Predictive big data analytics for drilling downhole problems: A review

Abstract

With the recent introduction of data recording sensors in exploration, drilling and production processes, the oil and gas industry has transformed into a massively data-intensive industry. Big data analytics has acquired a great deal of interest from researchers to extract and use all the possible information. This paper presents an outline for predictive big data analytics to forecast and analyze some downhole problems such as pipe sticking, dog leg and pipe failure depending on several variables. Different methodologies were studied under big data, enabling the identification of the paradigm change in data storage and processing while handling vast diversified data generated in a short span of life. The evaluated data pattern sets are fed into different established predictive models and risk prediction windows to highlight future irregularities for the prevention of accidents. Finally, the game theory is used to evaluate the best predictive model to discover the optimal model for the identification of downhole problems.