Method and system for multi-energy computed fault drilling cutting analysis

Abstract

The invention relates to the technical field of computed tomography, in particular to a multi-energy computed tomography drilling cutting analysis method and system, according to the method, multi-angle and multi-energy-level scanning is carried out on drilling cutting through the multi-energy CT scanning technology, and a high-resolution three-dimensional image of the drilling cutting is obtained. And in combination with a high-precision positioning system, the absolute position of the drilling cuttings in the scanning process is obtained in real time, and the relative position change of the drilling cuttings is analyzed through an image processing technology. The method comprises the steps of hole depth distribution characteristic analysis and lateral pressure analysis of drilling cuttings, stress distribution of a material in the drilling process is simulated through the finite element analysis (FEA) technology, and a pressure concentration area and potential damage points are predicted. And through a machine learning algorithm, the drilling cutting movement trend and the abnormal position change are further identified, and a dynamic analysis chart of the drilling cutting is generated. The three-dimensional structure and the motion trail of the drilling cuttings can be accurately captured, and the method is suitable for comprehensive evaluation of efficiency, equipment abrasion and underground rock stratum stability in the drilling process.

Description

Technical Field

[0001] The invention relates to the technical field of computer tomography, and in particular to a method and system for multi-energy computer tomography drilling cuttings analysis. Background Art

[0002] Drill cuttings analysis plays an important role in geological exploration, mineral mining, oil and gas drilling, etc. It is usually used to evaluate underground rock formations, mineral deposits, and equipment status during drilling. Traditional drill cuttings analysis techniques usually use physical screening, optical microscope observation, chemical analysis, etc. Although these methods can obtain some basic information about drill cuttings, they usually have the following shortcomings:

[0003] Low efficiency: Traditional analysis relies on manual operation, which is time-consuming and requires high operator requirements, and cannot meet the real-time and efficient analysis needs of modern engineering. With the advancement of drilling technology, the amount of data obtained is increasing, and traditional methods are difficult to process massive amounts of drill cuttings information in a short period of time.

[0004] Limited analysis dimensions: Traditional analysis methods mostly stay at the level of two-dimensional plane or single physical and chemical property analysis, and cannot fully understand the three-dimensional structure, hole characteristics and stress distribution of drill cuttings. Three-dimensional information such as the shape, distribution and movement trajectory of drill cuttings is crucial in some cases. For example, when judging the wear of drilling equipment and the distribution of cracks in underground rock formations, more accurate three-dimensional data is required.

[0005] Insufficient precision: Traditional methods such as physical screening and microscopic observation can only obtain limited surface information and cannot accurately detect the internal structural characteristics of drill cuttings, especially for the detection of tiny cracks, holes or internal defects. For geological analysis and resource assessment with high precision requirements, traditional methods often cannot meet the required technical standards.

[0006] Unable to achieve automation: Traditional drill cuttings analysis technology usually relies on manual operation and lacks the ability of intelligent and automated processing. In the modern oil and mining industry, with the application of large-scale automated drilling equipment, traditional manual operation methods cannot keep up with the speed and accuracy requirements of operations, especially remote monitoring and real-time data analysis.

[0007] In order to solve the above problems, in recent years, computed tomography (CT) technology has been gradually applied to drill cuttings analysis. CT technology can accurately reflect the internal structure of drill cuttings by obtaining three-dimensional tomographic images of drill cuttings. However, the existing CT drill cuttings analysis still has the following shortcomings:

[0008] Single energy range: Traditional CT scans are mostly single-energy CT, which cannot distinguish the absorption differences of different materials or components under multiple energies, resulting in the inability to accurately analyze the material properties of drill cuttings.

[0009] Lack of multi-dimensional data analysis capabilities: Most existing CT analyses remain at the structural imaging stage and lack the ability to combine multi-dimensional data analysis with other technologies such as high-precision positioning technology and finite element analysis. It is impossible to perform multi-level analysis of drill cuttings in a dynamic and complex environment.

[0010] Therefore, in response to the above problems, the present invention proposes a drill cuttings analysis method and system based on multi-energy CT scanning technology, which combines high-precision positioning, machine learning, image processing, finite element analysis and other technical means to perform multi-dimensional analysis of drill cuttings in real time and accurately. It can not only obtain the three-dimensional structure of the drill cuttings, but also analyze its movement trend, hole distribution and stress distribution, thereby greatly improving the accuracy and efficiency of drill cuttings analysis. Summary of the invention

[0011] The purpose of the present invention is to provide a drill cuttings analysis method and system based on multi-energy CT scanning, which can combine high-precision positioning technology, image processing technology, machine learning algorithm and finite element analysis method to track the three-dimensional position changes of drill cuttings in real time, analyze the hole depth distribution, relative position trend and lateral pressure distribution of drill cuttings, thereby improving the accuracy and efficiency of drill cuttings analysis.

[0012] A method and system for multi-energy computed tomography drill cuttings analysis, the method comprising the following steps:

[0013] 1. Obtain 3D image data of drill cuttings through multi-energy CT scanning equipment;

[0014] 2. Use high-precision positioning technology to obtain the absolute position data of drill cuttings

[0015] $(x, y, z)$, and generate its three-dimensional spatial position distribution;

[0016] 3. Use machine learning algorithms to analyze the temporal variation of the absolute position of the drill cuttings

[0017] $P_i(t)=(x_i(t), y_i(t), z_i(t)), and the direction and speed of the cuttings are predicted by the speed formula _i(t)_fracdP_i(t)dt;

[0018] 4. Use image processing technology to identify the relative position between the cuttings, the relative position SR _ {ij}(t)S is calculated by the following formula:

[0019]

[0020] 5. Based on the image data obtained by the multi-energy CT scanning, the hole depth distribution characteristics of the drill cuttings are analyzed, and the hole depth $\mathrm{SD(x, y)$~ is calculated by the following formula:

[0021] D(x, y) = z bottom (x, y)-z top (x, y)

[0022] And the depth gradient formula $G(x, y) = D(x, y)$ is used to automatically identify abnormal hole depth points and depth gradients;

[0023] 6. Combined with the finite element analysis method, the finite element analysis simulates the lateral pressure distribution in the cuttings area through the following formula to predict the pressure concentration area and the material failure point. The stress σ is calculated by the following formula:

[0024] σ=E·∈

[0025] Among them, SES is the Young's modulus of the material and $\c{c}psilon$ is the strain.

[0026] Furthermore, the high-precision positioning technology includes a laser radar, which is used to obtain the real-time three-dimensional position information of the drill cuttings, and is combined with a recurrent neural network (RNN) to perform nonlinear prediction of the motion trajectory of the drill cuttings.

[0027] Furthermore, the image processing technology includes edge detection and contour recognition, which are used to extract the relative position characteristics of the drill cuttings and generate a relative position change map of the drill cuttings.

[0028] Furthermore, finite element analysis is used to predict the material failure point by simulating the stress distribution in the drill cuttings area and to identify the stress concentration area based on the stress-strain distribution of the drill cuttings.

[0029] Furthermore, a multi-energy computer tomography cuttings analysis system is characterized in that the system comprises:

[0030] Multi-energy CT scanning equipment, used to obtain multi-dimensional image data of drill cuttings;

[0031] High-precision positioning module, used to obtain the three-dimensional spatial position data of drill cuttings;

[0032] Image processing unit for analyzing the relative position of drill cuttings and hole depth distribution;

[0033] A machine learning analysis module to predict the direction and speed of movement of drill cuttings;

[0034] Finite element analysis module for analyzing the lateral pressure distribution in the cuttings area.

[0035] To achieve the above object, the present invention provides a drill cuttings analysis method based on multi-energy CT scanning, comprising the following steps:

[0036] Acquire multi-dimensional image data of drill cuttings through multi-energy CT scanning equipment;

[0037] Introducing high-precision positioning technology to monitor the absolute position of drill cuttings in real time and generate three-dimensional spatial position information;

[0038] Analyze the absolute position trend of drill cuttings through machine learning algorithms to predict their movement direction and speed;

[0039] Use image processing technology to identify the relative position changes of drill cuttings and generate dynamic position change diagrams;

[0040] Combined with multi-energy CT scanning data, the hole depth distribution characteristics of drill cuttings are analyzed to automatically identify depth gradients and abnormal points;

[0041] Based on the finite element analysis method, the lateral pressure distribution is simulated to predict the pressure concentration area and possible material failure points.

[0042] Through the above method, the present invention can comprehensively analyze the multi-dimensional characteristics of drill cuttings and generate a detailed analysis report.

[0043] Beneficial effects of the present invention:

[0044] 1. The present invention uses multi-energy CT scanning technology and high-precision positioning technology to obtain the three-dimensional position and structural information of drill cuttings in real time and accurately, greatly improving the speed and accuracy of drill cuttings analysis. The method combines multi-dimensional analysis such as absolute position, relative position, hole depth distribution and lateral pressure, and can comprehensively evaluate the physical properties of drill cuttings, and realize multi-level analysis from macro to micro. The movement trend of drill cuttings is analyzed by machine learning algorithm, and the movement direction and speed of drill cuttings can be predicted, effectively improving the intelligence level of drill cuttings analysis. Through finite element analysis, the stress distribution of materials can be accurately simulated, and the pressure concentration area and potential material damage points can be predicted, thereby enhancing the safety and reliability of drill cuttings analysis. The method and system of the present invention are suitable for drill cuttings analysis in different drilling environments, can maintain high analysis accuracy under complex geological conditions, and have broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 It is a schematic flow chart of the drill cuttings analysis method based on multi-energy CT scanning of the present invention;

[0047] Figure 2 It is a schematic diagram of the working process of the high-precision positioning system of the present invention;

[0048] Figure 3 It is a schematic diagram of the image processing flow of the relative position change of drill cuttings according to the present invention. DETAILED DESCRIPTION

[0049] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0050] Embodiment 1:

[0051] The system of the present invention comprises: a multi-energy CT scanning device, a high-precision positioning module, an image processing unit, a machine learning analysis module, a finite element analysis module, and a terminal device for processing and displaying analysis results.

[0052] 1. Absolute position trend analysis

[0053] Obtain the real-time coordinates of the drill cuttings in space through a high-precision positioning module (such as laser radar) The machine learning algorithm is used to perform time series analysis on these coordinates to obtain the trajectory and speed of the drill cuttings. The change over time can be described as: i (t) = (x i (t), y i (t), z i (t))

[0054] The velocity Vi(t) can be calculated from the time derivative of the position:

[0055]

[0056] The future movement direction and speed of drill cuttings are predicted by linear regression or recurrent neural network models.

[0057] 2. Relative position trend analysis

[0058] Image processing technology is used to analyze the relative position relationship between different drill cuttings. The relative position SR{ij}(t)$ is calculated using the Euclidean distance formula between two points:

[0059]

[0060] 3. Analysis of hole depth distribution characteristics

[0061] Multi-energy CT scanning obtains the three-dimensional depth information of drill cuttings, and the hole depth $D(x, y)$ is expressed as:

[0062] D(x, y) = z bottom (x, y)-z top (x, y)

[0063] By calculating the depth gradient $G(x, y)$: Able to automatically identify abnormal points of hole depth change.

[0064] 4. Regional lateral pressure analysis

[0065] Based on the image data of multi-energy CT scanning, combined with the finite element analysis method (FEA), the lateral pressure distribution of the material is modeled. The stress $\siqma$ is calculated by the following formula:

[0066] σ=E·∈

[0067] Where SES is the Young's modulus of the material and S$ is the strain. Through finite element analysis, the pressure concentration area and possible failure points can be predicted.

[0068] 5. Single hole box type analysis

[0069] A three-dimensional model is established for a single hole to perform structural strength analysis. The volume $V$ of the hole is calculated by volume integration:

[0070] V = ∫ Ω dV

[0071] The stability of the hole is evaluated in combination with its mechanical properties.

[0072] The present invention is intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for multi-energy computed tomography cuttings analysis, characterized in that: The method comprises the following steps: (1) Obtain three-dimensional image data of drill cuttings through multi-energy CT scanning equipment; (2) Use high-precision positioning technology to obtain the absolute position data $(x, y, z)$ of the drill cuttings and generate its three-dimensional spatial position distribution; (3) Using machine learning algorithm to analyze the time variation of the absolute position of the drill cuttings $P_i(t)=(x_i(t), y_i(t), z_i(t)), and predict the movement direction and speed of the drill cuttings through the speed formula _i(t)_fracdP_i(t)dt: (4) Using image processing technology to identify the relative position between drill cuttings, the relative position SR_{ij}(t)S is calculated by the following formula: (5) Based on the image data obtained by the multi-energy CT scanning, the hole depth distribution characteristics of the drill cuttings are analyzed, and the hole depth $\mathrm{SD(x, y)$~ is calculated by the following formula: D(x,y)=z bottom (x,y)-z top (x,y) And the depth gradient formula $G(x, y) = D(x, y)$ is used to automatically identify abnormal hole depth points and depth gradients; (6) Combined with the finite element analysis method, the finite element analysis simulates the lateral pressure distribution in the drill cuttings area through the following formula to predict the pressure concentration area and material failure point. The stress σ is calculated by the following formula: σ=E·∈ Among them, SES is the Young's modulus of the material and $\c{c}psilon$ is the strain.

2. The method for multi-energy computed tomography cuttings analysis according to claim 1, characterized in that: The high-precision positioning technology includes a laser radar, which is used to obtain real-time three-dimensional position information of drill cuttings, and combines a recurrent neural network (RNN) to perform nonlinear prediction on the movement trajectory of the drill cuttings.

3. The method and system for multi-energy computed tomography drill cuttings analysis according to claim 1, characterized in that: The image processing technology includes edge detection and contour recognition, which are used to extract the relative position characteristics of drill cuttings and generate a relative position change diagram of drill cuttings.

4. The method and system for multi-energy computed tomography drill cuttings analysis according to claim 1, characterized in that: The finite element analysis is used to predict the material failure point by simulating the stress distribution in the drill cuttings area, and to identify the pressure concentration area according to the stress and strain distribution of the drill cuttings.

5. A multi-energy computer tomography cuttings analysis system, characterized in that: The system comprises: Multi-energy CT scanning equipment, used to obtain multi-dimensional image data of drill cuttings; High-precision positioning module, used to obtain the three-dimensional spatial position data of drill cuttings; Image processing unit for analyzing the relative position of drill cuttings and hole depth distribution; A machine learning analysis module to predict the direction and speed of movement of drill cuttings; Finite element analysis module for analyzing the lateral pressure distribution in the cuttings area.

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