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SOLVING
GEOPHYSICS
PROBLEMS
WITH PYTHON
PAIGE BAILEY
SEPTEMBER 29, 2015
STRATA + HADOOP WORLD 2015
YOUR MISSION, SHOULD YOU CHOOSE TO ACCEPT IT
WARNING!
…OR DISCLAIMER, RATHER
PAIGE BAILEY
@DynamicWebPaige
WHAT IS
“GEOPHYSICS”?
WHAT IS
“GEOPHYSICS”?
WHAT IS
“GEOPHYSICS”?
THEMES
• Gravity
• Heat flow
• Electricity
• Fluid dynamics
• Magnetism
• Radioactivity
• Mineral Physics
• Vibration
…han...
GRAVITY
HEAT FLOW
FLUID DYNAMICS
MAGNETISM
MINERAL PHYSICS
VIBRATION
(A.K.A., SEISMIC)
VIBRATION
(A.K.A., SEISMIC)
…WE’LL TALK ABOUT THIS MORE SOON
…AND UNEXPECTED USE CASES
3D-printing Geology with Python
LIBRARIES / SOFTWARE
MENTIONED
Madagascar
PySIT
Segpy
segpy-py
SLIMpy
Fatiando a Terra
ObsPy
PyGMI
SimPEG
Seismic Handler
...
ALMOST ALL
OF THAT IS
OPEN-SOURCE
BUT HERE’S THE KICKER:
ALMOST ALL
OF THAT IS
OPEN-SOURCE
(AND SO IS THE DATA)
BUT HERE’S THE KICKER:
GEOPHYSICS-FOCUSED
SCIPY TALKS
2012
ALGES: Geostatistics and Pythong
Py-ART: Python for Remote Sensing Science
Building a ...
LET’S TALK ABOUT ENERGY
FIRST WELL LOG?
FIRST
SEISMOGRAPH?
FIRST OIL WELL?
Drilling has been around for a long
time, but its success is due to
improved data acquisition and
data analysis methods.
NOW
WORLD’S LARGEST PUBLIC, STATE-OWNED,
AND PRIVATE BUSINESSES
WORLD’S LARGEST PUBLIC, STATE-OWNED,
AND PRIVATE BUSINESSES
7 out of 10
Profitability for oil companies is
directly tied to reserves.
UPSTREAM BIG DATA
(Seshadri M., 2013)
Mapping
Reservoir
Characterization
Cross-sections
Petrophysics
Reservoir Simulation
Well Planning &
Drilling Simulation St...
Mapping
Reservoir
Characterization
Cross-sections
Petrophysics
Reservoir Simulation
Well Planning &
Drilling Simulation St...
Mapping
Reservoir
Characterization
Cross-sections
Petrophysics
Reservoir Simulation
Well Planning &
Drilling Simulation St...
Data impacts the entire value chain.
THE
FUTURE
2000 – 2010 :
Decade of “Big Data”
2000 – 2010 :
Decade of “Big Data”
2010 – 2020 :
Decade of Sensing
“The oil and gas upstream sector
is a complex, data-driven business
with data volumes growing
exponentially.”
(Feblowitz, ...
’S
’S
VOLUME – VARIETY – VELOCITY – VERACITY
VOLUME
Seismic data acquisition (wide-azimuth)
Seismic processing
5D interpolated data sets
Fiberoptics
How big is “big”?
STRUCTURED
UNSTRUCTURED
VARIETY
• Structured
• Standard data models
• SEG-Y
• WITSML
• RESQML
• PRODML
• LAS
• .shp, .lyr, other GIS files
• Unstr...
VELOCITY
Real-time streaming data
Drilling equipment (EDR, LWD, MWD, mud logging…)
Sensors (flow, pressure, ROP, etc.)
VERACITY
…in other words, data quality.
VERACITY
…in other words, data quality.
…IT’S NOT
THAT GREAT.
VALUE
…ALL LEADING UP TO
“Analytic advantages could help
oil and gas companies improve
production by 6% to 8%.”
(Bain Energy Report)
’S
’S
CREATING – CLEANING – CURATING DATASETS
’S
CREATING – CLEANING – CURATING DATASETS
…CHALLENGES
BIG
ADVANCED ANALYTICS TODAY
UNCONVENTIONALS
Huge number of wells operating simultaneously
Operators need to make decisions very quickly, and are far
r...
CONVENTIONALS
Fewer wells in this scenario
Can still spot trends from the constant streams of
information, particularly se...
MIDSTEAM /
DOWNSTREAM
Monitoring pipelines and equipment for a more predictable
and precise approach to maintenance
Preven...
Historically, oil companies relied on
operating models that focused on
functional excellence and clear hand-
offs from one...
Each individual function may have a
wealth of data, but unless your model
can put it all in a single location,
analyze it,...
THANKS!
Solving Geophysics Problems with Python
Solving Geophysics Problems with Python
Solving Geophysics Problems with Python
Solving Geophysics Problems with Python
Solving Geophysics Problems with Python
Solving Geophysics Problems with Python
Solving Geophysics Problems with Python
Solving Geophysics Problems with Python
Solving Geophysics Problems with Python
Solving Geophysics Problems with Python
Solving Geophysics Problems with Python
Solving Geophysics Problems with Python
Solving Geophysics Problems with Python
Solving Geophysics Problems with Python
Solving Geophysics Problems with Python
Solving Geophysics Problems with Python
Solving Geophysics Problems with Python
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Solving Geophysics Problems with Python

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Presented at Strata + Hadoop World 2015 as part of the PyData track. September 29, 2015.

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Solving Geophysics Problems with Python

  1. 1. SOLVING GEOPHYSICS PROBLEMS WITH PYTHON PAIGE BAILEY SEPTEMBER 29, 2015 STRATA + HADOOP WORLD 2015
  2. 2. YOUR MISSION, SHOULD YOU CHOOSE TO ACCEPT IT
  3. 3. WARNING! …OR DISCLAIMER, RATHER
  4. 4. PAIGE BAILEY @DynamicWebPaige
  5. 5. WHAT IS “GEOPHYSICS”?
  6. 6. WHAT IS “GEOPHYSICS”?
  7. 7. WHAT IS “GEOPHYSICS”?
  8. 8. THEMES • Gravity • Heat flow • Electricity • Fluid dynamics • Magnetism • Radioactivity • Mineral Physics • Vibration …handshakes with atmospheric sciences, geology, engineering, hydrology, planetary sciences, global positioning systems…
  9. 9. GRAVITY
  10. 10. HEAT FLOW
  11. 11. FLUID DYNAMICS
  12. 12. MAGNETISM
  13. 13. MINERAL PHYSICS
  14. 14. VIBRATION (A.K.A., SEISMIC)
  15. 15. VIBRATION (A.K.A., SEISMIC) …WE’LL TALK ABOUT THIS MORE SOON
  16. 16. …AND UNEXPECTED USE CASES 3D-printing Geology with Python
  17. 17. LIBRARIES / SOFTWARE MENTIONED Madagascar PySIT Segpy segpy-py SLIMpy Fatiando a Terra ObsPy PyGMI SimPEG Seismic Handler sgp4 PyGMI SgFm laspy ParaView Geo 3ptScience Agile Geoscience - Bruges - Modelr - Pick This - G3.js - Striplog ArcPy PyQGIS …so many other geospatial libraries
  18. 18. ALMOST ALL OF THAT IS OPEN-SOURCE BUT HERE’S THE KICKER:
  19. 19. ALMOST ALL OF THAT IS OPEN-SOURCE (AND SO IS THE DATA) BUT HERE’S THE KICKER:
  20. 20. GEOPHYSICS-FOCUSED SCIPY TALKS 2012 ALGES: Geostatistics and Pythong Py-ART: Python for Remote Sensing Science Building a Solver Based on PyClaw for the Solution of the Multi-Layer Shallow Water Equations 2013 Modeling the Earth with Fatiando a Terra 2014 The Road to Modelr: Building a Commercial Web Application on an Open-Source Foundation Measuring Rainshafts: Bringing Python to Bear on Remote Sensing Data The History and Design Behind the Python Geophysical Modeling and Interpretation (PyGMI) Package Prototyping a Geophysical Algorithm in Python 2015 (and an entire Geophysics Track) Using Python to Span the Gap Between Education, Research, and Industry Applications in Geophysics Practical Integration of Processing, Inversion, and Visualization of Magnetotelluric Geophysial Data Striplog: Wranging 1D Subsurface Data Geodynamic Simulations in HPC with Python
  21. 21. LET’S TALK ABOUT ENERGY
  22. 22. FIRST WELL LOG?
  23. 23. FIRST SEISMOGRAPH?
  24. 24. FIRST OIL WELL?
  25. 25. Drilling has been around for a long time, but its success is due to improved data acquisition and data analysis methods.
  26. 26. NOW
  27. 27. WORLD’S LARGEST PUBLIC, STATE-OWNED, AND PRIVATE BUSINESSES
  28. 28. WORLD’S LARGEST PUBLIC, STATE-OWNED, AND PRIVATE BUSINESSES 7 out of 10
  29. 29. Profitability for oil companies is directly tied to reserves.
  30. 30. UPSTREAM BIG DATA (Seshadri M., 2013)
  31. 31. Mapping Reservoir Characterization Cross-sections Petrophysics Reservoir Simulation Well Planning & Drilling Simulation Stratigraphic Modeling Seismic Interpretation
  32. 32. Mapping Reservoir Characterization Cross-sections Petrophysics Reservoir Simulation Well Planning & Drilling Simulation Stratigraphic Modeling Seismic Interpretation
  33. 33. Mapping Reservoir Characterization Cross-sections Petrophysics Reservoir Simulation Well Planning & Drilling Simulation Stratigraphic Modeling Seismic Interpretation
  34. 34. Data impacts the entire value chain.
  35. 35. THE FUTURE
  36. 36. 2000 – 2010 : Decade of “Big Data”
  37. 37. 2000 – 2010 : Decade of “Big Data” 2010 – 2020 : Decade of Sensing
  38. 38. “The oil and gas upstream sector is a complex, data-driven business with data volumes growing exponentially.” (Feblowitz, 2012)
  39. 39. ’S
  40. 40. ’S VOLUME – VARIETY – VELOCITY – VERACITY
  41. 41. VOLUME Seismic data acquisition (wide-azimuth) Seismic processing 5D interpolated data sets Fiberoptics
  42. 42. How big is “big”?
  43. 43. STRUCTURED
  44. 44. UNSTRUCTURED
  45. 45. 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
  46. 46. VELOCITY Real-time streaming data Drilling equipment (EDR, LWD, MWD, mud logging…) Sensors (flow, pressure, ROP, etc.)
  47. 47. VERACITY …in other words, data quality.
  48. 48. VERACITY …in other words, data quality. …IT’S NOT THAT GREAT.
  49. 49. VALUE …ALL LEADING UP TO
  50. 50. “Analytic advantages could help oil and gas companies improve production by 6% to 8%.” (Bain Energy Report)
  51. 51. ’S
  52. 52. ’S CREATING – CLEANING – CURATING DATASETS
  53. 53. ’S CREATING – CLEANING – CURATING DATASETS …CHALLENGES
  54. 54. BIG ADVANCED ANALYTICS TODAY
  55. 55. UNCONVENTIONALS Huge number of wells operating simultaneously Operators 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.)
  56. 56. CONVENTIONALS Fewer wells in this scenario Can still spot trends from the constant streams of information, particularly sensors – spotting where a piece of equipment might fail Reducing the potential for environmental disasters
  57. 57. MIDSTEAM / DOWNSTREAM Monitoring pipelines and equipment for a more predictable and precise approach to maintenance Preventing shutdowns and launching interventions to prevent spills Ideally, we would have as few people operating in hazardous locations as possible
  58. 58. 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.
  59. 59. 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)
  60. 60. THANKS!

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