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Tuesday, September 18, 2018

AAPG Basic Wireline Log Interpretation (Petrophysics Technical Interest Group

The 2-day course "The Basic Wireline Log Interpretation" is designed to teach people who are not petrophysicists, but are in need of incorporating wireline log data and some basic log interpretation in their daily work.  The course will take place October 22-23 / Halliburton Campus Room TC-1214, 3000 N Sam Houston Pkwy E, Houston, TX 77032-3216 
(Note that the cost is only $300 for a full two-day course, with lunch and materials.)

Another goal of this course is to illustrate not only on how to understand the logs responses but also to know the wireline log data acquisition process.

The Course will begin with the principles and scope of the tools (gamma ray, resistivity, density, neutron, Sonic, image log, etc.). This will include the basics of the tool physics as well as how to apply the different responses to discern reservoir properties such as porosity, hydrocarbon pore volume, reservoir deliverability, etc.

There will also be some basic exercises that will be handed out to try real world problems using open hole wireline logs.

In addition, the class will take a field trip on Halliburton campus to the Test Well facility to see an actual logging job. While their wireline engineers will explain what one would need to know to acquire data correctly and instructors will explain how knowing this can be useful to log interpretation.

Lastly, the class will have an open forum for approximately an hour in talking about the challenges the students may be facing in their projects as well as viable solutions.

For the purposes of this class students will not need a computer for activities but as we as an industry are always on call computers are welcome in the class.

Additionally, no open toed shoes will be allowed for the field trip portion of the class.

Lunch will also be provided.



INSTRUCTORS

Bhaskar Bikash Sarmah
Senior Advisor, Petrophysics, SE Tech. Team, Halliburton
Dr. Bhaskar Sarmah is a Senior Technical Advisor, Petrophysics for the South East Technical Team under Halliburton, based at Houston, Texas. He has over twenty one years of Oil and Gas industry experience, with last eleven years in reservoir characterization mostly in unconventional plays and reservoir stimulation optimization processes in North America. In the first ten years of his career, he worked in the rig site in the field of well site geology, mostly in Middle East and North Africa. His formation evaluation experience ranges from the Darcy Carbonates of Ghawar basin in Saudi Arabia to the nano Darcy organic rich resources as well as tight gas sands and carbonates of North America. Bhaskar has wide experience in both open hole as well as cased-hole formation evaluation.
Bhaskar has a Master’s degree in Applied Geology from IIT Roorkee and PhD in Geology from Guwahati University, India. He is a member of AAPG, SPE, SPWLA. He has authored and co-authored multiple technical publications.

Hamdi Elnahhas
Sr. Technical Advisor, Wireline and Perforating, US Southern Region



 Hamdi Elnahhas is a Senior Technical Advisor for Wireline and Perforating, focusing on the Southern US region operations. His responsibilities include establishing the strategic direction in the work area through clear understanding of the local customers’ business drivers and technical challenges while collaborating with the Product Service Line, Region Business Development, Account and Tech Teams to identify opportunities to maintain awareness of the technology available.
Hamdi has worked for Halliburton for 12 years in both operational and business development roles within the wireline and perforating product service line. In addition, his business development experience included looking after the needs of two other majors in the Southeast / Eagle Ford area, as well as several independents. Hamdi has Bachelors in Computer Engineering from University of California, San Diego.

Ted Koon
Technical Advisor, Wireline and Perforating, US Southern Region

 Ted Koon is the Open Hole Logging Technical Advisor for Halliburton’s Business Development Group in Houston. Ted began his career with Halliburton in 2003 as a Field Professional in Casper, WY. Ted spent seven years running an open hole logging unit throughout the western US. In 2009, Ted transferred to Halliburton’s base in Cabinda, Angola, where he continued to run an offshore open hole logging unit with a primary focus on NMR and Pressure and Sampling tools for two years. At this point, Ted moved into a front line management role where he helped manage the day to day activities of the logging operations and provided technical support for Angola. After three years in Angola, Ted transitioned into the role of Global Technical Advisor for NMR and Open Hole Nuclear Logging tools in Halliburton’s Technical Services group. Under Technical Services, Ted’s responsibilities ranged from assisting in troubleshooting and repairing logging tools to real time data acquisition QC throughout the globe. As part of his daily workflow, Ted still assists globally with NMR data acquisition and provides open hole logging technical support to the Houston Business Development team and their customers.
Ted studied Civil Engineering at Montana State University, as well as, received a bachelor’s degree in Business Management from Colorado State University.

Sandeep Mukherjee
Geosciences Advisor, Callon Petroleum

Sandeep Mukherjee is a Geosciences Advisor for Callon Petroleum. His current focus is mainly on the characterization and development of unconventional reservoirs of the Permian Basin.
Sandeep began his career as a Geologist with Schlumberger in 2006 where he was primarily focused on the utilization of advanced techniques in the image interpretation realm to provide sophisticated geologic solutions. Through the years in Schlumberger, Sandeep and managed several responsibilities including that of Geology Team Lead for Schlumberger Data Services of the Permian Basin and Geology Domain Champion of the North American and Middle Eastern Geomarkets. After Schlumberger, Sandeep joined Halliburton in 2014, and managed several responsibilities as Technical Team lead for Halliburton’s Formation and Reservoir Solutions Group in Houston, as well as Geosciences Solutions Advisor for North America Land. In these positions Sandeep advised Halliburton’s varied clientele in designing the right approach towards advanced, reservoir specific, geologic and petrophysical characterization.
His broader research interests encompass interpretation of borehole images, constructing geologic reservoir models, analysis of fracture systems, sequence stratigraphy, heterogeneous rock analysis and characterizing carbonate reservoirs. He earned a Bachelor and a Masters in Geology from University of Calcutta, India in 1998, and 2000 respectively and a Masters in Geology from University of Minnesota in 2006. He is a member of AAPG, SPE, SPWLA, AGU and SEPM.



Saturday, September 15, 2018

Machine Learning and Python: Interview with Patrick Ng

The United States is now the world's largest producer of oil and gas, and machine learning played a large role in the transformation, which has occurred because of new techniques and technologies.

Welcome to an interview with Patrick Ng, geoscientist and pioneer of innovative ways to use analytics and specifically machine learning, to find new oil and gas reserves and to produce them more efficiently and sustainably.

https://youtu.be/6uQR8PO3l3A

https://youtu.be/6uQR8PO3l3A


LIFE EDGE with Patrick Ng Chat 2018 Q&A Notes

Background - I am a geophysicist by training, and experienced A to Z in  geosciences. 1) As - AVO amplitude versus offset to reduce risk, azimuthal features to map natural fractures, 2) transform seismic to rock properties, and 3) prestack depth imaging / model building to map subsalt reservoirs leading to 3 giant discoveries total over 2.5 billion boe in the Gulf of Mexico, and 4) the Z is drilling wells and learning from the drill bit all the way to total depth (Z).

And I learn through the drill bit that we drill anything but an average well, or rather a range of IP initial productions. The risk lies in the spread, and I make it a business managing risk at Real Core Energy.

Q1: how about examples of using Python in industry?

The hackathon focus was production forecast of a well. Given the flow rate data (courtesy of Halliburton, sponsor) and Python Notebook as template, and bootcamp to bring everyone up to speed. The exercise is to try use geoscience in machine learning, and play with the number of layers and neurons in neural network, and improve the forecast accuracy.

Q2: why Python?

Python is like the foundation, that my teenage daughter uses for make up. Depending on the event, she will put on other colors and things (not sure what to call those… so I won’t).  And the real power of Python comes from a set of libraries. For example:

1) Numpy, numeric Python for vectorized numerical computation
2) Pandas for handling lots of columns and rows
3) SK learn for machine learning algorithms, ready plug-n-play.

Think of.Python example, say write a few lines of codes, in a loop do something to each element in an array one at a time.

Numpy can collapse that into a single line, operates on an entire time series as a vector all at one go.

Often we may have a thousand wells, each with its production profiles. Think of wells as columns across the top with number of barrels per day, week or month hanging down. Pandas can operate on the entire collection of series of data all at once, like getting the mean, median, statistics with one line on an entire group of data. We also get the top 25%, next 50% and bottom 25% percentiles. Quickly we get a feel for how well the producing assets perform.

Q3: why is Python so popular with  machine learning?

It has to do with the availability of powerful libraries like Keras and Tensorflow well suited for neural network and deep learning. While SK Learn has been around for some time, Tensorflow was released by Google to open source consortium in November 2017.

Lets take deep learning as example. Microsoft had success using 158 layers in a deep neural network. Using keras, we specify one layer at a time, and we’d have 158 lines of codes.

But with Tensorflow, we can do that in one line albeit a long line, by listing the number of neurons in all 158 layers all at once. Again fewer lines of codes. But if we want to customize, and tune each layer, then we can do so with Python in a more granular way.

So we go from Python (the foundation), to Numpy, Pandas, Keras and Tensorflow, each provides the tools to do more, faster with fewer line of codes. In a nutshell, Python opens up a whole new way for geoscientists to explore data, do rapid experiments and gain new insights.

Q4: can machine learning make the industry more safe and clean?

Here are two examples. First predictive maintenance, we can better anticipate and schedule downtime for routine maintenance and repairs of equipments. Just as we do annual check up for our AC in Houston and keep them running top shape. That will prevent potential leaks and minimize surprises, so keep us safe.

On cleaner environment, one possibility is that we drill fewer wells and produce the same volume, if we can better predict the outcome with machine learning. Doing so, we reduce the footprint and impact on the environment.

(One more thought came after the Chat, is refracking. If we can use machine learning to better identify refracking candidate wells, we shall improve recovery factor and may also drill fewer new wells. Again reduce footprint and lessen impact on the environment.)

Q5: is there benefit of reprocessing data and machine learning together?

Yes. It has been standard business practice that every few years, with improved algorithm, we reprocess data, get higher resolution and a more detailed look. Like going from 4K to 8K HDTV, instead of 80 to 100 feet resolution in seismic, we may get that down to 40 ft. With higher resolution data, we’d retrain machine learning and get better results. Both go hand in hand.

That brings up a good point. In the world of geoscience, if we change the model, we also get different resulting imaged data. Unlike typical data used to feed machine learning algorithm, say what I bought from Amazon or movies streamed from Netflix, what I read and watched became record. That won’t change. But when imaging seismic, the model and resulting data are tightly coupled. Change one, we change the other.

So learning with machine beats machine learning alone.

Before 1995, the thinking in Gulf of Mexico was that salt bodies would become detached because of buoyancy (density of salt is lighter than that of surrounding rocks). So over time in geologic scale (millions of years, not weeks), salt moved up from great depth and ended up what looks like cup cakes (picture inside the lava lamp). But with the Crazy Horse (now called Thunder Horse) discovery, we learn there is salt mountain that goes forty five thousand feet deep below the seafloor. No cup cakes.

Python is a tool that can geoscientists explore and test their ideas with data. Better understanding of the geology and producing more. Last but not lease, is that Python while really powerful for numerically intense applications, it can go all the way to voice. Using Python-Flask libraries, I put together numerically rigorous app and deliver via Alexa.  That I see can draw more highschool students interested in geoscience.

Closing

As a closing thought, remember the old saying “The journey of a thousand miles begins with one step.” I see learning python is the first step. Just do it!

 Thank you, Patrick! 




Tuesday, September 04, 2018

Bolivia Plans to Expand Gas, Electricity, Green Fuel, and Petrochemicals Exports

Bolivia intends to expand its exports of LNG, electricity, petrochemicals, and green fuels in 2019 and beyond, announced the Bolivian Vice President, Alvaro Linero Garcia. In addition, exploration to develop reserves of gas are being encouraged through partnering with companies to conduct studies and to drill exploratory wells. In addition, mature fields will be the target of study and investment to revitalize the reservoirs through enhanced recovery methods.

Panel discussion with Luis Sanchez, Minister of Bolivian Ministry of Hydrocarbons, with experts discussing opportunities and expanded reserves.
The announcements were made at the closing ceremony of Bolivia's First International Forum on Gas, Petrochemicals, and Green Fuels, a four-day event in Santa Cruz (August 28-31) that had as a goal to encourage investment, and in doing so, presented a wide array of potential game-changers for partner companies, investors, and Bolivia.

With a goal of stimulating investment in exploring for hydrocarbons, the Minister of Hydrocarbons, Luis Sanchez, detailed the opportunities to participate in more than 10 blocks in Bolivia, many in the prolific Tarija and Chuquisaca regions.

First International Forum on Gas, Petrochemicals, and Green Fuels / Santa Cruz, Bolivia
Green fuels, including new ethanol sources from sugar cane grown by small cane farmers in the Santa Cruz region.

LNG terminals are being expanded, with the long-term goal of being a gas transportation hub for all of South America on the drawing board.

Exhibitions featured green fuel, LNG technology, pipelines, compressors, equipment for enhanced recovery, and more.
The importance of incentives for investors was stressed, along with access to new studies and data which can be reprocessed and analyzed to reevaluate existing reservoirs, and to identify stacked plays, shale plays, as well as improved producibility using new technologies.

Susan Nash, Ph.D. (center) after giving a talk on case studies of  successful exploration with new technology. Accompanied by YPFB engineers Ing Isabel Prudencio and (unidentified).





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