A method for identifying influencing factors of through-layer extension of a reservoir fracturing fracture of a directional well

Abstract

The application discloses a kind of directional well reservoir fracturing fracture translayer extension influence factor identification method, belong to oil and gas field development technical field, for each kind of working condition, according to factor set, generate multiple non-repeated factor combinations by orthogonal test;The extension path of fracture in reservoir and barrier under different factor combinations is obtained by simulation, to determine the reservoir reconstruction area efficiency of fracture under different factor combinations;According to the maximum and influence amplitude of reservoir reconstruction area efficiency of double factors under different factor combinations, the interference coefficient of double factors is determined;The proportion of each factor under different factor combinations is obtained, to determine the single-factor potential energy corresponding to each factor;According to interference coefficient and single-factor potential energy, by determining the size of the system interference coefficient of each factor in factor set, the influence degree of each factor on fracture translayer extension under different working conditions is determined.The method can accurately identify the key factors affecting fracture translayer behavior.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field development technology, and more specifically to a method for identifying factors influencing fracture propagation across layers in directional well reservoir fracturing. Background Technology

[0002] In today's oil exploration and development field, the use of directional wells combined with hydraulic fracturing technology has become an effective way to efficiently develop multi-layered reservoirs. Directional wells, through optimized wellbore trajectories, can penetrate multiple oil and gas layers in a single well, increasing the contact area between the wellbore and the reservoir, improving single-well production and ultimate recovery rate, and providing important technical support for the economical and efficient development of oil and gas fields. Directional wells can effectively connect high-quality reservoir units, and through segmented multi-cluster fracturing, can form a complex network of fractures in multiple vertical layers, achieving more thorough and uniform three-dimensional stimulation of multi-layered reservoirs.

[0003] Against this backdrop, controlling the vertical expansion range of hydraulic fractures to effectively penetrate interlayers and form sufficient "penetration" height is the core of directional well fracturing for effective communication between multiple reservoirs. However, the complex wellbore trajectory of directional wells significantly increases the difficulty of controlling fracture initiation and extension trajectories; in addition, the strong vertical heterogeneity of multi-layered reservoirs makes fracture penetration and propagation difficult to predict, posing certain challenges to fracturing design.

[0004] Traditional research on factors influencing fracture propagation across reservoir fracturing primarily employs single-factor sensitivity analysis combined with subjective weighting using the analytic hierarchy process (AHP). This involves assessing the independent impact of individual factors on fracture propagation height and then ranking factors by weight, clearly demonstrating the degree of influence of each factor on propagation and providing preliminary reference for subsequent analysis. However, for the complex conditions of multi-layered reservoirs, fracture propagation is affected by the coupling effects of multiple scenarios within the multi-layered reservoir. Current technologies only quantify the impact of factors on propagation within a single factor, limiting the results to simple correlations and failing to accurately reflect the mechanical mechanisms of fracture propagation under directional wellbore trajectories. This leads to deviations in practical fracturing design applications. Summary of the Invention

[0005] To address the problems existing in the above-mentioned fields, this invention proposes a method for identifying the influencing factors of fracture propagation across layers in directional well reservoir fracturing. The obtained two-factor interference coefficients can quantify the interference intensity and properties between factors under different combinations of factors. By determining the magnitude of the system interference coefficients of each factor under different combinations of factors, the potential energy of a single factor and the interference effect can be comprehensively considered, thereby achieving the objective identification of the main controlling factors under different combinations of factors.

[0006] To address the aforementioned technical problems, this invention discloses a method for identifying factors influencing fracture propagation across layers in directional well reservoir fracturing, comprising the following steps:

[0007] By simulating and obtaining the rock mechanical properties, geostress field and fracturing construction parameters of fractures under different test levels, a basic database of reservoirs and interlayers corresponding to fractures under different working conditions is constructed.

[0008] For each working condition, the basic database is screened through correlation analysis to construct a factor set; based on the factor set, multiple non-repeating factor combinations are generated through orthogonal experiments; numerical simulation is used to obtain the propagation path of fractures in the reservoir and interlayer under multiple non-repeating factor combinations; based on the propagation path, the reservoir stimulation area efficiency of fractures under different factor combinations is determined.

[0009] Obtain the average difference of reservoir stimulation area efficiency under different combinations of factors to determine the influence range of each factor on reservoir stimulation area efficiency; determine the interference coefficient of the two factors based on the maximum and minimum values ​​and influence range of reservoir stimulation area efficiency under different combinations of factors.

[0010] Obtain the proportion of each factor under different combinations of factors, and determine the single-factor potential energy corresponding to each factor;

[0011] Based on the interference coefficient and single-factor potential energy, the influence of each factor on crack propagation under the current working condition is determined by determining the magnitude of the system interference coefficient of each factor under different combinations of factors.

[0012] Preferably, the step of obtaining the fracture propagation paths in the reservoir and interlayer under multiple non-repeating combinations of factors through numerical simulation, and determining the reservoir stimulation area efficiency of fractures under different combinations of factors based on the propagation paths, specifically includes:

[0013] Based on the model geometry of the fracture, a three-dimensional hydraulic fracturing numerical simulation model of the fracture is established using a three-dimensional hydraulic fracturing simulation platform based on the discrete lattice method.

[0014] Multiple non-repeating factor combinations are input into a three-dimensional hydraulic fracturing numerical simulation model to simulate fracture propagation across layers in a multi-layered reservoir, and to obtain the propagation paths of fractures in the reservoir and interlayers under different factor combinations.

[0015] Based on the obtained expansion path, the grid area of ​​fractures located in the reservoir and interlayer regions under different combinations of factors is determined using the discrete lattice method.

[0016] Based on the grid area, determine the contact area between the fracture surface and the reservoir rock mass and the contact area between the fracture surface and the interlayer rock mass after penetrating the reservoir under different factor combinations, and obtain the reservoir fracture communication area and interlayer fracture communication area under different factor combinations.

[0017] The ratio of reservoir fracture connectivity area to interlayer fracture connectivity area under different combinations of factors is taken as the reservoir stimulation area effectiveness rate under different combinations of factors:

[0018] ;

[0019] In the formula: The efficiency of reservoir stimulation area under different combinations of factors; The reservoir fracture communication area under different combinations of factors; For the first The area of ​​interlayer crack communication under the combination of factors.

[0020] Preferably, the step of obtaining the average difference of the reservoir stimulation area efficiency under different combinations of factors, and determining the influence of each factor on the reservoir stimulation area efficiency, specifically includes:

[0021] Determining factors range for:

[0022] ;

[0023] In the formula: The factors in the orthogonal experimental table obtained through orthogonal experiments Horizontal serial number; As factors In the The average efficiency of reservoir stimulation area at the horizontal level; This represents the maximum value corresponding to the average value of reservoir stimulation area efficiency under different combinations of factors. This represents the minimum value corresponding to the average reservoir stimulation area efficiency under different combinations of factors.

[0024] Factors range As a factor The extent of the impact on the effectiveness of reservoir stimulation area.

[0025] Preferably, determining the interference coefficient of the two factors based on the maximum and minimum values ​​and influence range of the reservoir stimulation area efficiency under different combinations of factors specifically includes:

[0026] Build Asymmetric Interference Matrix Among them, the asymmetric interference matrix matrix elements Indicating interference factors Interfering factors The intensity and properties of interference, This demonstrates the asymmetry of interference.

[0027] For each pair of factors By constructing a two-factor interaction table, the interaction effect of the two factors was determined. for:

[0028] ;

[0029] In the formula: and Factors and The maximum and minimum values ​​of reservoir stimulation area efficiency under different combinations of factors; , Factors , The extent of the impact on the effectiveness of reservoir stimulation area;

[0030] The interaction effect of the two factors Quantify two factors The mutual influence effect when working together;

[0031] Based on interaction effect Determine the elements of the interference matrix to account for the two-factor interaction effect. Standardization, obtaining factors right The quantized value of the interference intensity is used as the two-factor interference coefficient;

[0032] Interference coefficient for:

[0033] ;

[0034] In the formula: The two-factor interaction table shows all two-factor interaction effects. The maximum value of the corresponding absolute value.

[0035] Preferably, obtaining the proportion of each factor under different combinations of factors and determining the single-factor potential energy corresponding to each factor specifically includes:

[0036] For the first The first factor combination The factors were normalized to obtain the normalized data. ;

[0037] Get the The first factor combination The proportion of each factor for:

[0038] ;

[0039] Where: specific gravity Reflecting this factor Distribution proportions under different combinations of factors The number of factor combinations;

[0040] According to the The first factor combination The proportion of each factor Determine the first Systematic variability of the factors for:

[0041] ;

[0042] In the formula: To measure the first The degree of dispersion of the data for each factor itself;

[0043] According to the Systematic variability of each factor Determine the first The intrinsic influence coefficient of each factor for:

[0044] ;

[0045] In the formula: For the number of factors;

[0046] Obtain the mean of the normalized data, and then use the mean of the normalized data and the first... The intrinsic influence coefficient of each factor , obtained the Single-factor potential of each factor for:

[0047] ;

[0048] In the formula: As factors The average value.

[0049] Preferably, the step of determining the influence of each factor on crack propagation under the current working condition by determining the magnitude of the system interference coefficient of each factor under different combinations of factors, based on the interference coefficient and single-factor potential energy, specifically includes:

[0050] The system interference coefficients for each factor under different combinations of factors are obtained as follows:

[0051] ;

[0052] In the formula: For the first under different combinations of factors The system interference coefficient of each factor; Interfering factors Single-factor potential energy; Interfering factors Interfering factors The interference coefficient; Interfering factors Single-factor potential energy;

[0053] The system interference coefficients of each factor under different combinations of factors are sorted from largest to smallest. The higher the ranking, the more significant the influence of the factor on the crack propagation under the current working condition.

[0054] Preferably, the step of obtaining the rock mechanical properties, in-situ stress field, and fracturing operation parameters of the fractures under different test levels through simulation, and constructing a basic database of reservoirs and interlayers corresponding to the fractures under different working conditions, specifically includes:

[0055] By using mechanical tests, well logging data, and acoustic emission tests at different experimental levels, the rock mechanical properties, geostress field, and fracturing construction parameters of fractures under different working conditions were simulated and obtained.

[0056] The rock mechanical properties, geostress field and fracturing parameters of the fractures obtained under different working conditions are sorted out to construct a basic database covering the reservoirs and interlayers corresponding to the fractures in the target layer.

[0057] Preferably, it also includes a system for identifying factors influencing the propagation of fractures across layers in directional well reservoir fracturing, comprising:

[0058] The data acquisition module is used to acquire the rock mechanical properties, geostress field and fracturing construction parameters of the fractures under different test levels through simulation, and to build a basic database of reservoirs and interlayers corresponding to the fractures under different working conditions.

[0059] The reservoir stimulation area efficiency determination module is used to screen the basic database and construct a factor set for each working condition through correlation analysis; based on the factor set, multiple non-repeating factor combinations are generated through orthogonal experiments; numerical simulation is used to obtain the fracture propagation path in the reservoir and interlayer under multiple non-repeating factor combinations; based on the propagation path, the reservoir stimulation area efficiency of fractures under different factor combinations is determined.

[0060] The interference coefficient determination module is used to obtain the average difference of the reservoir stimulation area efficiency under different combinations of factors, and to determine the influence range of each factor on the reservoir stimulation area efficiency; based on the maximum and minimum values ​​and influence range of the reservoir stimulation area efficiency under different combinations of factors, the interference coefficient of the two factors is determined.

[0061] The single-factor potential energy determination module is used to obtain the proportion of each factor under different combinations of factors and determine the single-factor potential energy corresponding to each factor.

[0062] The influencing factor identification module is used to determine the degree of influence of each factor on the crack propagation under the current working condition by determining the magnitude of the system interference coefficient of each factor under different combinations of factors, based on the interference coefficient and single-factor potential energy.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] The proposed method for identifying factors influencing fracture propagation across layers in directional well reservoir fracturing involves screening an established database to construct a factor set and generating multiple non-repeating factor combinations through orthogonal experiments. Numerical simulations are used to obtain the propagation paths of fractures in the reservoir and interlayers under different factor combinations, thereby determining the reservoir stimulation area efficiency of fractures under different factor combinations. Based on the maximum and minimum values ​​and influence amplitude of the reservoir stimulation area efficiency of the two factors under different factor combinations, the interference coefficient of the two factors is determined. Finally, based on the interference coefficient and the potential energy of a single factor, the influence of each factor on fracture propagation across layers is determined by assessing the magnitude of the system interference coefficient of each factor under different factor combinations.

[0065] This database encompasses various key factors influencing fracture propagation across layers under different operating conditions, providing comprehensive and systematic data support for subsequent in-depth analysis and avoiding analytical biases caused by missing or incomplete data. The constructed factor set can accurately identify key factors closely related to fracture propagation across layers. Orthogonal experimental design provides diverse samples for subsequent numerical simulations. The propagation paths of fractures in reservoirs and interlayers under different factor combinations obtained through numerical simulations can intuitively and clearly demonstrate the propagation morphology and trend of fractures under different conditions. The determined reservoir stimulation area efficiency quantifies the fracture propagation effect. By determining the influence range of each factor on the reservoir stimulation area efficiency, the individual influence of each factor on fracture propagation across layers can be clearly quantified, helping to identify key single factors with a significant impact on fracture propagation. Based on the maximum and minimum values ​​and influence ranges of the reservoir stimulation area efficiency under different factor combinations, the interference coefficient of the two factors is determined, fully considering the influence of the interaction between the two factors on fracture propagation across layers. In reality, factors often do not act in isolation; the determination of the two-factor interference coefficient makes the analysis closer to reality, improving the accuracy and reliability of the research results. The determined system interference coefficient comprehensively considers both single-factor effects and two-factor interactions, and can more accurately reflect the actual influence of each factor on the fracture penetration propagation process under each working condition, providing a scientific basis for optimizing fracturing construction parameters and improving the fracture penetration propagation effect. Attached Figure Description

[0066] Figure 1 This is a flowchart of the method for identifying factors influencing fracture propagation across layers in directional well reservoir fracturing proposed in this invention;

[0067] Figure 2 This is a numerical simulation model for directional well cross-layer fracturing provided in an embodiment of the present invention;

[0068] Figure 3 A schematic diagram of the discrete lattice method provided in an embodiment of the present invention;

[0069] Figure 4 This is a schematic diagram of the fracture communication area in sandstone reservoirs and the fracture communication area in mudstone interlayers provided in an embodiment of the present invention;

[0070] Figure 5 An example of the elastic modulus of crack propagation morphology provided in this embodiment of the invention;

[0071] Figure 6 A flowchart for calculating the multi-factor interference matrix provided in an embodiment of the present invention;

[0072] Figure 7 The multi-factor interference matrix for target layer penetration provided in the embodiments of the present invention. Detailed Implementation

[0073] The following will refer to the appendices in the embodiments of the present invention. Figures 1-7 The technical solutions in the embodiments of the present invention will be clearly and completely described. It should be understood that the terminology used in the present invention is only for describing particular implementation methods and is not intended to limit the present invention.

[0074] This invention aims to overcome the shortcomings of existing methods for identifying the main controlling factors in fracturing of multi-layered reservoirs in directional wells, particularly the deficiencies in static analysis and insufficient characterization of coupling effects. Specifically, static analysis is hampered by traditional methods that typically employ single-factor sensitivity analysis, such as fixing other parameters and only examining the independent influence of interlayer stress differences, neglecting the actual dynamic changes of multiple parameters during fracturing. Insufficient characterization of coupling effects is also evident in the difficulty of quantifying the interactions between factors, such as the inability to quantify complex coupling mechanisms like the impact of displacement changes on the elastic modulus of interlayers or the interference effect between well inclination angle and interface dip angle. This new method quantifies the interaction strength between factors by establishing a system interference matrix that integrates multi-factor coupling mechanisms; it introduces a system interference coefficient that comprehensively considers single-factor potential energy and system interference effects to achieve objective identification and ranking of the main controlling factors; ultimately forming an identification method aimed at optimizing fracturing design, providing a decision-making basis for improving the effectiveness of multi-layered reservoir stimulation.

[0075] Specifically, this invention establishes a standardized database of reservoir geomechanical parameters and fracturing engineering parameters to obtain key data such as rock mechanics parameters, interlayer stress differences, interface properties, and construction parameters of the target layer. Based on this, the reservoir stimulation area effectiveness rate is defined as a cross-layer evaluation index. An orthogonal experimental scheme is designed, and a numerical simulation model of the target block is established using a three-dimensional hydraulic fracturing simulation platform to simulate fracture cross-layer propagation results under different parameter combinations, thereby calculating the reservoir stimulation area effectiveness rate.

[0076] By defining single-factor potential energy, all influencing factors are uniformly quantified into comparable parameters. By normalizing each influencing factor, the intrinsic influence coefficient is determined based on system variability analysis, and the single-factor potential energy value is calculated to quantify the independent influence capacity of each factor. By defining two-factor interaction effect, the range and interaction effect of each factor are calculated, and an asymmetric multi-factor interference matrix is ​​established to accurately characterize the interference intensity between factors.

[0077] By defining the system interference coefficient, which integrates the potential energy of a single factor and its interference effect with all factors, a quantitative value of the factor's "comprehensive influence" in the system is obtained. ,according to The values ​​are sorted from largest to smallest. The factor ranked first is... The factor with the highest value is identified as the primary controlling factor; the second-ranked factor is the secondary controlling factor, and so on. These factors not only have strong individual influence but also occupy a central position in the multi-factor system, and their changes can significantly amplify or alter the behavior of the entire system through interference effects.

[0078] Example

[0079] like Figure 1 As shown in the figure, a method for identifying factors influencing fracture propagation across layers in directional well reservoir fracturing, provided by an embodiment of the present invention, includes the following steps:

[0080] S1: By simulating and obtaining the rock mechanical properties, geostress field and fracturing construction parameters of the fractures under different test levels, a basic database of reservoirs and interlayers corresponding to the fractures under different working conditions is constructed.

[0081] S2: For each working condition, the basic database is screened through correlation analysis to construct a factor set; based on the factor set, multiple non-repeating factor combinations are generated through orthogonal experiments; numerical simulation is used to obtain the propagation path of fractures in the reservoir and interlayer under multiple non-repeating factor combinations; based on the propagation path, the reservoir stimulation area efficiency of fractures under different factor combinations is determined.

[0082] S3: Obtain the average difference of the reservoir stimulation area efficiency under different combinations of factors, and determine the influence range of each factor on the reservoir stimulation area efficiency; determine the interference coefficient of the two factors based on the maximum and minimum values ​​and influence range of the reservoir stimulation area efficiency under different combinations of factors.

[0083] S4: Obtain the proportion of each factor under different combinations of factors, and determine the single-factor potential energy corresponding to each factor;

[0084] S5: Based on the interference coefficient and single-factor potential energy, determine the influence of each factor on the crack propagation under the current working condition by determining the magnitude of the system interference coefficient of each factor under different combinations of factors.

[0085] Specifically, in step S1, through well logging interpretation, triaxial compression test, acoustic emission test, direct shear test and fracturing design requirements, the rock mechanics parameters, interlayer stress difference, interface properties and construction parameters of the target section of the directional well under different test levels are obtained, and the basic database of the target section is constructed respectively.

[0086] In step S2, based on geomechanical principles and fracturing engineering experience, the basic database is screened through correlation analysis. The system identifies several key parameters that may affect fracture cross-layer behavior and constructs a factor set X={X1, X2, ..., X...} 10 The specific set of factors provided in this embodiment is shown in Table 1. Typical factors include, but are not limited to: interlayer stress difference, interface dip angle, fracturing fluid viscosity, fracturing flow rate, well inclination angle, elastic modulus, Poisson's ratio, fracture toughness, interface dip angle, reservoir thickness, etc.

[0087] Table 1. Set of factors affecting cross-layer influencing factors

[0088]

[0089] As shown in Table 2, this is the basic database of the target layer provided in this embodiment. The mechanical parameters of the target layer are calculated from well logging interpretation data, the interface cohesion is measured by triaxial compression test, and the stress field distribution is obtained by acoustic emission test.

[0090] Table 2 Basic Database of Target Layer

[0091]

[0092] like Figure 2 As shown, based on the model geometry of the target layer (10×10×10m), reservoir thickness (2.5~3m), and interlayer thickness (2m), a three-dimensional hydraulic fracturing numerical simulation model of the fracture is established using a three-dimensional hydraulic fracturing simulation platform based on the discrete lattice method.

[0093] The basic parameters used in the numerical simulation stage are derived directly from the basic database in Table 2, ensuring the representativeness of the mine and the geological authenticity of the simulation input parameters. For example, the elastic modulus, Poisson's ratio, compressive strength, and fracture toughness are grouped according to reservoir layers, and the maximum value of each group is taken to highlight the mechanical differences between layers. The basic parameters for modeling are shown in Table 3.

[0094] Table 3. Basic Parameters for Numerical Simulation

[0095]

[0096] Based on the key influencing factor set for cross-layering X={X1, X2, ..., X... 10 By designing corresponding orthogonal experimental tables, multiple sets of experiments with different combinations of factors were determined. Within a reasonable range of geological and construction parameters in the actual mine, the values ​​of influencing factors were systematically changed. The effective rate of reservoir stimulation area was used as the evaluation index. Simulation calculations were performed, and the calculation results of each scheme were recorded. Based on the formula for calculating the effective rate of reservoir stimulation area, the effective rate of reservoir stimulation area under different combinations of factors was calculated and used as the evaluation index.

[0097] The solution provided in this embodiment is L. 81 (3 10 The orthogonal array, its numerical simulation scheme and results are shown in Table 4. Based on the results of the embodiment and the analysis of visualized crack propagation morphology, A value between 70% and 100% indicates that the fractures are unlikely to penetrate the interlayer and only extend within the reservoir region. When the value is less than 70%, it indicates that the fracture can completely penetrate the interlayer, signifying that the fracture height has exceeded the reservoir. Numerical simulation of fracture propagation morphology is shown in [reference needed]. Figure 4 .

[0098] Table 4 Numerical simulation test scheme and results for the target layer

[0099]

[0100] Based on the three-dimensional hydraulic fracturing numerical simulation model, the construction parameters are input, and the hydraulic fracturing simulation is run to simulate the propagation of fractures through layers in a multi-layered reservoir. The three-dimensional hydraulic fracturing numerical simulation model based on the discrete lattice method obtains the propagation path of fractures in the reservoir and interlayers under multiple non-repeating combinations of factors.

[0101] like Figure 3The diagram illustrates the discrete lattice method provided in this embodiment of the invention. The three-dimensional hydraulic fracturing numerical simulation model based on the discrete lattice method abstracts the medium into a network structure composed of a spring-node system. Springs simulate the interaction forces (contact relationships) between particles, while nodes with mass represent particles. When the stress on a spring exceeds its strength limit, it fractures and forms microcracks. As these microcracks continue to propagate and merge, they eventually evolve into a three-dimensional non-planar fracture morphology. Simultaneously, fluid elements are distributed at the midpoints between adjacent nodes and interconnected through flow channels, forming a network of fluid flow channels to simulate the seepage process of fluid in the medium. This method does not require pre-setting the crack propagation path and can intuitively simulate the initiation and propagation of three-dimensional cracks and the flow behavior of fluid in the fracture network, providing an effective numerical simulation method for studying complex fracture-seepage coupling processes.

[0102] Based on the obtained expansion path, the grid area of ​​fractures located in the reservoir and interlayer regions under different combinations of factors is determined using the discrete lattice method.

[0103] Based on the grid area, the contact area between the fracture surface and the reservoir rock mass and the contact area between the fracture surface and the interlayer rock mass after penetrating the reservoir are determined under different combinations of factors, and the reservoir fracture communication area and interlayer fracture communication area are obtained accordingly under different combinations of factors.

[0104] like Figure 4 The diagram shows the fracture communication area of ​​sandstone reservoir and the fracture communication area of ​​mudstone interlayer provided in this embodiment. The present invention uses the ratio of the reservoir fracture communication area to the interlayer fracture communication area under different combinations of factors as the reservoir stimulation area efficiency of fractures under different combinations of factors, and as the cross-layer evaluation index corresponding to different combinations of factors. This not only conforms to the characteristics of alternating multi-layer reservoirs and interlayers and strong vertical heterogeneity, but also characterizes the fracture cross-layer capability, and meets the core needs of scarce development well locations and the pursuit of efficient utilization of reserves.

[0105] Among them, the effective rate of reservoir stimulation area under different combinations of factors is:

[0106] ;

[0107] In the formula: The efficiency of reservoir stimulation area under different combinations of factors; The reservoir fracture communication area under different combinations of factors, m 2 ; For the first The interlayer crack communication area under the combination of factors, m 2 .

[0108] like Figure 5The figure shows an example of the elastic modulus of the influencing factors of fracture propagation morphology provided in this embodiment. That is, the propagation path of fractures in the reservoir and interlayer is obtained by simulation of various combinations of factors using a three-dimensional hydraulic fracturing numerical simulation model based on the discrete lattice method. Figure 5 Subgraphs (a), (b), and (c) in the figure are visualizations of the model simulation of the interlayer under different elastic moduli. The elastic modulus is a measure of the stiffness of a material. The higher the elastic modulus of the interlayer, the stiffer the material, and the less resistance the crack encounters when it propagates (or the dynamic characteristics of crack propagation change), which allows the crack to penetrate higher interlayers. Figure 5 Each sub-figure in the image, from left to right, represents a 3D visualization of the crack, a planar visualization of the crack, and a crack height visualization. The crack height visualization shows the crack's propagation in the vertical direction (z-direction). As the elastic modulus of the interlayer increases from 7.5 GPa to 17.5 GPa, the vertical propagation range of the crack gradually increases, indicating that the crack can penetrate more or thicker interlayers. Therefore, as the elastic modulus of the interlayer increases, the penetration height of the crack also increases.

[0109] In step S3, construct Asymmetric Interference Matrix Among them, the asymmetric interference matrix matrix elements Indicating interference factors Interfering factors The intensity and properties of interference, This demonstrates the asymmetry of interference.

[0110] Based on the quantitative analysis results of numerical simulation, a multi-factor interference matrix was constructed using a combination of range analysis and interaction effects. The specific steps are as follows:

[0111] For each factor, multiple experimental levels are designed, and each experimental level corresponds to multiple schemes, with each scheme including different cross-layer evaluation indicators;

[0112] This invention uses one of the experimental levels as a benchmark, based on the reservoir stimulation area efficiency. As a quantitative first Factors affecting the evaluation index of cross-layers The extent of the impact. By calculating the difference in the average values ​​of the evaluation indicators for tunneling under different levels of factors, the range of influence of factor changes on the results is reflected.

[0113] Determining factors range for:

[0114] ;

[0115] In the formula: The factors in the orthogonal experimental table obtained through orthogonal experiments Horizontal serial number; As factors In the The average efficiency of reservoir stimulation area at the horizontal level; This represents the maximum value corresponding to the average value of reservoir stimulation area efficiency under different combinations of factors. This represents the minimum value corresponding to the average reservoir stimulation area efficiency under different combinations of factors.

[0116] For each pair of factors Construct a two-factor interaction table to determine its interaction effect. for:

[0117] ;

[0118] In the formula: and Factors and Maximum and minimum values ​​of the cross-layer evaluation index under different combinations of factors; , Factors , The single-factor range;

[0119] Among them, the interaction effect Quantify two factors The mutual influence effect when working together;

[0120] Based on interaction effect Determine the elements of the interference matrix and the two-factor interaction effect values. Standardization, obtaining factors right The quantized value of the interference intensity, i.e., the quantized interference coefficient. for:

[0121] ;

[0122] In the formula: For all interaction effects The maximum absolute value of the corresponding interference coefficient. The closer the value is to 1, the stronger the interaction interference between the two factors; the interference coefficient The closer the value is to 0, the weaker the interaction between the two factors.

[0123] like Figure 6 The diagram shown is a flowchart for calculating the multi-factor interference matrix, combining normalized data and intrinsic influence coefficients. The sum of the average values ​​of all factors comprehensively reflects the independent effect of a single factor. Single-factor potential energy. The value of reflects the potential influence of a factor based on its current state and physical nature, ignoring the interactions of other factors. This value distinguishes the strength of each factor's influence and provides a "benchmark value" for subsequent interference effect analysis.

[0124] In step S4, the present invention determines the systematic variability of each factor by obtaining the proportion of each factor at different experimental levels; based on the systematic variability of each factor, the intrinsic influence coefficient of each factor on the system is obtained; based on the average value and intrinsic influence coefficient of each factor, the single-factor potential of each factor is determined, specifically:

[0125] To eliminate the influence of dimensions, for the first The first factor combination The factors were normalized to obtain the normalized data. :

[0126] ;

[0127] In the formula: and Each of these factors Reasonable lower and upper limits in an engineering sense.

[0128] Get the The first factor combination The proportion of each factor for:

[0129] ;

[0130] Where: specific gravity Reflecting this factor Distribution proportions under different combinations of factors The number of factor combinations;

[0131] According to the The first factor combination The proportion of each factor Determine the first Systematic variability of the factors for:

[0132] ;

[0133] In the formula: Measure the first The degree of dispersion of the factor data itself reflects the differences in the distribution of factor values.

[0134] According to the Systematic variability of the factors Determine the first The intrinsic influence coefficient of the factor for:

[0135] ;

[0136] In the formula: For the number of factors;

[0137] Obtain the mean of the normalized data, and then use the mean of the normalized data and the first... The intrinsic influence coefficient of the factor The potential energy obtained is a single-factor potential. for:

[0138] ;

[0139] In the formula: As factors The average value.

[0140] In step S5, the system interference coefficients of each factor under different combinations of factors are obtained. for:

[0141] ;

[0142] In the formula: Interfering factors Single-factor potential energy; Interfering factors Interfering factors The interference coefficient; Interfering factors Single-factor potential energy;

[0143] System interference coefficients under different combinations of factors It can integrate the potential energy of a single factor with the interference effects of all other factors to obtain a quantitative value of the factor's "comprehensive influence" in the system, thus reflecting its own influence.

[0144] The system interference coefficients of each factor under different combinations of factors were obtained. The factors are sorted from largest to smallest. The higher the ranking, the greater the influence of the factor on the crack propagation under the current working conditions, and the more obvious the interference effect.

[0145] The number one factor is, The factor with the largest value is identified as the primary controlling factor for fracture propagation across layers under this operating condition; the second largest is the secondary controlling factor, and so on. These factors not only have strong individual influence but also occupy a core position in the multi-factor system, and their changes can significantly amplify or alter the behavior of the entire system through interference effects. The system interference coefficients provided in the example are shown in Table 5, and the top three controlling factors are: displacement, well inclination angle, and viscosity.

[0146] Table 5 Interference coefficients of the cross-layer system in the target layer segment

[0147]

[0148] like Figure 7 As shown in the figure, this is the multi-factor interference matrix provided in this embodiment. As can be seen from the figure, =1 indicates that the displacement of the interfering factor and the elastic modulus of the interfering factor have a strong interference effect. = 0.34545, indicating a weak interference effect between the elastic modulus of the interfering factor and the displacement of the interfering factor. This means that in this embodiment, the change in displacement has a very strong modulating effect on the elastic modulus factor (i.e., its influence on the fracture penetration effect); while the change in elastic modulus has a much weaker modulating effect on the displacement factor. This reveals that in this embodiment, displacement plays a more fundamental role, and its change will alter the actual effect of the interlayer elastic modulus on fracture penetration.

[0149] This invention also proposes a system for identifying factors influencing fracture propagation across layers in directional well reservoir fracturing, comprising:

[0150] The data acquisition module is used to acquire the rock mechanical properties, geostress field and fracturing construction parameters of the fractures under different test levels through simulation, and to build a basic database of reservoirs and interlayers corresponding to the fractures under different working conditions.

[0151] The reservoir stimulation area efficiency determination module is used to screen the basic database and construct a factor set for each working condition through correlation analysis; based on the factor set, multiple non-repeating factor combinations are generated through orthogonal experiments; numerical simulation is used to obtain the fracture propagation path in the reservoir and interlayer under multiple non-repeating factor combinations; based on the propagation path, the reservoir stimulation area efficiency of fractures under different factor combinations is determined.

[0152] The interference coefficient determination module is used to obtain the average difference of the reservoir stimulation area efficiency under different combinations of factors, and to determine the influence range of each factor on the reservoir stimulation area efficiency; based on the maximum and minimum values ​​and influence range of the reservoir stimulation area efficiency under different combinations of factors, the interference coefficient of the two factors is determined.

[0153] The single-factor potential energy determination module is used to obtain the proportion of each factor under different combinations of factors and determine the single-factor potential energy corresponding to each factor.

[0154] The influencing factor identification module is used to determine the degree of influence of each factor on the crack propagation under the current working condition by determining the magnitude of the system interference coefficient of each factor under different combinations of factors, based on the interference coefficient and single-factor potential energy.

[0155] This invention overcomes the static limitations of traditional single-factor sensitivity analysis and subjective weight assignment. It innovatively introduces the "system interference coefficient" as the core method. Based on the maximum and minimum values ​​and influence amplitudes of reservoir stimulation area efficiency under different combinations of factors, the interference coefficient of the two factors is determined, fully considering the impact of the interaction between the two factors on fracture propagation. The complex and abstract multi-factor coupling effect is transformed into calculable and sortable objective parameters. This method constructs an asymmetric interference matrix to reasonably characterize the dynamic interaction of multiple field parameters in multi-layered reservoirs.

[0156] Unlike traditional methods that consider the influence of factors in isolation, this invention places the "interference effect between factors" at the core of the analysis. Through a complete logical chain of "potential energy characterization - interference field construction - system evaluation," it achieves the objective identification and quantitative decision-making of the main controlling factors. This method not only focuses on the independent influence of each factor (single-factor potential energy) but also emphasizes its core position in the system (system interference coefficient), thereby effectively identifying the main controlling factors that have a global impact on fracturing and tunneling behavior.

[0157] This invention integrates database establishment, numerical simulation, potential energy calculation, matrix construction, and system evaluation. The method is logically rigorous, with clear steps, does not rely on subjective experience, is highly practical, and is easy to promote in the petroleum industry, providing novel theoretical support for fracturing design of multi-layered reservoirs in directional wells.

[0158] This invention overcomes the limitations of traditional methods in identifying the main controlling factors of fracturing in multi-layered reservoirs in directional wells. By establishing a multi-factor system interference analysis model, it combines the characterization of "single-factor potential energy" with the quantification of "multi-factor interference," effectively identifying the true impact of each factor on fracture penetration propagation in complex systems. This method can quantitatively identify the main controlling factors affecting fracture penetration behavior under specific geological and engineering conditions. Practical applications show that optimizing fracturing parameters based on the main controlling factors identified in this invention can effectively improve fracture penetration efficiency, providing theoretical guidance for optimizing directional wells to achieve "penetrating" fracturing.

[0159] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

[0160] Furthermore, unless otherwise stated, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. All references to this specification are incorporated by way of citation to disclose and describe methods relating to those references. In the event of any conflict with any incorporated reference, the content of this specification shall prevail.

Claims

1. A method for identifying factors influencing fracture propagation across layers in directional well reservoir fracturing, characterized in that, Includes the following steps: By simulating and obtaining the rock mechanical properties, geostress field and fracturing construction parameters of fractures under different test levels, a basic database of reservoirs and interlayers corresponding to fractures under different working conditions is constructed. For each working condition, the basic database is filtered through correlation analysis to construct a factor set; based on the factor set, multiple non-repeating factor combinations are generated through orthogonal experiments; numerical simulation is used to obtain the fracture propagation path in the reservoir and interlayer under multiple non-repeating factor combinations. Based on the expansion path, determine the reservoir stimulation area efficiency under different combinations of factors for fractures. Obtain the average difference of reservoir stimulation area efficiency under different combinations of factors to determine the influence range of each factor on reservoir stimulation area efficiency; determine the interference coefficient of the two factors based on the maximum and minimum values ​​and influence range of reservoir stimulation area efficiency under different combinations of factors. Obtain the proportion of each factor under different combinations of factors, and determine the single-factor potential energy corresponding to each factor; Based on the interference coefficient and single-factor potential energy, the influence of each factor on crack propagation under the current working condition is determined by determining the magnitude of the system interference coefficient of each factor under different combinations of factors. The propagation paths of fractures in reservoirs and interlayers are obtained through numerical simulation under multiple non-repeating combinations of factors. Based on the propagation path, the reservoir stimulation area efficiency under different combinations of factors is determined, specifically including: Based on the model geometry of the fracture, a three-dimensional hydraulic fracturing numerical simulation model of the fracture is established using a three-dimensional hydraulic fracturing simulation platform based on the discrete lattice method. Multiple non-repeating factor combinations are input into a three-dimensional hydraulic fracturing numerical simulation model to simulate fracture propagation across layers in a multi-layered reservoir, and to obtain the propagation paths of fractures in the reservoir and interlayers under different factor combinations. Based on the obtained expansion path, the grid area of ​​fractures located in the reservoir and interlayer regions under different combinations of factors is determined using the discrete lattice method. Based on the grid area, determine the contact area between the fracture surface and the reservoir rock mass and the contact area between the fracture surface and the interlayer rock mass after penetrating the reservoir under different factor combinations, and obtain the reservoir fracture communication area and interlayer fracture communication area under different factor combinations. The ratio of reservoir fracture connectivity area to interlayer fracture connectivity area under different factor combinations is taken as the reservoir stimulation area effectiveness rate under different factor combinations: In the formula: The efficiency of reservoir stimulation area under different combinations of factors; The reservoir fracture communication area under different combinations of factors; For the first The area of ​​interlayer crack communication under the combination of factors; The step of obtaining the average difference of the reservoir stimulation area efficiency under different combinations of factors, and determining the influence of each factor on the reservoir stimulation area efficiency, specifically includes: Determining factors range for: In the formula: The factors in the orthogonal experimental table obtained through orthogonal experiments Horizontal serial number; As factors In the The average efficiency of reservoir stimulation area at the horizontal level; This represents the maximum value corresponding to the average value of reservoir stimulation area efficiency under different combinations of factors. This represents the minimum value corresponding to the average reservoir stimulation area efficiency under different combinations of factors. Factors range As a factor The extent of the impact on the effectiveness of reservoir stimulation area.

2. The method for identifying factors influencing fracture propagation across layers in directional well reservoir fracturing according to claim 1, characterized in that, The determination of the interference coefficient of the two factors based on the maximum and minimum values ​​and influence range of the reservoir stimulation area efficiency under different combinations of factors specifically includes: Build Asymmetric Interference Matrix Among them, the asymmetric interference matrix matrix elements Indicating interference factors Interfering factors The intensity and properties of interference, This demonstrates the asymmetry of interference. For each pair of factors By constructing a two-factor interaction table, the interaction effect of the two factors was determined. for: In the formula: and Factors and The maximum and minimum values ​​of reservoir stimulation area efficiency under different combinations of factors; , Factors , The extent of the impact on the effectiveness of reservoir stimulation area; The interaction effect of the two factors Quantify two factors The mutual influence effect when working together; Based on interaction effect Determine the elements of the interference matrix to account for the two-factor interaction effect. Standardization, obtaining factors right The quantized value of the interference intensity is used as the two-factor interference coefficient; Interference coefficient for: In the formula: The two-factor interaction table shows all two-factor interaction effects. The maximum value of the corresponding absolute value.

3. The method for identifying factors influencing fracture propagation across layers in directional well reservoir fracturing according to claim 1, characterized in that, The process of obtaining the proportion of each factor under different combinations of factors and determining the single-factor potential energy corresponding to each factor specifically includes: For the first The first factor combination The factors were normalized to obtain the normalized data. ; Get the The first factor combination The proportion of each factor for: Where: specific gravity Reflecting this factor Distribution proportions under different combinations of factors The number of factor combinations; According to the The first factor combination The proportion of each factor Determine the first Systematic variability of the factors for: In the formula: To measure the first The degree of dispersion of the data for each factor itself; According to the Systematic variability of each factor Determine the first The intrinsic influence coefficient of each factor for: In the formula: For the number of factors; Obtain the mean of the normalized data, and then use the mean of the normalized data and the first... The intrinsic influence coefficient of each factor , obtained the Single-factor potential of each factor for: In the formula: As factors The average value.

4. The method for identifying factors influencing fracture propagation across layers in directional well reservoir fracturing according to claim 3, characterized in that, The method involves determining the influence of each factor on crack propagation under the current working condition by analyzing the system interference coefficients of each factor under different combinations of factors and the potential energy of each factor. Specifically, this includes: The system interference coefficients for each factor under different combinations of factors are obtained as follows: In the formula: For the first under different combinations of factors The system interference coefficient of each factor; Interfering factors Single-factor potential energy; Interfering factors Interfering factors The interference coefficient; Interfering factors Single-factor potential energy; The system interference coefficients of each factor under different combinations of factors are sorted from largest to smallest. The higher the ranking, the more significant the influence of the factor on the crack propagation under the current working condition.

5. The method for identifying factors influencing fracture propagation across layers in directional well reservoir fracturing according to claim 1, characterized in that, The process involves simulating and obtaining the rock mechanical properties, in-situ stress field, and fracturing parameters of fractures under different experimental levels. This leads to the construction of a basic database of reservoirs and interlayers corresponding to fractures under different operating conditions. Specifically, this includes: By using mechanical tests, well logging data, and acoustic emission tests at different experimental levels, the rock mechanical properties, geostress field, and fracturing construction parameters of fractures under different working conditions were simulated and obtained. The rock mechanical properties, geostress field and fracturing parameters of the fractures obtained under different working conditions are sorted out to construct a basic database covering the reservoirs and interlayers corresponding to the fractures in the target layer.

6. A system for identifying factors influencing fracture propagation across layers in directional well reservoir fracturing, characterized in that, include: The data acquisition module is used to acquire the rock mechanical properties, geostress field and fracturing construction parameters of the fractures under different test levels through simulation, and to build a basic database of reservoirs and interlayers corresponding to the fractures under different working conditions. The reservoir stimulation area efficiency determination module is used to screen the basic database and construct a factor set for each working condition through correlation analysis; based on the factor set, multiple non-repeating factor combinations are generated through orthogonal experiments; and numerical simulation is used to obtain the fracture propagation path in the reservoir and interlayer under multiple non-repeating factor combinations. Based on the expansion path, determine the reservoir stimulation area efficiency under different combinations of factors for fractures. The interference coefficient determination module is used to obtain the average difference of the reservoir stimulation area efficiency under different combinations of factors, and to determine the influence range of each factor on the reservoir stimulation area efficiency; based on the maximum and minimum values ​​and influence range of the reservoir stimulation area efficiency under different combinations of factors, the interference coefficient of the two factors is determined. The single-factor potential energy determination module is used to obtain the proportion of each factor under different combinations of factors and determine the single-factor potential energy corresponding to each factor. The influencing factor identification module is used to determine the degree of influence of each factor on the crack propagation under the current working condition by determining the magnitude of the system interference coefficient of each factor under different combinations of factors based on the interference coefficient and single-factor potential energy. Specifically, the step of obtaining fracture propagation paths in the reservoir and interlayers under multiple non-repeating combinations of factors through numerical simulation, and determining the reservoir stimulation area efficiency of fractures under different combinations of factors based on the propagation paths, includes: Based on the model geometry of the fracture, a three-dimensional hydraulic fracturing numerical simulation model of the fracture is established using a three-dimensional hydraulic fracturing simulation platform based on the discrete lattice method. Multiple non-repeating factor combinations are input into a three-dimensional hydraulic fracturing numerical simulation model to simulate fracture propagation across layers in a multi-layered reservoir, and to obtain the propagation paths of fractures in the reservoir and interlayers under different factor combinations. Based on the obtained expansion path, the grid area of ​​fractures located in the reservoir and interlayer regions under different combinations of factors is determined using the discrete lattice method. Based on the grid area, determine the contact area between the fracture surface and the reservoir rock mass and the contact area between the fracture surface and the interlayer rock mass after penetrating the reservoir under different factor combinations, and obtain the reservoir fracture communication area and interlayer fracture communication area under different factor combinations. The ratio of reservoir fracture connectivity area to interlayer fracture connectivity area under different factor combinations is taken as the reservoir stimulation area effectiveness rate under different factor combinations: In the formula: The efficiency of reservoir stimulation area under different combinations of factors; The reservoir fracture communication area under different combinations of factors; For the first The area of ​​interlayer crack communication under the combination of factors; The step of obtaining the average difference of the reservoir stimulation area efficiency under different combinations of factors, and determining the influence of each factor on the reservoir stimulation area efficiency, specifically includes: Determining factors range for: In the formula: The factors in the orthogonal experimental table obtained through orthogonal experiments Horizontal serial number; As factors In the The average efficiency of reservoir stimulation area at the horizontal level; This represents the maximum value corresponding to the average value of reservoir stimulation area efficiency under different combinations of factors. This represents the minimum value corresponding to the average reservoir stimulation area efficiency under different combinations of factors. Factors range As a factor The extent of the impact on the effectiveness of reservoir stimulation area.

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