92 results about "Mud logging" patented technology

A method for estimating a quantity of natural hydrogen in a subterranean formation includes degassing drilling fluid obtained from a wellbore to obtain a gas sample including a quantity of hydrogen gas, measuring a concentration of hydrogen in the gas sample, and applying a correction to the measured concentration of hydrogen to estimate the quantity of natural hydrogen in the subterranean formation.

The invention belongs to the technical field of oil-gas geological exploration and development, and relates to an oil-gas phase state type identification method and system, and the method comprises the steps: obtaining logging data and logging data of a target layer of a research area; selecting a phase prediction index of a target layer of the research area based on the logging data, and selecting a plurality of sample points of known phase types in the research area based on the logging data; calculating the principal component of the target layer according to the phase state prediction index, and establishing a geography-survey-recording integrated phase state prediction chart according to the plurality of sample points and the calculated principal component plumbing point; according to the gas logging response value of the hydrocarbon component of the oil and gas sample, determining the division standard of the oil and gas phase state type, and superposing the division standard to the geography-logging integrated phase state prediction chart to form a phase state prediction chart containing the critical phase state type; and according to the phase state prediction chart containing the critical phase state type, identifying the oil-gas phase state type of the target layer in the research area. According to the invention, the oil-gas phase state type of the oil-gas reservoir can be accurately identified, and the subdivision of the critical state type is realized.

The invention relates to the technical field of geological rock type identification, and discloses a deep carbonate rock lithology identification method based on gamma-ray spectrum logging, and the method comprises the steps: S1, collecting a large number of rock cores, slices and the like as verification data; s2, performing statistics on slice identification results of 4, 6, 7 and 8 well carbonate rock coring layer sections of the ancient city in the research area, and dividing the carbonate rock in the research area into five types, namely grit limestone, mud crystal, bright crystal grit limestone, mud crystal limestone, cloud limestone and dolomite; and S3, calibrating on a single well histogram, and counting gamma-ray energy spectrum (U, K, TH, PE) values corresponding to the slice samples. According to the method provided by the invention, the lithology of the carbonate rocks in the fourth, sixth, seventh and eighth wells of the ancient city is identified with relatively high accuracy, fine lithology changes are further identified in the originally explained large set of limestone, the accuracy is far higher than that of logging and debris logging through rock core inspection, and the accuracy of identifying the lithology of the carbonate rocks is greatly improved.

An annulus monitoring-based kick / loss monitoring system, comprising: an annulus monitoring assembly (107), configured to monitor annulus monitoring information at a fixed depth within a drilling annulus, wherein the annulus monitoring information comprises an annulus fluid velocity measurement value and / or fluid composition information of an annulus fluid; and a controller (101), coupled with the annulus monitoring assembly and configured to: receive the annulus monitoring information and mud logging data; determine drilling conditions associated with kick / loss monitoring; select a corresponding kick / loss monitoring model on the basis of the drilling conditions; determine a theoretical annulus fluid velocity value at a specified depth on the basis of the kick / loss monitoring model; determine whether the annulus fluid velocity measurement value falls within a predetermined range of the theoretical annulus fluid velocity value; and in response to the annulus fluid velocity measurement value deviating beyond the predetermined range of the theoretical annulus fluid velocity value, determine the presence of a kick or loss.

The present invention relates to a method for calculating the buried hill formation pressure based on the coupling of logging and mud logging binary data, which mainly includes the following steps: S1 Collect logging parameters and mud logging parameters; S2 Simulate the actual formation pressure based on the Dc index, drilling efficiency - mechanical specific energy monitoring, gas logging auxiliary judgment, measured formation pressure data, etc.; S3 Use the unary linear regression method to draw the intersection diagram between the logging data and the formation pressure value, and obtain the unary fitting equation between the formation pressure value and the logging parameters; S4 Use the binary linear regression method to draw the intersection diagram between the logging-mud logging binary data and the formation pressure value, and obtain the binary fitting equation between the formation pressure value and the logging-mud logging parameters; S5 Integrate the binary fitting equation between the formation pressure value and the logging-mud logging parameters, and establish a buried hill formation pressure calculation model based on the logging-mud logging binary coupling data.

The invention discloses a formation pressure monitoring method and device and a formation pressure monitoring model construction method and device. The method comprises the steps that by collecting logging information and logging information of at least one adjacent well of a target block, the actual formation pressure of the adjacent well is determined according to the logging information of the adjacent well; determining logging parameters influencing the formation pressure according to the logging data of the adjacent well; optimal sensitive logging parameters are obtained from the determined logging parameters, and a deep learning network model is established in combination with the optimal sensitive logging parameters; and inputting a pre-established pressure data set into the deep learning network model for training, and using the trained deep learning network model as a formation pressure monitoring model and putting the formation pressure monitoring model into practical application. The actual formation pressure can be predicted more accurately according to the established formation pressure monitoring model, the precision and accuracy of formation pressure prediction are improved, the time of drilling work is shortened, the drilling efficiency is improved, and the practical value is achieved.

The invention relates to a pressure-controlled drilling formation pressure calculation and control method and system based on multi-field coupling, and belongs to the field of oil exploration and development. The method comprises the steps that 1, an industrial personal computer is connected with a logging instrument, and basic data needed by calculation are obtained in real time; 2, shaft-stratum space domain grids are divided, and drill rod and annulus temperature distribution and stratum temperature distribution at different distances from a borehole are calculated; 3, considering the thermal-fluid-solid multi-field coupling influence, and calculating well circumference stresses at different well depth positions in elastic-plastic regions; 4, solving a real-time formation pore pressure profile after multi-factor coupling correction; 5, calculating real-time stratum fracture pressure profiles and collapse pressure profiles at the well walls at the different well depth positions after multi-factor coupling correction in the elastic-plastic areas; and 6, determining a bottom hole pressure control standard, calculating in real time based on a shaft hydraulic parameter calculation model, and controlling the wellhead back pressure in real time through an industrial personal computer. The method can effectively improve the calculation precision of the formation pressure during the pressure-controlled drilling period.

This application relates to the field of data technology and discloses an intelligent processing method for mud logging data. The method includes: collecting data through multi-source mud logging equipment and preprocessing to form a result set; identifying and classifying formation characteristics according to the result set; analyzing oil and gas parameters based on the formation atlas and the result set; establishing an early warning mechanism by identifying risks using the oil and gas evaluation table and the result set; evaluating reservoir physical properties according to the oil and gas evaluation table and the early warning mechanism; integrating the formation atlas, the oil and gas evaluation table and the reservoir report to reconstruct the geological structure and predict the enrichment area. This application realizes the full-process intelligent processing from the acquisition and preprocessing of multi-source heterogeneous mud logging data to formation identification, oil and gas evaluation, drilling risk early warning, reservoir evaluation and oil and gas reservoir description, overcomes problems such as data spatio-temporal mismatch, isolated parameter analysis, strong subjectivity in evaluation, and lagging early warning in traditional methods, and improves the efficiency, accuracy and systematicness of mud logging data processing.

The invention discloses a cleaning device and method, and the cleaning device comprises a cleaning main body which is internally provided with a cavity; the ultrasonic assembly is installed outside the cleaning body and used for emitting ultrasonic waves into the cleaning body; the bubble generator is mounted in the cavity and used for providing bubbles; the heating assembly is used for heating liquid in the cavity; the liquid inlet, the liquid circulating outlet, the rock debris sample inlet and the sand discharge port are all mounted on the cleaning main body and are communicated with the cavity; the stock solution outlet is provided with a stock solution electromagnetic valve, the sand discharge port is provided with a sand discharge electromagnetic valve, and the liquid inlet can be communicated with one of a synthetic base stock solution pipeline, a cleaning agent pipeline or a liquid circulation outlet of the drilling system; and the controller is connected with the ultrasonic assembly, the bubble generator, the stock solution electromagnetic valve and the sand discharging electromagnetic valve. According to the cleaning device provided by the invention, the automation level of cleaning the synthetic base logging rock debris is greatly improved, and a foundation is laid for realizing rock debris identification and stratum evaluation.

The invention discloses a mechanical drilling rate prediction method of a bidirectional LSTM model based on attention enhancement, and the method comprises the steps: selecting an open source data set from a Norway Volve oil field, carrying out the preprocessing, and enabling all data to be logging data based on a depth domain; establishing a mechanical drilling speed prediction model by using an attention-based bidirectional LSTM network, and training the model to obtain an optimized mechanical drilling speed prediction model; according to the invention, by introducing an attention mechanism, the model can automatically identify and preferentially process data which has great influence on a prediction result, so that the prediction accuracy and efficiency are remarkably improved. Compared with a traditional one-way LSTM, the two-way LSTM can consider past and future data information at the same time, and a more effective solution is provided for processing time series data with complex nonlinear characteristics.

The invention discloses a method for recognizing the batch stand column connecting and detaching working condition of a drilling machine through logging real-time data. The method comprises the steps that batch stand column connecting and detaching working condition samples in drilling historical data are counted; determining working condition charts of batched upright post connection and batched upright post disassembly; the normalized working condition charts of batch connection and batch disassembly of the stand columns are obtained; obtaining a standard comparison chart of the working conditions of batch connection and batch disassembly of the stand columns; obtaining height wave crest and wave trough values of the hook; obtaining possible batched connection and disassembly stand column charts in the real-time data; calculating the similarity of the two by using a Pearson's correlation coefficient method; if the similarity meets the judgment requirement, the captured window working condition is accurately recognized as the corresponding batched stand column connecting and disassembling working condition. According to the method, a specific rule is set to identify batch connection and batch disassembly data windows; a standard comparison plate of batch connection and batch disassembly is established, and accurate recognition of batch connection and batch disassembly of the stand columns is achieved by comparing real-time data with the standard comparison plate.

The invention discloses a thin reservoir horizontal well geosteering method based on real-time adjustment of a three-dimensional model, which is used for predicting lithology of sand bodies between wells and geosteering the horizontal well by normalizing gamma curves of logging and logging, and comprises the following steps of: 1, normalizing natural gamma curve data of the horizontal well while drilling obtained from logging; the strain is named as GRL; 2, normalizing a logging natural gamma curve according to the mudstone baseline value of the natural gamma curve, and naming the logging natural gamma curve as GRC; 3, performing data inspection on the GRL curve and the GRC curve of the same horizontal well, and splicing the two inspected curves of the horizontal well into GRU; according to the method, in the aspect of mismatching of the data obtained by the logging measurement method and the logging measurement method, the accuracy of the logging measurement data is improved through normalization processing; the matching of the logging data increases the transverse information of the continuous drilling sand body of the horizontal section, and greatly improves the accuracy of the inter-well sand body prediction.

The present invention relates to a method for calculating formation pressure by coupling multi-source data based on machine learning, comprising the following steps: S1 Collect logging data and mud logging data of multiple wells; S2 Simulate the actual formation pressure based on the Dc index, drilling efficiency - mechanical energy ratio monitoring, gas logging auxiliary judgment, measured pressure data, etc.; S3 Establish a univariate fitting equation between the formation pressure and the logging data; S4 Establish a bivariate fitting equation between the formation pressure and the logging - mud logging parameters; S5 Establish a formation pressure calculation model based on the logging - mud logging bivariate coupling data; S6 Calculate the formation pressure based on the formation pressure calculation model of the logging - mud logging bivariate coupling data; S7 Correct the formation pressure obtained in S6; S8 Establish a thermal map of the logging - mud logging parameters of each well; S9 Based on the results shown in the thermal map, eliminate variables using the correlation principle to obtain the final logging - mud logging parameter data set; S10 Establish a formation pressure calculation model based on the coupling of multi-source logging and mud logging data.

Embodiments of the invention relate to methods, systems, and software for identifying and quantifying subsurface hydrogen, helium, carbon dioxide, or other fluids using multiple indicia from geophysical well logs, other wireline logging tools, or mudlogging tools.

The invention discloses a logging instrument drilling fluid outlet flow monitoring system and method based on machine vision, and belongs to the technical field of drilling fluid detection. Collecting data; carrying out image convolution processing; extracting features; calculating a visual modal visibility probability; calculating a flow modal probability; calculating a pressure modal probability; determining a comprehensive cutoff probability based on multi-modal probability fusion; and determining a risk level and a hierarchical response mechanism based on the comprehensive cutoff probability. According to the method, three heterogeneous modes of vision, flow and pressure are fused, so that the problem that the reliability in the prior art is still difficult to meet the real-time decision-making requirement under the complex working condition is solved, the system reliability under the severe working condition is improved, and the technical effect of reducing the false alarm rate and the missing report rate is achieved.

The invention belongs to the technical field of oil and gas exploration and development, and discloses an oil well production initial stage yield splitting method based on logging gas chromatography data, electronic equipment and a medium, and the method comprises the following steps: establishing a standard interpretation evaluation chart marked with a water content interval boundary by using four pieces of water logging degree related data I obtained by early stage oil testing production; and determining the moisture content interval of each single layer according to the position where the water logging degree related data II of each single layer of the to-be-split layer falls into the standard interpretation and evaluation chart, and then performing splitting. According to the method, the influence of different water logging degrees on the single-layer water content is fully considered, the splitting result is closer to the real situation, the accuracy is higher, the application scene of electronic equipment and media based on the method is widened, and the method is suitable for oil well yield splitting so as to evaluate the single-layer remaining oil potential.

The application discloses a kind of paragenetic metamorphic rock weathering crust weathering alteration index calculation method, comprising: the mass percentage of element of mud logging cuttings is determined;The mass percentage of element is normalized;The mass percentage of element after normalization is converted into oxide molar ratio;According to the change trend of element oxide molar ratio, the relative loss and relative enrichment trend of various elements in paragenetic metamorphic rock weathering crust is judged;According to the trend of element relative loss or enrichment, the easily soluble element and the element easy to remain are determined, and the new paragenetic metamorphic rock weathering crust weathering alteration index calculation formula is fitted;The molar ratio of element oxide is brought into the new calculation formula established, according to the size of the weathering alteration index value calculated, paragenetic metamorphic rock weathering crust is identified, and paragenetic metamorphic rock weathering crust structure is divided.The application provides important technical support for oil and gas basin paragenetic metamorphic rock weathering crust reservoir research.

The application discloses a method for calculating a dynamic threshold value based on an MLP network to evaluate a risk of pipe sticking, and comprises the following steps: collecting real-time mud logging data on site; calculating a theoretical value of hook load and a theoretical value of torque by using a soft rod model; using an MLP neural network model to predict a dynamic threshold value of hook load and a dynamic threshold value of torque in combination with the mud logging data, the theoretical value of hook load and the theoretical value of torque; calculating a risk factor of pipe sticking; and monitoring a drilling state in real time, and issuing a warning in time according to the value of the risk factor to provide a scientific decision basis for an on-site operator. The application has the advantages of high calculation precision, strong adaptability and simple on-site operation, can self-adaptively identify the risk of pipe sticking, provides a scientific decision basis for the on-site operator, and effectively improves drilling efficiency and safety.

The present invention discloses a lost circulation warning method based on the PCA-XGBOOST algorithm, which includes the following steps: S1. Establish a comprehensive mud logging time series matrix, and mark the comprehensive mud logging time series data according to different working conditions based on the status information of drilling operations. The normal working condition is marked as 0, and the data under the lost circulation accident is marked as 1; S2. Data preprocessing, eliminating the outliers in the comprehensive mud logging time series data through the 3σ criterion, and performing smoothing processing on the comprehensive mud logging time series data after elimination by using the Savitzky-Golay filter. The present invention combs the outliers in the comprehensive mud logging time series by using the 3σ criterion; the present invention uses the principal component analysis algorithm to rank the importance of the parameter features of the comprehensive mud logging time series, and using these strongly correlated features extracted for subsequent prediction can effectively avoid the occurrence of overfitting and the problems of insufficient memory and slow calculation caused by too large data volume, and improve the operation efficiency.

The present invention discloses a method for evaluating the fracturability based on the mass fractal of rock fragmentation blocks. This method obtains the rock mechanical parameters of the core through triaxial core experiments; secondly, by using the fragmented core blocks after fracturing and introducing the mass fractal theory, the fracturability of the reservoir is characterized by the mass fractal dimension; finally, combining the rock mechanical parameters obtained from the experiments, the relationship between the rock mechanical parameters and the mass fractal dimension is fitted through multiple regression analysis. Then, by using data such as drilling, logging, and mud logging, the rock mechanical parameter profile along the wellbore is obtained, and the continuous fractal dimension along the wellbore is calculated through the fitted relationship for comprehensive fracturability evaluation. The fracturability evaluation method provided by the present invention combines the advantages of experimental evaluation and comprehensive evaluation, and can provide theoretical guidance for the selection of sweet spots in exploration and development projects.

The present invention provides a method for identifying apparent strike-slip faults with multi-phase tectonic stress field superposition. The method for identifying apparent strike-slip faults with multi-phase tectonic stress field superposition includes: Step 1, based on the logging and mud logging data of the drilled wells, conducting fine structural interpretation of the standard layer; Step 2, performing characterization of the direction conversion of the multi-phase tectonic stress field; Step 3, performing characterization of the changes in the strike-slip component and the extension component; Step 4, identifying the nature of the apparent strike-slip faults. The method for identifying apparent strike-slip faults with multi-phase tectonic stress field superposition systematically analyzes from three aspects of geometry, kinematics and dynamics, and takes the ratio of the calculated fault extension component to the strike-slip component as the basis for identifying apparent strike-slip faults, with high practical value and strong operability.

The invention belongs to the field of geological sedimentology, and particularly relates to a storm cause high-quality lithofacies identification method based on multi-source data comprehensive identification, which comprises the following steps of: A, data acquisition and preprocessing: S1, data acquisition and confirmation: automatically identifying the characteristics similar to a Baomah sequence in a rock core by using a convolutional neural network (CNN) according to the rock core, a field outcrop, a logging curve and logging data; outputting storm deposition sample information; s2, performing automatic lithofacies classification on the rock core image by adopting deep learning (U-Net) in combination with a sedimentary structure; and S3, automatically identifying curve characteristics of a'box-shaped '(storm dam main body) and'bell-shaped and finger-shaped' (storm dam side edge) by adopting time sequence analysis (LSTM or Transformer). According to the method, the high-quality lithofacies recognition model is established by integrating multivariate data, the recognition precision and efficiency are greatly improved, and a scientific basis is provided for oil and gas reservoir evaluation in a storm sedimentary environment.

The utility model provides a comprehensive logging air compressor convenient to install, and relates to the technical field of comprehensive logging air compressors, the comprehensive logging air compressor comprises a movable air compressor, the outer surface of the movable air compressor is fixedly connected with a connecting hose, the end, away from the movable air compressor, of the connecting hose is fixedly connected with an installation pipe, and the installation pipe is fixedly connected with an air inlet pipe. A comprehensive logging instrument external pipe is mounted at one end, far away from the connecting hose, of the mounting pipe; a mounting mechanism is arranged outside the mounting pipe and comprises a rotating pipe rotationally connected to the outer surface of the mounting pipe, four hinge seats fixedly connected to the outer surface of the mounting pipe and arranged circumferentially and four moving frames arranged circumferentially. According to the convenient-to-mount air compressor for comprehensive logging, through the arrangement of the mounting mechanism, the mounting pipe and the external connecting pipe of the comprehensive logging instrument can be quickly mounted while the sealing performance is guaranteed, and compared with an existing bolt mounting mode, the situation that multiple bolts are screwed slowly and repeatedly is avoided, and the mounting efficiency is improved.

The present application relates to a kind of mud logging rock chip oil-based mud cleaning device, wherein the filter assembly of circulating filter module is connected with circulating pool on one side, and the first circulating pipeline with vibration screen nozzle and stirring sand nozzle of output end is arranged on the other side, and vibration screen nozzle is arranged above vibration screen;Vibration screen is provided with first output port, and stirring lifting mechanism, rotating frame, spin-drying component and sample cylinder are arranged in the processing cavity of processing module, and the first output port and stirring sand nozzle are all aligned with sample cylinder located in material receiving station;After sample cylinder receives material, the stirring shaft of stirring lifting mechanism is lowered into the sample cylinder;The upper pressing mechanism of spin-drying component includes rotating disc and pressing disc connected by spring, and after lower support rotating disc rises, it is held sample cylinder in cooperation with pressing disc, then pressing disc is driven to rotate by rotating disc;Vibration screen and processing cavity are all communicated with rotating screen, and rotating screen outlet is arranged above circulating pool.The present application realizes oil-based mud cleaning and recycling of cleaning fluid.

Methods (and related apparatus) include obtaining data regarding a measured property. The measured property includes an amount of each of predetermined hydrocarbons in a gas sample extracted from drilling fluid exiting a wellbore having a hydrocarbon resource. An unknown characteristic of an investigated fluid property of the hydrocarbon resource is predicted utilizing the obtained input data and one or more predetermined models each built via statistical classification and regression analysis of a preexisting database containing records. Each record includes known characteristics of fluid properties of a different one of known reservoir fluids. The fluid properties include the investigated fluid property and the measured property. The investigated fluid property includes a fluid type of the hydrocarbon resource, an amount of at least one additional hydrocarbon, gas-oil ratio, or stock tank oil density.

The invention discloses a well leakage risk early warning method and device, and the method comprises the steps: collecting logging data in real time when a drilling tool micro-element is emptied in drilling operation, and carrying out time-depth conversion to obtain logging data for analysis; obtaining a well leakage judgment index value of a first preset window length from the well logging data for analysis; calculating a well leakage judgment index base value according to the well leakage judgment index value of the first preset window length; for each acquisition point of a second preset window length, calculating the deviation between each well leakage judgment index value and the well leakage judgment index base value, and obtaining a first well leakage judgment result according to the current well depth, the deviation corresponding to all the acquisition points, the first well leakage judgment threshold value and the second well leakage judgment threshold value; if the first well leakage judgment result is that the suspicious well leakage exists in the current shaft, whether well leakage early warning needs to be sent out or not is judged according to the hook height corresponding to the current well depth and the maximum value of the historical hook height. According to the invention, shaft leakage-prone points can be found in time.

The invention relates to the technical field of detection methods, in particular to a logging equipment gas concentration detection method which comprises the following steps: SI, detection preparation and primary treatment; s2, flow measurement and flow path control; s3, standard gas is introduced, and a gas path is cleaned; s4, pressure control and output; according to the detection method, by using the flow sensor and the needle valve, the flow of air entering the detection system can be accurately controlled, it is ensured that the air enters the detection system at a constant flow, and therefore detection errors caused by flow fluctuation are reduced, hydrocarbon compounds and moisture in the air are effectively removed by introducing the hydrocarbon remover and the dryer, and the detection accuracy is improved. The method avoids the influence of the impurities on the detection result, can adapt to the detection requirements of different gases through the steps of standard gas introduction and gas path cleaning, also can quickly calibrate and verify the accuracy of the detection system, greatly reduces the time required for calibration and verification, also can avoid cross contamination, and improves the detection efficiency. And the long-term stable operation of the detection system is ensured.

The utility model provides a portable hydrogen generator for comprehensive logging, which relates to the technical field of hydrogen generating equipment and comprises a hydrogen generator body, the hydrogen generator body comprises a water inlet arranged at the top of the hydrogen generator body, and a water injection stirring mechanism is arranged on the side surface of the hydrogen generator body. According to the portable hydrogen generator for comprehensive logging, water and potassium hydroxide can be poured in through a water storage barrel with a large opening and are not prone to scattering, in the ascending process of a mounting frame, a circular gear and a toothed plate are meshed to rotate, the circular gear sequentially drives a driving bevel gear, a driven bevel gear, a stirring shaft and a stirring plate to rotate, and the stirring shaft is driven to rotate; and then water and potassium hydroxide in the water storage barrel are fully stirred through the rotating stirring plate until the height of the joint of the hose and the water storage barrel is higher than that of the joint of the hose and the water inlet, at the moment, a solution in the water storage barrel can be conveyed into the hydrogen generator body from the hose, the operation steps are simplified, and convenience is achieved.

The invention relates to a condensate gas reservoir horizontal well section oil-gas phase state judgment method, and belongs to the technical field of condensate gas reservoir oil-gas exploration and development. According to the method, hydrocarbon component data information of logging gas logging in the drilling process is utilized, a gas-liquid hydrocarbon coefficient formula is established, then normalization processing is conducted, normalized data are displayed in the same scale through a curve and correspond to the actual productivity of a well, and a judgment standard of the oil-gas phase state is established. When the oil-gas phase state of the condensate gas reservoir horizontal well section is quickly and qualitatively predicted, the relative contents of the condensate oil and the natural gas in the stratum of the condensate gas reservoir horizontal well section are intuitively judged by comparing two curves of liquid hydrocarbon and gas-liquid hydrocarbon coefficients according to the established judgment standard. A reference basis is provided for optimizing a gas testing interval and optimizing a reservoir transformation process, the method is a novel method which is low in cost, simple, convenient, visual, efficient and reliable in result, and geological evaluation data is provided for a horizontal well multi-section fracturing technology.

The invention belongs to the technical field of petroleum and natural gas exploration and development, and particularly relates to a method for judging a bottom interface of a permian stone thousand peak group by applying element logging data, which comprises the following steps: S1, collecting logging rock debris while drilling; s2, performing coarse division on the bottom interface of the permian stone thousand-peak group by combining regional deposition characteristics and geological design information, and delimiting a coarse division interface; s3, performing X-ray fluorescence spectrum scanning on the logging rock debris while drilling to obtain strontium, copper and barium element data; s4, carrying out data processing, analysis and judgment on the strontium, copper and barium element data, and calculating an interface coefficient JM; and S5, correcting the depth of the coarse interface to obtain the bottom interface depth of the permian stone thousand-peak group. According to the method for judging the bottom interface of the permian stone thousand peak group by applying the element logging data, errors existing in a lithology combination judgment method which mainly depends on naked eyes of technicians to recognize rock debris are corrected, and the method has the advantages of being small in error, small in data processing amount, high in accuracy, low in cost, easy to operate and wide in application range.