793 results about "Field development" patented technology

The invention relates to the technical field of oil and gas field development, and discloses an ultra-large type true triaxial hydraulic fracturing fracture evolution path prediction method and system.The prediction method comprises the steps that a coupling geomechanical model integrating microscopic, macroscopic and wellbore flow scales is constructed; initializing the model and performing crack initial expansion simulation; collecting construction data in real time, and dynamically optimizing model parameters through a data assimilation algorithm; performing fracture evolution advanced prediction by using the updated model; and generating an optimization decision based on the prediction result and feeding back to the construction site. According to the method, fusion of a multi-scale physical mechanism and real-time dynamic prediction is realized, and accurate prediction and active control can be performed on crack expansion under the ultra-large true triaxial condition.

The invention discloses an intelligent optimization method for a multi-type well seam joint control fine injection-production mode, and relates to the technical field of oil-gas field development. The method comprises the following steps: setting a well seam joint control fine injection-production mode, establishing an oil reservoir numerical simulation model in oil reservoir numerical simulation software, obtaining multiple groups of oil reservoir injection-production schemes based on a Latin hypercube sampling method, performing simulation according to each group of oil reservoir injection-production schemes by utilizing the oil reservoir numerical simulation model, generating multiple pieces of sample data, and establishing a sample database; a deep learning agent model is established, after the sample database is utilized to train and train the deep learning agent model, a particle swarm optimization algorithm is adopted to carry out single-target pre-search global optimization to obtain a preferred reference strategy, a reinforcement learning dynamic decision model is established, and a reinforcement learning agent is obtained through training based on a PPO near-end strategy optimization algorithm; and the optimal injection-production development scheme of the oil reservoir is obtained by utilizing the reinforcement learning agent, so that rapid optimization and decision support of the oil reservoir injection-production scheme in a new multi-type well seam joint control mode are realized.

The invention discloses a polymer-flooding reservoir yield prediction method and system based on a double attention mechanism and a storage medium, relates to the field of oilfield development, and aims to solve the problems that a bidirectional dependency relationship and local features of long-time-series data are difficult to capture at the same time and feature weight distribution and time-series information focusing effects are insufficient in an existing method. According to the technical key points, the method comprises the following steps: S1, collecting field development data of the polymer flooding reservoir and screening optimization characteristics; s2, a CNN-BiLSTM-DAM fusion model is constructed, and the function implementation process of the model comprises the steps that local features of input data are extracted based on a convolutional layer, a dynamic weight is distributed to the local features of each time step through an attention mechanism layer, forward and backward dependency relationships of weighted time sequence feature data are captured through a bidirectional loop structure of a BiLSTM layer, and a dynamic weight is distributed to the local features of each time step; performing weight adjustment on the time sequence features output by the BiLSTM layer through the attention mechanism layer, and capturing global time dependence through the BiLSTM layer; and S3, the optimized features are input into the trained CNN-BiLSTM-DAM fusion model, and the yield of the polymer flooding reservoir is predicted.

The invention discloses a shaft operation state intelligent prediction method based on a hybrid deep learning framework, and belongs to the field of petroleum engineering oil-gas field development engineering, and the method comprises the following steps: building a geological sequestration shaft transient temperature-pressure coupling mathematical model, and carrying out the numerical solution; constructing a double sequence feature database covering CO2 injection and leakage working conditions; adopting a parallel architecture to process the double sequence features to construct an improved double attention network; the method comprises the following steps: establishing a hybrid deep learning framework DT-DANet by coupling an improved double attention network and a time fusion Transform; and carrying out collaborative optimization training on the hybrid deep learning framework by adopting a composite loss function, wherein the output of the framework is a wellbore pressure profile, a temperature profile and phase state distribution under different time steps. According to the method, high-precision prediction of shaft multi-parameter dynamic evolution in the CO2 geological storage process is realized.

The invention discloses a deep learning assisted acceleration fracturing construction parameter intelligent real-time optimization method, and belongs to the field of unconventional oil and gas field development, and the method comprises the steps: constructing a structured sample database covering geological attributes, engineering parameters and fracturing effect responses, and carrying out the unified preprocessing of multi-source data; constructing a multi-modal space-time collaborative prediction model, and combining a multi-expansion time permissible convolutional network, a three-dimensional residual network and a cross attention mechanism; supervised learning training is carried out based on a database, sparse regularization and learning rate scheduling are introduced, and the model precision and generalization ability are improved; a hierarchical collaborative optimization framework of outer-layer Bayesian search-inner-layer CMA-ES refinement is provided, and reservoir transformation volume maximization and construction feasibility are achieved under complex constraints; compared with an existing method, optimization parameters of the method include the cluster distance and the section distance and further include the displacement, the sand concentration, the fracturing fluid type and other construction parameters, and real-time optimization of the fracturing construction parameters can be achieved through the machine learning agent model.

The invention discloses an injection-production regulation imbalance quantitative diagnosis method based on fluid potential energy, and the method comprises the steps: extracting a streamline track space coordinate sequence and corresponding point oil reservoir parameters based on a dynamic permeability field, a pressure field and an injection-production streamline field; constructing a three-dimensional fluid potential energy field model in the injection-production unit, and dividing dominant seepage, a retention zone and a transition zone based on the injection-production unit through potential energy gradient tensor; establishing a seepage potential index LS by taking the flow line penetrating pore volume as a stability weighting factor and combining the potential energy gradient intensity; on the basis of LS sorting, a potential energy-volume two-factor accumulation curve is constructed, the development non-equilibrium degree is represented through curve form parameterization, and the envelope area of the accumulation curve is calculated and used for representing the oil reservoir flow field development non-equilibrium degree and the development effect of each type of injection-production unit. According to the method, fluid potential energy and a two-factor cumulative distribution concept are introduced into computational logic, and unified and quantitative characterization of an oil reservoir flow field and an injection-production unit is realized in combination with a seepage mechanism of the injection-production unit.

The invention discloses an EUR prediction model construction method and system of a tight gas reservoir gas well, and relates to the field of oil and gas field development technology and artificial intelligence cross technology, and the method comprises the steps: collecting multi-source data; screening the multi-source data by adopting a three-level feature screening method, and removing redundant features; constructing a heterogeneous integrated model comprising a plurality of machine learning models, inputting the screened features into the heterogeneous integrated model to carry out EUR prediction, obtaining respective prediction errors of different machine learning models, correcting each EUR prediction result based on the prediction errors in combination with a material balance equation MBE, a yield attenuation model DCA and fracture network simulation, and carrying out EUR prediction. And carrying out weight distribution on each machine learning model after the EUR prediction result is corrected by adopting an error reciprocal weighting method to generate a final combined EUR prediction model. According to the method, an optimization strategy combining data driving and physical constraint is adopted, and the precision and reliability of EUR prediction are improved.

The invention relates to an introduction structure of a fisher, which belongs to the field of oil-gas field development and comprises an expansion drive, a base is arranged at one end of the expansion drive, an expansion guide structure is arranged at the other end of the expansion drive, and a flow guide structure is arranged on the inner side of the expansion drive. The expansion guide structure comprises expansion connecting rods and a plurality of rigid expansion flaps, the end, away from the base, of the expansion drive is connected with the rigid expansion flaps, and the expansion connecting rods are connected between the adjacent rigid expansion flaps. The expansion guiding structure completes expansion and folding actions through expansion driving, the expansion guiding structure enlarges the guiding area through the expansion action and also enlarges the projection area, and the expansion guiding structure pushes a fish in the guiding area through the folding action, so that the fish is correctly guided into the fishing tool. The invention provides an effective guide area in a larger range, and enhances the adaptability of the introduction structure to the fish form, thereby improving the efficiency and success rate of the fishing operation.

The invention relates to the technical field of oil and gas field development, in particular to a fracture parameter inversion method based on a Bayesian neural network, which comprises the following steps: establishing a bottom hole net pressure conversion model based on an actual construction curve, and drawing a bottom hole net pressure curve; calculating a bottom hole net pressure index sequence and a corresponding time sequence; establishing a shaft bottom crack extension mode judgment criterion on the basis of a classic double logarithmic curve analysis method; taking the net pressure index sequence and the time sequence obtained in the previous step as input data, combining actual physical parameter constraints, and establishing an inversion fracture parameter model based on a Bayesian neural network; and inputting the pressure index sequence to be inverted into the Bayesian neural network model to obtain a specific fracture parameter inversion result. According to the technical scheme, the confidence interval of the prediction result can be given, the prediction result and uncertainty quantification capability can be synchronously provided, and the reliability and decision support value of the inversion result are greatly improved.

The present application relates to the technical field of shale oil and gas field development, and is a test system for simulating fault activation and induced earthquake under the action of horizontal well hydraulic fracturing, which comprises a horizontal well geological model, a distributed optical fiber monitoring system, a perforating system, a fracturing system, a formation temperature simulation system and a stress applying system. The present application has a reasonable and compact structure and is convenient to use. It can realize the application of true triaxial formation pressure, simulate the real formation temperature of the formation where the horizontal well is located, and continuously and real-timely monitor the multi-physical field of the fault-containing geological reservoir under the conditions of formation pressure, temperature, the whole process of horizontal well hydraulic fracturing and multi-step conditions. Therefore, the mechanism of hydraulic fracturing activating faults, the feature difference of multi-stage fracturing fracture network, the deformation of casing and the risk of induced earthquake can be quantitatively characterized and regularly analyzed, so as to study the formation and expansion of rock mass fracturing fracture network, the activation of faults and the deformation and damage process of wellbore casing during the whole process of fracturing at different stages of horizontal well.

The invention provides an oil-gas well connectivity evaluation method and device, and relates to the technical field of oil-gas field development. The method comprises the following steps: acquiring monthly production data of an oil-gas well, and performing feature extraction on the monthly production data of the oil-gas well to obtain multi-dimensional dynamic features of a quantifiable dynamic injection-production relationship; and performing oil-gas well connectivity evaluation on the multi-dimensional dynamic characteristics based on a preset oil-gas well connectivity evaluation model to obtain an oil-gas well connectivity evaluation result. The device executes the method. According to the oil and gas well connectivity assessment method and device, the oil and gas well connectivity can be efficiently and accurately assessed in real time.

The invention discloses digital intelligence powder type distribution-free variable viscosity fracturing fluid skid-mounted equipment and a control method thereof, and belongs to the technical field of oil and gas field development. The equipment comprises a stirring mechanism, a feeding mechanism, a water feeding mechanism and a discharging mechanism which are centrally controlled through a control box. The feeding mechanism adopts a first screw conveyor to convey solid materials to the stirring tank body; the water supply mechanism supplies water to the tank through a water supply pump and a water outlet pipe with an electromagnetic valve, and the water quantity is monitored by a water inlet flow sensor; the discharging mechanism outputs the mixed liquid through a discharging pump, and the viscosity of the fracturing fluid is detected in real time through a viscosity sensor. The control box is connected with all the sensors and execution components, and automatic adjustment and closed-loop control of the material proportion are achieved by collecting flow and viscosity data. According to the invention, accurate and automatic preparation of the fracturing fluid is realized, the problems of inaccurate traditional manual proportioning and poor stability are effectively solved, and the fluid preparation quality and the operation efficiency are remarkably improved.

The invention provides a heavy oil thermal-flow coupling development experimental device and method based on microwave-assisted gravity drainage, and belongs to the field of oil-gas field development. The experimental device comprises a three-dimensional oil reservoir simulation system, a microwave-assisted heating system, a horizontal well injection-production regulation and control system and a multi-field monitoring system; the microwave auxiliary heating system comprises a microwave generation regulation and control system, a microwave transmission system and a shaft integrated microwave radiator, the microwave generation regulation and control system comprises an industrial grade microwave generator, a power regulator and a PLC, and the microwave transmission system comprises a microwave coaxial cable and a microwave waveguide tube bundle; and the microwave generator is continuously connected with the microwave waveguide tube bundle and the shaft integrated microwave radiator. According to the heavy oil thermal-flow coupling development experimental device and method based on microwave-assisted gravity oil drainage, the technical problems that an existing deep heavy oil thermal recovery experimental device cannot reproduce a'thermal-flow-force 'multi-field coupling environment, the microwave heating adaptability is poor, and the experimental scale and the field deviation is large are solved. The heavy oil thermal-flow coupling development experimental device and method based on microwave-assisted gravity oil drainage solve the technical problems that an existing deep heavy oil thermal recovery experimental device cannot reproduce the'thermal-flow-force' multi-field coupling environment.

The invention relates to the technical field of oil and gas field development, in particular to a gas drive front description method based on a multi-modal fusion deep learning model. The method comprises the steps of collecting multi-modal data, and constructing a multi-modal comprehensive database; performing data preprocessing and cleaning on the multi-modal comprehensive database; constructing a multi-modal fusion deep learning model; carrying out training and hyper-parameter optimization on the multi-modal fusion deep learning model; inputting real-time data into the trained model, and outputting a leading edge prediction result; and according to an output prediction result, new data meeting requirements are added into a multi-modal comprehensive database, model incremental training is triggered, and parameters are updated to adapt to dynamic changes of the oil reservoir. And rapid, accurate and global dynamic prediction of the gas-driven front is realized.

The invention provides a method and a device for measuring the solid-phase deposition amount of crude oil under high-temperature and high-pressure conditions, and relates to the technical field of oil-gas field development. Under the reservoir temperature and pressure of a target oil reservoir, ground degassed crude oil, associated gas and an on-site solid-phase sediment sample are mixed and dissolved; an in-situ crude oil sample capable of truly reflecting in-situ crude oil components is prepared; then, reducing the pressure to a target pressure point in a stepped manner at a constant temperature, and delimiting a deposition monitoring area; spectral image data of the area is obtained, spectral reflection reference rate is analyzed and calculated pixel by pixel, and a spectral distribution matrix is constructed; judging whether the solid-phase deposition reaches balance or not by calculating a space consistency index and a time convergence index of the matrix; finally, the solid-phase deposition amount is calculated after deposition equilibrium; according to the method, the problem of solid-phase deposition measurement distortion under the ultrahigh-pressure oil reservoir condition is solved, and interference-free, real-time monitoring and quantitative analysis in the deposition process are achieved.

The invention relates to the technical field of oil and gas field development, in particular to an ultra-deep gas well production string multi-mode leakage simulation experiment device and method. According to the technical scheme, the outer side of a shaft temperature and pressure coupling simulation system is connected with a reservoir gas supply simulation system, the middle of the shaft temperature and pressure coupling simulation system is provided with a multi-mode leakage simulation assembly, the lower end of a production oil pipe is connected with a reservoir-shaft coupling connector, and the upper portion of one side of a layered sand filling seepage tank of the reservoir gas supply simulation system is connected with a confining pressure loading unit through a pipeline; the lower portion of the layered sand filling seepage tank is connected with a reservoir fluid supply unit, and the outlet end of the layered sand filling seepage tank is connected with a reservoir-shaft coupling connector through a pipeline. The experimental device has the beneficial effects that the experimental device for coupling the reservoir and the shaft is constructed, and the multi-mode leakage simulation assembly is adopted to realize body perforation leakage simulation and screw thread sealing failure simulation, so that the simulation of the ultra-deep gas well under complex working conditions is realized, and a scientific basis and a technical support are provided for leakage prevention and control of a production string of the ultra-deep gas well.

The invention relates to the technical field of oil and gas field development and production, and discloses a rapid detection device and method for oil and gas well tubular column sealing thread damage injection agent repair, and the device comprises a pressurization mechanism, a pressure container and a thread damage assembly; the pressurizing mechanism comprises a liquid supply assembly, a manual hydraulic pump and a pipeline assembly, the liquid supply assembly is installed on the manual hydraulic pump, and the pipeline assembly is installed at the output end of the manual hydraulic pump; the pressure container comprises a sealed container and a piston, the piston is slidably connected to the inner side wall of the sealed container, and the bottom of the sealed container communicates with the pipeline assembly; the thread damage assembly comprises a conduit assembly, a pressure gauge and an external plug; the conduit assembly is communicated with the top of the sealed container; the external plug is in threaded connection with the end, away from the sealed container, of the guide pipe assembly. According to the invention, two states of thread slippage and damage leakage can be simulated, and downhole conduit thread leakage simulation and rapid detection of chemical injection agent repair are realized.

The invention relates to the technical field of oil-gas field development and reservoir protection, in particular to a low-permeability tight reservoir clay stabilizer mixing amount optimization method, which comprises the following steps: firstly, enabling a rock sample and clay stabilizer solutions with different concentration gradients to generate imbibition to obtain nuclear magnetic signal peak areas corresponding to different imbibition moments; obtaining a liquid inlet amount change rate K sequence and a permeability change rate K sequence; then obtaining a first weight coefficient alpha and a second weight coefficient beta; and finally, calculating a comprehensive decision index CDI sequence. Taking the concentration of the clay stabilizer solution corresponding to the permeability change rate K less than or equal to the maximum permeability damage threshold value Kmax and the minimum value of the comprehensive decision index CDI as the optimal clay stabilizer concentration; according to the method, accurate and differentiated optimization of the mixing amount of the clay stabilizer is achieved, the defects of an existing static evaluation method are overcome, and the method has important significance for protecting a low-permeability tight reservoir and improving the recovery ratio.

The invention belongs to the technical field of oil and gas field development, and discloses a fractured gas reservoir fracturing crack complexity evaluation method and related device.The method comprises the steps that stratum data and construction data of an earlier-stage reconstructed well in a work area are collected; the G function pressure filtration occurrence frequency, the well bottom net pressure fluctuation frequency and average fluctuation amplitude of the prepad fluid stage and the fracture net extension duration proportion are calculated respectively; calculating correlation coefficients between the G function pressure filtration occurrence frequency, the bottom hole net pressure fluctuation frequency and average fluctuation amplitude of the prepad fluid stage, and the fracture network extension duration ratio and the transformation volume; and calculating a crack complexity index based on the correlation coefficient. According to the method, calculation is carried out according to on-site construction parameters, the complexity of cracks formed in the construction process can be quantitatively described conveniently, construction parameter optimization and production system optimization can be effectively guided, long-term stable production of gas reservoirs is guaranteed, and therefore large economic benefits are obtained.

The invention discloses a displacement potential energy gradient oriented injection-production reconstruction optimization method between fracturing segments, and relates to the technical field of oil and gas field development. The method comprises the following steps: firstly, establishing an oil reservoir three-dimensional numerical model according to oil reservoir interpretation data, simulating in an oil reservoir numerical simulator by using the oil reservoir three-dimensional numerical model to obtain a global pressure field and a fluid flow velocity change condition in the oil reservoir, constructing a fluid potential field model, and determining potential energy gradients among injection-production units; constructing an objective function and setting constraint conditions by taking the most balanced potential energy gradient distribution among all injection and production units in the fluid potential field model as an optimization objective, solving the objective function based on a particle swarm algorithm, and optimizing the horizontal well multi-stage fracturing inter-section injection and production scheme to obtain the optimal horizontal well multi-stage fracturing inter-section injection and production scheme. The control device is used for controlling the injection-production process device between the multistage seam sections of the horizontal well, quantitative characterization and active control of an oil reservoir fluid potential field are achieved, and a basis is provided for improving the injected fluid sweep volume and the crude oil recovery rate of an oil reservoir.

The invention discloses a CO2 well flooding oil casing pressure intelligent control and anti-freezing and anti-blocking system, and relates to the technical field of oil and gas field development engineering.The CO2 well flooding oil casing pressure intelligent control and anti-freezing and anti-blocking system comprises the steps that a controller starts a self-checking program, loads a preset target pressure value, a preset temperature early warning threshold value and a preset ice blocking critical temperature parameter, completes initial configuration, and sends the initial configuration to the controller; zero calibration and precision verification are carried out on the front-end pressure sensor, the rear-end pressure sensor and the temperature sensor, front-end pressure data, rear-end pressure data and temperature data are collected, data filtering and abnormal value removing processing are carried out, and the real-time temperature data and CO2 physical property parameters are obtained. According to the invention, hardware self-inspection and sensor precision calibration are realized through the controller initialization module, the data acquisition module completes multi-source data synchronous acquisition and filtering processing, and the dynamic risk assessment module based on an improved Span-Wagner equation and a fuzzy logic algorithm can accurately calculate a CO2 phase change critical point and output a quantitative risk index. The problems of energy waste and untimely freezing and blocking prevention caused by a traditional fixed threshold value method are solved.

The invention belongs to the technical field of oil and gas field development engineering and multiphase flow numerical simulation, and relates to a rapid prediction method for gas-liquid two-phase flow key flow parameters of a horizontal gas well. Constructing the mixed momentum equation, the mass conservation equation and the drift velocity constitutive relation into a partial differential equation residual group; secondly, constructing a physical information Fourier operator network architecture, and learning integral operator mapping from working condition parameters to flow field distribution in a frequency domain by using a lifting layer, a Fourier layer and a projection layer; and finally, calculating a partial derivative of an output variable relative to a space-time coordinate by utilizing an automatic differential technology, and constructing a loss function fusing a data error and a physical PDE residual error to carry out model training. According to the method, millisecond-level accurate prediction of the pressure, the liquid holdup and the flow velocity field of the whole wellbore is achieved, and the problems that traditional numerical simulation calculation is long in time consumption, the real-time monitoring requirement is difficult to meet, and a pure data driving model lacks physical consistency are solved.

The invention discloses a method and a system for evaluating yield-increasing and injection-increasing aerodynamic force composite plug removal capability of medium-low permeability reservoirs, and relates to the technical field of oil-gas field development. The method comprises the following steps: determining to-be-measured well types, including oil wells or water wells; based on the type of the well to be measured, whether measures are taken or not is judged according to well selection conditions; if measures are taken, well deviation data are collected, and the maximum well deviation position and the maximum well deviation size are found; determining a transmission type according to the well deviation; aerodynamic force operation is carried out according to the transmission type; acidizing operation is carried out after the aerodynamic force operation; and evaluating the effect of the oil well or the water well after the measures are taken. The method solves the problem that large-scale transformation of the fractured thin and poor layer of the medium-low permeability reservoir is difficult to realize, so that reserves are difficult to effectively use; the problems that traditional fracturing transformation measures are long in construction period, high in measure cost and poor in economic benefit are solved. The problem that there is no effective treatment means for a thin layer or a thin interlayer is solved; and the problem of difficulty in measure effect evaluation after traditional process measures is solved.

The invention discloses an oil-based drilling fluid performance parameter prediction method, and relates to the technical field of oil-gas field development. According to the method, firstly, multiple training sets are obtained, each training set comprises a drilling fluid formula and temperature which serve as input and drilling fluid performance parameters which serve as output, then the training sets are used for training an Adaboost-BP prediction model, and the trained model can be used for prediction; the Adaboost-BP prediction model takes a three-layer BP neural network as a sub-model, and meanwhile, a feature enhancement-fusion module is arranged on a hidden layer of the sub-model. According to the method, multiple key indexes of the oil-based drilling fluid can be predicted at a time, and compared with a traditional method, the prediction efficiency is improved, and the application range is widened.

The invention discloses an oil reservoir history fitting method based on graph lasso and set smooth multi-data assimilation, and relates to the technical field of oil and gas field development. The method comprises the following steps: firstly, acquiring to-be-optimized model parameters and observation data of a target oil reservoir, constructing an observation error covariance matrix and initializing a model parameter set, performing numerical simulation by utilizing an oil reservoir numerical simulator to obtain prediction data, constructing a joint state matrix, performing dimensionless standardization processing on the joint state matrix to obtain an empirical correlation coefficient matrix, and then, performing optimization on the joint state matrix. And executing a graph lasso algorithm combined with an extended Bayesian information criterion to obtain an optimal dimensionless sparse precision matrix, extracting correlation coefficient sub-blocks required by Kalman updating from the optimal dimensionless sparse precision matrix based on a Scherr's theorem, obtaining a robust data auto-covariance matrix through a reverse reduction physical quantity outline, calculating Kalman gain in combination with an observation error covariance matrix, and calculating a Kalman filter. And the model parameter set is updated until the preset condition is met, the reservoir history fitting model parameter set is output, and the stability and precision of reservoir automatic history fitting are improved.

The invention discloses an oil and gas shaft multiphase flow simulator MphaseSim modeling method and a PINN solving method, and relates to the technical field of oil and gas field exploitation, and the method comprises the following steps: pre-calibrating a modeling strategy, including calibrating gas-liquid two-phase flow, and calculating the average flow velocity according to the section occupied by the gas-liquid two-phase flow; mass transfer can occur between the calibrated gas and liquid phases, but the gas and liquid phases are always in a thermodynamic equilibrium state; in calibration steady-state analysis, the mass flow of any section of the shaft is equal, and the liquid holdup is calculated through a drift-assisted equation. According to the method, on the basis of a mass-momentum-energy coupling mathematical model, the SRK state equation and the drift flow model are combined, the phase change characteristics, momentum transfer and energy conversion rules of the gas phase and the liquid phase can be accurately represented, the model sealing performance is good, and the physical significance is clear; and the device can be stably applied to different types of oil and gas well scenes such as gas wells, deep oil wells and electric submersible pump wells, has no obvious numerical value divergence problem in long shaft flow simulation, and can effectively reflect the real production state of the shaft.

The embodiment of the invention relates to the technical field of oil and gas field development, in particular to a fracturing simulation device and method.The fracturing simulation device comprises a pump injection system, a reconstruction system, a flowback system and a monitoring system; the pump injection system is used for injecting sand-carrying fracturing fluid into the horizontal shaft; the reconstruction system is used for performing multi-cluster fracturing on the rock sample based on the sand-carrying fracturing fluid injected into the horizontal shaft; inverting rock sample multi-cluster fracturing to obtain fracture form data of the rock sample; the flow-back system is used for enabling sand-carrying fracturing fluid to flow back to the horizontal shaft from rock sample cracks after rock sample multi-cluster fracturing is finished; the monitoring system is used for acquiring migration data of the sand-carrying fracturing fluid corresponding to the fracture form data so as to determine a fracturing scheme according to the migration data, and the migration data at least comprises first migration state data of the sand-carrying fracturing fluid in the process of flowing back to the horizontal shaft.

The invention provides an oil reservoir inter-well communication path dynamic evolution intelligent prediction method and system based on permeability change, and relates to the technical field of intelligent oil and gas field development. The method comprises the following steps: preprocessing static seismic multi-attribute, initial permeability and production dynamic data, and extracting multi-scale features; establishing a dynamic permeability and static prior fusion mechanism, and generating a dynamic weight connectivity graph; on the basis, reinforcement learning path search is adopted, dynamic re-planning is achieved through time sequence rolling, and inter-well communication paths and migration rules are obtained; a non-dominated solution set and a Pareto frontier are formed in combination with multi-objective optimization of path length and cumulative flow capacity, and candidate schemes are provided for different engineering preferences; and finally outputting a spatio-temporal evolution prediction result of the path and a matched adjustment suggestion. The method realizes the fusion of static and dynamic data, breaks through the limitation of depicting the reservoir only by depending on static earthquakes, and remarkably improves the development effect and decision scientificity of the marine facies sandstone reservoir.

The invention discloses a fracturing crack fine characterization method and system based on microseism monitoring data, and relates to the technical field of oil and gas field development, and the method comprises the steps: obtaining basic parameters of a target well; according to the well completion data of the target well, the micro-seismic event points of all the fracturing sections of the target well are preprocessed, and the preprocessed micro-seismic event points of all the fracturing sections are obtained; noise reduction processing is carried out on the preprocessed micro-seismic event points of all the fracturing sections, and the micro-seismic event points of all the fracturing sections after noise reduction are obtained; clustering the microseismic event points after noise reduction according to the number of perforation clusters of the fracturing section, and determining effective microseismic event points of each perforation cluster; on the basis of the effective microseismic event points of each perforation cluster, through a crack surface fitting method, respectively obtaining characterization parameters of each perforation cluster crack; and according to the characterization parameter of each perforation cluster crack in the target well, obtaining a fracturing crack fine characterization result of the target well. According to the invention, fine characterization of the fractured crack can be realized.

The invention belongs to the technical field of oil and gas field development engineering, and particularly relates to a construction method of an emulsion generation kinetic model. The method is based on a micro-fluidic experiment, comprehensively considers seven factors of pore throat radius, pore throat form, pore throat coordination number, displacement environment temperature, surfactant concentration, injection speed and crude oil viscosity, and determines an undetermined coefficient of a function by utilizing a least square method through single factor analysis and orthogonal experiment scheme design; therefore, the functional relationship among the pore throat radius, the pore throat form, the pore throat coordination number, the displacement environment temperature, the surfactant concentration, the injection speed, the crude oil viscosity and the emulsion generation rate and the generation radius is defined, and an emulsion generation kinetic model is constructed, so that an emulsion generation theoretical system under the multi-factor coupling effect is perfected; and reference is provided for other similar chemical flooding.