A combined detection system of Stoneley wave and resistivity in well

Abstract

The present application relates to a kind of well in the joint detection system of Stoneley wave and resistivity, belong to geophysical well logging technical field, solve the problem of limited detection range of Stoneley wave and limited accuracy of well in the prior art.Electric method.A kind of well in the joint detection system of Stoneley wave and resistivity, comprising: for well in the first observation device of Stoneley wave detection, for well in the second observation device of electric method data detection, host computer;First observation device includes Stoneley wave acquisition station, transmitting sensor, receiving sensor;Second observation device includes high-density electrical method instrument, high-density electrical method instrument is also connected with the cable vertically into well, multiple electrodes are arranged on cable;High and low density electrical method instrument sends the measured data to host computer;Host computer obtains geological state according to the seismic data and electric method measurement raw data obtained.This application prompts the security risk through the water content of sandstone, improves detection accuracy and effect, and fuses rock mass state and lithology identification.

Description

Technical Field

[0001] This invention relates to the field of geophysical logging technology, and in particular to a combined Stoneley wave and resistivity detection system in a well. Background Technology

[0002] With the development of well drilling technology, the requirements for detection precision and accuracy are becoming increasingly stringent. Existing technologies often have their own applicable scope and limitations. For example, the widely used well drilling seismic wave method is suitable for studying physical parameters such as the density of subsurface structures and the velocity of signals corresponding to different strata; electrical resistivity methods are suitable for studying the resistivity distribution of subsurface structures. Well drilling seismic waves have low interference, high precision, and are sensitive to complex fractures and structures. Well drilling electrical resistivity methods have a wide detection range and are sensitive to water-bearing fracture structures.

[0003] In wellbore seismic wave technology, Stoneley waves are elastic waves that propagate along the surface of a pipe or wellbore wall. They are widely used in geological and engineering exploration for radial fracture detection, and their high sensitivity and high resolution make them an important tool for precise assessment of radial fractures. However, their effectiveness decreases over long distances due to their primarily one-dimensional propagation along the well wall. In wellbore electrical resistivity tomography (EDT), resistivity detection typically has a detection range of several meters to tens of meters. It is often used to detect low-resistivity bodies such as mudstone, water, and water-bearing sandstone in large fracture zones around the well. This is a two-dimensional detection method based on the well depth and radial direction. However, this method suffers from volume effects, representing a comprehensive response of various objects within the anomalous area, resulting in relatively low accuracy. In engineering applications, mudstone generally exhibits low resistivity, while anhydrous sandstone typically has high resistivity. However, sandstone is prone to lower resistivity due to water content in its pores. Furthermore, high water content in sandstone increases the difficulty of mine drainage, potentially leading to mine flooding accidents; it can also cause the dissolution and destruction of underground rock formations, resulting in ground subsidence and damage to buildings and facilities. Therefore, it is necessary to determine the lithology of the rock mass and the degree of development of its fractures, as well as whether the fractures contain water, in order to avoid safety issues. Summary of the Invention

[0004] Based on the above analysis, the present invention aims to provide a combined Stoneley wave and resistivity detection system in wells to solve the problem that existing Stoneley wave and resistivity detection technologies are limited by their own methodological principles and have difficulty in overcoming their own bottlenecks.

[0005] On one hand, embodiments of the present invention provide a combined wellstone wave and resistivity detection system, comprising:

[0006] The first observation device for Stoneley wave detection in wells, the second observation device for electrical resistivity data detection in wells, and the host computer;

[0007] The first observation device includes a Stoneley wave acquisition station located on the ground, a transmitting sensor embedded in a cable metal slot, and multiple receiving sensors;

[0008] The Stoneley wave acquisition station is used to acquire and receive seismic wave data received by the sensor and send it to the host computer.

[0009] The second observation device includes a high-density electrical resistivity tomography (ED tomography) instrument arranged at the wellhead, as well as a power supply electrode B, a reference electrode N, and multiple measuring electrodes. The high-density E tomography instrument is connected to the power supply electrode B arranged outside the well in the drilling site and the reference electrode N arranged in another direction outside the well. The high-density E tomography instrument is also connected to a cable that extends vertically into the well, and multiple electrodes are arranged on the cable through slots. The raw electrical resistivity measurement data obtained by the electrode measurements are transmitted to the high-density E tomography instrument via the cable, and the high-density and low-density E tomography instruments transmit the measurement data to the host computer.

[0010] The host computer obtains the geological conditions based on the acquired seismic wave data and the raw data from electrical resistivity tomography.

[0011] Furthermore, during testing, the first observation device moves from the wellhead to the bottom of the well. Each time a test is conducted, the transmitting sensor and multiple receiving sensors are moved downwards by a preset distance.

[0012] Furthermore, the Stoneley wave acquisition station includes an acquisition device with data storage function. The acquisition device can transmit the received seismic wave data from each receiving sensor to a host computer via cable or mobile storage medium.

[0013] Furthermore, the electrode distance between each measuring electrode in the second observation device is 2 meters. After each round of measurement is completed, all electrodes are moved from the bottom of the well to the surface via cables, with each movement step being 30 meters.

[0014] Furthermore, the high-density electrical resistivity meter has a data storage function, which can transmit the collected raw electrical resistivity measurement data to a host computer for processing via a mobile storage medium or data cable.

[0015] Furthermore, the host computer obtains the geological conditions based on the acquired seismic wave data and raw electrical resistivity tomography data, including:

[0016] The Stoneley wave spectrum was extracted based on the collected seismic wave data;

[0017] After frequency domain transformation of the seismic data, interference components are filtered out to obtain the interference-free seismic frequency domain signal. The interference-free seismic frequency domain signal is then converted to the time domain for static correction, and the down-going wave in the reflected wave of the Stoneley wave is extracted. The Stoneley wave profile is obtained after equal offset gather extraction of the down-going wave.

[0018] The Stoneley wave energy spectrum is obtained by normalizing the downflow wave.

[0019] Apparent resistivity is obtained based on the collected raw data from electrical resistivity measurements;

[0020] A resistivity inversion model is established, and the resistivity inversion model is optimized using apparent resistivity to obtain an optimized resistivity inversion model. Based on the optimized resistivity inversion model, the resistivity is obtained, and then a resistivity profile is obtained.

[0021] The geological state of the rock mass is determined based on the Stoneley wave spectrum, Stoneley wave profile, energy spectrum, and resistivity profile.

[0022] Furthermore, the determination of the geological state of the rock mass based on the Stoneley wave spectrum, Stoneley wave profile, energy spectrum, and resistivity profile includes:

[0023] The lithology and degree of fracture development can be determined by the Stoneley wave spectrum, or the lithology can be determined by the Stoneley wave profile. The location and degree of fracture development can be determined by the Stoneley wave profile and the Stoneley wave energy spectrum. The water-bearing state can be determined by the resistivity profile. The geological state of the rock mass can be determined by combining two or more of the following information: lithology, fracture location and degree of development, Stoneley wave energy spectrum or water-bearing state.

[0024] Furthermore, lithology is determined based on the Stoneley wave spectrum, including:

[0025] If the peak amplitude value of the signal in the Stoneley wave spectrum is greater than the first voltage threshold V1, and the frequency components and sidelobe state of the signal conform to the characteristics of sandstone layer, then it is judged to be sandstone layer; if the peak amplitude value of the signal in the Stoneley wave spectrum is less than the second voltage threshold V2, and the frequency components and sidelobe state of the signal conform to the characteristics of mudstone layer, then it is judged to be mudstone layer, where V1>V2.

[0026] Furthermore, the location and development degree of the fracture were determined based on the Stoneley wave profile and energy spectrum, including:

[0027] In the Stoneley wave profile, waveform omissions and / or discontinuities correspond to cracks; the corresponding average amplitude of the Stoneley wave energy spectrum is less than the energy threshold E. th The fracture is strongly developed; otherwise, it is weakly developed.

[0028] Furthermore, the step of determining the water content based on the resistivity profile includes:

[0029] The maximum resistivity of the same sandstone layer is obtained from the resistivity profile. After normalizing the resistivity of the same sandstone layer, those below 0.3 are highly water-rich sandstone, those between 0.3 and 0.7 are moderately water-rich sandstone, and those above 0.7 are waterless sandstone.

[0030] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0031] 1. The present invention provides a combined Stoneley wave and resistivity detection system in a well. Within a set range around the well, Stoneley wave information is first used to identify the lithology and radial fracture development at different depths. Based on this, a resistivity data interpretation standard is established. The resistivity parameters are optimized using a resistivity inversion model. The high-precision identification advantage of the Stoneley wave method for radial fractures is used to improve the accuracy of the resistivity method.

[0032] 2. Outside the wellbore setting range, the lithology determined within the wellbore setting range is continued. The characteristics of tens of meters that can be identified by the resistivity method are used to determine the geological state of the rock mass in this section; this makes up for the lack of fracture detection capability of Stoneley wave at long distances.

[0033] 3. By combining resistivity data with the interpretation of water content, it is possible to identify water-bearing sandstones that may pose safety hazards. After resistivity normalization, sandstones with a resistivity below 0.3 are considered highly water-bearing sandstones, those between 0.3 and 0.7 are considered moderately water-bearing sandstones, and those above 0.7 are considered non-water-bearing sandstones.

[0034] 4. The alternating occurrence of mudstone and sandstone indicates interbedded sandstone and mudstone; in sandstone, the normalized energy of Stoneley waves of 0.1-0.8 corresponds to fracture zones, and the normalized energy of 0-0.6 corresponds to breccia zones. This improves the interpretation effect and effectively integrates rock mass structure detection with lithological identification.

[0035] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from the description and drawings, which are particularly pointed out. Attached Figure Description

[0036] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0037] Figure 1 This is a diagram illustrating the composition of a combined well-hole Stoneley wave and resistivity detection system according to the present invention.

[0038] Figure 2 This is a schematic diagram of the Stoneley wave method observation system of the well-based Stoneley wave and resistivity combined detection system of the present invention;

[0039] Figure 3 This is a schematic diagram of the resistivity observation system of the well-based Stoneley wave and resistivity combined detection system of the present invention;

[0040] Figure 4This is a spectrum analysis result of the Stoneley wave mudstone layer from a well-based Stoneley wave and resistivity combined detection system according to the present invention.

[0041] Figure 5 This is a spectrum analysis result of the Stoneley wave sandstone layer using a combined well-hole Stoneley wave and resistivity detection system according to the present invention.

[0042] Figure 6 This is a Stoneley wave equidistant offset profile of a combined Stoneley wave and resistivity detection system in a well according to the present invention.

[0043] Figure 7 This is a diagram showing the combined interpretation results of a well-based Stoneley wave and resistivity joint detection system according to the present invention.

[0044] Figure label:

[0045] 1-Box 1;

[0046] 2-Box 2;

[0047] 3-Box 3;

[0048] 4-Box 4;

[0049] 5-Ellipse 5;

[0050] 6-Ellipse 6. Detailed Implementation

[0051] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0052] One embodiment of the present invention, such as Figure 1 As shown, a combined wellbore Stoneley wave and resistivity detection system is disclosed, aiming to improve the detection range of the original wellbore Stoneley wave method and address the accuracy issues of the resistivity method. The system includes:

[0053] The first observation device for Stoneley wave detection in wells, the second observation device for electrical resistivity data detection in wells, and the host computer;

[0054] The first observation device includes a Stoneley wave acquisition station located on the ground, a transmitting sensor embedded in a cable metal slot, and multiple receiving sensors;

[0055] The Stoneley wave acquisition station is used to acquire and receive seismic wave data received by the sensor and send it to the host computer.

[0056] The second observation device includes a high-density electrical resistivity tomography (ED tomography) instrument arranged at the wellhead, as well as a power supply electrode B, a reference electrode N, and multiple measuring electrodes. The high-density E tomography instrument is connected to the power supply electrode B arranged outside the well in the drilling site and the reference electrode N arranged in another direction outside the well. The high-density E tomography instrument is also connected to a cable that extends vertically into the well, and multiple electrodes are arranged on the cable through slots. The raw electrical resistivity measurement data obtained by the electrode measurements are transmitted to the high-density E tomography instrument via the cable, and the high-density and low-density E tomography instruments transmit the measurement data to the host computer.

[0057] The host computer obtains the geological conditions based on the acquired seismic wave data and the raw data from electrical resistivity tomography.

[0058] During testing, the first observation device moves from the wellhead to the bottom of the well. Each time a test is conducted, the transmitting sensor and multiple receiving sensors are moved downwards by a preset distance.

[0059] The Stoneley wave acquisition station includes an acquisition device with data storage function. The acquisition device can transmit the received seismic wave data from each receiving sensor to a host computer via cable or mobile storage medium.

[0060] In the second observation device, the electrode distance between each measuring electrode is 2 meters. After each round of measurement, all electrodes are moved from the bottom of the well to the surface via cables, with each movement step being 30 meters.

[0061] The high-density electrical resistivity meter has a data storage function, and the raw electrical resistivity measurement data collected can be transmitted to a host computer for processing via a mobile storage medium or data cable.

[0062] The host computer obtains the geological conditions based on the acquired seismic wave data and raw electrical resistivity tomography data, including:

[0063] The Stoneley wave spectrum was extracted based on the collected seismic wave data;

[0064] After frequency domain transformation of the seismic data, interference components are filtered out to obtain the interference-free seismic frequency domain signal. The interference-free seismic frequency domain signal is then converted to the time domain for static correction, and the down-going wave in the reflected wave of the Stoneley wave is extracted. The Stoneley wave profile is obtained after equal offset gather extraction of the down-going wave.

[0065] The Stoneley wave energy spectrum is obtained by normalizing the downflow wave.

[0066] Apparent resistivity is obtained based on the collected raw data from electrical resistivity measurements;

[0067] A resistivity inversion model is established, and the resistivity inversion model is optimized using apparent resistivity to obtain an optimized resistivity inversion model. Based on the optimized resistivity inversion model, the resistivity is obtained, and then a resistivity profile is obtained.

[0068] The geological state of the rock mass is determined based on the Stoneley wave spectrum, Stoneley wave profile, energy spectrum, and resistivity profile.

[0069] The method of determining the geological state of a rock mass based on Stoneley wave spectrum, Stoneley wave profile, energy spectrum, and resistivity profile includes:

[0070] The lithology and degree of fracture development can be determined by the Stoneley wave spectrum, or the lithology can be determined by the Stoneley wave profile. The location and degree of fracture development can be determined by the Stoneley wave profile and the Stoneley wave energy spectrum. The water-bearing state can be determined by the resistivity profile. The geological state of the rock mass can be determined by combining two or more of the following information: lithology, fracture location and degree of development, Stoneley wave energy spectrum or water-bearing state.

[0071] Lithology is determined based on Stoneley wave spectrum, including:

[0072] If the peak amplitude value of the signal in the Stoneley wave spectrum is greater than the first voltage threshold V1, and the frequency components and sidelobe state of the signal conform to the characteristics of sandstone layer, then it is judged to be sandstone layer; if the peak amplitude value of the signal in the Stoneley wave spectrum is less than the second voltage threshold V2, and the frequency components and sidelobe state of the signal conform to the characteristics of mudstone layer, then it is judged to be mudstone layer, where V1>V2.

[0073] Determining the location and development level of fractures based on Stoneley wave profiles and energy spectra, including:

[0074] In the Stoneley wave profile, waveform omissions and / or discontinuities correspond to cracks; the corresponding average amplitude of the Stoneley wave energy spectrum is less than the energy threshold E. th The fracture is strongly developed; otherwise, it is weakly developed.

[0075] The method of determining the water content based on the resistivity profile includes:

[0076] The maximum resistivity of the same sandstone layer is obtained from the resistivity profile. After normalizing the resistivity of the same sandstone layer, those below 0.3 are highly water-rich sandstone, those between 0.3 and 0.7 are moderately water-rich sandstone, and those above 0.7 are waterless sandstone.

[0077] Specifically, the system includes the following steps during use:

[0078] Step S1: Acquire seismic wave data and raw electrical resistivity tomography data along the well depth direction. This includes the following steps:

[0079] S11. Establish the observation system and determine its parameters. This includes:

[0080] Step S111, as follows Figure 2 , 3As shown, observation systems for Stoneley wave detection and electrical resistivity tomography (EDT) in wells were established respectively.

[0081] If there is too much gravel in the borehole during testing, flushing should be performed first.

[0082] like Figure 2 As shown, a Stoneley wave observation system employing one transmitting sensor and multiple receiving sensors is vertically placed inside the well. The spacing between the receiving sensor arrays is 0.4 meters, and the spacing between measurement points is 0.4-2.4 meters. The transmitting sensor is located 0.4-2.4 meters directly below all the sensors. The array spacing is the distance between two adjacent receiving sensors; the measurement point spacing is the downward translation distance of the entire sensor during each repeated test. Ground-based data acquisition equipment is equipped with a wireless communication module, a storage module, and a data transmission interface. The wireless communication module transmits seismic wave data with the sensors, the storage module stores the seismic wave data acquired from the receiving sensors, and the data transmission interface connects to cables or a portable storage medium to transmit the received seismic wave data from each receiving sensor to a host computer for processing. In this specific example, six receiving sensors are used, employing a one-transmitter-six-receiver configuration.

[0083] like Figure 3 As shown, a high-density electrical resistivity meter is positioned at the wellhead, and multiple electrodes are vertically arranged within the wellbore as part of a borehole electrical resistivity observation system. The power supply electrode B is positioned 300m outside the well at the drilling site, and the reference electrode N is positioned 300m outside the well in another direction. The electrode spacing between all electrodes is 2 meters. After each measurement cycle, all electrodes are moved from the bottom of the well towards the surface, with each movement taking a 30-meter step. Resistivity data is acquired after each movement. The reference electrode N serves as the reference electrode for calculating the potential difference; the voltage across a given electrode represents the voltage difference between that electrode and the reference electrode.

[0084] Specifically, the measuring line includes 32 electrodes. In each round of measurement, one of the electrodes numbered 1 to 32 is used as the power supply electrode, and the remaining electrodes are used as measuring electrodes. The voltage difference between each measuring electrode and the reference electrode N is measured, thus obtaining the receiving voltage and receiving current of the 31 measuring electrodes.

[0085] The observation system parameters for Stoneley wave detection include source excitation parameters and acquisition parameters.

[0086] Before formal data acquisition, tests are conducted to verify the source excitation parameters, followed by a full-wavelength acoustic test. The source excitation parameters refer to the power supply time for exciting the source; for example, it is 50ms.

[0087] The acquisition parameters include the number of sampling points, the number of leading points, and the sampling frequency. For example: the number of sampling points is 4K, the number of leading points is 256, and the sampling frequency is 250KHz.

[0088] The parameters of the well-drilled electrical resistivity tomography (EDT) system include the power supply voltage.

[0089] The power supply voltage is used to power the electrodes of the high-density electrical resistivity meter; the electrodes being powered are called the power supply electrodes. Specifically, the power supply voltage of the well-drilling electrical resistivity observation system of this invention is 72V.

[0090] Step S12: Evaluate the quality of the test seismic data from the perspectives of the time and frequency domains. After confirmation, proceed to step S13 for formal data acquisition. This includes:

[0091] A full-wavelength acoustic test was conducted to evaluate the test seismic data from both time and frequency domain perspectives.

[0092] The quality of the test seismic data is evaluated from both time and frequency domain perspectives. This includes observing whether the arrival time of the direct wave matches the spatial and wave velocity background, whether the frequency band and frequency meet the detection requirements, and whether the full wave train signal morphology is complete. If the test meets the requirements, formal data acquisition can proceed; otherwise, the sensor equipment needs to be readjusted.

[0093] Step S13: Use the observation system to collect data based on the determined observation system parameters to obtain raw data; the raw data of the Stoneley wave is the seismic wave data collected by each receiving sensor, and the raw data of the electrical method measurement includes the receiving current, receiving voltage, and power supply electrode number.

[0094] After receiving the aforementioned seismic wave data and raw electrical resistivity tomography data, the host computer processes the seismic wave data and raw electrical resistivity tomography data in the following manner to obtain the geological conditions:

[0095] Step S2: Extract the Stoneley wave spectrum based on the collected seismic wave data.

[0096] Specifically, the seismic wave data undergoes data export and spectral analysis, including steps S21-S22.

[0097] Step S21: Export the multi-channel raw data collected by the sensors according to the specified data format based on the model of the acquisition device. Common geophysical data formats include SEG-Y and ASCII. Select the data range to be exported as needed; this can be the entire dataset, a specific time window, spatial region, or measurement gather. Set the export parameters, such as sampling rate, data format, file naming rules, and compression options. Specifically, the raw data refers to the data output by each receiving sensor.

[0098] Step S22: Convert the seismic data of each channel to the frequency domain to obtain the Stoneley wave spectrum.

[0099] Spectrum analysis involves performing Fourier transform or wavelet transform on the raw time-domain data acquired after successful testing, converting it to the frequency domain. The Fourier transform decomposes the seismic wave waveform into components of different frequencies to analyze the spectral content of the seismic signal. By extracting the amplitude spectrum from the Fourier transform results, a seismic wave data spectrum diagram is obtained, representing the amplitude distribution of the seismic signal at different frequencies and reflecting the frequency characteristics of the signal. The horizontal axis represents frequency, and the vertical axis represents amplitude. By observing the seismic wave data spectrum diagram, the main frequency components, frequency range, and energy distribution of the seismic signal can be analyzed.

[0100] like Figure 4 , Figure 5 As shown, after converting the seismic wave data to the frequency domain, a seismic wave data spectrum is obtained. The seismic wave data spectrum contains a Stoneley wave spectrum, so the amplitude value, frequency components, and sidelobe state of the Stoneley wave signal can be analyzed in the seismic wave data spectrum.

[0101] Step S3: After frequency domain conversion of the seismic data, interference components are filtered out to obtain the interference-free seismic frequency domain signal. The interference-free seismic frequency domain signal is converted to the time domain and statically corrected. The down-going wave in the reflected wave of the Stoneley wave is extracted. The Stoneley wave profile is obtained after equal offset gather extraction of the down-going wave.

[0102] The seismic wave data undergoes interference filtering and static correction. A Stoneley wave is obtained through low-pass filtering, then converted to a time-domain signal. The reflected Stoneley wave is extracted using median filtering, and the downlink wave is extracted from the reflected wave using a two-dimensional frequency filtering method. This process includes steps S31-S33.

[0103] Step S31: Filter out interference components from the seismic data spectrum obtained in step S22 to obtain the seismic frequency domain signal with interference removed.

[0104] Interference components unrelated to formation information, such as signals, are identified from the raw data and spectral analysis results. This includes background interference within certain frequency ranges, such as white noise, which is an interference component unrelated to formation information. An amplitude threshold is set, and portions of the spectrum with amplitudes below this threshold are considered interference components unrelated to formation information. In this embodiment, the amplitude threshold is set to 5% of the average amplitude.

[0105] Step S32: After removing noise, convert the frequency domain signal of the seismic wave back to the time domain and perform static correction. After correction, convert it back to the frequency domain.

[0106] Specifically, the purpose of static correction is to suppress irrelevant signals and eliminate signal delays that may be caused by changes in formation velocities.

[0107] Static correction compensates for time delays caused by variations in formation velocity by measuring and correcting for velocity differences in signals from different formations after noise removal. By accurately estimating the velocities of different formations, the corresponding time delays can be calculated and applied to each data point in the seismic record to align the records. This eliminates the time delays caused by different formations, aligning the arrival times of the seismic records.

[0108] Step S33: Perform low-pass filtering on the statically corrected seismic wave frequency domain signal and transform it to the time domain to obtain the Stoneley wave; extract the Stoneley wave reflection wave using the median filtering method; extract the downlink wave from the reflection wave using the two-dimensional frequency filtering method.

[0109] The Stoneley wave signal was extracted from the statically corrected seismic frequency domain data using a low-pass filtering method.

[0110] Specifically, a low-pass filter with a cutoff frequency of 6kHz is designed to allow the main energy range of the Stoneley wave signal to pass through. The filtered frequency domain data is then subjected to an inverse transform to convert the data back to the time domain. The data after the inverse transform is the Stoneley wave signal. The obtained Stoneley wave includes both reflected and direct waves.

[0111] Specifically, the extracted Stoneley wave signal is processed using median filtering to separate the reflected Stoneley wave from the direct wave.

[0112] A median filter is designed with the period of the reflected Stoneley wave as the window size. The period of the reflected wave is obtained from the time-domain signal. By sorting the voltage amplitude data in each time window and selecting the median value as the output of the median filter, high-frequency noise and non-stationary signal components are suppressed. This filters out the reflected wave, high-frequency noise, and other non-stationary components from the Stoneley wave, while retaining the energy of the direct wave. By subtracting the median-filtered data from the Stoneley wave signal, the reflected Stoneley wave signal can be obtained, thus completing the separation of the reflected wave from the direct wave.

[0113] The Stoneley wave reflected signal was separated into uplink and downlink reflected waves using a two-dimensional frequency filtering method.

[0114] In the frequency domain, the ascending and descending waves are separated by selecting appropriate filtering functions or bandpass filters. Specifically, the passband range of the filtering function or bandpass filter can be set according to the dominant frequency range of the ascending and descending waves to filter out the remaining frequency data. An inverse Fourier transform is then performed on the filtered frequency domain data to convert the data back to the time domain. The data after the inverse transform is the separated ascending and descending waves. Equal-offset gathers are extracted from the descending waves for determining the location, development level, or lithology of fractures in the well.

[0115] Step S34: After extracting the downlink wave using equal offset gathers, the Stoneley wave profile is obtained.

[0116] The downwave data processed in step S33 is subjected to equal-offset gather extraction to obtain the Stoneley wave profile, as shown below. Figure 6 As shown.

[0117] By moving the transmitting and receiving sensors as a whole, and shifting them downwards by a distance 'a' (i.e., the distance between measuring points) each time, multiple seismic source excitations are performed from different locations to collect a series of seismic wave records. Stoneley wave profiles are obtained by extracting equal offset gathers. The range of the shift distance 'a' is 0.4-2.4m. In this embodiment, the time-depth conversion method is used to convert time into distance to form the Stoneley wave profile.

[0118] The distance between the vibration receiving sensor and the location of the vibration source is called the offset distance.

[0119] The data is offset using an offset algorithm. For each offset distance, a group of downlink waves with the same offset distance are superimposed to obtain an equal-offset channel. The resulting equal-offset channel profile is shown below. Figure 7 As shown, the vertical axis represents depth, and the horizontal axis represents time. Time can be converted to radial distance using the following methods: Obtain subsurface velocity model data, such as formation velocity, from well logging data, geological exploration data, etc. Based on the data, derive the functional relationship of sound wave velocity at different distances to form a velocity model. Based on the velocity model and the arrival time of the statically corrected seismic wave data, convert the time values ​​on the seismic profile into the time it takes for the seismic wave to propagate underground. Integrate the velocity values ​​in the velocity model with this time to convert the time in the Stoneley wave profile into radial distance.

[0120] Step S4: Normalize the downlink wave to obtain the Stoneley wave energy spectrum.

[0121] Obtain the maximum voltage data of the downlink reflected Stoneley wave extracted in step S33, and divide the voltage data of the downlink wave by the maximum voltage data extracted to obtain the normalized Stoneley wave energy spectrum.

[0122] Step S5: Obtain the apparent resistivity based on the collected electrical resistivity measurement raw data; establish a resistivity inversion model; optimize the resistivity inversion model using the apparent resistivity to obtain the optimized resistivity inversion model; obtain the resistivity based on the optimized resistivity inversion model; and then obtain the resistivity profile.

[0123] The apparent resistivity is obtained based on the raw electrical resistivity measurement data acquired in step 13, including data export, preprocessing, and calculation of apparent resistivity. Specifically, this includes steps S51-S53.

[0124] Step S51: Export the raw data collected from multiple electrodes according to the specified data format based on the acquisition device model. This is typically in CSV format. Select the valid dataset within the detection area for export, and specify the name and save location of the exported file.

[0125] Step S52: Preprocess the exported raw electrical measurement data.

[0126] The preprocessing of raw electrical measurement data mainly judges the accuracy of the signal by whether the signal conforms to the trend that the voltage is higher when it is closer to the power supply electrode and lower when it is farther away from the power supply electrode, and distorted points that do not conform to this trend are removed.

[0127] Step S53: Calculate the apparent resistivity at the corresponding location in the well based on the preprocessed electrical resistivity measurement data.

[0128] Specifically, the apparent resistivity is calculated by using electrical methods to measure the received voltage and received current in the raw data.

[0129] The arithmetic mean of the apparent resistivity obtained from each measurement at the same depth and radial distance within the well is taken as the apparent resistivity data for that location. The difference between the voltage measurement data of the two measuring electrodes is divided by the current measurement data to obtain the apparent resistivity at the corresponding point. The ordinate of this point is the average vertical depth of the two measured electrodes, and the abscissa is half the distance between the two measured electrodes. Thus, the apparent resistivity at each location in the well can be calculated.

[0130] Step S54: Establish a resistivity inversion model, optimize the resistivity inversion model using apparent resistivity to obtain an optimized resistivity inversion model, obtain the resistivity based on the optimized resistivity inversion model, and then obtain the resistivity profile.

[0131] Step S541: Establish a resistivity inversion model, identify the lithology based on the Stoneley wave spectrum, and set the initial resistivity based on the identified lithology.

[0132] Specifically, based on the Stoneley wave spectrum obtained in step S22, the lithology is initially determined according to the signal amplitude value. If the peak value of the signal amplitude in the Stoneley wave spectrum is greater than the first voltage threshold V1, and the frequency components of the Stoneley wave signal are relatively simple with few side lobes, it is initially determined to be a sandstone layer. If the peak value of the signal amplitude in the Stoneley wave spectrum is less than the second voltage threshold V2, and the frequency components of the signal are complex with many side lobes, it is initially determined to be a mudstone layer, where V1>V2.

[0133] Based on the "three-zone" theory of the overlying strata, the sandstone-mudstone interbedded layer, the sandstone fracture zone, and the sandstone fracture zone usually occur together, so the number of layers is set to three.

[0134] Shallower strata are typically more influenced by surface measurement data; therefore, using smaller layer thicknesses in these shallower layers simplifies the inversion calculations. For deeper strata, however, information transmission is relatively weaker due to data attenuation; therefore, setting progressively increasing layer thicknesses can improve the stability of the inversion. Empirically, layer thicknesses of 1.2-1.5m can match most strata conditions, and setting the initial layer thickness to 1.2m can reduce the computational load of the inversion.

[0135] The present invention is as follows Figure 7 In one embodiment shown, the modeling parameters are as follows: the number of horizontal rock layers is set to 3; the resistivity of each layer is 1 Ω·m, 5 Ω·m, and 10 Ω·m, respectively; the thickness of the first layer, h0, is 1.2 m, and the thickness of each subsequent layer is: h n =h0×1.2 n In the Stoneley wave spectrum, the alternating sandstone and mudstone in the 460-480 meter depth range indicates an interbedded sandstone and mudstone layer. Since the resistivity of mudstone is lower than that of sandstone, the resistivity of the interbedded sandstone and mudstone layer is the lowest among the three layers. In the second layer of sandstone fracture zone and the third layer of sandstone fracture zone, the fracture zone usually has a higher degree of fracture development than the fracture zone. Under the same conditions, the resistivity of the fracture zone is higher. Therefore, the resistivity of the second layer is the middle value among the three layers, and the resistivity of the third layer is the highest among the three layers.

[0136] Step S542: Based on the resistivity inversion model, calculate the theoretical value of underground resistivity using digital simulation methods.

[0137] Step S543: Input the apparent resistivity obtained from the collected electrical resistivity measurement raw data as the true value and the theoretical value of underground resistivity calculated by the resistivity inversion model into the error function. Minimize the error function result through the inversion algorithm; optimize the parameters of the resistivity inversion model so that the difference between the theoretical value and the apparent resistivity is less than the set error threshold ξ. th The optimized resistivity inversion model was obtained.

[0138] Specifically, the inversion algorithm includes: regularization algorithm, gradient descent algorithm, and simulated annealing algorithm.

[0139] Preferably, a regularized inversion algorithm is used to regularize the data to improve the stability and reliability of the inversion results. An objective function is constructed, and the objective function is minimized to find the optimal resistivity inversion model. The specific steps are as follows:

[0140] The resistivity inversion model *m* of the subsurface structure is optimized by measuring apparent resistivity data *d*, where the resistivity inversion model *m* includes the layer thickness *h* and resistivity *ρ* of each stratum. In the regularized inversion, the objective function consists of two parts: a data fitting term and a regularization term. The data fitting term measures the difference between the inversion model and the observed data. Specifically, the sum of squared data residuals is used as the data fitting term, which can be expressed as:

[0141] ||dG(m)|| 2

[0142] Where d is the measured apparent resistivity data, and G(m) is the resistivity data obtained from forward modeling based on the resistivity inversion model m. ||dG(m)|| represents the norm of the difference between the apparent resistivity data and the forward modeled resistivity data.

[0143] Regularization terms are used to constrain the inversion model to satisfy the properties of prior information or expectations. Using Tikhonov regularization, introducing the square of the L2 norm as a regularization term yields:

[0144] alpha×||L(m)|| 2

[0145] Where alpha is the regularization parameter, and its value ranges from 10. -3 -10 3 L(m) is the L2 norm of the inversion model m. Given the number of strata is 3, L(m) can be expressed as:

[0146] L(m)=(h0,ρ0,h1,ρ1,h2,ρ2)

[0147] Among them, h n and ρ n These are the layer thickness and resistivity of the nth stratum, respectively. Combining the data fitting term and the regularization term, we obtain the complete objective function:

[0148] J(m)=||dG(m)|| 2 +alpha×||L(m)|| 2

[0149] To minimize the objective function J(m), gradient descent is used. The optimization process continuously optimizes the model parameters h0, h1, h2 and ρ0, ρ1, ρ2 to gradually reduce the objective function until it reaches its minimum. During this process, the gradient of the objective function J(m) with respect to the parameters of the inverted model m needs to be calculated to update the parameters of model m in the gradient descent method. The gradient represents the direction of the fastest ascent of the objective function at the current point; therefore, the objective function value needs to be reduced along the opposite direction of the gradient. The gradient of the objective function with respect to the model parameters m can be expressed as:

[0150]

[0151] Where G'(m) and L'(m) are the Jacobian matrices of G(m) and L(m) with respect to the resistivity inversion model m, respectively.

[0152] Iterative optimization is performed using gradient descent. In each iteration, the gradient of the objective function with respect to each parameter of the resistivity inversion model m is calculated, and then the parameters of the resistivity inversion model m are updated.

[0153]

[0154] Where, m new For the updated new model, m old The model is the one from the previous iteration. K is the learning rate of gradient descent and represents the step size for each update, with a value ranging from 0.01 to 0.001.

[0155] Repeat the iterative steps. The error function used in this example is root mean square error (RMSE). When the RMS error is less than 10%, the model is considered to be consistent with the actual geological conditions, and the output model is determined. Based on the finally converged resistivity inversion model, the resistivity is calculated, and the resistivity distribution of the subsurface medium is interpreted to obtain information about the subsurface structure and properties.

[0156] Step S544: Obtain the resistivity based on the optimized resistivity inversion model, and plot the calculated resistivity onto a coordinate system with the wellhead as the origin and the well depth and radial distance around the well as the vertical and horizontal axes, respectively, to obtain a resistivity profile.

[0157] Step S6: Determine the geological state of the rock mass based on the Stoneley wave spectrum, Stoneley wave profile, energy spectrum, and resistivity profile.

[0158] The lithology and degree of fracture development can be determined by the Stoneley wave spectrum, or the lithology can be determined by the Stoneley wave profile; the location and degree of fracture development can be determined by the Stoneley wave profile and the Stoneley wave energy spectrum; the water-bearing state can be determined by the resistivity profile; and the geological state of the rock mass can be determined by combining two or more of the following information: lithology, fracture location and degree of development, Stoneley wave energy spectrum or water-bearing state.

[0159] Lithology is determined based on Stoneley wave spectrum, including:

[0160] If the peak amplitude value of the signal in the Stoneley wave spectrum is greater than the first voltage threshold V1, and the frequency components and sidelobe state of the signal conform to the characteristics of sandstone layer, then it is judged to be sandstone layer; if the peak amplitude value of the signal in the Stoneley wave spectrum is less than the second voltage threshold V2, and the frequency components and sidelobe state of the signal conform to the characteristics of mudstone layer, then it is judged to be mudstone layer, where V1>V2.

[0161] In a specific embodiment of the present invention, V1 = 5e +001 mV; V2=3.5e -001 The frequency components and sidelobe states of the sandstone layer signal are simpler, while those of the mudstone layer signal are relatively more complex.

[0162] Another method for determining the degree of fracture development is based on the Stoneley wave spectrum, including:

[0163] By extracting the Stoneley wave spectrum from adjacent sensors under the same seismic source, the attenuation coefficient calculated from the maximum amplitude difference of the Stoneley wave spectrum from adjacent sensors can preliminarily determine the degree of rock mass fracture development. An attenuation coefficient of 0.01dB / m-0.1dB / m indicates weak fracture development; an attenuation coefficient of 0.1dB / m-1dB / m indicates strong fracture development.

[0164] Lithology determination based on the Stoneleybo profile includes:

[0165] In the Stoneley wave profile, the amplitude of the Stoneley wave signal in the sandstone layer is on average 5-10 times larger than that in the mudstone layer. The lithology can be determined based on the amplitude in the profile.

[0166] like Figure 6 As shown in the Stoneley wave profile, it can be clearly seen that the mudstone layer absorbs Stoneley waves significantly, while the Stoneley wave signal amplitude in the sandstone layer is 5-10 times larger than the average. The lithology can be judged by the amplitude of the Stoneley wave profile.

[0167] Determining the location and development level of fractures based on Stoneley wave profiles and energy spectra, including:

[0168] In the Stoneley wave profile, waveform omissions and / or discontinuities correspond to cracks; the corresponding average amplitude of the Stoneley wave energy spectrum is less than the energy threshold E. th The fracture is strongly developed; otherwise, it is weakly developed.

[0169] Determining the water content based on resistivity profiles includes:

[0170] The maximum resistivity of the same sandstone layer is obtained from the resistivity profile. After normalizing the resistivity of the same sandstone layer, those below 0.3 are highly water-rich sandstone, those between 0.3 and 0.7 are moderately water-rich sandstone, and those above 0.7 are waterless sandstone.

[0171] The geological state of a rock mass is determined by combining two or more pieces of information, including lithology, fracture location and development degree, energy spectrum, or water-bearing state.

[0172] The alternating appearance of mudstone and sandstone indicates that the sandstone and mudstone are interbedded; the normalized energy of Stoneley waves in the sandstone is 0.1-0.8, which corresponds to the fracture zone, and the normalized energy is 0-0.6, which corresponds to the fracture zone.

[0173] Within a defined wellbore perimeter range (specifically within 5 meters), the Stoneley wave detection capability is strong enough to determine lithology, fracture location, and development level. Outside this range, because lithology doesn't change significantly laterally within tens of meters, any changes will be reflected in resistivity. For example, if mudstone transforms into sandstone, the resistivity will increase under otherwise unchanged conditions. Therefore, although the Stoneley wave detection capability is distance-limited, the lithology detected within the defined wellbore perimeter range can still be assessed. Specifically, this invention is applicable to a 15-meter wellbore perimeter range.

[0174] One specific embodiment of the present invention is as follows: Figure 7 As shown.

[0175] Figure 7 Box 1: Indicates sandstone with strong fractures and is waterless to moderately water-rich. The well depth is 498-503m, exhibiting relatively high resistivity of 1.2-1.6 Ω·m, normalized value of 0.6-0.8, partial loss of Stoneley wave, discontinuous waveform, and normalized average energy below 0.2.

[0176] Region B, located at a well depth of 497-506m, indicates sandstone with strong fracture development and low to moderate water content, gradually developing downwards. In the resistivity profile, the anomalous signal appears as a long, semi-elliptical strip, with its major axis indicating the direction of development; otherwise, it should be a regular semi-circle. The Stoneley wave signal has low energy, with a normalized average energy below 0.2, and the waveform is discontinuous. Region C, located at a well depth of 506-509m, shows a severe deficiency of the Stoneley wave signal, with its energy essentially zero and a normalized average energy below 0.1, indicating the development of large radial fractures, i.e., large fractures parallel to the bedding planes. Given the low resistivity of this region (0.2-0.5 Ω·m, normalized value 0.1-0.25), indicating strongly fractured, water-rich sandstone, consistent with the characteristics of a fracture zone, it is determined that the boundary between Region B and Region C, at a well depth of 506m, is the lower boundary of the fracture zone and the upper boundary of the fracture zone.

[0177] Box 2: Indicates waterless sandstone with weak overall fracture development in the fracture zone, except for area A. The well depth is 488m-497m, exhibiting high resistivity of 1.7-2Ω·m with a normalized value of 0.85-1. The Stoneley wave signal is strong with a normalized average energy greater than 0.3. Fractures exist near the borehole, but their radial depth is not large.

[0178] Box 3: Indicates moderately water-rich sandstone with strong fracture development. At a depth of 479-486m, the resistivity decreases to 1.2-1.3 Ω·m, with a normalized value of 0.6-0.65. The Stoneley wave signal shows leakage and discontinuity. Based on the degree of fracture development and lithology, it is inferred that the depth of 480-483m represents the upper boundary of the fracture zone.

[0179] Box 4: Indicates sandy mudstone with strong fractures in the overlying delamination region. The well depth is 458-463m, exhibiting locally low resistivity values ​​of 0.8-1.2 Ω·m, partial loss of Stoneley waves, and normalized average energy below 0.3.

[0180] Ellipse 5: Indicates that beyond 10m around the well, the upper part of the fracture zone has well-developed sandstone fractures and moderate water abundance, exhibiting relatively high resistivity of 1.3-1.4 Ω·m, with a normalized value of 0.65-0.7.

[0181] Ellipse 6 indicates that the lower part of the fracture zone beyond 5m around the well is characterized by strong sandstone fracture development, with water-free to moderately water-rich conditions, exhibiting relatively high resistivity of 1.3-1.7 Ω·m (normalized value 0.65-0.85). Since it belongs to the same fracture zone, its overall properties will have similarities, and the upper and lower parts are not entirely inconsistent. However, the lower part of this fracture zone has a higher degree of fracture development than the upper part, while the overall condition is water-free to moderately water-rich. Therefore, the higher degree of fracture development is reflected in the lower part, which contains areas with even higher resistivity, i.e., the 1.4-1.7 Ω·m region (normalized value 0.7-0.85). The degree of fracture development in the lower part is gradual, unlike abrupt changes at the interface; therefore, the lower part includes a transitional zone with properties not significantly different from the upper part. Beyond 5m, resistivity is the primary interpretation factor. Stoneley waves are mainly used to qualitatively characterize the degree of fracture development and determine lithology within 5m; therefore, Stoneley wave energy was not included in this area.

[0182] Compared to existing technologies, the Stoneley wave and resistivity combined detection system provided in this embodiment first uses Stoneley wave information to identify lithology and the degree of radial fracture development within a defined wellbore range. This information serves as a constraint to establish a resistivity data interpretation standard, leveraging the high-precision identification of radial fractures by the Stoneley wave method to improve the accuracy of the resistivity method. Outside the defined wellbore range, the lithology determined within that range is used, utilizing the tens of meters that the resistivity method can identify to determine the geological state of the rock mass in that section. The system optimizes the resistivity parameters' ability to identify fracture zones, improving detection accuracy and interpretation effectiveness. After resistivity normalization, values ​​below 0.3 are classified as strongly water-bearing sandstone, values ​​between 0.3 and 0.7 as moderately water-bearing sandstone, and values ​​above 0.7 as anhydrous sandstone. By combining resistivity data with the interpretation of water content, it can identify water-bearing sandstones that may pose safety hazards. The alternating occurrence of mudstone and sandstone indicates interbedded sandstone and mudstone; the normalized energy of Stoneley waves in the sandstone, ranging from 0.1 to 0.8, corresponds to fracture zones, while a normalized energy of 0 to 0.6 corresponds to breccia zones. This approach effectively integrates rock mass structure detection with lithological identification, while improving the interpretation results.

[0183] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0184] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A combined Stoneley wave and resistivity detection system for wells, characterized in that, The system includes: The first observation device for Stoneley wave detection in wells, the second observation device for electrical resistivity data detection in wells, and the host computer; The first observation device includes a Stoneley wave acquisition station located on the ground, a transmitting sensor embedded in a cable metal slot, and multiple receiving sensors; The Stoneley wave acquisition station is used to acquire and receive seismic wave data received by the receiving sensors and send it to the host computer. The Stoneley wave acquisition station includes an acquisition device, which transmits the received seismic wave data from each receiving sensor to the host computer via a cable. The acquisition device includes a wireless communication module, a data storage module, and a data transmission interface. The wireless communication module is used to transmit seismic wave data with the sensors. The storage module is used to store the seismic wave data acquired from the receiving sensors. The data transmission interface is used to connect to the cable and transmit the received seismic wave data from each receiving sensor to the host computer for processing via the cable. The second observation device includes a high-density electrical resistivity tomography (ERT) instrument arranged at the wellhead, as well as a power supply electrode B, a reference electrode N, and multiple measuring electrodes. The high-density ERT instrument is connected to the power supply electrode B arranged outside the well in the drilling site and the reference electrode N arranged in another direction outside the well. The high-density ERT instrument is also connected to a cable that extends vertically into the well, and multiple electrodes are arranged on the cable through slots. The raw electrical resistivity measurement data obtained by the electrodes is transmitted to the high-density ERT instrument via the cable, and the high-density ERT instrument transmits the raw electrical resistivity measurement data to the host computer via a data cable. The host computer obtains the geological conditions based on the acquired seismic wave data and raw electrical resistivity tomography data, including: The Stoneley wave spectrum was extracted based on the collected seismic wave data; After frequency domain transformation of the seismic data, interference components are filtered out to obtain the interference-free seismic frequency domain signal. The interference-free seismic frequency domain signal is then converted to the time domain for static correction, and the down-going wave in the reflected wave of the Stoneley wave is extracted. The Stoneley wave profile is obtained after equal offset gather extraction of the down-going wave. The Stoneley wave energy spectrum is obtained by normalizing the downflow wave. Apparent resistivity is obtained based on the collected raw data from electrical resistivity measurements; A resistivity inversion model is established, and the resistivity inversion model is optimized using apparent resistivity to obtain an optimized resistivity inversion model. Based on the optimized resistivity inversion model, the resistivity is obtained, and then a resistivity profile is obtained. The geological state of the rock mass is determined based on the Stoneley wave spectrum, Stoneley wave profile, energy spectrum, and resistivity profile.

2. The Stoneley wave and resistivity combined detection system according to claim 1, characterized in that, During testing, the first observation device moves from the wellhead to the bottom of the well. Each time a test is conducted, the transmitting sensor and multiple receiving sensors are moved downwards by a preset distance.

3. The Stoneley wave and resistivity combined detection system according to claim 1, characterized in that, In the second observation device, the electrode distance between each measuring electrode is 2 meters. After each round of measurement, all electrodes are moved from the bottom of the well to the surface via cables, with each movement step being 30 meters.

4. The Stoneley wave and resistivity combined detection system according to claim 1, characterized in that, The method of determining the geological state of a rock mass based on Stoneley wave spectrum, Stoneley wave profile, energy spectrum, and resistivity profile includes: The lithology and degree of fracture development can be determined by the Stoneley wave spectrum, or the lithology can be determined by the Stoneley wave profile. The location and degree of fracture development can be determined by the Stoneley wave profile and the Stoneley wave energy spectrum. The water-bearing state can be determined by the resistivity profile. The geological state of the rock mass can be determined by combining two or more of the following information: lithology, fracture location and degree of development, Stoneley wave energy spectrum or water-bearing state.

5. The Stoneley wave and resistivity combined detection system according to claim 4, characterized in that, Lithology is determined based on Stoneley wave spectrum, including: If the peak amplitude value of the signal in the Stoneley wave spectrum is greater than the first voltage threshold V1, and the frequency components and sidelobe state of the signal conform to the characteristics of sandstone layer, then it is judged to be sandstone layer; if the peak amplitude value of the signal in the Stoneley wave spectrum is less than the second voltage threshold V2, and the frequency components and sidelobe state of the signal conform to the characteristics of mudstone layer, then it is judged to be mudstone layer, where V1>V2.

6. The Stoneley wave and resistivity combined detection system according to claim 5, characterized in that, Determining the location and development level of fractures based on Stoneley wave profiles and energy spectra, including: In the Stoneley wave profile, waveform omissions and / or discontinuities correspond to cracks; the corresponding average amplitude of the Stoneley wave energy spectrum is less than the energy threshold E. th The fracture is strongly developed; otherwise, it is weakly developed.

7. The Stoneley wave and resistivity combined detection system according to claim 4, characterized in that, The method of determining the water content based on the resistivity profile includes: The maximum resistivity of the same sandstone layer is obtained from the resistivity profile. After normalizing the resistivity of the same sandstone layer, those below 0.3 are highly water-rich sandstone, those between 0.3 and 0.7 are moderately water-rich sandstone, and those above 0.7 are waterless sandstone.

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