How To Understand The Oil And Gas Analytics

Transcription

1 Big Value from Big Data Big Data Analytics for the Upstream Domain Duncan Irving, Jane McConnell, Teradata Oil and Gas Team 10 th February,

2 Upstream IT is a bit of a mess (and quite confusing) if we are being honest 2

3 Image Courtesy of Flicker Upstream IT is the way it is for a reason the 6 S s Size Science Spatial Speed Sustainability Sertainty 3

4 Big Data Analytics in Oil and Gas - what???????? Analytics has been around for ages! Big Data is an emerging term but is also mainstream Many industries have been using Big Data Analytical architectures for several years, notably retail and banking Retail and banking are well-established domains of competitive, strategic and operational analytics Oil and Gas analytics are mainly re-purposed engineering applications e.g. rotating equipment predictive maintenance domain-specific analytics are still emerging! Teradata

5 Where do you site a well to maximise profitability? Data-driven development Teradata 2014 Walmart Walmart, and many other retailers, routinely integrate functional domains and value chains. New stores are sited based on: Potential revenues of product mix Cost of supply to store from one of Walmart s 3200 Supercenters Daily/Monthly/Seasonal effects Long-term demographics

6 How do you optimise production and development on mature fields? Data-driven planning Walmart Since 2007, Daimler has had an integrated view of all production line, diagnostic and dealership data for cars and trucks. They identify emerging problems in vehicles within weeks, and correct production processes accordingly. This reduced long-term warranty cost and development risk.

7 How do you optimise logistics and maintenance to minimise shut-ins? Data-driven operations Teradata 2015 Google 2014 UPS Since 2004, UPS has eliminated millions of miles off delivery routes through advanced analytics. Resulting in: Saving 45 million litres of fuel ($3.5M/yr) Reducing CO 2 emissions by 100,000 tons, equivalent to 5,300 passenger cars off the road for an entire year.

8 How do we achieve insight today? The integration happens in a scientist s head! 8

9 How do we achieve insight today? lining things up with numbers around them 9

10 How do we achieve insight today? How many dimensions can your brain work with? 10

11 Our tools and teams don t work well together NEW WAY OLD WAY 11

12 What s missing? Data Integration Cartoonist Hugh MacLeod nailed it with this cartoon: ESRI SAP SAP WELLS SCADA OSI WELLS OPT PROJECT OPT PROJECT Survey PROD PROD Logs Logs Excel ESRI Survey SCADA OSI Excel There is a world of difference in the workplace between knowing facts, and knowing how those facts fit together. Even more important is knowing what to do about it. 1 Source: 1 Marc Cenedella, Founder The Ladders 12

13 leading to cross-domain insight Geology data Drilling data Borehole data 13

14 Why is it so difficult? Operating companies don t understand Big Data and Analytics Service companies show too much inertia in product development IT companies don t understand the Oil industry Functional and technical domains speak different languages Challenges are often very-domain specific Culture of providing the right solution offthe-shelf Teradata

15 Why is it so difficult? Our data managers are highly skilled librarians who want to deploy their domain expertise much more than they do Our application databases signpost the books, not the data in the books, there is a data lake of untapped information Data is collected and stored in chunks and these chunks must be broken down by niche applications for pre-defined tasks Data integration is possible but currently only occurs after complex and often one-off transformation Application workflows are rigid and compartmentalised architectures exist to enable more flexibility It is a long journey from the data to the decision; operationalization of data shortens time-to-decision 15

16 Business Value The power of becoming data-driven Analytics at scale: from descriptive to predictive Types of question (analytic class) Data usage and setting Optimise what happened? (Descriptive) Discover insights Data stored for indomain analysis in research or operations why did it happen? (Diagnostic) Operationalise insights Dashboards show what happened? and niche tools used for the why? what will happen? (Predictive) Trust what should happen? (Prescriptive) Modelling and planning based on historic data drives decisions Use near-realtime data for new business processes Teradata Implementation sophistication

17 How does an operating company get started? Wait for a service company to develop a Big Data Analytics offering? Wait for an IT company to develop the domain expertise? Wait for the IT department to develop a fit-for-purpose Big Data Analytics capability and architecture? Teradata

18 Introducing the Hackathon Prototype ideas Cross-fertilize experience Low risk Rapid value (or rapid failure) Try out some use cases 18

19 Cross-domain insights Teradata

20 An example of Big Data Analytics in O&G

21 Drilling Efficiency/Safety Stuck Pipe = NPT = cost Why stuck? Geology (link) e.g. swelling shales Rock properties e.g. weak rocks Deviation/deviated wells Bit type Mud type WBM vs OBM Other If we can analyse the conditions causing stuck pipe we can reduce the risk/cost Pilot for Big Data Analytics partnership between Teradata and CGG 21 Use of Big Data Analytics in O&G

22 Bad Hole Example Single Well The completion log gives no clues to the problems encountered. 22 Use of Big Data Analytics in O&G

23 Data Quantity It is widely recognised that data quantities have ballooned and continue to do so : O&G Data is: Seismic Well logs Formations tops Checkshot surveys Pressures Drilling data Core data Well test data Completions Production data Fluid data UKCS Data One TeraByte 200,000+ files 23 Use of Big Data Analytics in O&G

24 Data Links A lot of these connections are routine, check shots and seismic, fluids and pressures. Some of this data is used in combination in reservoir studies, seismic, well logs, formation tops, pressures, fluids, core data. However these are single instances, single wells or a field study. 24 Use of Big Data Analytics in O&G

25 Data Not Linked A lot of data types are not-linked or only linked occasionally. Why? Are all links equal or are some ridiculous? Sometimes new techniques are found by linking diverse data types for example - Seismic to Fluids is AVO - Seismic to pressures is overpressured zones 25 Use of Big Data Analytics in O&G

26 Data Visualisation Multi Well Visualisation of a number of parameters simultaneously. 26 Use of Big Data Analytics in O&G

27 Data Analysis Analysis of the data gives correlations and probabilities. 27 Use of Big Data Analytics in O&G

28 As a conclusion, we show that JAH 2014 It is possible to use Big Data Analytics on diverse data type such as employed in the Pilot Multivariate analysis are performed on the data without preconceptions A variety of techniques are available to display multiple types of data Unexpected correlations have been exhibited Correlations have been geo-localized across area and verticaly across formation Correlations allow predictive statistics to be computed The Pilot confirms the possibility to improve the Drilling Models using Big Data Analytics

29 Our Pilot open the door for numerous other applications JAH 2014 Possibility to perform the same pattern recognition in other basins using public or corporate data Possibility to add some other input in the pilot (eg, deviation, lihology ) Possiblity to query the data set using log curves Possibility to QC data and meta-data by pattern recognition Finally to analyse more data-type together give more value to your decision.

30 For more information please contact: JAH 2014

31 The quickest way to find a needle in a haystack is to burn the haystack crunch all the data Gary Class, Head of Digital Analytics, Wells Fargo Data Mining and Discovery Analytics 31

32 Statoil s offshore Big Data problem Permanent Reservoir Monitoring investment on the Snorre and Grane Fields $800M in seafloor cable 38 wells, 2 platforms LOF: th largest field and 3% of PRs 32

33 33 4D Seismic seeing what happened

34 Traditional Marine Seismic Surveys Planning 1 year Acquisition 1 month Processing 6+ months Interpretation 3-6 months Average 2 years between snapshots of the reservoir How much of this time is spent on data transfer and provenance checking as it moves through domains? 34

35 Permanent Reservoir Monitoring (PRM) Operationalizing the workflow With a fixed seabed receiver array: Simpler source vessel Cheaper per survey More weather independent Receiver geometry the same Surveys are more repeatable Faster processing turnaround Perform more frequent surveys 35

36 PRM: Shortens the time frame Planning 1 month Acquisition 3 weeks Processing <3 months Interpretation <1 month New survey at least every 6 months Decision making on the timescale of interventions Need a much more streamlined process for receiving new data and interpreting it 36

37 And brings so much data Base Monitor1 Monitor2 Monitor3. time shift time shift time shift time shift time shift time shift time shift. Difference base to TS Mon 1 Difference base to TS Mon 2 Difference Mon 1 to TS Mon 2 So many surveys, time shifts, differences between pairs, attributes to look at. And the governance..? Attribute maps on horizon 1 and horizon 2 37

38 Learning from other industries 38

39 We made a Reservoir Data Warehouse! And we brought Analytics to the Subsurface We store detailed subsurface data in an MPP Analytical database we integrate it in space and time as well as logical relationships 4D workflows not fully supported by today s tools Explaining 4D effects requires other data Identifying artefacts of processing or acquisition Identifying events that correlate and users can visualise detailed data and analysis, calculated onthe-fly 39

40 Example 1: Repeatability Analysis Good image of 4D effect requires seismic image taken from the same position. NRMS is measure for survey repeatability. NRMS map Areas with bad survey repeatability Good image of a 4D effect Bad image of a 4D effect 40

41 NRMS Offset devaition (m) Example 1: Repeatability Analysis Colored by base-monitor source + receiver deviation (meters) Colored by basemonitor azimuth deviation (degree) Bad Goo d Bad repeatability Not great repeat -ability OK repeatability Base receiver station nr Offset x Azimuth deviation Source/receiver positions repeated well Source/receiver angle repeated well, distance not Source/receiver distance repeated well, angle not Source/Receiver (base) Source/Receiver (monitor) 41 Classification: Internal

42 Example 2: Subsurface Analytics Repeatability (NRMS) Time shift Pressure difference 43

43 Example 2: Subsurface Analytics SQL query and result set (best correlation on top) Time Shift vs Pressure Further down the list. Time Shift vs Water Saturation 44

44 Example 2: Subsurface Analytics Clear correlation Weak correlation 45

45 46 Full project report in EAGE presentation

46 Teradata Predictive and Prescriptive analytics

47 More efficient development drilling Fewer bit failures Fewer Trips Reduced Opex Goal: consistently drill horizontal section in a single trip in hard formations As-is: It s just hard formation that s the way it is. Unpredictable and repeated failures occur. Some single-trip sections achieved, but success/failure criteria not understood. To-be: find combinations of a wide range of drilling parameters likely to avoid bit failure and model alarms to ensue efficient drilling insights. How? look for patterns to that will inform better operational decisions: increase drilling efficiency to avoid catastrophic bit damage 48

48 What data was used? Source data sets and derived properties surface and downhole drilling data MWD/LWD time series Logging notes metadata relating to well and drill string configuration Wellview schema CSD bit damage severity and profile Synthetic scoring from IADC codes well position and trajectory LAS, DLS and x,y,z trajectories petrophysical information Formation strength, density, elastic moduli Operations data Project logging (time allocations and costing) from ERP Teradata

49 50 Bit damage and rock hardness

50 51 Applying scores to run efficiency

51 52 Event Correlations to damage scores

52 53 Links between events and states

53 54 Tree diagram showing dominant paths to low efficiency

54 Concluding remarks What have we learnt in our three years of upstream Big Data Analytics projects? The upstream domain is still sceptical No single organisation has all the answers Technology companies Service companies Operating companies All need to work together it s as much about people and process as it is technology Mismatch in language and expectations Converting use cases to real case histories is: possible Challenging Fun! Teradata

55 Teradata