SOLVING GEOPHYSICS PROBLEMS WITH PYTHONPAIGE BAILEYSEPTEMBER 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”?

ISN’T

THEMES• Gravity• Heat flow• Electricity• Fluid dynamics• Magnetism• Radioactivity• Mineral Physics• Vibration

…handshakes with atmospheric sciences, geology, engineering, hydrology, planetary sciences, global positioning systems…

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

MadagascarPySITSegpysegpy-pySLIMpyFatiando a TerraObsPyPyGMISimPEGSeismic Handlersgp4PyGMI

SgFmlaspyParaView Geo3ptScience

Agile Geoscience- Bruges- Modelr- Pick This- G3.js- Striplog

ArcPyPyQGIS…so many other geospatial libraries

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

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

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 Stratigraphic Modeling

Seismic Interpretation

Mapping

Reservoir Characterization

Cross-sections

Petrophysics

Reservoir Simulation Well Planning & Drilling Simulation Stratigraphic Modeling

Seismic Interpretation

Mapping

Reservoir Characterization

Cross-sections

Petrophysics

Reservoir Simulation Well Planning & Drilling Simulation Stratigraphic Modeling

Seismic Interpretation

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, 2012)

V’S

V’S

VOLUME – VARIETY – VELOCITY – VERACITY

VOLUMESeismic data acquisition (wide-azimuth)Seismic processing 5D interpolated data setsFiberoptics

How big is “big”?

STRUCTURED

UNSTRUCTURED

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

VELOCITYReal-time streaming dataDrilling 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)

C’S

C’S

CREATING – CLEANING – CURATING DATASETS

C’S

CREATING – CLEANING – CURATING DATASETS

…CHALLENGES

BIG 3ADVANCED ANALYTICS TODAY

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.)

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

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

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.

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)

THANKS!