Storage type logging environment safety correction method and device in extreme environment and electronic equipment

Abstract

The invention discloses a storage type logging environment safety correction method, which is used for environment correction of an acoustic logging curve and comprises the following steps of: inputting original acoustic logging curve data; dividing the sound wave data in the logging file, setting the number of control processing data, and then performing circular processing until all the sound wave data in the logging file are processed; an acoustic logging environment correction method based on model inversion is adopted, an acoustic logging ray tracing forward modeling model is constructed, under the condition that the forward modeling model is known, a set of parameter initial values are firstly estimated and given, the stratum interval transit time is calculated through a known acoustic logging ray tracing model algorithm, the interval transit time is used as a theoretical value, and the acoustic logging ray tracing forward modeling model is obtained. Comparing the error between the observed value and the theoretical value for inversion to obtain a sound wave time difference value closer to the theoretical value, and obtaining a corrected sound wave time difference value; and outputting the corrected acoustic logging curve data so as to achieve the purpose of increasing the operation speed.

Description

Technical Field

[0001] The present invention relates to the field of geophysical exploration technology, and in particular to a storage-type well logging environmental safety correction method, device and electronic equipment under extreme environments. Background Art

[0002] With the continuous growth of global energy demand and the increasing difficulty of oil and gas resource development, horizontal well technology has been widely used in the oil industry as a key means of improving oil and gas recovery. Horizontal well drilling technology not only effectively increases the contact area between the wellbore and the formation, thereby increasing oil and gas production, but also reduces development costs and minimizes environmental impact. However, the complex structure and unique environment of horizontal wells place higher demands on logging technology, and traditional logging methods often fail to meet these requirements. Against this backdrop, horizontal well storage logging technology has emerged and has quickly become a key technology for addressing the challenges of horizontal well logging. Using advanced sensors and data acquisition systems, this technology can acquire and store large amounts of formation information in real time or with a delayed delay, providing accurate data support for oil and gas field exploration and development. Furthermore, storage logging technology is resistant to high temperatures and pressures, and can operate without power for extended periods, adapting to the complex conditions of horizontal wells and improving the efficiency and safety of logging operations.

[0003] Well logging data is crucial for evaluating reservoir properties, especially fluid properties, and is also indispensable for log-constrained seismic inversion and seismic profile calibration. However, the actual operating environment of well logging differs significantly from the ideal operating environment of the instrument. While the environmental correction charts provided with the instrument can be used to perform environmental correction for conventional well logging data, they cannot correct for environmental influences outside the calibration chart's application range. Therefore, research on environmental correction of unconventional factors affecting well logging data is of great significance for reservoir evaluation, log-constrained seismic inversion, and seismic profile calibration. Among well logging data, sonic logging is often used to create synthetic seismic records and calculate reservoir porosity, rock elastic modulus, formation pressure, and reservoir reserves. Its accuracy is crucial for exploration. Many wells suffer from severe irregular wellbore diameters, referred to here as extreme borehole expansion. Extreme borehole expansion has a significant impact on sonic logging data, and logging environmental safety is crucial to the success of logging. Therefore, environmental correction of sonic logging data is essential.

[0004] In oilfield exploration, sonic logging data is often used to calculate reservoir porosity, formation pressure, and oil reserves. Extreme borehole expansion can affect sonic logging data, and eliminating this effect is crucial for oilfield exploration. Existing sonic logging environmental correction methods can reduce the impact of extreme borehole expansion on sonic logging data, making the affected data more closely aligned with actual formation data. However, this method is inconvenient to run on a computer, exhibiting drawbacks such as long runtimes and large data storage requirements. This method cannot meet the requirements for rapid processing required for on-site logging interpretation. Summary of the Invention

[0005] The object of the present invention is to provide a storage-type well logging environment safety correction method, device and electronic equipment to solve the problems raised in the above background technology.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, a storage logging environmental safety correction method is provided for environmental correction of acoustic logging curves, comprising the following steps:

[0008] Step S1: input the data to be corrected, and input the original acoustic logging curve data;

[0009] Step S2: Data stratification: Before correcting the acoustic logging curve data, the acoustic data in the logging file is divided, the number of control processing data is set, and then the processing is cyclically performed until all the acoustic data in the logging file are processed;

[0010] Step S3: Using an acoustic logging environmental correction method based on model inversion, an acoustic logging ray tracing forward model is constructed. When the forward model is known, a set of initial parameter values are estimated first, and the formation acoustic transit time is calculated using the known acoustic logging ray tracing model algorithm. The acoustic transit time is used as a theoretical value, and the error between the observed value and the theoretical value is compared and inverted to obtain an acoustic transit time value that is closer to the theoretical value, thereby obtaining a corrected acoustic transit time value.

[0011] Step S4: Output the corrected data, and output the corrected acoustic logging curve data.

[0012] Furthermore, the step S3 of constructing the acoustic logging ray tracing forward model includes the following steps:

[0013] Step S31: Determine the coordinates of key points, including the coordinates of the transmitting point, the receiving point, the incident point, the exit point, and the maximum expansion point.

[0014] Step S32: determine the simulated propagation path of the sound wave, and fit a simulated propagation path based on the key point position coordinates determined in step S31 and the actual wellbore coordinates. Specifically, the key points of step S31 can divide the simulated propagation path into four sections: from the emission point to the incident point, from the incident point to the maximum diameter expansion point, from the maximum diameter expansion point to the exit point, and from the exit point to the receiving point. In each section, the horizontal coordinates of the points on the simulated propagation path are compared with the actual wellbore coordinates at the same depth. If the horizontal coordinates of the points on the simulated propagation path are smaller than the actual wellbore coordinates at the same depth, the actual wellbore coordinates are modified to the points on the simulated propagation path, and then the new simulated propagation path obtained after the modification is compared again in sequence, until the horizontal coordinates of all points on the simulated propagation path are finally greater than the horizontal coordinates of the actual wellbore at the same depth. In this way, the simulated propagation path finally obtained contains the actual wellbore.

[0015] Step S33: Calculate the acoustic wave propagation time between adjacent layers in the model. Starting from the launch point, calculate the acoustic wave propagation time from each point in each row of the acoustic logging grid model to each point in the next row, that is, the ratio of the straight-line distance between the two points to the starting point velocity;

[0016] Step S34: Calculate the shortest propagation time and shortest path of the sound wave. Given a search range value, with the simulated propagation path obtained in step S32 as the axis, calculate the propagation time of all nodes within the search step range on the left and right of the axis, and finally obtain the shortest propagation time and shortest path through comparison.

[0017] Furthermore, the inversion method used in step S3 is a least squares curve fitting inversion algorithm, which includes the following steps: step S35, in the same extreme expansion interval, multiple known data points are selected, and these known data points are forward modeled using an acoustic logging ray tracing model algorithm to calculate the forward time difference;

[0018] Step S36, establishing a functional relationship between the forward moveout and the known moveout, and using the functional relationship to calculate the error between the measured moveout and the forward moveout;

[0019] Step S37: Finally, the error value is corrected from the measured time difference value to obtain the corrected acoustic wave time difference value.

[0020] Furthermore, determining the key point position coordinates in step S31 includes the following steps:

[0021] Step S311: Establish a coordinate system with the transmitting point as the origin and the well axis as the y-axis to establish a rectangular coordinate system, and give the position coordinates of the receiving point according to the size of the acoustic system;

[0022] Step S312: Calculate the position coordinates of the actual well diameter from the transmitting point to the receiving point based on the actual well diameter;

[0023] Step S313: Calculate the critical angle for generating a glide wave based on the mud velocity and formation velocity in the model according to Snell's law;

[0024] Step S314: The coordinates of the launch point, the receiving point, the actual wellbore diameter, and the critical angle for generating the glide wave in the rectangular coordinate system are all known. The coordinates of the incident point and the exit point can be found based on these known quantities.

[0025] Step S315: After determining the position coordinates of the incident point and the exit point, the position coordinates of the maximum expansion point between the incident point and the exit point are calculated.

[0026] In a second aspect, a storage-type well logging environmental safety correction device is provided, comprising:

[0027] Data loading module: As the overall interface of the storage logging environment safety correction, it imports the data to be processed in preparation for subsequent operations by the user;

[0028] Well logging environment correction module: uses an optimized model-based inversion algorithm to complete the correction function of the acoustic well logging environment impact;

[0029] Curve display module: intuitive graphical display of data, which helps users compare processing results.

[0030] Furthermore, the logging environment correction module includes:

[0031] Layered processing control module: After reading the logging file data, it performs layered control on the data and divides the processing segment data input by the user according to certain values;

[0032] Acoustic correction forward modeling main module: used to construct a ray tracing model using actual logging acoustic wave data, and then forward calculate the acoustic wave time difference;

[0033] Acoustic correction and inversion main module: used for inversion correction of acoustic logging data;

[0034] Least square fitting module: The forward modeling results obtained by calling the acoustic wave correction forward modeling main module are fitted according to the least squares principle.

[0035] Furthermore, the acoustic correction inversion main module is used to perform inversion correction of acoustic logging data, including:

[0036] Step S71: defining two arrays of acoustic wave time difference and well diameter based on the processing control quantity determined in the layered processing control stage, and reading the acoustic wave and well diameter data in the well logging file;

[0037] Step S72: Calculate the radius of the wellbore and normalize the wellbore and acoustic wave time difference data into units;

[0038] Step S73: Calculate the maximum and minimum values of the acoustic wave time difference and the wellbore value respectively, and calculate the radial unit interval of the forward model grid according to the calculated maximum wellbore value;

[0039] Step S74: defining an array of a set number of interpolation post-processing data to store the inversion correction results;

[0040] Step S75: defining an array of predetermined forward modeling times for initializing the forward model formation velocity;

[0041] Step S76: Based on the maximum and minimum values of the acoustic time difference data calculated in step S73, insert values between the maximum and minimum values by the number of forward modeling times, i.e., divide the maximum and minimum values equally and store them in the array defined in step S75 respectively;

[0042] Step S77: calling the acoustic wave correction forward modeling main module in sequence to perform forward modeling calculations in a loop to obtain forward modeling results;

[0043] Step S78: calling the point-by-point inversion function of the least squares fitting module to perform least squares fitting inversion on the forward calculation results to obtain an inversion result;

[0044] Step S79: restoring the inversion results to the sampling interval of the original logging data according to the sampling interval of the data in the logging file, converting them back to the unit of the original logging file data, and arranging the inversion results;

[0045] Step S710: Write the sorted inversion correction results into the generated file.

[0046] Furthermore, the acoustic wave correction forward modeling main module is used to construct a ray tracing model through actual logging acoustic wave data, and then forward calculate the acoustic wave time difference, including: building a forward model, determining the key points of the simulated propagation path, calculating the time of acoustic wave propagation between adjacent layers, and calculating the shortest propagation time.

[0047] In a third aspect, an electronic device is provided, comprising: a memory for storing a computer program; and a processor for implementing the steps of a storage logging environment safety correction method when executing the computer program.

[0048] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the storage logging environment safety correction method are implemented.

[0049] By adopting the above technical solution, safe correction of the storage-type logging environment is achieved, especially correction of the sonic logging curve.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] 1. This invention addresses the drawbacks of existing acoustic logging environmental correction, such as long runtime and large storage space requirements. First, data layering is performed before correction processing to reduce the memory usage and speed issues caused by processing large amounts of data at once. This also satisfies the requirement for performing correction processing on the same formation during logging interpretation. Second, by exploring the patterns of simulated acoustic wave propagation paths, an improved method and algorithm is proposed that simulates a simulated propagation path and then searches for the shortest path around it. This reduces the search space for acoustic wave propagation within the acoustic logging grid model, thereby reducing memory usage and improving execution speed.

[0052] 2. The present invention integrates the improved method into the existing "read data-display curve" platform in the form of a module to form acoustic logging environment correction software. The implementation method of the software and the design method and functions of the key functions in the program and the matching data structure are introduced. The compiled acoustic logging environment correction software is applied to actual extreme expansion formation logging data for acoustic correction processing and achieves very good results. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A flowchart of a method for implementing storage-type well logging environmental safety correction according to an embodiment of the present invention;

[0054] Figure 2 A structural diagram of a device unit for storage-type well logging environmental safety correction according to an embodiment of the present invention;

[0055] Figure 3 A structural diagram of a well logging environment correction module unit according to an embodiment of the present invention;

[0056] Figure 4 A grid partition diagram of acoustic logging provided according to an embodiment of the present invention;

[0057] Figure 5 The first wave trajectory diagram of acoustic logging at different expansion points of the dual-transmitter and dual-receiver acoustic system provided according to an embodiment of the present invention;

[0058] Figure 6 This is a diagram showing the first wave trajectory of the sound wave at different points of the rectangular expansion diameter of the dual-transmitter and dual-receiver sound system provided according to an embodiment of the present invention;

[0059] Figure 7 This is a diagram showing the acoustic wave first wave trajectory at different points of the actual wellbore of the dual-transmitter and dual-receiver acoustic system provided by an embodiment of the present invention;

[0060] Figure 8 This is an internal flow chart of a well logging environment correction module provided according to an embodiment of the present invention;

[0061] Figure 9 A diagram showing the relationship between the forward moveout and the inversion moveout provided according to an embodiment of the present invention;

[0062] Figure 10 A diagram showing the relationship between the inverted time difference and the theoretical time difference provided according to an embodiment of the present invention;

[0063] Figure 11 This is a comparison diagram of an actual well before and after correction according to an embodiment of the present invention. DETAILED DESCRIPTION

[0064] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0065] It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including," "having," and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products, or apparatus.

[0066] The following is a detailed description of the method for storage logging environmental safety correction

[0067] Explanation of the effect of diameter expansion on acoustic time difference:

[0068] In sonic logging instruments, the combination of a sonic transmitter and receiver, formed according to specific requirements, is called the sonic logging instrument's acoustic system. A system consisting of one transmitter and one receiver is called a single-transmitter, single-receiver system, and the distance between the transmitter and receiver is called the source distance. A system consisting of one transmitter and two receivers is called a single-transmitter, dual-receiver system, and the distance between the two receivers is called the standoff. A system consisting of two transmitters and two receivers is called a dual-transmitter, dual-receiver system, and the distance between the two transmitters is called the instrument length. The midpoint of the two receivers is defined as the recording point of the sonic logging instrument. The sound velocity logging result is a curve showing how the sound velocity or acoustic time difference measured at the recording point at various depths changes with depth.

[0069] The dual-transmitter, dual-receiver acoustic system can reduce the impact of changes in wellbore diameter on sound velocity measurements. When the acoustic system is placed at a certain location downhole, the recorded sound velocity or acoustic wave time difference value represents the sound velocity (or time difference) at the depth where the midpoint of the two receiving probes is located. Figure 5 Figure 1 shows the first-wave propagation trajectories of the dual-transmitter, dual-receiver acoustic system at different expansion positions. The thin solid line represents the first-wave propagation trajectories of the TIR1 and T2R2 acoustic systems, the dot-dash line represents the first-wave propagation trajectories of the T2R1 system, and the short-dash line represents the first-wave propagation trajectories of the TIR2 system. The thick solid line represents the wellbore curve in the expansion mode. T1 represents the upper transmitting probe, R1 represents the upper receiving probe, T2 represents the lower transmitting probe, and R2 represents the lower receiving probe.

[0070] Figure 5 In (a), the downward-transmitting dual-receiving acoustic system composed of T2R2R1 is located below the expanded diameter, and the influence of the expanded diameter will cause the measured value to increase; the upward-transmitting dual-receiving acoustic system composed of T1R1R2 is affected by the expanded diameter, causing the measured time difference value to decrease. The two groups of acoustic systems are affected by the expanded diameter to a similar extent, so the increased time difference is offset by the decreased time difference: the time difference value measured by the dual-transmitting dual-receiving acoustic system is closest to the actual formation time difference. Figure 5 (b) shows the case where the midpoint of the acoustic system coincides with the midpoint of the expansion path. Due to the symmetry of the acoustic system, the effect of the expansion path on the time difference is reduced. However, due to the irregularities of the expansion path, the propagation path along the edge of the expansion path is not a straight line, but a longer curve: this increases the time required, resulting in a discrepancy between the time difference at the expansion path and the theoretical value. Figure 5 (c) When the acoustic system is positioned above the expansion, the measured values for the T1R1R2 system are larger than the theoretical values, while the measured values for the T2R2R1 system are smaller. However, due to the asymmetry of the expansion position, the dual-transmitter, dual-receiver acoustic system can only reduce the effect of the expansion on the measured time difference but cannot completely eliminate the time difference error caused by unequal paths in the mud.

[0071] Description of the layered processing method:

[0072] In existing calculations, the acoustic logging grid model uses a 0.025-meter interval in the depth direction, while actual logging file data is typically sampled at 0.1-meter or 0.125-meter intervals. Therefore, even if the logging data is 1 meter thick, 40 data points are required to interpolate it to 0.025-meter intervals when inserted into the grid model. However, the actual expanded formation thickness can reach tens or even hundreds of meters. Therefore, the number of data points involved in the calculation increases rapidly as the actual formation thickness increases. When these data are inserted into the acoustic logging ray tracing model algorithm for forward modeling, they inevitably occupy a large amount of computer memory. During program execution, a large virtual memory allocation is required to ensure proper operation; otherwise, insufficient memory may occur. Furthermore, during well logging interpretation, processing and interpretation are typically performed based on formation type, with the same set of formation data analyzed. However, formation data from different categories reflects different formation conditions, making them incomparable. If the entire acoustic logging curve is mechanically included in the calculation without distinguishing the formation type, the calculation results will inevitably be inaccurate.

[0073] To address the data processing volume issue, the present invention employs a layered processing method. Specifically, before correcting acoustic data affected by extreme expansion, the acoustic data in the logging file is divided, the number of processed data is controlled, and the processing is then repeated until all acoustic data in the logging file has been processed. The number of processed data can be manually set based on the actual extreme expansion thickness of the formation in the field, or it can be fixed to a single value based on empirical data from the formation expansion. This solves the problem of excessive data volume and memory usage caused by the original algorithm processing the entire acoustic time difference data at once. Furthermore, it also achieves the goal of flexibly classifying and categorizing data according to formation type, selecting the same set of formation parameters for correction, and thus achieving more accurate calculation results.

[0074] Description of the improved shortest path ray tracing method for diameter expansion in extreme environments:

[0075] From the principle of acoustic logging, it can be known that in the double-transmitter and double-receiver acoustic logging, the acoustic wave signal is emitted from the transmitting point, passes through the mud, refracts on the well wall to generate a sliding longitudinal wave, then propagates along the well wall, and then refracts again and returns to the receiving point through the mud. Figure 5 、 Figure 6 and Figure 7 Tracking the trajectory of the first wave of acoustic logging shows that for formations with extreme diameter expansion, the first wave almost always starts from the launch point, passes through the incident point, the wellbore point with the largest diameter expansion, the exit point, and finally reaches the receiving point or propagates along the path near it. Figure 4The acoustic logging grid model uses five points: the launch point, the entry point, the wellbore point with maximum expansion, the exit point, and the receiving point. A simulated propagation path is first assumed. A search step is then set to search for network nodes within the given step size around this simulated propagation path, and the minimum propagation time difference within that range is calculated. This improves upon the original algorithm by reducing the ray tracing range and, consequently, the computational complexity.

[0076] By observing and summarizing the true propagation path of the first wave of acoustic logging, the search for the shortest acoustic wave propagation time difference in the forward model is made directional, the search for invalid areas is reduced, and the amount of calculation is reduced, thereby saving time and space for the operation of the algorithm and improving the algorithm execution efficiency.

[0077] Based on the principle of improving the acoustic logging ray tracing model algorithm, the algorithm improvement design is carried out below.

[0078] (1) First determine the position coordinates of the five key points.

[0079] ① Establish a coordinate system with the transmitting point T as the origin and the well axis as the y-axis to establish a rectangular coordinate system. The position coordinates of the receiving point are given according to the size of the acoustic system.

[0080] ② Based on the actual well diameter, calculate the position coordinates of the actual well diameter from the transmitting point to the receiving point.

[0081] ③ According to the law in physics and ultrasonics that "the angle of incidence is equal to the angle of reflection, and the sines of the angles of incidence and refraction are proportional to the speed of sound in the two media", the critical angle for generating sliding waves is calculated based on the mud velocity and formation velocity in the model.

[0082] ④ At this point, the coordinates of the launch point, the receiving point, the actual wellbore diameter, and the critical angle for generating a glide wave are all known in the rectangular coordinate system. Therefore, the coordinates of the entry and exit points can be found based on these known quantities.

[0083] ⑤ After determining the position coordinates of the incident point and the exit point, calculate the position coordinates of the maximum expansion point between the incident point and the exit point.

[0084] (2) Determine the pseudo propagation path of the sound wave. In step (1), the position coordinates of the five key points have been determined. Next, a pseudo propagation path is fitted based on the position coordinates of the five key points and the actual wellbore coordinates. The five key points can divide the pseudo propagation path into four sections: from the emission point to the incident point, from the incident point to the maximum expansion point, from the maximum expansion point to the exit point, and from the exit point to the receiving point. Each section compares the horizontal coordinates of the points on the pseudo propagation path with the actual wellbore coordinates at the same depth. If the horizontal coordinates of the points on the pseudo propagation path are smaller than the actual wellbore coordinates at the same depth, the actual wellbore coordinates are modified to the points on the pseudo propagation path, and then the new pseudo propagation path obtained after the modification is compared again in sequence until the horizontal coordinates of all points on the pseudo propagation path are greater than the horizontal coordinates of the actual wellbore at the same depth. In this way, the final pseudo propagation path contains the actual wellbore.

[0085] (3) Calculate the acoustic wave propagation time between adjacent layers within the model. Starting from the launch point, calculate the acoustic wave propagation time from each point in each row of the acoustic logging grid model to each point in the next row, that is, the ratio of the straight-line distance between the two points to the starting point velocity.

[0086] (4) Calculate the shortest propagation time and shortest path of the sound wave. Given a search range value, with the simulated propagation path obtained in step (2) as the axis, calculate the propagation time of all nodes within the search step range to the left and right of the axis, and finally obtain the shortest propagation time and shortest path through comparison.

[0087] Description of the least squares curve fitting inversion method:

[0088] Model-based inversion is the simplest form of inversion. It is based on a forward model, specifically the acoustic logging ray tracing model mentioned above. Given a known forward model, a set of initial parameter values is estimated. The known acoustic logging ray tracing model algorithm is then used to calculate the formation acoustic transit time, which is then used as the theoretical value. Using the method of inferring the unknown from the known, the observed values are passed through the forward model to obtain the theoretical value. The error between the observed and theoretical values is then compared, and the effect of the error is corrected to produce a transit time value that is closer to the theoretical value.

[0089] The optimization method is to find the minimum value of a function. The function that finds the minimum value is called the objective function. The objective function is generally a multi-source function, which is recorded as: φ(b)=φ(b1,b2,…,b m ), where b=(b1, b2, ..., b m ) T .make A set of parameters that achieve a minimum It is called the minimum point, and the corresponding objective function is called the optimal value, and together (b * ,φ(b *)) is called the optimal solution. Since the ultimate goal of most problems is to find b * Instead of φ(b * ), so it is customary to put b * It is called the optimal solution.

[0090] Among the optimization methods, the methods that minimize the objective function in the form of the sum of squares are collectively called least squares optimization methods. Therefore, least squares fitting belongs to this type of optimization method. Its objective function is the sum of the squares of the deviations between the observation values of each measurement point and the trend value (fitting function value). The optimal solution is Take a set of fitting parameters b=(b1,b2,…,b m ) T In optimization theory, the function relationship describing the theoretical value of the measurement point is called the regression equation. The function of the least squares fit is the regression equation, which is the parameter to be determined b = (b1, b2, ..., b m ) T The least squares fitting is a linear function of , so it is called the linear least squares parameter estimation problem or the linear least squares optimization method. Its characteristic is to obtain the optimal solution by solving the equation once.

[0091] The design idea of the least squares curve fitting inversion algorithm is as follows: in the same extreme expansion layer section, multiple known data points are selected: these known data points are forward calculated using the acoustic logging ray tracing model algorithm to obtain the forward time difference; a functional relationship between the forward time difference and the known time difference is established, and the error value between the measured time difference value and the forward time difference value is calculated using this functional relationship: finally, the error value is corrected from the measured time difference value to obtain the corrected acoustic wave time difference value.

[0092] The high-order polynomial can be fitted by line to construct the equation system according to the following formula:

[0093]

[0094] Where n is the number of fittings, xi is the known point, Ci is the unknown coefficient, and yi is the objective function value.

[0095] The specific algorithm steps are as follows:

[0096] (1) First, select known data points and find the maximum and minimum acoustic transit times from the actual measured acoustic transit times affected by extreme expansion. Then, interpolate three points between the maximum and minimum acoustic transit times, and use these five points (Y1, Y2, Y3, Y4, and Y5) as known data points. These five known data points are then substituted into the acoustic logging ray tracing model algorithm for forward modeling, resulting in the corresponding forward transit times (T1, T2, T3, T4, and T5).

[0097] (2) Calculate the coefficient according to the following formula

[0098]

[0099] (3) Coefficient matrix

[0100] C 11 =S1 C 21 =S2 C 31 =S3

[0101] C 21 =S2 C 22 =S3 C 32 =S4

[0102] C 31 =S3 C 23 =S4 C 33 =S5

[0103] (4) Calculate the constant column vector (the value to the right of the "=" sign)

[0104]

[0105] (5) Forming an augmented matrix

[0106]

[0107] (6) Solve the coefficient

[0108]

[0109] (7) Substitute the obtained unknown coefficients into the equation, solve the error between the measured time difference and the forward time difference, and judge whether the error is small enough. If the error does not meet the conditions, continue to solve the equation until the coefficients that meet the model parameters are obtained.

[0110] (8) The error between the measured time difference value and the forward time difference value is corrected from the actual measured acoustic wave time difference value to obtain the corrected acoustic wave time difference data.

[0111] The following is a detailed description of the device for storage logging environmental safety correction

[0112] Description of the function allocation of each module of the storage logging environmental safety correction device:

[0113] ① Data loading module: As the overall interface of the acoustic wave inversion correction and curve scale processing module, it imports the data to be processed in preparation for subsequent operations by the user.

[0114] ② Sonic logging extreme diameter expansion correction module: This module uses an optimized model-based inversion algorithm to correct the extreme diameter expansion effect of sonic logging.

[0115] ③ Curve display module: intuitive graphical display of data, which helps users compare processing results.

[0116] The extreme expansion effect correction module for acoustic logging is the core module. Its internal structure follows the function flow sequence. Here we mainly introduce the design and implementation of several key functions, including the functions used and the corresponding data structures:

[0117] Description of the hierarchical processing control function:

[0118] In order to achieve the purpose of layered processing, after reading the logging file data, it is necessary to first control the data in layers and design the Layer() function. The main function of this function is to divide the processing segment data input by the user according to a certain value (this value can also be given by the user). The specific implementation method is as follows:

[0119] (1) First, the data file is processed according to the user input, and the processing depth segment data and the sampling step of the data in the logging data file are analyzed. According to the sampling step, the logging data of each column in the logging data file is marked according to the depth sampling point at the processing depth input by the user. For example, if the user processes the data of the 800-850 meter formation and the data sampling interval is 0.1 meter, the Layer() function will first convert each column of the logging data in this section into 500 points of data.

[0120] (2) Set a fixed value (such as 80) to control the number of data processed each time. The fixed value can be set according to the actual formation thickness or set as an empirical value. Finally, the number of well logging data is processed. This process is a one-time processing process.

[0121] (3) A certain amount of data is processed in a loop until all the depth data input by the user is processed.

[0122] Description of the main module of acoustic correction inversion:

[0123] In the above, the logging file data has been hierarchically controlled. Now we need to perform inversion correction on the acoustic logging data, which is mainly achieved through a function named MainCorrect().

[0124] The specific implementation method is as follows:

[0125] (1) First, define two arrays of wave time difference and well diameter with the processing control number A determined in the layered processing control stage as the size, and read the acoustic wave and well diameter data in the A number of logging files.

[0126] (2) Calculate the radius of the wellbore and normalize the wellbore and acoustic transit time data. As mentioned above, the forward model selected has a depth step of 0.025 meters. Therefore, the original logging file data is interpolated to meet the model requirement of a 0.025-meter interval in the wellbore depth direction. The number of processed data after interpolation is calculated as A_c. Similarly, the wellbore and acoustic transit time arrays are defined based on the number A_c to store the differenced wellbore and acoustic transit time data. This data is then used as the calculation data in the subsequent steps.

[0127] (3) Calculate the maximum and minimum values of the acoustic wave time difference and the wellbore value respectively, and calculate the radial unit interval of the forward model grid based on the calculated maximum wellbore value.

[0128] (4) Define an array of interpolated post-processing data size A_c to store the inversion correction results. Note that since the depth recording point of the dual-transmitter, dual-receiver acoustic rangefinder is located in the middle of the instrument, half of the acoustic range range is unmeasured, which adds up to the length of one acoustic range. Therefore, when defining the inversion array, you can allocate an array with less acoustic range data size to avoid wasting memory.

[0129] (5) Define an array of the size of the forward modeling number M to initialize the forward model formation velocity. Then define a two-dimensional array with the number of forward modeling times M and the amount of interpolated data to store the forward velocity obtained after each forward modeling calculation.

[0130] (6) The maximum and minimum values of the acoustic time difference data calculated in step (3) are inserted between the maximum and minimum values in equal amounts according to the number of forward models M, that is, the maximum and minimum values are divided into M parts, and these M parts are stored in the array defined in step (5).

[0131] (7) Call the forward modeling function ZhengYan() in sequence to perform forward modeling calculations M times in a loop to obtain the forward modeling results.

[0132] (8) Call the point-by-point inversion function FanYan() to perform least squares fitting inversion on the forward calculation results to obtain the inversion results.

[0133] (9) Restore the inversion results to the sampling interval of the original logging data according to the sampling interval of the data in the logging file, convert them back to the unit of the original logging file data, and organize the inversion results.

[0134] (10) Write the sorted inversion correction results into the generated file.

[0135] Description of the main module of the model forward model:

[0136] The main function of the model forward modeling program is to construct a ray tracing model using actual well logging acoustic wave data and then forward-model the acoustic wave moveout process. The forward modeling function, ZhengYan(), is designed. This function also includes three sub-functions: KeyDot(), which simulates the propagation path to determine key points; ACLayer(), which calculates the acoustic wave propagation time between adjacent layers; and ZuiDuan(), which calculates the shortest path for acoustic wave propagation. These functions are described below.

[0137] (1) Constructing a forward model

[0138] The forward modeling process is implemented by a function called ZhengYan(). The specific implementation method is as follows:

[0139] ① Before building the forward model, define a two-dimensional array to store the velocity points in the forward model. The position of a point in the model can be determined by the row and column coordinates of the model.

[0140] ② After defining the forward model velocity variable, we construct the forward model by assigning a value to this variable. Within the model, we set the velocity within the wellbore and formation zones by comparing the coordinates of each point on the model with the actual wellbore diameter corresponding to that point. Velocity points within the wellbore use the mud velocity value, while velocity points within the formation zone use the value passed to the ZhengYan() function by the MainCorrect() function.

[0141] ③ After building the model, define a three-dimensional array to store the sound wave time difference obtained by ray tracing. The first dimension of the array represents the row where the point is located, the second dimension represents the column where the point is located, and the third dimension represents the column where the next row of the point is located. This variable mainly stores the minimum sound wave time difference from a certain point to a certain point in the next row.

[0142] ④The ZhengYan() function calls the KeyDot() function to simulate the propagation path and determine the key points.

[0143] (2) Determine the key points of the proposed propagation path

[0144] The key points of the simulated propagation path are determined by the KeyDot() function. The specific implementation method is as follows:

[0145] ① Before calling the function to determine key points along the simulated propagation path, first define variables to record the coordinates of points along the simulated propagation path in the model. To do this, construct a one-dimensional array equal to the number of rows in the model and record the column values of each row of the simulated propagation path point to determine the location of that point.

[0146] ② Determine the key points of the propagation path simulation, namely the launch point, the incident point, the point of maximum diameter expansion, the exit point, and the receiving point. The launch point and the receiving point can be determined based on the modeling coordinate position and the acoustic system length.

[0147] ③ Find the location of the incident point. The principle of refraction shows that, given the interface velocities of the mud velocity and the formation velocity, the critical angle for generating a sliding wave at that interface can be calculated. Using the actual wellbore column values for each row in the model and the coordinates of the emission point, calculate the row within the model where the actual wellbore points satisfying the incident angle greater than or equal to the critical angle are located. This allows the location of the incident point to be determined.

[0148] ④ Similarly, the position of the exit point is calculated based on the actual well diameter column value of each row in the model and the coordinate value of the receiving point: and the restriction condition that the row coordinate of the incident point is smaller than the row coordinate of the exit point is added.

[0149] ⑤ After determining the coordinates of the incident point and the exit point, find the coordinates of the point with the maximum expansion diameter between the two points.

[0150] ⑥ In this way, the locations of the five key points—the launch point, the incident point, the point at maximum expansion, the exit point, and the receiving point—are determined. Next, the simulated propagation path of the acoustic wave can be determined. These five key points can be used to divide the simulated propagation path into four segments: from the launch point to the incident point, from the incident point to the point of maximum expansion, from the point of maximum expansion to the exit point, and from the exit point to the receiving point. The paths between these four segments can be calculated using the equation of a straight line between two points.

[0151] ⑦ For each segment, compare the horizontal coordinates of the points on the simulated propagation path with the coordinates of the actual wellbore at the same depth. If the horizontal coordinate of a point on the simulated propagation path is smaller than the coordinates of the actual wellbore at the same depth, modify the actual wellbore coordinates to match the coordinates of the point on the simulated propagation path. Then, recompare the coordinates using the modified simulated propagation path until the horizontal coordinates of all points on the simulated propagation path are larger than the horizontal coordinates of the actual wellbore at the same depth. This means that the final simulated propagation path contains the actual wellbore.

[0152] (3) Calculate the time of sound wave propagation between adjacent layers

[0153] Calculating the acoustic wave propagation time between adjacent layers is accomplished using the function named ACLayer(). The three-dimensional array defined in the ZhengYan() function stores the minimum acoustic wave travel time difference from a point in an adjacent layer to a point in the next row. Within the search range set to the left and right of the proposed propagation path, the start point and the end point in the next adjacent row are first determined. The acoustic wave travel time difference between the two points is calculated based on the ratio of the distance between the start and end points to the velocity at the start point. The end point is then moved to the right. After the search range for the row containing the end point has been traversed, the start point is again moved to the right until the acoustic wave travel time difference for all combinations of the start and end points within the search range between the two adjacent layers has been calculated. Based on the positional relationship between the start and end points, three situations can be identified: the column coordinates of the start point are smaller than the column coordinates of the end point, the column coordinates of the start point are larger than the column coordinates of the end point, and the column coordinates of the start point are equal to the column coordinates of the end point.

[0154] (4) Calculate the shortest propagation time

[0155] After obtaining the acoustic time differences between all adjacent layers within the model search range, the next step is to calculate the shortest propagation time difference from the transmitting point to the receiving point. This is achieved using the ZuiDuan() function, which is implemented as follows:

[0156] ① First, define a two-dimensional array to store the shortest propagation time difference. Additionally, define four one-dimensional arrays to store the time differences for the four sound system combinations: a dual-transmitter, dual-receiver system (upper-transmitter-upper-receiver system), an upper-transmitter-lower-receiver system, a lower-transmitter-upper-receiver system, and a lower-transmitter-lower-receiver system.

[0157] ② Set the coordinates of the transmitting and receiving points of the dual-transmitting and dual-receiving acoustic system according to the transmitting and receiving point positions during modeling.

[0158] ③ Assuming the launch point coordinates are (m, 0), for each acoustic system combination, first calculate the minimum acoustic time difference between the launch point and row m+2. This acoustic time difference is the sum of the acoustic time difference between the launch point and row m+1 and the acoustic time difference between row m+1 and row m+2. The minimum value is taken by comparing them.

[0159] ④ Then, starting from row m+3, calculate the minimum acoustic wave time difference of each receiving layer between the row before the receiving point, using the same method as step ③, and obtain the minimum acoustic wave time difference from the transmitting point to the row before the receiving point.

[0160] ⑤ The minimum acoustic wave time difference obtained in step ④ is added to the acoustic wave time difference to the receiving point to obtain the minimum acoustic wave time difference from the transmitting point to the receiving point.

[0161] ⑥Finally, follow steps ③④⑤ to calculate the minimum sound wave time difference of the four sound systems in a loop, and obtain the total minimum propagation time difference based on the combination of the four sound systems in the double-transmitting and double-receiving sound system.

[0162] Description of the least squares fitting module:

[0163] By calling the forward modeling function ZhengYan() M times, M forward modeling results have been obtained. The least squares principle is used for fitting, which is implemented here by the function named FanYan(). The specific implementation method is as follows:

[0164] (1) Define two one-dimensional arrays of size M to store the forward time difference and the known time difference (i.e., the M values obtained by interpolating the actual acoustic wave time difference).

[0165] (2) Based on the calculated coefficients, a one-dimensional array with a size of N fitting times is used to store the calculated coefficients; then the matrix is divided into N*N matrices, and the constant column vector is calculated to form an augmented matrix.

[0166] (3) Call the function QiuJie() to solve the coefficients of the equation group that meets the given error condition.

[0167] (4) Substitute the obtained coefficients into the original acoustic logging file data for correction calculation to obtain the inversion correction results.

[0168] The following demonstrates the implementation effect of the present invention

[0169] like Figure 9 As shown in the figure, the dotted line is the forward acoustic wave time difference curve, and the solid line is the inversion correction acoustic wave time difference curve. It can be seen that the acoustic wave time difference value after inversion correction is about 380 microseconds / meter, which can restore the original appearance of the original layer. In order to more clearly compare the difference between the corrected acoustic wave time difference value and the original experimental theoretical value (380 microseconds / meter), the two are plotted in the same figure for comparison. Figure 10 As shown, the solid line is the experimental theoretical value of 380 microseconds per meter, and the dotted line is the corrected acoustic wave time difference curve obtained by inversion. Figure 10 It can be seen that the change of the corrected curve near the experimental theoretical value of 380 μs / m does not exceed 0.5 μs / m, which shows that the acoustic logging environment correction method based on model inversion can accurately correct the influence of extreme expansion on acoustic wave movement.

[0170] Taking the actual serious expansion interval as an example, the logging data Figure 11 The solid line is the actual measured acoustic wave time difference curve, and the dotted line is the acoustic wave time difference curve after correction based on the model inversion. Figure 11 As can be seen, the greater the diameter expansion, the greater the acoustic transit time correction. In intervals without diameter expansion, the acoustic transit time correction is almost zero. This example demonstrates that the model-based inversion-based acoustic logging environmental correction method effectively corrects for the effects of extreme diameter expansion on acoustic transit time. This demonstrates that both experimental theoretical data and actual measurement data confirm the effective correction of the model-based inversion-based acoustic logging environmental correction method for extreme formation diameter expansion.

[0171] The above is a detailed introduction to the method, device, equipment and medium for storage-type well logging environmental safety correction provided by the present invention. The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description. It should be pointed out that for ordinary technicians in this technical field, without departing from the principle of the present invention, the present invention can also be improved and modified in several ways, and these improvements and modifications also fall within the scope of protection of the present invention.

[0172] It should also be noted that, in this specification, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

Claims

1. A storage logging environmental safety correction method for environmental correction of acoustic logging curves, characterized in that: The steps include: Step S1: input the data to be corrected, and input the original acoustic logging curve data; Step S2: Data stratification: Before correcting the acoustic logging curve data, the acoustic data in the logging file is divided, the number of processed data is set, and then the processing is cyclically performed until all the acoustic data in the logging file are processed; Step S3: Using an acoustic logging environmental correction method based on model inversion, an acoustic logging ray tracing forward model is constructed. When the forward model is known, a set of initial parameter values are estimated first, and the formation acoustic transit time is calculated using the known acoustic logging ray tracing model algorithm. The acoustic transit time is used as a theoretical value, and the error between the observed value and the theoretical value is compared and inverted to obtain an acoustic transit time value that is closer to the theoretical value, thereby obtaining a corrected acoustic transit time value. Step S4: Output the corrected data, and output the corrected acoustic logging curve data.

2. The storage logging environment safety correction method according to claim 1, characterized in that: The step S3 constructs the acoustic logging ray tracing forward model, including the following steps: Step S31: Determine the coordinates of key points, including the coordinates of the transmitting point, the receiving point, the incident point, the exit point, and the maximum expansion point. Step S32: determine the simulated propagation path of the sound wave, and fit a simulated propagation path based on the key point position coordinates determined in step S31 and the actual wellbore coordinates. Specifically, the key points of step S31 can divide the simulated propagation path into four sections: from the emission point to the incident point, from the incident point to the maximum diameter expansion point, from the maximum diameter expansion point to the exit point, and from the exit point to the receiving point. In each section, the horizontal coordinates of the points on the simulated propagation path are compared with the actual wellbore coordinates at the same depth. If the horizontal coordinates of the points on the simulated propagation path are smaller than the actual wellbore coordinates at the same depth, the actual wellbore coordinates are modified to the points on the simulated propagation path, and then the new simulated propagation path obtained after the modification is compared again in sequence, until the horizontal coordinates of all points on the simulated propagation path are finally greater than the horizontal coordinates of the actual wellbore at the same depth. In this way, the simulated propagation path finally obtained contains the actual wellbore. Step S33: Calculate the acoustic wave propagation time between adjacent layers in the model. Starting from the launch point, calculate the acoustic wave propagation time from each point in each row of the acoustic logging grid model to each point in the next row, that is, the ratio of the straight-line distance between the two points to the starting point velocity; Step S34: Calculate the shortest propagation time and shortest path of the sound wave. Given a search range value, with the simulated propagation path obtained in step S32 as the axis, calculate the propagation time of all nodes within the search step range on the left and right of the axis, and finally obtain the shortest propagation time and shortest path through comparison.

3. The storage logging environment safety correction method according to claim 1, characterized in that: The inversion method used in step S3 is a least squares curve fitting inversion algorithm, which includes the following steps: Step S35, in the same extreme expansion interval, multiple known data points are selected, and these known data points are forward modeled using an acoustic logging ray tracing model algorithm to calculate the forward modeling moveout; Step S36, establishing a functional relationship between the forward moveout and the known moveout, and using the functional relationship to calculate the error between the measured moveout and the forward moveout; Step S37: Finally, the error value is corrected from the measured time difference value to obtain the corrected acoustic wave time difference value.

4. The storage logging environment safety correction method according to claim 2, characterized in that: Determining the key point position coordinates in step S31 includes the following steps: Step S311: Establish a coordinate system with the transmitting point as the origin and the well axis as the y-axis to establish a rectangular coordinate system, and give the position coordinates of the receiving point according to the size of the acoustic system; Step S312: Calculate the position coordinates of the actual well diameter from the transmitting point to the receiving point based on the actual well diameter; Step S313: Calculate the critical angle for generating a glide wave based on the mud velocity and formation velocity in the model according to Snell's law; Step S314: The coordinates of the transmitting point, receiving point, actual wellbore diameter, and critical angle for generating the glide wave in the rectangular coordinate system are all known. The coordinates of the incident point and the exit point can be found based on these known quantities. Step S315: After determining the position coordinates of the incident point and the exit point, the position coordinates of the maximum expansion point between the incident point and the exit point are calculated.

5. A storage-type well logging environment safety correction device based on the method according to any one of claims 1 to 4, characterized in that: include: Data loading module: As the overall interface of the storage logging environment safety correction, it imports the data to be processed in preparation for subsequent operations by the user; Well logging environment correction module: uses an optimized model-based inversion algorithm to complete the correction function of the acoustic well logging environment impact; Curve display module: intuitive graphical display of data, which helps users compare processing results.

6. The storage-type well logging environmental safety correction device according to claim 5, characterized in that: The logging environment correction module includes: Layered processing control module: After reading the logging file data, it performs layered control on the data and divides the processing segment data input by the user according to certain values; Acoustic correction forward modeling main module: used to construct a ray tracing model using actual logging acoustic wave data, and then forward calculate the acoustic wave time difference; Acoustic correction and inversion main module: used for inversion correction of acoustic logging data; Least square fitting module: The forward modeling results obtained by calling the acoustic wave correction forward modeling main module are fitted according to the least squares principle.

7. The storage-type well logging environmental safety correction device according to claim 6, characterized in that: The acoustic correction inversion main module is used to perform inversion correction of acoustic logging data, including: Step S71: defining two arrays of acoustic wave time difference and well diameter based on the processing control quantity determined in the layered processing control stage, and reading the acoustic wave and well diameter data in the well logging file; Step S72: Calculate the radius of the wellbore and normalize the wellbore and acoustic wave time difference data into units; Step S73: Calculate the maximum and minimum values of the acoustic wave time difference and the wellbore value respectively, and calculate the radial unit interval of the forward model grid according to the calculated maximum wellbore value; Step S74: defining an array of a set number of interpolation post-processing data to store the inversion correction results; Step S75: defining an array of predetermined forward modeling times for initializing the forward model formation velocity; Step S76: Based on the maximum and minimum values of the acoustic time difference data calculated in step S73, insert values between the maximum and minimum values by the number of forward modeling times, i.e., divide the maximum and minimum values equally and store them in the array defined in step S75 respectively; Step S77: calling the acoustic wave correction forward modeling main module in sequence to perform forward modeling calculations in a loop to obtain forward modeling results; Step S78: calling the point-by-point inversion function of the least squares fitting module to perform least squares fitting inversion on the forward calculation results to obtain an inversion result; Step S79: restoring the inversion results to the sampling interval of the original logging data according to the sampling interval of the data in the logging file, converting them back to the unit of the original logging file data, and arranging the inversion results; Step S710: Write the sorted inversion correction results into the generated file.

8. The storage-type well logging environmental safety correction device according to claim 7, characterized in that: The acoustic wave correction forward modeling main module is used to construct a ray tracing model using actual well logging acoustic wave data, and then forward calculate the acoustic wave time difference. The process includes: building a forward model, determining key points of the simulated propagation path, calculating the time for acoustic wave propagation between adjacent layers, and calculating the shortest propagation time.

9. An electronic device, characterized in that: include: memory for storing computer programs; A processor is configured to implement the steps of the storage logging environment safety correction method according to any one of claims 1 to 4 when executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the storage logging environmental safety correction method according to any one of claims 1 to 4 are implemented.

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