity coefficient should be added with phase permeability terms or component concentration terms. For example, for two-phase flow, its conductivity can be expressed as:

[0083]

[0084] Among them, the total fluid flow rate

[0085] Among them, k ro represents the relative permeability of the oil phase, k rw Represents the relative permeability of the water phase, μ o Indicates the oil phase viscosity, μ w Indicates the viscosity of the water phase in mPa / s.

[0086] 2) Find the connected volume:

[0087] Through the proportional relationship between the connected geometric factor and the volume, the sum of the control volumes of all connected units is equal to the total volume of the reservoir, and the connected volume of the connected unit can be calculated:

[0088]

[0089] Among them, V ij represents the connected volume of the connected unit formed by node i and node j, G ij Indicates the geometric factor of the connection unit, in m; L ij It is the distance between the centers of two grids, in m. From this result, it can be seen that the calculation of the connected unit control volume depends only on the overall volume of the reservoir, but not on the physical properties of the reservoir.

[0090] Furthermore, the controlled pore volume of the node can be expressed as:

[0091]

[0092] Among them, V p,i represents the volume of connected pores in the control area of ​​node i, represents the average porosity in the control area of ​​node i, represents the average thickness in the control area of ​​node i, V i represents the control volume of node i, Represents the average thickness of the current oil layer, i=1,2,3...,N.

[0093] The volume of the interconnected pores of the connected unit is:

[0094]

[0095] Among them, V p,ij represents the connected pore volume of the connection unit formed by nodes i and j, It is expressed as the average porosity of the connected unit (i, j), It is expressed as the average thickness of the connected unit (i, j).

[0096] That is, the conductivity and the connected volume of the connection unit are obtained by the above-mentioned manner and method, which are used as characteristic parameters to characterize the connection unit. The characteristic parameters of the connection unit of the fracture-cavity reservoir are inverted to obtain the data-driven model of the fracture-cavity reservoir.

[0097] Among them, in the process of inversion, a historical fitting objective function can be established. Specifically, based on Bayesian theory and taking reservoir numerical simulation as the research object, dynamic parameters that are mathematically consistent with the actual situation, can reflect reservoir characteristics, and are closely related to reservoir characteristics can be constructed. At the same time, in view of the special reservoir characteristics of fracture-cavity reservoirs, oil and water wells may communicate with the surrounding marginal and bottom water. Therefore, when considering the constant volume water body, the water body volume can also be used as an important influencing factor, and can also be used as a parameter that needs to be adjusted in the fitting process. Based on this, the following set of equations can be established as the historical fitting objective function:

[0098]

[0099] Among them, O(m) is the mathematical function of history fitting, m is the vector composed of inversion parameters, and is the vector composed of connected volume, conductivity and phase permeability parameters; d obs represents the actual observation value (i.e., the observation value of the real model in the model study); G(m) represents the vector of production dynamic data generated by the data-driven model calculation; C d represents the dynamic covariance matrix, V R represents the total effective pore volume of the model, m3, V R,low represents the lower limit of the total effective pore volume of the model, V R,up represents the upper limit of the total effective pore volume of the model; m low The vector representing the upper limit of the characteristic parameters, m up A vector representing the lower bounds of the feature parameters.

[0100] Based on the optimal principle, the data-driven model of fracture-vuggy reservoirs is historically fitted and iteratively obtained to obtain the local minimum of the objective function and the corresponding model parameters when the local minimum is met, thereby obtaining the optimized data-driven model of fracture-vuggy reservoirs.

[0101] Using the existing fracture-cavity reservoir connectivity data fitting method, a mathematical model for fracture-cavity reservoir connectivity data fitting was established. According to the optimal principle, historical fitting was performed and iteratively obtained to obtain its local minimum and its corresponding model parameters. However, in the calculation of O, m was not explicitly included, so it was very difficult to obtain the actual slope through analysis.

[0102] When optimizing the data-driven model of fracture-cavity reservoirs, the SPSA algorithm can be used to randomly interfere with the model at the same time, and the desired optimization direction can be obtained in each iteration step. Among them, the general formula of the SPSA approximate gradient method considers the first-order Taylor expansion and the expected value. Under certain conditions, the SPSA gradient based on the covariance matrix of the control variable is still upward, and the expected value is the product of the covariance matrix and the actual gradient.

[0103] S5: Path tracing calculation:

[0104] The data-driven model of the fracture-vuggy reservoir can reflect the connection relationship between well nodes, that is, the connectivity of the connection unit. Specifically, the dynamic connection volume of the connection unit and the splitting coefficient of the connection unit can be calculated by path tracing. The splitting coefficient can be calculated according to the following formula:

[0105]

[0106] in, represents the splitting coefficient, n c ′ represents the number of well points connected to injection well j and with a pressure lower than that of injection well j, represents the liquid outflow from injection well j, n represents the nth time step, and q represents the liquid flow of the connected unit.

[0107] After calculating the splitting coefficient of each connection unit, starting from the concept of "path", the water injection well is regarded as the "source", the oil production well is regarded as the "sink", and the series of directed sequences composed of the different well points passed by the "source" and "sink" are regarded as the path. That is, the path represents various situations of well points from high pressure (injection well) to low pressure (oil production well).

[0108] In the above example, according to the quantitative identification method of 3D connectivity of fracture-cavity reservoirs based on point cloud technology, the connectivity paths of different stages of 3D space of fracture-cavity reservoirs can be identified and the connectivity quantitative judgment can be given. Furthermore, combined with the actual development status of the oil field, case tests and actual block applications were carried out. The degree of approximation between the 3D connectivity model of fracture-cavity reservoirs studied and the actual model is as high as 80%, the dynamic simulation efficiency is high, the accuracy is accurate, and the fitting effect is as high as 90% compared with the actual model.

[0109] For a specific example, the total number of grids in the block is 990,000, the total oil production of the well group is 47,600 tons, and there are elastic production, well shut-in, and water injection for oil recovery in different time periods. In the CEM model, the number of nodes is 536, the number of injection and production wells is 1, the number of connections is 11,000, the average porosity is 0.034, the initial water saturation is 0.3, the reservoir pore volume is 310.16, and the 104m 3 , initial reservoir average pressure 88MPa, rock compression coefficient 0.00014, MPa -1 , oil phase compression coefficient 0.0005, MPa -1 , water phase compressibility coefficient 0.000426, unit is MPa -1 , oil phase viscosity 0.65, unit is mPa·s, oil phase volume coefficient 1.51, water phase viscosity 0.233, unit is mPa·s, water phase volume coefficient 1.0438. Taking the dynamic indicators such as block cumulative oil production, water content, single well oil production rate, water content, bottom hole flowing pressure as historical fitting observation values, and the conductivity and connected volume of each connected unit as the characteristic parameters that need to be corrected, dynamic fitting is performed, and the fitting accuracy can reach more than 90%.

[0110] Figure 6 It is a method flow chart of an embodiment of the method for determining the connectivity of fracture-cavity reservoirs provided by the present application. Although the present application provides method operation steps or device structures as shown in the following embodiments or drawings, more or fewer operation steps or module units may be included in the method or device based on routine or without creative labor. In the steps or structures that do not logically have a necessary causal relationship, the execution order of these steps or the module structure of the device is not limited to the execution order or module structure described in the embodiments of the present application and shown in the drawings. When the method or module structure is applied in an actual device or terminal product, it can be connected according to the method or module structure shown in the embodiments or drawings for sequential execution or parallel execution (for example, a parallel processor or a multi-threaded processing environment, or even a distributed processing environment).

[0111] Specifically, Figure 6 As shown, the above-mentioned method for determining the connectivity of fracture-cavity reservoirs may include the following steps:

[0112] Step 601: extracting sample points from a target block of a fracture-cavity oil reservoir to restore the fracture-cavity body morphology of the fracture-cavity oil reservoir and obtain a target point cloud;

[0113] During implementation, data from the external environment of the target block can be acquired through a variety of preset sensors; the data can be classified and preprocessed according to preset rules to reduce noise in the data; image segmentation and edge detection can be performed based on the denoised data to extract feature information of each sample point, where the feature information may include but is not limited to: shape, size, depth; and target point clouds of multiple resolutions and densities can be dynamically generated based on the feature information of each sample point.

[0114] Step 602: constructing a plurality of connection units based on the influence domain radius of each node in the target point cloud, and performing flow simulation on the plurality of connection units to form a connection unit system of the fracture-vuggy oil reservoir;

[0115] Step 603: Calculating characteristic parameters of each connection unit in the connection unit system, wherein the characteristic parameters include: conductivity characterizing the fluid permeability of the connection unit, and connected volume characterizing the geometric properties of the connection unit;

[0116] Specific:

[0117] 1) The conductivity between the first node and the second node can be calculated according to the following formula as the conductivity corresponding to the connection unit formed by the first node and the second node:

[0118]

[0119]

[0120] Among them, T ij represents the conductivity between nodes i and j when i is the central node and first node j is the second node, k ij represents the permeability between node i and node j, λ ij represents the conduction coefficient between node i and node j, α is the unit conversion coefficient, V i represents the control volume of node i, the generalized difference coefficient of the mth row of node j in the node i's influence domain, m represents the row number of the generalized difference coefficient matrix, k ro represents the relative permeability of the oil phase between nodes i and j, k rw represents the relative permeability of the water phase between nodes i and j, μ o represents the oil phase viscosity between nodes i and j, μ w represents the viscosity of the water phase between nodes i and j.

[0121] 2) The connected volume of each connected unit and the connected pore volume of each connected unit can be calculated according to the following formula:

[0122]

[0123]

[0124] Among them, V p,ij represents the connected pore volume of the connected unit formed by nodes i and j, V ij represents the connected volume of the connection unit formed by node i and node j, represents the average porosity of the connected unit formed by nodes i and j, represents the average thickness of the connection unit formed by nodes i and j, L ij represents the distance between node i and node j, G ij represents the geometric factor of the connection unit formed by nodes i and j, V R represents the total volume of the reservoir, and p represents the consideration of pores;

[0125] 3) The volume of connected pores within the control area of ​​each node can be calculated according to the following formula:

[0126]

[0127]

[0128] Among them, V p,i represents the volume of connected pores in the control area of ​​node i, represents the average porosity in the control area of ​​node i, represents the average thickness in the control area of ​​node i, V i represents the control volume of node i, in units of 10 4 m 3 , V p,j represents the volume of connected pores in the control region of node j, represents the average porosity in the control region of node j, represents the average thickness in the control area of ​​node j, V j represents the control volume of node j, in units of 10 4 m 3 , the subscript p represents the volume of the pores considered, the unit is MPa, Indicates the average thickness of the current oil layer, in meters.

[0129] Step 604: Invert the characteristic parameters of the fracture-cavity oil reservoir connection unit to obtain a data-driven model of the fracture-cavity oil reservoir;

[0130] Step 605: Perform path tracing on the data-driven model to determine the connectivity of the inter-well communication paths in the target block.

[0131] In one embodiment, the data-driven model is path-traced to determine the connectivity of the well-to-well connection paths in the target block, which may be: the splitting coefficient of the connection unit is calculated according to the following formula:

[0132]

[0133] in, represents the splitting coefficient of the connection unit formed by node i and node j, node j represents the injection well, n c ′ represents the number of well points connected to node j and with lower pressure than node j, represents the total outflow of liquid from node j, n represents the nth time step, k represents the kth path, and q represents the liquid flow of the connected unit;

[0134] Then, the splitting coefficient and the connected volume of the connection unit are used as the connectivity of the inter-well communication path formed by the connection unit.

[0135] In order to make the obtained data-driven model more accurate, it can be iteratively optimized. Specifically, after the data-driven model of the fracture-cavity oil reservoir is obtained by inverting the characteristic parameters of the connection unit of the fracture-cavity oil reservoir, a historical fitting objective function can be established for the data-driven model; then, based on the optimal principle, the data-driven model of the fracture-cavity oil reservoir is historically fitted and iteratively obtained to determine the local minimum of the historical fitting objective function; the model parameters of the data-driven model are determined when the historical fitting objective function takes the local minimum to obtain the optimized data-driven model.

[0136] The above history matching objective function can be:

[0137]

[0138] m low ≤m≤m up

[0139]

[0140] Among them, O(m) is the historical matching objective function, m is the vector composed of connected volume, conductivity and phase permeability parameters, G(m) is the vector composed of production dynamic data generated by data-driven model calculation, d obs represents the actual observed value, C d represents the dynamic covariance matrix, m low The vector representing the upper limit of the characteristic parameters, mup The vector representing the lower limit of the characteristic parameters, V R represents the total effective pore volume of the data-driven model, V R,low represents the lower limit of the total effective pore volume of the data-driven model, V R,up represents the upper limit of the total effective pore volume of the data-driven model, R represents the reservoir, and N w represents the number of well nodes, Represents the initial connection volume of the connection unit.

[0141] The method embodiments provided in the above embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on an electronic device as an example, Figure 7 1 is a hardware structure block diagram of an electronic device for determining the connectivity of a fracture-cavity reservoir provided in this application. Figure 7 As shown, the electronic device 10 may include one or more (only one is shown in the figure) processors 02 (the processor 02 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 04 for storing data, and a transmission module 06 for communication functions. It can be understood by those skilled in the art that Figure 7 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 7 More or fewer components as shown, or with Figure 7 Different configurations are shown.

[0142] The memory 04 can be used to store software programs and modules of application software, such as program instructions / modules corresponding to the method for determining the connectivity of fracture-hole type oil reservoirs in the embodiment of the present application. The processor 02 executes various functional applications and data processing by running the software programs and modules stored in the memory 04, that is, the method for determining the connectivity of fracture-hole type oil reservoirs of the above-mentioned application is realized. The memory 04 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 04 may further include a memory remotely arranged relative to the processor 02, and these remote memories may be connected to the electronic device 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0143] The transmission module 06 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the electronic device 10. In one example, the transmission module 06 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission module 06 can be a radio frequency (Radio Frequency, RF) module, which is used to communicate with the Internet wirelessly.

[0144] At the software level, the above-mentioned device for determining the connectivity of fracture-cavity reservoirs can be as follows: Figure 8 As shown, including:

[0145] The extraction module 801 is used to extract sample points from the target block of the fracture-vuggy oil reservoir to restore the fracture-vuggy body morphology of the fracture-vuggy oil reservoir and obtain a target point cloud;

[0146] A construction module 802 is used to construct a plurality of connection units based on the influence domain radius of each node in the target point cloud, and perform flow simulation on the plurality of connection units to form a connection unit system of the fracture-vuggy oil reservoir;

[0147] The calculation module 803 is used to calculate the characteristic parameters of each connection unit in the connection unit system, wherein the characteristic parameters include: conductivity characterizing the fluid permeability of the connection unit, and connected volume characterizing the geometric properties of the connection unit;

[0148] An inversion module 804 is used to perform inversion through characteristic parameters of the fracture-cavity oil reservoir connection unit to obtain a data-driven model of the fracture-cavity oil reservoir;

[0149] The determination module 805 is used to perform path tracing on the data driven model to determine the connectivity of the inter-well connection paths in the target block.

[0150] The embodiment of the present application also provides a specific implementation of an electronic device capable of implementing all the steps in the method for determining the connectivity of fracture-hole type reservoirs in the above embodiment, wherein the electronic device specifically includes the following contents: a processor, a memory, a communication interface, and a bus; wherein the processor, the memory, and the communication interface communicate with each other through the bus; the processor is used to call a computer program in the memory, and when the processor executes the computer program, all the steps in the method for determining the connectivity of fracture-hole type reservoirs in the above embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0151] Step 1: extracting sample points from the target block of the fracture-cavity oil reservoir to restore the fracture-cavity body morphology of the fracture-cavity oil reservoir and obtain a target point cloud;

[0152] Step 2: constructing a plurality of connection units based on the influence domain radius of each node in the target point cloud, and performing flow simulation on the plurality of connection units to form a connection unit system of the fracture-vuggy oil reservoir;

[0153] Step 3: Calculate characteristic parameters of each connection unit in the connection unit system, wherein the characteristic parameters include: conductivity characterizing the fluid permeability of the connection unit, and connected volume characterizing the geometric properties of the connection unit;

[0154] Step 4: Invert the characteristic parameters of the connection unit of the fracture-cavity oil reservoir to obtain a data-driven model of the fracture-cavity oil reservoir;

[0155] Step 5: Perform path tracing on the data-driven model to determine the connectivity of the inter-well connection paths in the target block.

[0156] The embodiment of the present application also provides a computer-readable storage medium capable of implementing all the steps in the method for determining the connectivity of fracture-cavity reservoirs in the above embodiment. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, all the steps in the method for determining the connectivity of fracture-cavity reservoirs in the above embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0157] Step 1: extracting sample points from the target block of the fracture-cavity oil reservoir to restore the fracture-cavity body morphology of the fracture-cavity oil reservoir and obtain a target point cloud;

[0158] Step 2: constructing a plurality of connection units based on the influence domain radius of each node in the target point cloud, and performing flow simulation on the plurality of connection units to form a connection unit system of the fracture-vuggy oil reservoir;

[0159] Step 3: Calculate characteristic parameters of each connection unit in the connection unit system, wherein the characteristic parameters include: conductivity characterizing the fluid permeability of the connection unit, and connected volume characterizing the geometric properties of the connection unit;

[0160] Step 4: Invert the characteristic parameters of the connection unit of the fracture-cavity oil reservoir to obtain a data-driven model of the fracture-cavity oil reservoir;

[0161] Step 5: Perform path tracing on the data-driven model to determine the connectivity of the inter-well connection paths in the target block.

[0162] From the above description, it can be seen that the embodiment of the present application discretizes the target block of the fracture-hole reservoir into a series of spatial nodes, and then constructs a fracture-hole reservoir connection unit system in combination with the node influence domain, and then applies the path tracking algorithm in the established connection unit system to find the well-to-well connection path, so that the dynamic connectivity between any wells and at any time can be quantitatively calculated to accurately determine the spatial development changes of the fracture-hole reservoir. The above scheme solves the problem that the existing connectivity of the fracture-hole reservoir can only be qualitatively evaluated, and achieves the technical effect of quantitatively evaluating the connectivity of the fracture-hole reservoir to guide oil and gas development.

[0163] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the hardware + program embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0164] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0165] Although the present application provides method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-creative labor. The order of steps listed in the embodiments is only one way of executing the order of many steps and does not represent the only execution order. When the actual device or client product is executed, it can be executed in the order of the method shown in the embodiments or the drawings or in parallel (for example, in a parallel processor or multi-threaded processing environment).

[0166] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a vehicle-mounted human-computer interaction device, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0167] Although the present specification embodiment provides the method operation steps as described in the embodiment or flow chart, more or less operation steps may be included based on conventional or non-creative means. The order of steps listed in the embodiment is only one way in the order of execution of many steps, and does not represent a unique execution order. When the device or terminal product in practice is executed, it can be executed in sequence or in parallel (such as a parallel processor or a multi-threaded processing environment, or even a distributed data processing environment) according to the method shown in the embodiment or the accompanying drawings. The term "include", "comprise" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, product or equipment including a series of elements not only includes those elements, but also includes other elements not clearly listed, or also includes elements inherent to such process, method, product or equipment. In the absence of more restrictions, it is not excluded that there are other identical or equivalent elements in the process, method, product or equipment including the elements.

[0168] For the convenience of description, the above devices are described in various modules according to their functions. Of course, when implementing the embodiments of this specification, the functions of each module can be implemented in the same or more software and / or hardware, or the module implementing the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0169] Those skilled in the art also know that, in addition to implementing the controller in a purely computer-readable program code, the controller can be made to implement the same function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered as a hardware component, and the devices for implementing various functions included therein can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules for implementing the method and structures within the hardware component.

[0170] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0171] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0172] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0173] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0174] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0175] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0176] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, the embodiments of this specification may take the form of complete hardware embodiments, complete software embodiments or embodiments combining software and hardware. Moreover, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0177] The present specification embodiments may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present specification embodiments may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0178] Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. In the description of this specification, the description of the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of this specification. In this specification, the schematic representation of the above terms does not necessarily target the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, in the absence of contradiction, a person skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0179] The above is only an example of the embodiment of the present specification and is not intended to limit the embodiment of the present specification. For those skilled in the art, the embodiment of the present specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiment of the present specification shall be included in the scope of the claims of the embodiment of the present specification.

Claims

1. A method for determining the connectivity of a fracture-vuggy reservoir, characterized in that: The method comprises: Extracting sample points from a target block of a fracture-cavity oil reservoir to restore the fracture-cavity body morphology of the fracture-cavity oil reservoir and obtain a target point cloud; Constructing a plurality of connection units based on the influence domain radius of each node in the target point cloud, and performing flow simulation on the plurality of connection units to form a connection unit system of the fracture-vuggy oil reservoir; Calculating characteristic parameters of each connection unit in the connection unit system, wherein the characteristic parameters include: conductivity characterizing the fluid permeability of the connection unit, and connected volume characterizing the geometric properties of the connection unit; Inverting the characteristic parameters of the fracture-cavity oil reservoir connection unit to obtain a data-driven model of the fracture-cavity oil reservoir; Path tracing is performed on the data driven model to determine the connectivity of the inter-well communication paths in the target block.

2. The method according to claim 1, characterized in that Calculating characteristic parameters of each connection unit in the connection unit system includes: The conductivity between the first node and the second node is calculated according to the following formula as the conductivity corresponding to the connection unit formed by the first node and the second node: Among them, T ij represents the conductivity between nodes i and j when i is the central node and first node j is the second node, k ij represents the permeability between node i and node j, λ ij represents the flow coefficient between node i and node j, α is the unit conversion coefficient, V i represents the control volume of node i, represents the generalized difference coefficient of the mth row of node j in the influence domain of node i, m represents the row number of the generalized difference coefficient matrix, k ro represents the relative permeability of the oil phase between nodes i and j, k rw represents the relative permeability of the water phase between nodes i and j, μ o represents the oil phase viscosity between nodes i and j, μ w represents the viscosity of the water phase between nodes i and j.

3. The method according to claim 1, characterized in that Calculating characteristic parameters of each connection unit in the connection unit system includes: The connected volume of each connected unit and the connected pore volume of each connected unit are calculated according to the following formula: Among them, V p,ij represents the connected pore volume of the connected unit formed by nodes i and j, V ij represents the connected volume of the connection unit formed by node i and node j, represents the average porosity of the connected unit formed by nodes i and j, represents the average thickness of the connection unit formed by nodes i and j, L ij represents the distance between node i and node j, G ij represents the geometric factor of the connection unit formed by nodes i and j, V R represents the total volume of the reservoir, and p represents the consideration of pores; The volume of connected pores in the control area of ​​each node is calculated according to the following formula: Among them, V p,i represents the volume of connected pores in the control area of ​​node i, represents the average porosity in the control area of ​​node i, represents the average thickness in the control area of ​​node i, V i represents the control volume of node i, V p,j represents the volume of connected pores in the control region of node j, represents the average porosity in the control region of node j, represents the average thickness in the control area of ​​node j, V j represents the control volume of node j, Indicates the average thickness of the current oil layer.

4. The method according to claim 1, characterized in that: Performing path tracing on the data driven model to determine the connectivity of the well-to-well communication paths in the target block includes: The splitting coefficient of the connection unit is calculated according to the following formula: in, represents the splitting coefficient of the connection unit formed by node i and node j, node j represents the injection well, n c ′ represents the number of well points connected to node j and with lower pressure than node j, represents the total outflow of liquid from node j, n represents the nth time step, k represents the kth path, and q represents the liquid flow of the connected unit; The splitting coefficient and connected volume of the connection unit are taken as the connectivity of the inter-well connection path formed by the connection unit.

5. The method according to claim 1, characterized in that Sample points are extracted from the target block of the fracture-cavity oil reservoir to restore the fracture-cavity body morphology of the fracture-cavity oil reservoir and obtain the target point cloud, including: Acquire data from the external environment of the target block through a variety of preset sensors; Classify and preprocess the data according to preset rules to reduce the noise in the data; Based on the denoised data, image segmentation and edge detection are performed to extract the feature information of each sample point, where the feature information includes: shape, size, and depth; According to the characteristic information of each sample point, the target point cloud with multiple resolutions and densities is dynamically generated.

6. The method according to claim 1, characterized in that After inverting the characteristic parameters of the fracture-cavity reservoir connection unit to obtain the data-driven model of the fracture-cavity reservoir, the method further includes: Establishing a history matching objective function for the data driven model; Based on the optimal principle, the data-driven model of the fracture-vuggy reservoir is historically matched, and iteratively obtained to determine the local minimum value of the historical matching objective function; Determine the model parameters of the data-driven model when the history matching objective function takes a local minimum value to obtain an optimized data-driven model.

7. The method according to claim 6, characterized in that The history matching objective function is: m low ≤m≤m up Among them, O(m) is the historical matching objective function, m is the vector composed of connected volume, conductivity and phase permeability parameters, G(m) is the vector composed of production dynamic data generated by data-driven model calculation, d obs represents the actual observed value, C d represents the dynamic covariance matrix, m low The vector representing the upper limit of the characteristic parameters, m up The vector representing the lower limit of the characteristic parameters, V R represents the total effective pore volume of the data-driven model, V R,low represents the lower limit of the total effective pore volume of the data-driven model, V R,up represents the upper limit of the total effective pore volume of the data-driven model, R represents the reservoir, and N w Represents the number of well nodes, V ij 0 k Represents the initial connection volume of the connection unit.

8. A device for determining the connectivity of a fracture-cavity reservoir, characterized in that: include: An extraction module is used to extract sample points from a target block of a fracture-cavity oil reservoir to restore the fracture-cavity body morphology of the fracture-cavity oil reservoir and obtain a target point cloud; A construction module, used to construct a plurality of connection units according to the influence domain radius of each node in the target point cloud, and perform flow simulation on the plurality of connection units to form a connection unit system of the fracture-vuggy oil reservoir; A calculation module, used to calculate characteristic parameters of each connection unit in the connection unit system, wherein the characteristic parameters include: conductivity characterizing the fluid permeability of the connection unit, and a connected volume characterizing the geometric properties of the connection unit; An inversion module, used for inverting characteristic parameters of the fracture-cavity oil reservoir connection unit to obtain a data-driven model of the fracture-cavity oil reservoir; A determination module is used to perform path tracing on the data-driven model to determine the connectivity of the inter-well connection paths in the target block.

9. An electronic device comprising a processor and a memory for storing instructions executable by the processor, characterized in that: When the processor executes the instructions, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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