Shale reservoir fracture and cement analysis method based on nuclear magnetic resonance technology

Abstract

The invention discloses a shale reservoir crack and cement analysis method based on a nuclear magnetic resonance technology, and relates to the technical field of geological prospecting, and the method comprises the following steps: obtaining a shale reservoir core sample, and cleaning and drying the core sample; the processed rock core sample is placed in a nuclear magnetic resonance spectrometer, measurement parameters are set, nuclear magnetic resonance data are collected, and the collected data comprise transverse relaxation time distribution and longitudinal relaxation time distribution; the collected nuclear magnetic resonance data are preprocessed, and noise interference is removed; according to the shale reservoir fracture and cement analysis method provided by the invention, the shale reservoir core sample is obtained, and the nuclear magnetic resonance technology is utilized to perform data acquisition and analysis, so that the porosity of the core sample can be accurately calculated, the existence condition of the fracture and the cement can be accurately judged, and the fracture volume can be further calculated; the components and the content of the cement are analyzed, so that the analysis accuracy and the analysis reliability are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of geological exploration, and particularly to a method for analyzing fractures and cements in shale reservoirs based on nuclear magnetic resonance technology. Background Art

[0002] In the field of oil exploration and development, the analysis of fractures and cements in shale reservoirs is a key link in evaluating reservoir quality and predicting oil and gas production. Fractures not only affect the permeability of the reservoir but also serve as important channels for oil and gas migration, while the type and content of cements are directly related to the stability of the reservoir and the pore structure. Traditional analysis methods, such as microscopic observation and X-ray diffraction, although they can provide certain information, often have problems such as complex operation, long time consumption, and destructiveness to samples, making it difficult to meet the requirements of high efficiency and accuracy in modern oil and gas exploration.

[0003] With the progress of technology, nuclear magnetic resonance technology has gradually been applied to shale reservoir analysis due to its advantages of non-destructiveness, high resolution, and the ability to directly reflect pore structure and fluid properties. However, when the existing nuclear magnetic resonance technology is applied to the analysis of fractures and cements in shale reservoirs, many challenges still remain. On the one hand, the complexity of shale reservoirs makes the nuclear magnetic resonance signals of fractures and cements interfere with each other, making it difficult to accurately distinguish; on the other hand, under different geological conditions, the relaxation characteristics of fractures and cements may vary, and the existing analysis methods often lack pertinence, resulting in deviation of analysis results. In addition, the existing technology is also difficult to accurately model the three-dimensional distribution of fractures and cements in shale reservoirs, thus limiting its in-depth application in oil and gas exploration. For this reason, we propose a method for analyzing fractures and cements in shale reservoirs based on nuclear magnetic resonance technology. Summary of the Invention

[0004] To solve the above technical problems, a method for analyzing fractures and cements in shale reservoirs based on nuclear magnetic resonance technology is provided, and this technical solution solves the above problems.

[0005] To achieve the above object, the technical solution adopted by the present invention is as follows: A method for analyzing fractures and cements in shale reservoirs based on nuclear magnetic resonance technology, comprising: Obtaining a core sample of a shale reservoir, and performing cleaning and drying treatments on the core sample; Placing the processed core sample in a nuclear magnetic resonance instrument, setting measurement parameters, and collecting nuclear magnetic resonance data. The collected data includes transverse relaxation time distribution and longitudinal relaxation time distribution; Preprocessing the collected nuclear magnetic resonance data to remove noise interference, and converting the data into an analyzable format through an algorithm; Calculate the porosity of the core sample based on the transverse relaxation time distribution data. By analyzing the peak position and amplitude of the transverse relaxation time distribution, and combining the characteristic relaxation time ranges of fractures and cements, determine the presence of fractures and cements; Calculate the fracture volume for fractures. For cements, by comparing the nuclear magnetic resonance data under different pretreatment conditions and combining the relaxation characteristics of known cements, analyze the composition and content of cements; Based on all the analysis results, establish a distribution model of fractures and cements in the shale reservoir.

[0006] Preferably, the cleaning treatment includes a three-stage cleaning process, and the specific implementation steps are as follows: Use toluene solvent for continuous soaking treatment at a constant temperature of 45 ± 1 °C for 24 hours. The treatment container is equipped with a magnetic stirring device to maintain a rotation speed of 200 rpm. The treatment termination condition is detected by a UV-visible spectrophotometer, and the solvent transmittance reaches more than 90%, and the detection wavelength λ = 400 nm; Use an analytical pure methanol solution in a 40 kHz ultrasonic cleaner for 30 minutes. A sample fixing rack is set in the cleaning tank to ensure that the axial direction of the core forms a 45° angle with the ultrasonic propagation direction to enhance the cleaning effect; Adopt a deionized water circulation rinsing system, control the water flow rate at 0.5 m / s ± 5%, and continuously monitor the conductivity of the drain until the detection value is stable below 5 μS / cm for 3 consecutive minutes; The drying treatment is carried out in a vacuum drying oven. The temperature control adopts a three-stage gradient heating program: the initial stage is maintained at 50 °C for 2 hours to evaporate the surface moisture, the second stage is maintained at 65 °C for 4 hours to remove the adsorbed water, and the third stage is vacuum dried at 80 °C for 12 hours until the mass change rate ≤ 0.05% / h, and the vacuum degree is maintained at -0.095 MPa ± 2%.

[0007] Preferably, the setting of the nuclear magnetic resonance parameters includes the following optimized configurations: Set the magnetic field strength to 0.3 T ± 3% and use dynamic shimming technology to make the magnetic field uniformity reach the full width at half maximum of the FID signal ≤ 50 Hz; Echo spacing is determined according to the formula: , In the formula, the gyromagnetic ratio , the gradient field strength , the effective pore radius is obtained by measuring the initial value through the pre-experiment mercury intrusion method; The waiting time TW is set to 6 seconds ± 10% according to the maximum longitudinal relaxation time T1max, which is determined by the inversion recovery method through preliminary experiments. The specific operation includes setting 16 recovery time points for signal acquisition, logarithmically distributed from 0.1 ms to 10 s. The number of signal acquisition channels is set to 32, and the number of accumulations per channel is dynamically adjusted to the baseline noise ≤ 0.5 mV according to the signal-to-noise ratio formula SNR ∝ √Navg.

[0008] Preferably, the data preprocessing includes the following precise processing procedures: Noise filtering uses an improved wavelet packet transform algorithm: select the db8 wavelet basis function for 6-layer decomposition, and dynamically calculate the thresholds for each layer. The formula for the threshold is: , In the formula, the noise variance is obtained by statistical analysis of the highest frequency subband coefficients, the number of sampling points , and the maximum echo amplitude is taken as the average value of the first 10 echoes; Spectral inversion uses the regularized least squares method with non-negativity constraints. The regularization parameter is automatically selected by the L-curve inflection point method. The construction of the kernel matrix K uses 200 logarithmically distributed T2 time points. The stop condition for the inversion iteration is set to the residual norm change rate ≤ 0.1% / step or the maximum number of iterations is 100 times; The post-processing of the inversion results includes applying a Gaussian smoothing filter to eliminate artifacts and removing minor peaks with amplitudes less than 1% of the maximum peak height.

[0009] Preferably, the crack volume calculation uses a multi-dimensional correction model, and the specific expression is: , In the formula, the morphological correction coefficient is obtained by statistically analyzing 100 groups of samples with different crack morphologies through CT scans. The temperature correction factor is used to compensate for the influence of the experimental temperature T on the surface relaxation intensity; The calculation of the crack aperture introduces surface roughness correction, and the expression is: , In the formula, takes 1.2 μm / ms, and the roughness factor is graded and assigned values between 1.2 and 1.8 according to the SEM image analysis results, and 1.0 is the theoretical smooth surface reference value.

[0010] Preferably, the cement analysis includes establishing a mineral characteristic database and a quantitative analysis algorithm. The specific implementation is as follows: Prepare standard samples containing single minerals in the laboratory to obtain their characteristics Response spectra and establish interval division criteria. The standard samples include: 20 groups each of quartz, calcite, and illite. Requirements: Quartz corresponds to 0.1 - 0.5 ms, quartz cementation coefficient ; Calcite corresponds to 0.5 - 1.2 ms, calcite cementation coefficient ; Illite corresponds to 1.2 - 2.0 ms, illite cementation coefficient ; During sample analysis, use the constrained least squares method for spectral decomposition. The expression of the constrained least squares method is: , In the formula, represents the measured rock sample's relaxation spectrum amplitude distribution function, represents the characteristic reference spectrum of the nth type of standard mineral, represents the weight coefficient of the nth type of mineral in the mixed spectrum; The weight coefficient i.e., the corresponding mineral content ratio. The final cement content calculation formula is: , And set the confidence index as the result validity criterion.

[0011] Preferably, the construction of the distribution model adopts multi-source data fusion technology, specifically including: Perform three-dimensional registration on NMR data and micro-CT scan results through an improved ICP algorithm, with the registration accuracy requirement ≤ 5 μm; Construct the pore network model using the maximum sphere algorithm, set the minimum sphere radius rmin = 0.1 μm, and the throat identification threshold dth = 0.05 μm; Calculate the permeability using the modified Hagen-Poiseuille equation: , In the formula, represents the absolute permeability, represents the effective porosity of the rock sample, represents the tortuosity factor. The tortuosity τ is simulated and calculated by the random walk method, representing the nth throat radius, represents the nth throat length. The throat length is taken from the three-dimensional topological analysis result, Denotes the axial length of the rock sample; The model verification adopts the material balance method, and it is required that the deviation between the calculated pore volume and the measured value is ≤ 3%.

[0012] Preferably, the data verification method includes establishing a three - level quality control system: The first level uses a quartz sand - epoxy resin composite standard sample for instrument calibration, with a porosity of 12% ± 0.5% and a fracture density of 2 fractures / cm³, and it is required that The geometric mean relative error ; The second level implements three - repeated measurements of the same sample, and the relative standard deviation of the fracture porosity detection results ; The third level sets an automatic identification rule for abnormal data. When it is detected that the value mutation exceeds 15% or the single - point jump of the cement content exceeds 5%, the micro - CT re - inspection process is automatically triggered, and the correlation coefficient is required when comparing the re - inspection result with the original data .

[0013] Preferably, it includes multi - scale data fusion analysis: At the micro - scale, NMR relaxation spectrum analysis is combined with SEM image processing to establish the relationship between the pore surface fractal dimension and the distribution slope as: , where, Denotes the pore surface fractal dimension, Denotes the cumulative area of the relaxation spectrum; At the meso - scale of 1 - 100 Integrate micro - CT scan data, extract the fracture network skeleton through image segmentation algorithms, and calculate the fracture connectivity ; At the macro - scale > 100 Combine well - logging data to establish a conversion model , R² ≥ 0.92, and apply the Kriging interpolation method to generate a three - dimensional geological model.

[0014] Preferably, it includes a temperature - pressure coupling correction module: For formation conditions: temperature T = 20 - 150 °C, pressure P = 5 - 60 MPa, develop a porosity correction model expression as: , where, Denotes the corrected porosity, Denotes the measured porosity, Denotes the formation temperature, Denotes the laboratory reference temperature, represents the formation pressure, represents the laboratory reference pressure, represents the temperature correction coefficient, represents the pressure correction coefficient; where the temperature coefficient =-2.3×10-4 / ℃ is calibrated through experiments in a high-temperature and high-pressure chamber, and the pressure coefficient =5.6×10-3 / MPa is obtained from triaxial compression test data; The surface relaxation strength correction adopts the Arrhenius formula, the activation energy Ea = 15 kJ / mol is determined through variable-temperature NMR experiments, and the gas constant R = 8.314 J / (mol·K).

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: The method for analyzing fractures and cements in shale reservoirs proposed by the present invention can accurately calculate the porosity of core samples, accurately judge the presence of fractures and cements, further calculate the fracture volume, and analyze the composition and content of cements by obtaining core samples of shale reservoirs and using nuclear magnetic resonance technology for data collection and analysis. The technical means such as the multi-dimensional correction model, mineral characteristic database, and quantitative analysis algorithm greatly improve the accuracy and reliability of the analysis. By constructing a distribution model of multi-source data fusion technology, an intuitive display of the three-dimensional distribution of fractures and cements in shale reservoirs is realized, providing more detailed geological information for the exploration and development of oil and gas resources. The established three-level quality control system and temperature-pressure coupling correction module ensure the stability and credibility of the analysis results, effectively avoid the interference of abnormal data, and improve the accuracy and efficiency of the analysis. Description of the Drawings

[0016] Figure 1 is the method flow chart of the present invention. Detailed Embodiments

[0017] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments in the following description are only examples, and those skilled in the art can think of other obvious variations.

[0018] Referring to Figure 1 as shown, a method for analyzing fractures and cements in shale reservoirs based on nuclear magnetic resonance technology includes: Obtain core samples from the shale reservoir, which are the basis for subsequent analysis. To ensure the purity of the samples and avoid interference from impurities, the core samples need to be carefully cleaned to remove the attached soil, impurities, etc. on the surface, and then dried to an appropriate dryness.

[0019] Carefully place the processed core samples into the nuclear magnetic resonance instrument. According to the characteristics of shale reservoirs and the analysis requirements, accurately set the measurement parameters, and then carry out the nuclear magnetic resonance data acquisition work. During this process, the collected data mainly includes the transverse relaxation time distribution and the longitudinal relaxation time distribution, and these data contain key information about the internal structure of shale reservoirs.

[0020] The collected data may be interfered by noise, so it needs to be preprocessed. Through professional denoising algorithms, remove the noise to make the data more accurate and reliable. At the same time, use specific algorithms to convert the data into a format convenient for subsequent analysis.

[0021] Based on the transverse relaxation time distribution data, the porosity of the core samples can be accurately calculated. In this process, by carefully analyzing the peak position and amplitude of the transverse relaxation time distribution, and combining the characteristic relaxation time ranges of fractures and cements, it is possible to accurately judge the presence of fractures and cements.

[0022] For the fractures that have been determined to exist, further calculate their fracture volumes; for cements, by comparing the nuclear magnetic resonance data under different pretreatment conditions and combining the relaxation characteristics of known cements, deeply analyze the composition and content of cements.

[0023] Based on all the above analysis results, establish a distribution model of fractures and cements in shale reservoirs, providing strong support for comprehensively understanding the internal structure and characteristics of shale reservoirs.

[0024] The cleaning treatment includes a three-level cleaning process, and the specific implementation steps are as follows: Use toluene solvent for continuous soaking treatment at a constant temperature of 45 ± 1 °C for 24 hours. The treatment container is equipped with a magnetic stirring device to maintain a rotation speed of 200 rpm. The treatment termination condition is detected by a UV-visible spectrophotometer, and the solvent transmittance reaches more than 90%, and the detection wavelength λ = 400 nm; Use an analytical pure methanol solution to treat in a 40 kHz ultrasonic cleaner for 30 minutes. A sample fixing rack is set in the cleaning tank to ensure that the axial direction of the core forms a 45° angle with the ultrasonic propagation direction to enhance the cleaning effect; Adopt a deionized water circulation rinsing system, control the water flow rate at 0.5 m / s ± 5%, and continuously monitor the conductivity of the drainage outlet until the detection value is stable below 5 μS / cm for 3 consecutive minutes; The drying process is carried out in a vacuum drying oven, and the temperature control adopts a three-stage gradient heating program: in the initial stage, it is maintained at 50 °C for 2 hours to evaporate the surface moisture; in the second stage, it is kept at 65 °C for 4 hours to remove the adsorbed water; in the third stage, it is vacuum dried at 80 °C for 12 hours until the mass change rate ≤ 0.05% / h, and the vacuum degree is maintained at -0.095 MPa ± 2%. In the three-stage cleaning process, constant-temperature soaking in toluene combined with magnetic stirring can effectively dissolve stubborn oil and dirt impurities, and the cleaning effect is ensured by detecting the light transmittance of the solvent. Methanol ultrasonic cleaning, with a specific angle setting to enhance the cleaning strength, removes tiny attachments. Rinsing with deionized water combined with conductivity monitoring ensures no residual ions. The three-stage gradient heating for drying can not only effectively evaporate different types of water but also avoid damaging the core samples due to sudden temperature changes. The control of the vacuum degree makes the drying more efficient, ensures the stability of the sample quality, provides pure and stable core samples for subsequent nuclear magnetic resonance analysis, and guarantees the accuracy of the data.

[0025] The setting of the nuclear magnetic resonance parameters includes the following optimized configurations: The magnetic field strength is set to 0.3 T ± 3%, and the magnetic field uniformity is made to reach the full width at half maximum of the FID signal ≤ 50 Hz through dynamic shimming technology; Echo spacing is determined according to the formula: , wherein, the gyromagnetic ratio , the gradient field strength , and the effective pore radius is obtained through pre-experiment mercury injection method to measure the initial estimate; The waiting time TW is set to 6 s ± 10% according to the maximum longitudinal relaxation time T1max, and this T1max value is determined by the inversion recovery method through pre-experiment. The specific operation includes setting 16 recovery time points for signal acquisition, with a logarithmic distribution from 0.1 ms to 10 s; The number of signal acquisition channels is set to 32, and the number of accumulations for each channel is dynamically adjusted according to the signal-to-noise ratio formula SNR ∝ √Navg until the baseline noise ≤ 0.5 mV. The precise magnetic field strength and shimming technology ensure stable and accurate signals; the echo spacing is determined according to the formula, fitting the sample characteristics; the waiting time is set according to T1max to fully recover the longitudinal magnetization; multiple signal acquisition channels and dynamic adjustment of the number of accumulations improve the signal-to-noise ratio, reduce noise interference, and comprehensively guarantee the data quality, facilitating the precise analysis of fractures and cements in shale reservoirs.

[0026] The data preprocessing includes the following precise processing procedures: Noise filtering adopts an improved wavelet packet transform algorithm: select the db8 wavelet basis function for 6-layer decomposition, and dynamically calculate the thresholds for each layer. The formula for the threshold is: , where the noise variance is obtained by statistically analyzing the highest-frequency sub-band coefficients, the number of sampling points , and the maximum echo amplitude is the average value of the first 10 echoes; Spectral inversion uses the regularized least squares method with non-negativity constraints. The regularization parameter is automatically selected by the L-curve inflection point method. The kernel matrix K is constructed using 200 logarithmically distributed T2 time points. The inversion iteration stop condition is set as the change rate of the residual norm ≤ 0.1% / step or the maximum number of iterations of 100 times; The post-processing of the inversion results includes applying a Gaussian smoothing filter to eliminate artifacts and removing minor peaks with amplitudes less than 1% of the maximum peak height. The improved wavelet packet transform algorithm can accurately filter out noise and retain the effective signal to the greatest extent. The regularized least squares method with non-negativity constraints combined with automatic parameter selection makes spectral inversion more accurate. The post-processing of the inversion eliminates artifacts and simplifies the results through smoothing filtering and removing small peaks, making the data clearer and more reliable, providing a high-quality basis for subsequent analysis.

[0027] The crack volume calculation uses a multi-dimensional correction model, and the specific expression is: , where the morphological correction coefficient is obtained by statistically analyzing 100 groups of samples with different crack morphologies through CT scanning. The temperature correction factor is used to compensate for the influence of the experimental temperature T on the surface relaxation intensity; The calculation of the crack aperture introduces surface roughness correction, and the expression is: , where takes 1.2 μm / ms, and the roughness factor is graded and assigned values between 1.2 and 1.8 according to the analysis results of SEM images, and

[0028] The cement analysis includes establishing a mineral characteristic database and a quantitative analysis algorithm, and the specific implementation is: By preparing standard samples containing a single mineral in the laboratory, obtaining their characteristic response spectra and establishing interval division criteria. The standard samples include: 20 groups each of quartz, calcite, and illite, and the requirements are: Quartz corresponds to 0.1 - 0.5 ms, quartz cementation coefficient ; Calcite corresponds to 0.5 - 1.2 ms, calcite cementation coefficient ; Illite corresponds to 1.2 - 2.0 ms, illite cementation coefficient ; During sample analysis, constrained least squares method is used for spectral decomposition. The expression of the constrained least squares method is: , In the formula, represents the relaxation spectrum amplitude distribution function of the measured rock sample, represents the characteristic reference spectrum of the th type of standard mineral, weight coefficient i.e., the corresponding mineral content ratio. The final calculation formula for the cement content is: , And a confidence index is set as the criterion for judging the result validity. A mineral characteristic database is established, and the response spectrum intervals of each mineral are clarified through standard samples to provide an accurate reference for analysis. The quantitative analysis algorithm uses the constrained least squares method for spectral decomposition, which can effectively separate the mixed spectrum to determine the mineral weight coefficient, i.e., the content ratio. Setting the confidence criterion ensures the result validity and helps to accurately analyze the cement composition and content.

[0029] The construction of the said distribution model adopts multi-source data fusion technology, specifically including: Performing three-dimensional registration on the NMR data and the micro-CT scan results through an improved ICP algorithm, with the registration accuracy requirement ≤ 5 μm; The pore network model is constructed using the maximum ball algorithm, with the minimum sphere radius rmin = 0.1 μm and the throat identification threshold dth = 0.05 μm set; The permeability calculation applies the modified Hagen-Poiseuille equation: , In the formula, represents the absolute permeability, represents the effective porosity of the rock sample, represents the tortuosity factor. The tortuosity τ is calculated by simulating the random walk method, represents the radius of the th One throat length, the throat length Taken from the results of three-dimensional topological analysis Indicates the axial length of the rock sample The material balance method is used for model verification, and it is required that the deviation between the calculated pore volume and the measured value is ≤ 3%

[0030] Multi-source data fusion, using the improved ICP algorithm to achieve high-precision registration of NMR and micro-CT data. The maximum sphere algorithm constructs an accurate pore network model. The modified equation calculates permeability considering multiple factors. The material balance method verifies the model, strictly controls the pore volume deviation, and ensures that the model can accurately reflect the distribution characteristics of fractures and cements in shale reservoirs

[0031] The data verification method includes establishing a three-level quality control system In the first level, a quartz sand-epoxy resin composite standard sample is used for instrument calibration, with a porosity of 12% ± 0.5% and a fracture density of 2 fractures / cm³, and it is required that Geometric mean relative error ; In the second level, the same sample is measured three times repeatedly, and the relative standard deviation of the fracture porosity detection results ; In the third level, an automatic abnormal data recognition rule is set. When it is detected that The value mutation exceeds 15% or the single-point jump of the cement content exceeds 5%, the micro-CT re-inspection process is automatically triggered, and the correlation coefficient is required for the comparison between the re-inspection results and the original data .

[0032] In the first level, the instrument is calibrated with a standard sample to ensure the accuracy of the measurement basis. In the second level, the same sample is measured repeatedly to test the stability of the results. In the third level, an abnormal recognition rule is set and the re-inspection is triggered to timely discover and correct the deviation. Comprehensively guarantee the data quality, making the shale reservoir analysis based on this data more reliable and the conclusion more accurate

[0033] Including multi-scale data fusion analysis Microscopic scale Using NMR relaxation spectrum analysis combined with SEM image processing to establish the relationship between the pore surface fractal dimension And The distribution slope is as follows , In the formula Represents the pore surface fractal dimension Represents The cumulative area of the relaxation spectrum Mesoscopic scale 1 - 100 Integrating micro-CT scan data, extracting the fracture network skeleton through image segmentation algorithms, and calculating the fracture connectivity ; Macroscopic scale > 100 Establish a conversion model by combining well logging data , R²≥0.92, and apply Kriging interpolation method to generate a three-dimensional geological model. At the microscopic scale, combine NMR and SEM to reveal pore characteristics; at the mesoscopic scale, analyze fracture connectivity through micro-CT scanning and image algorithms; at the macroscopic scale, establish a model by combining well logging data and interpolate to generate a three-dimensional geological model. Each scale complements each other to comprehensively and deeply characterize the shale reservoir characteristics, providing strong support for accurate analysis.

[0034] Including a temperature-pressure coupling correction module: For formation conditions: temperature T = 20 - 150 °C, pressure P = 5 - 60 MPa, develop a porosity correction model expression as: , In the formula, represents the corrected porosity, represents the measured porosity, represents the formation temperature, represents the laboratory reference temperature, represents the formation pressure, represents the laboratory reference pressure, represents the temperature correction coefficient, represents the pressure correction coefficient; Among them, the temperature coefficient =-2.3×10-4 / °C is calibrated through high-temperature and high-pressure chamber experiments, and the pressure coefficient =5.6×10-3 / MPa is taken from triaxial compression test data; The surface relaxation strength correction adopts the Arrhenius formula , the activation energy Ea = 15 kJ / mol is determined through variable-temperature NMR experiments, and the gas constant R = 8.314 J / (mol·K). It develops a porosity correction model for specific formation temperature and pressure conditions, calibrates the temperature and pressure coefficients through experiments, and effectively corrects the measured porosity. At the same time, it corrects the surface relaxation strength with the Arrhenius formula and determines the activation energy. This module can accurately restore the true formation conditions, provide more practical data for shale reservoir analysis, and improve the analysis accuracy.

[0035] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection required by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for analyzing shale reservoir fractures and cements based on nuclear magnetic resonance technology, characterized in that: include: Obtain shale reservoir core samples, and clean and dry the core samples; The processed core sample is placed in a nuclear magnetic resonance instrument, and measurement parameters are set to collect nuclear magnetic resonance data, where the collected data include transverse relaxation time distribution and longitudinal relaxation time distribution; Preprocess the collected NMR data to remove noise interference and convert the data into an analyzable format through algorithms; The porosity of the core samples is calculated based on the transverse relaxation time distribution data. The presence of fractures and cements is determined by analyzing the peak position and amplitude of the transverse relaxation time distribution and combining the characteristic relaxation time range of fractures and cements. For cracks, the volume of the cracks is calculated. For cementing materials, the composition and content of the cementing materials are analyzed by comparing the NMR data under different pretreatment conditions and combining the relaxation characteristics of known cementing materials. By integrating all analysis results, a distribution model of shale reservoir fractures and cements is established.

2. The method for analyzing shale reservoir fractures and cements based on nuclear magnetic resonance technology according to claim 1, characterized in that: The cleaning process includes a three-stage cleaning process, and the specific implementation steps are: The toluene solvent was used for continuous immersion treatment at a constant temperature of 45±1°C for 24 hours. The treatment container was equipped with a magnetic stirring device to maintain a rotation speed of 200 rpm. The treatment termination condition was that the solvent transmittance reached more than 90% by UV-visible spectrophotometer, and the detection wavelength λ=400nm; Use analytical grade methanol solution to treat in a 40kHz ultrasonic cleaning machine for 30 minutes. Set a sample holder in the cleaning tank to ensure that the core axis is at an angle of 45° to the ultrasonic propagation direction to enhance the cleaning effect. A deionized water circulation flushing system is used, the water flow rate is controlled at 0.5m / s±5%, and the conductivity of the drain outlet is monitored in real time until the detection value is stable below 5μS / cm for 3 consecutive minutes; The drying process was carried out in a vacuum drying oven, and the temperature was controlled using a three-stage gradient heating program: the initial stage was maintained at 50°C for 2 hours to evaporate the surface moisture, the second stage was maintained at 65°C for 4 hours to remove the adsorbed water, and the third stage was vacuum drying at 80°C for 12 hours until the mass change rate was ≤0.05% / h, and the vacuum degree was maintained at -0.095MPa±2%.

3. The method for analyzing shale reservoir fractures and cements based on nuclear magnetic resonance technology according to claim 1, characterized in that: The NMR parameter setting includes the following optimization configurations: The magnetic field strength was set to 0.3T±3% and the magnetic field uniformity was made to reach the FID signal half-width ≤50Hz through dynamic shimming technology; Echo interval The determination is based on the formula: ， In the formula, the gyromagnetic ratio , gradient field strength , effective pore radius An initial estimate was obtained by preliminary experimental mercury intrusion porosimetry measurements; The waiting time TW was set to 6 seconds ± 10% according to the maximum longitudinal relaxation time T1max, which was determined by the inversion recovery method in a preliminary experiment. The specific operation included setting 16 recovery time points for signal acquisition, which were logarithmically distributed from 0.1 ms to 10 s; The number of signal acquisition channels is set to 32, and the accumulation times of each channel are dynamically adjusted to a baseline noise ≤ 0.5 mV according to the signal-to-noise ratio formula SNR∝√Navg.

4. The method for analyzing shale reservoir fractures and cements based on nuclear magnetic resonance technology according to claim 1, characterized in that: The data preprocessing includes the following precise processing flow: The noise filtering adopts the improved wavelet packet transform algorithm: the db8 wavelet basis function is selected for 6-layer decomposition, and the threshold of each layer is dynamically calculated. The calculation formula of the threshold is: ， In the formula, the noise variance Obtained through statistics of the highest frequency sub-band coefficients, the number of sampling points , maximum echo amplitude Taken from the average of the first 10 echoes; The spectral inversion uses a regularized least squares method with a non-negative constraint, and the regularization parameter The kernel matrix K was constructed using 200 logarithmically distributed T2 time points through the automatic selection of the L-curve inflection point method, and the inversion iteration stop condition was set to the residual norm change rate ≤ 0.1% / step or the maximum number of iterations 100 times; Post-processing of the inversion results included applying a Gaussian smoothing filter to remove artifacts and removing minor peaks with amplitudes less than 1% of the maximum peak height.

5. The method for analyzing shale reservoir fractures and cements based on nuclear magnetic resonance technology according to claim 1, characterized in that: The fracture volume calculation adopts the multi-repair correction model, and the specific expression is: ， In the formula, the morphology correction coefficient is The temperature correction factor was obtained by CT scanning and statistics of 100 groups of samples with different crack shapes. Used to compensate for the effect of experimental temperature T on surface relaxation intensity; The surface roughness correction is introduced into the calculation of crack opening, and the expression is: ， In the formula, Take 1.2μm / ms, roughness factor According to the SEM image analysis results, the values ​​are graded between 1.2-1.

8. The theoretical smooth surface reference value is 1.

0.

6. The method for analyzing shale reservoir fractures and cements based on nuclear magnetic resonance technology according to claim 1, characterized in that: Cement analysis includes the establishment of a mineral feature database and quantitative analysis algorithm, which is specifically implemented as follows: Prepare standard samples containing a single mineral in the laboratory to obtain its characteristics The response spectrum and interval division standards are established. The standard samples include 20 groups of quartz, calcite and illite, and the requirements are: Quartz corresponds to 0.1-0.5ms, quartz cementation coefficient ; Calcite corresponds to 0.5-1.2ms, calcite cementation coefficient ; Illite corresponds to 1.2-2.0ms, illite cementation coefficient ; When analyzing the sample, the constrained least squares method is used for spectral decomposition. The constrained least squares method expression is: ， In the formula, Indicates the measured rock sample Relaxation spectrum amplitude distribution function, Indicates Characteristics of standard minerals Reference spectrum, Indicates The weight coefficient of the mineraloid in the mixed spectrum; Weight coefficient That is, corresponding to the mineral content ratio, the final cement content calculation formula is: ， And set the confidence index as a criterion for the effectiveness of the results.

7. The method for analyzing shale reservoir fractures and cements based on nuclear magnetic resonance technology according to claim 1, characterized in that: The distribution model is constructed by using multi-source data fusion technology, which specifically includes: The NMR data and micro-CT scan results are 3D registered using an improved ICP algorithm, with a registration accuracy requirement of ≤5μm; The maximum sphere algorithm was used to construct the pore network model, with the minimum sphere radius rmin = 0.1 μm and the throat identification threshold dth = 0.05 μm; The permeability calculation uses the modified Hagen-Poiseuille equation: ， In the formula, represents the absolute permeability, Indicates the effective porosity of the rock sample. represents the tortuosity factor, and the tortuosity τ is simulated and calculated by the random walk method. Indicates The throat radius, Indicates throat length, throat length Taken from the results of 3D topological analysis, Indicates the axial length of the rock sample; The model is verified using the material balance method, which requires that the deviation between the calculated pore volume and the measured value be ≤3%.

8. The method for analyzing shale reservoir fractures and cements based on nuclear magnetic resonance technology according to claim 1, characterized in that: The data verification method includes establishing a three-level quality control system: The first level uses quartz sand-epoxy resin composite standard samples for instrument calibration, with a porosity of 12%±0.5% and a crack density of 2 / cm³. Geometric mean relative error ; The second level implements three repeated measurements on the same sample, and the relative standard deviation of the fracture porosity test results is ; The third level sets the rules for automatic identification of abnormal data. When the value mutation exceeds 15% or the single point jump of cement content exceeds 5%, the micro-CT re-inspection process is automatically triggered, and the re-inspection result is compared with the original data with the correlation coefficient. .

9. The method for analyzing shale reservoir fractures and cements based on nuclear magnetic resonance technology according to claim 1, characterized in that: Including multi-scale data fusion analysis: Microscopic scale The fractal dimension of the pore surface was established by combining NMR relaxation spectrum analysis with SEM image processing. and The relationship for the slope of the distribution is: ， In the formula, represents the fractal dimension of the pore surface, express Relaxation spectrum cumulative area; Mesoscopic scale 1-100 Integrate micro-CT scanning data, extract the crack network skeleton through image segmentation algorithm, and calculate the crack connectivity rate ; Macro scale>100 Establishing conversion model by combining well logging data , R²≥0.92, and the Kriging interpolation method was used to generate a three-dimensional geological model.

10. The method for analyzing shale reservoir fractures and cements based on nuclear magnetic resonance technology according to claim 1, characterized in that: Including temperature and pressure coupling correction module: According to the formation conditions: temperature T = 20-150 ° C, pressure P = 5-60 MPa, the porosity correction model expression is developed as follows: ， In the formula, represents the corrected porosity, represents the measured porosity, represents the formation temperature, represents the laboratory reference temperature, Indicates the formation pressure, Indicates the laboratory reference pressure, represents the temperature correction coefficient, Indicates the pressure correction factor; The temperature coefficient =-2.3×10-4 / ℃ Calibrated by high temperature and high pressure chamber experiment, pressure coefficient =5.6×10-3 / MPa taken from triaxial compression test data; The surface relaxation intensity was corrected using the Arrhenius formula , the activation energy Ea = 15 kJ / mol was determined by variable temperature NMR experiments, and the gas constant R = 8.314 J / (mol·K).

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