Method, system and medium for determining critical radius based on nuclear magnetic logging

Abstract

The present invention discloses a method, system and medium for determining a critical radius based on nuclear magnetic resonance logging; it relates to the technical field of reservoir simulation; according to the distance from each grid to the well point grid, this solution obtains the permeability and porosity of the first part of the grid based on the principle of nuclear magnetic resonance logging, and obtains the permeability and porosity of the second part of the grid based on the principle of Kriging interpolation; based on the critical path seepage theory, the actual rock pore throat characteristics and seepage channel characteristics are considered in the well location grid model to construct a critical radius model; this solution combines the respective advantages of reservoir simulation technology and pore network simulation technology, and on the basis of ensuring the scale of the reservoir model, considers the microscopic critical path seepage characteristics, and considers the actual rock pore throat characteristics and seepage channel characteristics in the well location grid model to construct a critical radius model; thereby obtaining a more accurate critical radius, laying the foundation for high-precision reservoir simulation analysis results.

Description

Technical Field

[0001] The present invention relates to the technical field of oil reservoir simulation, and in particular to a method, system and medium for determining a critical radius based on nuclear magnetic logging. Background Art

[0002] After years of research, reservoir development methods have matured. Researchers generally use numerical simulation methods to reduce development costs and formulate reasonable development plans and provide scientific guidance based on simulation results. Numerical reservoir simulation methods use models to study the dynamic changes of reservoirs, including physical simulation and mathematical (numerical) simulation. Physical simulation refers to the indoor study of reservoir development dynamics, while the main principle of numerical reservoir simulation methods is to describe the reservoir production status using a set of partial differential equations and obtain changes in development indicators through computer numerical solution. Numerical reservoir simulation methods can consider the impact of factors such as reservoir geometry, heterogeneity, changes in rock and fluid properties, well patterns, and production on dynamics. To date, this method considers the most factors in reservoir dynamics research and has become one of the important means of reservoir development research. The main feature of numerical simulation methods is the analysis and prediction of development dynamics through simulation analysis of fluid and energy distribution within the reservoir.

[0003] Current numerical simulation methods for reservoir research are categorized by scale: reservoir simulation calculations centered on black oil models and pore-scale flow simulation techniques based on pore network models. Reservoir models are large-scale and encompass a wide range of research areas, enabling analysis of subsurface reservoir fluid flow states and simulation of development processes at a larger scale. However, traditional reservoir models cannot directly account for the impact of microscopic factors, such as rock pore throat characteristics and the dynamic flow characteristics of multiphase fluids at the pore scale, on macroscopic flow processes. Pore network models can maximize the reproduction of microscopic features such as the pore throat structure within real reservoir rocks and can investigate factors influencing the critical paths (dominant channels) and sweep efficiency of multiphase fluid flow within pore throats from a microscopic perspective. Pore network models have become a popular approach for studying microscopic flow mechanisms in reservoirs in recent years. However, due to limitations in computer hardware and algorithms, the scale of the resulting pore-scale models differs significantly from the actual reservoir scale (typically at the centimeter level), making them inappropriate for direct application in large-scale industrial applications.

[0004] To summarize, reservoir simulation technology has the advantages of large scale and wide application range. However, the selection of some parameters in the model is difficult and their physical meaning is unclear, resulting in low accuracy. The pore network model has high accuracy, but is limited by scale issues and cannot be applied on a large scale in actual reservoirs. Summary of the Invention

[0005] The technical problem to be solved by the present invention is: reservoir simulation technology has the advantages of large scale and wide application range, but the selection of some parameters in the model is difficult and the physical meaning is unclear, resulting in low accuracy; the pore network model construction process is limited by the reservoir scale, and the critical radius accuracy is not high, which affects the large-scale industrial application of actual reservoirs; the purpose of the present invention is to provide a method, system and medium for determining the critical radius based on nuclear magnetic resonance logging, and to improve the method on the basis of the existing pore network model construction technology. This scheme combines the respective advantages of reservoir simulation technology and pore network simulation technology, and considers the microscopic critical path seepage characteristics on the basis of ensuring the scale of the reservoir model, and considers the actual rock pore throat characteristics and seepage channel characteristics in the well grid model to construct a critical radius model; thereby obtaining a higher accuracy critical radius, laying the foundation for high-precision reservoir simulation analysis results.

[0006] The present invention is achieved through the following technical solutions:

[0007] This solution also provides a method for determining the critical radius based on nuclear magnetic logging, including:

[0008] Construct a grid model and calculate the basic data of the grid model;

[0009] Well points are deployed on each grid in the grid model to obtain a well location grid model; the grid where the well points are deployed is a well point grid;

[0010] The well location grid model is divided into a first part of grids and a second part of grids according to the distance between each grid and the well point grid; wherein the distance between the second part of grids and the well point grid is greater than the distance between the first part of grids and the well point grid;

[0011] The permeability and porosity of the first grid are obtained based on the principle of nuclear magnetic logging, and the permeability and porosity of the second grid are obtained based on the principle of Kriging interpolation.

[0012] Based on the critical path seepage theory, the critical radius model is constructed by considering the actual rock pore throat characteristics and seepage channel characteristics in the well location grid model.

[0013] The permeability and porosity of each grid are input into the critical radius model to calculate the critical radius.

[0014] Working principle of this scheme: Reservoir simulation technology has the advantages of large scale and wide application range, but the selection of some parameters in the model is difficult and the physical meaning is unclear, which makes its accuracy low; the pore network model construction process is limited by the reservoir scale, and the critical radius accuracy is not high, which affects the large-scale industrial application of actual reservoirs; the purpose of the present invention is to provide a method, system and medium for determining the critical radius based on nuclear magnetic resonance logging, and to improve the method on the basis of the existing pore network model construction technology. This scheme combines the respective advantages of reservoir simulation technology and pore network simulation technology, and considers the micro-scale critical path seepage characteristics on the basis of ensuring the scale of the reservoir model, and considers the actual rock pore throat characteristics and seepage channel characteristics in the well grid model to construct a critical radius model; thereby obtaining a higher-precision critical radius, laying the foundation for high-precision reservoir simulation analysis results.

[0015] For highly heterogeneous reservoirs, traditional pore grid simulation technology uses logging data that can only reflect partial information near the wellbore at the well point. The area far from the wellbore is filled using completely random kriging interpolation based on the grid at the well point. This results in a large error between the constructed geological model and the actual situation, affecting the accuracy of subsequent reservoir simulation analysis results. This solution uses nuclear magnetic T2 spectra containing the well point and the area near the wellbore (the first part of the grid) to build a model. This can amplify the modeling accuracy from the well point to the near-wellbore (i.e., the area near the wellbore), and then use kriging interpolation to model the second part of the grid. This model is more accurate and better suited for modeling highly heterogeneous reservoirs.

[0016] A further optimization scheme is to construct a grid model and calculate basic data of the grid model; deploy well points in the grid model to obtain a well location grid model; including the following methods:

[0017] Construct a regular grid model of a three-dimensional cube; construct a spatial coordinate system with one vertex of the regular grid model as the origin and along the edges of the regular grid model as the x-axis, y-axis, and z-axis directions; calculate the grid center of each node with each grid body as a node;

[0018] Taking the z-axis direction as the height direction, obtain the height of each grid body center and divide the grid model into multi-layer sub-grid models;

[0019] A well point is deployed on each layer of the sub-grid model according to the actual oil reservoir to obtain a well location grid model; the well point is deployed on the center of the grid body.

[0020] A further optimization scheme is to divide the well location grid model into a first part of grids and a second part of grids according to the distance between each grid and the well point grid, including the following method:

[0021] Calculate the distance D between each grid and the well point grid;

[0022] A distance threshold De is preset, and the grids with distance D less than the distance threshold De and the well point grids are divided into the first part of grids;

[0023] The grids with distance D ≥ distance threshold De are divided into the second part of grids.

[0024] A further optimization scheme is to obtain the permeability and porosity of the first part of the grid based on the principle of nuclear magnetic logging, including the following method:

[0025] The basic parameters of each layer sub-grid model are obtained based on nuclear magnetic resonance logging, including: nuclear magnetic resonance T2 spectrum, macro-average permeability K, macro-average porosity φ and macro-average pore throat radius r m ; The first part of the grid mainly contains the effective oil-bearing reservoir section.

[0026] The nuclear magnetic resonance T2 spectrum of the effective oil-bearing reservoir section can be obtained through nuclear magnetic logging. The macro-average permeability K and average porosity φ of the effective oil-bearing reservoir section are obtained by formation testing.

[0027] Macro-average permeability K, average porosity φ and average pore throat radius r m Satisfies the Gozenikalman equation:

[0028]

[0029] Where τ is the tortuosity;

[0030] The average pore throat radius r m Expressed as:

[0031]

[0032] The average value T2 of the T2 spectrum of nuclear magnetic logging m The average pore throat radius r of the block m The first conversion coefficient c can be obtained:

[0033]

[0034] The current layer sub-grid model is evenly divided into N small blocks, and the average pore throat radius r of the small block i in the current layer sub-grid model is expressed based on the basic parameters i ;

[0035] [r i ] i＝1...N =c·[T2 i ] i＝1...N

[0036] Among them, T2 i Represents the nuclear magnetic signal value of small block i.

[0037] Based on the average pore throat radius r i Determine the porosity φ of small block i i and permeability K i :

[0038] Because the average porosity φ is equal to the average value of the nuclear magnetic logging T2 spectrum T2 m Positive correlation: φ=x·T2 m ;

[0039] Where x is the second conversion coefficient, which realizes the correlation between the NMR T2 spectrum and the grid porosity φ i The conversion of

[0040] [φ i ] i＝1...N =x·[T2 i ] i＝1...N

[0041] And because the permeability K of small block i i , porosity φ i With r i The Gornica-Mann equation is satisfied between them, so the permeability K of the small block i is i It can be expressed as:

[0042]

[0043] Where c is the first conversion coefficient; x is the second conversion coefficient; τ is the tortuosity; T2 i represents the nuclear magnetic signal value of small block i; y is the third conversion coefficient, [] i＝1...N Indicates the traversal of grid i from sequence number 1 to sequence number N.

[0044] It can be found that the third conversion coefficient y can be represented by the first conversion coefficient c and the second conversion coefficient x, and its value is only related to the physical properties of the target block. According to the above formula, the signal value of a certain section of the nuclear magnetic T2 spectrum can be related to the permeability K of a certain grid block. i For different depth layers Z, the above steps are used to establish the grid attribute model.

[0045] A further optimization scheme is to obtain the permeability and porosity of the second part of the grid based on the Kriging interpolation principle, including the following method:

[0046] The actual permeability and actual porosity corresponding to the second part of the grid are obtained by converting the T2 spectrum of nuclear magnetic resonance logging; the first part of the grid mainly contains the effective oil-bearing reservoir section.

[0047] The Kriging interpolation method is used to take the actual permeability and actual porosity of the well point grid in the bottom grid model as the benchmark, and the second part of the grid in the bottom grid model is randomly interpolated based on the random function to obtain the permeability and porosity of the second part of the grid. i The ratio of the average permeability and porosity of the well point and the grid around the well to the average permeability and porosity of the first layer of grid around the well is recorded as I, then the layer Z i The porosity and permeability of any other grids are obtained by multiplying the porosity and permeability values of the grid corresponding to the z direction of the first layer by I.

[0048] A further optimization scheme is to consider the actual rock pore throat characteristics and seepage channel characteristics in the well location grid model based on the critical path seepage theory and construct a critical radius model, including the following methods:

[0049] The permeability k between all adjacent grids i and j in the well location grid model is calculated based on the harmonic mean method. ij and porosity φ ij

[0050]

[0051] where k i is the permeability of grid i, k j is the permeability of grid j, φ i is the porosity of grid i, φ j is the porosity of grid j;

[0052] The effective porosity φ between grid i and grid j cij Less than or equal to porosity φ ij , based on the seepage channel characteristics between grid i and grid j, the effective porosity φ cijy and porosity φ ij The relationship is:

[0053]

[0054] Among them, σ / <r>is the rock pore throat variation coefficient, z represents the rock pore throat coordination number; h represents the parameter related to rock type. Taking tight sandstone as an example, the value range of h is [-3.97, -2.55];

[0055] Then the critical radius r between grids i and j is cij for:

[0056]

[0057] A further optimization scheme is that the critical path length l c and critical radius r cij Characterize the seepage channel characteristics between grids i and j, including methods:

[0058] The seepage channel formed by multiple pore throats connected in series between grid i and grid j is taken as the critical path, and the seepage channel A with the smallest radius is screened out. The radius of seepage channel A is taken as the critical radius r cij ;

[0059] The porosity between grid i and grid j corresponding to the critical path is the effective porosity φ cij ; The tortuosity of the seepage channel between grid i and grid j is τ c =l c / l ij ; l ij is the distance between the center points of grid i and grid j.

[0060] A further optimization scheme includes the steps of: expressing the volume flow q between grid i and grid j based on the critical radius ij :

[0061]

[0062] Δp ij =(p i -p j )=(p oi -p oj +ρgZ i -ρgZ j )

[0063] Among them, κ ij is the conductivity of the critical path between grid i and grid j; B s is the volume coefficient of the single-phase fluid; μ s is the viscosity of the single-phase fluid; p i =p oi +ρgZ i ;p oi is the pore pressure of grid i; ρgZ i is the gravity of the fluid in grid i; ρ is the fluid density; g is the acceleration due to gravity; p j =p oj +ρgZ j ;p oj is the pore pressure of grid j, ρgZ j is the gravity of the fluid in grid j; Z i is the vertical height of grid i; Z j is the vertical height of grid j, Δp ij Represents the pressure difference between grid i and grid j.

[0064] This solution also provides a system for determining a critical radius based on nuclear magnetic logging, which is used to implement the above-mentioned method for determining a critical radius based on nuclear magnetic logging. The system includes:

[0065] The first calculation module is used to construct a grid model and calculate basic data of the grid model;

[0066] A deployment module is used to deploy well points on each grid in the grid model to obtain a well location grid model; wherein the grid on which the well points are deployed is a well point grid;

[0067] A division module is used to divide the well location grid model into a first part of grids and a second part of grids according to the distance between each grid and the well point grid; wherein the distance between the second part of grids and the well point grid is greater than the distance between the first part of grids and the well point grid;

[0068] The second calculation module is used to obtain the permeability and porosity of the first part of the grid based on the principle of nuclear magnetic logging, and to obtain the permeability and porosity of the second part of the grid based on the principle of Kriging interpolation;

[0069] A construction module is used to construct a critical radius model based on the critical path seepage theory, taking into account the actual rock pore throat characteristics and seepage channel characteristics in the well location grid model;

[0070] The third calculation module is used to input the permeability and porosity of each grid into the critical radius model to calculate the critical radius.

[0071] The present solution also provides a computer-readable medium having a computer program stored thereon. The computer program is executed by a processor to implement the above-mentioned method for determining the critical radius based on nuclear magnetic logging.

[0072] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0073] 1. The method, system, and medium for determining the critical radius based on nuclear magnetic resonance logging provided by the present invention improve upon existing pore network model construction technology by combining the respective advantages of reservoir simulation technology and pore network simulation technology. While ensuring the scale of the reservoir model, the critical radius model is constructed by considering the microscopic critical path seepage characteristics and the actual rock pore throat characteristics and seepage channel characteristics in the well location grid model. This results in a more accurate critical radius, laying the foundation for high-precision reservoir simulation analysis results.

[0074] 2. This solution combines the T2 spectrum of nuclear magnetic logging with the kriging interpolation method to establish a reservoir-scale grid model and assign porosity and permeability attributes. For grids near well points and the well periphery, a new conversion relationship between T2 data and porosity and permeability is proposed in combination with the nuclear magnetic logging T2 spectrum. For grids far from the well periphery, kriging interpolation is used to interpolate each grid based on the average porosity and permeability of the well point grid. Compared with the previous random interpolation modeling technology, the modeling accuracy is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the examples. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be considered as limiting the scope. A person of ordinary skill in the art can also derive other relevant drawings based on these drawings without inventive effort. In the drawings:

[0076] Figure 1 Schematic diagram of the process of determining the critical radius based on nuclear magnetic logging;

[0077] Figure 2 Schematic diagram of T2 spectra of different layers obtained from nuclear magnetic logging;

[0078] Figure 3 Schematic diagram of the reservoir-scale model established based on nuclear magnetic logging T2 spectrum and Kriging interpolation;

[0079] Figure 4 It is a two-dimensional plane diagram of the grid level;

[0080] Figure 5 Schematic diagram of the critical path between adjacent grids i and j;

[0081] Figure 6 Schematic diagram of the critical radius in the critical path. DETAILED DESCRIPTION

[0082] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with examples and drawings. The exemplary embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0083] Example 1

[0084] This embodiment provides a method for determining the critical radius based on nuclear magnetic logging, such as Figure 1 Shown, including:

[0085] Step 1: Build a grid model and calculate the basic data of the grid model;

[0086] Step 2: Deploy well points on each grid in the grid model to obtain a well location grid model; the grid where the well points are deployed is called a well point grid;

[0087] The above steps specifically include the following methods:

[0088] S1, construct a three-dimensional cube regular grid model; use a vertex of the regular grid model as the origin and construct a spatial coordinate system along the edges of the regular grid model as the x-axis, y-axis, and z-axis directions; use each grid body as a node and calculate the grid body center of each node; basic data includes the coordinates of each grid body, the coordinates of each grid body center, the height of each grid body center, etc.

[0089] Specifically, a total grid number T is constructed o =X×Y×Z three-dimensional simple cube regular grid model, where X, Y, and Z are the grid numbers in the x, y, and z directions respectively, and the number of nodes in each direction is set to n x , n y , n z Among them, each grid is a unit, and the grid side lengths are l x , l y , l z , and l x =l y =l z = l); the grids are connected to each other. In the grid model thus established, each grid (except the grids at the edges and corners) is connected to the six surrounding grids. The real reservoir (reservoir model) is considered to have a volume of L x ×L y ×L z The length of each side of the reservoir model is L x =(n x -1)×l x , L y =(n y -1)×l y , L z =(n z -1)×l z . Grid volume V b =l x ×l y ×l z =l 3 .

[0090] Starting from the first grid in the lower left corner of the grid model, the coordinates of each node in the network model are calculated in sequence. The calculation formula is: (x d ,y d , z d )=[(i-1)l x , (j-1)l y , (k-1)l z ]; record the coordinates of the center of each grid in the network model, and the calculation formula is: (x g ,y g , z g )=[(i-1)l x +(l x / 2), (j-1)l y +(l y / 2), (k-1)l z +(l z / 2)], where i, j, and k are the node numbers in the x-, y-, and z-directions, respectively, and their values range from 1 to n. x , 1 to n y , 1 to n z Therefore, the grid numbers range from 1 to T. o .

[0091] S2, taking the z-axis direction as the height direction, obtains the height of each grid body center and divides the grid model into multi-layer sub-grid models;

[0092] S3, deploying a well point on each layer of the sub-grid model according to the actual oil reservoir to obtain a well location grid model; the well point is deployed on the center of the grid body.

[0093] The established grid model takes the first layer at the bottom of the xoz plane as the starting layer, and records the distance between the center of the grid body of the starting layer and the horizontal plane. The height of each grid body center in the starting layer is Z1 = (1-1)l z +(l z / 2)=(l z / 2), the grid numbers of the starting layer are from 1 to X·Y; the second layer of grids are stacked upwards, and the center height of the second layer of grids is Z2=(2-1)l z +(l z / 2), the order of the grids in the second layer is from ((2-1)·X·Y+1) to 2·X·Y; the height of the center of the grid in any layer is Z i =(i-1)l z +(l z / 2), the grid numbers of this layer are from ((i-1)·X·Y+1) to i·X·Y. This step can determine any grid T i (1≤T i ≤T o ) and its height from the horizontal plane. It should be noted that to ensure the symmetry of the model, the wells in the reservoir model are deployed at the center of the grid model (that is, all grids in the z direction of the model center are well point grids), and X and Y are odd numbers.

[0094] Step 3: Divide the well location grid model into a first part of grids and a second part of grids according to the distance between each grid and the well point grid; wherein the distance between the second part of grids and the well point grid is greater than the distance between the first part of grids and the well point grid;

[0095] Nuclear magnetic resonance logging (NMR) T2 spectroscopy can effectively reflect the pore-throat radius distribution of reservoir rocks. However, due to the limited accuracy of NMR logging instruments, NMR T2 spectroscopy can only measure the pore-throat radius distribution of rocks within a few to tens of meters around the wellbore. Therefore, different value assignment methods are used for the regional grids near the wellbore and the grids far from the wellbore.

[0096] Specific methods include:

[0097] S31, calculating the distance D between each grid and the well point grid;

[0098] S32, presetting a distance threshold De (the distance threshold De is 1 / 16 of the side length of the well location grid model from the well point), dividing the grids with distance D less than the distance threshold De and the well point grids into a first part of grids;

[0099] S33, dividing the grids where the distance D is greater than or equal to the distance threshold De into a second part of grids.

[0100] Step 4: Obtain the permeability and porosity of the first grid based on the principle of nuclear magnetic logging, and obtain the permeability and porosity of the second grid based on the principle of Kriging interpolation;

[0101] The method of obtaining the permeability and porosity of the first part of the grid based on the principle of nuclear magnetic logging includes:

[0102] The basic parameters of each layer sub-grid model are obtained based on nuclear magnetic resonance logging, including: nuclear magnetic resonance T2 spectrum, macro-average permeability K, average porosity φ and pore throat radius r m ; The first part of the grid mainly contains the effective oil-bearing reservoir section.

[0103] like Figure 2 As shown, the nuclear magnetic resonance T2 spectrum of the effective oil-bearing reservoir section is obtained by nuclear magnetic logging, and the macro-average permeability K and average porosity φ of the effective oil-bearing reservoir section are obtained by formation testing;

[0104] Macro-average permeability K, macro-average porosity φ and macro-average pore throat radius r m Satisfies the Gozenikalman equation:

[0105]

[0106] Where τ is the tortuosity;

[0107] Then the macro-average pore throat radius r m Expressed as:

[0108]

[0109] The average value T2 of the T2 spectrum of nuclear magnetic logging m The average pore throat radius r of the block m The first conversion coefficient c can be obtained:

[0110]

[0111] The current layer sub-grid model is evenly divided into N small blocks, and the average pore throat radius r of the small block i in the current layer sub-grid model is expressed based on the basic parameters i ;

[0112] [r i ] i＝1...N =c·[T2 i ] i＝1...N (4)

[0113] Among them, T2 i Represents the nuclear magnetic signal value of small block i.

[0114] Based on the average pore throat radius r i Determine the porosity φ of small block i i and permeability K i :

[0115] Because the average porosity φ is equal to the average value of the nuclear magnetic logging T2 spectrum T2 m Positive correlation: φ=x·T2 m (5);

[0116] Where x is the second conversion coefficient, which realizes the correlation between the NMR T2 spectrum and the grid porosity φ i The conversion of

[0117] [φ i ] i＝1...N =x·[T2 i ] i＝1...N (6)

[0118] And because the permeability K of small block i i , porosity φ i With r i The equation (1) is satisfied between them, so the permeability K of the small block i is i It can be expressed as:

[0119]

[0120] Where c is the first conversion coefficient; x is the second conversion coefficient; τ is the tortuosity; T2 i represents the nuclear magnetic signal value of small block i; y is the third conversion coefficient, [] i＝1...N Indicates the traversal of grid i from sequence number 1 to sequence number N.

[0121] It can be found that the third conversion coefficient y can be represented by the first conversion coefficient c and the second conversion coefficient x, and its value is only related to the physical property parameters of the target block. According to formula (7), the signal value of a certain section of the nuclear magnetic T2 spectrum and the permeability K of a certain grid block can be realized. i For different depth layers Z, the above steps are used to establish the grid attribute model.

[0122] For the grid far away from the wellbore, the permeability and porosity of the second part of the grid are obtained based on the Kriging interpolation principle, including the following methods:

[0123] The actual permeability and actual porosity corresponding to the second part of the grid are obtained by converting the T2 spectrum of nuclear magnetic resonance logging; the first part of the grid mainly contains the effective oil-bearing reservoir section.

[0124] The Kriging interpolation method is used. Taking the actual permeability and actual porosity of the well point grid in the bottom grid model as the benchmark, random interpolation processing is performed on the second part of the grid in the bottom grid model based on the random function to obtain the permeability and porosity of the second part of the grid.

[0125] Based on the permeability and porosity values of the actual reservoir around the well obtained by converting the T2 spectrum of nuclear magnetic resonance logging, after the actual depth around the well position is matched with the model well point grid position, the Kriging interpolation method is used. Based on the porosity and permeability at the first layer grid well point, random functions (such as random distribution, uniform distribution, normal distribution function) are used to perform random interpolation processing on the remaining grids in the first layer. For any layer Z i The ratio of the average permeability and porosity of the well point and the grid around the well to the average permeability and porosity of the first layer of grid around the well is recorded as I, then the layer Z i The porosity and permeability of any other grids are obtained by multiplying the porosity and permeability values of the grid corresponding to the z direction of the first layer by I; the reservoir scale model is as follows: Figure 3 shown.

[0126] Step 5: Based on the critical path seepage theory, the critical radius model is constructed by considering the actual rock pore throat characteristics and seepage channel characteristics in the well location grid model. The specific methods include:

[0127] For the established well location grid model, each grid body center is used to replace the grid, and the horizontal two-dimensional surface of the grid is used as an example to illustrate. Figure 4 As shown, the permeability k between all adjacent grids i and j in the well location grid model is calculated based on the harmonic mean method. ij and porosity φ ij

[0128]

[0129] where k i is the permeability of grid i, k j is the permeability of grid j, φ i is the porosity of grid i, φ j is the porosity of grid j;

[0130] The effective porosity φ between grid i and grid j cij Less than or equal to porosity φ ij , based on the seepage channel characteristics between grid i and grid j, the effective porosity φ cijy and porosity φ ij The relationship is:

[0131]

[0132] Among them, σ / <r>is the rock pore throat variation coefficient, z represents the rock pore throat coordination number; h is a parameter related to the rock type. Taking tight sandstone as an example, the value range of h is [-3.97, -2.55].

[0133] Then the critical radius r between grids i and j is cij for:

[0134]

[0135] Step 6: Input the permeability and porosity of each grid into the critical radius model to calculate the critical radius.

[0136] The critical path length l c and critical radius r cij Characterize the seepage channel characteristics between grids i and j, including methods:

[0137] The seepage channel formed by multiple pore throats connected in series between grid i and grid j is taken as the critical path, and the seepage channel A with the smallest radius is screened out. The radius of seepage channel A is taken as the critical radius r cij ;

[0138] The porosity between grid i and grid j corresponding to the critical path is the effective porosity φ cij ; The tortuosity of the seepage channel between grid i and grid j is τ = l c / l ij ; l ij is the distance between the center points of grid i and grid j.

[0139] In actual reservoirs, the pore space between any grid i and j can be represented by a pore network model. When the fluid flows inside the pore throat, it is easy to form a dominant seepage channel along the high permeability zone, that is, the critical path. Therefore, the seepage channel characteristics between grids i and j can be expressed by the critical path length l c (like Figure 5 shown) and the critical radius r cij (like Figure 6 At the same time, the porosity between the critical path corresponding to grid i and grid j is the effective porosity φ cij In the model, the distance between grids i and j is l ij (like Figure 5 As shown, its value is equal to l), then the tortuosity (pore throat curvature) of the seepage channel (critical path) inside the grid is τ c =l c / l ij , τ c The value is between 1.2 and 2.0. There are multiple critical paths in the model, and the equivalent tube bundle is a regular circular tube. From a microscopic point of view, the critical path can also be regarded as a seepage channel formed by multiple pore throats in series ( Figure 6 ), critical radius r cij is the minimum radius among these pore throat channels.

[0140] The step 7 is further included: expressing the volume flow rate q between the grid i and the grid j based on the critical radius ij :

[0141]

[0142] Δp ij =(p i -p j )=(p oi -p oj +ρgZ i -ρgZ j )(13)

[0143] Among them, κ ij is the conductivity of the critical path between grid i and grid j; B s is the volume coefficient of the single-phase fluid; μ s is the viscosity of the single-phase fluid; p i =p oi +ρgZ i ;p oi is the pore pressure of grid i; ρgZ i is the gravity of the fluid in grid i; ρ is the fluid density; g is the acceleration due to gravity, 9.8 m / s 2 ;p j =p oj +ρgZ j ;p oj is the pore pressure of grid j, ρgZ j is the gravity of the fluid in grid j; Z i is the vertical height of grid i; Z j is the vertical height of grid j, Δp ij Represents the pressure difference between grid i and grid j.

[0144] The linear velocity v of the fluid flow between grids i and j ij Expressed as:

[0145]

[0146] Among them, l ij is the distance between the center points of grids i and j, and its value is equal to the grid side length l, m;

[0147] This scheme takes into account the compressibility of the actual fluid, the density ρ and viscosity μ of the fluid s etc. will change with pressure, introducing the volume coefficient B of single-phase fluid s ,have:

[0148]

[0149] κ ij The conductivity of the critical path between grids i and j is expressed as:

[0150]

[0151] κ ij The unit is m 3 / (Pa·s).

[0152] Example 2

[0153] This embodiment provides a system for determining a critical radius based on nuclear magnetic logging, which is used to implement the method for determining a critical radius based on nuclear magnetic logging described in Example 1. The system includes:

[0154] The first calculation module is used to construct a grid model and calculate basic data of the grid model;

[0155] A deployment module is used to deploy well points on each grid in the grid model to obtain a well location grid model; wherein the grid on which the well points are deployed is a well point grid;

[0156] A division module is used to divide the well location grid model into a first part of grids and a second part of grids according to the distance between each grid and the well point grid; wherein the distance between the second part of grids and the well point grid is greater than the distance between the first part of grids and the well point grid;

[0157] The second calculation module is used to obtain the permeability and porosity of the first part of the grid based on the principle of nuclear magnetic logging, and to obtain the permeability and porosity of the second part of the grid based on the principle of Kriging interpolation;

[0158] A construction module is used to construct a critical radius model based on the critical path seepage theory, taking into account the actual rock pore throat characteristics and seepage channel characteristics in the well location grid model;

[0159] The third calculation module is used to input the permeability and porosity of each grid into the critical radius model to calculate the critical radius.

[0160] Example 1

[0161] This embodiment provides a computer-readable medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the method for determining the critical radius based on nuclear magnetic logging as described in Example 1. Specifically, the following steps are performed:

[0162] Step 1: Build a grid model and calculate the basic data of the grid model;

[0163] Step 2: Deploy well points on each grid in the grid model to obtain a well location grid model; the grid where the well points are deployed is called a well point grid;

[0164] Step 3: Divide the well location grid model into a first part of grids and a second part of grids according to the distance between each grid and the well point grid; wherein the distance between the second part of grids and the well point grid is greater than the distance between the first part of grids and the well point grid;

[0165] Step 4: Obtain the permeability and porosity of the first grid based on the principle of nuclear magnetic logging, and obtain the permeability and porosity of the second grid based on the principle of Kriging interpolation;

[0166] Step 5: Based on the critical path seepage theory, the actual rock pore throat characteristics and seepage channel characteristics are considered in the well location grid model to construct a critical radius model;

[0167] Step 6: Input the permeability and porosity of each grid into the critical radius model to calculate the critical radius.

[0168] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.< / r> < / r>

Claims

1. A method for determining the critical radius based on nuclear magnetic logging, characterized in that: include: Construct a grid model and calculate the basic data of the grid model; Well points are deployed on each grid in the grid model to obtain a well location grid model; the grid where the well points are deployed is a well point grid; According to the distance between each grid and the well point grid, the well location grid model is divided into a first part grid and a second part grid; The distance between the second part of the grid and the well point grid is greater than the distance between the first part of the grid and the well point grid; The permeability and porosity of the first grid are obtained based on the principle of nuclear magnetic logging, and the permeability and porosity of the second grid are obtained based on the principle of Kriging interpolation. Based on the critical path seepage theory, the critical radius model is constructed by considering the actual rock pore throat characteristics and seepage channel characteristics in the well location grid model. The specific methods include: The permeability k between all adjacent grids i and j in the well location grid model is calculated based on the harmonic mean method. ij and porosity φ ij : where k i is the permeability of grid i, k j is the permeability of grid j, φ i is the porosity of grid i, φ j is the porosity of grid j; Based on the seepage channel characteristics between grid i and grid j, the effective porosity φ cij and porosity φ ij The relationship is: Among them, σ / <r> is the rock pore throat variation coefficient; z represents the rock pore throat coordination number;< / r> Then the critical radius r between grids i and j is cij for: Where, τ c is the tortuosity of the seepage channel between grid i and grid j; Among them, based on the critical path length l c and critical radius r cij Characterize the seepage channel characteristics between grid i and grid j: take the seepage channel formed by multiple pore throats connected in series between grid i and grid j as the critical path, screen out the seepage channel A with the smallest radius, and use the radius of seepage channel A as the critical radius r cij ; The porosity between grid i and grid j corresponding to the critical path is the effective porosity φ cij ; The tortuosity τ of the seepage channel between grid i and grid j is c =l c / l ij ; l ij is the distance between the center points of grid i and grid j; The permeability and porosity of each grid are input into the critical radius model to calculate the critical radius.

2. The method for determining the critical radius based on nuclear magnetic logging according to claim 1, characterized in that: The grid model is constructed and basic data of the grid model is calculated; well points are deployed in the grid model to obtain a well location grid model; including method: Construct a regular grid model of a three-dimensional cube; construct a spatial coordinate system with one vertex of the regular grid model as the origin and along the edges of the regular grid model as the x-axis, y-axis, and z-axis directions; calculate the grid center of each node with each grid body as a node; Taking the z-axis direction as the height direction, obtain the height of each grid body center and divide the grid model into multi-layer sub-grid models; A well point is deployed on each layer of the sub-grid model according to the actual oil reservoir to obtain a well location grid model; the well point is deployed on the center of the grid body.

3. The method for determining the critical radius based on nuclear magnetic logging according to claim 2, characterized in that: The method of dividing the well location grid model into a first part of grids and a second part of grids according to the distance between each grid and the well point grid includes: Calculate the distance D between each grid and the well point grid; A distance threshold De is preset, and the grids with distance D less than the distance threshold De and the well point grids are divided into the first part of grids; The grids with distance D ≥ distance threshold De are divided into the second part of grids.

4. The method for determining the critical radius based on nuclear magnetic logging according to claim 2, characterized in that: The method of obtaining the permeability and porosity of the first part of the grid based on the principle of nuclear magnetic logging includes: The basic parameters of each layer sub-grid model are obtained based on nuclear magnetic resonance logging, including: nuclear magnetic resonance T2 spectrum, macro-average permeability K, macro-average porosity φ and macro-average pore throat radius r m ; The current layer sub-grid model is evenly divided into N small blocks, and the average pore throat radius r of the small block i in the current layer sub-grid model is expressed based on the basic parameters i : [r i ] i＝1...N =c·[T2 i ] i＝1...N Where c is the first conversion coefficient; T2 i Indicates the nuclear magnetic signal value of small block i; [] i＝1...N Indicates the traversal of grid i from sequence number 1 to sequence number N; The average pore throat radius r based on small blocks i Determine the porosity φ of small block i i and permeability K i : [f i ] i＝1...N =x·[T2 i ] i＝1...N ; Where x is the second conversion coefficient; τ is the tortuosity; y is the third conversion coefficient, 5. The method for determining the critical radius based on nuclear magnetic logging according to claim 4, characterized in that: The method of obtaining the permeability and porosity of the second part of the grid based on the Kriging interpolation principle includes: The actual permeability and actual porosity corresponding to the second part of the grid are obtained by converting the T2 spectrum of nuclear magnetic resonance logging; The Kriging interpolation method is used. Taking the actual permeability and actual porosity of the well point grid in the bottom grid model as the benchmark, random interpolation processing is performed on the second part of the grid in the bottom grid model based on the random function to obtain the permeability and porosity of the second part of the grid.

6. The method for determining the critical radius based on nuclear magnetic logging according to claim 1, characterized in that: The method further includes the steps of: expressing the volume flow rate q between the grid i and the grid j based on the critical radius; ij : Δp ij =(p i -p j )=(p oi -p oj +ρgZ i -ρgZ j ) Among them, κ ij is the conductivity of the critical path between grid i and grid j; B s is the volume coefficient of the single-phase fluid; μ s is the viscosity of the single-phase fluid; p i =p oi +ρgZ i ;p oi is the pore pressure of grid i; ρgZ i is the gravity of the fluid in grid i; ρ is the fluid density; g is the acceleration due to gravity; p j =p oj +ρgZ j ;p oj is the pore pressure of grid j, ρgZ j is the gravity of the fluid in grid j; Z i is the vertical height of grid i; Z j is the vertical height of grid j, Δp ij Represents the pressure difference between grid i and grid j.

7. A system for determining the critical radius based on nuclear magnetic logging, characterized in that: A method for determining a critical radius based on nuclear magnetic logging according to any one of claims 1 to 6, the system comprising: The first calculation module is used to construct a grid model and calculate basic data of the grid model; A deployment module is used to deploy well points on each grid in the grid model to obtain a well location grid model; wherein the grid on which the well points are deployed is a well point grid; A division module is used to divide the well location grid model into a first part of grids and a second part of grids according to the distance between each grid and the well point grid; wherein the distance between the second part of grids and the well point grid is greater than the distance between the first part of grids and the well point grid; The second calculation module is used to obtain the permeability and porosity of the first part of the grid based on the principle of nuclear magnetic logging, and to obtain the permeability and porosity of the second part of the grid based on the principle of Kriging interpolation; A construction module is used to construct a critical radius model based on the critical path seepage theory, taking into account the actual rock pore throat characteristics and seepage channel characteristics in the well location grid model; The third calculation module is used to input the permeability and porosity of each grid into the critical radius model to calculate the critical radius.

8. A computer-readable medium having a computer program stored thereon, characterized in that: The computer program is executed by a processor to implement the method for determining the critical radius based on nuclear magnetic logging as described in any one of claims 1 to 6.

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