An exemple of Big Data Analytics in E&P presented by CGG at SPE France - Ecole des Mines de Paris on 18 Nov. 2014

1. An example of Big Data Analytics in O&G
Ecole des Mines de Paris - November 2014

2. Use of Big Data Analytics in O&G
Contents
Drilling Wells
Current Situation
Big Data Analytics
Pilot Study
Results
Use of Big Data Analytics in O&G
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3. It is expensive to drill
It is even more and more expensive.
Drilling involves a large number of unknowns especially in Exploration phase and a large number or parameters, mud weight, torque, weight on bit, and so on.
Adjusting these parameters while drilling is a modern practice because of downhole drilling measurements
Use of Big Data Analytics in O&G
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…and sometime drilling creates poor borehole conditions …
Poor boreholes are at the origin of:
Potential stuck pipe
More wiper trips
Cementing problems
Logging problem
Stuck, sticking tools, poor quality data, poor interpretation, poor decisions
Poor boreholes are due to:
Geology, eg: swelling shales
Rock properties
Deviation scheme
Drilling parameters
An analytical approach to the conditions causing stuck pipe, over large data volumes, taking into account a large number of parameters, has the potential to assist in the drilling model and the in-situ drilling parameters choice with a resulting decrease in the instances of stuck pipe.

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Sonic wrong-poor correlation with seismic
Porosity wrong Hydrocarbons wrong
Incorrect seismic processing may be done due to the bad hole interval
Expensive testing decisions may be made
Bad hole consequneces

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Evil is in the details and in the data format …

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Big data is about:
•Volume
•Velocity
•Variety
•Veracity Big data is about accessing structured and unstructured data
•RDBMS but also
•Social network, emails, knowledge DB, transactional DB, image, video, audio, GIS, documents
… and then the big data …

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…it was in 2004, when Google published MapReduce

9. An appliance is a Hardware-Software system designed for performance, scalability and analytics
…This architecture is now implemented in Appliances specifically designed for Big Data Analytics …

10. Aster functions
Example style for optional footer
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… this new architecture open new doors to analyse data …

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Sometimes because there is just too much data
Few links are created on the truly enormous scale, the entire North Sea for example. Thousand of wells, thousand of 2D lines, thousand of 2D km2 … is just too much for conventional analysis techniques to handle in its enirety
But why it hasn’t been done before ?

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TERADATA
Data integration
Performance and scalability
Advanced analytics
Seismic
Well logs
Formations tops
Checkshot surveys
Pressures
Drilling data
Core data
Well test data
Completions
Production data
Fluid data
Cultural data
…So, to understand the reasons of bad hole occurrences on the UK Continental Shelf …

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CGG
has access to data, e.g. Drilling & Wells, geology, petrophysics...
... and provides subject- matter expertise to interpret and enrich the value of this data
Teradata
provides the analytical platform to run complex data analyses...
... and deliver deep data science, math and stats competences
… CGG and Teradata have been working on a common pilot…

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•Loading numerous different type de data
•Using different formats
•Linking the data types
•Using a ‘generic’ well data viewer
•Finding usable correlations
… and have solved together several challenges …

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This view was built using drilling parameters and well logs directly with Teradata technology – and without the need for data preparation, modelling and indexation
•It is possible to load lots of differing types even with non specific loading tools.
•All data is available from metadata such as Quad number to individual logging curves.
•The manipulation and querying of data is done without any preconception of the analysis to be made.
…initial data loading …

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… Vizualization using a generic tool …

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…Analyzing …

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•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 pre- conceptions
•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
… As a conclusion, we show that …

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•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.
…Our Pilot open the door for numerous other applications …

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•Aurelien.guichard@teradata.com
•Joe.johnston@cgg.com
If you want to try, to know more, to have a private demo, please contact: