Petroleum and Coal
The Petroleum and Coal Products Manufacturing subsector is based on the transformation of crude petroleum and coal into usable products. The dominant process is petroleum refining that involves the separation of crude petroleum into component products through such techniques as cracking and distillation.In addition, this subsector includes establishments that primarily further process refined petroleum and coal products and produce products, such as asphalt coatings and petroleum lubricating oils.
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🛢️🧠 ENEOS and PFN Begin Continuous Operation of AI-Based Autonomous Petrochemical Plant System
ENEOS Corporation (ENEOS) and Preferred Networks, Inc. (PFN) announced today that their artificial intelligence (AI) system, which they have been continuously operating since January 2023 for a butadiene extraction unit in ENEOS Kawasaki Refinery’s petrochemical plant, has achieved higher economy and efficiency than manual operations.
Jointly developed by ENEOS and PFN, the AI system is designed to automate large-scale, complex operations of oil refineries and petrochemical plants that currently require operators with years of experience. The new AI system is one of the world’s largest for petrochemical plant operation according to PFN’s research, with a total of 363 sensors for prediction and 13 controlled elements. The companies co-developed the system to improve safety and stability of plant operations by reducing dependence on technicians’ varying skill levels.
A Data Architecture to assist Geologists in Real-Time Operations
Data plays a crucial role in making exploration and drilling operations for Eni a success all over the world. Our geologists use real-time well data collected by sensors installed on drilling pipes to keep track and to build predictive models of key properties during the drilling process.
Data is delivered by a custom dispatcher component designed to connect to a WITSML Server on all oil rigs and send time-indexed and / or depth-indexed data to any supported applications. In our case, data is delivered to Azure ADLS Gen2 in the format of WITSML files, each accompanied by a JSON file for additional custom metadata.
The visualizations generated from this data platform are used both on the oil rigs and in HQ, with operators exploring the curves enriched by the ML models as soon as they’re generated on a web application made in-house, which shows in real time how the drilling is progressing. Additionally, it is possible to explore historic data via the same application.
Detecting dangerous gases to improve safety and reduce emissions
The primary advantage of differential optical absorption spectroscopy is its scalability. Two elements are required: a calibrated light source tuned to emit a specific wavelength, and a receiver able to read the same wavelength. In some cases, the receiver must also read a reference source for comparison. The two elements can be within the same housing to function as a point detector, but the source and receiver can also be separated, sending a beam across an open path, looking for a cloud of the target gas to move into its field of view.
Additive Manufacturing Poised to Make a Value Impact on Oil & Gas Supply Chain
An end-to-end metal AM system allows OEMs to quickly manufacture mission-critical parts for O&G operators without extensive redesigns. Such a fully integrated solution consists of print preparation software that applies a generalized set of recipes based on the design’s native CAD file, a printer that executes the print file, and quality assurance software that ensures the health of the tool and monitors the build, layer-by-layer.
Additionally, the American Petroleum Institute has now published API20S, the first-ever O&G-industry sanctioned specification for metal AM. This spells out processes, testing, documentation and traceability, among other requirements, for manufacturers of metal AM components being used in O&G facilities of all types.
Why ExxonMobil, Sinopec and Dow Are Betting On Plastic
Why Gas Prices In The U.S. Vary
Predictive Monitoring: Gas Turbines Demo
Real-Time Sensors Allow Data-Driven Monitoring of Flow-Measurement Systems
The downtime of manufacturing machinery, engines, or industrial equipment can cause an immediate loss of revenue. Reliable prediction of such failures using multivariate sensor data can prevent or minimize the downtime. With the availability of real-time sensor data, machine-learning and deep-learning algorithms can learn the normal behavior of the sensor systems, distinguish anomalous circumstances, and alert the end user when a deviation from normal conditions occurs.
Robotic Inspection for Aboveground Storage Tanks
Aboveground Storage Tanks (AST) are vital assets for many industries including, power, paper and pulp, oil and gas, chemical, and even beverage production. Routine inspection of external and internal tank components is beneficial for understanding its condition and is required by federal and local laws and regulations. Robot-enabled ultrasonic testing (UT) offers a unique solution to AST inspections because they save plant operators valuable resources while providing more asset coverage and actionable data.
The Cost of Unplanned Downtime for Refineries
According to the American Institute of Chemical Engineers (AlChE), the cost of missed production for a U.S. refinery with an average-sized fluid catalytic cracking unit of 80,000 barrels per day will range from $340,000 a day at profit margins of $5 per barrel, to $1.7 million a day at profit margins of $25 per barrel, based on a conservative estimate. A single, unplanned shutdown that lasts hours can lead to the release of a year’s worth of emissions into the atmosphere, according to John Hague, Aspen Technology Inc.
One type of innovative inspection process is Rapid Ultrasonic Gridding (aka RUG), which creates data-rich visual grid maps that identify areas of corrosion and other damage mechanisms. It is 10 times faster than traditional gridding and competing methods. In most situations, the operator can quickly make the decision of whether to proceed with maintenance measures to resolve the issue, or to return the inspected asset to operation.
Getting Industrial About The Hybrid Computing And AI Revolution
Beyond Limits is applying such techniques as deep reinforcement learning (DRL), using a framework to train a reinforcement learning agent to make optimal sequential recommendations for placing wells. It also uses reservoir simulations and novel deep convolutional neural networks to work. The agent takes in the data and learns from the various iterations of the simulator, allowing it to reduce the number of possible combinations of moves after each decision is made. By remembering what it learned from the previous iterations, the system can more quickly whittle the choices down to the one best answer.
IIoT builds new bridges to new adventures
Engenuity Inc. in Conroe, Tex., provides control automation and data integration for oil and gas and other industries, and recently found deficiencies in validation pressure testing of blowout preventers (BOP) and well-control devices. Because pressure tests are needed every few weeks for regulatory compliance, executed and recorded manually over several hours, and can cost up to $6 per second to run in offshore valve arrays, testing can cost millions of dollars per year. To reduce these expenses, Engenuity collaborated with clients like Shell International Exploration and Production Co., and developed automated, hydrostatic, test execution and reporting solutions, which use Opto 22’s groov Edge Programmable Industrial Controller (EPIC) for process control, automatic notification, and process history storage and replication.
Why resources companies are looking to evented APIs
Resources companies that want to get the most value from their data will process it the instant that it is created. The longer that data is left unprocessed, the more it diminishes in value. Operational excellence can be driven by evented APIs that can produce, detect, consume, and react to events occurring within the technology ecosystem.
Evented APIs can be applied to our example use case to deliver an autonomous feedback loop that incorporates smarter decision making in real-time.
Application of AI to Oil Refineries and Petrochemical Plants
Artificial intelligent (AI), machine learning, data science, and other advanced technologies have been progressing remarkably, enabling computers to handle labor- and time-consuming tasks that used to be done manually. As big data have become available, it is expected that AI will automatically identify and solve problems in the manufacturing industry. This paper describes how AI can be used in oil refineries and petrochemical plants to solve issues regarding assets and quality.