Artificial intelligence (AI) is already proving its value to oil and gas companies, and yet widespread adoption of AI technologies within the industry still faces lots of hurdles. "The barriers for oil companies to adopting new AI technologies are many, ranging from resistance to change, a belief that what they already have is sufficient, and skepticism about whether new technologies will deliver," said Ray Hall, energy sector director at Tessella, a provider of engineering and consulting services that has helped global energy companies identify ways to improve drilling and operational efficiency with data. "Many of our own customers have invested in large technology vendors promising the world from analytics, only to be left disappointed with the result," Hall said. Oil companies for years have been using analytics approaches such as model predictive control (MPC) in supply chain platforms and linear programs in refinery planning, Hall said. "They are no strangers to the use of structured data approaches and analytics," Hall said.

In the past year, you've probably heard something about machine learning. This branch of science, which involves crunching massive datasets to find hidden patterns, is helping companies solve problems that used to be unsolvable. Machine learning algorithms keep spam out of your inbox, and sound an early warning when someone else might be using your credit card. Down the road, they might save your life. At their core, machine learning tools capture lots of complex information, learn from it, then apply what they learn to better estimate unknowns and predict future events.

For several years, the oil and gas industry has experienced a drain of experienced personnel, creating a gap between very experienced people and lower-level resources. Reimagining business for the digital age is the number-one priority for many of today's top executives. We offer practical advice and examples of how to do it right. "This demographic issue has affected the industry, since the knowledge retained by these more experienced individuals has historically been a blend of'art and science,' and has been relied on to make decisions in the exploration, production, trading, and refining of petroleum and petrochemicals," said Tony Mataya, director of the Energy Practice at technology consulting firm Information Services Group (ISG). One potential way to deal with the challenge is to deploy cognitive technologies.

The longest amount of time that a spacecraft has survived on the surface of Venus is 127 minutes. On March 1, 1982, the USSR's Venera 13 probe parachuted to a gentle landing and managed to keep operating for just over two hours by hiding all of its computers inside of a hermetically sealed titanium pressure vessel that was pre-cooled in orbit. The surface temperature on Venus averages 464 C (867 F), which is hotter than the surface of Mercury (the closest planet to the sun), and hot enough that conventional electronics simply will not work. It's not just the temperature that makes Venus a particularly nasty place for computers--the pressure at the surface is around 90 atmospheres, equivalent to the pressure 3,000 feet down in Earth's ocean. And while you can be relieved that the sulfuric acid rain that you'll find in Venus' upper atmosphere doesn't reach the surface, it's also so dark down there (equivalent to a heavily overcast day here on Earth) that solar power is horrendously inefficient.

Surrogate models provide a low computational cost alternative to evaluating expensive functions. The construction of accurate surrogate models with large numbers of independent variables is currently prohibitive because it requires a large number of function evaluations. Gradient-enhanced kriging has the potential to reduce the number of function evaluations for the desired accuracy when efficient gradient computation, such as an adjoint method, is available. However, current gradient-enhanced kriging methods do not scale well with the number of sampling points due to the rapid growth in the size of the correlation matrix where new information is added for each sampling point in each direction of the design space. They do not scale well with the number of independent variables either due to the increase in the number of hyperparameters that needs to be estimated. To address this issue, we develop a new gradient-enhanced surrogate model approach that drastically reduced the number of hyperparameters through the use of the partial-least squares method that maintains accuracy. In addition, this method is able to control the size of the correlation matrix by adding only relevant points defined through the information provided by the partial-least squares method. To validate our method, we compare the global accuracy of the proposed method with conventional kriging surrogate models on two analytic functions with up to 100 dimensions, as well as engineering problems of varied complexity with up to 15 dimensions. We show that the proposed method requires fewer sampling points than conventional methods to obtain the desired accuracy, or provides more accuracy for a fixed budget of sampling points. In some cases, we get over 3 times more accurate models than a bench of surrogate models from the literature, and also over 3200 times faster than standard gradient-enhanced kriging models.

A couple of weeks ago, I spent a few hours at GE Digital's headquarters in San Ramon, CA. It was a great overview by several executives of how GE is using their Predix platform to create software to design, build, operate, and manage the entire asset lifecycle for the Industrial IoT. A big part of this transformation for GE involves hiring tons of software developers, acquisitions, and partnerships. One of those partnerships is with Silicon Valley based FogHorn Systems (GE Ventures, Dell Ventures, March Capital and a few others are investors). FogHorn is a developer of "edge intelligence" software for industrial and commercial IoT applications.

LONDON, July 28 (Thomson Reuters Foundation) - A new technique using artificial intelligence to predict where deforestation is most likely to occur could help the Democratic Republic of Congo (DRC) preserve its shrinking rainforest and cut carbon emissions, researchers have said. Congo's rainforest, the world's second-largest after the Amazon, is under pressure from farms, mines, logging and infrastructure development, scientists say. Protecting forests is widely seen as one of the cheapest and most effective ways to reduce the emissions driving global warming. But conservation efforts in DRC have suffered from a lack of precise data on which areas of the country's vast territory are most at risk of losing their pristine vegetation, said Thomas Maschler, a researcher at the World Resources Institute (WRI). "We don't have fine-grain information on what is actually happening on the ground," he told the Thomson Reuters Foundation.

Google parent Alphabet Inc. GOOGL 0.58% is pitching an idea to store power from renewable energy in tanks of molten salt and cold liquid, an example of the tech giant trying to marry its far-reaching ambitions with business demand. Alphabet's research lab, dubbed X, said Monday that it has developed plans to store electricity generated from solar panels or wind turbines as thermal energy in hot salt and cold liquids, such as antifreeze. The lab is seeking partners in the energy industry, including power-plant developers and utilities, to build a prototype to plug into the electrical grid. Whether the project, called Malta, ever comes to market depends as much on a sound business model as it does on science. Academics said the technology is likely years away from market, if it ever makes it.

The following essay is reprinted with permission from The Conversation, an online publication covering the latest research. As an Artificial Intelligence researcher, I often come across the idea that many people are afraid of what AI might bring. It's perhaps unsurprising, given both history and the entertainment industry, that we might be afraid of a cybernetic takeover that forces us to live locked away, "Matrix"-like, as some sort of human battery. And yet it is hard for me to look up from the evolutionary computer models I use to develop AI, to think about how the innocent virtual creatures on my screen might become the monsters of the future. Might I become "the destroyer of worlds," as Oppenheimer lamented after spearheading the construction of the first nuclear bomb?

One of the main challenges in the parametrization of geological models is the ability to capture complex geological structures often observed in subsurface fields. In recent years, Generative Adversarial Networks (GAN) were proposed as an efficient method for the generation and parametrization of complex data, showing state-of-the-art performances in challenging computer vision tasks such as reproducing natural images (handwritten digits, human faces, etc.). In this work, we study the application of Wasserstein GAN for the parametrization of geological models. The effectiveness of the method is assessed for uncertainty propagation tasks using several test cases involving different permeability patterns and subsurface flow problems. Results show that GANs are able to generate samples that preserve the multipoint statistical features of the geological models both visually and quantitatively. The generated samples reproduce both the geological structures and the flow properties of the reference data.