Oil, Gas, and Data High-Performance Data Tools in the Production of Industrial Power Daniel Cowles ISBN: 978-1-491-92289-7 Make Data Work strataconf.com Presented by O’Reilly and Cloudera, Strata + Hadoop World is where cutting-edge data science and new business fundamentals intersect— and merge n n n Learn business applications of data technologies Develop new skills through trainings and in-depth tutorials Connect with an international community of thousands who work with data Job # 15420 Oil, Gas, and Data High-Performance Data Tools in the Production of Industrial Power Daniel Cowles Oil, Gas, and Data by Daniel Cowles Copyright © 2015 O’Reilly Media, Inc All rights reserved Printed in the United States of America Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472 O’Reilly books may be purchased for educational, business, or sales promotional use Online editions are also available for most titles (http://safaribooksonline.com) For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com Editor: Tim McGovern Production Editor: Kara Ebrahim Interior Designer: David Futato Cover Designer: Ellie Volckhausen First Edition April 2015: Revision History for the First Edition 2015-04-10: First Release The O’Reilly logo is a registered trademark of O’Reilly Media, Inc Oil, Gas, and Data, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limi‐ tation responsibility for damages resulting from the use of or reliance on this work Use of the information and instructions contained in this work is at your own risk If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsi‐ bility to ensure that your use thereof complies with such licenses and/or rights 978-1-491-92289-7 [LSI] Table of Contents Oil, Gas, and Data Introduction Overview Upstream Well Optimization and Mature Wells Remote Sensors and Network Attached Devices/I of T Security Health, Safety, and Environment High-Performance Computing and Beyond More Cloud and Mobile Midstream and Downstream Emerging Tech Summary Innovation in Tough Economic Times Post-Mortem 11 11 12 13 13 18 18 19 iii Oil, Gas, and Data Introduction When you hear “innovation in oil and gas,” your first thoughts might go to hardware—bigger, faster, deeper drilling; more powerful pumping equipment; and bigger transport—or to the “shale revolu‐ tion”—unconventional wells, hydraulic fracturing, horizontal drill‐ ing, and other enhanced oil recovery (EOR) techniques But, just like any other industry where optimization is important—and due to large capital investment and high cost of error, it’s perhaps even more important in oil and gas than in most other industries—the potential benefits of predictive analytics, data science, and machine learning, along with rapid increases in computer processing power and speed, greater and cheaper storage, and advances in digital imaging and processing, have driven innovation and created a rich and disruptive movement among oil and gas companies and their suppliers The truth is, the oil and gas industry has been dealing with large amounts of data longer than most, some even calling it the “original big data industry.”1,2 Large increases in the quantity, resolution, and frequency of seismic data, and advances in “Internet-of-Things"-like network-attached sensors, devices, and appliances, are being com‐ bined with large amounts of historical data—both digital and Karren, Charles “Insight Report: Data Centre Developments Get up Close and Per‐ sonal.” OFFCOM News CTLD Publishing Ltd Web 20 Mar 2015 Boman, Karen “What Upstream Oil, Gas Can Learn About Big Data from Social Media.” Rigzone News Dice Holdings, Inc., 10 Dec 2014 Web 20 Mar 2015 physical—to create one of the most complex data science problems out there, and a new industry is developing to help solve it In oil and gas more than in almost any other industry, efficiency and accuracy is highly valued, and small improvements in efficiency and productivity can make a significant economic difference When a typical well can cost upwards of ten million dollars—and often much more—the cost of error is great, and managing cost versus benefit can mean the difference between profitability and loss And not unlike a tech startup, where a meaningful investment upfront is required before knowing how much the return will be—if any—you may have to dig many holes to find a successful well Obviously, the more certainty you can have, the better, and incrementally increas‐ ing certainty is a place where data science and predictive analytics promise to help The payoff from analytics isn’t limited to explora‐ tion: once a well has been successfully drilled, production efficiency and optimization remains important in the lifetime ultimate recov‐ ery of a well In addition, given crude price fluctuations and many other unpre‐ dictable outside variables, capital project planning itself is rife with uncertainty, and large-scale projects often face significant overages “In 2011, upstream offshore oil and gas projects…around 28% had a cost blowout of more than 50% and the root cause of that is…they got the numbers wrong,” says Dominic Thasarathar, who watches the energy sector for the Thought Leadership team at Autodesk “Their costs have gone up, they’re dealing in everything from fron‐ tier environments to difficulties raising finance.” According to the International Energy Agency, capital investments in energy projects have more than doubled since 2000, and are expected to grow by $2 trillion annually by 2035, so accurately predicting cost versus benefit is extremely important.3 “Where we see big data fitting in,” contin‐ ues Thasarathar, “is…if you look at the performance for those big projects, it’s pretty much a horror story in terms of how it’s dropped off over the last 15–20 years, and the root cause of that is, there’s so much that project teams need to understand and assimilate in terms of information to make the right decision.” But exploration and production aren’t the only areas that can benefit from innovative data and data science driven solutions From “WORLD ENERGY INVESTMENT OUTLOOK 2014 FACTSHEET - OVERVIEW.” International Energy Agency OECD/IEA, Jan 2014 Web 20 Mar 2015 | Oil, Gas, and Data health, safety, and environmental, to cyber security, to transporta‐ tion and manufacturing—opportunities to create greater efficiencies exist throughout the entire hydrocarbon production and delivery cycle Overview The oil and gas industry is traditionally broken down into three broader categories: upstream, which includes exploration, discovery, and both land and sea drilling and production; midstream, which includes transportation, wholesale markets, and manufacturing and refinement of crude; and downstream, which is primarily concerned with the delivery of refined products to the consumer The majority of big and fast data related innovation is found upstream, in the dis‐ covery and exploration phase, where risk and uncertainty are high, conditions can be—to put it mildly—challenging, and where failure is very expensive The industry is a mature and unique one, built on experience and hard-won knowledge, and employing the world’s leading geological scientists and engineers They’re very good at what they do, and they’ve been doing it for a long time, but there is an imperative to add more big data and data science skills like machine learning and predictive analytics into the mix, skills that oil companies haven’t traditionally and broadly had in-house According to Boaz Nur, for‐ mer VP of Energy at data science startup Kaggle, energy analysts think big data and analytics are the next frontier in oil and gas, but they’re only now in the early adoption phase “They [oil and gas companies] don’t shy away from technology, they’re just careful,” Nur says “A lot of snake oil has been sold to the oil and gas compa‐ nies over the years They’ve also historically done a pretty good job of producing oil They’re [already] doing OK; what we’re proposing will help them take it up to the next level.” Adds Nur: “They’re cau‐ tious but they’re optimistic.” Halliburton is using big data and data science techniques to try to solve a variety of problems in the E & P (exploration and produc‐ tion) upstream phase “We are looking at trying to optimize seismic space, trying to optimize drilling space, well planning,” says Dr Satyam Priyadarshy, Halliburton’s recently hired Chief Data Scien‐ tist Priyadarshy is bringing some big data techniques to the space: “For example, we are looking at how to optimize in the seismic Overview | world through distributed computing [techniques] because it takes a long time to process the data.” But Priyadarshy says that it’s a mis‐ take to think that data science methods and techniques are new to oil and gas “They’ve actually been using machine learning for many years,” he says “People have been using neural networks, fuzzy logic, SVM, SVRs—pretty much any algorithm you want to talk about in machine learning, they have been using it But, they have been using these in limited cases, to limited value, and the goal is now for peo‐ ple like us (data scientists) to build this into a more valuable prod‐ uct.” He says that the oil and gas industry is unique in terms of the complexity of the data and models, and that turnkey solutions from other traditional big data industries can’t be easily applied here “It’s a complex challenge It’s not the same as the other big data players,” says Priyadarshy, who has worked widely on big data projects in the news, media, Internet, and insurance spaces “The complexity in the oil and gas industry outweighs any other.” Because of that complexity, Priyadarshy stresses the need for domain area expertise when dealing with petrotechnical data, and he has his own definition of the skills a data scientist should have for the space “You need a person who has domain expertise, a person who is a computer scientist, and a business person—these three actually form a real domain data scientist” for the oil and gas space Another complication is legacy and historical data: some is digital, but much is still found in binder and paper form From a predictive modeling standpoint, there’s value to be had, but dealing with old systems and documents, often at isolated physical properties, or—as often happens in the industry—inherited through acquisitions and neglected, makes integrating these pieces into your model challenging Remote standalone locations and physical records and manuals also hamper efforts to digitally connect a company’s systems and assets— the much discussed “digital oilfield” idea, where systems are integra‐ ted and automated to tune and optimize operations across the breadth of the production cycle “The move to digital operations is increasing steadily, but there’s an awful lot of legacy out there, things going back decades, where the drawings were done with, literally, pen and paper,” says Neale Stidolph, Head of Information Manage‐ ment at Lockheed Martin and based in Aberdeen, where he primar‐ ily deals with North Sea oil fields, including many older legacy wells “A large part of the industry is very much tied to documents and | Oil, Gas, and Data Remote Sensors and Network Attached Devices/I of T There is already a lot of application and ongoing interest in network-attached devices, appliances, and Internet of Things-like connected devices in the oil and gas space Halliburton’s Priyadarshy prefers the term “emerging technology devices,” to the “Internet of Things” label, which causes some confusion and resistance in the industry In any event, remoteness, geographic breadth of facilities and pipelines, hazardous environments, and inaccessibility of many aspects of the oil and gas production cycle make it highly disposed to automation and remote monitoring and optimization Remotely monitored and controlled devices can help lower cost, effort, and error in resource tracking, and can decrease workforce overhead, improve logistics, and drive well and operations automation and optimization It’s a big piece of the “digital oilfield” concept, and one that the industry has already embraced Sensors of all kinds are already used throughout the detection, pro‐ duction, and manufacturing cycle to better understand and monitor processes and gather data Sensors can capture fluid pressure, veloc‐ ity and flow, temperature, radiation levels (gamma ray energy is a useful indication in hydrocarbon discovery), relative orientation and position, as well as chemical and biological make-up of physical materials Trending toward cheaper, smaller, and connected arrays, newer microsensors can communicate with each other and with external networks From exploration to the gas pump, there are opportunities to use networked devices Offshore, submersible devices that gather infor‐ mation can be remotely controlled and are safer alternatives to human-piloted crafts Pumps can be remotely monitored and adjus‐ ted, and can be far more economical than manual maintenance Midstream in the transportation phase, networked devices can help track resources through the many and various stages and handoffs that happen throughout the crude transport process Pipelines and remote equipment can be monitored and even maintained remotely Biomonitoring workers could increase safety Gartner has predicted Remote Sensors and Network Attached Devices/I of T | as many as 30 billion connected devices by 2020, with 15% of those in the manufacturing sector.6 The data gathered from all these devices will be valuable for predic‐ tive analytics and other applications: from well sensor data that can be analyzed to help optimize productivity, to operations data that can monitor and calibrate operational systems, to transportation data that can help identify bottlenecks and inefficiencies, to work‐ force data that can help drive safety But, as Halliburton’s Priyadar‐ shy points out, with those benefits also come some new challenges; for example, sensor data veracity in different physical environments: “Imagine a situation where you build a sensor for Texas weather If you were to take it to some Middle Eastern country like Kuwait, where temperatures are [significantly] higher…if the sensor starts sending data and you are trying to predict based on what you know from Texas, then you may be in deep trouble.” Security As discussed, there is significant pressure to lower costs and opti‐ mize, and remote-controlled and network-attached devices of all types are a means to that end The downside is, the more connected you are, the more vulnerable you are to network intrusions, inten‐ tional or otherwise And while remote monitoring is also crucial to improved security, it can open holes itself “There are gains from the automation; you can get more protection, you can better sensing of what’s happening along your line, there’s lots and lots of opportu‐ nities for managing and monitoring the line using automation,” says industrial and oil and gas cyber-security expert Eric Byres, “but your automation system, which is supposed to be protecting your pipe‐ line, [can become] the problem.” A series of events and attacks have made the oil and gas industry keenly aware of the need to dramatically improve their cybersecurity After 9/11, the industry became more concerned about intentional and coordinated attacks, but it wasn’t until the Stuxnet worm attack in 2010 that they started to really address the problem Stuxnet hit an Iranian nuclear facility in 2010, causing the failure of uranium-enriching centrifuges Written specifically to exploit van der Meulen, Rob “Gartner Says Personal Worlds and the Internet of Everything Are Colliding to Create New Markets.” Gartner.com Gartner, Inc., 11 Nov 2013 Web 20 Mar 2015 | Oil, Gas, and Data Microsoft and Siemens vulnerabilities, Stuxnet was the first promi‐ nent attack against the PLC/SCADA (programmable logic control‐ ler/supervisory control and data acquisition) systems used by indus‐ trial plants of all types, including the oil and gas industry, and previ‐ ously assumed to be safe from cyber attack To make things even scarier, Stuxnet—widely reported to be a joint Israeli/US made cyber-weapon—found its way onto the Natanz enrichment facility while not connected to the Internet, via sneakernet, on USB drives In the case of Stuxnet, the collateral damage—and what might even be called friendly fire in this new battlefield—spread into the wider industrial ecosystem, infecting Chevron, with unconfirmed reports of at least three other major oil companies being affected as well.7,8 Given the sociopolitically charged nature of the industry, oil compa‐ nies were justifiably worried by Stuxnet Suddenly, the ability for remote and unaffiliated parties to influence operational and safety systems was very real: spills, blowouts, explosions, and the potential for loss of life In addition to wells and refineries, pipelines, trains, and other transportation methods are vulnerable to attack, and beyond any human disaster, the repercussions could be environ‐ mentally catastrophic as well as disruptive to business Since Stuxnet, there have been other attacks, including the Shamoon virus that hit Saudi Aramco in 2012 Initiated by a “disgruntled insider,” Shamoon wiped out the contents of between 30,000 and 55,000 Saudi Aramco workstations These attacks, coupled with environmental and PR disasters like Deepwater Horizon, have given the industry all the motivation it needs to get serious about security “I think the oil and gas industry is ahead of all the other compa‐ nies [in terms of security],” continues Byres “There is a real serious attempt to try and get security under control…that’s the good news.” But while the majors like Shell, Exxon, Chevron, Total, and in par‐ ticular BP (where Paul Dorey was an early and vocal security advo‐ cate) have become very serious about security, you’re only as strong as your weakest link, and the industry is dependent on and tightly integrated with suppliers, contractors, and vendors, many with less sophisticated approaches to security “That terrifies the guys at BP, Sale, Richard “Stuxnet Hit Oil Companies.” Isssource.com Industrial Safety and Secu‐ rity Source, 15 Nov 2012 Web 20 Mar 2015 King, Rachael “Virus Aimed at Iran Infected Chevron Network.” Wall Street Journal Dow Jones and Company, Inc., Nov 2012 Web 20 Mar 2015 Security | that’s why they started becoming evangelists in 2006, 2005—because they realized they could a good job on their site, and gain noth‐ ing because of the integration to all the other companies around them The other companies were so insecure.” And it’s not just production that is threatened The Night Dragon attacks—thought to be started in China—targeted intellectual prop‐ erty.9 In the PR space, a Sony-type attack on internal email and pro‐ prietary information systems could also have huge ramifications in a competitive and secretive industry While some facilities remain off the net by virtue of being old and isolated, and instances of air-gapped systems may persist, in general the digitally attached genie is out of the bottle: the industry is mov‐ ing rapidly toward digital openness, and it won’t be going back As Byres notes, “The reality is, modern networks in the oil and gas industry need a steady diet of data Data going in and out; security patches, lab results, remote maintenance, [and] interactions with customers So, there’s no way you can isolate a refinery anymore There’s just too much need for data on the plant floor now with the way we’ve built our systems.” Technology might not always be the best solution in an industry as fundamentally physical as this one Byres relates a story of how one Nigerian delta oil company battled the theft of sections of pipe that were being taken and sold as scrap metal They started making them heavy enough to sink the boats that were used to carry them off, and the thefts stopped But anecdotes aside, security is now primary for oil and gas IT, and while prevention is still important, most now agree that 100% impenetrability is unlikely, and rapid detection is the most important security tool This is an area where data science and threat analytics can possibly help Applying machine learning and pattern recognition to noisy and ever-larger data streams can preemptively detect anomalies and identify attacks But Byres thinks the complexity of the problem means the industry is a ways off from really leveraging big data and data science solutions in the security space: “There is an opportunity, there’s no question…but we’re still a few years away before anyone uses it effectively.” Kirk, Jeremy “Night Dragon” Attacks from China Strike Energy Companies.” PCWorld.com IDG, 12 Feb 2011 Web 20 Mar 2015 10 | Oil, Gas, and Data Health, Safety, and Environment There is also a lot of optimism around the ways big data can help in the health, safety, and environmental space (HSE), and around the ways that predictive analytics and machine learning can be applied to anticipate well and manufacturing downtimes, malfunctions, accidents, and spills As increasing energy demand pushes oil and gas production into untapped frontiers and deeper waters, with ever harsher and unpredictable environments, the potential for ecologi‐ cal, human, economic, and public relations disaster increases So, companies are highly incentivized to everything they can to anticipate and proactively address potential problems This is a space where historical data can be analyzed to predict future issues, and where models can also tie in new data sources, like weather In addition, unconventional resource plays have introduced a whole new set of environmental and safety concerns, from water, air, and soil pollution to earthquakes There is thought to be a significant opportunity to tune and improve all aspects of unconventional drill‐ ing to reduce the harmful side effects Data science and predictive models can help drive optimization of fluid injection and more accurate drilling, and by incorporating ever more underground sen‐ sor data into the model, further improvements can be made High-Performance Computing and Beyond To handle the massive increase in the amount of data—for example, in 2013 BP stated their computing needs were 20,000 times greater than in 199910—companies have turned to expensive highperformance computing centers, and are building out data science expertise in-house, or engaging data science partners Some of the largest private supercomputing facilities in the world are now run by oil companies, with Italian energy company Eni, France-based Total Group, and now BP all recently building HPC centers capable of greater than petaflops.11 Eni—which utilizes a CPU/GPU cluster— claims upward of petaflops, while BP—whose facility cost upwards of $100 million—claims 3.8 petaflops of computing power and 23.5 10 “BP Opens New Facility in Houston to House the World’s Largest Supercomputer for Commercial Research.” BP.com British Petroleum, 22 Oct 2013 Web 20 Mar 2015 11 Trader, Tiffany “Eni Joins Oil and Gas Petaflop Club.” HPCWire.com Tabor Communi‐ cations, Inc., 20 Nov 2013 Web 20 Mar 2015 Health, Safety, and Environment | 11 petabytes of disk space, all geared toward processing seismic imag‐ ing and hydrocarbon exploration data.12 GPU processing is now commonplace for seismic data-crunching, and Intel, with their Xeon Phi processor, claims similar or better cost/benefit performance As falling crude prices impact IT spending, and with cloud comput‐ ing prices dropping almost as fast as that of crude, it seems that open-source distributed data management computing, in the cloud or on commodity hardware, could be poised to become a real pres‐ ence in the oil and gas space, particularly for smaller companies who can’t afford their own HPC center, or skunkworks projects where resources are scarce But it’s a cautious IT culture, with many com‐ panies waiting to see what others before them “It’s usually cul‐ tural problems that get in the way more than technical capabilities,” says Stidolph, about adopting new technology solutions “We often call it ‘the race to be second.’ In most industries, people want to innovate, to be the leader, which means you take certain risks, and you make certain investments In oil and gas, everybody kind of queues up to see who steps out of line to make that investment and take that risk” and follow suit only once it’s proved successful But, while crunching seismic data may continue to live in the HPC world, there are many other use cases where open source and NoSQL distributed data management systems like Hadoop could provide cost-effective alternatives to HPC Hadoop providers like Hortonworks have begun working with the industry, and they see opportunities throughout the exploration to delivery petro cycle (read more about Hortonworks next) Meanwhile, Cloudera has developed a “Seismic Hadoop” project to demonstrate “how to store and process seismic data in a Hadoop cluster” on commodity hardware.13 More Cloud and Mobile Cloud-based processing of large datasets is also driving innovation and disruption in the supply chain, and allowing for an untethered workforce Products like Autodesk’s ReCap allow customers to create large (many billion) point-cloud datasets, and render them as 12 “Number Crunching with Big Data.” BP.com British Petroleum, 22 Dec 2014 Web 20 Mar 2015 13 Wills, Josh “Seismic Data Science: Reflection Seismology and Hadoop.” Cloudera.com 25 Jan 2012 Web 20 Feb 2015 12 | Oil, Gas, and Data 3D models to mobile devices quickly In the oil and gas manufactur‐ ing space, this can mean visualizing wells or facilities on-site via tab‐ let or mobile device “It’s advances like the cloud that allow things like ReCap to be able to crunch those numbers and stitch photo‐ graphs together,” says Autodesk’s Thasarathar And using the cloud to crunch those datasets is becoming attractive to oil and gas com‐ panies for a few different reasons Not only does it allow them to lessen their capital investment in soon-to-be-obsolete hardware, but they can also move the infrastructure cost/benefit burden to the cloud provider “The fact that they can it on demand, and you pay for what you use…that consumption-based business model is incredibly attractive to the industry,” says Thasarathar Midstream and Downstream Primarily because there is less uncertainty, and the cost of failure is lower, there is less innovative data science activity in the midstream and downstream sectors, but there is opportunity there as well Many of the same principles and techniques apply, especially in midstream activities like crude transport and pipeline security and safety, refinery maintenance, and failure monitoring, logistics, and people and resource management Emerging Tech Things are changing within the sector, where cluster compute plat‐ forms, massive and affordable storage, and new techniques have enabled companies to evolve their existing tools and methods Data science as a practice is being adopted within the industry, but many companies lack the needed internal data science resources While they have abundant expertise in geosciences and engineering, among other things, they don’t typically have big and unstructured data, machine learning, predictive analytics, artificial intelligence, or other data science specific expertise “They’re recognizing that they have a lot of data, both historical as well as new, that they aren’t get‐ ting everything they can out of,” says Kaggle’s Nur So oil and gas related companies are taking different approaches, building out data science teams internally or turning to outside companies for exper‐ tise Let’s take a look at some of these outside companies Midstream and Downstream | 13 Hortonworks Hortonworks is a leading provider of Hadoop solutions, well known in the tech sector, but relatively new to oil and gas They bring tech‐ nical expertise and provide solutions with a toolset that oil and gas isn’t familiar with, and they bring an open-source approach to a sec‐ tor that isn’t known for its openness or its willingness to share But that’s changing as the industry starts to understand the potential in data science and predictive analytics “They all want to get into a modern data architecture, and they realize Hadoop is a cornerstone for that,” says Ofer Mendelevitch, Hortonwork’s Director of Data Science Hortonworks sees opportunities to provide insight throughout the upstream sector, from using predictive analytics to improve production optimization by providing a better sense of when a well might go down, to being better able to predict and pro‐ actively handle safety and environmental hazards, to providing a broader and more multidimensional dashboard, including services like weather and social feeds Mendelevitch also sees potential in niche cases, like automatically processing LAS (Log ASCII Standard) files, using algorithms and fitting curves to identify redundancy and greatly reduce work cur‐ rently done manually In addition, with a lot of buzz around data security and Internet of Things, they see companies adjusting their IT processes to collect more and new data, and becoming more in touch with their social media streams and presence And there are opportunities midstream and downstream as well, in areas like equipment failure prediction, safety analytics, and portfolio analysis Kaggle Kaggle, a startup with roots as an analytics competition platform, is another tech sector company to have brought data science expertise to the oil and gas space Kaggle took a different approach, hoping to provide expertise to the oil and gas sector by leveraging its large data science competition community and platform to provide expertise to an industry who might not always have the data science skills inhouse to find the solution Though this business model ultimately did not pan out, Kaggle did achieve successes using data science and well logs, production data, and completion data to optimize drilling parameters like well spacing, orientation, length, and more “Of course all these decisions they have to make have an economic cost 14 | Oil, Gas, and Data component to them,” said Boaz Nur, former VP of Energy at Kaggle “The longer you drill the well, the more it costs; the more proppant14 you use, the more it costs; the more fluid you use, the more it costs…there is some sort of optimal solution,” Nur continues “We ingest all the data…and we basically come up with that optimal sol‐ ution By helping guide them, they’re able to think about the param‐ eters that are most important Our strategy is to find where data sci‐ ence solutions add the most value, where the challenging problems are, and where data science is the most applicable solution.” SparkBeyond Unlike some pure data science oriented startups, SparkBeyond uses domain area experts to work with their data scientist team, to help ask the right questions and pick the right inputs for their models and machine learning They emphasize the value of expertise, and stress sound methodology when building complex models They use Apache Spark, yes, but many other tools as well, and apply a broad multidisciplinary approach and diverse datasets to their oil and gas sector work, a sector full of uncertainty throughout the entire pro‐ duction chain In addition to standard seismic, production, opera‐ tional, and log data, they pull from a variety of other sources “You need to incorporate weather data and APIs with geological data, financial data with news articles to see how geopolitical events can affect (production) cycles,” says Sagie Davidovich, SparkBeyond’s CEO They also incorporate data from other energy sectors, since it has a direct impact on oil and gas cost/benefit models Currently most of the work they is in the unconventional well space, which makes building models challenging because of the rela‐ tively small and incomplete sample set for wells of the shale boom era “Wells drilled since, say 2009, 2010…are very different from the wells drilled 20 years ago,” says Meir Maor, SparkBeyond’s Chief Architect “So, there’s actually less relevant data to learn from, and most of these wells have not completed their lifetime, [which makes ultimate recovery predictions difficult] We’re looking for the areas where there is a lot of uncertainty in the exploration process…how much oil is going to be produced, how fast it’s going to come out,” says Meir, “and we’re also looking for places that decisions can be made, that if we can manage to lower that uncertainty with a 14 “Hydraulic Fracturing Proppants.” Wikipedia.com Wiki Foundation, Feb 2015 Web 20 Mar 2015 Emerging Tech | 15 predictive model, it will be actionable.” And with so many new tech‐ niques and methods emerging in the unconventional oil space, how one drills is becoming as much of an issue as if and where to drill “When you are talking about extracting hydrocarbons from solid rock, it becomes exponentially more difficult The techniques have advanced, so there are many different ways [to drill], which can behave differently…so the decision space is wide and there is a lot of money at stake and a lot of uncertainty as to what will come out.” Given the high cost of error in the industry, trust and adoption of new technologies doesn’t come easily Even if you have sound pre‐ dictive models, actual economic success can take years to prove So, in the meantime, SparkBeyond works hard to remain transparent and to build models that are easy to understand Says Davidovich: “What we learned is that being 30–40% more successful than our competitors is only the first step to get in the door, then you go through other steps.” But they’re seeing things change, partly due to external forces “If you think about it, the big data hype actually creates a lot of pressure on companies to introduce predictive analytics…and the oil and gas industry is no different,” says Davidovich, and that’s an exciting prospect to him “There are so many new undiscovered opportuni‐ ties to bring more certainty to this space, which affects every aspect of our lives.” Adds Maor: “What’s even more interesting is, when our client actually acts on this…when the insights we deliver drive action to change the world in a meaningful way.” WellWiki Joel Gehman started the WellWiki project when he was a grad stu‐ dent, at a time when the Pennsylvania Marcellus Formation and debates around fracking first appeared in mainstream conscious‐ ness Hoping to become the Wikipedia for wells, WellWiki scrapes public databases to compile a wiki of North American well info They then combine the database feed with contributions from the community to create a structured dataset tied to user-driven narra‐ tive content The goal is to have information on all the wells—4 mil‐ lion by Gehman’s estimate—drilled in North America since the Drake Well in 1859 Neither a watchdog nor industry-backed entity, WellWiki remains neutral while trying to provide information and bring transparency to a fragmented and controversial space “I think 16 | Oil, Gas, and Data of it as giving wells biographies.” Gehman says, “Every well has a story.” While the data was generally publically available, Gehman found it difficult to consume in its nonstandardized form He wants to har‐ monize the data and information, which is regulated and recorded inconsistently by state and province in the United States and Can‐ ada Landowners, community members, citizens, journalists, and attorneys have used the site But maybe the real power of WellWiki is what happens behind the scenes, where all the data that has been collected, scrubbed, and nor‐ malized can then be joined to other datasets using standardized and unique keys, including parent company financial reports and other business data, to help researchers, academics, journalists, and others understand and report on the industry There are other well-monitoring organizations, some working to keep an eye on the industry’s activities FracTracker is another datadriven organization, providing maps and analysis to “shine a light” on the impact of fracking and other oil and gas development projects SkyTruth is a nonprofit that uses satellite and aerial remote sensor data and imagery to identify and quantify the effects of oil and gas production on the environment According to their site, “SkyTruth was the first to publicly challenge BP’s inaccurate reports of the rate of oil spilling into the Gulf.” Other Disruptors There are other relatively new technologies beginning to be adopted that may impact niche segments of the industry 3D printing has the potential to bring disruption to the supply chain Drones are begin‐ ning to be used to acquire aerial images and remote-sensor data Crowd-funding platforms like crudefunders.com allow individuals to participate directly in the oil business DIY spill cleanup and monitoring organizations have sprung up in the aftermath of the Deepwater Horizon spill Emerging Tech | 17 Summary “It’s early times, but one of the important things to remember is this is iterative, [it’s] low hanging fruit at first, but over time you can really dial in models to become really good at predicting, safety, or maintenance,” says Hortonwork’s Mendelevitch, and the capabilities of these new tools and platforms are in turn changing the way oil and gas does business; for example, cheaper storage allows them to change retention policies, and keep more data longer “In the past, a lot of this data was thrown away pretty quickly, because the cost of storing it was very high That’s the disruptive part of Hadoop—data storage is so inexpensive.” “It’s really important to understand the industry well and the pain points of the industry in order to develop the appropriate solutions,” says Kaggle’s Nur, adding that cultural differences don’t matter if you deliver “If you have a solution that creates value, and is proven, then clients will use it.” Despite it’s massive size, the oil and gas industry operates on small margins, and depends on efficiency and optimization for profitabil‐ ity Given extraordinary capital costs, a wide and deep array of risk, and high cost of error, machine learning and predictive analytics— driven by faster, distributed cluster computing and larger, cheaper storage—becomes an increasingly important factor to address the efficiency and optimization required to extract profits along with hydrocarbons Innovation in Tough Economic Times Recently, of course, oil prices have plummeted The question is, how will lower crude prices affect investments in innovation and data science? At a time when margins are being squeezed even further, and vendors up and down the supply chain are being asked to cut back, will resources be found for new investment? The consensus seems to be: yes, at first, projects will be cut But then, innovation becomes a necessity “The first reaction is usually, we’ll look at our suppliers, and we’ll look at our staff, and we’ll look at our freelance contractors, and we’ll ask them all to take a haircut,” says Lockheed’s Stidolph, “and then they’ll look and see ‘Have we got any projects we can suspend or defer?'…but then they have to look at how they can 18 | Oil, Gas, and Data be smarter, and collaborate more, share drilling rigs, move the data more efficiently, mine it harder.” Autodesk’s Thasarathar also sees opportunity in collaboration, and shared IP, as well as a chance to get leaner and smarter: “Once the dust has settled on the initial kneejerk of ‘cut capital spending by 20%’ or whatever it might be…I think they’re going to look to the supply chains to deliver those costs that are going to be cut…and one way to that is through innova‐ tion and technology.” But the current drop in prices may require more than a “haircut,” as falling prices are causing a steady decline in the number of rigs open for business, with over 500 US rigs hav‐ ing closed in the past year, and Halliburton, Schlumberger, and Baker Hughes have all signaled significant layoffs to come While some people might see downtimes as an opportunity to inno‐ vate, others become even more risk averse But SparkBeyond’s Maor sees even more reason to embrace data solutions in that case: “When you become risk averse…uncertainty becomes even more of an issue You want to have a really good idea of how much oil is going to come out They aren’t going to stop drilling…and they still have great uncertainty Our solution has proven to dramatically reduce the amount of uncertainty.” “The cost of error is higher now [and] you cannot make as many trial and errors as you could before, because you need to slow down some of your activities,” adds Davidovich “But this also cre‐ ates a higher pressure to innovate It just takes the inherent chal‐ lenges of applying predictive analytics in such a risk averse space, and it makes them even more acute.” Halliburton’s Priyadarshy sees the commitment to big data as a longterm play: “Data projects are an investment, it’s not like you can get the return tomorrow You have to invest in it, you have to build a team, and you have to come up with a plan,” he says “Think of it like building a startup within a company.” Post-Mortem The falling price of crude is already having an impact on data sci‐ ence forays into the industry Kaggle—mentioned earlier in this arti‐ cle—recently eliminated their energy-industry consulting business.15 15 McMillan, Robert “DATA-SCIENCE DARLING KAGGLE CUTS A THIRD OF ITS STAFF.” Wired.com Condé Nast, Feb 2015 Web 20 Mar 2015 Post-Mortem | 19 In addition to falling prices, it’s possible that petro companies were uncomfortable with Kaggle’s “competition-based” business model, which could require them to share their private data with the data science community While Kaggle’s innovative algorithms and pre‐ dictive expertise may very well have contributed insights and improved efficiencies to the century-old effort of supplying petro‐ leum to the industrial world, it seems that not enough companies were willing to make that leap quite yet, particularly given the cur‐ rent environment In the data era, however, we look forward to see‐ ing more experiments—and successes—in this high-stakes industry 20 | Oil, Gas, and Data About the Author Dan Cowles is a writer, filmmaker, and data geek who has worked in the tech sector for the last 20+ years Dan is interested in human beings and their stories, and all manner and method of telling them He lives in Berkeley, California, with his wife and son ... storing it was very high That’s the disruptive part of Hadoop data storage is so inexpensive.” “It’s really important to understand the industry well and the pain points of the industry in order... Byres thinks the complexity of the problem means the industry is a ways off from really leveraging big data and data science solutions in the security space: “There is an opportunity, there’s... production on the environment According to their site, “SkyTruth was the first to publicly challenge BP’s inaccurate reports of the rate of oil spilling into the Gulf.” Other Disruptors There are other