A quantitative evaluation method for the connectivity of natural cracks

Abstract

This invention provides a method for quantitatively evaluating the connectivity of natural fractures, applicable to the fields of oil and gas geology and coalbed methane geology. First, by collecting imaging logging data, natural fractures are identified, and the fracture density f is calculated. d , fracture orientation dispersion f c and the fractional dimension of crack distribution f w Then, based on the entropy weight method, a natural fracture connectivity index λ and f are constructed. d f c and f w A mathematical model between them was established; a three-dimensional geological model of the target layer was created, generating a fault distance attribute body D, a rock mechanics parameter attribute body E, and a stratigraphic thickness attribute body T, and f were constructed respectively. d f c and f w The method establishes relationships between natural fractures and three attribute entities (D, E, and T) to achieve a quantitative spatial evaluation of their connectivity. This method is comprehensive, rigorous, highly operable, and yields reliable evaluation results.

Description

Technical Field

[0001] This invention relates to a method for quantitatively evaluating the connectivity of natural fractures, applicable to the fields of petroleum and natural gas geology and coalbed methane geology. Background Technology

[0002] Natural fractures developed within basin reservoirs are important reservoir spaces and seepage channels for oil and gas, and are crucial parameters for reservoir evaluation and sweet spot selection, thus being vital for oil and gas development. The connectivity between all fractures in a region refers to the ability of fractures to connect with each other. The greater the fracture density, the more dispersed the occurrence, and the more uniform the distribution, the greater the probability of communication between them.

[0003] Patent CN112504928B, authorized by patent number CN112504928B, proposes a method and apparatus for determining the connectivity of fractures in reservoir rocks. Based on porosity, pore size range, and pore size distribution function, a porous media model of the reservoir rock is constructed to obtain the fracture characteristics. A fracture-pore dual-media model is then constructed to represent the relationship between the pore distribution, matrix distribution, and fracture distribution of the reservoir rock, thereby determining the connectivity coefficient of fractures in the reservoir rock and quantitatively characterizing the connectivity of fractures. This method determines the fracture connectivity coefficient from the perspective of porosity, but fractures and pores belong to different types of media. Patent CN116400405A, published under application number CN116400405A, proposes a structural fracture connectivity prediction model and its application method. Based on the ratio of single-well isotope intensity to well logging natural gamma, it classifies water absorption profile types, calculates single-well rock mechanical parameters, establishes a single-well geomechanical model, simulates the paleostress magnitude during fracture formation, establishes a structural fracture connectivity database model and prediction model, predicts the connectivity of structural fractures, and verifies the reliability of the results using dynamic development data. The uncertainty of this method lies in how to verify the determination of the paleostress magnitude, thus raising questions about the reliability of the fracture connectivity evaluation results obtained based on this. Patent application CN108875163A proposes a method and system for evaluating the connectivity of a three-dimensional fracture network. This method obtains actual three-dimensional fracture network parameters, calculates the normalized radius and first and second critical values ​​of the equivalent disk of the fracture surface, and compares the normalized radius of the equivalent disk with the first and second critical values. Based on the comparison results, it obtains the evaluation results of the actual three-dimensional fracture network connectivity. Does the "equivalence" determined by this method meet the actual conditions of petroleum geology? Patent application CN116432811A discloses a method, apparatus, electronic device, and storage medium for determining fracture network connectivity. The method determines the target fracture network connectivity coefficient associated with a target tight conglomerate reservoir and the target grey relational degree of the corresponding target influencing factor data. This grey relational degree is used to characterize the inter-well fracture connectivity of the target tight conglomerate reservoir. The method also determines the target influence weight of the target influencing factor data on the target fracture network connectivity coefficient and the target membership degree of the target influencing factor data relative to the target fracture network connectivity coefficient. Based on the target influence weight and the target membership degree, the method predicts and evaluates the fracture network connectivity of the target tight conglomerate reservoir. However, this method uses a simple relationship determination approach, which does not fully consider the factors affecting natural fracture connectivity, and the evaluation results are mostly qualitative. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a quantitative evaluation method for the connectivity of natural fractures that considers all factors and yields highly reliable results. It comprehensively considers the distance from the fault, rock mechanical parameters, and formation thickness, and constructs a natural fracture connectivity index λ based on fracture density, fracture orientation dispersion, and fracture distribution fractal dimension to achieve a quantitative evaluation of natural fracture connectivity.

[0005] To achieve the above objectives, this invention provides a method for quantitatively evaluating the connectivity of natural fractures. First, imaging logging data of the area to be evaluated is collected, and natural fractures are identified. Then, the fracture density f of the natural fractures in the area to be evaluated is calculated. d , fracture orientation dispersion f c and the fractional dimension of crack distribution f w Define a natural fracture connectivity index λ, and construct a natural fracture connectivity index λ and f based on the entropy weight method. d f c and f w A mathematical model was established between the two; a three-dimensional geological model of the target oil and gas reservoir in the area to be evaluated was constructed, generating the fault distance attribute body D, the rock mechanics parameter attribute body E, and the formation thickness attribute body T, and the fracture density f was constructed respectively. d , fracture orientation dispersion f c and the fractional dimension of crack distribution f w The relationship between the distance to the fault attribute D, the rock mechanics parameter attribute E, and the formation thickness attribute T is used to achieve a spatial quantitative evaluation of the connectivity of natural fractures.

[0006] The specific steps are as follows:

[0007] Step 1: Collect imaging logging data from all wells within the evaluation area, obtain the dip and dip angle information of natural fractures in each well, record the depth of the center of each fracture, and calculate the fracture density f of each well. d , fracture orientation dispersion f c and the fractional dimension of crack distribution f w ;

[0008] Step 2: Calculate the crack density f obtained in Step 1. d , fracture orientation dispersion f c and the fractional dimension of crack distribution f w Normalization is performed to eliminate the influence of parameter units, and then the entropy weight method is used to obtain the crack parameters f. d f c and f w We use the weights a, b, and c to establish a mathematical model for the connectivity index λ of natural fractures.

[0009] Step 3: Collect basic geological and seismic data of the study area, establish a three-dimensional geological model of the target oil and gas reservoir, divide the three-dimensional geological model of the target oil and gas reservoir into three-dimensional grid units, calculate the distance from the fault, rock mechanical parameters and formation thickness of each grid unit, and then obtain the fault distance attribute body D, rock mechanical parameter attribute body E and formation thickness attribute body T of the target oil and gas reservoir.

[0010] Step 4: Based on the calculation results of Step 1 and Step 3, construct the crack parameters: f d f c and f w The relationship between the three attribute bodies D, E, and T;

[0011] Step 5, based on the mathematical model of the natural fracture connectivity index λ described in Step 2 and the fracture parameter f constructed in Step 4. d f c and f w The spatial distribution of the natural fracture connectivity index λ in the area to be evaluated is obtained by calculating the relationship between the three attribute bodies D, E and T. The larger the value of λ, the better the natural fracture connectivity; the smaller the value, the worse the fracture connectivity. This enables a quantitative spatial evaluation of the natural fracture connectivity.

[0012] Furthermore, in step 1, the crack density f d Expressed in linear density form, that is, the number of cracks per unit length:

[0013]

[0014] In the formula: f d Where n is the crack density, n is the number of cracks, and l is the length of the measurement section;

[0015] The dispersion of fracture orientation in the well was calculated based on Fisher distribution analysis. c Its probability density function is expressed as:

[0016]

[0017] In the formula: f c The crack orientation dispersion is represented by θ, which is the angular deviation of the average vector, and k is Fisher's constant or the coefficient of dispersion; the larger the k is, the more concentrated the data clustering is.

[0018] The fractional dimension f of crack distribution was calculated using the differential box method. w :

[0019]

[0020] In the formula: f w is the fractional dimension of the crack distribution, and Nr is the number of subsets formed by scaling down the initial crack set by a factor of r that do not overlap with or cover the initial crack set.

[0021] Furthermore, to eliminate the influence of different units for the parameters, step 2 specifies the fracture density f for each well. d , fracture orientation dispersion f c and the fractional dimension of crack distribution f w Normalization is performed using the following formula:

[0022]

[0023] In the formula: P * P represents the parameter values ​​after normalization, and P represents the parameter values ​​before normalization. max and P min These are the maximum and minimum values ​​of the parameter data volume before normalization, respectively;

[0024] The mathematical model expression for the established natural fracture connectivity index λ is as follows:

[0025] λ=a·f d +b·f c +c·f w

[0026] In the formula: f d f is the crack density. c f represents the dispersion of fracture orientation. w Let f be the fractal dimension values ​​of the crack distribution, a, b, and c, obtained using the entropy weight method, and respectively be f. d f c and f w The weighting coefficient is λ, which is the natural fracture connectivity index.

[0027] Furthermore, in step 3, the basic geological data includes the structural contour map of the target layer, the lithological columnar section, and the rock mechanics experimental test data; using Petrel software, the Gaussian sequential interpolation method is used to obtain the fault distance attribute volume D, the rock mechanics parameter attribute volume E, and the formation thickness attribute volume T of the three-dimensional model of the oil and gas target reservoir.

[0028] Furthermore, in step 4, f d f c and f w The relationship between the three attribute bodies D, E, and T is as follows:

[0029]

[0030] In the formula: f d f is the crack density. c f represents the dispersion of fracture orientation. w The fractional dimension of the fracture distribution is represented by D, E, and T, which are the distance from the fault, rock mechanics parameter, and formation thickness attributes in the three-dimensional model, respectively.

[0031] Beneficial effects:

[0032] Natural fractures are the main reservoirs and seepage channels for oil and gas, and their connectivity is crucial for oil and gas development. This invention constructs a natural fracture connectivity index λ based on fracture density, fracture occurrence dispersion, and fracture distribution fractal dimension, enabling a quantitative evaluation of natural fracture connectivity. It considers comprehensive and rigorous factors, employs a refined mathematical model, is highly operable, and yields highly reliable evaluation results. Attached Figure Description

[0033] Figure 1 This is a flowchart illustrating a method for quantitatively evaluating the connectivity of natural cracks according to the present invention. Detailed Implementation

[0034] The embodiments of the present invention will be further described below with reference to the accompanying drawings:

[0035] like Figure 1 As shown, the present invention provides a method for quantitatively evaluating the connectivity of natural fractures, which involves collecting imaging logging data, identifying natural fractures, and calculating the fracture density f. d , fracture orientation dispersion f c and the fractional dimension of crack distribution f w Based on the entropy weight method, a connectivity index λ and f of natural fractures are constructed. d f c and f w A mathematical model between them is established; a three-dimensional geological model of the target layer is constructed, generating a fault distance attribute body D, a rock mechanics parameter attribute body E, and a stratigraphic thickness attribute body T, and f is constructed respectively. d f c and f w The relationship between the three attribute bodies D, E and T is used to achieve a spatial quantitative evaluation of the connectivity of natural cracks.

[0036] The specific steps are as follows:

[0037] Step 1: Collect imaging logging data within the study area, identify the dip and dip angle of natural fractures, record the depth of the center of each fracture, and calculate the fracture density f. d , fracture orientation dispersion f c and the fractional dimension of crack distribution f w ;

[0038] Crack density is expressed as linear density, which is the number of cracks per unit length:

[0039]

[0040] In the formula: f d denoted as crack density, n as the number of cracks, and l as the length of the measurement section.

[0041] The dispersion of fracture orientation is analyzed and calculated based on the Fisher distribution, and its probability density function is expressed as:

[0042]

[0043] In the formula: f c The value represents the dispersion of crack orientation, θ is the angular deviation of the mean vector, and k is Fisher's constant, also known as the coefficient of dispersion. The larger the value of k, the more concentrated the data clustering.

[0044] The fractional dimension of crack distribution was calculated using the differential box method:

[0045]

[0046] In the formula: f w is the fractional dimension of the crack distribution, and Nr is the number of subsets formed by scaling down the initial crack set by a factor of r that do not overlap with or cover the initial crack set.

[0047] Step 2, calculate the crack parameters f obtained in Step 1 respectively. d f c and f w Normalization is performed to eliminate the influence of parameter units, and then the entropy weight method is used to obtain f. d f c and f w We use the weights a, b, and c to establish a mathematical model for the connectivity index λ of natural fractures.

[0048] Data normalization uses the following formula:

[0049]

[0050] In the formula: P * P represents the parameter values ​​after normalization, and P represents the parameter values ​​before normalization. max and P min These are the maximum and minimum values ​​of the parameter data volume before normalization, respectively.

[0051] The general expression for the mathematical model of the natural fracture connectivity index λ is as follows:

[0052] λ=a·f d +b·f c +c·f w

[0053] In the formula: f d f is the crack density. c f represents the dispersion of fracture orientation. w Let f be the fractional dimension of the crack distribution, and let a, b, and c be f', respectively. d f c and f w The weighting coefficient is λ, which is the natural fracture connectivity index.

[0054] Step 3: Collect basic geological and seismic data of the study area, establish a three-dimensional geological model of the target layer, divide it into grids, and calculate the distance from the fault, rock mechanics parameters, and stratum thickness of each grid cell. Then, obtain the fault distance attribute volume D, rock mechanics parameter attribute volume E, and stratum thickness attribute volume T of the three-dimensional model. The basic geological data should at least include the structural contour map of the target layer, the lithological columnar section, and the rock mechanics experimental test data. The attribute volumes of the three-dimensional model are obtained using the Gaussian sequential interpolation method.

[0055] Step 4: Based on the calculation results of Step 1 and Step 3, construct f respectively. d f c and f w The relationship between the three attribute bodies D, E, and T; f d f c and f w The general expression for the relationship between the three attribute bodies D, E, and T is:

[0056]

[0057] In the formula: f d f is the crack density. c f represents the dispersion of fracture orientation. w The fractional dimension of the fracture distribution is represented by D, E, and T, which are the distance from the fault, rock mechanics parameter, and formation thickness attributes in the three-dimensional model, respectively.

[0058] Step 5: Based on the calculation results of Step 2 and Step 4, calculate and obtain the spatial distribution of the natural fracture connectivity index λ in the study area. The larger the value of λ, the better the natural fracture connectivity; the smaller the value, the worse the fracture connectivity. This enables a spatial quantitative evaluation of the natural fracture connectivity.

Claims

1. A method for quantitatively evaluating the connectivity of natural fractures, characterized in that, Collect imaging logging data of the area to be evaluated, identify natural fractures, and calculate the fracture density f of the natural fractures in the area to be evaluated. d , fracture orientation dispersion f c and the fractional dimension of crack distribution f w Define a natural fracture connectivity index λ, and construct a natural fracture connectivity index λ and f based on the entropy weight method. d f c and f w A mathematical model was established between the two; a three-dimensional geological model of the target oil and gas reservoir in the area to be evaluated was constructed, generating the fault distance attribute body D, the rock mechanics parameter attribute body E, and the formation thickness attribute body T, and the fracture density f was constructed respectively. d , fracture orientation dispersion f c and the fractional dimension of crack distribution f w The relationship between the distance to the fault attribute D, the rock mechanics parameter attribute E, and the formation thickness attribute T is used to achieve a spatial quantitative evaluation of the connectivity of natural fractures.

2. The method for quantitatively evaluating the connectivity of natural fractures according to claim 1, characterized in that, The specific steps are as follows: Step 1: Collect imaging logging data from all wells within the evaluation area, obtain the dip and dip angle information of natural fractures in each well, record the depth of the center of each fracture, and calculate the fracture density f of each well. d , fracture orientation dispersion f c and the fractional dimension of crack distribution f w ; Step 2: Calculate the crack density f obtained in Step 1. d , fracture orientation dispersion f c and the fractional dimension of crack distribution f w Normalization is performed to eliminate the influence of parameter units, and then the entropy weight method is used to obtain the crack parameters f. d f c and f w We use the weights a, b, and c to establish a mathematical model for the connectivity index λ of natural fractures. Step 3: Collect basic geological and seismic data of the study area, establish a three-dimensional geological model of the target oil and gas reservoir, divide the three-dimensional geological model of the target oil and gas reservoir into three-dimensional grid units, calculate the distance from the fault, rock mechanical parameters and formation thickness of each grid unit, and then obtain the fault distance attribute body D, rock mechanical parameter attribute body E and formation thickness attribute body T of the target oil and gas reservoir. Step 4: Based on the calculation results of Step 1 and Step 3, construct the crack parameters: f d f c and f w The relationship between the three attribute bodies D, E, and T; Step 5, based on the mathematical model of the natural fracture connectivity index λ described in Step 2 and the fracture parameter f constructed in Step 4. d f c and f w The spatial distribution of the natural fracture connectivity index λ in the area to be evaluated is obtained by calculating the relationship between the three attribute bodies D, E and T. The larger the value of λ, the better the natural fracture connectivity; the smaller the value, the worse the fracture connectivity. This enables a quantitative spatial evaluation of the natural fracture connectivity.

3. The method for quantitatively evaluating the connectivity of natural fractures according to claim 2, characterized in that, Crack density f in step 1 d Expressed in linear density form, that is, the number of cracks per unit length: ， In the formula: f d Where n is the crack density, n is the number of cracks, and l is the length of the measurement section; The dispersion of fracture orientation in the well was calculated based on Fisher distribution analysis. c Its probability density function is expressed as: ， In the formula: f c The crack orientation dispersion is represented by θ, which is the angular deviation of the average vector, and k is Fisher's constant or the coefficient of dispersion; the larger the k is, the more concentrated the data clustering is. The fractional dimension f of crack distribution was calculated using the differential box method. w : ， In the formula: f w is the fractional dimension of the crack distribution, and Nr is the number of subsets formed by scaling down the initial crack set by a factor of r that do not overlap with or cover the initial crack set.

4. The method for quantitatively evaluating the connectivity of natural fractures according to claim 2, characterized in that, To eliminate the influence of different units for the parameters, step 2 specifies the fracture density f for each well. d , fracture orientation dispersion f c and the fractional dimension of crack distribution f w Normalization is performed using the following formula: ， In the formula: P * P represents the parameter values ​​after normalization, and P represents the parameter values ​​before normalization. max and P min These are the maximum and minimum values ​​of the parameter data volume before normalization, respectively; The mathematical model expression for the established natural fracture connectivity index λ is as follows: ， In the formula: f d f is the crack density. c f represents the dispersion of fracture orientation. w Let f be the fractal dimension values ​​of the crack distribution, a, b, and c, obtained using the entropy weight method, and respectively be f. d f c and f w The weighting coefficient is λ, which is the natural fracture connectivity index.

5. The method for quantitatively evaluating the connectivity of natural fractures according to claim 2, characterized in that, In step 3, the basic geological data include the structural contour map of the target layer, the lithological columnar section, and the rock mechanics experimental test data. The Petrel software is used to obtain the fault distance attribute volume D, the rock mechanics parameter attribute volume E, and the formation thickness attribute volume T of the three-dimensional model of the oil and gas target reservoir using the Gaussian sequential interpolation method.

6. The method for quantitatively evaluating the connectivity of natural fractures according to claim 2, characterized in that, In step 4, f d f c and f w The relationship between the three attribute bodies D, E, and T is as follows: ， In the formula: f d f is the crack density. c f represents the dispersion of fracture orientation. w The fractional dimension of the fracture distribution is represented by D, E, and T, which are the distance from the fault, rock mechanics parameter, and formation thickness attributes in the three-dimensional model, respectively.

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