3457 results about "Well logging" patented technology

The invention relates to an array electric imaging logging based reservoir oil-water identification method and system. Aiming at the resistivity distribution characteristics of mud-invaded formations, a 'five-parameter formation model' is established, which is adapted to the requirement for simulating the resistivity distribution characteristics of different mud types and different invaded formations, and is pioneering in the aspects of forward-inversion models and processing methods of array electric imaging logging; and based on a 'five-parameter inversion' method for array electric imaging logging, an operation of carrying out resistivity distribution profile reconstruction on mud-invaded formations is performed, which reflects the actual differences between different resistivity distribution characteristics or change rules of mud-invaded oil formations or water formations. The method has stronger practicability, and is pioneering in the actual application aspects of qualitative identification and quantitative calculation on reservoir fluid properties.

The invention provides an intelligent drilling speed prediction method based on physical feature guidance and multi-source information fusion, and relates to the technical field of intelligent drilling speed prediction, and the method specifically comprises the following steps: collecting multi-source heterogeneous data from a drilling real-time database, a logging system, a logging system and a geological database; constructing a dual-channel deep learning prediction model, wherein the dual-channel deep learning prediction model comprises a dual-channel convolution feature extraction module, a feature fusion module, a time sequence fusion module, a time sequence modeling module and a full connection layer which are connected in sequence; obtaining a predicted drilling speed by using a dual-channel deep learning prediction model; a joint loss function is constructed by considering a data driving error and a physical constraint error, an error is calculated according to the joint loss function, and network parameters are updated through back propagation; carrying out loop iteration training until convergence; and the trained dual-channel deep learning prediction model is used for drilling speed prediction. According to the technical scheme, the problems that in the prior art, a mechanism model is insufficient in precision, and a data driving model is poor in reliability are solved.

The invention discloses a radioactive measurement data processing method for improving uranium exploration efficiency. The method comprises the following steps: standardized data acquisition: integrating a multi-parameter module, carrying out time-space synchronous acquisition of geological geophysical environment parameters, standardizing protocol alignment data, and supporting three-dimensional modeling; a three-dimensional coupling model is used for processing interference in a sub-module mode, LiDAR-DEM is used for correcting gamma attenuation in the terrain, an optical fiber thermopermeability instrument is used for correcting daughter errors in the hydrology, signal attenuation of a borehole is compensated through a drilling fluid chart, and a space-time continuous interference field is formed; performing dynamic equilibrium coefficient inversion: constructing equilibrium coefficient isoparametric mapping, and inputting rock core, logging and geochemical data into a random forest model; the transfer learning is trained by using historical data, and dynamic inversion and updating of a new area are carried out; intelligent data processing three-dimensional visualization: self-adaptive denoising and spectral shape matching reinforcement abnormity are carried out; constructing a three-dimensional model, and carrying out transfer learning to optimize the precision; the WebGL platform integrates multi-parameter display, virtual drilling functions and high-dimensional data intuitive interpretation.

The invention relates to the technical field of reservoir evaluation, in particular to a deep learning-based reservoir evaluation model construction method and system. The method comprises the following steps: acquiring logging data and core analysis data of a target work area, and respectively carrying out abnormal value processing and missing value filling to obtain complete logging data and complete core data; calculating reservoir pore structure parameters based on the complete core data; and inverting the macroscopic porosity of the reservoir based on the complete logging data. According to the method, the accuracy, the stability and the robustness of reservoir evaluation are remarkably improved by fusing the core data and the logging data and combining the pore structure parameters, the macroscopic porosity and the geological attention mechanism, the problems of data missing and small samples are solved, and the interpretability and the geological consistency of the model are enhanced.

A pore pressure prediction method for fine-grained hybrid sedimentary rock based on variable P-wave velocity includes: carrying out a P-wave velocity test of rock under different effective stresses, determining a function form of the relationship between acoustic velocity and effective stress, fitting the parameters in the function by optimization, and establishing an evaluation model of effective stress influence term by lithology. According to the measured formation pressure data and the effective stress influence term model, the acoustic velocity background value is deduced, the logging curve is preferred, and the acoustic velocity background value logging evaluation model is established. According to the logging curves of DT and DEN, combined with the lithology identification results, the acoustic velocity background value is calculated, and then the effective stress of the formation is obtained, and then the pore pressure is determined according to the overburden pressure.

The invention relates to the technical field of data processing, and discloses a coal mine area gas control and safety guarantee method and system under complex geological conditions. The method comprises the steps that geophysical prospecting, drilling and logging exploration are conducted on a coal mine area, and geological parameters are obtained; performing statistical analysis on the data, and constructing a three-dimensional gas model; identifying a high-gas area based on the model, and arranging a directional drilling network; differential extraction and anti-reflection measures are implemented according to the drilling network; fusing extraction and sensor data, and predicting a gas change trend by using a neural network; and performing intelligent diagnosis based on the early warning matrix to form a disposal scheme. Under the complex geological condition, the gas distribution rule is accurately mastered, scientific arrangement of directional drilling and optimization design of differential extraction parameters are achieved, meanwhile, a gas early warning decision support system based on artificial intelligence is established, the accuracy of gas treatment and the timeliness of early warning are improved, and therefore safe production of a coal mine is guaranteed.

The invention discloses a discrete element-based three-dimensional fault / fracture network modeling method and device under a backlash pushing and covering system, and relates to the technical field of three-dimensional fault modeling, and the method comprises the steps: building an initialized fault plane model based on the seismic logging information parameters of a target region; extracting stratum parameters of the target area, and determining a deformation process in combination with an equilibrium profile recovery method; the method comprises the following steps: calibrating mesoscopic parameters of discrete elements according to stratum parameters, depositing a first discrete element model, optimizing and generating a second discrete element model, setting boundary conditions to carry out back-flushing pushing-covering simulation, comparing an output result of the second discrete element model with an actual geological section, adjusting model parameters until the output result and the actual geological section are matched, and establishing a three-dimensional fault and fracture network. And analyzing and establishing spatial distribution characteristics of the target area, and carrying out dynamic evaluation and trend prediction on the activity of the three-dimensional fault. According to the method, the geomechanical model is fused, the subjectivity of manual interpretation is overcome, and the problems of fault tip extension and the like in grid reconstruction are effectively avoided.

The present disclosure discloses a seismic inversion method based on joint constraint of a physical model and priori information. The method includes: extracting seismic wavelets based on seismic data, and determining an amplitude scaling factor of the wavelets; counting priori information of impedance parameters; establishing an initial impedance parameter model by using seismic structural interpretation information and logging data; obtaining a simplified approximate equation based on an interface weak elasticity difference hypothesis, forward modeling a seismic gather by using the simplified equation, and calculating an inversion residual; rewriting an objective function into a function related to the impedance parameters by using a generalized linear inversion idea, solving the impedance parameters by using an iterative reweighted least squares algorithm, and updating the impedance parameters; and repeating the above steps until the inversion residual reaches the requirements or reaches the maximum number of iterations, and outputting a final processing result.

The invention discloses a shale oil reservoir geology-engineering double-dessert compressibility comprehensive evaluation method, and relates to the technical field of petroleum and natural gas yield increase transformation and geotechnical engineering, in particular to a shale oil reservoir geology-engineering double-dessert compressibility comprehensive evaluation method, which comprises the following steps of performing normalization processing on a logging basic parameter standard deviation; geological dessert parameters are established by calculating the weight of each factor; influence factors influencing the longitudinal expansion capacity of the hydraulic fracture of the target reservoir are obtained; constructing a geological-engineering double-dessert compressibility comprehensive evaluation model of three types of lithofacies'reservoir performance-transformation potential 'synergistic mechanisms; and establishing a geological-engineering double-dessert compressibility kernel density estimation map, and dividing the reservoir compressibility on the premise of adaptive bandwidth. According to the method, the operability and prediction precision of an evaluation result are remarkably improved, and an innovative decision support tool is provided for horizon optimization and scheme design of shale oil reservoir fracturing transformation.

The invention relates to a depth domain time-lapse seismic joint inversion method and device, equipment and a storage medium, and the method comprises the steps: extracting depth domain seismic wavelets according to obtained logging data, and building a depth domain seismic wavelet matrix; based on the depth domain seismic wavelet matrix, well-seismic calibration is carried out on the basic logging data and the monitoring logging data; solving a difference between the corrected basic logging data and the corrected monitoring logging data, determining differential logging data, performing full-band interpolation according to the differential logging data and the corrected basic logging data to construct an initial model, and obtaining a low-frequency model according to the initial model; establishing a time-lapse seismic simultaneous inversion objective function based on the forward operator of the time-lapse seismic simultaneous inversion; and substituting the low-frequency model into the target function for iterative solution, and outputting a time-lapse seismic inversion result meeting an iteration ending condition, thereby overcoming the limitation of the existing time-lapse seismic method while reducing the workload of time-domain time-lapse seismic inversion processing.

The invention relates to the technical field of petroleum and natural gas engineering, and discloses an oil and gas reservoir optimized mining method based on multi-modal data, which comprises the following steps: constructing an oil and gas reservoir multi-modal data acquisition system, seismic wave field data, logging interpretation data, production dynamic data, micro-seismic monitoring data, underground temperature and pressure time sequence data and shaft structure parameter data are obtained through the acquisition system; and performing space-time alignment processing on the acquired multi-modal data, establishing a unified geological coordinate system and a time reference, and eliminating data isomerism caused by different acquisition frequencies and spatial resolutions. A six-dimensional heterogeneous data acquisition system covering a seismic wave field, well logging interpretation, production dynamics, micro-seismic monitoring, an underground temperature and pressure time sequence and shaft structure parameters is constructed, so that the depiction precision of a reservoir porosity field, a permeability field, a saturation field and a pressure field is essentially improved.

Systems and methods for characterizing a subterranean formation that include a drill bit for drilling a deviated wellbore through the formation and that repeatedly changes direction. Also included is a directional drilling system and measurement equipment to detect a property of the formation. A trajectory control system is operable to: receive a well log; determine a property of the wellbore; segment the well log based on the property; and correlate one of the segments with the well log previous to the segment to determine a matching score for the segment. If the matching score is equal to or greater than a minimum threshold, the segment was taken in the formation and at the same stratigraphical true vertical depth (“STVD”). If the matching score is less than the minimum threshold, the segment was not taken in the formation and not at the same STVD and the STVD is estimated.

A grading evaluation method for high-maturity gas source rock based on hydrocarbon generation and expulsion simulation includes: establishing a change curve of hydrocarbon generation yield based on a hydrocarbon generation thermal simulation experiment; establishing a change curve of hydrocarbon expulsion of source rocks based on a principle of material balance; determining a classification boundary of source rocks based on the change curve of hydrocarbon generation yield and the change curve of hydrocarbon expulsion of source rocks; and obtaining a spatial distribution of different types of source rocks by combining logging interpretation according to the classification boundary of source rocks. In the method, the evaluation model and change curve of hydrocarbon generation yield, retained gas volume and hydrocarbon expulsion volume of source rock are established, and the classification standard of gas source rock at different maturity stages is determined by combining the change curve of hydrocarbon expulsion efficiency under different maturities.

The invention relates to the technical field of geological exploration, and particularly discloses a geological structure automatic identification and interpretation method based on deep learning, and the method comprises the steps: obtaining seismic data, logging data, gravity data and / or magnetic data, and generating a fusion tensor through space-time alignment and dynamic weight distribution; performing space-frequency domain joint coding on the fusion tensor by using an improved Transform encoder, and generating multi-scale geologic feature representation; a generative adversarial network is constructed, a generator of the generative adversarial network outputs a geological structure pseudo label, and a discriminator and a geological expert rule base collaboratively optimize a prediction result; and quantizing a confidence interval of a prediction result based on Bayesian deep learning, and combining geological priori knowledge to generate a dynamic interpretation scheme. Through multi-source data fusion, an innovative algorithm and model collaboration, accurate recognition and scientific interpretation of the geological structure are achieved, the accuracy and efficiency are improved, the cost is reduced, and the method has remarkable advantages in complex geological exploration.

The invention discloses a while-drilling pressure monitoring and regulating device for a complex fractured reservoir, and relates to the technical field of well drilling automatic control, and the while-drilling pressure monitoring and regulating device comprises a data acquisition module which comprises a micro-resistivity imaging logging device and a well drilling data acquisition device; the micro-resistivity imaging logging device is used for acquiring well wall imaging data to obtain crack characteristic parameters; the drilling data acquisition device is used for acquiring operation parameters in the drilling process; the data analysis module is used for evaluating the total fracture pressure, calculating the pressure gradient while drilling and constructing a seepage model to obtain the fracture flow according to the fracture characteristic parameters and the operation parameters; and the self-adaptive control module is used for calculating the optimal bottom hole pressure according to the fracture flow in combination with the operation parameters, and controlling the pressure while drilling based on the optimal bottom hole pressure. Real-time evaluation of the fracture pressure state and self-adaptive regulation and control of the bottom hole pressure can be achieved, and the safety of the drilling process is effectively improved.

The invention is suitable for the technical field of volcanic rock reservoir gas saturation and permeability prediction, and provides a volcanic rock reservoir gas saturation and permeability seismic inversion method and system, and the method comprises the following steps: collecting data, and building a rock physical model of a volcanic rock reservoir; establishing a rock physical parameter in-well inversion method, and calculating a permeability indication parameter # imgabs0 # and a micro-fracture porosity parameter # imgabs1 #; based on the parameters # imgabs2 # and # imgabs3 #, constructing a gas-bearing seismic indication factor # imgabs4 # and a permeability seismic indication factor # imgabs5 # in combination with logging data; the quantitative relation between the # imgabs6 # and the gas saturation # imgabs7 # and the quantitative relation between the # imgabs8 # and the permeability # imgabs9 # are established through measured data in the well; in combination with rock physics and seismic reflection theories, a PP wave reflection coefficient formula about # imgabs10 # and # imgabs11 # is constructed; the pre-stack seismic inversion method suitable for the volcanic rock reservoir is developed, seismic indication factors # imgabs12 # and # imgabs13 # are calculated, and the seismic indication factors # imgabs14 # and the seismic indication factors # imgabs13 # are quantified into gas saturation # imgabs14 # and permeability # imgabs15 #. According to the method, the quantitative prediction of the spatial distribution of the volcanic rock reservoir gas saturation and permeability is realized.

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 discloses a satellite-air-ground-well multi-method collaborative shallow coverage area copper-gold ore exploration method, and relates to the technical field of mineral resource exploration, regional alteration mineral information is extracted in a shallow coverage area, and a porphyry type copper-gold ore prospecting prospective area is determined according to alteration mineral space distribution and zoning characteristics thereof; in the prospecting prospective area, a key exploration area is selected according to metallogenic geological conditions, aviation geophysical prospecting and aviation hyperspectral remote sensing investigation are carried out, and a prospecting favorable area is delineated; performing prospecting prediction on related ore types in the favorable prospecting area, and delineating a prospecting target area; determining a new drilling position in the target area; drilling verification and well logging are carried out, ground-well geophysical prospecting is carried out to describe the spatial form of the metallogenic geologic body, and the prospecting effect is expanded. According to the method, the favorable prospecting area and the prospecting target area can be more accurately delineated, the spatial form of the ore body is finely delineated, a reliable basis is provided for prospecting of the porphyry type copper-gold ore, and the prospecting success rate and efficiency of the porphyry type copper-gold ore in the shallow coverage area are greatly improved.

The invention relates to the technical field of oil exploration, and discloses a rock mechanical parameter logging intelligent evaluation method considering continental facies shale fabric characteristics. The method comprises the following steps: 1, dividing shale lithofacies, identifying and determining the bedding development thickness of a sample through an optical microscope, determining a granularity experiment of the sample through granularity analysis, and forming mineral composition and bedding factor lithofacies division; 2, constructing a typical logging facies based on a rock electricity response relationship, and dividing the imaging logging facies of the research area according to natural gamma curves of conventional logging and imaging logging, core deposition structure characteristics and imaging logging image characteristics; 3, testing the longitudinal and transverse wave velocities and the porosity of different shale lithofacies, and calculating the dynamic elastic modulus and the Poisson's ratio. Through fine division and description, the method for accurately evaluating the bedding development condition, the particle size characteristics and the mineral components of the shale sample is provided, the testing time is short, the cost is low, and accurate evaluation of shale can be achieved.

The present invention relates to a computer implemented method, the main objective of which is to predict real-time LWD profiles in the intervals from the drill tip to the limit of the profile offsets. This becomes a methodology that serves as yet another input to improve assertiveness in decision-making during well drilling operations.

The invention provides a rock burst and mine earthquake collaborative treatment method based on regional fracturing, and the method comprises the following steps: selecting a working surface, determining a plurality of rock burst control layers with rock burst risks, then determining a plurality of mine earthquake control layers with mine earthquake risks, and determining a plurality of mine earthquake control layers with mine earthquake risks; performing main control layer analysis on all the rock burst control layers in the fissure zone preferentially to determine a multi-stage rock burst main control layer, performing main control layer analysis on all the mine earthquake control layers above the fissure zone to determine a multi-stage mine earthquake main control layer, and performing logging correction analysis; according to the length and width of the working face, the thickness of the rock burst control layer and the number of the mine earthquake control layers, the layout design of separate layer fracturing is determined, the treatment method achieves comprehensive prevention and control of the mine area risk, the treatment effect on the rock burst and the mine earthquake is remarkable, the disaster risk is reduced, and a powerful guarantee is provided for mine safety production.

The invention discloses a boundary-preserving seismic wave impedance inversion method, device, equipment and medium, and relates to the technical field of geological prospecting. Historical seismic data of a target area and logging curve data including logging impedance parameters are acquired, and seismic wavelet data are extracted from the historical seismic data; carrying out data processing on the seismic wavelet data to obtain target seismic wavelet data, constructing an initial impedance parameter model according to the logging curve data, carrying out forward modeling on the initial impedance parameter model, constructing a composite objective function comprising a boundary preserving constraint term and a prior information constraint term, and carrying out the forward modeling on the initial impedance parameter model based on the composite objective function. And the initial impedance parameter model is updated to obtain the target impedance parameter model, and the output impedance parameter inversion curve is obtained through the target impedance parameter model, so that the inversion precision and efficiency are improved, and the inversion stability and the geological reduction capability are improved.

The invention discloses a deep geothermal fluid cause and migration path tracing method, and relates to the technical field of earth scientific exploration, and the method specifically comprises the following steps: A1, collecting a geothermal fluid sample, A2, fusing high-precision three-dimensional seismic data, logging data and drilling data, constructing a multi-scale fracture network model, quantifying fracture connectivity and permeability anisotropy, and calculating the fracture connectivity and permeability anisotropy according to the fracture connectivity and permeability anisotropy. A3, developing a long short-term memory (LSTM) network model, inputting temperature, pressure and chemical field data monitored in real time, dynamically predicting a fluid migration path, and introducing a generative adversarial network (GAN) optimization model, and A4, deploying a distributed optical fiber sensor network. According to the sensing method for the aviation high-speed moving target, through the core-shell structure tracer agent, high fluorescence efficiency and geological compatibility are considered, a complete innovation chain is formed in the aspects of material design, detection method and environmental adaptability of the nano tracer agent, and the precision and sustainability of tracing of a migration path of deep geothermal fluid are remarkably improved.

The invention discloses a while-drilling electromagnetic wave advanced detection sensitivity analysis method, and belongs to the field of oil-gas exploration and development. The method comprises the following steps: constructing a while-drilling electromagnetic wave advanced detection model, quantifying the influence of different measurement parameters on advanced detection signals, then analyzing the response characteristics of the advanced detection signals, extracting key sensitivity factors, and defining a single-parameter sensitivity function and a multi-parameter coupling sensitivity function; the sensitivity of advanced detection signals is qualitatively and quantitatively analyzed from the four angles of the distance to the interface, the formation resistivity, the interlayer and the stratigraphic dip angle, so that the while-drilling electromagnetic wave advanced detection range is optimized, and the adaptability in the well logging process is enhanced. And meanwhile, the advanced detection second-order sensitivity is approximately solved by adopting a central difference formula, influence factors suffered by advanced detection signals are analyzed from a numerical angle, the interaction of multi-parameter coupling is analyzed, and a scientific basis is provided for electromagnetic wave logging while drilling accurate inversion and instrument design.

The invention provides a deep coal seam fracture density azimuth seismic inversion method and device, and relates to the technical field of seismic exploration, and the method comprises the steps: obtaining pre-stack seismic trace set data and logging data of a research work area; constructing an elastic stiffness matrix of which each stratum is a VTI medium based on the logging data; processing the porosity of the coalbed methane reservoir in the logging data and an elastic stiffness matrix of the coalbed methane reservoir as a VTI medium according to a linear slip theory to obtain an elastic stiffness matrix of the coalbed methane reservoir as an orthotropic medium so as to determine an initial model parameter vector; combining a reflection coefficient approximation formula based on pre-stack seismic trace set data and orthoanisotropy to construct an objective function used for inverting model parameters; solving the target function to obtain a target model parameter vector of the research work area; and the target model parameters of the coal bed gas reservoir are processed by using the target prediction model to obtain the fracture density of the coal bed gas reservoir, so that an effective technical means is provided for inversion of the fracture density of the deep coal bed.

The invention provides an ultra-deep high-pressure gas well variable-flow-state sand-carrying capacity evaluation method, and relates to the technical field of oil-gas exploration, and the method comprises the steps: collecting well logging data of a target gas well, constructing a gas-liquid two-phase flow parameter expression, and carrying out the quantitative analysis of key flow characteristics such as the gas content and the speed; a mathematical model is established by applying an H-K flow state judgment criterion, and numerical simulation is performed in combination with fluid dynamics simulation software, so that the flow state in the shaft is accurately identified and distinguished; then, according to high-temperature and high-pressure characteristics under actual working conditions and sand shape information monitored in a mine field, carrying out secondary correction on the resistance coefficient CD so as to improve a sand-carrying capacity prediction model; and finally, the critical sand-carrying flow velocity and yield are calculated based on the corrected model, and a scientific basis is provided for setting the sand-carrying capacity and reasonable production parameters of the gas well in different flow states. Safe and efficient development of a gas reservoir is guided, production parameter setting is optimized, and long-term stable and efficient operation of a gas well is ensured.

The invention is suitable for the technical field of oil and gas resource exploration, and provides a tight sandstone gas reservoir microfracture prediction method based on elastic modulus frequency dispersion inversion. According to the method, a rock physical model is constructed based on an equivalent embedded body stress averaging theory, frequency dispersion attributes are defined through elasticity modulus differences in a non-relaxed state and a relaxed state, and a microfracture parameter influence mechanism is disclosed; inversion is driven by a non-relaxed state model, the calculated elastic modulus frequency dispersion attribute and the microfracture density are in significant positive correlation, the quantitative correlation of the two is verified through measured data, and the inversion precision of the microfracture characteristics in the well is improved; an L-BFGS algorithm is introduced to carry out seismic inversion, frequency dispersion attribute quantitative prediction of pre-stack seismic data is realized, physical consistency between logging and seismic data is ensured, traditional limitation is broken through, a micro-fracture development area of a gas production well target layer is successfully identified in practical application, and the method has a good application prospect. And an effective solution is provided for high-precision prediction of the regional scale of the compact sandstone gas reservoir microcracks.

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.

The invention relates to the technical field of oil and gas field exploration and development, in particular to a drilling guidance method, system and device based on a dynamic three-dimensional geologic model.The method comprises the steps that a data real-time monitoring service is deployed on a drilling data platform to automatically sense all real-time data on site; transmitting real-time data through the data interface, and preprocessing model update data in the real-time data to obtain standard update data; dynamically updating the three-dimensional geologic model based on a machine learning algorithm and the standard update data; whether a drill bit adjusting strategy is made or not is determined according to the actual drilling track data and the actual drilling well logging curve; and determining a drill bit adjustment strategy according to a judgment result of formulating the drill bit adjustment strategy in combination with reservoir parameters. According to the invention, while real-time updating and real-time guiding are carried out, the requirements of drilling site requirements on guiding accuracy and timeliness are met.

The invention provides a petroleum drilling geomechanical characteristic estimation method based on deep learning, and aims to solve the problems of high measurement cost and poor real-time performance of shear wave velocity Vs in traditional geomechanical analysis by fusing logging while drilling LWD data and real-time drilling engineering parameters. According to the method, a Transformer model is adopted to carry out depth correction on logging-while-drilling data, depth offset of a drill bit and a logging sensor is eliminated, and real-time drilling parameters such as torque T, bit pressure WOB and drilling speed ROP are combined and input into a multi-layer perceptron MLP network to predict the shear wave speed Vs. And further calculating key geomechanical parameters based on the predicted shear wave velocity Vs. In practical application, the method improves the shear wave velocity prediction accuracy to 97.2%, the mean absolute error MAE of the shear modulus is reduced from 0.186 to 0.059, and the bulk modulus is reduced from 0.189 to 0.040. The method can be used for outputting stratum elastic parameters, optimizing bit pressure, pre-warning well wall instability and adjusting a well track, well drilling safety and efficiency are improved, and meanwhile dependence on an expensive well logging technology is reduced.