Upcoming Division Talk

Predicting Potential Reservoirs in Reservoir Plays by DNA Fingerprinting and Machine Learning 
Speaker: Robert Chelak | Biodentify

June 12, 2019 | 12:00 Noon 
Selkirk Building, Westwind Boardroom, 555-4th Ave. SW, Calgary AB 

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Biodentify is unlike any other company in the industry as it uses new DNA analytical methods and advanced machine learning algorithms machine, from near surface soil samples, to identify potential hydrocarbon reservoirs.
The company’s technology is borrowed from a still emerging medical science breakthrough that uses saliva to test for tumors as opposed to a much more invasive biopsy. In looking for alternative uses, it was thought the same process is used to predict oil and gas deposits based on microbial reactions to micro-seepages of gas molecules.
This presentation will present a method to generate a >70% accurate predictive map of potential hydrocarbon locations in reservoir prior to drilling. It indicates where to drill, and where not to drill. The approach uses DNA analysis of shallow soil samples, to derive information on the mix of microbial species in the samples. Using a database to correlate DNA in soil samples and production data of earlier drilled areas, the new DNA fingerprint is an indicator of the presence of vertical micro seepage to the surface from hydrocarbon accumulations in the subsurface. 
First technological break-through: DNA ‘fingerprinting’, biotechnology.  The occurrence of vertical upward micro-seepage has been known for decades, but the microbial life is much more complicated than just a few species that were known to be hydrocarbon oxidizing bacteria.  It is necessary to determine the complex composition of microbes -not only those that flourish at micro-seepage, but also those that are eliminated and are therefore found in reduced concentrations above hydrocarbons. Recent developments in DNA analysis techniques have made this complex and previously expensive problem efficiently and economically solvable.  
Second technological break-through: Big Data, Machine Learning, supercomputing.  The millions of microbes counted in thousands of soil samples by applying 16SrDNA ‘fingerprinting’ techniques create terabytes of data that must be correlated with the presence of hydrocarbons. This is a huge mathematical and computational big data problem. Advancements in machine learning applications together with parallel computing (Hadoop in the cloud, GPU) have made it possible to construct robust and reliable predictive DNA based models for hydrocarbon potential locations. 
The combination of both technologies will be presented in combination with two case studies: 1) a validation case in the Haynesville shale, an area with known production data, and 2) two areas in The Netherlands where the prospectivity of two shale formations was estimated.

Robert is an accredited Professional Geoscientist in Texas, and graduated from the University of Alberta with a Bachelor’s degree in Geology and Cartography.  He has 25 years of experience in the oil and gas industry working for a major oil and gas operator, software development companies, consulting services, that all dealt with geology, geophysics, modeling, new innovations.  His growth and experience over the years has allowed him to excel in business development, leading teams, project management, product development, marketing, and implementation of strategic business transformations and innovations.

Division Sponsor

Division Profile
The mandate of the Geomodeling Division is to provide CSPG members with opportunities for education and information related to technical developments in the subject areas of geomathematics and computer technologies as they are used in the pursuit of petroleum exploration and development. As a main contribution of the division, technical luncheon presentations are held once a month, usually on the last Wednesday of the month. 

The subjects that are presented in these technical talks include, for example, The latest developments in geomathematical applications, Geological modeling technology, Geostatistical approaches to modeling and risk analysis, Geological case studies using computer technology and the benefits, Digital data organization - storage and retrieval. In addition, ad hoc forums may be organized where members can discuss geomathematical and geological computer issues with experts in the field.The Geomodeling Division does not endorse or promote the use of specific commercial software products, nor does it perform any testing or comparative studies of such products.We do encourage volunteers to present public talks on case histories that illustrate the use of technology and methods.

The success of the Division depends on volunteer participation. CSPG members are encouraged to attend the activities of the Geomodeling Division and to be involved in organizing these activities. Division meetings are held once a month over lunch. If you are interested in joining this committee or if you have suggestions for luncheon talks or other activities, please contact any members of the committee.

Committee Members

Weishan Ren | Chairperson         Tom Cox                      Hayley Silberg             
David Garner | Co-chair              Sasan Ghanbari           Darcy Novak

Olena Babak                               Damien Thenin             Eric Niven  

There is no charge. Non-members of the CSPG are also welcome. Please bring your lunch. For details or to present a talk in the future, please contact Weishan Ren via email at