Amid falling prices and concerns about production, industrial oil and gas producers constantly search for ways to operate ever more efficiently. As they do, some have turned to analytics to remain competitive in a downturning market. Learn more as we study the cases of ENOC, Santos Ltd. and Gasmart.
Many oil and gas organizations that face tremendous growth in unstructured data are implementing industrial-grade predictive analytics and object-oriented data storage solutions to keep up with demand. As a result, traditional RAID schemes are being replaced by object storage solutions, and the
Outokumpu, a global leader in the production of high-performance stainless steel, recently discovered something interesting when it started treating machines as individuals. From reactive to proactive maintenance planning, Outokumpu’s philosophical shift underpins its new process with a focus on
Weather is both asset and liability to energy providers. Even in a world of renewable energy, adverse weather events can create power outages on a citywide scale. Using weather data analysis, energy providers can forecast outages with high levels of reliability, then prevent or mitigate them ahead
IBM Asset Analytics for Rotational Equipment in Oil and Gas addresses the problem of monitoring a large number of wells through the efforts of relatively few engineers while analyzing large volumes of operational data in real time.
When assets fail, production continuity is disrupted and output reduced, affecting an operation’s profitability. An asset analytics solution takes a number of data sets related to operational data and generates a set of insights that include predicted failure modes, ranking for priority attention
The Internet of Things will create significantly more data and require significantly faster communications networks than ever before, increasing demand on producers to apply sophisticated analytical tools and methods to make sense of operational data, separate the signal from the noise and, most
Operational insight offers understanding of asset performance and process productivity in the oil and gas industry. Producers who employ predictive analytics can move toward a maintenance strategy that is highly cost-effective and efficient.
Big data and analytics today greatly helps any industry in leveraging the power of realizing greater insights from the constant and overflowing influx of data, be that data-at-rest or data-in-motion. This data, although complexly unstructured and in abundant, when subjected to the right Analytics
Harnessing the power of big data and analytics is valuable when conducting competitive analyses. Organizations can search and analyze a range of industry information to identify trends, anticipate changes and uncover emerging opportunities. To use big data in this way, organizations need solutions
Solutions designed for big data can help the oil and gas industry integrate operational analysis with business intelligence so they can optimize processes in order to meet specific business goals. For example, a company might want to determine the best offshore drilling location for maximizing oil
Big data presents important opportunities for enhancing the efficiency, safety, productivity and cost-effectiveness of oil and gas operations. Yet it comes with an array of operational technology challenges that often impede the use of big data for operational gains. For example, companies need
As I wrote in Part 1 of this blog series, big data and analytics can help companies develop the “digital oilfield”— integrated operations that unite operational technology (OT) with information technology (IT) to improve decision making and enhance operational and business performance. Adding
The petroleum industry is no stranger to large volumes of data. Operating in arguably the original sensor-based industry, oil and gas companies have for decades used tens of thousands of data-collecting sensors installed in subsurface wells and surface facilities to provide continuous, real-time