Fault connectivity prediction method based on PCA-CRITIC weights

Abstract

This invention provides a method for predicting fault connectivity based on PCA-CRITIC weights, relating to the field of oil and gas exploration technology. It includes determining the influencing factors of fault vertical connectivity, including the thickness of the displaced caprock, clay content, fault plane normal stress, formation fluid pressure, caprock brittleness index, and fault displacement. A comprehensive mathematical formula for the fault vertical connectivity coefficient is established. Based on the oil, gas, and water data from drilled wells and paleoreservoir tracing data near the displaced strata, the vertical connectivity coefficient is calculated using the comprehensive mathematical formula and by well and well depth segment. The vertical connectivity is analyzed based on the fault vertical connectivity coefficient and the oil and gas content of the near displaced strata, predicting the fault vertical connectivity in oil and gas basins. This invention accurately characterizes the oil and gas migration paths within faults, significantly improving prediction accuracy and exploration efficiency.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration technology, and in particular to a method for predicting fault connectivity based on PCA-CRITIC weights. Background Technology

[0002] In regions with complex geological structures and well-developed faults resulting from multiple periods of geological activity, faults play a crucial role in the migration and accumulation of oil and gas. When a fault exists within a trap, oil and gas can migrate vertically from deeper areas to shallower areas. However, this migration capacity is significantly reduced when encountering faults with strong sealing properties. Furthermore, for faults that cut through the reservoir-caprock system, once the vertical sealing capacity of the caprock is destroyed by the fault, oil and gas migration is only possible during the active phase and the active-intermittent transition phase of the fault. During the active-intermittent transition phase, the rocks are in the fracture healing stage, and although the conductivity of oil and gas is weakened and permeability decreases, there is still some fluid migration. Oil and gas move along the fracture zone primarily driven by buoyancy. When the fracture gradually closes due to water-rock action, and permeability gradually decreases to zero, the fault zone becomes sealed. The sealing performance of faults plays a vital role in the regional distribution of oil reservoirs, and its sealing performance evaluation is a key part of finding favorable exploration areas and high-quality oil and gas reservoir traps.

[0003] A fault is not a simple cross-section, but a complex zone composed of numerous structural units, including fractures, breccia zones, cemented zones, and mudstone smears. These components exhibit significantly different physical properties under varying geological periods and temperature / pressure conditions, leading to varying impacts on hydrocarbon migration. Their conduction or closure is also controlled by multiple geological factors. Previous studies have extensively evaluated the comprehensive effects of various factors on fault connectivity, achieving relatively good results. However, the weights of each influencing parameter have not been determined, and the specific migration paths of hydrocarbons within faults cannot be precisely characterized, resulting in imprecise evaluation results. Therefore, it is essential to design a fault connectivity prediction method based on PCA-CRITIC weights. Summary of the Invention

[0004] To overcome the shortcomings of the prior art, the purpose of this invention is to provide a fault connectivity prediction method based on PCA-CRITIC weights.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] This invention provides a fault connectivity prediction method based on PCA-CRITIC weights, comprising:

[0007] Step 1: Determine the influencing factors of vertical connectivity of the fault, including the thickness of the displaced overburden, clay content, normal stress of the fault plane, formation fluid pressure, brittleness index of the overburden rock, and fault displacement.

[0008] Step 2: Establish a comprehensive mathematical relationship between influencing factors and fault vertical connectivity, i.e., fault vertical connectivity coefficient;

[0009] Step 3: Based on the oil, gas and water data of drilled wells and the oil reservoir tracing data of the deep and shallow layers near the faulted strata, the vertical connectivity coefficient of the fault is calculated by well and well depth segment using the comprehensive mathematical relationship of the vertical connectivity coefficient of the fault.

[0010] Step 4: Analyze the vertical connectivity of the fault based on the vertical connectivity coefficient and the oil and gas content in the shallow and deep layers near the faulted strata, and predict the vertical connectivity of the fault in the oil and gas basin.

[0011] Preferably, in step 2, a comprehensive mathematical relationship is established between influencing factors and fault vertical connectivity, namely, the fault vertical connectivity coefficient, specifically as follows:

[0012] The comprehensive mathematical formula for the vertical connectivity coefficient (FTI) of the fault is:

[0013] (1)

[0014] In the formula, P is the formation fluid pressure, in MPa, and σ N The value represents the normal stress at the fault plane, in MPa. SGR is the mudstone smearing factor, and BDI is the brittleness index of the caprock.

[0015] Preferably, in step 3, based on the oil, gas, and water data of drilled wells and the oil and gas content of ancient reservoir tracing data in the vicinity of the faulted strata, the vertical connectivity coefficient of the fault is calculated by dividing the data into wells and depth segments using a comprehensive mathematical formula. Specifically:

[0016] In ResForm software, data such as well logging GR, sonic transit time, and rock density are extracted to calculate formation clay content and rock brittleness index. Data is extracted from the oil, gas and water data of drilled wells and ancient reservoir tracer data in the vicinity of faulted strata by well-by-well and well-by-deep-segment method.

[0017] The extracted data on formation clay content and rock brittleness index were imported into IBM SPSS software. The variance contribution of the two was calculated and principal component analysis was performed to obtain the principal components.

[0018] By substituting the principal components into the comprehensive mathematical formula for the fault vertical connectivity coefficient (FTI), the fault vertical connectivity coefficient is obtained.

[0019] Preferably, before performing principal component analysis on the extracted data, the following steps are also required:

[0020] The extracted data is then subjected to natural logarithm calculation and standardization.

[0021] Preferably, by substituting the principal components into the comprehensive mathematical formula for the fault vertical connectivity coefficient (FTI), the fault vertical connectivity coefficient is obtained, specifically as follows:

[0022] The principal component is indexed and substituted into the calculation formula of the mudstone smearing factor to update the mudstone smearing factor;

[0023] The updated mudstone smearing factor is substituted into the comprehensive mathematical formula of the fault vertical connectivity coefficient (FTI) to obtain the updated comprehensive mathematical formula of the fault vertical connectivity coefficient (FTI).

[0024] The natural logarithm is used to obtain the comprehensive mathematical relation of the updated fault vertical connectivity coefficient (FTI).

[0025] The CRITC weights of the three influencing factors in the vertical fault connectivity coefficient (FTI) are calculated. The calculated weights are then substituted into the comprehensive mathematical formula of the vertical fault connectivity coefficient (FTI) obtained by the natural logarithm to obtain the final comprehensive mathematical formula of the vertical fault connectivity coefficient (FTI).

[0026] The final fault vertical connectivity coefficient (FTI) is obtained by taking the exponent of the comprehensive mathematical formula and substituting the specific values ​​of the three influencing factors into the FTI.

[0027] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0028] This invention provides a method for predicting fault connectivity based on PCA-CRITIC weights. The method includes determining the influencing factors of fault vertical connectivity, including the thickness of the displaced caprock, clay content, fault plane normal stress, formation fluid pressure, caprock brittleness index, and fault displacement. A comprehensive mathematical relationship between these influencing factors and fault vertical connectivity is established, namely, the fault vertical connectivity coefficient. Based on the oil, gas, and water data from drilled wells and paleoreservoir tracing data near the displaced strata, the vertical connectivity coefficient is calculated using the comprehensive mathematical relationship and by well and well depth segment. The vertical connectivity is then analyzed based on the fault vertical connectivity coefficient and the oil and gas content of the deep and shallow layers near the displaced strata, thus predicting the fault vertical connectivity in oil and gas basins. This invention considers the correlation and repetitive information of various fault factors. Since the relationship between rock brittleness index and clay content differs across wells and depths, principal component analysis (PCA) cannot be performed on all wells or all depth segments of each well. Data extraction by well and by depth segment better reflects the changes in their correlation across depth segments. PCA is used to process clay content and brittleness index data at different well segments. During dimensionality reduction, the information of the original data is preserved as much as possible. Furthermore, the CRITIC weighting method is used to calculate objective factor weights. This method uses standard deviation and correlation coefficient to reflect sample dispersion and inter-factor conflict. A larger standard deviation indicates greater dispersion and thus a larger factor weight; a larger correlation coefficient indicates stronger inter-factor conflict and thus a smaller weight. This weighting method is well-suited for evaluating fault connectivity and overcomes the shortcomings of subjective evaluation, which leads to different evaluation standards for faults by different individuals. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0031] Figure 2 This is a schematic diagram of the well section segmentation points;

[0032] Figure 3 This is a schematic diagram of vertical connectivity prediction of fault planes based on the PCA-CRITIC method;

[0033] Figure 4 This is a schematic diagram of the vertical connectivity prediction of the fault plane obtained without assigning weights. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] The purpose of this invention is to provide a fault connectivity prediction method based on PCA-CRITIC weights. By introducing PCA-CRITIC combined weight analysis, a significant breakthrough has been achieved in the qualitative to quantitative evaluation of vertical fault connectivity. This method innovatively employs principal component analysis for different wells and depth sections, accurately capturing the dynamic correlation between clay content and brittleness index under different geological conditions, effectively eliminating information redundancy between parameters. The CRITIC weighting method objectively quantifies the contribution of each influencing factor, overcoming the subjective limitations of traditional methods that rely on expert experience. Practical application shows that this technology can more accurately characterize the dominant migration paths of oil and gas within faults, significantly improving the predictive ability of oil and gas distribution patterns in complex structural areas, providing a more reliable scientific basis for exploration target selection and well location deployment, and effectively reducing exploration decision-making risks.

[0036] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0037] like Figure 1 As shown, this invention provides a fault connectivity prediction method based on PCA-CRITIC weights, comprising:

[0038] Step 1: Determine the influencing factors of vertical connectivity of the fault, including the thickness of the displaced overburden, clay content, normal stress of the fault plane, formation fluid pressure, brittleness index of the overburden rock, and fault displacement.

[0039] Step 2: Establish a comprehensive mathematical relationship between influencing factors and fault vertical connectivity, i.e., fault vertical connectivity coefficient;

[0040] Step 3: Based on the oil, gas and water data of drilled wells and the oil reservoir tracing data of the deep and shallow layers near the faulted strata, the vertical connectivity coefficient of the fault is calculated by well and well depth segment using the comprehensive mathematical relationship of the vertical connectivity coefficient of the fault.

[0041] Step 4: Analyze the vertical connectivity of the fault based on the vertical connectivity coefficient and the oil and gas content in the shallow and deep layers near the faulted strata, and predict the vertical connectivity of the fault in the oil and gas basin.

[0042] In step 2, a comprehensive mathematical relationship is established between influencing factors and fault vertical connectivity, namely the fault vertical connectivity coefficient, which is as follows:

[0043] The comprehensive mathematical formula for the vertical connectivity coefficient (FTI) of the fault is:

[0044] (1)

[0045] In the formula, P is the formation fluid pressure, in MPa, and σ N The normal stress of the fault plane is expressed in MPa. SGR is the mudstone smearing factor (including the thickness of the displaced caprock, mud content, and fault displacement), which is dimensionless. BDI is the brittleness index of the caprock, which is dimensionless. The vertical connectivity coefficient of the fault, FTI, is also dimensionless.

[0046] In step 3, based on the oil, gas, and water data from drilled wells and the hydrocarbon-bearing information from paleoreservoir tracing data near the faulted strata, the vertical connectivity coefficient of the fault is calculated using a comprehensive mathematical formula based on well depths. Specifically:

[0047] Data on the hydrocarbon content of drilled oil, gas and water data and ancient reservoir tracer data in the vicinity of fault-triggered strata were extracted by using well-by-well and well-by-deep-section methods.

[0048] The extracted data were subjected to natural logarithm calculation and standardization.

[0049] Principal component analysis (PCA) was performed on the data after natural logarithm calculation and standardization using IBM SPSS software to obtain the principal components.

[0050] By substituting the principal components into the comprehensive mathematical formula for the fault vertical connectivity coefficient (FTI), the fault vertical connectivity coefficient is obtained.

[0051] Principal component analysis was performed on the data after obtaining the natural logarithm and standardization to obtain the principal components, which are:

[0052] (2)

[0053] In the formula, Main component, , The coefficients of the independent variable are... Vsh The value represents the mud content of the formation, and the BDI represents the brittleness index of the caprock.

[0054] Substituting the principal components into the comprehensive mathematical formula for the fault vertical connectivity coefficient (FTI), we obtain the fault vertical connectivity coefficient, specifically:

[0055] Taking the exponents of the principal components, we get:

[0056] (3)

[0057] Substitute it into the formula for calculating the mudstone smearing factor. Vsh In part, the mudstone smearing factor is updated to:

[0058] (4)

[0059] In the formula, SGR is the mudstone smearing factor. Vsh The content of clay in the formation; ∆Z i denoted by , represents the thickness of the i-th mudstone layer displaced by the fault; D represents the vertical displacement of the fault.

[0060] Substituting the updated mudstone smear factor into the comprehensive mathematical formula for the fault vertical connectivity coefficient (FTI), we obtain the updated comprehensive mathematical formula for the fault vertical connectivity coefficient (FTI):

[0061] (5)

[0062] The natural logarithm of the updated comprehensive mathematical formula for the fault vertical connectivity coefficient (FTI) is:

[0063] (6)

[0064] For the three influencing factors (LnP, Lnσ, LnSGR) in the vertical connectivity coefficient of faults (FTI), the data are first standardized, and then CRITC weights are calculated. The specific process for calculating the weights is as follows:

[0065] 1. For m samples containing n indicators, the evaluation matrix is:

[0066] (7)

[0067] In the formula, a ij Let be the value of the j-th factor in the i-th sample;

[0068] 2. Factor normalization:

[0069] (8)

[0070] 3. Calculation of coefficient of variation:

[0071] (9)

[0072] (10)

[0073] (11)

[0074] In the formula, a j S represents the average value of each evaluation factor. j The standard deviation is μ. j The coefficient of variation;

[0075] 4. Calculation of the correlation coefficient matrix:

[0076] (12)

[0077] In the formula, κ ij Represents the correlation coefficient between factors, (y k y l ) cov Indicates the covariance between factors;

[0078] 5. Factor information content calculation:

[0079] (13)

[0080] The weights of each evaluation factor are as follows:

[0081] (14)

[0082] Substituting the calculated weights into the comprehensive mathematical formula for the fault vertical connectivity coefficient (FTI) obtained from the natural logarithm, we obtain the final comprehensive mathematical formula for the fault vertical connectivity coefficient (FTI):

[0083] (15)

[0084] The final fault vertical connectivity coefficient (FTI) is obtained by taking the exponent of the comprehensive mathematical formula and substituting the specific values ​​of the three influencing factors into the FTI.

[0085] This invention provides an embodiment, which is illustrated by selecting a fault within the Qikou Depression of the Bohai Bay Basin. A schematic diagram of its well and segment divisions is shown below. Figure 2 As shown;

[0086] Based on the FTI values ​​calculated from the above data, and through the known well hydrocarbon shows, the vertical migration paths of hydrocarbons in the faults are determined. A comparison with the fault connectivity calculated using the original FTI evaluation formula without weights reveals that the Np calculated by PCA-CRITIC without weights easily leads to the assumption that the Dongying Formation is not connected. However, this result cannot explain the hydrocarbon sources in the shallow Minghua and Guantao Formations of wells Z74 and ZH3X1, given the good sealing of the lower source rock (Shahe Formation). Furthermore, it cannot adequately explain the early hydrocarbon migration records in the Shahe Formation present in well ZH2X1. It only shows relatively good connectivity in this segment of the Guantao Formation. By assigning weights, the following results are obtained... Figure 3 It can be seen that in well ZH3X1, within the Shahejie Formation, there is indeed a good caprock that preserves oil and gas within the Shahejie Formation, preventing vertical migration. However, the distribution area of ​​this sealed area is not very large. Although the oil-water correlation of the well and the conclusion drawn without weighting both indicate that the upper caprock has good sealing capacity, weighting better characterizes the range of its sealing capacity. Based on the shallow Minghua Formation oil and gas shows in wells Z74 and ZH3X1, and the deep source rock oil and gas shows and migration history in well ZH2X1, it can be inferred that the shallow oil and gas source in wells Z74 and ZH3X1 may originate from the source rocks on the right side of the profile and migrate there. Figure 3 and Figure 4 The comparison shows that the PCA-CRITIC weight allocation method can better reduce the repetitive information at different depths in the selected parameters, and the fault connectivity probability it calculates can better characterize the migration channels inside the fault.

[0087] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0088] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A PCA-CRITIC weight-based fault connectivity prediction method, characterized by, The method comprises the following steps: Step 1: determining factors affecting vertical fault connectivity, including thickness of a faulted cap rock, shale content, normal stress of a fault surface, formation fluid pressure, rock brittleness index of the cap rock and fault throw; Step 2: establishing a comprehensive mathematical relationship between the factors and the vertical fault connectivity, i.e. a vertical fault connectivity coefficient; Step 3: based on oil and gas and water data and paleo-reservoir tracer data of deep and shallow layers near a faulted formation, the vertical fault connectivity coefficient is calculated by using a comprehensive mathematical relationship of the vertical fault connectivity coefficient in a well-by-well and depth-by-depth manner; specifically: In the ResForm software, logging GR, acoustic time difference and rock density data are extracted to calculate the shale content and the rock brittleness index, wherein the data are extracted from oil and gas and water data and paleo-reservoir tracer data of deep and shallow layers near a faulted formation in a well-by-well and depth-by-depth manner; The extracted shale content and rock brittleness index data are imported into the IBM SPSS software to calculate the variance contribution and perform principal component analysis to obtain principal components; The principal components are brought into the comprehensive mathematical relationship of the vertical fault connectivity coefficient FTI to obtain the vertical fault connectivity coefficient; specifically: The principal components are taken as an index and brought into a calculation formula of a shale smear factor to update the shale smear factor; The updated shale smear factor is brought into the comprehensive mathematical relationship of the vertical fault connectivity coefficient FTI to obtain an updated comprehensive mathematical relationship of the vertical fault connectivity coefficient FTI; The updated comprehensive mathematical relationship of the vertical fault connectivity coefficient FTI is taken as a natural logarithm; CRITIC weights of three factors in the vertical fault connectivity coefficient FTI are calculated, and the calculated weights are brought into the comprehensive mathematical relationship of the vertical fault connectivity coefficient FTI after natural logarithm calculation to obtain a final comprehensive mathematical relationship of the vertical fault connectivity coefficient FTI; The final comprehensive mathematical relationship of the vertical fault connectivity coefficient FTI is taken as an index, and specific values of the three factors in the vertical fault connectivity coefficient FTI are brought into the final comprehensive mathematical relationship of the vertical fault connectivity coefficient FTI to obtain an FTI value; Step 4: analyzing the vertical fault connectivity according to the vertical fault connectivity coefficient and the oil and gas content of deep and shallow layers near a faulted formation to predict the vertical fault connectivity in an oil and gas basin.

2. The method of claim 1, wherein, In step 2, the comprehensive mathematical relationship between the factors and the vertical fault connectivity, i.e. the vertical fault connectivity coefficient, is established; specifically: The comprehensive mathematical relationship of the vertical fault connectivity coefficient FTI is: （1） In the formula, P is the formation fluid pressure, in MPa, σ N is the normal stress of the fault plane, in MPa, SGR is the shale gouge ratio, and BDI is the cap rock brittleness index.

3. The method of claim 1, wherein, Before principal component analysis of the extracted data is performed, the following steps are further needed: The extracted data are taken as a natural logarithm and subjected to standardization.

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