Using AI tools, upstream oil and gas companies can shift their approach from production at all costs to producing in context. They will need to do profit and loss management at the well level to optimize the production cost per barrel. To do this, they must integrate all aspects of production management, collect the data for analysis and forecasting, and leverage artificial intelligence to optimize operations.
On a macro scale, deep machine learning can help increase awareness of macroeconomic trends to drive investment decisions in exploration and production. Economic conditions and even weather patterns can be considered to determine where investments should take place as well as intensity of production.
For example drilling is an expensive and risky investment, and applying AI in the operational planning and execution stages can significantly improve well planning, real-time drilling optimization, frictional drag estimation, and well cleaning predictions.
Today, AI systems form the backbone of digital oil field concepts and implementations. However, there is still great potential for new ways to optimize field development and production costs, prolong field life, and increase the recovery factor.
The promise of AI is already being realized in the oil and gas industry. Early adopters are taking advantage of their position to get a head start on the competition and protect their assets. The industry has always leveraged technology to adapt to change, and early adopters have always benefited the most. As competition in the oil and gas industry continues to heat up, companies cannot afford to be left behind. For those that understand and seize the opportunities inherent in adopting machine learning technologies, the future looks bright.