Big Data and Data Analytics In Oil and Gas Industry

Big Data and Data Analytics: huge quantities of knowledge can now be analyzed. To enhance efficiency and safety within the oil and gas industry. Image by fresh from Pixabay.

This has brought with it problems with managing and analyzing such vast quantities of data. Analysis of such data has been a serious area of focus and innovation. Within the last five years within the oil and gas industry (as well as many other industries) with a view to potential improvements in exploration and production efficiency and safety.

At this time there are many samples of research and development projects watching the way to utilize Big Data within the industry. But fewer world samples of how Big Data is being utilized in practice. Research projects have covered applications in exploration, drilling, reservoir engineering, and production engineering. Some examples include the analysis of giant micro-seismic datasets using the Hadoop1 platform to model fracture propagation during hydraulic fracturing; the utilization of massive Data to optimize steam-assisted gravity drainage and cyclic steam operations. On important oil reservoirs by analyzing data from over 14,200 wells in Chevron’s San Joaquin valley fields; and optimizing the performance of electrical submersible pumps (ESPs) by using data from over 200 million logs from 1,649 wells during one year. To gauge ESP performance and identify emergency situations like overheating and unsuccessful start-ups.

Massive Data Analysis

GE Digital, a GE Baker Hughes subsidiary, is an early adopter of massive Data analysis. GE Digital has developed ‘Predix’, a digital platform that may be wont to produce ‘Digital Twins’ – software representations of a physical asset. The application’s machine learning as well as algorithms are ready to process. An enormous amount of knowledge collected by sensors, like equipment or parts performance, environmental data and weather associated. With a processing facility. The algorithms then compare these against the perfect performance data contained within the database to look for discrepancies between the present and ideal state. If such discrepancies are identified, the appliance is triggered to send an awareness of technicians. Who successively conduct preventative maintenance or part replacement.

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