Well wall surrounding rock breaking degree prediction method for to-be-drilled formation based on well-seismic fusion

Abstract

The application relates to the technical field of drilling exploration, and discloses a method, device, equipment and medium for predicting the broken degree of the well wall surrounding rock of a to-be-drilled formation based on well-seismic fusion, which comprises the following steps: obtaining the single-channel seismic attribute data of a drilled well, the hole enlargement rate, the logging data and the seismic attribute data of an un-drilled formation; performing correlation analysis based on the single-channel seismic attribute data and the hole enlargement rate to obtain target seismic attribute data; performing multi-attribute fusion based on the target seismic attribute data and a hierarchical analysis fuzzy mathematics method to obtain a formation broken seismic attribute index; obtaining an evaluation index of the broken degree of the well wall surrounding rock of the formation based on the formation broken seismic attribute index, the logging data and an evaluation index formula of the broken degree of the well wall surrounding rock of the formation; and obtaining the broken degree of the well wall surrounding rock of the un-drilled formation based on the evaluation index and the seismic attribute data of the un-drilled formation. The broken degree of the well wall surrounding rock of the un-drilled formation is effectively predicted.

Description

Technical Field

[0001] This application relates to the field of drilling exploration technology, specifically to a method, device, equipment, and medium for predicting the degree of fracture of the surrounding rock of the wellbore in the formation to be drilled based on well-seismic fusion. Background Technology

[0002] With the continuous innovation of petroleum exploration technology, the accurate prediction of underground rock mechanical parameters has become a core element of wellbore stability control in drilling operations. Accurately assessing the degree of fracturing of the surrounding rock in the formation to be drilled is of paramount importance for effectively preventing wellbore collapse and leakage risks, and ensuring the safety and smooth progress of drilling operations.

[0003] However, most of the currently used technical methods rely on collecting and analyzing acoustic data after drilling operations to assess the degree of fragmentation of the surrounding rock of the formation and wellbore. This method has obvious limitations because it lacks effective pre-drilling prediction methods, making it difficult to provide timely and accurate guidance for drilling design. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus, equipment, and medium for predicting the degree of fracture of the surrounding rock of the wellbore in a drilling formation based on well-seismic fusion, in order to solve the obvious limitations of the existing technology that assesses the degree of fracture of the surrounding rock of the wellbore by collecting and analyzing acoustic data.

[0005] To achieve the above objectives, the first aspect of this application provides a method for predicting the degree of fracturing of the surrounding rock of a wellbore in a formation to be drilled based on well-seismic fusion, comprising:

[0006] Acquire single-channel seismic attribute data, wellbore enlargement rate, and logging data of drilled wells and seismic attribute data of undrilled formations. The logging data includes P-wave velocity of formation rocks and P-wave velocity of intact formation rocks.

[0007] Correlation analysis was performed based on the single-channel seismic attribute data of the bypass passage and the well diameter enlargement rate to obtain the target seismic attribute data. The target seismic attribute data is the single-channel seismic attribute data of the bypass passage that is highly correlated with the degree of fracture of the surrounding rock of the formation and well wall.

[0008] Based on the target seismic attribute data and the hierarchical analysis fuzzy mathematics method, multi-attribute fusion is performed to obtain the seismic attribute index of strata fracture.

[0009] Based on the seismic attribute index of the formation fracture, well logging data, and the evaluation index formula of the degree of fracture of the surrounding rock of the formation wellbore, the evaluation index of the degree of fracture of the surrounding rock of the formation wellbore is obtained.

[0010] Based on evaluation indicators and seismic attribute data of un-drilled formations, the degree of fracturing of the surrounding rock of the wellbore in un-drilled formations is obtained.

[0011] In this embodiment of the application, the seismic attribute data of the drilled well bypass passage, wellbore enlargement rate, logging data, and seismic attribute data of the undrilled formation are acquired. The logging data includes the P-wave velocity of the formation rock and the P-wave velocity of the intact formation rock, including:

[0012] Acquire back-stack seismic data from drilled wells;

[0013] Based on well logging data from drilled wells, a synthetic seismic record is obtained;

[0014] The calibrated post-stack seismic data are obtained by calibrating post-stack seismic data based on synthetic seismic records;

[0015] Based on the calibrated post-stack seismic data, single-channel seismic attribute data of the well bypass is extracted.

[0016] In this embodiment of the application, correlation analysis is performed based on the single-channel seismic attribute data of the bypass passage and the wellbore enlargement rate to obtain the target seismic attribute data, including:

[0017] Correlation analysis was performed based on the single-channel seismic attribute data of the bypass well and the well diameter enlargement rate to obtain the correlation coefficient. The correlation analysis included at least one of Pearson correlation, Kendall correlation and Spearman correlation analysis.

[0018] Seismic attribute data of the bypass passage with a correlation coefficient greater than a preset threshold are used as the target seismic attribute data.

[0019] In this embodiment of the application, multi-attribute fusion is performed based on target seismic attribute data and hierarchical analysis fuzzy mathematics method to obtain stratigraphic fracture seismic attribute indicators, including:

[0020] Based on the target seismic attribute data and the Saaty scaling method, a wellbore surrounding rock fracture degree judgment matrix is ​​established;

[0021] Based on the wellbore surrounding rock fracture degree judgment matrix, the eigenvector corresponding to the largest eigenvalue of the wellbore surrounding rock fracture degree judgment matrix is ​​obtained;

[0022] Based on the feature vectors, an evaluation matrix of seismic attribute elements is established;

[0023] Based on the degree of fracturing of the surrounding rock of the wellbore for each target seismic attribute data, the target seismic attribute data is converted into a fracturing element index matrix.

[0024] Based on the degree of fracturing of the surrounding rock in the wellbore, a fracturing standard assignment matrix is ​​established;

[0025] Based on the seismic attribute element evaluation matrix, the fracture element index matrix, and the fracture standard assignment matrix, the seismic attribute index of the strata fracture is obtained.

[0026] In this embodiment of the application, based on the seismic attribute index of the formation fracture, well logging data, and the evaluation index formula for the degree of fracture of the surrounding rock of the formation wellbore, the evaluation index for the degree of fracture of the surrounding rock of the formation wellbore is obtained, including:

[0027] Based on the maximum value of the seismic attribute index of fractured strata, the seismic attribute index of fractured strata is normalized to obtain the normalized seismic attribute index of fractured strata.

[0028] Based on well logging data and formation integrity evaluation formula, the formation integrity evaluation index is obtained;

[0029] Based on the normalized seismic attribute index of strata fracturing, the evaluation index of strata integrity, and the evaluation index formula of the degree of fracturing of the surrounding rock of the wellbore, the evaluation index of the degree of fracturing of the surrounding rock of the wellbore is obtained.

[0030] In this embodiment of the application, the formula for evaluating stratigraphic integrity includes:

[0031] K v =(V pm / V pr ) 2

[0032] In the formula, K v V represents the stratigraphic integrity evaluation index. pm V represents the longitudinal wave velocity of the rock strata. pr This indicates the longitudinal wave velocity of intact strata rocks.

[0033] In this embodiment of the application, the evaluation index formula for the degree of fracturing of the surrounding rock of the formation wellbore includes:

[0034] K = D1(1-K) v )

[0035] In the formula, K represents the evaluation index of the degree of fracturing of the surrounding rock of the wellbore, D1 represents the normalized seismic attribute index of the fracturing of the formation, and K v This represents the stratigraphic integrity evaluation index.

[0036] The second aspect of this application provides a device for predicting the degree of fracturing of the surrounding rock of a wellbore in a formation to be drilled based on well-seismic fusion, comprising:

[0037] The acquisition module is used to acquire single-channel seismic attribute data, wellbore enlargement rate, logging data, and un-drilled formation seismic attribute data of drilled wells and surrounding rock. The logging data includes P-wave velocity of formation rocks and P-wave velocity of intact formation rocks.

[0038] The first module is used to perform correlation analysis based on the single-channel seismic attribute data of the bypass passage and the well diameter enlargement rate to obtain the target seismic attribute data. The target seismic attribute data is the single-channel seismic attribute data of the bypass passage that is highly correlated with the degree of fracture of the surrounding rock of the formation and well wall.

[0039] The second module is used to perform multi-attribute fusion based on target seismic attribute data and hierarchical analysis fuzzy mathematics method to obtain the seismic attribute index of strata fracture.

[0040] The third module is used to obtain the evaluation index of the degree of fracture of the surrounding rock of the wellbore based on the seismic attribute index of the formation fracture, well logging data and the evaluation index formula of the degree of fracture of the formation wellbore.

[0041] The fourth module is used to obtain the degree of fracturing of the surrounding rock of the un-drilled wellbore based on evaluation indicators and seismic attribute data of the un-drilled formation.

[0042] A third aspect of this application provides a device for predicting the degree of fracture of the surrounding rock of a wellbore in a formation to be drilled based on well-seismic fusion, comprising: a memory configured to store instructions;

[0043] The processor is configured to retrieve instructions from memory and, when executing the instructions, to implement the aforementioned first aspect of the wellbore surrounding rock fragmentation prediction method based on well-seismic fusion.

[0044] The fourth aspect of this application provides a computer-readable storage medium storing a computer program, which, when run on a processor, executes the method for predicting the degree of fracture of the surrounding rock of the wellbore in the formation to be drilled based on well-seismic fusion as described in the first aspect.

[0045] The above technical solutions enable the prediction of the degree of fracture of the surrounding rock of the wellbore before drilling operations, providing more detailed and reliable data support for drilling operations, significantly enhancing the understanding of the mechanical properties of underground strata, and thus providing support for promoting safe, efficient and rapid drilling technologies.

[0046] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0047] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings:

[0048] Figure 1 The illustration shows a flowchart of a method for predicting the degree of fracture of the surrounding rock of a wellbore in a formation to be drilled based on well-seismic fusion, according to an embodiment of this application.

[0049] Figure 2 This illustration schematically shows a single-channel seismic attribute data diagram of a bypass tunnel according to an embodiment of this application;

[0050] Figure 3 This illustration schematically shows the results of correlation analysis between single-channel seismic attribute data and wellbore enlargement rate of the bypass passage according to an embodiment of this application;

[0051] Figure 4 This diagram illustrates the degree of fracturing of the surrounding rock of a drilled well according to an embodiment of this application.

[0052] Figure 5 The diagram illustrates the distribution of the degree of fracturing of the surrounding rock in an un-drilled wellbore according to an embodiment of this application. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0054] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with the relevant provisions of national laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0055] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0056] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0057] Figure 1 The illustration schematically shows a flowchart of a method for predicting the degree of fracture of the surrounding rock of a wellbore in a formation to be drilled based on well-seismic fusion, according to an embodiment of this application. Figure 1 As shown in the figure, this application provides a method for predicting the degree of fracture of the surrounding rock of the wellbore in the formation to be drilled based on well-seismic fusion. The method may include the following steps.

[0058] Step S110: Obtain the single-channel seismic attribute data, wellbore enlargement rate, logging data, and un-drilled formation seismic attribute data of the drilled well. The logging data includes the P-wave velocity of the formation rock and the P-wave velocity of the intact formation rock.

[0059] In step S110, the single-channel seismic attribute data of the drilled well bypass and the seismic attribute data of the un-drilled formation both include steering phase, variance, coherence, maximum negative curvature and maximum positive curvature, and the wellbore enlargement rate = (measured wellbore diameter - drill bit diameter) / drill bit diameter × 100%.

[0060] Furthermore, step S110 includes the following steps:

[0061] Step S111: Obtain post-stack seismic data from the drilled wells.

[0062] Post-stack seismic data refers to seismic data obtained after stacking common midpoint (CMP) gathers during seismic data processing.

[0063] Step S112: Based on the logging data of the drilled wells, obtain the synthetic seismic record.

[0064] In step S112, the well logging data undergoes a quality check to remove outliers and noise, ensuring the accuracy and reliability of the data. By analyzing the spectral characteristics of the well logging data and post-stack seismic data, parameters corresponding to the wavelet can be extracted. Using parameters such as velocity and density from the well logging data, the reflection coefficients of each subsurface rock layer are calculated. The reflection coefficient is a physical quantity describing the intensity of seismic wave reflection between different rock layers. The extracted wavelet is convolved with the calculated reflection coefficient sequence to generate a synthetic seismic record.

[0065] Step S113: Based on the synthetic seismic record, calibrate the post-stack seismic data to obtain the calibrated post-stack seismic data.

[0066] In step S113, the synthetic seismic records and post-stack seismic data are time-aligned. The frequency ranges of the synthetic and post-stack seismic records are consistent by filtering or adjusting them. The amplitudes of the synthetic and post-stack seismic records are corrected to ensure comparability in amplitude. After time alignment, frequency matching, and amplitude correction, the post-stack seismic data is calibrated by comparing the synthetic and post-stack seismic records to find the optimal matching relationship between them. This optimal matching relationship is used to map information from the synthetic seismic records onto the post-stack seismic data, thereby obtaining the calibrated post-stack seismic data.

[0067] Step S114: Based on the calibrated post-stack seismic data, extract the single-channel seismic attribute data of the bypass tunnel.

[0068] In step S114, the single-channel seismic attribute data of the well bypass is extracted based on the calibrated post-stack seismic data. Figure 2 This illustration schematically shows a single-channel seismic attribute data diagram of a bypass tunnel according to an embodiment of this application, such as... Figure 2 As shown in the embodiments of this application, the single-channel seismic attribute data of the well-side trace includes coherence, maximum negative curvature, maximum positive curvature, steering phase, and variance. Coherence is used to measure the similarity between seismic traces. The maximum negative curvature and maximum positive curvature attributes reflect the curvature characteristics of the seismic amplitude data as a function of space; positive curvature typically corresponds to the convexity of the top interface of the formation, while negative curvature is associated with depression. Steering phase can be used to trace the continuity of the formation and identify possible formation inversions or phase transitions. Variance is used to measure the dispersion of the seismic amplitude data to reveal the inhomogeneity of subsurface lithological variations or fluid content.

[0069] Step S120: Based on the single-channel seismic attribute data of the bypass passage and the well diameter enlargement rate, perform correlation analysis to obtain the target seismic attribute data, wherein the target seismic attribute data is the single-channel seismic attribute data of the bypass passage that is highly correlated with the degree of fracture of the surrounding rock of the formation and well wall.

[0070] Correlation analysis was conducted between the single-channel seismic attribute data of the bypass passage and the wellbore enlargement rate to obtain single-channel seismic attribute data of the bypass passage that are highly correlated with the degree of fracture of the surrounding rock of the formation and wellbore.

[0071] Furthermore, step S120 includes the following steps:

[0072] Step S121: Perform correlation analysis based on the single-channel seismic attribute data of the bypass well and the well diameter enlargement rate to obtain the correlation coefficient. The correlation analysis includes at least one of Pearson correlation, Kendall correlation and Spearman correlation analysis.

[0073] In step S121, in the embodiments of this application, three correlation analysis methods, namely Pearson correlation, Kendall correlation and Spearman correlation, are used to perform correlation analysis between the single-channel seismic attribute data of the well bypass and the well diameter enlargement rate, so as to more accurately screen out the single-channel seismic attribute data of the well bypass that are highly correlated with the degree of fracture of the surrounding rock of the formation well wall.

[0074] Pearson correlation measures the degree of linear correlation between two continuous variables, and is calculated using the following formula:

[0075]

[0076] In the above formula, γ represents the Pearson correlation coefficient, cov(X,Y) represents the covariance of the single-channel seismic attribute data of the well bypass and the wellbore enlargement rate, σX represents the standard deviation of the single-channel seismic attribute data of the well bypass, and σY represents the standard deviation of the wellbore enlargement rate.

[0077] Kendall correlation assesses the degree of association between variables based on the consistency and inconsistency of data pairs. The calculation formula is as follows:

[0078]

[0079] In the above formula, τ represents the Kendall correlation coefficient, C represents the number of pairs where the direction of change of the single-channel seismic attribute data of the bypass tunnel and the wellbore enlargement rate is consistent, D represents the number of pairs where the direction of change of the single-channel seismic attribute data of the bypass tunnel and the wellbore enlargement rate is inconsistent, and n represents the sample size.

[0080] Spearman correlation is a nonparametric method based on the rank (or order) of variables to measure the monotonic relationship between two variables. The formula is as follows:

[0081]

[0082] In the above formula, ρ represents the Spearman correlation coefficient, and di The difference in rank between the single-channel seismic attribute data and the wellbore enlargement rate at each data point represents the difference in rank between the data and the wellbore enlargement rate. n represents the sample size.

[0083] Step S122: Select the single-channel seismic attribute data of the bypass tunnel with a correlation coefficient greater than the preset threshold as the target seismic attribute data.

[0084] In step S122, Figure 3 This illustration schematically shows the results of correlation analysis between single-channel seismic attribute data and wellbore enlargement rate in a bypass passage according to an embodiment of this application. Figure 3 As shown in the embodiment of this application, the preset threshold is set to 0.7, and the single-channel seismic attribute data of the well bypass that have a Pearson correlation coefficient, Kendall correlation coefficient and Spearman correlation coefficient all greater than 0.7 are used as the target seismic attribute data.

[0085] Step S130: Based on the target seismic attribute data and the hierarchical analysis fuzzy mathematics method, perform multi-attribute fusion to obtain the seismic attribute index of strata fracture.

[0086] In step S130, multi-attribute fusion is performed based on the target seismic attribute data and the analytic hierarchy process (AHP) fuzzy mathematics method to obtain the seismic attribute index of stratigraphic fracturing. The AHP fuzzy mathematics method is a decision analysis method that combines the Analytic Hierarchy Process (AHP) and fuzzy mathematics theory.

[0087] Furthermore, step S130 includes the following steps:

[0088] Step S131: Based on the target seismic attribute data and the Saaty scaling method, establish a wellbore surrounding rock fracture degree judgment matrix.

[0089] In step S131, the Saaty scaling method is a scaling method commonly used in the analytic hierarchy process (AHP), often employing a scale of 1-9 to represent the relative importance of different factors. Based on the target seismic attribute data and the Saaty scaling method, the wellbore surrounding rock fragmentation judgment matrix S1 is obtained as follows:

[0090]

[0091] Step S132: Based on the wellbore surrounding rock fracture degree judgment matrix, obtain the eigenvector corresponding to the largest eigenvalue of the wellbore surrounding rock fracture degree judgment matrix.

[0092] In step S132, the eigenvalues ​​of the wellbore surrounding rock fracture degree judgment matrix S1 are calculated, the maximum eigenvalue of the wellbore surrounding rock fracture degree judgment matrix S1 is determined, and the eigenvector corresponding to the maximum eigenvalue is calculated.

[0093] Step S133: Based on the feature vector, establish the seismic attribute element evaluation matrix.

[0094] In step S133, the seismic attribute element evaluation matrix S2 is obtained based on the eigenvector corresponding to the largest eigenvalue.

[0095] Step S134: Based on the degree of fracturing of the surrounding rock of the wellbore for each target seismic attribute data, convert the target seismic attribute data into a fracturing element index matrix.

[0096] Schematic, in step S134, based on the wellbore enlargement rate, the degree of fragmentation of the surrounding rock is classified into four levels: intact, relatively intact, relatively fragmented, and severely fragmented. Specifically, when the wellbore enlargement rate is 0–0.25, the degree of fragmentation of the surrounding rock is intact. When the wellbore enlargement rate is 0.25–0.5, the degree of fragmentation is relatively intact. When the wellbore enlargement rate is 0.5–0.75, the degree of fragmentation is relatively fragmented. When the wellbore enlargement rate is 0.75–1, the degree of fragmentation is severely fragmented. Based on the degree of fragmentation of the surrounding rock corresponding to each target seismic attribute data, the target seismic attribute data is converted into a fragmentation element index matrix S3 using a binary assignment method. In one example, the fragmentation levels and their binary representations are defined as follows: completeness is [1,0,0,0], relatively completeness is [0,1,0,0], relatively fragmentedness is [0,0,1,0], and completeness is [0,0,0,1]. Assuming there are 5 target seismic attribute data points, and the fragmentation levels of these 5 data points are completeness, relatively fragmentedness, relatively completeness, completeness, and completeness, the resulting fragmentation element index matrix S3 is:

[0097]

[0098] Step S135: Based on the degree of fracturing of the surrounding rock of the wellbore, establish the fracturing standard assignment matrix S4.

[0099] In step S135, the fracture standard assignment matrix S4, established based on the degree of fracture of the surrounding rock in the wellbore, is as follows:

[0100]

[0101] S4 is a column vector, with each element corresponding to a level of breakage. The four levels of breakage are assigned the values ​​1, 2, 3, and 4, respectively. The numerical value reflects the increase in the level of breakage: 1 represents the degree of integrity, 2 represents the degree of integrity, 3 represents the degree of breakage, and 4 represents the degree of breakage.

[0102] Step S136: Obtain the seismic attribute index of the strata fracture based on the seismic attribute element evaluation matrix, the fracture element index matrix, and the fracture standard assignment matrix.

[0103] In step S136, the seismic attribute index of the fractured strata is D = S2 × S3 × S4.

[0104] Step S140: Based on the seismic attribute index of the formation fracture, well logging data, and the evaluation index formula of the degree of fracture of the surrounding rock of the formation wellbore, the evaluation index of the degree of fracture of the surrounding rock of the formation wellbore is obtained.

[0105] In step S140, based on the seismic attribute index of the formation fracture and the well logging data obtained in step S130, the evaluation index formula for the degree of fracture of the surrounding rock of the wellbore can be determined.

[0106] Furthermore, step S140 includes the following steps:

[0107] Step S141: Based on the maximum value of the seismic attribute index of the fractured strata, normalize the seismic attribute index of the fractured strata to obtain the normalized seismic attribute index of the fractured strata.

[0108] In step S141, the seismic attribute index D of the fractured strata is normalized to obtain the normalized seismic attribute index D1 of the fractured strata.

[0109] D1 = D / D max

[0110] In the formula, D max This represents the maximum value of the seismic attribute index indicating strata fracturing.

[0111] Step S142: Based on well logging data and formation integrity evaluation formula, obtain formation integrity evaluation index.

[0112] In step S142, the formation integrity evaluation formula includes:

[0113] K v =(V pm / V pr ) 2

[0114] In the formula, K v V represents the stratigraphic integrity evaluation index. pm V represents the longitudinal wave velocity of the rock strata. pr The P-wave velocity of intact strata rocks is expressed in km / s.

[0115] Step S143: The evaluation index of the formation surrounding rock fracture degree of the drilled well is obtained by formulating the normalized seismic attribute index of formation fracture, the formation integrity evaluation index and the evaluation index of formation wellbore fracture degree.

[0116] In step S143, Figure 4 This diagram schematically illustrates the degree of fracturing of the surrounding rock in a drilled well according to an embodiment of this application. Based on the normalized seismic attribute index of formation fracturing, the formation integrity evaluation index, and the evaluation index formula for the degree of fracturing of the surrounding rock in the wellbore, the following is obtained: Figure 4 The evaluation indicators for the degree of fracturing of the surrounding rock in drilled wells are shown. The formulas for these evaluation indicators include:

[0117] K = D1(1-K) v )

[0118] In the formula, K represents the evaluation index of the degree of fracturing of the surrounding rock of the wellbore, D1 represents the normalized seismic attribute index of the fracturing of the formation, and K v This represents the stratigraphic integrity evaluation index.

[0119] Step S150: Based on the evaluation index and the seismic attribute data of the un-drilled formation, obtain the degree of fracturing of the surrounding rock of the un-drilled wellbore.

[0120] In step S150, Figure 5 This diagram illustrates the distribution of rock fragmentation in the un-drilled wellbore according to an embodiment of this application. Based on the evaluation index obtained in step S140 and combined with the seismic attribute data of the un-drilled formation, the following can be obtained: Figure 5 The diagram shows the distribution of the degree of fracturing of the surrounding rock in the un-drilled formation wellbore.

[0121] The wellbore surrounding rock fragmentation prediction method based on well-seismic fusion of the present application can predict the fragmentation degree of the surrounding rock of the un-drilled formation. It breaks through the limitations of existing technologies that mostly rely on collecting and analyzing acoustic data after drilling operations to assess the fragmentation degree of the surrounding rock of the formation wellbore. It provides more comprehensive and reliable data for drilling operations, effectively improves the understanding of the mechanical behavior of underground formations, and reduces engineering risks such as wellbore collapse and drilling fluid loss.

[0122] Optionally, embodiments of this application also provide a device for predicting the degree of fracturing of the surrounding rock of the wellbore in the formation to be drilled based on well-seismic fusion, comprising:

[0123] The acquisition module is used to acquire single-channel seismic attribute data, wellbore enlargement rate, logging data, and un-drilled formation seismic attribute data of drilled wells and surrounding rock. The logging data includes P-wave velocity of formation rocks and P-wave velocity of intact formation rocks.

[0124] The first module is used to perform correlation analysis based on the single-channel seismic attribute data of the bypass passage and the well diameter enlargement rate to obtain the target seismic attribute data. The target seismic attribute data is the single-channel seismic attribute data of the bypass passage that is highly correlated with the degree of fracture of the surrounding rock of the formation and well wall.

[0125] The second module is used to perform multi-attribute fusion based on target seismic attribute data and hierarchical analysis fuzzy mathematics method to obtain the seismic attribute index of strata fracture.

[0126] The third module is used to obtain the evaluation index of the degree of fracture of the surrounding rock of the wellbore based on the seismic attribute index of the formation fracture, well logging data and the evaluation index formula of the degree of fracture of the formation wellbore.

[0127] The fourth module is used to obtain the degree of fracturing of the surrounding rock of the un-drilled wellbore based on evaluation indicators and seismic attribute data of the un-drilled formation.

[0128] It is understood that the wellbore surrounding rock fragmentation prediction device based on well-seismic fusion provided in this application embodiment can realize each process of the wellbore surrounding rock fragmentation prediction method based on well-seismic fusion in the above embodiment, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0129] Optionally, embodiments of this application also provide a device for predicting the degree of fracturing of the surrounding rock of the wellbore in the formation to be drilled based on well-seismic fusion, comprising:

[0130] The memory is configured to store instructions;

[0131] The processor is configured to retrieve instructions from memory and, when executing instructions, to implement the wellbore surrounding rock fracturing degree prediction method based on well-seismic fusion of the aforementioned formation. It achieves the same technical effect, and to avoid repetition, will not be elaborated upon here.

[0132] Optionally, embodiments of this application also provide a computer-readable storage medium storing a computer program. When the computer program runs on a processor, it executes the above-described method for predicting the fracture degree of the surrounding rock in the wellbore of a formation to be drilled based on well-seismic fusion. This achieves the same technical effect, and to avoid repetition, it will not be described further here.

[0133] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0134] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0135] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0136] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0137] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0138] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0139] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0140] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0141] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for predicting the degree of fracturing of the surrounding rock of a wellbore in a formation to be drilled based on well-seismic fusion, characterized in that, include: Acquire single-channel seismic attribute data, wellbore enlargement rate, logging data, and un-drilled formation seismic attribute data for drilled wells, including P-wave velocity of formation rocks and P-wave velocity of intact formation rocks. Correlation analysis was performed based on the single-channel seismic attribute data of the bypass passage and the well diameter enlargement rate to obtain the target seismic attribute data, wherein the target seismic attribute data is the single-channel seismic attribute data of the bypass passage that is highly correlated with the degree of fracture of the surrounding rock of the formation well wall. Based on the target seismic attribute data and the hierarchical analysis fuzzy mathematics method, multi-attribute fusion is performed to obtain the seismic attribute index of strata fracture. Based on the seismic attribute index of the fractured formation, the well logging data, and the evaluation index formula for the degree of fracture of the surrounding rock of the wellbore, the evaluation index for the degree of fracture of the surrounding rock of the wellbore is obtained. Based on the evaluation index and the seismic attribute data of the un-drilled formation, the degree of fracturing of the surrounding rock of the un-drilled wellbore is obtained.

2. The method according to claim 1, characterized in that, The acquisition of single-channel seismic attribute data, wellbore enlargement rate, logging data, and undrilled formation seismic attribute data for drilled wells, including P-wave velocities of formation rocks and P-wave velocities of intact formation rocks, includes: Obtain the post-stack seismic data of the drilled wells; Based on the logging data from the drilled wells, a synthetic seismic record was obtained; The post-stack seismic data are calibrated based on the synthetic seismic record to obtain calibrated post-stack seismic data; Based on the calibrated post-stack seismic data, the single-channel seismic attribute data of the bypass tunnel is extracted.

3. The method according to claim 1, characterized in that, The correlation analysis based on the single-channel seismic attribute data of the bypass passage and the wellbore enlargement rate yields the target seismic attribute data, including: Correlation analysis is performed based on the single-channel seismic attribute data of the bypass passage and the well diameter enlargement rate to obtain the correlation coefficient. The correlation analysis includes at least one of Pearson correlation, Kendall correlation and Spearman correlation analysis. Seismic attribute data of the bypass tunnel with a correlation coefficient greater than a preset threshold are used as the target seismic attribute data.

4. The method according to claim 1, characterized in that, The multi-attribute fusion based on the target seismic attribute data and the hierarchical analysis fuzzy mathematics method yields the stratigraphic fracture seismic attribute index, including: Based on the target seismic attribute data and the Saaty scaling method, a wellbore surrounding rock fracture degree judgment matrix is ​​established; Based on the wellbore surrounding rock fracture degree judgment matrix, the eigenvector corresponding to the largest eigenvalue of the wellbore surrounding rock fracture degree judgment matrix is ​​obtained; Based on the aforementioned feature vectors, an evaluation matrix for earthquake attribute elements is established; Based on the degree of fracturing of the surrounding rock of the wellbore for each of the target seismic attribute data, the target seismic attribute data is converted into a fracturing element index matrix; Based on the degree of fracturing of the surrounding rock of the wellbore, a fracturing standard assignment matrix is ​​established; The seismic attribute index of the strata is obtained based on the seismic attribute element evaluation matrix, the fracture element index matrix, and the fracture standard assignment matrix.

5. The method according to claim 1, characterized in that, The evaluation index for the degree of fracturing of the surrounding rock of the formation is obtained by the formula based on the seismic attribute index of the formation fracturing, the well logging data, and the evaluation index formula for the degree of fracturing of the surrounding rock of the formation wellbore, including: Based on the maximum value of the fractured seismic attribute index, the fractured seismic attribute index is normalized to obtain the normalized fractured seismic attribute index. Based on the well logging data and the formation integrity evaluation formula, the formation integrity evaluation index is obtained; Based on the normalized seismic attribute index of strata fracturing, the evaluation index of strata integrity, and the evaluation index formula of the degree of fracturing of the surrounding rock of the wellbore, the evaluation index of the degree of fracturing of the surrounding rock of the wellbore is obtained.

6. The method according to claim 5, characterized in that, The formula for evaluating stratigraphic integrity include: K v =(V pm / V pr ) 2 In the formula, K v V represents the stratigraphic integrity evaluation index. pm V represents the longitudinal wave velocity of the rock strata. pr This indicates the longitudinal wave velocity of intact strata rocks.

7. The method according to claim 5, characterized in that, The evaluation index formula for the degree of fracturing of the surrounding rock of the formation wellbore includes: K=D1(1-K w ) In the formula, K represents the evaluation index of the degree of fracturing of the surrounding rock of the wellbore, D1 represents the normalized seismic attribute index of the fracturing of the formation, and K v This represents the stratigraphic integrity evaluation index.

8. A device for predicting the degree of fracturing of the surrounding rock in a wellbore based on well-seismic fusion, characterized in that, include: The acquisition module is used to acquire the single-channel seismic attribute data of the drilled well bypass, the wellbore enlargement rate, the logging data, and the seismic attribute data of the undrilled formation. The logging data includes the P-wave velocity of the formation rock and the P-wave velocity of the intact formation rock. The first obtaining module is used to perform correlation analysis based on the single-channel seismic attribute data of the bypass passage and the well diameter enlargement rate to obtain target seismic attribute data, wherein the target seismic attribute data is the single-channel seismic attribute data of the bypass passage that is highly correlated with the degree of fracture of the surrounding rock of the formation well wall; The second module is used to perform multi-attribute fusion based on the target seismic attribute data and hierarchical analysis fuzzy mathematics method to obtain the seismic attribute index of strata fracture. The third module is used to obtain the evaluation index of the degree of fracture of the surrounding rock of the wellbore based on the seismic attribute index of the formation fracture, the logging data and the evaluation index formula of the degree of fracture of the surrounding rock of the wellbore. The fourth module is used to obtain the degree of fracturing of the surrounding rock of the un-drilled wellbore based on the evaluation index and the seismic attribute data of the un-drilled formation.

9. A device for predicting the degree of fracturing of the surrounding rock in a wellbore based on well-seismic fusion, characterized in that, include: The memory is configured to store instructions; The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the method for predicting the degree of fracture of the surrounding rock of the wellbore in the formation to be drilled based on well-seismic fusion according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when run on a processor, executes the method for predicting the degree of fracture of the surrounding rock of the wellbore in the formation to be drilled based on well-seismic fusion as described in any one of claims 1 to 7.

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