57 results about "Reservoir management" patented technology

The invention discloses an intelligent water level monitoring system and a data transmission method thereof, and belongs to the technical field of Internet of Things sensing. The system comprises a distributed water level sensing node, an edge computing gateway and a cloud management platform, wherein the sensing node is integrated with a piezoresistive water level transmitter and an adaptive Kalman filtering module; the data transmission method comprises a dynamic threshold triggering mechanism and a compression algorithm based on improved Huffman coding, and low-power-consumption and high-reliability transmission is realized by optimizing transmission frequency and a data packet structure. The edge computing gateway is internally provided with an LSTM water level prediction model, historical data are analyzed through an LSTM neural network, and an early warning signal is generated in real time. And the cloud management platform stores the key data by adopting a block chain technology. Through multi-sensor data fusion and layered encryption transmission, the water level monitoring error rate is reduced to + / -0.5 cm, the data transmission energy consumption is reduced by 40%, and the monitoring efficiency and the data safety are remarkably improved in flood early warning and reservoir management scenes.

The invention discloses a reservoir management optimization method and system based on digital twinning. The method comprises the steps of multi-source data acquisition and virtualization mapping, digital twinning modeling, reservoir risk early warning and reservoir management decision optimization. The invention relates to the technical field of reservoir intelligent management, in particular to a reservoir management optimization method and system based on digital twinning, and the method comprises the steps: constructing a virtualization data field through multi-Bezi wavelets and fractal interpolation, and carrying out the self-learning of dynamic weight fusion through a time sequence convolution network; constructing a macroscopic twinborn body and a microcosmic twinborn body, and coupling through an asynchronous message bus; constructing multi-source risk vectors of water pressure gradient, over-overflow water level, dam body stress and seepage flow, adopting clustering and spatial diffusion, generating a dynamic risk thermodynamic diagram, and performing early warning; by taking flood control safety, water supply guarantee and ecological environment as multiple targets, a multi-target particle swarm optimization model and flow constraints are established, an optimal scheduling scheme is solved and executed in real time, and high-precision simulation, precise early warning and intelligent scheduling are realized.

The invention discloses an oil reservoir injection-production optimization method based on an improved multi-objective particle swarm algorithm, and belongs to the technical field of oil and gas field development. According to the method, an attention mechanism, a Transform and a long-short-term memory network are introduced based on a relational graph convolutional network, geological features and production system information are combined, inter-well water content prediction is taken as a target, an HSTMF model is constructed, an injection-production well production system does not need to be adjusted, the calculated amount is greatly reduced, and the prediction precision is remarkably superior to that of a traditional method; in the aspect of optimization algorithms, a Cauchy disturbance variation mechanism is introduced into a genetic algorithm and combined with a particle swarm optimization algorithm to form a new optimization strategy, a real-time closed-loop collaborative oil reservoir management framework is constructed in combination with the moisture content prediction capability of an HSTMF model, the net present value and the total oil production are effectively increased, the total moisture content is reduced, and good practical application value is shown.

The embodiment of the invention provides a reservoir capacity dynamic monitoring method and system based on an unmanned aerial vehicle, and the method comprises the steps: obtaining a reservoir surface feature data set through the unmanned aerial vehicle, determining the boundary of a water area, scanning the bottom of the reservoir, obtaining the underwater terrain elevation and sediment distribution data, and constructing a three-dimensional terrain model; and determining terrain and deposit change data based on the three-dimensional terrain model dynamic change data of the continuous time sequence. Sedimentation space distribution characteristics are obtained through change rate analysis, and the sediment transportation amount and the sedimentation and deposition space rule are determined in combination with the water flow speed. Key driving factors are screened through correlation analysis, a sedimentation high-risk area is determined according to the key driving factors, and the change of sediments in the area is monitored, so that the change of the reservoir capacity is mastered. The reservoir siltation can be accurately and dynamically monitored, the high-risk area and the change mode are defined, the dynamic database is constructed to update the capacity change in real time, and important technical support is provided for reservoir management and flood control and disaster reduction.

The invention discloses a reservoir dam safety risk level assessment method, and particularly relates to the technical field of reservoir dam risk assessment. The method comprises the following steps: acquiring real-time stress data of a reservoir dam, spillway drainage capacity data and reservoir water level change data, and performing abnormal feature extraction and prediction on the data to generate related abnormal values; performing weighted analysis on the plurality of risk factors by adopting a fuzzy comprehensive evaluation method, and calculating a comprehensive risk score of the dam; comparing the comprehensive risk score with the classification threshold of the risk level, and dividing the safety risk level of the reservoir dam; corresponding early warning information and emergency treatment measures are output based on the evaluation result, safe operation of the reservoir dam is ensured, the method can monitor the safety state of the reservoir dam in real time, potential risks can be early warned in time, and scientific decision support is provided for reservoir management personnel.

A system for managing well site operations comprising a well site operations module, a chain of blocks of a distributed network, and a sensor bank and control module. The operations module generates earth model variables using a physics model, well log variables or seismic variables, or both, and a trained AI / ML algorithmic model. The chain of blocks comprises a plurality of subsequent blocks. Each subsequent block comprises a well site entry and a hash value of a previous well site entry. A well site entry comprises transacted operation control variables. The well site operations module generates production operation control variables or development operation control variables from earth model variables. The well site entry can also include transacted earth model variables and sensor variables. The sensor bank and control module provides well log variables and the operations module couples control variables to the control module to control well site equipment.

The invention provides a reservoir water level real-time monitoring and early warning system and method based on the Internet of Things, and the method comprises the steps: extracting credible water level time sequence information from the water level monitoring data of a target reservoir dam body, and determining the potential risk intensity of the target reservoir dam body under the water level change according to a preset sliding time window in combination with the credible water level time sequence information; the potential risk intensity is input to a preset water level early warning unit, the water level early warning unit is synchronously connected into a reservoir capacity-pressure distribution diagram deduced by the hydrological model in real time, and a safe bearing boundary of the target reservoir dam body structure under the current water level is determined according to the reservoir capacity-pressure distribution diagram; determining a water level grading early warning value of the target reservoir dam body under the current water level according to the safety bearing boundary in combination with the potential risk intensity; and sending a water level grading early warning signal of the target reservoir dam body to a reservoir management terminal based on the water level grading early warning value. According to the technical scheme provided by the invention, water level grading early warning can be carried out under the time-varying risk caused by accumulative damage of the dam body structure of the reservoir.

The embodiment of the application provides a reservoir capacity dynamic monitoring method and system based on a UAV, which comprises the following steps: obtaining a reservoir surface layer feature data set through a UAV, determining a water area boundary and scanning a reservoir bottom according to the data set, obtaining underwater topographic elevation and silt distribution data, and constructing a three-dimensional topographic model; determining topographic and silt change data based on dynamic change data of the three-dimensional topographic model of a continuous time sequence. Through change rate analysis, silt space distribution characteristics are obtained, and the amount of silt transport and the silt deposition space rule are determined in combination with water flow velocity. Key driving factors are screened through correlation analysis, a high-risk silt area is determined, silt change in the area is monitored, and thus the reservoir capacity change is mastered. The application can accurately and dynamically monitor reservoir siltation, clearly determine a high-risk area and a change mode, construct a dynamic database, update the capacity change in real time, and provide important technical support for reservoir management and flood control and disaster reduction.

The invention discloses equipment and a method for predicting, controlling and dispatching the scouring and silting amount of a downstream river channel of a dam, which are taken into account the influence of a flood process on the scouring and silting amount of the river channel and comprehensively consider the influence of water and sediment elements and flood characteristics on the scouring and silting amount of the downstream river channel of the dam. Water surface gradient, flow, peak pattern coefficient, jacking coefficient, sediment content and sediment grading elements are incorporated to predict the erosion and deposition amount of the downstream river channel of the dam, a calculation formula structure of the erosion and deposition amount of the downstream river channel of the dam and a matched calculation method are constructed, a corresponding table of the erosion and deposition amount of the downstream river channel of the dam is compiled according to the method, and reverse derivation can be achieved through table query. When the scouring and silting amount of the downstream river channel of the dam is controlled to be a fixed value, key dispatching parameters such as reservoir outlet flow and peak pattern coefficient required to be regulated and controlled by an upstream reservoir; a reservoir management department can formulate a scientific reservoir optimization scheduling scheme through the method, technical guarantee is provided for river regime stability and bank slope protection of a downstream river channel of a dam, flood control standard improvement and shipping safety maintenance are effectively supported, and a decision basis is provided for exerting comprehensive benefits of the reservoir.

This invention relates to the field of reservoir engineering management technology, specifically a reservoir lifecycle management system based on spatiotemporal matrix and digital twin. The system includes a digital twin model construction module, a spatiotemporal matrix module, a data fusion module, a lifecycle simulation module, a real-time data acquisition and update module, a model dynamic adjustment module, and a management decision support module. The system uses a spatiotemporal matrix to uniformly organize and manage multi-source data throughout the reservoir's lifecycle, utilizes a digital twin model to construct a virtual representation of the reservoir, achieves bidirectional synchronization through data fusion, and dynamically adjusts model parameters based on real-time data, ultimately generating lifecycle management decision outputs. This invention solves the problems of scattered data and static models in existing technologies, improving the accuracy and reliability of reservoir management.

The invention provides a method, system and device for depicting a strike-slip fault zone transverse structure and a medium, and the method comprises the following steps: obtaining fault zone data, and building a strike-slip fault zone transverse structure geologic model; depicting the strike-slip fault zone in the strike-slip fault zone transverse structure geologic model by applying geophysical attributes to obtain a depicting result; three-dimensional space modeling is carried out on the strike-slip fault zone based on the depiction result, and transverse structure depiction of the strike-slip fault zone is completed; according to the method, accurate depiction of transverse different zone spaces can be achieved, when the method is used for fault zone well pattern deployment, the drilling encounter rate, the drilling success rate and the efficient well proportion of a reservoir can be increased, and then the carbonate oil and gas reservoir management and development level is improved.

The embodiment of the invention provides a thin interlayer and phase control physical property cause sand body configuration model construction method and system, and belongs to the technical field of reservoir modeling. The method comprises the following steps: identifying fluvial facies reservoir distribution characteristics based on exploration data, and constructing a corresponding cause sand body reservoir configuration model; obtaining a corresponding small-layer sand body deterministic model based on the cause sand body reservoir configuration model; on the basis of the deterministic model of the small-layer sand body, performing representation of an internal configuration interface of the sand body based on a lateral lamination model and an adaptive triangular mesh generation algorithm; and generating a distribution model of each lithofacies unit based on representation of a configuration interface in the sand body, and constructing a physical property model for fine modeling and heterogeneity description of high and low permeability sections in the configuration based on a phase control stochastic simulation method. According to the scheme, the limitations of insufficient precision, unreasonable transition and low calculation efficiency in traditional modeling are effectively overcome, and high-precision geologic model support is provided for reservoir management and oil reservoir development.

A reservoir management method includes: in response to receiving input data to which label information is mapped, determining whether to add the input data to a reservoir based on a sampling probability; in response to determining to add the input data to the reservoir when the reservoir is filled, selecting candidate data to be removed from among sets of sample data included in the reservoir based on a target label distribution and a current label distribution of the reservoir, and removing the selected candidate data from the reservoir; and training a neural network model using sample data of the reservoir from which the selected candidate data is removed.

Implementations described and claimed herein provide systems and methods for an integrated development optimization platform for well sequencing and unconventional reservoir management. The platform integrates key elements of unconventional development planning, such as production forecast, lease obligations, surface facilities, and economics and provides analysis and data associated with past and future field development and production. In addition, development optimization platform includes the parent-child relationship as one of the determining factors of production performance, which can provide valuable insights into the frac-hit impact and infill performance. The defensive re-fracs may also be incorporated to provide a more holistic view on project investment and field development. The development optimization platform is not only an optimization platform for well sequence and development planning, but also a reservoir management tool.

The invention relates to the technical field of reservoir water level monitoring, and discloses a multi-sensor integrated reservoir water level monitoring method, system and device and a medium, and the method comprises the steps: obtaining the measurement values of all water level monitoring sensors in a target reservoir range, and carrying out the first elimination operation of the measurement values of all water level monitoring sensors; establishing a measurement input vector according to the measurement values of the plurality of water level monitoring sensors after the first elimination operation is completed; and establishing a filter set, and inputting the measurement input vector into the filter set to obtain a target state value. The precision of water level monitoring is improved, and the stability and reliability of the system are remarkably enhanced. In practical application, the system can accurately reflect the change condition of the water level of the reservoir area in real time, and provides an important decision basis for reservoir management, flood control scheduling and the like.

The application provides a reservoir water level real-time monitoring and early warning system and method based on Internet of Things, which extracts credible water level time sequence information from water level monitoring data of a target reservoir dam body, determines potential risk intensity of the target reservoir dam body under water level change according to a preset sliding time window and the credible water level time sequence information, inputs the potential risk intensity into a preset water level early warning unit, synchronously accesses a reservoir capacity-pressure distribution map derived in real time by a hydrological model, and determines a safe bearing boundary of the target reservoir dam body structure under the current water level according to the reservoir capacity-pressure distribution map, determines a water level grading early warning value of the target reservoir dam body under the current water level according to the safe bearing boundary and the potential risk intensity, and sends a water level grading early warning signal of the target reservoir dam body to a reservoir management terminal based on the water level grading early warning value. The technical scheme provided by the application can perform water level grading early warning under time-varying risk caused by cumulative damage of a reservoir dam body structure.

The utility model relates to the technical field of reservoir management, in particular to a reservoir ice melting device which is characterized in that a movable frame is connected in an open frame in a sliding manner, connecting plates are fixedly connected to two sides of the upper end of the movable frame, each connecting plate penetrates through the movable frame, a plurality of limiting holes are formed in each connecting plate at equal intervals in the length direction, and the limiting holes are communicated with the movable frame. Fixing bolts are connected to the two sides of the open frame, each fixing bolt penetrates through the corresponding limiting hole, an icebreaking mechanism is fixedly connected into the movable frame, and a driving machine is fixedly connected to the movable frame. The connecting plate drives the movable frame to move, the movable frame drives the icebreaking mechanism to move in the vertical direction, the height of the icebreaking mechanism is adjusted, after the height of the icebreaking mechanism is determined, the fixing bolts are connected to the open frame, the fixing bolts penetrate through the limiting holes to fix the connecting plate, and therefore the movable frame is fixed, and the icebreaking mechanism is fixed. The height of the icebreaking mechanism can be adjusted according to the thickness of the ice surface, and the ice chiseling effect on the ice layer is improved.

The application discloses a kind of reservoir water resource optimal allocation method based on multi-stage multi-objective belonging to water resource management technical field, comprising the following steps: A, predict the water resource demand, water resource available quantity, social economy and other basic data in basin;B, with the maximum unit water resource utilization benefit as target, with reservoir water balance, irrigation and power generation water demand, salinity leaching and so on as constraint condition, construct water resource optimal allocation model;C, coupling fixed combination stochastic programming and fractional programming method is solved to model, according to the water resource shortage obtained under different probability level arrangement production to reach maximum unit allocation water benefit.The application fully considers the necessity of joint management of upstream hydropower generation and downstream agricultural irrigation in reservoir management system, reasonably allocates water resources, land resources and energy in the basin, obtains the maximum unit allocation water benefit, so as to relieve water contradiction and improve the utilization efficiency of resources.

This invention relates to the field of water resource management technology, specifically to an artificial intelligence-based cascade reservoir ecological scheduling system, comprising a multi-source data integration and processing module, an ecological impact prediction module, a scheduling strategy optimization module, an adaptive water quality management module, and an intelligent user interaction module. Specifically: the multi-source data integration and processing module collects various data from meteorological stations, hydrological stations, and ecological monitoring points; the ecological impact prediction module constructs an ecological impact model; the scheduling strategy optimization module formulates reservoir scheduling strategies; the adaptive water quality management module adjusts the reservoir's outflow and regulation strategies; and the intelligent user interaction module provides a human-machine interface. This invention, by integrating multi-source real-time data, applying reinforcement learning to optimize scheduling strategies, and providing an efficient user interface, significantly improves the real-time responsiveness and decision-making efficiency of reservoir management, while ensuring optimal utilization of water resources and protection of the ecological environment.

A method and system for real-time monitoring of the dynamic water level and storage capacity of a reservoir based on GNSS-RTK belongs to the fields of environmental monitoring, environmental governance and computer control technology. The system includes a power supply system, a water level monitoring system, a control system and a data storage and display system. The steps are: select several fixed measuring stations around the reservoir, install high-precision GNSS receivers, and conduct real-time continuous observation of mobile stations in the reservoir area; transmit the real-time collected data to the data processing center via wireless communication or the Internet; calculate the real-time water level of the reservoir based on the buoy position information and the functional relationship between the water level and the position; substitute the real-time measured water level data into the volume calculation formula to calculate the real-time storage capacity of the reservoir; store the calculated water level and storage capacity data in a database, and display them in real time using visualization software. The present invention has the advantages of high real-time performance, high precision and strong comprehensiveness, and is suitable for the field of reservoir management and operation.

The application discloses a reservoir data matrix management platform and method based on artificial intelligence, relates to the technical field of reservoir data management, and comprises a reservoir three-dimensional simulation module, a reservoir data analysis module, a water quality factor index prediction module, a warning module, a water supply and diversion scheduling plan generation module and a reservoir management module; the reservoir three-dimensional simulation module is used for constructing a real-time three-dimensional simulation model of a target reservoir; the reservoir data analysis module is used for generating real-time water supply and diversion scheduling characteristics and basin flow characteristics of the target reservoir in each basin; the water quality factor index prediction module is used for predicting real-time water quality factor indexes of each basin of the target reservoir; and the warning module selectively sends a warning signal according to real-time water supply and diversion scheduling warning values of each basin of the target reservoir. Through mutual support among the modules, the application realizes matrix management of the target reservoir, and further guarantees safe operation of the target reservoir.

This invention discloses a method for assessing the safety risk level of a reservoir dam, specifically relating to the field of reservoir dam risk assessment technology. It involves acquiring real-time stress data of the reservoir dam, spillway discharge capacity data, and reservoir water level change data, extracting and predicting abnormal features from these data, and generating relevant outliers. A fuzzy comprehensive evaluation method is used to perform weighted analysis on multiple risk factors to calculate the dam's comprehensive risk score. The comprehensive risk score is compared with a risk level classification threshold to determine the reservoir dam's safety risk level. Based on the assessment results, corresponding early warning information and emergency response measures are output to ensure the safe operation of the reservoir dam. This method can monitor the safety status of the reservoir dam in real time, provide timely early warning of potential risks, and offer scientific decision support for reservoir management personnel.

The invention relates to the field of hydraulic engineering gate control, and particularly discloses an integrated intelligent hoist which comprises a volume measuring and calculating module which divides gridding subunits based on a DEM (digital elevation model), performs curved surface integral in combination with terrain curvature parameters and real-time water depth, dynamically calculates reservoir volume, and calculates the volume of the reservoir; the problem of water storage capacity misjudgment caused by complex terrain and sludge interference in a traditional water level method is solved; the gate opening trigger module generates a flood discharge instruction through volume threshold value comparison; the flood discharge monitoring module extracts a gate contour line through high-definition video monitoring and calibrates the actual opening degree in combination with an image edge detection algorithm, and it is ensured that the overflowing area is accurate and controllable. And the automatic gate closing module calculates the flood discharge required duration according to the flood discharge water amount, the overflowing area and the flow velocity, dynamically corrects the gate closing time point, and prevents excessive flood discharge or insufficient water storage. According to the method, the limitation of a traditional warning water level method and fixed opening control is systematically solved, and the accuracy, safety and resource utilization efficiency of reservoir management are improved.

The invention relates to a method, a system, a medium and equipment for acquiring a social and economic data survey range in a safety risk area of a river-shaped reservoir, and the method comprises the following steps: according to a multi-period remote sensing image of an area where a water level observation station near an upstream main dam of a reservoir main dam is located and the same-period water level monitoring data of the multi-period remote sensing image; calculating to obtain a water level elevation correction value so as to correct a reservoir check flood level and a flood control high water level; according to the DEM data, calculating the elevation of the intersection point of the river channel line and the reservoir management range surface in the reservoir tail direction, namely the reservoir tail elevation; according to the elevation of the reservoir tail and the corrected check flood level and flood control high water level of the reservoir, calculating the number of subareas of the reservoir area and the elevation reference value extracted from the survey range of each area according to the set height difference, performing subarea processing on the reservoir area along the river channel according to the number of the subareas, and extracting the survey range area by area according to the elevation reference value extracted from the survey range of each area; and obtaining a final survey range. According to the method, the social and economic data survey range in the safety risk area of the river-shaped reservoir can be quickly and accurately acquired.

The present disclosure relates to a computer-implemented method (100) for oil rig move operations, comprising: obtaining reservoir management data (110), obtaining real-time drilling data (120), estimating, using a trained artificial intelligence (Al) release date estimation engine, a release date of the oil rig based on the reservoir management data and the real-time drilling data (130), and allocating at least one resource for the oil rig move operation based on the estimated release data (140). In addition, the present disclosure relates to a corresponding system and computer program.

The present application relates to a kind of river shape reservoir safety risk area social economic data investigation range acquisition method, system, medium and equipment, it includes: according to the multiple period remote sensing image of the area where the water level observation station of reservoir main dam upstream main dam is located and its synchronous water level monitoring data, water level elevation correction value is obtained to correct reservoir check flood level, flood control high water level;According to DEM data, the intersection of river channel line and reservoir management range surface in the direction of reservoir tail is calculated, i.e.the elevation of reservoir tail;According to the elevation of reservoir tail and the corrected reservoir check flood level, flood control high water level, according to the elevation reference value of the extraction of each area investigation range, according to the number of partition, along river channel, the reservoir area is partitioned, and according to the elevation reference value of the extraction of each area investigation range, the investigation range is extracted in each area, to obtain final investigation range.The present application can realize the fast, accurate acquisition of river shape reservoir safety risk area social economic data investigation range.

The invention belongs to the technical field of reservoir management, and particularly relates to a reservoir operation and maintenance monitoring method, a terminal and a storage medium, and the method comprises the steps: dividing a reservoir into a plurality of grid monitoring regions, deploying a plurality of monitoring devices in each monitoring region, and enabling the monitoring devices to be connected with a configured looped network data collection system; the ring network data acquisition system is of a star topology structure, comprises a master node deployed in a reservoir management center and slave nodes in one-to-one correspondence with monitoring areas, and acquires data according to a preset acquisition period under the synchronous time reference, each slave node adds a synchronous timestamp to the acquired data and then uploads the acquired data to the master node, and the master node and the slave nodes are in one-to-one correspondence with the monitoring areas. And the main node integrates and generates a reservoir operation and maintenance data set. The method effectively solves the problem that the data transmission quality is difficult to guarantee due to the fact that most devices depend on wireless transmission, the position of the small reservoir is remote, and the data flow is large, avoids the situations of data loss, delay and the like, and provides reliable basis for real-time decision making of the reservoir.

The invention relates to the technical field of reservoir flood prevention devices, and provides a flood prevention early warning device for reservoir management, which comprises a support column, the upper end of the support column is connected with a solar panel, an angle adjusting structure is connected between the solar panel and the support column, the outer surface of the support column is fixedly connected with a control box through a screw, and the outer surface of the support column is provided with a water gathering cooling structure; the angle adjusting structure comprises a support fixedly connected to the top end of the supporting column, a second hinge piece is connected between the top end of the support and the solar panel, a multi-stage telescopic rod is connected between the other end of the support and the solar panel, and first hinge pieces are connected between the two ends of the multi-stage telescopic rod and the solar panel and between the two ends of the multi-stage telescopic rod and the support. The water gathering and cooling structure sequentially comprises a scraping assembly, a water collecting assembly, a cooling assembly and a recycling assembly from top to bottom. By means of the technical scheme, the problems that an existing flood prevention early warning device for reservoir management cannot collect rainwater, and a solar panel is not provided with a cleaning structure are solved, and better use prospects can be brought.

This invention discloses a digital twin modeling method for a reservoir intelligent scheduling decision support system, belonging to the field of decision modeling technology. A multi-source data fusion module integrates data from multiple sources such as satellites and UAVs, improving data quality through cleaning and spatiotemporal alignment. A physical mechanism modeling module constructs hydrodynamic and joint scheduling models to ensure simulation accuracy. A data-driven modeling module uses LSTM, graph convolutional networks, and other techniques to predict hydrological and equipment status. A twin mapping and interaction module establishes a bidirectional mapping to achieve virtual-real interaction. A decision support module provides scientific decision-making solutions based on multi-objective optimization algorithms. All modules work together to improve the intelligence level of reservoir scheduling. This invention extends the flood forecast lead time and reduces errors; optimizes multi-objective scheduling to improve the comprehensive benefits of water resources; achieves precise synchronization between the digital twin model and the physical entity, enhancing dam safety monitoring capabilities; provides intelligent decision support, shortens response time, and promotes the transformation of reservoir management towards intelligence and efficiency.

The invention discloses a multi-system combination self-control method for an offshore oilfield. The method comprises the steps that a drilling track is optimized through multi-sensor data and a geologic model, oil reservoir data are monitored in real time in combination with a seabed sensing network, and injection-production parameters and production balance are optimized through the technologies of a genetic algorithm, a neural network and the like. Meanwhile, a resource allocation scheme is generated based on an intelligent scheduling algorithm, a risk signal is handled, the digital twinborn model is updated, an oil reservoir management strategy and a production deployment scheme are finally generated, and intelligent oil field management is achieved. According to the invention, intelligent management of the whole life cycle of the submarine oil field is realized, the production efficiency and safety are improved, and a new technical scheme is provided for oil and gas development in a complex environment.