Communities along the east coast are steeling themselves for a fresh round of angst and protest over offshore drilling, with Donald Trump set to throw open vast swaths of the Atlantic seaboard to oil and gas companies. On Friday, the president signed an executive order that asks the interior department to review offshore areas potentially rich in fossil fuels that were put out of reach of drilling by Barack Obama's administration. At the signing of the order, Trump said: "It's a great day for America workers, unleashing American energy and clearing the way for thousands and thousands of high-paying American energy jobs. Our country is blessed with incredible natural resources, including abundant offshore oil and natural gas reserves." Asked about his first 100 days in office, which Trump has admitted has been harder than he expected, the president said: "We're moving awfully well.
A new geological survey has revealed the biggest continuous oil field ever discovered in the America hidden under west Texas. The Midland Basin, of the Wolfcamp Shale area in the Permian Basin, has an estimated 20 billion barrels of oil - worth up to $900 billion - and 1.6 billion barrels of natural gas, according to the US Geological survey. The discovery is nearly three times larger than the shale oil found in 2013 in the Bakken and Three Forks formations in the Dakotas and Montana, said Chris Schenk, a Denver-based research geologist for the agency. A new geologist survey has revealed the biggest continuous oil field ever discovered in the America hidden under west Texas (Robinson Drilling rig No. 4 in Midland County, Texas in February 2016) The oil, which is contained within layers of shale, is worth around $900 billion based on the current market price of oil. Yet, oil companies will have to pay for the extraction and processing of the oil.
The US Geological Survey (USGS) said that it assessed in Texas the largest oil deposit ever discovered in the country, the agency announced this week. The USGS says an estimated 20 billion barrels of oil, 16 trillion cubic feet of associated natural gas and 1.6 billion barrels of natural gas liquids were located in the Wolfcamp shale in the Midland Basin area of the Permian Basin province in west Texas. The discovery is valued at $900 billion, based on the current West Texas Intermediate $45 per barrel price. This is the largest deposit ever found in the United States and is three times bigger that the one in the North Dakota Bakken-Three Forks oilfields assessed in 2013. "The fact that this is the largest assessment of continuous oil we have ever done just goes to show that, even in areas that have produced billions of barrels of oil, there is still the potential to find billions more," said Walter Guidroz from the USGS Energy Resources Program.
(ABRIDGED) In previous work, two platforms have been developed for testing computer-vision algorithms for robotic planetary exploration (McGuire et al. 2004b,2005; Bartolo et al. 2007). The wearable-computer platform has been tested at geological and astrobiological field sites in Spain (Rivas Vaciamadrid and Riba de Santiuste), and the phone-camera has been tested at a geological field site in Malta. In this work, we (i) apply a Hopfield neural-network algorithm for novelty detection based upon color, (ii) integrate a field-capable digital microscope on the wearable computer platform, (iii) test this novelty detection with the digital microscope at Rivas Vaciamadrid, (iv) develop a Bluetooth communication mode for the phone-camera platform, in order to allow access to a mobile processing computer at the field sites, and (v) test the novelty detection on the Bluetooth-enabled phone-camera connected to a netbook computer at the Mars Desert Research Station in Utah. This systems engineering and field testing have together allowed us to develop a real-time computer-vision system that is capable, for example, of identifying lichens as novel within a series of images acquired in semi-arid desert environments. We acquired sequences of images of geologic outcrops in Utah and Spain consisting of various rock types and colors to test this algorithm. The algorithm robustly recognized previously-observed units by their color, while requiring only a single image or a few images to learn colors as familiar, demonstrating its fast learning capability.