Predictive Analytics Will Help Oil Companies Forecast the Future
The ability to predict the future to optimize operations has been the aim of oil and gas companies for some time. For the past 50 years, planners have chased accurate models to predict future energy supply, demand, and prices and the significant developments that affect them.
Could that time finally be here? Thanks to recent advances in data analytics technologies, oil and gas companies now have the prospect to considerably widen and systematize the scope of their forecasting to include operational processes up and down the value chain.
“The oil and gas industry may have emerged from its last downturn, but the pressure on companies to find new capital and operating efficiencies remains unrelenting,” Nial McCollam, chief technology officer at Lloyd’s Register, said. “As in other industries, demands also grow stronger from regulators and other stakeholders to improve environmental performance and safety. Advanced data technologies such as predictive analytics offer oil and gas companies a means to navigate this increasingly complex landscape.”
A Maturing Technology
In recent years, predictive analytics has matured as a technology with practical applications for the oil and gas industry. The next stage in analytics development is the application of prescriptive capabilities.
“Big data analytics are no stranger to this industry, and companies have long understood the potential value that predictive capabilities can bring them,” McCollam said. “Along with our clients and partners, we believe that the use of predictive analytics can lead not only to improved performance but also to enhanced safety and risk management. Encouragingly, the findings of the latest edition of the Lloyd’s Register Technology Radar show that this potential is beginning to be converted into reality.”
Balancing the Risks
Naturally, in a risk-averse industry such as oil and gas, executives are demanding more than anecdotal evidence that their investment in such technologies is bearing fruit. The report confirms that demonstrating returns from the early use of predictive analytics is not straightforward. Some companies, however, have been able to quantify their gains in terms appreciated by the board and executive suite.
“Demonstrating return on investment should prove less difficult as years of experience with, and evidence from, the technology’s use accumulate,” McCollam said. “Needless to say, realizing the full potential of such predictive capabilities will take a considerable amount of effort—both internally by companies themselves (adapting processes, cleaning data, acquiring skills), and by the industry overall.
“Indeed, the power of predictive tools relies on the richness and variety of aggregated data that they can access.”
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