Drilling physical detection data processing method and system

Abstract

The invention relates to the technical field of drilling data processing, in particular to a drilling physical detection data processing method and system, and the method comprises the following steps: analyzing the response change of adjacent measuring points based on a sound wave detector signal, judging the change trend through combining depth and time, fusing the direction consistency of various logging devices, and extracting a response section with concentrated energy; and integrating the characteristics of the overlapping zone, and dividing the strata into a structural stability type or a structural instability type to obtain a strata structure distribution type. According to the method, by integrating the trend change and spatial distribution characteristics of the multi-source logging signals, the cooperative relation between different device responses is dynamically analyzed, deep continuous residual tracking, trend consistency comparison and energy distribution aggregation are adopted, the multi-parameter characteristic extraction capacity of an interface area is enhanced, spatial linkage recognition of an abnormal response section is achieved, and the accuracy of the abnormal response section is improved. The multi-dimensional accuracy of section structure type judgment is improved, and layered attribution judgment of stratum continuity, structure change and unstable areas is enhanced.

Description

Technical Field

[0001] This invention relates to the field of borehole data processing technology, and in particular to a method and system for processing borehole physical exploration data. Background Technology

[0002] Borehole data processing involves digitizing, structuring, and organizing various raw physical measurement data obtained during underground drilling to extract key information that can be used for geological structure, stratigraphic characteristics, and resource distribution analysis. It is widely applied in fields such as geological exploration, oil and gas development, geothermal resource evaluation, and geological hazard monitoring. Traditional borehole physical exploration data processing methods utilize physical signals such as seismic waves, acoustic waves, electromagnetic waves, gamma rays, or neutrons. After acquiring raw physical data related to the stratigraphy through measuring devices installed in the borehole, preliminary data cleaning, feature extraction, and physical property parameter calculation are performed using methods such as manual identification, one-dimensional filter processing, formula correction, fixed window sampling, and simplified impedance inversion.

[0003] Existing technologies in borehole data processing generally rely on single physical signals and static analysis processes. Manual identification and one-dimensional filtering methods are difficult to cope with the interaction between multiple source signals under complex geological conditions. Fixed window sampling methods limit the effective capture of continuous changes and spatial response characteristics. Simplified inversion modes can easily lead to the loss of signal linkage in the interface identification process. In practical applications, it is impossible to reveal the discontinuity and instability risk of the geological structure in a timely manner. Key information is easily buried by local anomalies or noise, affecting the real-time and comprehensiveness of geological structure identification and early warning. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide a method and system for processing borehole physical exploration data. The technical solution is as follows: On the one hand, a method for processing borehole physical exploration data is provided, including the following steps: S1: Based on the acoustic wave detector, the raw signals collected inside the borehole are analyzed, the response changes of adjacent measuring points are compared, the change state of the signal along the depth direction is identified, and the acoustic residual characteristic sequence is obtained. S2: Based on the acoustic residual characteristic sequence, the signal change direction of each logging device in the same depth range is compared, the consistency of the direction of acoustic wave, resistivity and gamma response is analyzed, the depth segment with consistent direction is determined, and multi-source trend consistency data is obtained. S3: Based on the multi-source trend consistency data, analyze the response change amplitude of each logging device within the depth range, identify the depth segment where the changes are concentrated, and collect the amplitude distribution state to obtain the energy integrated response segment. S4: Based on the energy integration response section, the overlap relationship of each response section in the depth direction is analyzed, the sections with spatial overlap are merged and sorted, and the response information of each logging equipment in the section is integrated to obtain the interface composite feature zone. S5: Based on the interface composite feature zone, analyze the change state of each segment, classify and determine the segment characteristics, divide the depth segment into types at the structural stability level, and obtain the stratigraphic structure distribution type.

[0005] On the other hand, the acoustic residual feature sequence includes acoustic response waveform, time-series residual signal and acquisition depth information; the multi-source trend consistency data includes directional consistency zone identifier, equipment coordinated change label and trend synchronization depth segment; the energy integrated response segment includes energy concentration zone number, response amplitude peak value and segment location information; the interface composite feature zone includes interface depth interval, composite response characteristics and structural boundary indication; and the stratigraphic structure distribution type includes stable segment label, unstable segment label and interface attribute parameters.

[0006] On the other hand, the specific steps for obtaining the acoustic residual feature sequence are as follows: S101: Based on the acoustic wave detector, analyze the collected raw signal, determine the directional change of the sound pressure amplitude at continuous measurement points, compare the signal strength between adjacent measurement points, and calibrate each set of data with depth and time labels to determine the trend and continuity of signal changes, and obtain the measurement point response direction sequence. S102: Based on the response direction sequence of the measurement points, compare the change directions, identify continuous and consistent trend segments, sort out the continuation segments of trend changes according to spatial order, determine the start and end depth and duration of each trend segment, and obtain a set of continuous trend intervals. S103: Based on the set of continuous trend intervals, determine the waveform sequence of the original signal in the corresponding depth interval, analyze the temporal characteristics and local deviation phenomena of each waveform segment, optimize the arrangement of residual signals in each segment, and obtain the acoustic residual feature sequence.

[0007] On the other hand, the specific steps for obtaining the multi-source trend consistency data are as follows: S201: Based on the acoustic residual feature sequence, by comparing the signal change directions collected by the acoustic logging equipment, resistivity logging equipment and gamma logging equipment in the same depth range one by one, the response direction status of each group of measuring points is marked and classified, and combined with the change trend of each logging equipment, a multi-equipment direction identification sequence is obtained. S202: Based on the multi-device direction identification sequence, determine whether the direction markings of multiple logging devices at continuous measuring points are consistent, analyze the consistency of the response symbols of each device within a continuous depth segment, and aggregate the measuring point segments with the same direction of change to obtain a set of coordinated change segments. S203: Based on the aforementioned set of coordinated change zones, a joint analysis is performed on the response direction of each logging device within the zone. The response changes of multiple devices in the same zone are compared to see if they are synchronized. Zones with consistent trends are aggregated and archived to obtain multi-source trend consistency data.

[0008] On the other hand, the specific steps for obtaining the energy integrated response segment are as follows: S301: Based on the multi-source trend consistency data, calculate the signal response change amplitude of each logging device in the target depth range, analyze the amplitude change of each acquisition point, and by comparing the response amplitude of each device in the same range, aggregate the amplitude characteristics of all acquisition segments to obtain the spatial distribution of response amplitude. S302: Based on the spatial distribution of the response amplitude, determine the amplitude distribution status of each acquisition segment, mark the segments where the amplitude changes in the spatial range show a clustering trend, identify data segments with concentrated distribution characteristics, and obtain a set of amplitude-concentrated segments; S303: Based on the set of amplitude concentration segments, compare the distribution performance of each group in the depth interval, summarize the amplitude characteristics and spatial location of each segment, perform classification and interval aggregation on the amplitude concentration segments, integrate them into response interval information, and obtain the energy integrated response segment.

[0009] On the other hand, the specific steps for obtaining the interface composite feature region are as follows: S401: Based on the energy integrated response segment, analyze the start and end depth parameters of each segment, make a continuity judgment on the depth coordinates of adjacent segments, compare the depth coverage between segments, identify the segment group with overlapping depth intervals, and obtain the interval overlap feature group. S402: Based on the overlapping feature group of the intervals, analyze the depth coordinates of each overlapping interval, compare the acoustic response, resistivity change and gamma measurement data corresponding to each interval, optimize the correspondence between each logging response, and obtain logging parameter coordination data. S403: Based on the well logging parameter coordination data, determine the joint response characteristics of multiple types of well logging data in each depth interval, summarize the linkage change performance of each well logging parameter in the interval, assign a composite attribute identifier to each depth interval, and obtain the interface composite feature zone.

[0010] On the other hand, the specific steps for obtaining the distribution type of the stratigraphic structure are as follows: S501: Based on the composite feature zone of the interface, analyze the logging response sequence of each section, determine the direction of change of each group of response sequences, divide the change segments according to the response rise and fall relationship between continuous measuring points, combine the depth coordinate information of the segments, sort out the trend combination of each group of segments, and obtain the segment trend structure set. S502: Based on the segment trend structure set, compare the response trend sequence of each segment, calculate the number of times the response direction changes continuously within each group of segments, identify the intervals with consistent direction and stable fluctuations, and obtain the trend consistent interval set. S503: Based on the aforementioned trend-consistent interval set, determine the trend continuity and response offset characteristics of each segment, analyze the correspondence between the trend parameters and the change amplitude of each segment, classify each segment according to its stability number, and obtain the stratigraphic structure distribution type.

[0011] On the other hand, the original signal refers to the measurement signal data collected by the borehole acoustic detector at each sampling time and depth point without any processing, and the depth segment with consistent direction refers to the depth segment in the same interval where the signal change direction of multiple devices is consistent.

[0012] On the other hand, the depth range refers to the depth segment in the borehole that needs further attention and analysis after screening, and the response change amplitude refers to the absolute magnitude of the change in the signal value collected by each logging equipment within the target range.

[0013] On the other hand, a borehole physical exploration data processing system is provided, which is applied to a borehole physical exploration data processing method, including: The residual feature generation module is based on an acoustic wave detector. It analyzes the raw signals collected inside the borehole, compares the response changes of adjacent measuring points, identifies the change state of the signal along the depth direction, and obtains the acoustic residual feature sequence. The trend consistency discrimination module compares the signal change directions of each logging device within the same depth range based on the acoustic residual feature sequence, analyzes the consistency of the directions of acoustic waves, resistivity and gamma response, determines the depth segment with consistent directions, and obtains multi-source trend consistency data. Based on the multi-source trend consistency data, the energy segment extraction module analyzes the response change amplitude of each logging device within the depth range, identifies the depth segments where the changes are concentrated, and collects the amplitude distribution state to obtain the energy integrated response segment. The interface zone fusion module analyzes the overlap relationship of each response zone in the depth direction based on the energy integrated response zone, merges and organizes the spatially overlapping zones, and then integrates the response information of each logging equipment in the zone to obtain the interface composite feature zone. The structure type determination module analyzes the change state of each segment based on the interface composite feature zone, classifies and determines the segment characteristics, divides the depth segment into types at the structural stability level, and obtains the stratigraphic structure distribution type.

[0014] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: By integrating the trend changes and spatial distribution characteristics of multi-source logging signals, the collaborative relationship between the responses of different equipment is dynamically analyzed. By employing depth continuous residual tracking, trend consistency comparison, and energy distribution aggregation, the ability to extract multi-parameter features in the interface area is enhanced, enabling spatial linkage identification of abnormal response segments, improving the multi-dimensional accuracy of segment structure type determination, and strengthening the determination of stratigraphic continuity, structural changes, and the stratification of unstable zones, thus providing support for structural interpretation and risk assessment in complex borehole environments. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of the main steps of the present invention; Figure 2 This is a flowchart of steps S1 of the present invention; Figure 3 This is a flowchart of steps S2 of the present invention; Figure 4 This is a flowchart of steps S3 of the present invention; Figure 5 This is a flowchart of step S4 of the present invention; Figure 6 This is a flowchart of steps S5 of the present invention; Figure 7 This is a system block diagram of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0019] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0020] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0021] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0022] This invention provides a method for processing borehole physical exploration data, such as... Figure 1 As shown, it includes the following steps: S1: Based on the acoustic wave detector, analyze the original signals collected inside the borehole, compare the changes in response strength between continuous measurement points, combine the depth and time labels corresponding to each group of signals, determine the change characteristics of the response with depth extension, track the continuity and trend of changes at each collection point, and obtain the acoustic residual characteristic sequence. S2: Based on the acoustic residual feature sequence, compare the signal change direction of each logging device in the same depth range, determine the change trend of acoustic device, resistivity device and gamma device in the corresponding range, compare the response sign of each group of measuring points one by one, accumulate the ranges with consistent directions, and obtain multi-source trend consistency data. S3: Based on multi-source trend consistency data, calculate the response change amplitude of each group of logging equipment within the target depth range, identify the acquisition segments with concentrated change amplitudes, sort out the clustering of amplitude distribution in each segment, and identify the intervals with concentrated responses by judging the distribution characteristics of amplitude changes, thereby obtaining the energy integrated response segment. S4: Based on the energy integration response section, determine the spatial interval overlap, analyze the degree of overlap of each group of response sections on the depth coordinate, merge the overlapping sections, integrate the response performance of each logging equipment in the interval, and obtain the interface composite feature zone. S5: Based on the interface composite feature zone, adjust the change trend of each segment, analyze the continuous directional consistency and the number of amplitude fluctuations in each segment, compare the various characteristics of segment changes, classify each depth segment into structurally stable or unstable types according to the trend continuity and amplitude offset characteristics, and obtain the stratigraphic structure distribution type.

[0023] The acoustic residual feature sequence includes acoustic response waveform, time-series residual signal and acquisition depth information; the multi-source trend consistency data includes directional consistency zone identifier, equipment coordinated change label and trend synchronization depth segment; the energy integrated response segment includes energy concentration zone number, response amplitude peak value and segment location information; the interface composite feature zone includes interface depth interval, composite response characteristics and structural boundary indication; and the stratigraphic structure distribution type includes stable segment label, unstable segment label and interface attribute parameters.

[0024] In S1, the raw signal refers to the measurement signal data collected by the borehole acoustic wave detector at each sampling time and specific depth point without any processing, commonly including sound pressure amplitude and sound time; continuous measurement points refer to the measurement points distributed along the borehole depth direction and obtained by sampling at equal intervals, with the point data arranged continuously in space; response strength variation refers to the rise or fall or magnitude change of the acquired signal at adjacent measurement points, usually reflected by calculating the difference between two points; depth and time labels refer to the spatial (borehole depth) and sampling time (time) information of each set of acquired data, used for subsequent data positioning and sorting; variation characteristics refer to the numerical variation law of the signal in different depth segments, such as overall rise, fall, sudden change or stability; persistence and trend refer to the continuity of signal change (such as continuous rise / fall) and the overall direction of change (rise, fall or no obvious change).

[0025] In S2, each logging device refers to a different type of logging instrument used in borehole physical exploration, such as acoustic logging tools, resistivity logging tools, and gamma logging tools; acoustic equipment, resistivity equipment, and gamma equipment refer to instruments specifically designed to measure the acoustic propagation characteristics, resistivity parameters, and natural gamma radiation intensity of the borehole formation, respectively; the trend of change refers to the spatial direction of change of the signals of each logging device within the same depth range (e.g., rising, falling, or unchanged); the response sign converts the direction of change of the logging signal (e.g., positive increase / negative decrease) into a positive or negative sign for comparison of consistency; the interval with consistent direction refers to the depth segment within the same interval where the signal change direction of multiple devices is consistent (all rising or all falling).

[0026] In S3, the target depth range refers to the specific depth segment in the borehole that needs further attention and analysis after being selected through previous steps; the response change amplitude refers to the absolute magnitude of the signal value change acquired by each logging equipment within the target range (such as the difference between the maximum and minimum values ​​in a certain segment); the acquisition segment refers to a continuous set of sampling points divided according to the borehole depth or data analysis requirements, constituting the basic block of analysis; the amplitude distribution refers to the spatial distribution of the signal change amplitude within a certain range, which can be manifested as uniform distribution, local concentration, etc.; the distribution characteristics refer to the description of the amplitude distribution pattern, such as whether amplitude aggregation (drastic change) occurs in certain depth segments; the response concentration interval refers to the depth segment in the amplitude distribution where the signal changes drastically (energy accumulation).

[0027] In S4, spatial interval overlap refers to the overlapping or coincident parts of multiple depth segments obtained from different analysis steps along the longitudinal direction of the borehole; depth coordinates refer to the specific depth values ​​corresponding to each measurement point inside the borehole, used to distinguish spatial locations; degree of overlap refers to the length of the intersection of two or more intervals on the depth coordinates, quantifying the size or proportion of the overlapping area; mutually overlapping segments refer to analysis segments that actually intersect in depth, i.e., multiple data features appear at the same location; response performance refers to the signal change characteristics and trends detected by each device within the interval.

[0028] In S5, directional consistency refers to the continuity and intensity of the consistent direction of change of all logging signals within a section (all rising or all falling); amplitude fluctuation refers to the repeated changes of signal values ​​around a certain level within a specified section, describing the degree of signal fluctuation; the characteristics of section changes describe the trend, fluctuation, stability, and aggregation of signals in a depth section; trend continuity refers to the ability of the signal change direction to remain consistent within a depth range; amplitude deviation characteristics refer to the magnitude of the signal value's deviation from the mean or benchmark, i.e., the severity of signal fluctuation; structural stability or instability type refers to the classification of the stability of the section in terms of formation structure based on the indicators. Structural stability indicates that the formation is continuous and reliable, while instability type indicates the presence of signs of instability such as fracturing and loosening.

[0029] like Figure 2 As shown, the specific steps for obtaining the acoustic residual feature sequence are as follows: S101: Based on the acoustic wave detector, analyze the collected raw signal, determine the directional change of the sound pressure amplitude at continuous measurement points, compare the signal strength between adjacent measurement points, and calibrate each set of data with depth and time labels to determine the trend and continuity of signal changes, and obtain the measurement point response direction sequence. The raw acoustic pressure signals from continuous sampling points inside the borehole are processed. First, acoustic data is acquired downhole at a set sampling interval, for example, recording one sampling point every 0.1 meters. The depth and acquisition time of each sampling point are also recorded. During analysis, the acoustic pressure amplitudes of all measuring points are read sequentially and arranged in ascending order of depth. For every two adjacent measuring points, their acoustic pressure values ​​are extracted and directly subtracted to determine if the signal shows an increase, decrease, or no significant change. The result is marked as rising, falling, or stable. This result is then combined with the current measuring point's depth and time to construct a directional information record with spatial and temporal identifiers. This process is repeated until all measuring point data has been processed. In actual drilling... During the exploration, for example, if the sound pressure at a certain depth of 100.0 meters is 1.25 and the sound pressure at 100.1 meters is 1.40, it is marked as rising. If the sound pressure at the next point of 100.2 meters is 1.32, it is marked as falling, and so on. After calibrating the directionality of each group of signals, the search begins to check whether the changing trend between adjacent points has continuity. By traversing the measurement point sequence through a sliding window, the measurement point segments that continuously maintain the same directional change are counted. For example, if 5 consecutive sampling points are rising, it is determined to be a segment with a continuous rising trend, and the starting depth and ending depth of the segment are recorded. In this way, the spatial changing trend and continuity of each measurement point in the entire borehole are obtained, forming a measurement point response direction sequence.

[0030] S102: Based on the response direction sequence of the measurement points, compare the change direction, identify continuous and consistent trend segments, sort out the continuation segments of trend changes according to spatial order, determine the start and end depth and persistence of each trend segment, and obtain a set of continuous trend intervals. The data is read line by line from the data record, with each group of measurement points marked with a direction of change. The direction of change of the current measurement point is compared with that of the previous measurement point. If the directions are consistent, the next measurement point is checked. If a direction change is found, the previous continuous direction segment is considered to have ended, and its start and end measurement point positions are recorded. This direction segment is added to the trend segment list. In the trend segment identification process, judgment conditions are set: only when the number of measurement points with consistent directions exceeds the minimum sampling number is it considered a valid trend segment. For example, at least three points must maintain an upward direction to constitute a segment usable for trend continuation analysis. Then, the entire measurement point direction data is processed sequentially to obtain multiple trend segments with consistent directions. Finally, each segment is spatially ordered, i.e., the starting and ending points are sorted. Information such as starting depth, ending depth, direction, and number of points is assembled into a trend interval description record and arranged from shallow to deep according to the starting depth. Further analysis is conducted on the continuity of each trend segment, mainly based on the following conditions: within the same trend segment, the number of trend direction reversals does not exceed 1, the number of measurement points that continuously maintain the same direction of change is not less than 5, and there are no frequent alternations of direction within the segment. In practical applications, for example, if a trend segment continuously decreases in depth within the range of 120.0 meters to 120.8 meters, and the number of continuous measurement points is 9 and the number of direction reversals is 0, then this segment is recorded as a downward trend segment, and its trend continuity is judged to be good. This forms a set of trend continuity intervals composed of multiple continuous trend segments.

[0031] S103: Based on the set of trend continuous intervals, determine the waveform sequence of the original signal in the corresponding depth interval, analyze the temporal characteristics and local deviation of each waveform segment, optimize the arrangement of residual signals in each segment, and obtain the acoustic residual feature sequence. The signal sequence corresponding to the depth range in the original acoustic wave data is called segment by segment. The waveform signal of each segment is extracted into a separate segment. Each segment contains the sound pressure value of multiple continuous measurement points. During the analysis, the change of waveform data on the time axis is observed first to identify whether there are local abnormal fluctuation points, that is, measurement point records that deviate significantly from the average level of surrounding points. When multiple such offset points are concentrated in a certain sub-region, it is judged as a waveform segment with obvious local deviation. Then, the number of all deviation points in the data of this segment is counted and it is determined whether it exceeds a fixed proportion of the total number of samples in the segment, such as higher than 10%. If so, it is recorded as a waveform segment with significant deviation. After subtracting the segment average from the original sound pressure value of all measurement points in this segment, a residual signal sequence is constructed, and the deviation direction of each point in the sequence is recorded. The numerical sorting process involves checking for symmetrical distribution structures. If the deviation values ​​of certain points are extremely small, they are considered noise points and removed, retaining only representative fluctuation terms to form the final residual structure. During implementation, if multiple consecutive deviation values ​​appear in the same direction in a waveform segment, they will be represented as continuous positive or negative deviations in the residual signal. For example, in the depth range of 130.5 meters to 130.9 meters, if there are no fewer than three measurement points in this depth segment that are higher than the average value of the segment, and these measurement points are spatially continuous and show the same deviation direction, then a continuous positively biased segment will be formed in the residual sequence. This process is repeated to process all trend segments, resulting in multiple structured residual signal sequences, which are used to form an acoustic residual feature sequence for subsequent multi-source consistency comparison or structural stability discrimination tasks.

[0032] like Figure 3 As shown, the specific steps for obtaining multi-source trend consistency data are as follows: S201: Based on the acoustic residual feature sequence, by comparing the signal change direction of acoustic logging equipment, resistivity logging equipment and gamma logging equipment in the same depth range one by one, the response direction status of each group of measuring points is marked and classified, and combined with the change trend of each logging equipment, a multi-equipment direction identification sequence is obtained. Data from acoustic logging, resistivity logging, and gamma logging devices at the same depth were read separately. Within the same depth range, single-point measurement results from each of the three devices were extracted in groups, with each group corresponding to a unique depth number. The signal directions of the three devices at that depth were used for comparison. First, the change direction label of the acoustic residual signal was extracted. The resistivity value at the corresponding depth was read and compared with the previous depth point to determine whether the resistivity signal change direction was increasing, decreasing, or unchanged. The gamma logging signal was then processed in the same way to obtain the gamma response change direction at that depth point. Through these three directional judgments, the combination of response directions at that depth point under the three devices was obtained. For example, for a certain depth point, the response direction might be "acoustic increase, resistivity decrease." "Decrease, gamma decrease", then mark the direction combination of each measurement point and classify them into the predefined direction combination type. For example, define "++-" to indicate that the sound wave and resistivity are increasing and the gamma is decreasing. Perform the above judgment and classification operation on all measurement points. If a measurement point signal has an invalid value or data outside the acquisition range, mark its direction status as null and skip the classification. After processing, uniformly number and record all valid measurement point results to form a direction status sequence arranged by depth. For the section with an actual sampling depth of 150 meters to 160 meters, there is a sampling point every 0.1 meters, for a total of 101 sets of data. Generate a list of direction combination information of the three devices at each depth point in sequence to obtain the multi-device direction identification sequence.

[0033] S202: Based on the multi-device directional identification sequence, determine whether the directional markings of multiple logging devices at continuous measuring points are consistent, analyze the consistency of the response symbols of each device within a continuous depth segment, and aggregate the measuring point segments with the same direction of change to obtain a set of coordinated change segments; The direction markers at each depth point are read and compared, with a focus on determining whether the response directions of the three devices at the same depth point are completely consistent. Points with completely consistent directions are marked as "unified direction points," while those without are marked as "divergent direction points." The sequence is traversed in depth order, connecting adjacent consecutive unified direction points to form continuous consistent trend segments. A minimum of three consecutive points is set during processing; that is, a valid coordinated trend segment is considered only if three or more consecutive measurement points satisfy the requirement of consistent directions across all three devices. If only one or two points have consistent directions, they are considered isolated consistent points and ignored. The process proceeds point by point downwards, terminating the current segment when the number of consecutive consistent direction points is insufficient. The starting and ending depths of the current segment are recorded. For example, a segment starts at 152... If seven consecutive points from 0 meters to 152.6 meters have the same direction, they are grouped into a coordinating segment. The direction is determined by the unified direction, either "all upward" or "all downward". If the direction is a combination of "upward-upward-upward" or "downward-downward-downward", it is also grouped into a coordinating segment. Conversely, if a point has the direction "upward-downward-upward", it does not meet the consistency judgment rule and is not included. All consecutive consistent segments are uniformly identified, and their direction type, depth start and end range, and number of participating measurement points are recorded to form a complete set of coordinating change segments. In the sample implementation, the sampling depth range was 160 meters to 170 meters, with a total of 101 sampling points. Six coordinating segments were actually identified, with the longest segment spanning 1.2 meters and the direction being all downward. The set of coordinating change segments was thus generated.

[0034] S203: Based on the set of coordinated change sections, the response direction of the logging points of each logging equipment in the section is jointly analyzed, and the response changes of multiple equipment in the same interval are compared to see if they are synchronized. Sections with consistent trends are aggregated and archived to obtain multi-source trend consistency data. For each segment in the dataset, all logging point records are extracted. Each logging device within that segment is checked for temporal synchronization of its direction of change; that is, whether they exhibit the same directional trend at the same logging point location. During execution, the start and end depth ranges of each segment are traversed, and the data direction markers of all logging points within the segment are compared laterally. If all three devices maintain a consistent direction of change from the start to the end point of a segment, it is considered a segment with a consistent trend. If some points within a segment show a reversal of device direction (e.g., the acoustic wave change in the middle of the segment changes from rising to falling), the number of reversal points needs to be counted. If the proportion of reversal points does not exceed 20% of the total number of points in the segment, it is considered to have an overall consistent trend; otherwise, the segment is discarded as an invalid segment. During the joint analysis, the data direction of each point is compared once. Yes, ensure that the labels of all devices at this point are in the same direction; otherwise, the point will be included in the inconsistency point set. During the statistical process, a reversal ratio threshold of 0.2 is set to limit the maximum proportion of measurement points with inconsistent device response directions within the same analysis section. This threshold is determined by statistical analysis of relatively stable formation sections in actual well logging data and is used to reflect the allowable range of device response deviations without affecting the overall trend judgment. If a section has a total of 10 points and 2 of them show inconsistent directions, it can still be judged as a consistent trend section. Subsequently, the verified trend consistent sections are merged into the trend consistent archive list, recording their depth range, direction type, consistency score (calculated according to the proportion of consistent points), participating device type, etc., to form multi-source trend consistent data results.

[0035] like Figure 4 As shown, the specific steps for obtaining the energy integrated response section are as follows: S301: Based on multi-source trend consistency data, calculate the signal response change amplitude of each logging device in the target depth range, analyze the amplitude change of each acquisition point, and by comparing the response amplitude of each device in the same range, aggregate the amplitude characteristics of all acquisition segments to obtain the spatial distribution of response amplitude. Extract each group of marked measurement points with consistent trends within the target depth range. For each group, read the raw response signal values ​​of the sonic logging, resistivity logging, and gamma logging equipment within that range. Summarize the signal value sequences of each equipment's acquisition points within that depth segment according to equipment classification. Then, calculate the signal variation amplitude of each equipment within that segment. Specifically, select the difference between the maximum and minimum values ​​within that segment as the amplitude. During execution, if the minimum signal value of the sonic logging equipment in the depth range of 140.0 to 140.5 meters is 1.05 and the maximum value is 2.60, then the amplitude is 1.55. Calculate the amplitude values ​​of the resistivity and gamma responses in a similar manner. Next, perform item-by-item analysis on each measurement point, extracting the response values ​​of each point under the three equipment, constructing measurement point amplitude sample values, and recording their corresponding depth coordinates. Summarize the amplitude samples of all measurement points to form an amplitude variation distribution data set within the current target range. Organize this data set and construct a spatial distribution map with depth as the x-axis and response amplitude as the y-axis. When analyzing distribution characteristics, sliding statistics are performed in windows of 1 meter depth. Within each window, the set of amplitude values ​​of the corresponding measuring points is extracted, and the average value and difference range of the set are calculated to determine whether there are obvious amplitude jumps. For example, if the average amplitude value within a certain depth window is higher than the average amplitude value of the adjacent depth window, and the difference between the two exceeds 1.5 times the difference between the average amplitude values ​​of the adjacent windows, and the increase in the amplitude difference within the window compared to the amplitude difference of the adjacent windows exceeds 0.3, then the depth range corresponding to the window is marked as a segment with significant amplitude changes. The statistical results of all windows are normalized, and the amplitude results of different devices are scaled uniformly. Based on the response amplitude distribution range of each logging device in the target depth range, the values ​​are normalized and adjusted to make the amplitude values ​​of different devices comparable. Then, the adjusted amplitude data is uniformly organized, and all measuring points or segments are combined and aggregated, and integrated and sorted in the depth direction according to the sequence to form a response amplitude spatial distribution data group covering the target depth range.

[0036] S302: Based on the spatial distribution of response amplitude, determine the amplitude distribution status of each acquisition segment, mark the segments where amplitude changes show a clustering trend within the spatial range, identify data segments with concentrated distribution characteristics, and obtain a set of amplitude-concentrated segments; The amplitude value list of each sampling segment at the standard scale is read sequentially. First, an amplitude value scan is performed within the segment to extract the range, mean, and median of the amplitude values ​​within the current segment. Their distribution is statistically analyzed, and the judgment is based on whether the spatial distribution of amplitude values ​​exhibits high clustering, i.e., whether most amplitude values ​​are concentrated within the range of the average ±0.2. If eight out of ten measuring points in a segment have amplitude values ​​between 1.0 and 1.4, and the average value of that segment is 1.2, then it is determined to be an amplitude-concentrated segment. The basic criteria for amplitude clustering are: amplitude standard deviation less than 0.25 and the concentration point ratio not less than 70%. When a segment meets this condition, it is marked as a "concentrated segment"; otherwise, it is marked as a "non-concentrated segment." Then, the... The collection of segments marked as "concentrated segments" is extracted to construct a list of amplitude concentrated segments. The start and end depths, the proportion of concentrated points within the segment, the maximum amplitude, the minimum amplitude, and the center value are recorded for each segment. To enhance the accuracy of the identification process, a concentration threshold is added during the judgment process. Segments with a concentration point proportion between 60% and 70% require manual verification to confirm whether they are included in the concentrated segment. After filtering, a set of amplitude concentrated segments is generated. In practical applications, for example, within a depth range of 150 to 160 meters, five segments are marked as amplitude concentrated segments. The longest segment has a span of 1.8 meters, the highest concentration point proportion is 92%, and the amplitude range is 0.5. This set is output as the data basis for subsequent analysis.

[0037] S303: Based on the set of amplitude concentration segments, compare the distribution performance of each group in the depth interval, summarize the amplitude characteristics and spatial location of each segment, perform classification and interval aggregation on the amplitude concentration segments, integrate them into response interval information, and obtain the energy integrated response segment. Extract all paragraph information sequentially, reading their start and end depths, intra-segment amplitude characteristics, concentration point ratios, and center values. Then, classify and compare them according to depth intervals. First, determine the relationship between multiple paragraphs on the depth coordinates, calculating the depth distance between adjacent paragraphs. If the interval is less than 0.5 meters, they are considered neighboring paragraphs and merged into a single wide segment. If the amplitude characteristics within a segment show a consistent trend, such as multiple segments showing an increasing center value, they can be considered continuous trend segments, merged, and archived to form a continuous energy interval information group. Perform amplitude characteristic statistics on each merged interval, outputting the maximum, minimum, range, and average values, and recording the number of originating segments and the distribution width. This process is then repeated during the classification process. The process sets a segment spacing merging threshold of 0.5 meters, meaning that segments with a spacing less than this value and consistent amplitude characteristics can be merged. If a segment has a reversed direction or an amplitude difference greater than 0.8 during the merging process, the merging process is not performed. This rule is used to ensure the consistency of the final segments and the stability of the data structure. After processing, multiple energy integrated response segments are obtained, each segment is marked with a start and end depth, energy amplitude index, and structure number. In the embodiment, after merging, a continuous energy integrated response segment is formed within the depth range of 155 meters to 157.2 meters. This segment consists of 3 original concentrated segments with a total span of 2.2 meters, an average amplitude of 1.35, and a range of 0.6, thus constructing a response segment set.

[0038] like Figure 5 As shown, the specific steps for obtaining the composite feature bands of the interface are as follows: S401: Based on the energy integrated response segment, analyze the start and end depth parameters of each segment, make a continuity judgment on the depth coordinates of adjacent segments, compare the depth coverage between segments, identify the segment group with overlapping depth intervals, and obtain the interval overlap feature group. Read the starting and ending depth values ​​of each segment and arrange them into an ordered list in ascending order of depth. Then, compare the depth boundaries of adjacent segments. First, determine if the ending depth of the preceding segment is greater than the starting depth of the following segment. If it is, it indicates that the two segments overlap in vertical depth. A minimum overlap depth threshold of 0.2 meters is set during this process. If the overlap interval between two segments is greater than or equal to 0.2 meters, it is recorded as a valid overlapping segment group. Combine these segment numbers and add them to the overlap candidate set. Perform the same operation on all segment pairs, iterating through all combinations to form a preliminary overlap feature set. Subsequently, in the overlap group, read the depth range of each segment and calculate the overlap length value, while simultaneously determining the overlap length. Whether the coverage areas between segments have a clear overlapping relationship is determined. The overlapping relationship is formed by segments with smaller starting depths and larger ending depths, which constitute the main segment. The segments that are covered are classified as sub-segments and belong to the main segment. If the main segment overlaps with multiple sub-segments, an overlapping group is formed. For example, if the starting and ending depths of segment A are 142.0 meters to 144.0 meters, segment B is 143.5 meters to 145.0 meters, and segment C is 144.2 meters to 146.0 meters, then A and B, and B and C have overlapping relationships and form a continuous overlapping feature group spanning multiple segments. The overall depth range of this group is 142.0 meters to 146.0 meters. Record the segment numbers, overlapping starting and ending depths, and overlapping length values ​​of all segments participating in the overlap, and output the interval overlapping feature group.

[0039] S402: Based on the overlapping feature group of intervals, analyze the depth coordinates of each overlapping interval, compare the corresponding acoustic response, resistivity change and gamma measurement data of each interval, optimize the correspondence between each logging response, and obtain logging parameter coordination data. The acoustic response, resistivity change, and gamma measurement data for each interval were compared using the following formula: ; The mean value of the well logging parameter coordination difference is calculated, and the correspondence between the various well logging responses is optimized to obtain well logging parameter coordination data. This represents the mean of the coordinated difference in well logging parameters within the c-th overlapping interval. The c-th overlapping interval number represents the feature group of overlapping intervals. This represents the identifier of the well logging parameter combination within the overlapping interval. This represents the number of valid depth sampling points participating in the calculation within the c-th overlapping interval. This represents the index of the i-th depth sampling point within the c-th overlapping interval. This represents the acoustic response residual value corresponding to the i-th depth point within the c-th overlapping interval. This represents the residual value of resistivity change at the i-th depth point within the c-th overlapping interval. This represents the gamma measurement residual value corresponding to the i-th depth point within the c-th overlapping interval; Mean of well logging parameter coherence difference ( ) refers to: within a certain overlapping interval (the first... Within the group, for each depth sampling point Calculate the pairwise differences between the acoustic response residual, resistivity change residual, and gamma measurement residual. Specifically, calculate the absolute difference between the acoustic response residual and the resistivity change residual, and the absolute difference between the resistivity change residual and the gamma measurement residual. Add these two absolute differences, iterate through all sampling points within the interval, sum the results, and then divide by the number of sampling points within the interval. The average value obtained reflects the degree of difference and consistency in the changes of the three logging response parameters within the overlapping interval. The larger the value, the worse the coordination and consistency among the three and the greater the difference; conversely, the smaller the value, the more consistent they are.

[0040] It reflects the absolute difference between the acoustic wave and resistivity response at the same depth point, focusing on the synchronicity and difference between the two parameters; It reflects the absolute difference between resistivity and gamma response at the same depth point; it compares the differences of the three logging responses point by point, accumulates them over the entire interval, and normalizes them to the average difference, thereby quantifying the level of coordination and consistency of logging parameters within the interval.

[0041] Of course, the following is the final version of paragraph S402 of the embodiment after omitting the decimal point and any extra zeros. Its content is the same as before, only the numerical representation is adjusted to remove unnecessary decimal points and trailing zeros: Regarding the first For the identified overlapping regions, sampling points with an equal interval of 0.5m are selected within a depth range of 210m to 212m to construct an index set of valid sampling points. That is, the total number Subsequently, five sets of original residual values ​​corresponding to this interval were extracted from the sonic logging response residual dataset, namely 182, 175.4, 189.2, 180.6, and 177.3. At the same time, the residual values ​​at the same depth were obtained from the resistivity logging residual data: 24.3, 22.8, 25.1, 23.5, and 24, and the residual values ​​of gamma logging were 76.5, 74.2, 77.1, 75, and 76.3. Due to the different physical dimensions of the three types of residual values, the minimum-maximum normalization method was used to perform standard normalization processing on each type of residual sequence. Before the normalization processing, the minimum and maximum values ​​of each type of data were calculated. For the sonic residual data, the minimum value was 175.4, the maximum value was 189.2, and the interval span was 13.8. The normalized data were 0.478, 0, 1, 0.377, and 0.138. The minimum resistivity residual data is 22.8, the maximum is 25.1, the interval is 2.3, and the normalized data are 0.652, 0, 1, 0.304, and 0.522. The minimum value for the gamma residual data is 74.2, the maximum value is 77.1, the range is 2.9, and the normalized values ​​are 0.793, 0, 1, 0.276, and 0.724. Then, the mean of the co-existing difference is calculated according to the formula, and the calculation is performed item by item for each sampling point: Point 1: , Total: 0.315; Point 2: , Total: 0; Point 3: , Total: 0; Point 4: , Total: 0.101; Point 5: , The total is 0.586.

[0042] The sum of the collaborative differences among the above 5 depth points is: ; Substitute into the formula: ; The decision interval is divided as follows: when When the interval is determined to be a highly consistent section, it indicates that the residuals of the three types of logging responses—sonic, resistivity, and gamma—have a clear synchronous change trend in the depth direction, with minimal response differences, and belong to a highly synergistic region. when When the interval is determined to be a low-difference zone, it means that the three types of logging responses have a consistent direction and trend of change in this zone, and there are some minor differences but they do not affect the overall synergy judgment. when When the interval is defined as a medium-difference zone, it indicates that there are significant local differences in the logging responses of each well in this interval, and the consistency of the trend is weakened. when When the interval is determined to be a high-discrepancy zone, it indicates that the residuals of the three types of logging responses deviate significantly in this depth range, and the changing trends are inconsistent or mutually interfering, belonging to a low-coordination area.

[0043] This result indicates that the currently calculated mean of the cooperational difference is... satisfy Therefore, it was determined to be a low-difference section. This determination indicates that the acoustic, resistivity, and gamma logging responses within this overlapping section show a strong consistent trend, and the residual differences fluctuate within a reasonable range. In subsequent steps, this section will be marked as a candidate segment with multi-source response synergy, serving as an important basic segment for constructing the interface composite feature zone. Its corresponding depth segment index and The numerical values ​​will be aggregated and compared horizontally with the results of other segments, and then aggregated vertically.

[0044] S403: Based on the collaborative data of logging parameters, determine the joint response characteristics of multiple types of logging data in each depth interval, summarize the linkage changes of each logging parameter in the interval, assign a composite attribute identifier to each depth interval, and obtain the interface composite feature zone. The system reads the combination records and amplitude values ​​of the three types of equipment in each overlapping section. First, it organizes and statistically analyzes the combination labels of each measuring point to identify any cross-equipment linkage behavior. If, in a certain section, the signal changes of the acoustic wave, resistivity, and gamma devices are in the same direction and their amplitudes are relatively synchronized (i.e., the difference fluctuates within the allowable range and does not exceed the preset deviation threshold of 0.15), then that section is determined to be a linkage response section. In this determination, the signal difference of each measuring point is compared horizontally, and the maximum and minimum response values ​​are recorded. If the difference between the maximum and minimum values ​​is less than the set threshold, it indicates a high linkage strength, and the point is marked as "strong linkage." Subsequently, the proportion of points in the strong linkage state is statistically analyzed at the section level. If it exceeds 70% of the total number of measuring points in the section... If the response rate is 0%, the segment is classified as a "high-linkage segment"; otherwise, it is classified as a "weak-linkage segment." After determining the linkage strength of all segments, corresponding composite attribute identifiers are assigned. The attributes are divided into three categories: "full linkage," "partial linkage," and "non-linkage," representing that the response direction and amplitude of the devices are consistent, some devices show a consistent trend, and the response directions of the three devices differ significantly. Finally, all identifier information is integrated and accompanied by segment depth coordinates, linkage type, and device performance summary to form a set of interface composite feature zones. In the actual example, within the depth range of 147.0 to 148.6 meters, the sound waves, resistivity, and gamma all show a consistent trend and good amplitude synchronization. The composite attribute of "full linkage" is assigned and the interface composite zone is marked with the number IFZ-07.

[0045] like Figure 6 As shown, the specific steps for obtaining the distribution type of stratigraphic structure are as follows: S501: Based on the interface composite feature zone, analyze the logging response sequence of each section, determine the direction of change of each group of response sequences, divide the change segments according to the response rise and fall relationship between continuous measuring points, combine the depth coordinate information of the segments, sort out the trend combination of each group of segments, and obtain the segment trend structure set. Read the well logging response data sequence contained in each section, and extract the measurement value lists of equipment such as acoustic, resistivity, and gamma sensors in sequence. Construct a complete response sequence array according to the measurement point order. Then, determine the direction change of each group of response sequences. Specifically, read the response values ​​of two adjacent measurement points and perform direct difference calculation. If the response value of the latter measurement point is greater than that of the former, it is defined as an upward direction; otherwise, it is a downward direction. If the difference between the two does not exceed the equipment noise threshold of 0.02, it is defined as a stationary direction. Assign a corresponding direction label to each measurement point to form a direction sequence record set indexed by depth. Continue to perform the change segment division operation. Using the change of direction label as the dividing point, group measurement points with the same continuous direction into a change segment. For example, if all measurement points in the range of 146.0 meters to 146.6 meters are in a change segment... If the direction is upward, it is marked as an "ascending segment". If the direction changes to downward midway, a new segment is started. During the segmentation process, the minimum segment length is set to three measurement points to ensure the stability of the segment division. Then, the start and end depths of each changing segment are read, and the segment type and depth information are structurally combined and recorded as {segment number, segment type, depth start point, depth end point, number of points}. After traversing the entire composite feature zone, all segment records are summarized. The changing segments in each group of segments are further classified and organized, and combined and classified according to the order of appearance and type of the segments. For example, if a segment presents a "rising-rising-falling-falling-rising" combination, its trend combination is recorded as "up-down-up". The trend combination structure of each composite segment is output to form a segment trend structure set.

[0046] S502: Based on the segment trend structure set, compare the response trend sequence of each segment, calculate the number of times the response direction changes continuously within each segment, identify the intervals with consistent direction and stable fluctuations, and obtain the trend consistent interval set. For each segment, the identified trend combinations are read, and all trend segment types in that segment are analyzed sequentially. The number of directional changes between adjacent segments is calculated. Each change from upward to downward or from downward to upward is recorded as a change. Stationary segments are ignored during the calculation; continuous stationary segments are not counted as directional changes. The number of changes and the total number of measurement points are recorded during the calculation for subsequent trend consistency assessment. A threshold of 2 directional changes is set. If a segment has no more than 2 directional changes and at least 15 measurement points, the directional trend of that segment is considered relatively consistent. The next segment is then read... For the response value sequence of all measuring points within a segment, calculate the maximum, minimum, and range of the response values ​​respectively to determine whether there are abnormal and drastic fluctuations. If the range is less than 0.5 and the signal standard deviation is less than 0.3, the response fluctuation is considered stable. Combined with the trend consistency judgment result, segments that meet the above two conditions are marked as "trend consistent segments". Otherwise, they are not marked. For example, if a segment has 1 direction change, a maximum response value of 2.4, a minimum value of 1.9, a range of 0.5, and a standard deviation of 0.25, it is judged to be consistent in direction and stable in fluctuation. All segments that meet the conditions are summarized to form a trend consistent interval set.

[0047] S503: Based on the trend-consistent interval set, determine the trend continuity and response offset characteristics of each segment, analyze the correspondence between the trend parameters and the change amplitude of each segment, classify the stability of each segment according to the segment number, and obtain the stratigraphic structure distribution type. The directional trend records and response value statistics for each segment are read segment by segment. First, the trend continuity is judged by extracting the trend segment sequence for each segment and analyzing the spacing of directional changes. If the span between adjacent directional change segments is greater than the set trend continuation threshold of 0.8 meters, it is considered a discontinuous trend. If, within the same segment, the number of trend segments with a span greater than the threshold is not less than 2 / 3 of the total number of trend segments in that segment, the trend continuity of that segment is determined to be strong. If, the number of trend segments with a span less than the threshold is not less than 2 / 3 of the total number of trend segments in that segment, the segment is marked as having violent trend fluctuations. Next, the average response value within each trend segment is calculated, and the average values ​​of each segment are compared longitudinally to obtain the maximum offset of the response values ​​between each segment. If, within a segment, the offset change between adjacent trend segments does not exceed the preset offset threshold for at least 2 / 3 of the total number of adjacent segments, and no sudden jump occurs, the segment is determined to have a weak response offset. If the offset between adjacent trend segments exceeds the offset threshold two or more times, it is judged as a strong response offset. During the analysis, stability classification conditions are constructed by combining trend continuity and response offset characteristics, and the judgment criteria are set as follows: if the trend is continuous and the response offset is weak, it is marked as "structurally stable"; if the trend is discontinuous but the response offset is weak, it is marked as "substable"; if the trend is continuous but the response offset is strong, it is marked as "critically unstable"; if the trend is discontinuous and the response offset is strong, it is marked as "instable". All segments are classified according to the above criteria, and a structural distribution classification table is generated, recording the segment number, trend continuity, offset characteristics and stability type. In specific implementation, for example, segment numbered FZ-11 has three segments, one directional change, a trend span of 1.2 meters and an offset of 0.35, and is classified as "structurally stable". The stability classification of all segments is completed, and the stratigraphic structure distribution type is obtained.

[0048] like Figure 7 As shown, a borehole physical exploration data processing system includes: The residual feature generation module is based on an acoustic wave detector. It analyzes the raw signals collected inside the borehole, compares the response changes of adjacent measuring points, identifies the change state of the signal along the depth direction, and obtains the acoustic residual feature sequence. The trend consistency discrimination module compares the signal change directions of each logging device within the same depth range based on the acoustic residual feature sequence, analyzes the consistency of the directions of acoustic waves, resistivity and gamma response, determines the depth segment with consistent directions, and obtains multi-source trend consistency data. The energy zone extraction module analyzes the response variation amplitude of each logging device within the depth range based on multi-source trend consistency data, identifies the depth segments where the changes are concentrated, and collects the amplitude distribution to obtain the energy integrated response zone. The interface zone fusion module is based on the energy integrated response zone. It analyzes the overlap relationship of each response zone in the depth direction, merges and organizes the spatially overlapping zones, and then integrates the response information of each logging equipment in the zone to obtain the interface composite feature zone. The structure type determination module analyzes the change state of each segment based on the interface composite feature zone, classifies and determines the segment characteristics, divides the depth segment into types at the structural stability level, and obtains the stratigraphic structure distribution type.

[0049] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of protection of the described technical solutions.

Claims

1. A method for processing borehole physical exploration data, characterized in that, The method includes: S1: Based on the acoustic wave detector, the raw signals collected inside the borehole are analyzed, the response changes of adjacent measuring points are compared, the change state of the signal along the depth direction is identified, and the acoustic residual characteristic sequence is obtained. S2: Based on the acoustic residual characteristic sequence, the signal change direction of each logging device in the same depth range is compared, the consistency of the direction of acoustic wave, resistivity and gamma response is analyzed, the depth segment with consistent direction is determined, and multi-source trend consistency data is obtained. S3: Based on the multi-source trend consistency data, analyze the response change amplitude of each logging device within the depth range, identify the depth segment where the changes are concentrated, and collect the amplitude distribution state to obtain the energy integrated response segment. S4: Based on the energy integration response section, the overlap relationship of each response section in the depth direction is analyzed, the sections with spatial overlap are merged and sorted, and the response information of each logging equipment in the section is integrated to obtain the interface composite feature zone. S5: Based on the interface composite feature zone, analyze the change state of each segment, classify and determine the segment characteristics, divide the depth segment into types at the structural stability level, and obtain the stratigraphic structure distribution type.

2. The borehole physical exploration data processing method according to claim 1, characterized in that, The acoustic residual feature sequence includes acoustic response waveform, time-series residual signal and acquisition depth information; the multi-source trend consistency data includes directional consistency zone identifier, equipment coordinated change label and trend synchronization depth segment; the energy integrated response segment includes energy concentration zone number, response amplitude peak value and segment location information; the interface composite feature zone includes interface depth interval, composite response characteristics and structural boundary indication; and the stratigraphic structure distribution type includes stable segment label, unstable segment label and interface attribute parameters.

3. The borehole physical exploration data processing method according to claim 1, characterized in that, The specific steps for obtaining the acoustic residual feature sequence are as follows: S101: Based on the acoustic wave detector, analyze the collected raw signal, determine the directional change of the sound pressure amplitude at continuous measurement points, compare the signal strength between adjacent measurement points, and calibrate each set of data with depth and time labels to determine the trend and continuity of signal changes, and obtain the measurement point response direction sequence. S102: Based on the response direction sequence of the measurement points, compare the change directions, identify continuous and consistent trend segments, sort out the continuation segments of trend changes according to spatial order, determine the start and end depth and duration of each trend segment, and obtain a set of continuous trend intervals. S103: Based on the set of continuous trend intervals, determine the waveform sequence of the original signal in the corresponding depth interval, analyze the temporal characteristics and local deviation phenomena of each waveform segment, optimize the arrangement of residual signals in each segment, and obtain the acoustic residual feature sequence.

4. The borehole physical exploration data processing method according to claim 1, characterized in that, The specific steps for obtaining the multi-source trend consistency data are as follows: S201: Based on the acoustic residual feature sequence, by comparing the signal change directions collected by the acoustic logging equipment, resistivity logging equipment and gamma logging equipment in the same depth range one by one, the response direction status of each group of measuring points is marked and classified, and combined with the change trend of each logging equipment, a multi-equipment direction identification sequence is obtained. S202: Based on the multi-device direction identification sequence, determine whether the direction markings of multiple logging devices at continuous measuring points are consistent, analyze the consistency of the response symbols of each device within a continuous depth segment, and aggregate the measuring point segments with the same direction of change to obtain a set of coordinated change segments. S203: Based on the aforementioned set of coordinated change zones, a joint analysis is performed on the response direction of each logging device within the zone. The response changes of multiple devices in the same zone are compared to see if they are synchronized. Zones with consistent trends are aggregated and archived to obtain multi-source trend consistency data.

5. The borehole physical exploration data processing method according to claim 1, characterized in that, The specific steps for obtaining the energy integrated response section are as follows: S301: Based on the multi-source trend consistency data, calculate the signal response change amplitude of each logging device in the target depth range, analyze the amplitude change of each acquisition point, and by comparing the response amplitude of each device in the same range, aggregate the amplitude characteristics of all acquisition segments to obtain the spatial distribution of response amplitude. S302: Based on the spatial distribution of the response amplitude, determine the amplitude distribution status of each acquisition segment, mark the segments where the amplitude changes in the spatial range show a clustering trend, identify data segments with concentrated distribution characteristics, and obtain a set of amplitude-concentrated segments; S303: Based on the set of amplitude concentration segments, compare the distribution performance of each group in the depth interval, summarize the amplitude characteristics and spatial location of each segment, perform classification and interval aggregation on the amplitude concentration segments, integrate them into response interval information, and obtain the energy integrated response segment.

6. The borehole physical exploration data processing method according to claim 1, characterized in that, The specific steps for obtaining the interface composite feature region are as follows: S401: Based on the energy integrated response segment, analyze the start and end depth parameters of each segment, make a continuity judgment on the depth coordinates of adjacent segments, compare the depth coverage between segments, identify the segment group with overlapping depth intervals, and obtain the interval overlap feature group. S402: Based on the overlapping feature group of the intervals, analyze the depth coordinates of each overlapping interval, compare the acoustic response, resistivity change and gamma measurement data corresponding to each interval, optimize the correspondence between each logging response, and obtain logging parameter coordination data. S403: Based on the well logging parameter coordination data, determine the joint response characteristics of multiple types of well logging data in each depth interval, summarize the linkage change performance of each well logging parameter in the interval, assign a composite attribute identifier to each depth interval, and obtain the interface composite feature zone.

7. The borehole physical exploration data processing method according to claim 1, characterized in that, The specific steps for obtaining the stratigraphic structure distribution type are as follows: S501: Based on the composite feature zone of the interface, analyze the logging response sequence of each section, determine the direction of change of each group of response sequences, divide the change segments according to the response rise and fall relationship between continuous measuring points, combine the depth coordinate information of the segments, sort out the trend combination of each group of segments, and obtain the segment trend structure set. S502: Based on the segment trend structure set, compare the response trend sequence of each segment, calculate the number of times the response direction changes continuously within each group of segments, identify the intervals with consistent direction and stable fluctuations, and obtain the trend consistent interval set. S503: Based on the aforementioned trend-consistent interval set, determine the trend continuity and response offset characteristics of each segment, analyze the correspondence between the trend parameters and the change amplitude of each segment, classify each segment according to its stability number, and obtain the stratigraphic structure distribution type.

8. The borehole physical exploration data processing method according to claim 1, characterized in that, The raw signal refers to the measurement signal data collected by the borehole acoustic wave detector at each sampling time and depth point without any processing. The depth segment with consistent direction refers to the depth segment in the same interval where the signal change direction of multiple devices is consistent.

9. The borehole physical exploration data processing method according to claim 1, characterized in that, The depth range refers to the depth segment in the borehole that has been selected for further attention and analysis, and the response change amplitude refers to the absolute magnitude of the change in the signal values ​​collected by each logging device within the target range.

10. A borehole physical exploration data processing system, the system being used to implement the borehole physical exploration data processing method as described in any one of claims 1-9, characterized in that, The system includes: The residual feature generation module is based on an acoustic wave detector. It analyzes the raw signals collected inside the borehole, compares the response changes of adjacent measuring points, identifies the change state of the signal along the depth direction, and obtains the acoustic residual feature sequence. The trend consistency discrimination module compares the signal change directions of each logging device within the same depth range based on the acoustic residual feature sequence, analyzes the consistency of the directions of acoustic waves, resistivity and gamma response, determines the depth segment with consistent directions, and obtains multi-source trend consistency data. Based on the multi-source trend consistency data, the energy segment extraction module analyzes the response change amplitude of each logging device within the depth range, identifies the depth segments where the changes are concentrated, and collects the amplitude distribution state to obtain the energy integrated response segment. The interface zone fusion module analyzes the overlap relationship of each response zone in the depth direction based on the energy integrated response zone, merges and organizes the spatially overlapping zones, and then integrates the response information of each logging equipment in the zone to obtain the interface composite feature zone. The structure type determination module analyzes the change state of each segment based on the interface composite feature zone, classifies and determines the segment characteristics, divides the depth segment into types at the structural stability level, and obtains the stratigraphic structure distribution type.

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