Eni's Sannazzaro oil refinery, 60km south-west of Milan, is an industrial island surrounded by agriculture. But as well as the jumble of pipes, furnaces and storage tanks that characterise such sites, there is a less familiar scene. Rising from a football pitch-sized parcel of land on the edge of the refinery are six futuristic buildings -- grey, rectangular and windowless -- without any obvious association to the production of petroleum and diesel taking place nearby. These buildings are home to one of the world's most powerful computers with capacity of 18.6 petaflops, a measure of computing speed. That is three times quicker than Facebook's fastest and twice that of Nasa's, according to the Top 500 global ranking of supercomputers.
Klyuchnikov, Nikita, Zaytsev, Alexey, Gruzdev, Arseniy, Ovchinnikov, Georgiy, Antipova, Ksenia, Ismailova, Leyla, Muravleva, Ekaterina, Burnaev, Evgeny, Semenikhin, Artyom, Cherepanov, Alexey, Koryabkin, Vitaliy, Simon, Igor, Tsurgan, Alexey, Krasnov, Fedor, Koroteev, Dmitry
In order to bridge the gap of more than 15m between the drilling bit and high-fidelity rock type sensors during the directional drilling, we present a novel approach for identifying rock type at the drilling bit. The approach is based on application of machine learning techniques for Measurements While Drilling (MWD) data. We demonstrate capabilities of the developed approach for distinguishing between the rock types corresponding to (1) a target oil bearing interval of a reservoir and (2) a non-productive shale layer and compare it to more traditional physics-driven approaches. The dataset includes MWD data and lithology mapping along multiple wellbores obtained by processing of Logging While Drilling (LWD) measurements from a massive drilling effort on one of the major newly developed oilfield in the North of Western Siberia. We compare various machine-learning algorithms, examine extra features coming from physical modeling of drilling mechanics, and show that the classification error can be reduced from 13.5% to 9%.
Parts of Texas, Oklahoma, and Kansas have experienced increased rates of seismicity in recent years, providing new datasets of earthquake recordings to develop ground motion prediction models for this particular region of the Central and Eastern North America (CENA). This paper outlines a framework for using Artificial Neural Networks (ANNs) to develop attenuation models from the ground motion recordings in this region. While attenuation models exist for the CENA, concerns over the increased rate of seismicity in this region necessitate investigation of ground motions prediction models particular to these states. To do so, an ANN-based framework is proposed to predict peak ground acceleration (PGA) and peak ground velocity (PGV) given magnitude, earthquake source-to-site distance, and shear wave velocity. In this framework, approximately 4,500 ground motions with magnitude greater than 3.0 recorded in these three states (Texas, Oklahoma, and Kansas) since 2005 are considered. Results from this study suggest that existing ground motion prediction models developed for CENA do not accurately predict the ground motion intensity measures for earthquakes in this region, especially for those with low source-to-site distances or on very soft soil conditions. The proposed ANN models provide much more accurate prediction of the ground motion intensity measures at all distances and magnitudes. The proposed ANN models are also converted to relatively simple mathematical equations so that engineers can easily use them to predict the ground motion intensity measures for future events. Finally, through a sensitivity analysis, the contributions of the predictive parameters to the prediction of the considered intensity measures are investigated.
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.
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 topic of industry disruption -- "a process whereby a smaller company with fewer resources is able to successfully challenge established incumbent businesses" -- is rife with misconceptions. One of the biggest is that it is a mysterious, random, and unpredictable event. Another is that it happens to you in ways that are beyond your control. Those views may have been valid at one time, but they no longer apply. Industry disruption, as Accenture research has found, is reasonably predictable.
A pair of autonomous robots developed by Carnegie Mellon University's Robotics Institute will soon be driving through miles of pipes at the U.S. Department of Energy's former uranium enrichment plant in Piketon, Ohio, to identify uranium deposits on pipe walls. The CMU robot has demonstrated it can measure radiation levels more accurately from inside the pipe than is possible with external techniques. In addition to savings in labor costs, its use significantly reduces hazards to workers who otherwise must perform external measurements by hand, garbed in protective gear and using lifts or scaffolding to reach elevated pipes. DOE officials estimate the robots could save tens of millions of dollars in completing the characterization of uranium deposits at the Portsmouth Gaseous Diffusion Plant in Piketon, and save perhaps $50 million at a similar uranium enrichment plant in Paducah, Kentucky. "This will transform the way measurements of uranium deposits are made from now on," predicted William "Red" Whittaker, robotics professor and director of the Field Robotics Center.
The identification of sources of advection-diffusion transport is based usually on solving complex ill-posed inverse models against the available state- variable data records. However, if there are several sources with different locations and strengths, the data records represent mixtures rather than the separate influences of the original sources. Importantly, the number of these original release sources is typically unknown, which hinders reliability of the classical inverse-model analyses. To address this challenge, we present here a novel hybrid method for identification of the unknown number of release sources. Our hybrid method, called HNMF, couples unsupervised learning based on Nonnegative Matrix Factorization (NMF) and inverse-analysis Green functions method. HNMF synergistically performs decomposition of the recorded mixtures, finds the number of the unknown sources and uses the Green function of advection-diffusion equation to identify their characteristics. In the paper, we introduce the method and demonstrate that it is capable of identifying the advection velocity and dispersivity of the medium as well as the unknown number, locations, and properties of various sets of synthetic release sources with different space and time dependencies, based only on the recorded data. HNMF can be applied directly to any problem controlled by a partial-differential parabolic equation where mixtures of an unknown number of sources are measured at multiple locations.
NVIDIA's (NASDAQ: NVDA) graphics processing unit (GPU)-based approach to high-performance computing and deep learning, a category of artificial intelligence (AI) in which machines are trained to make inferences from data the way humans do, has begun making inroads into the global oil and gas industry. This is great news for investors, as this is a multitrillion-dollar industry that forms the foundation of the global economy. While renewable forms of energy have been steadily displacing fossil fuels to generate electricity and electric vehicles (EVs) have begun lessening the transportation industry's ravenous appetite for petroleum products, full transformations of these realms will take decades. Moreover, beyond being used to produce just about everything, oil derivatives are key ingredients in products ranging from plastics and fertilizers to the asphalt that paves our roads and the synthetic fibers that clothe many of us. In 2018, NVIDIA has announced two wins in the oil and gas space.
The cave formations in China's southwest Guangxi Zhuang region almost look like a different world. Centuries of dissolving limestone rock, a geological process known as karst, have resulted in expansive caverns, with stone towers and massive sinkholes. This region of China is home to the world's largest concentration of these karst formations, the largest cluster of sinkholes, and the world's most enormous cavern. The relatively pristine condition of these caves also means it's been a trove for scientific discovery. During their most recent expedition exploring the caves, Chinese and French cavers spotted an extremely rare fish called a Sinocyclocheilus grahami, or golden-line barbel.