Well casing change prediction method, device, equipment and medium

Abstract

The present application provides a method, device, equipment and medium for predicting well casing change. The method includes: obtaining a target seismic data body, which is obtained based on the pure wave seismic data body of the block where the target well is located; performing multi-attribute calculation on the target seismic data body to obtain at least two attribute bodies; screening at least two attribute bodies based on the fault characteristics and fracture characteristics of the block to obtain a screened first attribute body; filtering the value range of the first attribute body based on the extreme amplitude limit value to obtain a filtered second attribute body, the extreme amplitude limit value is obtained based on the amplitude value of the first attribute body of the completed casing change well, and the completed casing change well is a well on the block; predicting the casing change of the target well based on the second attribute body. The method of the present application improves the prediction effect of well casing change.

Description

Technical Field

[0001] The present application relates to the technical field of oil and gas field development, and in particular to a method, device, equipment and medium for predicting well casing changes. Background Art

[0002] In the process of oil and gas production, by drilling directly on the ground to reach the target layer, the oil and gas structures are found and confirmed, and industrial oil and gas flows are obtained. This process not only includes the exploration of the oil (gas) area and reserves of the confirmed oil (gas) structures, but also involves obtaining geological data and development data of the relevant oil (gas) fields, and establishing channels for crude oil (gas) production. In order to ensure the normal operation of the entire oil (gas) well during the drilling process and after completion, it is necessary to place a steel pipe in the well to support the well wall of the oil (gas) well to prevent the well wall from collapsing. This steel pipe is the well casing. When the well casing is subjected to formation pressure, the casing is broken, the casing leaks, and the elliptical diameter is changed, which is called well casing change. The occurrence of well casing change directly affects the stability and safety of drilling, and then affects the efficiency and cost of oil and gas production. Therefore, how to predict well casing change has become an urgent problem to be solved.

[0003] The existing technology predicts the casing change by using the fault characteristics near the well, but has the problem of low prediction accuracy. Summary of the invention

[0004] The present application provides a method, device, equipment and medium for predicting well casing changes, so as to solve the problem of low prediction accuracy.

[0005] In a first aspect, the present application provides a method for predicting well casing changes, comprising:

[0006] Acquire a target seismic data volume, where the target seismic data volume is obtained based on a pure wave seismic data volume of a block where the target well is located;

[0007] Perform multi-attribute calculation on the target seismic data volume to obtain at least two attribute volumes;

[0008] According to the fault characteristics and fracture characteristics of the block, at least two attribute bodies are screened to obtain a screened first attribute body;

[0009] According to the extreme amplitude limit value, the value range of the first attribute body is filtered to obtain a filtered second attribute body, wherein the extreme amplitude limit value is obtained according to the amplitude value of the completed casing change well in the first attribute body, and the completed casing change well is a well on the block;

[0010] The casing change of the target well is predicted according to the second attribute body.

[0011] In some implementations, acquiring a target seismic data volume includes:

[0012] Acquire the pure wave seismic data volume of the block where the target well is located;

[0013] The pure wave seismic data volume is subjected to bilateral amplitude preservation preprocessing to obtain the target seismic data volume. The bilateral amplitude preservation preprocessing includes structure-oriented fracture enhancement filtering processing and structure filtering processing based on diffusion equation.

[0014] In some embodiments, at least two attribute bodies are screened according to the fault characteristics and fracture characteristics of the block to obtain the screened first attribute body, including:

[0015] For each attribute body, the attribute body is matched according to the fault characteristics and fracture characteristics of the block to obtain a first matching result, and the first matching result is used to characterize the matching degree between the attribute body and the fault characteristics and fracture characteristics;

[0016] Determine the attribute body with the largest first matching result among the at least two attribute bodies as the third attribute body;

[0017] If the first matching result of the third attribute body meets the preset screening requirement, the third attribute body is determined as the first attribute body.

[0018] In some embodiments, the method further comprises:

[0019] If the third attribute body does not meet the preset screening requirements, then at least two attribute bodies are attribute-fused to obtain at least one fused attribute body;

[0020] According to the fault characteristics and fracture characteristics of the block, each fused attribute body is matched to obtain a second matching result, and the second matching result is used to characterize the matching degree between the fused attribute body and the fault characteristics and fracture characteristics;

[0021] Determine the attribute body with the largest second matching result in the at least one fused attribute body as the fourth attribute body;

[0022] If the second matching result of the fourth attribute body meets the preset screening requirement, the fourth attribute body is determined as the first attribute body.

[0023] In some embodiments, the method further comprises:

[0024] Confirm that the well has been drilled and the casing has been changed;

[0025] Determine the amplitude value of each casing change point in the first attribute body according to at least two casing change points of the casing change well that has been drilled;

[0026] According to the amplitude value of each set of variation points in the first attribute body, a limit amplitude boundary value is determined.

[0027] In some implementations, filtering the value range of the first attribute body according to the limit amplitude boundary value to obtain the second attribute body includes:

[0028] If the limit amplitude limit value is a positive value, the interval in the value range of the first attribute body that is less than the limit amplitude limit value is filtered to obtain the second attribute body;

[0029] If the extreme amplitude limit value is a negative value, the interval in the value range of the first attribute body that is greater than the extreme amplitude limit value is filtered to obtain the second attribute body.

[0030] In some embodiments, predicting the casing change of the target well according to the second attribute body includes:

[0031] Performing attribute enhancement processing on the second attribute body to obtain a fifth attribute body;

[0032] Performing crack extraction processing on the fifth attribute body to obtain at least one crack;

[0033] According to the geometric parameters of each crack, network reconstruction is performed to obtain a discrete crack network model;

[0034] According to the block's geomechanical parameters and fault occurrence parameters, the discrete fracture network model is analyzed and processed for fracture activation to obtain the fault slip probability of the target well.

[0035] The casing change risk level of the target well is determined based on the preset casing change risk level requirements and fault slip probability.

[0036] In a second aspect, the present application provides a well casing change prediction device, comprising:

[0037] An acquisition module is used to acquire a target seismic data volume, where the target seismic data volume is obtained based on a pure wave seismic data volume of a block where the target well is located;

[0038] A calculation module, used for performing multi-attribute calculation on a target seismic data volume to obtain at least two attribute volumes;

[0039] A screening module, used for screening at least two attribute bodies according to the fault characteristics and fracture characteristics of the block to obtain a screened first attribute body;

[0040] A filtering module is used to filter the value range of the first attribute body according to the extreme amplitude limit value to obtain a filtered second attribute body, wherein the extreme amplitude limit value is obtained according to the amplitude value of the completed casing change well in the first attribute body, and the completed casing change well is a well on the block;

[0041] The prediction module is used to predict the casing change of the target well according to the second attribute body.

[0042] In a third aspect, the present application provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0043] Memory stores computer-executable instructions;

[0044] The processor executes the computer-executable instructions stored in the memory to implement the method of the present application.

[0045] In a fourth aspect, the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used for the method of the present application.

[0046] The method, device, equipment and medium for predicting casing changes provided by the present application perform multi-attribute calculation on the target seismic data body to obtain at least two attribute bodies, and then screen the at least two attribute bodies according to the fault characteristics and fracture characteristics of the block, so that the fault characteristics and fracture characteristics of the first attribute body obtained have a high degree of matching with the fault characteristics and fracture characteristics of the block, and then further filter the value range of the first attribute body according to the limit amplitude limit value, remove data in the value range that are not related to casing changes, and obtain a filtered second attribute body, so that the fracture characteristics of the second attribute body obtained are more obvious, and finally predict the casing changes through the second attribute body, thereby improving the prediction effect of the casing changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0048] Figure 1 A schematic diagram of a scenario for predicting casing change in a shale gas well provided in an embodiment of the present application;

[0049] Figure 2 A schematic diagram of a flow chart of a shale gas well casing change prediction method provided in an embodiment of the present application;

[0050] Figure 3 A schematic flow chart of another shale gas well casing change prediction method provided in an embodiment of the present application;

[0051] Figure 4 A schematic diagram of the structure of a shale gas well casing change prediction device provided in an embodiment of the present application;

[0052] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0053] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0054] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0055] In order to clearly understand the technical solution of the present application, the solution of the prior art is first introduced in detail.

[0056] In the process of oil and gas production, wells are drilled directly on the ground to reach the target layer, so as to find and confirm the oil and gas structures and obtain industrial oil and gas flows. This process not only includes the exploration of the oil (gas) bearing area and reserves of the confirmed oil (gas) bearing structures, but also involves obtaining geological data and development data of the relevant oil (gas) fields, as well as establishing channels for crude oil (gas) production. In order to ensure the normal operation of the entire oil (gas) well during the drilling process and after completion, it is necessary to place a steel pipe in the well to support the well wall of the oil (gas) well to prevent the well wall from collapsing. This steel pipe is the well casing. When the well casing is subjected to formation pressure, the casing is broken, the casing leaks, and the elliptical diameter is changed, which is called well casing change. The occurrence of well casing change directly affects the stability and safety of drilling, and thus affects the efficiency and cost of oil and gas production.

[0057] For example, shale gas extraction usually requires drilling a vertical well several thousand meters underground, then drilling hundreds to thousands of meters horizontally, and using large-scale hydraulic fracturing technology, that is, by injecting a mixture of clean water, ceramic particles, chemicals, etc. into the ground, the rock layer containing natural gas flow is "prised open" at a pressure of tens to hundreds of megapascals, allowing the shale gas hidden deep in the shale layer to be released, achieving the purpose of extraction. However, in the fracturing process, some existing cracks in the formation are prone to stress, causing the formation to break and slip, and then the well casing changes. Therefore, how to predict the well casing change has become an urgent problem to be solved.

[0058] The existing technology predicts the casing change by using the fault characteristics near the well, but has the problem of low prediction accuracy.

[0059] In order to solve the above-mentioned problem of low prediction accuracy, the inventors found in their research that it is possible to determine a first attribute body that best matches the fault characteristics and fracture characteristics of the block, and then obtain the amplitude value corresponding to the first attribute body of the casing change wells that have been drilled on the block, determine the extreme amplitude limit value of the first attribute body according to the distribution of each amplitude value, and finally filter the first attribute body according to the extreme amplitude limit value to obtain a second attribute body, so that the casing change of the target well on the block can be predicted based on the second attribute body.

[0060] The following introduces the application scenarios of the well casing change prediction method provided in the embodiments of the present application.

[0061] Figure 1 A schematic diagram of a scenario for predicting a well casing change provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the scenario includes a block, a target well located in the block, and a completed casing conversion well.

[0062] The target well may be a well location to be predicted.

[0063] A completed casing change well may refer to a horizontal well that has been completed and casing changed.

[0064] Casing deformation can refer to the deformation of casing in a horizontal well.

[0065] Figure 2 A flow chart of a shale gas well casing change prediction method provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the method includes:

[0066] S201, acquiring a target seismic data volume.

[0067] In this solution, the target seismic data volume can be obtained based on the pure wave seismic data volume of the block where the target well is located.

[0068] Optionally, the method for acquiring the target seismic data volume may include:

[0069] Acquire the pure wave seismic data volume of the block where the target well is located;

[0070] The pure wave seismic data volume is subjected to bilateral amplitude preservation preprocessing to obtain the target seismic data volume. The bilateral amplitude preservation preprocessing includes structure-oriented fracture enhancement filtering processing and structure filtering processing based on diffusion equation.

[0071] In this step, the pure wave seismic data volume of the block is preprocessed with bilateral amplitude preservation to obtain a target seismic data volume with a high signal-to-noise ratio.

[0072] The pure wave seismic data volume may refer to a three-dimensional seismic data volume with fidelity and amplitude preservation in the post-stack time domain or depth domain of the block.

[0073] The structural orientation fault enhancement filtering process can refer to the use of a "heat diffusion" smoothing algorithm, that is, the smoothing operation is only performed on the information of the background seismic phase axis, and the information of the fault direction is not smoothed. It can eliminate random noise and eliminate non-random noise intersecting with the seismic reflection phase axis, which can effectively enhance the breakpoints of the layer, and while eliminating noise, it will not change the frequency and phase of the seismic data.

[0074] Structural filtering based on diffusion equations may refer to filtering pure wave seismic data volumes by means of linear diffusion equations, nonlinear diffusion equations, etc., thereby retaining structural features while suppressing noise.

[0075] S202: Perform multi-attribute calculation on the target seismic data volume to obtain at least two attribute volumes.

[0076] In this step, in order to obtain the characteristics of fractures in the target seismic data volume, multi-attribute calculation is performed on the target seismic data volume to obtain at least two attribute volumes.

[0077] The attribute volume may refer to the kinematic, dynamic and statistical seismic special measurement value volume extracted from the target seismic data volume.

[0078] The attribute body may include a variety of seismic attribute bodies reflecting the characteristics of fault distribution, such as coherence attribute body, curvature attribute body, dip attribute body and variance attribute body.

[0079] Multi-attribute calculation can refer to the process of obtaining an attribute body by using a calculation method corresponding to the attribute body. For example, a coherent attribute body is obtained by coherent processing, which is to determine the coherence body by analyzing the similarity between each seismic profile and the adjacent seismic traces in the seismic data. There are many parameter settings for extracting the coherence body, and the common ones are 4-point scanning, 8-point scanning, etc. When performing coherent processing, first select a coherence method, then select the analysis time window method, select the layer and the appropriate time window, type the output name, select the scanning mode and click OK. In this way, the coherent attribute body can be extracted to show the distribution law of the discontinuity points or mutation points of the seismic reflection phase axis in three-dimensional space.

[0080] S203: Screen at least two attribute bodies according to the fault characteristics and fracture characteristics of the block to obtain a screened first attribute body.

[0081] In this step, after obtaining at least two attribute bodies, the at least two attribute bodies need to be screened. Therefore, the first attribute body is screened and obtained by matching the fault characteristics and fracture characteristics of the block with each attribute body.

[0082] Fault characteristics can refer to the fault results of fine seismic interpretation of the block, and the fault characteristics can be characterized by three-dimensional images.

[0083] Fault characteristics can refer to the fault results interpreted by imaging logging on the block, as well as the fault results obtained based on the completed drilling implementation. Among them, the completed drilling implementation can refer to the complex working conditions such as well leakage, overflow, casing change, pressure channeling, etc. that are closely related to the development of faults. Fault characteristics can be characterized by three-dimensional images.

[0084] Optionally, at least two attribute bodies are screened according to the fault characteristics and fracture characteristics of the block to obtain a screened first attribute body, including:

[0085] For each attribute body, the attribute body is matched according to the fault characteristics and fracture characteristics of the block to obtain a first matching result, and the first matching result is used to characterize the matching degree between the attribute body and the fault characteristics and fracture characteristics;

[0086] Determine the attribute body with the largest first matching result among the at least two attribute bodies as the third attribute body;

[0087] If the first matching result of the third attribute body meets the preset screening requirement, the third attribute body is determined as the first attribute body.

[0088] In this step, the images of fault features and fracture features are matched with the image of the attribute body, and the matching degree of the image features corresponding to the fault features and fracture features in the attribute body in the same coordinates is determined to obtain a first matching result, and then the attribute body with the largest first matching result among at least two attribute bodies is determined as the third attribute body, and then the first matching result of the third attribute body is compared with the preset screening requirements, and if the first matching result of the third attribute body meets the preset screening requirements, the third attribute body is determined as the first attribute body. In this way, the fracture features of the obtained first attribute body are more obvious, and the prediction effect is improved.

[0089] The preset screening requirement may mean that the matching degree needs to be greater than or equal to a preset ratio value, and the preset ratio value is, for example, 85%.

[0090] For example, suppose there are 10 features in total for the fault features and fracture features of the block. According to the coordinate value of each feature, feature matching is performed at the same position in each attribute body. After matching, the statistical matching results are as follows: attribute body A has 9 features, and the matching result is 90%; attribute body B has 8 features, and the matching result is 80%; attribute body C has 10 features, and the matching result is 100%. Then attribute body C has the highest matching degree. Then compare 100% of attribute body C with the preset screening requirement of not less than 85%. 100% is higher than 85% and meets the preset screening requirement, so attribute body C is determined as the first attribute body.

[0091] Optionally, if the third attribute body does not meet the preset screening requirement, attribute fusion processing is performed on at least two attribute bodies to obtain at least one fused attribute body;

[0092] According to the fault characteristics and fracture characteristics of the block, each fused attribute body is matched to obtain a second matching result, and the second matching result is used to characterize the matching degree between the fused attribute body and the fault characteristics and fracture characteristics;

[0093] Determine the attribute body with the largest second matching result in the at least one fused attribute body as the fourth attribute body;

[0094] If the second matching result of the fourth attribute body meets the preset screening requirement, the fourth attribute body is determined as the first attribute body.

[0095] In this step, if the third attribute body does not meet the preset screening requirements, multiple attribute bodies are fused to obtain at least one fused attribute body including multiple fracture features, and then each fused attribute body is matched to select a fused attribute body that meets the preset screening requirements.

[0096] Attribute fusion can refer to a seismic multi-attribute fusion method based on principal component analysis. On the basis of correlation analysis, by removing the correlation between two or more attributes, the variance of the principal components of each attribute is used as the fusion weight coefficient, and the principal components are reconstructed to enhance the fusion results.

[0097] Optionally, when there are more than three attribute bodies, attribute fusion may be performed on any two or three attribute bodies.

[0098] S204 , filtering the value range of the first attribute body according to the extreme amplitude limit value to obtain a filtered second attribute body.

[0099] In this solution, the extreme amplitude limit value is obtained according to the amplitude value of the completed casing-changed well in the first attribute body, and the completed casing-changed well is a well on the block.

[0100] In this step, after obtaining the first attribute body, the extreme amplitude limit value is obtained according to the amplitude value of the completed casing change well on the block on the first attribute body, and then the value range of the first attribute body is filtered according to the extreme amplitude limit value to obtain the filtered second attribute body.

[0101] The extreme amplitude limit value may be a fixed value, and when it is higher or lower than the fixed value, the possibility of set change is greater.

[0102] The value domain may refer to the range of amplitude values ​​of the first attribute body, and the value domain may be a positive value or a negative value, indicating the direction of the amplitude.

[0103] Optionally, the method for determining the limit amplitude limit value may include:

[0104] Confirm that the well has been drilled and the casing has been changed;

[0105] Determine the amplitude value of each casing change point in the first attribute body according to at least two casing change points of the casing change well that has been drilled;

[0106] According to the amplitude value of each set of variation points in the first attribute body, a limit amplitude boundary value is determined.

[0107] The change point may refer to the point where the change occurs, and the change point may be represented by a coordinate point.

[0108] The amplitude value can reflect the characteristics of the formation and lithology, such as lithology difference, formation continuity, formation space, porosity, etc. In this embodiment, the amplitude value is used to reflect the parameters of the casing change point of the completed casing change well in the first attribute body.

[0109] The method for determining the limit amplitude limit value according to the amplitude value of each set change point in the first attribute body may include: counting the amplitude values ​​of multiple set change points in the first attribute body, determining the distribution of each amplitude value, if the amplitude value is a positive number, selecting the minimum amplitude value as the limit amplitude limit value, and if the amplitude value is a negative number, selecting the maximum amplitude value as the limit amplitude limit value.

[0110] Optionally, filtering the value range of the first attribute body according to the limit amplitude boundary value to obtain the second attribute body includes:

[0111] If the limit amplitude limit value is a positive value, the interval in the value range of the first attribute body that is smaller than the limit amplitude limit value is filtered to obtain a second attribute body;

[0112] If the extreme amplitude limit value is a negative value, the interval in the value range of the first attribute body that is greater than the extreme amplitude limit value is filtered to obtain the second attribute body.

[0113] In this step, the extreme amplitude limit value is a positive value or a negative value, indicating that the direction of the extreme amplitude limit value is different. Therefore, the absolute value of the extreme amplitude limit value is used to indicate the strength of the amplitude. When the amplitude is stronger, casing change is more likely to occur. Therefore, if the extreme amplitude limit value is a positive value, the interval in the value range of the first attribute body that is less than the extreme amplitude limit value is filtered. If the extreme amplitude limit value is a negative value, the interval in the value range of the first attribute body that is greater than the extreme amplitude limit value is filtered, so that the fracture characteristics of the obtained second attribute body are more obvious, thereby improving the prediction effect of casing change.

[0114] For example, assuming that the range of the first attribute is [50, 100], the limit amplitude limit value is 70, and after filtering the range of the first attribute according to the limit amplitude limit value, the range of the second attribute body is [70, 100]. Assuming that the range of the first attribute is [-50, -100], the limit amplitude limit value is -70, and after filtering the range of the first attribute according to the limit amplitude limit value, the range of the second attribute body is [-70, -100].

[0115] S205: Predict the casing change of the target well according to the second attribute body.

[0116] In this step, after the second attribute body is obtained, the second attribute body is analyzed to predict the casing change of the target well.

[0117] A method for predicting well casing changes provided in an embodiment of the present application performs multi-attribute calculation on a target seismic data body to obtain at least two attribute bodies, and then screens the at least two attribute bodies according to the fault characteristics and fracture characteristics of a block, so that the fault characteristics and fracture characteristics of the first attribute body obtained have a high degree of matching with the fault characteristics and fracture characteristics of the block, and then further filters the value range of the first attribute body according to the limit amplitude limit value, removes data in the value range that are not related to casing changes, and obtains a filtered second attribute body, so that the fracture characteristics of the second attribute body obtained are more obvious, and finally predicts the well casing changes through the second attribute body, thereby improving the prediction effect of the well casing changes.

[0118] Figure 3 A flow chart of another method for predicting well casing change provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, in Figure 2 Based on the embodiment shown, the method further includes:

[0119] S301, performing attribute enhancement processing on the second attribute body to obtain a fifth attribute body.

[0120] In this step, by performing attribute addition processing on the second attribute body, the resolution of the obtained fifth attribute body is made higher, and the cracks of the fifth attribute body are more easily identified, thereby improving the prediction effect of the casing change of the target well.

[0121] The attribute enhancement process may refer to improving the resolution of the fractures in the second attribute volume by using an ant volume tracing algorithm or a maximum likelihood volume algorithm.

[0122] S302: Perform crack extraction processing on the fifth attribute body to obtain at least one crack.

[0123] In this step, by performing crack extraction processing on the fifth attribute body to obtain at least one crack, data irrelevant to the crack in the fifth attribute body is removed to facilitate subsequent crack processing.

[0124] The crack extraction process may refer to automatically tracing the fifth attribute body along the layer slices using an ant body tracking algorithm, thereby extracting cracks.

[0125] S303, performing network reconstruction according to the geometric parameters of each crack to obtain a discrete crack network model.

[0126] In this step, after the cracks are extracted, each crack is distributed in three-dimensional space according to the geometric parameters of the cracks, so as to analyze the effects and influences between the cracks and analyze the fracture-slip trends related to the cracks.

[0127] The geometric parameters of the crack may include spatial coordinates, length, orientation, etc.

[0128] Network reconstruction can refer to constructing an overall fracture model through a fracture network group composed of various fractures distributed in three-dimensional space, thus achieving a realistic and detailed effective description of the fracture system from its geometric shape to its seepage behavior.

[0129] S304. According to the geomechanical parameters and fracture occurrence parameters of the block, the discrete fracture network model is subjected to fracture activation analysis and processing to obtain the fracture slip probability of the target well.

[0130] In this step, after obtaining the discrete fracture network model, the stress conditions of each fracture in the discrete fracture network model are analyzed by using the geomechanical parameters and fracture occurrence parameters of the block, thereby obtaining the fracture slip probability of the target well.

[0131] Geomechanical parameters may include maximum horizontal principal stress, minimum horizontal principal stress, overlying formation pressure, pore pressure, pore pressure increment, maximum horizontal principal stress direction and internal friction coefficient.

[0132] The fault attitude parameter can refer to the average dip of the fault.

[0133] Fracture activation analysis processing can refer to the fracture activation analysis carried out based on the Mohr-Coulomb criterion and using the average mechanics model.

[0134] The average mechanical model can refer to a mechanical model obtained based on the geomechanical parameters and fault occurrence parameters of the block. The average mechanical model can be used to simulate and predict the stress conditions at various locations in the block.

[0135] Fault slip probability refers to the slip and extension trend of the fault obtained after performing a fracture activation analysis on the well location of the target well.

[0136] S305. Determine the casing change risk level of the target well according to the preset casing change risk level requirement and the fracture slip probability.

[0137] In this step, the casing change risk level of the target well is determined by judging whether the fault slip probability is within the level of casing change risk level requirements.

[0138] The casing change risk level requirement may refer to the correspondence between the casing change risk level and the fault slip probability. For example, if the fault slip probability of the target well is ≥0.6, it means that the fault is prone to activation slip and the casing change risk level is high; if the slip trend of the target well is <0.6, it means that the fault is not prone to activation slip and the casing change risk level is low.

[0139] Another method for predicting well casing changes provided in an embodiment of the present application performs attribute enhancement processing on a second attribute body to obtain a fifth attribute body, performs fracture extraction processing on the fifth attribute body to obtain at least one fracture, and then performs network reconstruction based on the geometric parameters of each fracture to obtain a discrete fracture network model, and then performs fracture activation analysis processing on the discrete fracture network model based on the geomechanical parameters and fracture occurrence parameters of the block to obtain the fracture slip probability of the target well, and finally determines the casing change risk level of the target well based on the preset casing change risk level requirements and the fracture slip probability, thereby realizing the prediction of the casing change risk of the target well and improving the prediction effect of the well casing change risk.

[0140] Figure 4 A schematic diagram of a well casing change prediction device provided in an embodiment of the present application is shown in FIG. Figure 4 As shown, the device comprises:

[0141] An acquisition module 401 is used to acquire a target seismic data volume, where the target seismic data volume is obtained based on a pure wave seismic data volume of a block where a target well is located;

[0142] A calculation module 402 is used to perform multi-attribute calculation on the target seismic data volume to obtain at least two attribute volumes;

[0143] A screening module 403 is used to screen at least two attribute bodies according to the fault characteristics and fracture characteristics of the block to obtain a screened first attribute body;

[0144] A filtering module 404 is used to filter the value range of the first attribute body according to the extreme amplitude limit value to obtain a filtered second attribute body, wherein the extreme amplitude limit value is obtained according to the amplitude value of the completed casing change well in the first attribute body, and the completed casing change well is a well on the block;

[0145] The prediction module 405 is used to predict the casing change of the target well according to the second attribute body.

[0146] In some implementations, the acquisition module 401 is further configured to:

[0147] Acquire the pure wave seismic data volume of the block where the target well is located;

[0148] The pure wave seismic data volume is subjected to bilateral amplitude preservation preprocessing to obtain the target seismic data volume. The bilateral amplitude preservation preprocessing includes structure-oriented fracture enhancement filtering processing and structure filtering processing based on diffusion equation.

[0149] In some embodiments, the screening module 403 is further configured to:

[0150] For each attribute body, the attribute body is matched according to the fault characteristics and fracture characteristics of the block to obtain a first matching result, and the first matching result is used to characterize the matching degree between the attribute body and the fault characteristics and fracture characteristics;

[0151] Determine the attribute body with the largest first matching result among the at least two attribute bodies as the third attribute body;

[0152] If the first matching result of the third attribute body meets the preset screening requirement, the third attribute body is determined as the first attribute body.

[0153] In some embodiments, the screening module 403 is further configured to:

[0154] If the third attribute body does not meet the preset screening requirements, then at least two attribute bodies are attribute-fused to obtain at least one fused attribute body;

[0155] According to the fault characteristics and fracture characteristics of the block, each fused attribute body is matched to obtain a second matching result, and the second matching result is used to characterize the matching degree between the fused attribute body and the fault characteristics and fracture characteristics;

[0156] Determine the attribute body with the largest second matching result in the at least one fused attribute body as the fourth attribute body;

[0157] If the second matching result of the fourth attribute body meets the preset screening requirement, the fourth attribute body is determined as the first attribute body.

[0158] In some implementations, the filtering module 404 is further configured to:

[0159] If the limit amplitude limit value is a positive value, the interval in the value range of the first attribute body that is smaller than the limit amplitude limit value is filtered to obtain a second attribute body;

[0160] If the extreme amplitude limit value is a negative value, the interval in the value range of the first attribute body that is greater than the extreme amplitude limit value is filtered to obtain the second attribute body.

[0161] In some implementations, the prediction module 405 is further configured to:

[0162] Performing attribute enhancement processing on the second attribute body to obtain a fifth attribute body;

[0163] Performing crack extraction processing on the fifth attribute body to obtain at least one crack;

[0164] According to the geometric parameters of each crack, network reconstruction is performed to obtain a discrete crack network model;

[0165] According to the block's geomechanical parameters and fault occurrence parameters, the discrete fracture network model is analyzed and processed for fracture activation to obtain the fault slip probability of the target well.

[0166] The casing change risk level of the target well is determined based on the preset casing change risk level requirements and fault slip probability.

[0167] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 5 As shown, the electronic device 50 includes:

[0168] The electronic device 50 may include a processor 501 with one or more processing cores, a memory 502 with one or more computer-readable storage media, a communication component 503 and other components. The processor 501 , the memory 502 and the communication component 503 are connected via a bus 504 .

[0169] In the specific implementation process, at least one processor 501 executes the computer execution instructions stored in the memory 502, so that at least one processor 501 executes the above shale gas well casing change prediction method.

[0170] The specific implementation process of the processor 501 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0171] In the above Figure 5 In the illustrated embodiment, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0172] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.

[0173] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0174] In some embodiments, a computer program product is also proposed, including a computer program or instructions, which, when executed by a processor, implements the steps in any of the above-mentioned shale gas well casing change prediction methods.

[0175] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.

[0176] A person of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0177] To this end, an embodiment of the present application provides a computer-readable storage medium, in which multiple instructions are stored, and the instructions can be loaded by a processor to execute the steps in any shale gas well casing change prediction method provided in the embodiment of the present application.

[0178] The storage medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0179] According to one aspect of the present application, a computer program product or a computer program is provided. The computer program product or the computer program comprises computer instructions stored in a computer-readable storage medium.

[0180] Since the instructions stored in the storage medium can execute the steps in any shale gas well casing change prediction method provided in the embodiments of the present application, the beneficial effects that can be achieved by any shale gas well casing change prediction method provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0181] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0182] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for predicting well casing change, characterized in that: include: Acquire a target seismic data volume, wherein the target seismic data volume is obtained based on a pure wave seismic data volume of a block where the target well is located; Performing multi-attribute calculation on the target seismic data volume to obtain at least two attribute volumes; Screening the at least two attribute bodies according to the fault characteristics and fracture characteristics of the block to obtain a screened first attribute body; According to the extreme amplitude limit value, filtering the value range of the first attribute body to obtain a filtered second attribute body, wherein the extreme amplitude limit value is obtained according to the amplitude value of the completed casing change well in the first attribute body, and the completed casing change well is a well on the block; predicting the casing change of the target well according to the second attribute body; The step of acquiring the target seismic data volume comprises: Acquire the pure wave seismic data volume of the block where the target well is located; Performing bilateral amplitude-preserving preprocessing on the pure-wave seismic data volume to obtain a target seismic data volume, wherein the bilateral amplitude-preserving preprocessing includes structure-oriented fracture enhancement filtering processing and structure filtering processing based on a diffusion equation; The predicting of the casing change of the target well according to the second attribute body includes: Performing attribute enhancement processing on the second attribute body to obtain a fifth attribute body; Performing crack extraction processing on the fifth attribute body to obtain at least one crack; According to the geometric parameters of each crack, network reconstruction is performed to obtain a discrete crack network model; According to the geomechanical parameters and fracture occurrence parameters of the block, the discrete fracture network model is subjected to fracture activation analysis and processing to obtain the fracture slip probability of the target well; Determining the casing change risk level of the target well according to the preset casing change risk level requirement and the fracture slip probability; If the fracture slip probability is ≥ 0.6, it means that the fracture is prone to activation slip and the sleeve change risk level is high; if the fracture slip probability is < 0.6, it means that the fracture is not prone to activation slip and the sleeve change risk level is low.

2. The method according to claim 1, characterized in that The step of screening the at least two attribute bodies according to the fault characteristics and fracture characteristics of the block to obtain a screened first attribute body includes: For each attribute body, matching the attribute body according to the fault feature and the fracture feature of the block to obtain a first matching result, wherein the first matching result is used to characterize the matching degree between the attribute body and the fault feature and the fracture feature; Determine the attribute body with the largest first matching result among the at least two attribute bodies as the third attribute body; If the first matching result of the third attribute body meets the preset screening requirement, the third attribute body is determined as the first attribute body.

3. The method according to claim 2, characterized in that The method further comprises: If the third attribute body does not meet the preset screening requirement, then fusing the at least two attribute bodies to obtain at least one fused attribute body; According to the fault characteristics and fracture characteristics of the block, each fused attribute body is matched to obtain a second matching result, wherein the second matching result is used to characterize the matching degree between the fused attribute body and the fault characteristics and the fracture characteristics; Determine the attribute body with the largest second matching result in the at least one fused attribute body as the fourth attribute body; If the second matching result of the fourth attribute body meets the preset screening requirement, the fourth attribute body is determined as the first attribute body.

4. The method according to any one of claims 1 to 3, characterized in that: The method further comprises: Confirm that the well has been drilled and the casing has been changed; Determine the amplitude value of each casing change point in the first attribute body according to at least two casing change points of the completed casing change well; According to the amplitude value of each set of change points in the first attribute body, the limit amplitude boundary value is determined.

5. The method according to any one of claims 1 to 3, characterized in that: The filtering process is performed on the value range of the first attribute body according to the limit amplitude boundary value to obtain the second attribute body, including: If the extreme amplitude limit value is a positive value, filtering the interval in the value range of the first attribute body that is smaller than the extreme amplitude limit value to obtain a second attribute body; If the extreme amplitude limit value is a negative value, the interval in the value range of the first attribute body that is greater than the extreme amplitude limit value is filtered to obtain a second attribute body.

6. A well casing change prediction device, characterized in that: include: An acquisition module, used for acquiring a target seismic data volume, wherein the target seismic data volume is obtained based on a pure wave seismic data volume of a block where a target well is located; A calculation module, used for performing multi-attribute calculation on the target seismic data volume to obtain at least two attribute volumes; A screening module, configured to screen the at least two attribute bodies according to the fault characteristics and fracture characteristics of the block to obtain a screened first attribute body; A filtering module, configured to filter the value range of the first attribute body according to an extreme amplitude limit value to obtain a filtered second attribute body, wherein the extreme amplitude limit value is obtained according to the amplitude value of the completed casing change well in the first attribute body, and the completed casing change well is a well on the block; A prediction module, used for predicting the casing change of the target well according to the second attribute body; The acquisition module is specifically used to acquire a pure wave seismic data volume of the block where the target well is located; perform bilateral amplitude preservation preprocessing on the pure wave seismic data volume to obtain a target seismic data volume, wherein the bilateral amplitude preservation preprocessing includes structural guidance fracture enhancement filtering processing and structural filtering processing based on diffusion equation; The prediction module is specifically used to perform attribute enhancement processing on the second attribute body to obtain a fifth attribute body; perform fracture extraction processing on the fifth attribute body to obtain at least one fracture; perform network reconstruction according to the geometric parameters of each fracture to obtain a discrete fracture network model; perform fracture activation analysis processing on the discrete fracture network model according to the geomechanical parameters and fracture occurrence parameters of the block to obtain the fracture slip probability of the target well; determine the casing change risk level of the target well according to the preset casing change risk level requirements and the fracture slip probability; If the fault slip probability of the target well is ≥0.6, it means that the fault is prone to activation slip and the casing change risk level is high; if the slip trend of the target well is <0.6, it means that the fault is not prone to activation slip and the casing change risk level is low.

7. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 5 when executed by a processor.

9. A computer program product, characterized in that The computer program product stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 5 when executed by a processor.

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