One of the biggest decisions that Completion Engineers need to make when implementing Hydraulic fracking to improve well productivity is how much fracking fluid and proppant to inject into the well to maximize the rate of return on investment. This is a very hard decision since so many factors impact well performance.
In this use case, we will demonstrate the application of machine learning to detect trends and patterns in a complex system of a tight oil & gas reservoir. You will see how to easily train a machine learning model for forecasting initial production, and you will gain an understanding of how the same workflow can be applied for predicting expected ultimate recovery (EUR).
SpeedWise® ML (SML) is a web-based software platform hosted in the AWS secure cloud environment, allowing anyone in any size oil and gas company to conduct cutting-edge machine learning practices and predictive analysis.
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Learn more about using SML for solving universal Oil & Gas problems: [ Ссылка ]
