SPWLA Distinguished Speaker Webinar - January 2022

SPWLA Distinguished Speaker Webinar - January  2022


Paper Ref:
SPWLA-2021-0036 (full abstract)
Authors : 
Andrew McDonald (Lloyd’s Register) 
Speaker : Andrew McDonald

Speaker Bio: Andy McDonald is a Petrophysicist with Lloyd’s Register in Aberdeen and has over 15 years of industry experience. He currently provides petrophysical expertise to software development projects and specialises in Python development, artificial intelligence and applications of machine learning to petrophysics. Andy holds an MSc in Earth Science from the Open University, and a BSc (Hons) in Geology & Petroleum Geology from the University of Aberdeen. He has also co-authored several technical conference papers for the SPWLA and SPE on topics covering machine learning, heavy oil, geomechanics and low salinity waterflooding.

Decades of subsurface exploration and characterisation have led to the collation and storage of large volumes of well related data. The amount of data gathered daily continues to grow rapidly as technology and recording methods improve. With the increasing adoption of machine learning techniques in the subsurface domain, it is essential that the quality of the input data is carefully considered when working with these tools. If the input data is of poor quality, the impact on precision and accuracy of the prediction can be significant. Consequently, this can impact key decisions about the future of a well or a field.

This study focuses on well log data, which can be highly multi-dimensional, diverse and stored in a variety of file formats. Well log data exhibits key characteristics of Big Data: Volume, Variety, Velocity, Veracity and Value. Well data can include numeric values, text values, waveform data, image arrays, maps, volumes, etc. All of which can be indexed by time or depth in a regular or irregular way. A significant portion of time can be spent gathering data and quality checking it prior to carrying out petrophysical interpretations and applying machine learning models. Well log data can be affected by numerous issues causing a degradation in data quality. These include missing data - ranging from single data points to entire curves; noisy data from tool related issues; borehole washout; processing issues; incorrect environmental corrections; and mislabelled data.

There are two identical sessions:

Thursday, Jan. 27th    Early Morning Session: 3am – 4am US Central Time.
Thursday, Jan. 27th    Morning Session: 10
am – 11am US Central Time.

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1/27/2022 3:00 AM - 11:00 AM

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