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SOLVING GEOPHYSICS PROBLEMS WITH PYTHONPAIGE BAILEYSEPTEMBER 29, 2015
STRATA + HADOOP WORLD 2015
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YOUR MISSION, SHOULD YOU CHOOSE TO ACCEPT IT
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WARNING!…OR DISCLAIMER, RATHER
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PAIGE BAILEY
@DynamicWebPaige
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WHAT IS “GEOPHYSICS”?
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WHAT IS “GEOPHYSICS”?
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WHAT IS “GEOPHYSICS”?
ISN’T
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THEMES• Gravity• Heat flow• Electricity• Fluid dynamics• Magnetism• Radioactivity• Mineral Physics• Vibration
…handshakes with atmospheric sciences, geology, engineering, hydrology, planetary sciences, global positioning systems…
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GRAVITY
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HEAT FLOW
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FLUID DYNAMICS
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MAGNETISM
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MINERAL PHYSICS
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VIBRATION(A.K.A., SEISMIC)
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VIBRATION(A.K.A., SEISMIC)
…WE’LL TALK ABOUT THIS MORE SOON
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…AND UNEXPECTED USE CASES
3D-printing Geology with Python
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LIBRARIES / SOFTWARE MENTIONED
MadagascarPySITSegpysegpy-pySLIMpyFatiando a TerraObsPyPyGMISimPEGSeismic Handlersgp4PyGMI
SgFmlaspyParaView Geo3ptScience
Agile Geoscience- Bruges- Modelr- Pick This- G3.js- Striplog
ArcPyPyQGIS…so many other geospatial libraries
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ALMOST ALL OF THAT IS OPEN-SOURCE
BUT HERE’S THE KICKER:
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ALMOST ALL OF THAT IS OPEN-SOURCE
(AND SO IS THE DATA)
BUT HERE’S THE KICKER:
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GEOPHYSICS-FOCUSED SCIPY TALKS
2012ALGES: Geostatistics and PythongPy-ART: Python for Remote Sensing ScienceBuilding a Solver Based on PyClaw for the Solution of the Multi-Layer Shallow Water Equations
2013Modeling the Earth with Fatiando a Terra
2014 The Road to Modelr: Building a Commercial Web Application on an Open-Source FoundationMeasuring Rainshafts: Bringing Python to Bear on Remote Sensing DataThe History and Design Behind the Python Geophysical Modeling and Interpretation (PyGMI) PackagePrototyping a Geophysical Algorithm in Python
2015(and an entire Geophysics Track)Using Python to Span the Gap Between Education, Research, and Industry Applications in GeophysicsPractical Integration of Processing, Inversion, and Visualization of Magnetotelluric Geophysial DataStriplog: Wranging 1D Subsurface DataGeodynamic Simulations in HPC with Python
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LET’S TALK ABOUT ENERGY
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FIRST WELL LOG?
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FIRST SEISMOGRAPH?
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FIRST OIL WELL?
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Drilling has been around for a long time, but its success
is due to improved data acquisition and data analysis
methods.
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NOW
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WORLD’S LARGEST PUBLIC, STATE-OWNED, AND PRIVATE BUSINESSES
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WORLD’S LARGEST PUBLIC, STATE-OWNED, AND PRIVATE BUSINESSES
7 out of 10
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Profitability for oil companies is directly tied to reserves.
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UPSTREAM BIG DATA
(Seshadri M., 2013)
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Mapping
Reservoir Characterization
Cross-sections
Petrophysics
Reservoir Simulation Well Planning & Drilling Simulation Stratigraphic Modeling
Seismic Interpretation
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Mapping
Reservoir Characterization
Cross-sections
Petrophysics
Reservoir Simulation Well Planning & Drilling Simulation Stratigraphic Modeling
Seismic Interpretation
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Mapping
Reservoir Characterization
Cross-sections
Petrophysics
Reservoir Simulation Well Planning & Drilling Simulation Stratigraphic Modeling
Seismic Interpretation
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Data impacts the entire value chain.
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THE FUTURE
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2000 – 2010 :
Decade of “Big Data”
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2000 – 2010 :
Decade of “Big Data”
2010 – 2020 :
Decade of Sensing
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“The oil and gas upstream sector is a complex, data-driven business with data volumes growing exponentially.”
(Feblowitz, 2012)
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V’S
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V’S
VOLUME – VARIETY – VELOCITY – VERACITY
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VOLUMESeismic data acquisition (wide-azimuth)Seismic processing 5D interpolated data setsFiberoptics
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How big is “big”?
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STRUCTURED
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UNSTRUCTURED
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VARIETY• Structured
• Standard data models• SEG-Y• WITSML• RESQML• PRODML• LAS• .shp, .lyr, other GIS files
• Unstructured• Images (maps, embedded well logs in .PDF’s)• Audio, video• …and more, on both fronts
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VELOCITYReal-time streaming dataDrilling equipment (EDR, LWD, MWD, mud logging…)Sensors (flow, pressure, ROP, etc.)
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VERACITY…in other words, data quality.
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VERACITY…in other words, data quality.
…IT’S NOT THAT GREAT.
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VALUE…ALL LEADING UP TO
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“Analytic advantages could help oil and gas companies improve production by 6% to 8%.”(Bain Energy Report)
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C’S
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C’S
CREATING – CLEANING – CURATING DATASETS
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C’S
CREATING – CLEANING – CURATING DATASETS
…CHALLENGES
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BIG 3ADVANCED ANALYTICS TODAY
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UNCONVENTIONALSHuge number of wells operating simultaneouslyOperators need to make decisions very quickly, and are far removed from central business units – autonomy
• Geology interpretation – comparing geology to production• New well delivery – improving drilling and completions,
reducing lag time and minimizing the number of wells in process at any given moment in time
• Well and field optimization – well spacing and completions techniques (cluster spacing, number of stages, proppants and fluids used, etc.)
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CONVENTIONALSFewer wells in this scenarioCan still spot trends from the constant streams of information, particularly sensors – spotting where a piece of equipment might failReducing the potential for environmental disasters
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MIDSTEAM / DOWNSTREAMMonitoring pipelines and equipment for a more predictable and precise approach to maintenancePreventing shutdowns and launching interventions to prevent spillsIdeally, we would have as few people operating in hazardous locations as possible
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Historically, oil companies relied on operating models that
focused on functional excellence and clear hand-offs
from one function to the next. This process takes time, and it breaks down
when you have to make decisions quickly.
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Each individual function may have a wealth of data, but unless your
model can put it all in a single location,
analyze it, and place that information
in the right hands at the right time, it’s difficult to improve
performance.
(Bain Energy Report)
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THANKS!