High Performance Compu2ng and High Performance Data: exploring the growing use of Supercomputers in Oil and Gas Explora2on & Produc2on

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1 High Performance Compu2ng and High Performance Data: exploring the growing use of Supercomputers in Oil and Gas Explora2on & Produc2on Lesley Wyborn1, Ben Evans1, David Lescinsky2 and Clinton Foster2 16 September Na2onal Computa2onal Infrastructure (NCI), 2Geoscience Australia (GA) HPC and HPD in E&P Perth, September

2 Outline 1. Current drivers for supercomputers in the Oil and Gas Explora2on and Produc2on (E & P) 2. Overview of the concepts of: High Performance Compu2ng (HPC) High Performance Data (HPD) Data- intensive Science 3. Present some new research direc2ons in HP environments: are they applicable to Oil and Gas E & P? 4. Discuss advantages of the Oil and Gas Industries, Academia and Government collabora2vely working together in Data- intensive Science, but s2ll enabling compe22ve E & P analy2cs 5. Key take home messages

3 Poten2al drivers: relevant facts on Oil and Gas E & P Easy oil is running out: easily accessible fields are becoming scarcer People are no longer drilling wildcat wells and hoping for the best As explora2on goes deeper and into harsher environments (e.g., Arc2c, deeper water) the risk of miscalcula2ng drill sites increases The cost of finding and then bringing discoveries into produc2on are now substan2ally higher (e.g., offshore rigs can cost $1,000,000 per day) Exponen2ally growing volumes of E & P data are being collected In all parts of E & P the risks of ge_ng it wrong are far greater than ever Source: Source:

4 Background of Paper: Government working with Academia in partnership Work done in GA and its predecessors in the management of scien2fic digital data since 1977 Collabora2ve work since 2010 by GA and NCI, in par2cular research into large- scale, High Performance Data (HPD), High Performance Compu2ng (HPC) and mul2- disciplinary Data- intensive Science NCI is a partnership between Academia and Government: ANU, Bureau of Meteorology, GA and CSIRO Funding of ~ $360M in eresearch Infrastructure by the Australian Government (former Department of Innova2on, Industry, Science and Research) since 2007 (2 Petaflop computers, 24,000 node research cloud, ~30 PB of data storage at 8 nodes, data services, networks and 12 virtual laboratories) Raijin: The NCI 57,000 core Petascale machine (currently No 38 on the Top 500 Supercomputer list)

5 We are entering the 4 th Paradigm of Scien2fic Discovery First paradigm: Thousands of years ago Empirical Science describing natural phenomena Second paradigm: Last few hundred years: Theoretical Science using models, generalizations Third paradigm: Last few decades: Computational Science cpu intensive or simulating complex phenomena ~250 BC Archimedes of Syracuse Source: ~1650 AD Sir Isaac Newton Source: ~1940 AD Alan Turing Source:

6 The 4th Paradigm of Data- intensive Science Concept developed in 2007 by Jim Gray Data- intensive Supercompu2ng is where large volume data stores and large capacity computa2on are co- located Such HP hybrid systems are designed, programmed and operated to enable users to interac2vely invoke different forms of computa2on in situ over large volume data collec2ons High Performance Data (HPD) is data that is carefully prepared, standardised and structured to be used in Data- intensive Science on HPC Very different to compute intensive paradigm cf the iphone5 of th_paradigm_book_complete_lr.pdf HPC and HPD in E&P Perth, September 2014

7 Petascale: >100,000 cores Australian HPC in Top 500: June 2014 No 1: PFLOPS Tianhe-2 (China) Tier 0 (Top 10) External Terascale: >10,000 cores No 10: 3.14 PFLOPS No 38 (No 11: ENI, No 16: TOTAL) No 38: NCI (979 TFlops) No 57: LS Vic (715 TFlops) No 181: CSIRO (168 TFlops) No 266: Pawsey (192 Tflops) No 363: Defence (162 TFlops) No 364: Defence (162 TFlops) No 500 (134.2 TFLOPS) Tier 1 (Top 500) Gigascale: >1,000 cores Institutional Facilities Grid, Cloud GA usage!! Tier 2 Internal Megascale: >100 cores Desktop: 2 8 cores Local Machines and Local Machines and Clusters Local Local Condor Condor Pools Tier 3 Based on European Climate Computing Environments, Bryan Lawrence (http://home.badc.rl.ac.uk/lawrence/blog/2010/08/02 ) & Top 500 list June 2014 (http://www.top500.org)

8 Oil and Gas E&P in the global Top 500 Supercomputers list From the earliest days, Oil and Gas E & P has had a high demand for HPC 1 No 11 Pangea No 38 Raijin iphone 5S Developments in geophysical data processing sokware closely tracked (drove?) developments in HPC architecture 1 Oil and Gas E & P use cases appear on the Top 500 list, but not all users are recorded or iden2fiable June 2013, Pangea (TOTAL) was No 11 (2.09 Pflops) June 2014, ENI (Italy) was No 11 (3 Pflops) marks significant shiks in HPC everywhere iphone5 in 2012 was ~80 Gflops 1 Supercomputing and Energy in china How Investment in HPC affects Oil Security

9 The growth in HPC capacity is no longer driven by increasing the No. of CPU s Moore s law Transistor density doubles every 2 years Limita2ons Power, heat dissipa2on Atomic limits Impacts CPU clock speeds plateaued Power wall forced shik to mul2- core Number of cores increased Parallelisa2on became king New algorithms required for parallelism Many commercial sokware does not scale and/or the business model is inappropriate Sutter 2009 The Free Lunch is over:. Slide Courtesy of Brett Bryan CSIRO

10 New algorithms are being developed for Supercompu2ng by Oil and Gas E&P Source:

11 Supercomputers assis2ng Oil and Gas E&P Supercomputers assist in Oil and Gas E&P in three primary ways: seismic data processing reservoir simula2on computa2onal visualisa2on at all stages of the process from Explora2on to Produc2on (e.g., can produce four- dimensional visualisa2ons that iden2fy how oil, gas and water flow through the reservoir during produc2on that are hard to see algorithmically) Source:

12 Supercomputers de- risking Oil and Gas E&P They help "de- risk" the whole process from explora2on to produc2on by enabling processing and combina2on of vast amounts of data from well logs, seismic, gravity and magne2c surveys to produce 3D models of the subsurface assis2ng in iden2fying drilling loca2ons that maximise the chance of finding exploitable resources and minimise drilling of dry holes producing four- dimensional visualisa2ons that iden2fy how oil, gas and water flow through the reservoir during produc2on enabling field engineers to plan the op2mum layout for producing and injec2on wells, and to extract residual oil and gas from primary produc2on allowing ensemble runs to test mul2ple scenarios and to quan2fy uncertain2es on all parts of the process from explora2on through to produc2on above all, enabling integra2on with non- Oil and Gas data sets to maximise extrac2on Infrastructure from the 2014 subsurface safely and with minimal risks and environmental impacts

13 Quotes on Supercomputers assis2ng Oil and Gas E &P They save the industry 3me: Projects that used to take two years now take six months Pangea helped analyze seismic data from TOTAL s Kaombo project in Angola in just 9 days, or 4months quicker than it would have taken previously They produce be<er products It is like having a bigger lens, so that you get a sharper picture They allow for more interac3on within teams faster processors allow those collec2ng the data and the geologists, who interpret the data, to exchange informa2on and made needed adjustments They open new possibili3es: BP s industry- leading development of digital rocks.. enable calcula2ng petrophysical rock proper2es and modeling fluid flow directly from high- resolu2on 3D images at a scale equivalent to 1/50th of the thickness of a human hair

14 Impact on cost- benefit analysis Supercomputers can change an oil/gas company s cost- benefit calcula2ons by: allowing it to process data more quickly crea2ng a more accurate model with fewer assump2ons that help pinpoint the best drilling loca2on, thus reducing the number of dry holes monitoring changes in a site/field over 2me de- risking the process to make drilling in complex environments more affordable and safer Explorer-Starts-Drilling-1st-Well-for-CNOOC-Congo-SA.jpg

15 In HPC parallelising code is only one part of it The elephant in the room is data access No 11 Pangea No 38 Raijin iphone 5S The needs to be a balance between processing power and ability to access data (data scaling) The focus in no longer on feeds and speeds The focus is for on- demand direct access to large data sources It is now on content and on enabling HPC analy2cs directly on that content

16 Ways to beuer u2lise HPC capacity and transi2on to petascale compu2ng Increase Model Complexity Monte Carlo Simulations, multiple ensemble runs Increase Model Size and Data types Single passes at larger scales: integrate more data types Local Giga Timescale Use longer duration runs: use more and shorter time intervals Terascale Increase Data Resolution Use higher resolution data Petascale Speed up Data access Self describing data cubes and data arrays Based on European Climate Computing Environments, Bryan Lawrence (http://home.badc.rl.ac.uk/lawrence/blog/2010/08/02 )

17 The High Performance systems tetrahedron in balance High Performance Computing Infrastructures Data Accessibility Tools Bandwidth

18 The High Performance systems tetrahedron in 2014 High Performance Computing Infrastructures Totally out of balance! Bandwidth Data Accessibility Tools, Codes

19 HPD is now an essen2al prelude to Data- intensive Science We have new opportuni2es to process large volumes of data at resolu2ons and at scales never before possible But data volumes are growing exponen2ally: scalable data access is increasingly difficult Tradi2onal data find/download technologies are well past their effec2ve limit for Data- intensive Science Big data IS the new oil but unrefined it is of li6le value: it must be refined, processed and analysed We need to convert Big data collec2ons into High Performance Data (HPD) by Aggrega2ng data into seamless pre- processed data products Crea2ng hyper- cubes and self describing data arrays Source: images/student/art/ hokusai.jpg

20 Crea2ng HPD collec2ons: eg the Landsat Cube A research project with 15 Years of Landsat Data ( ) funded by the Department of Innova2on, Industry, Science and Research The Landsat cube arranges 636,000 Landsat Source scene spa2ally and temporally to allow flexible but efficient large- scale analysis The data is par22oned into spa2ally- regular, 2me- stamped, band- aggregated 2les which can be presented as temporal stacks. Temporal Stack Spa2ally par22oned 2les HPC and HPD in E&P Perth, September 2014

21 Current Landsat Holdings as HPD 636,000 Landsat Source Scenes (~52 x Pixels) 4M Spa2ally- Regular Time- Stamped Tiles (0.5 PB)

22 High- Resolu2on, Mul2- Decade, Con2nental- Scale Analysis of HPD Sampled 1,312, tiles => 21x10 12 pixels Water detection over15 Years from High 25m Nominal Pixel Resolution Actual data can be sampled at national or local farm scale

23 Can we created an equivalent HPD array of seismic reflec2on data? What would a calibrated HPD array of all Australian Seismic data look like? That is, direct access to actual data content rather than to metadata on files of data which then need to be downloaded, integrated and processed locally Such an array could be sampled and processed directly at a na2onal, basin or prospect scale And then integrated with HPD full resolu2on point clouds of magne2c, gravity and magneto- telluric survey data Source: data/assets/image/0003/15645/ allstates.jpg

24 Reali2es of HPD Data Collec2ons HPD collec2ons are just too large to move Bandwidth limits the capacity to move them: data transfers are too slow Even if they can be moved, few can afford to store them locally: the energy costs are also substan2al HPD is about moving processing to the data, moving users to the data and about having online applica2ons to process the data HPD enables cross- domain integra2on Domain- neutral interna2onal standards for data collec2ons and interoperability are cri2cal for allowing complex interac2ons in HP environments both within and between HPD collec2ons HPD enables scalable data access but also means rethinking the algorithms of the data (not again?)

25 The Oil and Gas Industry can take a bow They have been amongst the leaders in development of global standardised formats (e.g., SEG- Y) Energis2cs have driven the next genera2on of the ISO metadata standard THE global standard for discovery of geospa2al data Energis2cs are a global consor2um that facilitates the development, management and adop2on of data exchange standards for the upstream oil and gas industry (e.g., WITSML, PRODML and RESQML) In 2012, the Oil and Gas Industries formed the Standards Leadership Council which links many oil and gas standards bodies as well as the OGC and the SEG But these standards may need to evolve to increase uptake of data in HP environments, par2cularly at exascale

26 Rethinking Hardware Architectures for Data- intensive Science Work at NCI has highlighted the need for balanced systems to enable Data- intensive Science including: Interconnec2ng processes and high throughput to reduce inefficiencies The need to really care about placement of data resources Beuer communica2ons between the nodes Large persistent storage (on spinning disk) in addi2on to the tradi2onal scratch spaces I/O capability to match the computa2onal power NCI s I/O speed to persistent storage is ~50 GBytes/sec and to scratch is ~120 Gbytes/sec Close coupling of cluster, cloud and storage NCI s Integrated Infrastructure High Performance 2014 Environment Since star2ng on the NCI/GA Data- intensive journey in 2010 sokware is being progressively rewriuen: data and hardware architectures also need to change to create balanced systems

27 Warning: Exascale is just around the corner Exascale Petascale Terascale No11 Pangea No38 Raijin Next Pangea or ENI?? Next NCI?? Addressing the data access problem is the highest priority as Supercompu2ng heads towards exascale Climate/Weather research are already there: can we learn from these academic communi2es? Gigascale iphone 5S Looking backwards: the capacity of an iphone 5 today is equivalent to Supercomputers of 1995 Looking forwards: we are star2ng to slip.

28 Energy: the main limita2on on growth in HP environments Future HP systems will be energy- limited (both storage and HPC): are we reaching the tops in flops? Architecture will mauer more: energy efficiency will be achieved through careful architecture Increased performance will be determined by new algorithms and far more efficient data access Top 200: June 2014

29 Future HPC Challenges for the Oil and Gas E&P Future HPC challenges for everyone are: Power, programmability and scalability: programming needs to be at an extreme scale, using massive parallelism Data movement is THE current bouleneck: the precise and efficient flow of data will take center stage and hardware that will enable that level of control will become cri2cal Balanced systems will be crucial (architecture, sokware, data access) Specific HPC challenges for Oil and Gas E & P are: There will probably need to be a transi2on to high volume collabora2ve high performance data stores against which compe22ve algorithms can be deployed by individual companies The compe22ve advantage will now no longer be in what data a company holds: the advantage will be in smarter proprietary algorithms that are applied to collabora2ve HPD collec2ons that are closely sited next to HPC

30 Can we do this as a 3- way collabora2on? Government Agencies: (Data Rich)? Industry: Driving developments in HPD/HPC? Academia and Industry in partnerships on HPC developing new systems Academia: Cutting edge HPC/HPD research, particularly scaling to exascale Data-intensive Science

31 Take home messages for Oil and Gas E&P HPC is now an integral part of the Oil and Gas E&P: there IS capacity for way more growth Moving to HPD collec2ons will be key to enable data to be integrated and processed at high resolu2ons to give more accurate models and predic2ons The Oil and Gas Industry will need to con2nue to drive standards globally and ensure they can are compa2ble with the rapidly on- coming exascale HPC/HPD environments Three way partnerships need to be inves2gated (Government, Industry, Academia) Then together we can con2nue to drive New Oil from Raw Materials via standard specifica2ons to feed HPC environments to fuel quality assessments and provide burning new insights to support environmentally sustainable and safe development of our Oil and Gas Resources

32 Any Ques2ons? Dr Lesley Wyborn Dr Ben Evans Dr David Lescinsky Dr Clinton Foster Source: HPC and HPD in E&P Perth, September

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