Recent developments in SCADA (Supervisory Control and Data Acquisition) systems for physical infrastructure, such as high pressure gas pipeline systems and electric grids, have generated enormous amounts of time series data. This data brings great opportunities for advanced knowledge discovery and data mining methods to identify system failures faster and earlier than operation experts. This paper presents our effort in collaboration with a utility company to solve a grand challenge; namely, to use advanced data mining methods to detect leaks on a high pressure gas transmission system. Leak detection models with unsupervised learning tasks were developed analyzing billions of data records to identify leaks of different sizes and impacts, with very low false positive rates. In particular, our solution was able to identify small leaks leading to rupture events. The model also identified small leaks not identifiable with current detection systems. Such high-fidelity early identification enables operation personnel to take preventive measures against possible catastrophic events. We then formulate several generic detection methods with models derived from time series anomaly detection methods. We show that our leak detection models are superior to the SCADA alarm system, a mass balance model and other generic time series anomaly detection models in terms of both detection accuracy and computation time.
U.S. stocks were slightly higher Wednesday morning as utility companies climbed. Energy companies were trading lower as the price of oil continued to slip. Stocks are at their lowest levels in two months after large losses in two of the last three days. The Dow Jones industrial average advanced 31 points, or 0.2%, to 18,097 as of 10:05 a.m. The Standard & Poor's 500 index rose 5 points, or 0.2%, to 2,132.
Fox News Flash top headlines for Dec. 21 are here. Check out what's clicking on Foxnews.com Near-simultaneous attacks believed to have been carried out by drones hit three government-run oil and gas installations in central Syria, state TV and the Oil Ministry said Saturday. No one claimed responsibility for the attacks, which targeted the Homs oil refinery -- one of only two in the country -- as well as two natural gas facilities in different parts of Homs province. Syria has suffered fuel shortages since earlier this year amid Western sanctions blocking imports, and because most of the country's oil fields are controlled by Kurdish-led fighters in the country's east.
A commonly used stochastic model for derivative and commodity market analysis is the Barndorff-Nielsen and Shephard (BN-S) model. Though this model is very efficient and analytically tractable, it suffers from the absence of long range dependence and many other issues. For this paper, the analysis is restricted to crude oil price dynamics. A simple way of improving the BN-S model with the implementation of various machine learning algorithms is proposed. This refined BN-S model is more efficient and has fewer parameters than other models which are used in practice as improvements of the BN-S model. The procedure and the model show the application of data science for extracting a "deterministic component" out of processes that are usually considered to be completely stochastic. Empirical applications validate the efficacy of the proposed model for long range dependence.
Fixing the damage done by the drone attack on the Saudi oil processing plant may be the easy part. The hard part will be calming energy markets, where oil prices have jumped faster than at any time in over a decade. The attack on Saudi Arabia's Abqaiq plant, which accounts for 5 percent of global oil supplies, and a nearby facility took 5.7 million barrels a day of production off line for at least a few days. It also revealed the significant danger that drones pose to the Persian Gulf's sprawling processing plants, pipelines and refineries. "The psyche has been altered," said Tom Kloza, global head of energy analysis for Oil Price Information Service.