Algorithms and software for optimal management of raw materials, fuel and energy resources in blast furnace production

The structure of optimization model of optimal management of raw materials, fuel and energy resources in the blast-furnace shop of iron and steel works is represented. The following blocks are taken as system basis: 1) calculation of the set of parameters that characterize the thermal, gas-dynamic, slag and blasting modes for every blast furnaces of the shop during the base period; 2) calculation of linearized model coefficients (constants of transferring via different exposure pathways) individually for every blast furnace as well as properties of iron ore raw materials, fluxing additions, blasting parameters, parameters of fuel-enriched blast influencing the technical-and-economic indices of separate furnaces performance, their thermal, gas-dynamic and slag operation modes in the course of blast-furnace melting according to UrFU-MMK blast-furnace production model within the base period; 3) solution of tasks that consider the optimal allocation of raw materials, fuel and energy resources for the project period of blast furnaces operation; 4) analysis of obtained results and providing of recommendations on the optimization of blast furnaces parameters. The developed functional model of optimal distribution of raw materials, fuel and energy resources for the engineering and technology personnel of blast-furnace shop is illustrated; the main functions and interconnections between the separate functional blocks are defined. The functions of created "Optimal management of raw materials, fuel and energy resources in the blast-furnace production"software that is realized in the Microsoft Visual Studio 2017 (C# programming language) programming environment in the form of web application are pointed out. The program product provides the engineering and technology personnel of blast furnace shop of iron and steel works with the opportunity to solve the tasks of optimal distribution of fuel and energy resources (natural gas and oxygen consumption) within the group of blast furnaces in the different technological situations. © Published under licence by IOP Publishing Ltd

A New Automated Workflow for Well Monitoring Using Permanent Pressure and Rate Measurements

Pressure and rate measurements are essential for the well and reservoir surveillance workflows used in petroleum, geological carbon storage, and geothermal industries. Such well monitoring data are now analyzed in manual, semi-automated and automated modes or in a combination. Automated workflows are widely adopted by the industry nowadays, enabling most efficient knowledge extraction from the data both already accumulated and being received in real-time. The paper describes a new integrated workflow for automated well monitoring using pressure and rate measurements obtained with permanent gauges and flowmeters. The workflow is based on time-lapse Pressure Transient Analysis (PTA) and integrates the following components: virtual flow-metering, transient identification, feature extraction and pattern recognition in transient pressure responses, and assessment of well performance based on PTA-metrics. The methodology behind the workflow combines different physics-informed and data-driven methods described in the paper. Application of the workflow is illustrated on a field case example from the Norwegian Continental Shelf, where changes in the well, reservoir, and well-reservoir connection performances are automatically monitored during its three-year long injection history. Reliability and accuracy of the automated monitoring results are verified via comparison with the conventional model-based time-lapse PTA. The automated workflow may be used for a variety of use cases. Being applied to the well history, the workflow enables establishing a historical performance profile and identifying trends and issues in the past. In everyday well monitoring, it may be employed to detect well performance issues early and indicate their possible reasons. Further, it may provide valuable input for in-depth model-based analysis and other reservoir engineering studies. Using the workflow unlocks knowledge hidden in abundant well-monitoring datasets available at operating companies and empowers reservoir engineers to instantly assess well and reservoir performances, understand their interconnectivity, and make prompt, informed decisions