Volume 21 Number 10


Geo-microservices

The next big thing in IT? Microservices! Landmark and DELL/EMC, with backing from majors and service companies, to port monolithic legacy apps to the cloud in OpenEarth Community effort.

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Energy management

Yokogawa acquires Soteica Visual Mesa, adding cloud-based energy management to its Industrial Knowledge/KBC Advanced units.

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Watson and the weather

Neil McNaughton asks if the current big data and analytics hoopla is justified. In some fields, like finance, and probably oil and gas, it can be hard to test AI objectively. In weather forecasting though, there is a vast body of data and also, a rich forward modeling tradition that should allow for a head-to-head comparison of the two approaches. Looks like IBM is on the case already.

Hedge funds, day traders and others seeking get rich quick schemes are looking intensely at what has come to be known as big data and analytics. On the information technology side, the analytics approach is being supported by trendy new technologies – data lakes, machine learning, artificial intelligence and so on. Current media reporting focuses on the promise of such technology, the upside; along with the its’ risks to humanity, the downside. What is not so clear is the possibility that the ‘big data and analytics’ (BDA) movement may be another false dawn for artificial intelligence, the blindside.

It is a good idea to step back from the fray and look at what BDA is trying to do. On the one hand, there is the expectation that, by analyzing a very large amount of data, hidden trends and truths may emerge. The day traders are hoping for some a hitherto unseen correlation that foretells the future value of an investment. BDA promises to beat traditional analyses using charts or fundamentals. Unfortunately, finance does not lend itself to rigorous testing because if a new martingale is discovered, it is unlikely to be widely reported. If it is made public, investors will quickly adapt and lessen its effectiveness.

In science and engineering, BDA is up against a different kind of traditional analysis, generally known as forward modeling. If you are manufacturing an airplane wing or other mechanical part, or if you are studying the flow of chemicals in a reactor, the science behind these activities is well understood. Building detailed models of the part or process in the computer is now used to test proposed modifications to deformations, flow rates and such, that were previously done with real physical models. Should there remain uncertainty in a digital model’s validity, other measurements can be added to ‘ground truth’ and refine the model.

Forward modeling is a very powerful tool and it has been used for decades to model fluid flow in an oil reservoir. But this activity is different from the engineering fields above in that models of oil fields are built on incomplete data. A few sample boreholes, flow rate tests on wells, a relatively short period of oil or gas production do not make for a sufficiently detailed numerical model for unequivocal results. Here, forward modeling becomes an exercise in approximation and interpolation, with results that may vary greatly depending on what assumptions were used.

It is this kind of under-specified problem that is the predilection of the BDA enthusiasts. Instead of using forward modeling using physics, just throw all the information you have into the computer and let the machine figure out what is happening using machine learning/artificial intelligence. This approach is presented in some circles as a ‘no-brainer.’ To quote ConocoPhillips’ Richard Barclay, a strong advocate of the use of AI in the oilfield, ‘If you are still using physics-based models [as opposed to BDA/AI] then you are leaving money on the table.’ Strong words indeed, backed up enthusiastically by the IT/consulting community who see a whole new field of opportunities for getting their foot into the door and doing ‘disruption.’

Oilfield modeling is, and will likely remain, closer to financial modeling in that it is difficult to know when you have got something right. There are far too many ways of doing the calculations. Again, truth lies in the future, folks will keep on arguing about the outcomes and how much, if any, money is still ‘on the table.’ Acquiring more data is often prohibitively expensive. In reality, the techniques behind todays’ big data approaches have been used for decades in the oil and gas business and in many other verticals without much apparent ‘disruption.’

Weather forecasting on the other hand is an area that ought to provide a good test of full physics, forward modeling and BDA/AI. Currently, weather forecasters use forward modeling of the physics of air motion, a spinning earth, sunlight, water uptake from the oceans and so on to provide quite accurate forecasts of the weather a few days out. Weather forecasters also collect and have been collecting data, lots of it, for decades if not centuries. All of which should make a great litmus test for forward modeling versus analytics.

I say ‘should make,’ but weather data is not all that easy to access. The national weather authorities have invested heavily in computing resources that generally use the forward modeling approach and may not be all that keen on sharing their data with folks who may use it to compete with them in their commercial forecasting, or perhaps to develop a better, cheaper means of forecasting. There are notable exceptions. The US NOAA has opened its data for public use in its ‘Big data project, ‘but even here, public access is ‘limited to 10% of NOAA’s 20 terabytes of daily output’.

Alongside the ‘official’ met data collected by NOAA, a more grass roots data collection effort is exemplified by WeatherUnderground which links together hundreds of thousands of private individuals’ weather stations (on of them which I own!) In 1995 WU started out as a militant ‘underground’ activity but its potential was spotted early on by The Weather Company (owner of the US TV station The Weather Channel) which acquired WU in 2012. Earlier in 2016, TWC signed with IBM which is now to host its weather data and apply its Jeopardy-winning Watson’s ‘cognitive’ BDA technology to weather forecasting. The question now is, will IBM’s Watson beat the traditional forecasters? If it does, will we even know?

@neilmcn


Oil IT interview, Mike Jones (Landmark)/David Holmes (Dell/Emc)

Oil IT Journal gets the low-down on Landmark's ambitious OpenEarth initiative in this exclusive interview with CTO Mike Jones and David Holmes from partner Dell/EMC. The 'free, global and open' community of scientists, engineers and software developers, with backing from majors and service companies, proposes 'horizontal' interoperability as opposed to Petrel's 'single app' approach.

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EnergyIQ targets upstream data automation

'Rendezvous' meet up hears from users and from developers on software QA.

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Nimbix rolls-out 'push to compute' HPC

Novel container technology for HPC workflows offers 'bare metal' OS-less performance.

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Roxar announces Python API for reservoir modelers

'Extensibility solution' for upstream workflow development builds cross-discipline apps.

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Sindbad seismic imaging consortium year-end meet

OpeSci's symbolic math for finite difference models, wave propagators. Modeling in Juila.

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Software, hardware short takes ...

Roxar RMS 10, Sintef Matlab reservoir simulation toolbox, Tecplot RS, ExproSoft WellMaster, Schneider Electric RefineryWise, Aggreko's genny, CGG GeoSoftware HampsonRussell, GE Field Agent, IFS Business Connector, Seven Lakes Joyn for oil and gas, Kepware KEPServerEX, OpendTect Petrel plugin, PetroVR 2017, PHDWin 2.10, Ikon Science RokDoc/Ji-Fi, Pegasus Vertex CemLife/PlugPro, Quorum Land Canada, Streamsim StudioSL/3DSL. Schlumberger Olga 2016.1.

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OSIsoft PI System user conference, Berlin

Internet of things? Industrie 4.0? OSIsoft and clients done 'em for decades! Flowserve pump monitored live with AI from SparkCognition. PI marketplace announced. Shell's PI 'super collectives.' Mitsui's PI/SAP cloud. Honeywell on 'ubiquitous but problematic' OPC. OSIsoft's main competition? In-house IT. The PI big data debate. Tullow builds digital oilfield from scratch.

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Siemens takes-on GE in IoT, with help from Watson

Siemens Innovation event reports progress on Vision 2020 $5bn/year R&D program. Siemens to bundle IBM cognitive analytics with Mindsphere IoT. On 'nurturing' the digital twin.

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Folks, facts, orgs ...

Aereon, Asset Guardian, CGG, CH2M, Chevron, DMC Global, Energy Navigator, Exterran, Exxon-Mobil, Freedom CNG, H2scan, KBR, Noble, Petrowest, Shell, Siemens, TrendMiner, Technical Toolboxes, Wellsite, Weatherford, Amphora, Calgary Scientific, Rockwell, Object Management Group.

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Done deals

NOV/Fjords Processing. Premier Oilfield Labs/MUD Geochemical. Pure Technologies/E-MAC Corrosion. RS Energy/NavPort. Software AG/Zementis. ESIA/JSI Services. BJ Services. Automic/CA Technologies. EPI Group/PDF. Cognizant/Frontica. Rockwell/ACP.

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Going, going ... green!

PG&E/Acutect. US Energy Department CarbonSafe. ExxonMobil FuelCell Energy. Global CCS Institute. National Academic Press. Oil and gas climate initiative. HyMeAir.

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Battelle Institute on big data in oil and gas

TAMU big data conference paper compares different machine learning approaches.

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Blockchain in oil and gas?

Accenture on 'reinventing' the order-to-cash process.

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Sales, deployments, partnerships ...

Rockwell Automation, SAP, GE Digital, Wipro, Bertin Technologies, Onera, Cognizant, Honeywell, DSI, Emerson/Roxar, Ikon Science, Jacobs Engineering, KBR, Petrofac, Ansys, Rollie Systems, Accenture, SolidThinking, Soothsayer Analytics, Technip, DNV GL, Geosoft, Corescan, PTC.

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Standards stuff

International Foundation for Information Technology, DNV GL, NAOEMC, Petrotechnical Data Systems, Energistics, PODS, PPDM, CO-LaN.

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Safety first

BP on well safety software. CSB 5 year plan. IOGP 2015 record, WEC charter. SPE safety report.

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Hot swapping control systems

CIMA Canada's Tempus appliance supports uninterrupted, live control systems upgrade.

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The Castrol Brain, BP's chatbot

Pioneering artificial intelligence app for BP's mariners taste of things to come.

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Emerson standardizes Plantweb on Azure IoT

Digital ecosystem and connected services move to the Windows 10 cloud.

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Forecasts and fantasies ...

Oil and gas analytics. Cognitive computing. 3D printing. Drilling data management systems.

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EPCs, operators moot capital projects 'operating system'

San Antonio ECC conference hears call to fix 'management, technology and mindset' problems.

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Synopsis SimpleWare for Repsol

ScanIP-based tool underpins digital petrophysics workflows.

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API squeals as its own standard gets adopted!

American Standards Institute complains as Phmsa applies RP 1170/71 standards 'too soon!'

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