This paper addresses the general problem of accurate identification of oil reservoirs. Recent improvements in well or borehole logging technology have resulted in an explosive amount of data available for processing. The traditional methods of analysis of the logs characteristics by experts require significant amount of time and money and is no longer practicable. In this paper, we use the semi-supervised learning to solve the problem of ever-increasing amount of unlabelled data available for interpretation. The experts are needed to label only a small amount of the log data. The neural network classifier is first trained with the initial labelled data. Next, batches of unlabelled data are being classified and the samples with the very high class probabilities are being used in the next training session, bootstrapping the classifier. The process of training, classifying, enhancing the labelled data is repeated iteratively until the stopping criteria are met, that is, no more high probability samples are found. We make an empirical study on the well data from Jianghan oil field and test the performance of the neural network semi-supervised classifier. We compare this method with other classifiers. The comparison results show that our neural network semi-supervised classifier is superior to other classification methods.

Editor's Note: The following is a guest post from Iain Ellwood, chief growth officer at WPP's Group XP. If you've noticed a seamless feel when interacting with Tesla, from its vehicles to its customer service, or enjoyed similar approaches from Amazon or Ikea, you know that meaningful, connected brand experiences have become the lifeblood of today's most successful companies. The more we study the nature of these experiences, the more their benefits are becoming measurable and a key tool for growth. In fact, backed by 4.6 billion data points, our Group XP Experience Index ranked the world's biggest brands according to the quality of their experience, finally proving what we all knew to be true: Brands that invest in experience outperform the market by over 50%. As more of the world's top brands invest in experience strategy and design, the writing's on the wall for product-driven CEOs -- it's time to take these brand experiences seriously or die.

Around the world last year, unprovoked shark attacks (where humans do not initiate physical contact) resulted in just five fatalities–one fewer than the global average over the last decade. Meanwhile, humans carve up 100 million sharks and rays annually (a conservative estimate), causing enormous gaps in the aquatic ecosystem that directly disrupts the carbon cycle and incidentally reduces carbon capture by sea grasses, further heating the planet. Still, the fear of being bitten by a shark–literally any shark–is understandable. As rare as incidents already are, they might soon be even rarer, thanks to a new solution that comes out of Australia. Called the Clever Buoy, it's an eco-friendly ocean monitoring system that uses dual-wave sonar and artificial intelligence to detect and identify large marine life underwater more accurately than your standard fish-finder.

An autonomous robot will be deployed to an offshore oil and gas platform in the North Sea later this year, in a first for the sector. The £4m project's backers said the move was designed to take humans out of dangerous and dull jobs, and reinvent oil and gas as an industry of the future. Under the pilot scheme, the robot will initially be deployed at the French oil firm Total's gas plant on Shetland before being sent to join the 120 workers on the company's Alwyn platform, 440km north-east of Aberdeen. The machine, made by Austrian firm Taurob and supported on the software side by German university TU Darmstadt, will be used for visual inspections and detecting gas leaks. Rebecca Allison, asset integrity solution centre manager at the publicly-funded Oil and Gas Technology Centre, insisted autonomous robots would not be used to cut the wage burden of offshore workers who are paid a premium for working in tough, remote conditions.

I originally wrote and published a version of this article in September 2016. Since then, quite a bit has happened, further cementing my view that these changes are coming and that the implications will be even more substantial. I decided it was time to update this article with some additional ideas and a few changes. As I write this, Uber just announced that it just ordered 24,000 self-driving Volvos. Tesla just released an electric, long-haul tractor trailer with extraordinary technical specs (range, performance) and self-driving capabilities (UPS just preordered 125!). And, Tesla just announced what will probably be the quickest production car ever made -- perhaps the fastest. It will go zero to sixty in about the time it takes you to read zero to sixty. And, of course, it will be able to drive itself. The future is quickly becoming now.

All reinforcement learning algorithms must handle the trade-off between exploration and exploitation. Many state-of-the-art deep reinforcement learning methods use noise in the action selection, such as Gaussian noise in policy gradient methods or $\epsilon$-greedy in Q-learning. While these methods are appealing due to their simplicity, they do not explore the state space in a methodical manner. We present an approach that uses a model to derive reward bonuses as a means of intrinsic motivation to improve model-free reinforcement learning. A key insight of our approach is that this dynamics model can be learned in the latent feature space of a value function, representing the dynamics of the agent and the environment. This method is both theoretically grounded and computationally advantageous, permitting the efficient use of Bayesian information-theoretic methods in high-dimensional state spaces. We evaluate our method on several continuous control tasks, focusing on improving exploration.

Outages are fairly common in power distribution networks [1], [2], and this number is increasing in some countries because of aging infrastructure and changing weather patterns [3], [4]. While good design and maintenance reduce the number of outages, they cannot be eliminated completely. When an outage is required to perform maintenance or upgrade the equipment, the utility can minimize the disruption of service to customers by carefully planning the deployment of the crews and the sequence of operations. On the other hand, a fault in the system usually causes an unplanned outages, which can lead to long service interruptions and significant inconvenience to the customers. Therefore, reducing the number of unplanned outages and better managing their duration is a priority for most utilities [5]. The first step towards mitigating the negative consequences of unplanned outages is to gain a better understanding of their number and duration, as well as the number of customers affected.

When you first buy a house, your insurance company doesn't know very much about it or how much insuring it will cost. That's because it first has to send out an inspector to look at the exterior of your house, take measurements, and check out your roof to see what kind of shape it's in. Cape Analytics, a Mountain View-based data analytics startup, aims to change all that. Its API-pipeline can feed an insurance company information about a house's exterior square footage, roof type, roof condition, changes in a home and more - all thanks to the use of machine learning to analyze aerial imagery. The company announced Monday that it's launching data coverage for the entirety of the continental United States - over 70 million American homes - though it's already been providing information to insurance customers like national reinsurer XL Catlin and the Florida-based Security First Insurance.

An autonomous robot will be deployed to an offshore oil and gas platform in the North Sea later this year, in a first for the sector. The £4m project's backers said the move was designed to take humans out of dangerous and dull jobs, and reinvent oil and gas as an industry of the future. Under the pilot scheme, the robot will initially be deployed at the French oil firm Total's gas plant on Shetland before being sent to join the 120 workers on the company's Alwyn platform, 440km north-east of Aberdeen. The machine, made by Austrian firm Taurob and supported on the software side by German university TU Darmstadt, will be used for visual inspections and detecting gas leaks. Rebecca Allison, asset integrity solution centre manager at the publicly-funded Oil and Gas Technology Centre, insisted autonomous robots would not be used to cut the wage burden of offshore workers who are paid a premium for working in tough, remote conditions.

The number of utility companies in the United States is creating value with drone technology. On the other hand, Europe has also been able to use the UAV's in faster, cheaper and safer completion of the project. One of the companies named'ENGIE' used the drone technology to inspect vital components in power plants. Innovation is the major aim of the ENGIE's development. With the usage of drones in the inspection the risk have been reduced plus the work completion have become much faster.