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Optimization of Tripping IN/OUT in Drilling Operations using Machine Learning


Description:

Method 1) Database Development: A database will be developed to manage the enormous data from high-frequency telemetry (wired drill pipe data) [M. Reeves et. al. 2006]. Today, information is sent from the near bit area of the drill string via mud-pulse telemetry. The signal rate for this form of telemetry is quite low. There are several drawbacks with this form of data transmission in addition to a low data rate: • Poor data quality • Limited number of sensors (a point in time and/or a point in the well) • Communication is unavailable under tripping/connection Wired drill pipe (WDP) [M. Reeves et. al. 2006], developed by NOV can greatly reduce the mentioned problems. Wired drill pipe breaks these constraints by having effectively continuous and multiple sensor measurements along the length of the wellbore [Sanna Z. et. al. 2016]. It has been long recognized that bad data quality is hampering our attempts to make use of the drilling data [Dan Sui, et. al. 2018]. Bad data obstruct integrated planning, burden collaborative environments and hamper workflows. Consistent data quality represents a challenge for the industry and represents a prerequisite for quality decision support. Both technological and systemic challenges must be addressed. If not, bad data quality will remain a barrier to safe and efficient drilling. Method 2) Validation of WDP Data: Develop a method for the validation of the quality of the data while tripping data. Method 3) Quality of the sensors and sensor error will be addressed. Machine learning enables identifying the key variables from thousands of attributes to minimize the noise and errors in the predictive models and reveal hidden relationships between dependent and independent variables. A data-driven control system will be developed while using deep learning & machine learning techniques to automate the tripping in and tripping out of hole process.


Project leader: Øystein Stray

Started: 2020

Ends: 2023

Category: Næringsliv

Sector: Næringsliv

Budget: 2374000

Institution: VISCO COMPUTER GRAPHICS AS

Address: Stavanger