Oil and gas research is off to a flying start in 2019, with big conglomerates taking a prominent bite of the conventional resources found in the first quarter, as per the Rystad Energy.
Global findings of conventional sources in the first quarter touched a robust 3.2 billion barrels of oil equivalent (Boe). Most of the accumulations were recorded in February, posting 2.2 billion barrels of discovered reserves – the best monthly match on record since August 2015.
In the area of oil and natural gas, geologists, engineers, and drilling and production units tend to get the most significant share of the credit when good things happen, and most of the criticism when they do not make things work. That is fair, given the essential roles, these groups of employees perform within the thousands of businesses that make up the U.S. oil and gas industry.
But in contemporary years, as overall domestic production has increased at a pace no one could have predicted even five years ago, the credit has started to shift. These human workforces remain necessary to the success of any company, but the deployment of a boat of advancing technologies has represented an ever-advancing role over time in allowing businesses to maximize returns and profits.
Advanced-intelligence (AI), and machine-learning applications aggregate one area of technology that is receiving widespread use across the industry. Unplanned machine outages and the resulting loss of production cost businesses billions of dollars each year. Any technology that can assist in avoiding such disruptions can have a substantial, positive influence on a company’s bottom line.
As per reports, PRT, a recent acquisition of DrillingInfo is a new solution that empowers companies to significantly decrease their electricity costs by accurately foretelling weather and wind patterns up to two weeks in progress. Given that electricity is the singular most significant element of contract operating expenses industry-wide, that is a big opportunity.
Admittedly, machine-learning tools that can contribute intelligence about future indeterminate events can be extensive money-savers. AspenTech, another organization in the machine-learning business, has a unique technology called Aspen Mtell that utilizes machine-learning not only to monitor machine degradation but also truly predict when pieces of equipment are about to have a breakdown. As per the company’s literature, Mtell can present users with up to two to four weeks of notification of impending equipment failures. AspenTech also markets a flow support application that enables businesses to anticipate and evade interruptions in their capacity to get their products to market.
