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.
After several years of research on machine learning algorithms running on oil and gas production data, Solution Seeker has developed a hierarchical neural network model that improves the predictive power for real-time production optimization. The model leverages the power of neural network learning algorithms combined with domain knowledge in the form of first principle physics and production system logic.
Shell U.K. Exploration and Production (Aberdeen, U.K.) has implemented an advanced forecasting system for predicting oil field production. The expert system helped Shell achieve over $1.6 million in cost savings for its Brent Field site within 2 months of implementation. The National Research Council has awarded Nestor (Providence, R.I.) a grant to develop a neural network-based video sensor system, crossingguard Arvin Industries (Columbus, Ind.) is working with the U.S. Air Force to develop a neural network system that can determine the quality of noise in such vehicles as automobiles and aircraft. The neural network will help determine what exactly an annoying sound is and how it can be fixed. Using virtual reality hardware and software, Parke-Davis (Morris Plains, N.J.) has been able to improve the molecular modeling research techniques it uses to develop new pharmaceutical products.
Many years ago, during my first assignment at (super) major oil company, I was in charge of significant decisions for wells drilled in an onshore gas field. Each of these wells were drilled quickly, on average taking 5–7 days. The geology was well known, the reservoirs were, generally speaking, economic and the operational risks from drilling were rather low and manageable.