Machine learning, AI pump new energy into oil and gas industry

Oil and gas (O&G) companies operate in dynamic and complex environments, where they face constant challenges especially in terms of supply and demand. O&G companies have been adopting digital technologies for years, helping to increase the recovery of fossil resources, improve production processes, reduce costs and improve safety. O&G companies are emerging from the 2014 industry-wide downturn leaner and more efficient. Although technology and broader forms of innovation certainly deserve some of the credit for these gains – especially around enabling lower-cost design concepts, raising well productivities, and improving coordination among functional groups and assets – the call on technology is likely to rise substantially over the coming years.

As the O&G industry continues to adapt to a sustained period of volatility, companies are taking concrete steps to reduce costs and raise efficiencies. Technology and innovation are at the heart of many of these efforts and in certain “pockets of excellence” are helping to reduce facility costs by 5 percent to 15 percent, lower operating costs by 10 percent to 70 percent, and raise production efficiencies by 5 percent to 20 percent.

The O&G sector has a relatively long history with digital technologies, notably in upstream, and significant potential remains for digitalization to further enhance operations. Now, with the oil prices steadily increasing, the time has come to evaluate, adapt and embrace new technological initiatives. Machine learning and artificial intelligence (AI) are the two key technological initiatives driving the tectonic shift within the O&G industry.

Machine learning and other big data applications could save the oil and gas industry as much as $50 billion in the coming decade, according to McKinsey. Since the cratering of the global oil price in late 2014, companies have increasingly been looking at technology to reduce costs, improve efficiency and minimize downtime.

Of all the parts of the O&G industry ripe for the rollout of machine learning, the upstream sector is the obvious choice. The exploration phase, dependent on the interpretation of layers of information riddled with uncertainties, is a perfect fit for the machine-learning approach. The rapid identification of patterns working across multiple variables was a time-intensive process; this can now be partially automated and optimized with oversight from experienced professionals.

Reservoir modeling – trying to find out how a formation will react to particular drilling techniques – can also be verified using a combination of algorithms and fuzzy logic, a technique used for prediction when information is either unreliable and/or incomplete. “Virtually” testing the waters before a drill ever disappears downhole could save a company millions if not billions of dollars.

Looking back at past failures and applying the lessons learned is a fundamental problem-solving technique. Machine learning can speed up this process and widen the search net when seeking prior instances of similar problems in a case library. By trawling through a database of logged events for a description that matches the issue at hand, solutions can be accessed in real time to provide an essential guide for personnel on the ground to use as a troubleshooting tool.

Deep learning (one of the methods of machine learning) and the Internet of Things (IoT) are two aspects of AI that could potentially revolutionize O&G industries. Having already made quite a storm in various other industries including consumer electronics, this couldn’t come at a better time for the oil industry as it currently faces dramatic drops in the price of oil. While there’s no doubt several AI practical applications already in place will indeed help these industries improve, following are three aps that have the potential to make a significant difference across the board.

1. Rig diagnostic bots. In the same way that bots are being used in customer service departments, oilfield technicians can interact with diagnostic applications through voice controls.
2. Using deep learning algorithms to spot anomalies. Various O&G companies have benefited from using sensors attached to equipment such as rack rods or rod pumps to gather data. However, trying to detect anomalies in this way is very difficult. Using deep learning algorithms, experts can see anomalies that conventional methods would have missed and can alert the rig’s command center in advance.
3. Using AI in sourcing the best drilling locations. Currently, there are far too many false positives when it comes to locating suitable places to drill for oil; AI could cut the number down significantly. Other areas that AI can be used in the energy industry include robotic automation of various tasks including ship vetting and improved automated drilling.

Road Ahead

Using deep learning and IoT, new predicting and monitoring technologies have emerged that could completely transform O&G industries. Being able to predict what’s coming allows companies to deal with potential problems before they happen, saving them time, money and bad publicity. A future where we’re surrounded by AI incorporating deep learning and IoT is imminent.

The impact of ML and AI has already been realized in the industry. Early adopters are taking advantage of the technologies to optimally exploit and protect their assets. Tightening research and development budgets are prompting O&G firms and their suppliers to think differently about how they source new technologies and where they direct scarce resources. These evolving approaches are likely to outlast the current downturn and could lead to real changes in companies’ overall business strategies.