24 results about "Production forecasting" patented technology

Lowest cost usage scheduling of an energy system during a time interval of interest is achieved by utilizing two components of learning and optimization. First, several learning methods are used to forecast energy production and if needed energy consumption of a given group of energy components as a function of historical production data, weather, and solar irradiance data collected from weather and geo reports. A classification approach is used to select the learning model. A classification approach is used to select the learning model and approach. Then, an optimization problem is formulated and solved to create the optimal schedule of energy usage during time intervals of interests for the given group of energy components subject to scheduling and equipment constraints. Accordingly, integrated iterative methods, programs, and systems are described aiming at minimizing the cost of energy consumption for the given group of energy components within time intervals of interest.

The invention relates to a discrete industrial agent-based production management method and system, and relates to the field of production management, and the method comprises the steps: collecting a production prediction sample data set and a quality inspection decision sample data set, carrying out the data weight division of the two data sets, and obtaining two sample weight sets; obtaining a production prediction and quality inspection decision path array, a first prediction accuracy rate set and a decision accuracy rate set after integrated training; combining the two path arrays to obtain a discrete industrial agent array, carrying out joint optimization training, and testing to obtain a second prediction and decision accuracy set; obtaining current production basic data, inputting the current production basic data into the agent array, outputting a predicted production yield and a decision quality inspection parameter, performing compensation according to an error between the second prediction and decision accuracy set and the first prediction and decision accuracy set, and obtaining a predicted production yield and decision quality inspection parameter interval for production management. The technical problem that data interaction and business collaboration of a plurality of complex and independent scenes in the discrete manufacturing industry are difficult to realize in production management is solved.

Implementations described and claimed herein provide systems and methods for an innovative machine learning-driven approach, rooted in the fundamental physics of flow within fractured tight reservoirs for production forecasting of unconventional reservoirs. A first component of the method is to automatically analyze production data and generate characteristic attributes for linear flow and boundary-dominated flow. Following this, a Markov chain Monte Carlo process is utilized to integrate actual production data with flow regime analysis, resulting in probabilistic multi-segment decline models for production forecasting with uncertainty ranges and confidence estimation. Further, the method may include a two-step machine learning model to predict future planned wells. The two-step machine learning model may include a first aspect to generate predicted flow regime characteristics for one or more unconventional reservoirs and a second aspect to utilize the flow regime characteristics to generate the production forecast for the reservoirs.

The invention discloses a production prediction method for a flexible manufacturing system. The production prediction method comprises the steps of 1, setting algorithm preconditions and scheduling strategy requirements, 2, constructing a core element model, and 3, executing production whole-process simulation prediction. The method effectively solves the problem of production prediction of a flexible manufacturing system caused by uncertainty of varieties, processes and cutters and influence of a scheduling strategy, can simulate the whole production processing process, outputs multi-dimensional results such as work order completion time, a blank and cutter preparation list, a production Gantt chart, a production trend chart and an equipment starting rate, and improves the production efficiency. And reliable data support is provided for a user to adjust a production plan and configure resources.

This invention relates to an energy production forecasting method using a partial grey model with fractional derivatives, belonging to the field of energy production forecasting. It first selects the current monthly production values ​​of different energy sources as a database to construct an original matrix sequence X. (0) As input to the model; secondly, fractional derivatives and fractional accumulation operators are introduced when constructing the model to dynamically predict energy output under the grey effects of exponential and sine functions; finally, the simulated value X of the model is calculated. (r) , Restore value X (0) Furthermore, the model was compared with a control model in various indicators; the particle swarm optimization algorithm was used to find the optimal parameter vector that minimizes the MAPE value; finally, the new model was applied to energy production forecasting. This invention introduces exponential and trigonometric functions, giving the model's time response function oscillatory characteristics, thus accurately capturing and effectively mapping data volatility, significantly improving adaptability and flexibility; the integration of fractional derivatives and fractional accumulation operators into the model significantly improves prediction accuracy.

The application relates to the technical field of intelligent factories, and discloses a traditional handicraft and intelligent factory information fusion system and method, which comprises a data acquisition module, a data processing module, a first data analysis module, a backend management module and a second data analysis module.The data acquisition module is used for collecting first production data on a handicraft production line; the data processing module is used for obtaining the first production data, cleaning and deduplicating the first production data to obtain second production data; the first data analysis module is used for establishing a production prediction model based on the second production data to obtain a predicted production situation; the backend management module is used for receiving real-time production preset information; the second data analysis module is used for obtaining the real-time production preset information and obtaining production optimization suggestions; and the judgment module compares the predicted production situation with the production optimization suggestions to obtain a production optimization scheme.The application can effectively fuse the information of traditional handicraft and intelligent factories and improve production efficiency.

This invention belongs to the field of numerical simulation technology for oil and gas reservoir development, specifically relating to a numerical simulation method for enhanced oil recovery (EOR) through carbon dioxide injection into coalbed methane. The method includes: establishing a three-dimensional geological model, a three-dimensional geomechanical model, and a fracture network model using Petrel; establishing a numerical simulation model of coalbed methane components including fractures using the Intersect simulator, and performing historical data fitting based on historical production data; importing the geological attribute field, pressure field, and saturation field at the end of the historical data fitting into the CMG-GEM simulator to conduct thermal simulation of carbon dioxide injection into coalbed methane; importing the simulated pressure and temperature fields back into Petrel, and then using the Intersect simulator to complete the dynamic prediction of thermal drive production. This invention can consider the influence of temperature on CH4 and CO2 adsorption / desorption, improving model accuracy and significantly enhancing the accuracy of production prediction.

The invention provides a new tight oil fracturing transformation production prediction method based on modern yield decline analysis. The new tight oil fracturing transformation production prediction method based on the modern yield decline analysis comprises the steps that 1, the development condition of a fracture section is determined according to the geological characteristics of a reservoir, the reservoir fracturing transformation construction condition and microseism observation data; step 2, establishing a fracture physical model after pressure transformation; 3, establishing a matrix system mathematical model; 4, establishing a seepage mathematical model of the hydraulic fracturing system; 5, establishing a relation between the fracture conductivity and time, space and stress sensitivity; and step 6, carrying out model fitting and model solving. According to the new tight oil fracturing transformation production prediction method based on modern yield decline analysis, the influence of the space-time heterogeneity of the fractures on the production speed is considered, the method is suitable for modern yield decline analysis of the space-time heterogeneity of the fractures for tight reservoir fracturing transformation, and the fractures and reservoir parameters can be well explained.

The invention relates to the technical field of oil and gas exploitation, and provides a gas well blockage prediction method and device. The method comprises the following steps: acquiring gas well production historical data; establishing a training sample according to gas well production historical data; training a gas well production prediction model by using the training sample to predict and obtain gas well production prediction data; based on the dynamic flow process of the gas well gas, gas well blockage historical parameters corresponding to the gas well production historical data and gas well blockage prediction parameters corresponding to the gas well production prediction data are calculated respectively; whether the gas well is blocked or not is judged according to the change trend of the gas well blocking historical parameters and the gas well blocking prediction parameters; and if the gas well is blocked, calculating and comparing the maximum productivity of the wellhead when the gas well is not blocked and the maximum productivity of the wellhead when the gas well is blocked by utilizing a stratum inflow dynamic relation in the gas well according to production prediction data of the gas well, so as to determine the blocking degree of the gas well according to a comparison result. According to the embodiment of the invention, accurate and efficient prediction of gas well blockage can be realized.

The application discloses a kind of based on Monte Carlo simulation's bayesian random fluctuation natural gas production prediction method, it is related to natural gas production prediction technical field.The application obtains natural gas historical collection data, carries out target function construction, calculates the initial value of yield probability;According to yield probability initial value, introduce Monte Carlo error judgment mechanism, carry out mean square error estimation calculation to natural gas production by natural gas collection historical factor data;Natural gas collection historical factor data is unfolded to multiple factors, and a multiple factor judgment weight model is formed, and the multiple factor judgment weight model is predicted to natural gas production by bayesian training formula.The application can better avoid natural gas prediction data overfitting phenomenon by using Monte Carlo simulation combined with bayesian calculation, and the robustness of data is enhanced, the approximate weight distribution calculation is carried out by the whole natural gas data multiple factor calculation model, and the accuracy of natural gas prediction judgment is improved.

The invention discloses an intelligent circulation method and system for product production, and relates to the field of artificial intelligence, and the method comprises the steps: obtaining the receiving order information of a target production enterprise, which comprises the number of processed products and the delivery date of finished products; generating a production prediction order of the received order information; obtaining current production plan information of the target production enterprise, wherein the current production plan information comprises current production product information and a product delivery ending date; performing simulation training by using a product production intelligent circulation system, and generating a pre-production plan of the received order information; and the pre-production plan is distributed to the product production branch departments to obtain production plan information of each department, and orderly production is carried out on the received order information. The technical problem that in the prior art, when an enterprise formulates a production plan for a newly-added order, manpower cannot comprehensively consider the actual production capacity of the enterprise and reasonably arrange the newly-added order based on the existing order completion condition, and consequently the production benefit of the enterprise is poor is solved.

A method of predicting production characteristics of a hydrocarbon well using time lapse geochemistry fingerprinting and using machine learning to train a reservoir model to accurately predict production characteristics.

The present disclosure relates to the technical field of oil and gas extraction, and provides a gas well blockage prediction method and apparatus. The method comprises: acquiring historical gas well production data; establishing training samples on the basis of the historical gas well production data; using the training samples to train a gas well production prediction model, so as to predict gas well production prediction data; on the basis of a dynamic flow process of gas in a gas well, separately calculating a historical gas well blockage parameter corresponding to the historical gas well production data and a gas well blockage prediction parameter corresponding to the gas well production prediction data; on the basis of change trends in the historical gas well blockage parameter and the gas well blockage prediction parameter, determining whether the gas well is blocked; and if the gas well is blocked, on the basis of the gas well production prediction data, using a formation inflow dynamic relationship in the gas well to calculate and compare the maximum wellhead productivity when the gas well is not blocked with the maximum wellhead productivity when the gas well is blocked, thereby determining a degree of gas well blockage on the basis of a comparison result. By means of the embodiments of the present disclosure, accurate and efficient prediction of gas well blockage can be achieved.

Systems and methods are disclosed relating to production prediction and in some instances production optimization. For example, a method can include receiving multiphase data that can include production data and pressure data. The method can include upscaling the multiphase data to produce upscaled multiphase data and segmenting upscaled production data of the upscaled multiphase data to provide segmented production datasets based on segmentation criteria. The segmentation criteria can be provided based on upscaled pressure data of the upscaled multiphase data. The method can include predicting a future production of the one or more wells based on a decline curve analysis and the segmented production datasets. In some examples, the method can include optimizing a production of the one or more wells based on the predicted future production.

The invention discloses an enterprise intelligent data space construction method based on big data, and belongs to the technical field of petroleum production, and the method comprises the steps: obtaining the multi-modal production data of a petroleum production enterprise, the petroleum production enterprise comprises a well site node, an operation node and a headquarter node, the multi-modal production data comprises well site data, operation area data and headquarter data; constructing a production knowledge graph of the petroleum production process based on the multi-modal production data; a production prediction model is constructed based on the production knowledge graph, and the production prediction model is used for detecting whether leakage diffusion exists in the petroleum production process or not; and the production prediction model and the production knowledge graph are safely shared in the petroleum production enterprise based on the hierarchical encryption algorithm, so that the enterprise data space of the petroleum production enterprise is obtained, and the effect of synchronously storing the petroleum production data is realized.

The invention provides a carbon flow regulation and control method, device and equipment for a multi-energy flow system, and relates to the technical field of carbon flow regulation and control. The method comprises the steps that current data of a source-network-load-storage end in a target multi-energy-flow system are collected, and the current data comprise energy data, transmission data, load data, energy storage data and carbon emission data; according to the current data, predicting a future state of the target multi-energy-flow system to obtain energy production prediction data, user demand prediction data and a carbon emission trend; and determining a target carbon flow regulation strategy according to the energy production prediction data, the user demand prediction data, the transmission data and the carbon emission trend. According to the invention, by predicting the collected current data of the source-network-load-storage end, system dynamic states such as renewable energy power generation fluctuation and user load change can be captured. Compared with a traditional static model, the method can greatly improve the regulation and control response efficiency, and reduces the energy waste and the total carbon emission.

This application provides a data-driven method, equipment, and medium for creating industrial mechanism models. The method generates corresponding data models based on preset production management elements. The data models include model parameter identifiers, model parameter values, model parameter types, and model data formats. Based on process configuration information from a user terminal and the data model, custom model data combination information is determined. The process configuration information includes at least: intermediate process indicators, indicator formulas, and indicator formula calculation frequencies. Based on the mechanism model corresponding to the custom model data combination information, production forecast data corresponding to the industrial production data is determined, and the production forecast data is sent to the corresponding monitoring terminal. The mechanism model is updated based on the operation of the monitoring terminal and / or the actual production data corresponding to the production forecast data.

The application discloses a kind of oilfield production robust prediction method and system based on neural additive model, it is related to petroleum well production prediction technical field, including: based on neural additive model SMART, by obtaining the input data consisting of multidimensional time series data, model training is carried out, and prediction model is constructed;Based on prediction model, using sparse learning strategy, mode-based measurement method and non-convex optimization algorithm, model optimization is carried out, and the production of oilfield is predicted according to the optimized prediction model.The application combines neural network and additive model, introduces mode-based measurement, sparse learning and non-convex optimization algorithm, effectively improves the accuracy, robustness, interpretability and efficiency of oilfield production prediction method, so that it is more suitable for application in actual scene.

A method for predicting power production of a wind turbine (10) includes obtaining local weather data at an area of the wind turbine over a first time period, obtaining ice formation data of ice formed on blades (30) of the wind turbine (10) over a second time period, obtaining power production data of power output of the wind turbine over a third time period, and predicting power production of the wind turbine at a future time point based on the obtained power production data, the obtained ice formation data, the obtained local weather data as well as current weather forecast data for the future time point and current ice formation data.

Systems and methods are provided for performing decline curve analysis. The system can obtain historical production data as a function of time for at least one well drilled into a reservoir. The data can be smoothed and clustered into at least one cluster corresponding to a region of the reservoir. For the region, the system can generate an initial probability distribution for each decline parameter in a corresponding decline curve model and apply a Bayesian function iteratively to each initial probability distribution to generate a posterior probability distribution for each decline parameter to estimate an expected ultimate recovery (EUR) for each well. The system can generate a graphical representation of each posterior distribution for each well and display the graphical representations on a display.

This paper proposes a PSO-VMD-LSTM-based oil well production forecasting method. This method builds on the traditional VMD-LSTM forecasting method by applying the PSO algorithm to both the VMD model and the LSTM network. Specifically, the PSO algorithm optimizes the VMD model to obtain the optimal variational mode decomposition (VMD) and penalty factor, avoiding the errors caused by observational VMD and penalty factor calculations. Simultaneously, the PSO algorithm optimizes the LSTM network, enabling optimal parameterization, further improving the accuracy of the forecasting method.

The invention discloses an oil field yield robust prediction method and system based on a neural additive model, and relates to the technical field of oil well production prediction, and the method comprises the steps: obtaining input data composed of multi-dimensional time series data based on the neural additive model SMART, carrying out the model training, and constructing a prediction model; and on the basis of the prediction model, a sparse learning strategy, a mode-based measurement method and a non-convex optimization algorithm are adopted to carry out model optimization, and the oil field yield is predicted according to the optimized prediction model. According to the method, the neural network and the additive model are combined, and the mode-based measurement, sparse learning and non-convex optimization algorithms are introduced, so that the accuracy, robustness, interpretability and efficiency of the oil field yield prediction method are effectively improved, and the method is more suitable for being applied to actual scenes.

Embodiments relate to acquiring well, reservoir and production data, synthesizing training and test data, and then constructing, training, and utilizing machine-learning models to: (i) predict whether or not gas lift should be applied to facilitate the production of subsurface fluids from an oil well, (ii) predict an optimal range of gas lift values to be used in the production of fluids from the oil well, and (iii) predict an optimal liquid production rate for the oil well when the gas lift value is within the predicted optimal range. Unlike traditional approaches, the disclosed embodiments do not require the use of well interventions, which eliminates production losses, delays, and costs. The disclosed embodiments also avoid the delays, biases, and non-optimized values associated with existing trial-and-error-based approaches to gas lift injection optimization. Disclosed embodiments enable the efficient and reliable determination of the optimal range of gas lift values and the optimal liquid production rate.

Implementations described and claimed herein provide systems and methods for an innovative machine learning-driven approach, rooted in the fundamental physics of flow within fractured tight reservoirs for production forecasting of unconventional reservoirs. A first component of the method is to automatically analyze production data and generate characteristic attributes for linear flow and boundary-dominated flow. Following this, a Markov chain Monte Carlo process is utilized to integrate actual production data with flow regime analysis, resulting in probabilistic multi-segment decline models for production forecasting with uncertainty ranges and confidence estimation. Further, the method may include a two-step machine learning model to predict future planned wells. The two-step machine learning model may include a first aspect to generate predicted flow regime characteristics for one or more unconventional reservoirs and a second aspect to utilize the flow regime characteristics to generate the production forecast for the reservoirs.