About this Virtual Instructor Led Training (VILT)

The classical use of decline curve analysis across a reservoir's life cycle is well documented. We have oil, gas, and water production data. Critical petrophysical data are interpreted from an array of well logs and cores. Shedding light on complex multidimensional and multivariate subsurface systems is vital to reduce re-engineering decision-cycles and optimize forecasting accuracy. This Virtual Instructor-Led Training (VILT) course will discuss key Machine Learning (ML) and Deep Learning (DL) techniques to help the reservoir engineer analyze the production time-series data to forecast well performance. Participants will explore and fully understand the difference between supervised and unsupervised learning and how to use the ML/DL algorithms under each learning technique. By the end of this VILT course, you can immediately see the value by applying ML/DL on your upstream well data and fluids production. Are you someone in E&P who is interested in using Python to be a key member of your company's digitalization project to optimize field production and employ data-driven forecasting techniques? This two half-day session will then get you going with sound background understanding, illustrated by real case studies. Some open-source Jupyter Notebooks will be shared with participants to start their journey towards accurate and reliable Forecasting.

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