770 results about "Logging" patented technology

The invention relates to the technical field of geological reservoirs, in particular to a reservoir microcosmic connectivity characterization method based on pore throat boundary intelligent identification, which comprises the following steps of: performing threshold segmentation processing on a rock casting body slice by using image analysis software, and depicting partition images of pore throats and skeleton particles; based on the partition image, combining image analysis software to measure and extract the diameter, perimeter and area of the pore throat, and calculating quantitative parameters of the pore throat structure; constructing a one-dimensional Gaussian distribution model of the nuclear magnetic logging of the reservoir; based on a one-dimensional Gaussian distribution model, establishing a Gaussian collaborative factor for pore structure evaluation; based on the quantitative parameters of the pore throat structure and the established Gaussian collaborative factors, establishing a multi-factor evaluation mathematical method to establish a multi-element evaluation model of the pore structure; and performing multivariate nonlinear fitting on the porous structure multivariate evaluation model, the nuclear magnetic movable fluid saturation and the reservoir pressure measurement fluidity, and constructing a quantitative characterization model for characterizing the reservoir microscopic connectivity. According to the method, the microscopic connectivity of the reservoir can be accurately, rapidly and quantitatively evaluated.

The invention relates to a reservoir porosity and permeability prediction method based on a dynamic committee integration model, and the method comprises the following steps: obtaining an original data set which comprises shale content, porosity, permeability, GR, AC, CNL, DEN, RT, RXO, SP, CALC, CALI, depth and lithologic labels; performing data enhancement on the original data set; determining main control factors influencing the porosity and the permeability; constructing a dynamic committee integration model; inputting the main control factors into a dynamic committee integration model; and utilizing the trained dynamic committee integration model to respectively predict the porosity and the permeability. According to the method, the logging data is processed by adopting a machine learning method, reservoir parameters can be efficiently and accurately predicted, and favorable support can be provided for oil-gas exploration and development; the dynamic committee integrated model constructed by the invention can dynamically adjust the weight of each model according to different geological conditions and data features, can more flexibly adapt to the geological condition of a research area compared with a single model, and improves the prediction precision and generalization of the model.

The invention belongs to the technical field of carbonate rock oil-gas exploration, and particularly discloses a complex carbonate rock logging lithology identification method based on a diffusion model, and the method comprises the following steps: determining the lithology types of a plurality of observation wells based on the on-site rock core observation and slice analysis, and synchronously obtaining the logging data of the corresponding observation wells, constructing a lithology training data set in combination with a depth corresponding relationship between the lithology category and the logging data; a diffusion model is adopted to generate and supplement lithology categories with insufficient samples; logging data is adopted as an input feature, the lithology category is adopted as an output label, and a convolutional neural network fused with a Bayesian optimization algorithm and a Transformer hybrid model are utilized to train a lithology identification model; and inputting to-be-identified logging data into the trained lithology identification model, and outputting a lithology identification result. According to the method, high-precision lithology identification can be realized under the conditions of deep layers and complex stratums.

A method for evaluating shale compressibility logging based on game theory, comprising: step 1 of preparing rock core data, conducting experiments, and obtaining rock mechanical parameters and experimental rock physical parameters; step 2 of establishing a relationship between the rock mechanical parameters and the experimental rock physical parameters; step 3 of establishing a relationship between the rock mechanical parameters and the experimental rock physical parameters to predict the formation pore pressure based on the well logging rock physical parameters; step 4 of establishing a relationship between the laboratory and field logging rock physical parameters; step 5 of constructing a geostress calculation model for the research block formation and further obtaining related parameters; step 6 of obtaining the main controlling factors of shale compressibility geomechanics by normalizing the oil recovery intensity; step 7 of obtaining the main expression parameters and ranking of the relatively independent shale compressibility; and step 8 of determining the shale compressibility index. [Effects] A method for evaluating shale compressibility has been established with high accuracy.

The invention discloses a method and system for evaluating rock drillability in real time, and the method comprises the steps: determining a well section with logging data and element logging data at the same time in a target area, and obtaining a rock drillability grade value through the logging data; selecting element types conforming to geological characteristics of a current target area from the element logging data to form optimal element combination data of a corresponding stratum matched with the optimal element types, and based on the optimal element combination data, performing data fitting on a preset multiple regression model in combination with rock drillability levels to obtain a drillability level calculation model; the current drillability grade value calculation model is corrected by utilizing a rock core taken from a target stratum to obtain an experimental rock drillability grade value; and optimal element combination data of a target stratum is obtained in real time in the drilling process, and a dynamic rock drillability grade value is obtained by using the corrected drillability grade value calculation model, so that a rock drillability real-time evaluation result is obtained. According to the invention, real-time evaluation of the drillability of the rock is realized.

The invention discloses a drilling-measurement constrained three-dimensional geothermal geologic attribute modeling and quantification method, and relates to the technical field of data analysis and processing, and the method comprises the following steps: 1, constructing a drilling-measurement constrained three-dimensional geothermal geologic voxel basic model, and forming a three-dimensional geothermal geologic voxel basic model covering a modeling range; 2, constructing an implicit signed distance field on the three-dimensional geothermal geologic voxel basic model to express fracture geometry, generating a unified implicit geothermal geologic voxel field, and redividing voxel categories according to the unified implicit geothermal geologic voxel field; and step 3, executing moving cube surface reconstruction and prior sensitivity analysis on the unified implicit geothermal geologic voxel field to give a space confidence body and a volume interval, and dividing the space confidence body and calculating the volume interval according to the voxel occurrence frequency. Evaluation of the geothermal body is changed from traditional single volume estimation to interval and confidence expression, and a reliable basis is provided for well position deployment, risk management and development planning.

The invention discloses an intelligent natural gas productivity prediction method and device for a logging small layer joint test, and the method comprises the steps: dividing a plurality of target small layers of a target gas well reservoir according to the obtained various logging curve data of a target gas well, constructing corresponding productivity prediction machine learning models, and carrying out the prediction of the productivity of the target gas well. Predicting single-layer daily gas yield and single-layer daily water yield; for the plurality of target small layers, determining an interference coefficient representing interlayer interference of the plurality of target small layers; according to the interference coefficient and the single-layer daily gas production rate and the single-layer daily water production rate of each target small layer, obtaining a combined test productivity prediction result of the logging small layer after the interference loss is deducted; and / or, a deep learning algorithm is adopted to establish a well logging small layer combined test productivity prediction model, and a combined test productivity prediction result corresponding to the target small layer combined test is output. The method is used for improving the productivity prediction efficiency and accuracy of the natural gas combined test well.

The invention provides a method and system for identifying an omissive hydrocarbon reservoir of an old oil field based on rock debris and optical fiber monitoring, and the method comprises the steps: carrying out the multi-mode digital analysis of the stock rock debris of a target well of the old oil field, and obtaining the rock physical characteristic parameters of the rock debris; the method comprises the following steps: obtaining stratum dynamic response parameters through a distributed optical fiber sensing system arranged in a sleeve of a target well of an old oil field in advance; historical logging data of the target well of the old oil field are called, and standardization processing is conducted on the historical logging data to obtain a historical logging parameter set; static errors of a historical logging parameter set are corrected through rock physical characteristic parameters, and real-time dynamic constraints are provided through stratum dynamic response parameters; according to the type characteristics of the reservoir where the target well of the old oil field is located, the corresponding multi-parameter fusion identification method is adopted to obtain the position of the missing oil and gas reservoir, and the accuracy and reliability of missing oil and gas reservoir identification are improved.

The present description relates to the field of oil and gas field exploration and development, and in particular, to a shale oil sweet spot logging evaluation method and apparatus based on source-reservoir coupling characteristics. The method comprises: on the basis of the effective porosity and effective thickness of a shale oil reservoir in a research area, determining reservoir quality, wherein the effective porosity is greater than a preset porosity lower threshold; on the basis of effective points of kerogen total organic carbon content in the research area, the effective thickness of an interval corresponding to each effective point, and the kerogen total organic carbon content of each effective point, evaluating the quality of source rock intervals; on the basis of an estimated longitudinal distance range of the positions where the effective points of kerogen total organic carbon content are located, determining a coupling relationship between the reservoir quality and the quality of the source rock intervals; and on the basis of the coupling relationship, evaluating a shale oil sweet spot within the estimated longitudinal distance range.

The invention discloses a deep niobium phosphate ore positioning physical and chemical exploration method based on multi-method cooperation, and belongs to the technical field of geological mineral exploration. According to the method, a three-stage exploration system of region optimization, target region delineation and fine verification is constructed, multi-parameter cooperation of a magnetic method, an electromagnetic method, an induced polarization method, a three-component magnetic logging method and a geochemical method is combined, and each step is constrained by a basic rock-niobium phosphorite metallogenic model. An ore prospecting mark is determined through regional geological background analysis and metallogenic model construction, a metallogenic prospective region is delineated through regional optimization, a specific target region is delineated and locked, engineering verification is carried out through fine verification, and finally, a result is integrated to estimate the resource quantity. According to the method, the multiplicity of solutions of a single method is effectively reduced, the positioning precision of the deep niobium phosphorite is improved, the exploration efficiency and the target area ore finding rate are improved, and the method is suitable for accurate exploration of the deep niobium phosphorite.

The application provides a method for identifying mud shale fractures based on conventional logging data, comprising the following steps: step 1, obtaining acoustic travel time logging curves and natural gamma logging curves of a mud shale section; step 2, performing discrete wavelet transform and box dimension calculation on the acoustic travel time curves and the natural gamma curves to obtain wavelet coefficients and box dimensions; step 3, calculating the difference between the wavelet coefficients of the acoustic travel time curves and the wavelet coefficients of the natural gamma curves to obtain wavelet coefficient differences, and calculating the difference between the box dimensions of the acoustic travel time curves and the box dimensions of the natural gamma curves to obtain box dimension differences; step 4, averaging the wavelet coefficient differences and the fractal dimension differences to construct a fracture development response index; and step 5, identifying fractures in the mud shale section according to the relatively high value of the fracture development response index. The method for identifying mud shale fractures based on conventional logging data can accurately and reliably identify mud shale fracture development sections.

The invention provides an auxiliary rock core geological logging method based on drilling parameters, and relates to the technical field of geological investigation, the method is applied to a logging control terminal, and the method mainly comprises the following steps: obtaining regional typical rock core samples and five types of drilling parameter signals, classifying and extracting static characteristics of the rock core samples, and carrying out parallel noise reduction analysis on the parameter signals; obtaining a matching relationship between the rock core and the parameter signal, and constructing a lithologic parameter bidirectional association reference library; on the basis of the matching relation of the lithologic parameter bidirectional association reference library, the model is embedded into an incremental learning module to update the weight, and a subsection lithologic pre-judgment result is output; and calling a preset catalog template to load a pre-judgment result, generating a catalog report after man-machine interaction recheck and correction, collecting correction data, reversely transmitting the correction data back to the reference library to update a matching relationship, and triggering incremental learning of the model to complete a closed loop. The technical problems that in traditional rock core logging, regional lithology adaptation is poor, parameter interference is large, a model is not dynamically optimized, the deep operation risk is high, and efficiency is low are effectively solved.

The invention relates to a shale reservoir stratum pore pressure logging prediction method based on an improved Bowers equation, and the method comprises the steps: building an acoustic wave velocity response equation considering the influence of density and organic matter content in combination with the Bowers equation; carrying out a soaking experiment on the rock sample, and obtaining formation acoustic velocity and density subjected to hydration correction based on interval transit time, density and depth resistivity in logging information: obtaining a Bowers equation of a research formation based on the acoustic velocity and density of an undisturbed formation subjected to hydration correction; the Bowers equation of the research stratum is corrected, and an improved Bowers equation of the research stratum is obtained; based on an improved Bowers equation, effective stress and vertical stress of the stratum are calculated; and based on the effective stress and the vertical stress of the stratum, calculating to obtain the stratum pore pressure of the research stratum. According to the corrected Bowers formation pore pressure prediction model established by the invention, the prediction precision is obviously improved, and the development cost is reduced while the occurrence of construction accidents is reduced.

The techniques as described herein enhance the accuracy and precision of mineralogy analysis in elemental spectroscopy logging by utilizing oxide standards and / or rock-forming mineral compounds as reference materials that provide more representative and realistic signatures of geological formations. A method comprises logging a wellbore with a tool and fitting reference spectra from oxide compounds and / or rock-forming mineral compounds against measured gamma spectra obtained with the tool, to characterize a subterranean formation that the wellbore extends through.

The invention provides a sandstone-type uranium deposit sedimentary facies identification method, system and device and a medium, and belongs to the technical field of sedimentary facies identification, and the method comprises the steps: obtaining sandstone-type uranium deposit sedimentary facies multi-source data; time-frequency domain feature data, texture feature and waveform feature data and element ratio feature and statistical feature data related to sedimentary facies in the sedimentary facies multi-source data are extracted; time sequence features, three-dimensional structure features and metallogenic environment features are extracted from the feature data and weighted fusion is carried out to obtain the sedimentary facies category recognition probability. Multi-dimensional information complementation is realized by combining logging, earthquake and geochemical data, covering vertical lithology change, three-dimensional sand body distribution and a sedimentary facies state in a redox environment scene, and the focusing capability and the recognition precision of the model are effectively improved.

The invention relates to the technical field of oil exploitation and processing, in particular to a logging-based two-dimensional nuclear magnetic resonance low-permeability conglomerate fluid property identification method, which comprises the following steps of: obtaining the sum of original fluid areas in an original state of a low-permeability conglomerate core through two-dimensional nuclear magnetic resonance; obtaining the sum of manganese-saturated fluid areas of the low-permeability conglomerate core in a manganese-saturated state through two-dimensional nuclear magnetic resonance; according to the sum of the original fluid areas of the low-permeability conglomerate core in the original state and the sum of the manganese-saturated fluid areas of the low-permeability conglomerate core in the manganese-saturated state, the remaining oil index of the low-permeability conglomerate is obtained; and determining the fluid property of the low-permeability conglomerate reservoir according to the remaining oil index of the low-permeability conglomerate. According to the method, the residual oil index of the low-permeability conglomerate reservoir is established in an intuitive representation mode, the calculation formula of the residual oil index is based on extraction of parameter characteristics in a two-dimensional nuclear magnetic resonance original state and a manganese-saturated state, and the low-permeability conglomerate fluid property can be effectively recognized.

The invention discloses a core drilling method automatic recording and defect intelligent identification method based on deep learning, and relates to the technical field of core sample defect intelligent discrimination. Core sample photos shot on site are acquired, and a deep learning model is used for performing a series of processing; comprising the steps of determining boundary points, extracting angular points, preprocessing photos, extracting return information, identifying defects and the like, and finally generating core sample catalog information and a drilling comprehensive histogram. The method is technically characterized in that a corner point recognition model, a return extraction model, a defect extraction model and a defect classification model are combined with algorithms such as a rotary clamping shell and image preprocessing, and automatic analysis and recognition of a core sample photo are achieved; according to the method, various defect types and degrees can be accurately identified, an efficient, accurate and visual catalog result and visual display are provided for engineering detection, and the detection efficiency and reliability of a core drilling method are remarkably improved.

The utility model discloses a transfer type multi-arm caliper logging instrument which comprises a shell, vertical holes are formed in the outer wall of the shell at equal intervals, a vertical pipe is fixedly connected to the interior of the shell, electric push rods are installed on the outer side of the vertical pipe at equal intervals, sliding rails are fixedly connected to the outer wall of the vertical pipe at equal intervals, and the sliding rails correspond to the vertical holes. A moving seat is slidably connected to the outer wall of the sliding rail, the output end of the electric push rod is in clearance fit with the moving seat, a compression spring is arranged in the moving seat, and the two ends of the compression spring abut against the electric push rod and the moving seat respectively. The utility model relates to the technical field of oil well logging, and solves the problems that in the prior art, measuring arms of a multi-arm hole diameter logging instrument need to be unfolded or folded simultaneously, the complexity and failure rate of a mechanical structure are increased due to synchronous operation, the stability is poor, the maintenance cost is correspondingly increased, the measuring arms are prone to abrasion due to long-time friction with a well wall, and the measurement precision is high. And the measurement precision and the service life of the equipment are influenced.

The invention discloses a goaf remaining coal pillar stability comprehensive evaluation method based on InSAR and acoustics fusion, and belongs to the field of mine safety evaluation.The goaf remaining coal pillar stability comprehensive evaluation method comprises the steps that an SAR satellite suitable for a mining area is selected, SAR satellite image data of the mining area is obtained, sequential deformation analysis is conducted, the deformation rate and an accumulated deformation graph of the mining area are obtained, and a settlement abnormal area of the mining area is recognized; a coal pillar area of a mining area is drilled, and an ultrasonic logging device, an inter-well sound wave chromatography system and a sound emission monitor are arranged; performing comprehensive analysis on all the obtained data, and constructing a coal pillar stability evaluation model; and through the coal pillar stability evaluation model, grading the coal pillar stability, and predicting a coal pillar instability risk. According to the InSAR and acoustics fused goaf remaining coal pillar stability comprehensive evaluation method provided by the invention, high-precision evaluation of the coal pillar stability is realized through combination of earth surface deformation monitoring and deep coal pillar structure detection.

The invention discloses a tunnel plateau complex geology accurate exploration method based on ultra-deep horizontal drilling, which comprises the following steps: step (1) horizontal drilling layout: selecting a construction site on the ground of a to-be-tunneled tunnel, and planning a horizontal drilling track along a tunnel line; (2) horizontal drilling and multi-dimensional geological exploration are constructed, specifically, according to the planned horizontal drilling track, a petroleum drilling machine is used for constructing horizontal drilling; logging operation is carried out in the horizontal drilling construction process, and comprehensive logging and transient electromagnetic detection are carried out after horizontal drilling construction is completed; and step (3), integration and analysis of geological exploration data information: integrating and analyzing the data information collected by multi-dimensional geological exploration, determining geological conditions along the tunnel line, and identifying geological abnormal sections. According to the invention, the problems of discontinuous exploration and poor accuracy of traditional vertical drilling and geophysical prospecting means can be solved, and high-precision and continuous exploration of geological conditions near the tunnel line is realized.

The invention provides a sandstone reservoir oil-water layer logging identification method and system, and belongs to the field of reservoir evaluation. According to the method, an identification plate is obtained according to oil testing and production testing data and gas logging data, and then the fluid type of a reservoir to be identified is identified through the identification plate. According to the method, a conventional gas logging technology is adopted, new technical means and measurement time do not need to be added, the fluid type of the sandstone reservoir can be simply, conveniently and rapidly recognized based on deep excavation of gas logging data, the recognition coincidence rate is increased, a basis is provided for exploration and development decision and benefit development, and the method has the advantages of being economical, real-time, accurate and the like and has good application prospects. The popularization and application prospects are wide.

The invention provides a volcanic rock matrix-fracture dual-medium type oil reservoir oil layer evaluation method and system, and belongs to the technical field of geological engineering, oil reservoir processes and well logging interpretation. According to the method, a matrix porosity calculation chart is established through core scale logging, crack pores are added into an Archie sandstone conductive model to serve as conductive channels, the crack porosity is deduced, and finally, the matrix pores, the crack pores, the oil saturation and an oil testing result are combined to obtain the conductivity of the Archie sandstone. And the quantitative distinguishing method for the matrix-fractured dual-medium oil and gas reservoir oil layer is formed. According to the method, the gap of simultaneous research of the matrix and the crack is filled, the oil layer recognition precision is improved, the judgment standard is more strict and more reasonable, and the reliability is high.

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

The invention relates to the technical field of gas well reservoir identification methods, in particular to a method and device for eliminating coal seam interference in gas well reservoir accurate identification, and the method comprises the following steps: determining the existence of a coal seam by using a while-drilling gamma curve, a resistivity curve and a rock debris logging coal dust proportion; calculating a dynamic influence factor; on the basis of a gas survey data correction formula, obtaining a gas survey correction value of any depth point, and on the basis of a resistivity correction formula, obtaining correction resistivity; and determining the reservoir type by combining the gas logging correction value, the correction resistivity, the gas logging humidity ratio and the interval transit time. According to the method, firstly, the gas logging data and the logging resistivity are corrected, the influence of the coal seam on the gas logging data and the resistivity data is eliminated, the abnormal interference of the coal seam on the gas logging data and the logging data is effectively removed, and then the gas logging correction value obtained after correction, the correction resistivity, the gas logging humidity ratio and the interval transit time are combined, so that the gas logging data and the logging resistivity are obtained. According to the method, the precision and the efficiency of gas reservoir identification are improved.

The utility model provides a guide butt joint for over-bit logging, which comprises a butt joint body, a first end of the butt joint body is provided with external threads, the butt joint body is provided with a guide through hole, the guide through hole comprises a first hole section and a second hole section, the first hole section is positioned at the first end of the butt joint body, and the second hole section is positioned at the second end of the butt joint body. The first hole section is located at the first end of the butt joint body, the second hole section is located at the second end of the butt joint body and is conical, the diameter of the second hole section is gradually reduced in the direction from the second end of the butt joint body to the first end of the butt joint body, and the diameter of the first hole section is equal to the minimum diameter of the second hole section. The problems that in the prior art, butt joint of an upper suspension and a lower suspension is difficult, and the success rate of well logging instrument grabbing is low are solved.

The application discloses a downhole multi-parameter logging device and belongs to the technical field of logging while drilling, comprising a drill collar, a battery module and a circuit module are respectively arranged at the two ends of the inner cavity of the drill collar and are axially fixed through locking modules, a metal centralizer is fixedly arranged in the inner cavity of the drill collar, a plurality of independent centralizing blocks are arranged at the center of the metal centralizer, the drill collar comprises a drill collar body, an antenna housing, a strip-shaped magnetic core and a coil, and stable signal transmission is guaranteed, the battery module comprises annular batteries and butterfly springs, can be replaced on site and has a damping function, the circuit module comprises resistivity, azimuth gamma and inclination measurement modules, is integrally designed and has strong anti-interference performance, the metal centralizer realizes self-adaptive centralizing through centrifugal force and hydraulic damping, and the independent centralizing blocks can compensate temperature errors, the device is stable in structure, is suitable for complex well conditions, is convenient to maintain and can realize accurate and synchronous measurement of multiple parameters, thereby providing reliable data support for logging while drilling.

The application discloses a kind of based on sequence stratigraphy and seismic sedimentology's braided river channel reservoir fine characterization method, comprising the following steps: obtaining target area core, well logging and high-resolution three-dimensional seismic data;By analyzing the sedimentology mark and logging facies mark in the target area, to determine the sedimentary facies type and sedimentary microfacies division;Using lithology and well logging curve, using the theory of sedimentology, high-resolution sequence stratigraphy identifies sequence interface type and feature, and carries out high-resolution sequence stratigraphic sequence division;Division reservoir architecture interface;According to the internal structure of sedimentary system, sedimentary facies distribution, focus on high-frequency sequence and sedimentary body scale, generate the braided river channel reservoir fine characterization result of target area architecture unit.The present application effectively solves the deficiencies of prior art in reservoir fine description, boundary identification and parameter inversion, improves the efficiency and success rate of oil and gas reservoir development.

The embodiment of the invention provides a distributed optical fiber acoustic logging depth correction method and device, equipment and a medium, and the method comprises the steps: analyzing a distributed optical fiber acoustic logging waterfall plot, and determining an optical fiber length range corresponding to the position of a blowout preventer; performing fast Fourier transform on the distributed optical fiber acoustic logging data within the optical fiber length range, and determining the bottom position of the blowout preventer; and performing logging depth correction on the distributed optical fiber acoustic logging curve according to the bottom position of the blowout preventer to obtain the corrected logging depth. In the embodiment of the invention, the optical fiber length range roughly corresponding to the position of the blowout preventer is determined by analyzing the distributed optical fiber acoustic logging waterfall plot, the specific position of the bottom of the blowout preventer is determined by using the fast Fourier transform of the distributed optical fiber acoustic logging data, and then logging depth correction is performed on the distributed optical fiber acoustic logging curve. The corrected logging depth is obtained, and the accurate logging depth is provided for a distributed optical fiber acoustic logging curve.

The utility model discloses a storage type gamma magnetic positioning combined logging instrument which comprises a shell, a thermos bottle and a CCL magnetic detection assembly are fixedly arranged in the shell, and a gamma detection assembly and a circuit module are arranged in the thermos bottle and used for collecting, processing and storing gamma data and CCL signal data. The gamma detection assembly is arranged below the circuit module; the gamma detection assembly and the CCL magnetic detection assembly are electrically connected with the circuit module. The two ends of the shell are provided with a protective cap and a protective plug in a threaded connection mode respectively. According to the storage type gamma magnetic positioning combination logging instrument, the two logging units of the gamma energy spectrum instrument and the CCL instrument are designed and combined into one logging unit, the length of the instrument is shortened, the logging construction risk is greatly reduced, the pumping-out or direct pushing technology is adopted, real-time data storage is achieved in the logging process, and the logging efficiency is improved. The defects existing in cable type logging are well overcome.

The invention relates to the technical field of formation data acquisition, in particular to a logging formation information intelligent analysis system which comprises a response model construction module, a dynamic weight generation module, a composite evidence distribution module and a multi-source information fusion module. According to the method, core calibration data are obtained, a probability density distribution function of basic lithologic logging response is constructed through a kernel density estimation algorithm, dependence on a fixed plate and a static cut-off value is eliminated, dynamic probabilistic description of stratum response characteristics is achieved, and then the initial reliability and sensitivity weight of response numerical values are calculated based on distribution; the method is used for adjusting evidence distribution, then calling an evidence theory combination rule to fuse adjusted multi-source information, determining the stratum state according to the highest cumulative reliability, effectively solving the problem of misjudgment caused by response overlapping, and remarkably improving the accuracy and objectivity of stratum lithology and fluid property identification.