Artificial Intelligence (AI) technologies are being increasingly used in the Oil and Gas (O&G) industry to optimize production, reduce operational costs and maximize efficiency. According to a Markets and Markets report, AI in the global oil and gas market is expected to grow from an estimated USD 1.57 billion in 2017 to USD 2.85 billion by 2022, at a CAGR of 12.66%. The oil and gas enterprises are seeking novel approaches to address the issues that plague the industry at present. In view of the falling fuel prices, concerns over the environmental impact of energy production and personnel safety, companies are leveraging technological innovations such as AI to optimize processes and maximize the returns on investment. In this report, we present insights and trends related to the AI technologies used in the Oil and Gas industry, through a study of patents related to petroleum exploration and refining technology segments.

Let us consider a scenario: one night, an executive responsible for operations for a remote downstream oil and gas refinery gets a call from one of their subordinates saying things started acting up ever since they plugged in a USB they brought from home. Multiple processes have become unstable and commands sent to equipment are not executed as requested. Panicking, they say there has been a cyber attack on the supervisory control and data acquisition (SCADA) system. Valves, pumps, and compressors connected to the system are going haywire, and the organisation's legacy systems were not equipped to prevent whatever new malware snuck into the system. Production comes to a halt for two days.

Much like we have Chemical Engineering and Electrical Engineering and Mechanical Engineering, it is time to formalize of field of Data Engineering. This is a special two-part series on trends and requirements leading to the formalization of the Field of Data Engineering. "Data is the new oil…in much the same way that oil fueled economic growth in the 20th century, data will fuel economic growth in the 21st century." To further raise the credibility of data as the economic fuel for the next century, "The Economist" Special Report on the Data Economy asks "Are data more like oil or sunlight?" Still, it is hard to put a definitive value on data. If data is to be the fuel for economic growth in the 21st century, don't we need to find a way to accurately determine what data is worth?

The adoption of artificial intelligence and machine learning technologies has never been more critical. Due to COVID-19, many organizations need to find a new way of working. Ensuring production rates are reliable, if not increased, while limiting the number of personnel - in some cases down to 50%. Many asset heavy industries, such as water, transportation & energy are considered critical infrastructure. Every effort needs to be made to maintain these.

What other trends are shaping the future of energy extraction, refinement, and consumption. These industry insiders provided their takes on the #1 trend shaping energy this year, and into the future. "Some areas where we see nascent AI is in predictive maintenance and asset monitoring. There are a few who are beginning to look at utilizing AI to analyze images from drones for surveillance and also for acoustic listening." "Advances in the'time-series' AI world (as opposed to AI for images or audio) are shaping the energy industry today. These include techniques for time series forecasting, anomaly detection, optimization etc. Specifically, probabilistic techniques and algorithms are showing significant improvements and becoming the driver of the next wave of optimization and value creation. These techniques augment today's unilateral AI predictions with additional information about the confidence in these predictions. This is not unlike the trend shaping the peer to peer transportation industry."

Recognizing the pervasivetalent gapthat exists between data scientists and data workers in the line of business, Assisted Modeling helps teach data science with a guided walk-through and aims to help all data workers, regardless of technical acumen, advance their skill sets in the process of building machine learning models. Our approach in building Assisted Modeling is to advance the skills of the data worker, creating next-level citizen data scientists capable of building the machine learning models required to tackle the advanced analytic challenges of the future. Assisted Modeling provides users the transparency and control needed to build trustworthy machine learning models that drive business outcomes without writing a line of code. As an output of the application, users can access code-free machine learning tools directly within the Alteryx Designer interface. Assisted Modeling allows any data worker to construct machine learning models, understand how and why their models work, and capture modeling decisions, turning raw data into informed business decisions with unprecedented speed and confidence.

Enterprise seems to be entering a new era ruled by data. What was once the realm of science fiction, AI in business intelligence is evolving into everyday business as we know it. Companies can now use machines algorithms to identify trends and insights in vast reams of data and make faster decisions that potentially position them to be competitive in real-time. It's not a simple process for companies to incorporate machine learning into their existing business intelligence systems, though Skymind CEO and past Emerj podcast guest Chris Nicholson advises that it doesn't have to be daunting. "AI is just a box," he says.

In retail, supply chain efficiency is essential. Inventory management, picking, packing and shipping are all time and resource-intensive processes which can have a dramatic impact on a business's bottom line. The problem is these are complex processes, particularly when it comes to large scale operations covering multiple outlets and territories. The fact they are often dependent on outside forces – suppliers, service providers and even weather – make getting it right even more difficult. This is why retailers – both big and, increasingly, smaller operations too – are keen adopters of Big Data-driven analytics technology. Creating efficiencies in complex systems which involve multiple, often compartmentalized processes is an area where this technology excels. In short, it's about the ability of machines to make lots of little savings and efficiencies, which together add up to very large ones. Monte Zweben – CEO of Splice Machine, which provides predictive systems for industry, talked me through three key areas where retailers are increasingly looking towards data-driven analytics in order to drive efficiencies in their supply chains. We also talked about why this approach is going to become increasingly important for businesses in all sectors which want to stay ahead of the pack and foster innovation. Filling your customers' needs more quickly Today's Internet of Things industry means that everything is connected and capable of collecting and sharing data on how it is operating. This means that everything can be measured and – through the use of advanced analytics tools such as machine learning – rigorously interrogated until it gives up all its secrets on how it works, and, crucially, how it interacts with every other part of an operation. All of that data can be collected on an inventory – origins, transit routes, times when it is scanned or its location and status are reported by RF (Radio Frequency) tags. "So, now you can build a machine learning model," Zweben says, "and that model could make a prediction about any aspect of the operation based on the data it's got. "What's the likelihood you're not going to be late with this order? What's the likelihood you'll be a day late? Five days? It's basically a classification problem." This means that in-depth simulations can be run, allowing the implications and knock-on effects of lateness or missed deadlines to be assessed before they become an issue, even if they can't be entirely eliminated due to a reliance on external influences. Where this is the case, remedial action can be taken ahead of inconvenience being caused to customers, who are certainly likely to be appreciative of an email apology when a shipment is likely to be delayed, rather than simply to be kept waiting.

Earlier this year, we ran an article explaining why machine learning is much bigger than Google and RankBrain. The technology isn't just making our search engines and devices more intelligent; it's transforming the way we approach and manage our marketing campaigns. The machine learning revolution has already begun and things are going to get a lot more exciting over the next few years. So, to give you a taste of what's to come, here are five ways machine learning will transform your marketing workflow. As things stand, most marketers are swimming in more data than they can handle.

Earlier this year, we ran an article explaining why machine learning is much bigger than Google and RankBrain. The technology isn't just making our search engines and devices more intelligent; it's transforming the way we approach and manage our marketing campaigns. The machine learning revolution has already begun and things are going to get a lot more exciting over the next few years. So, to give you a taste of what's to come, here are five ways machine learning will transform your marketing workflow. As things stand, most marketers are swimming in more data than they can handle.