Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Risk Factors Evaluation for Monitoring of Well Drilling

Version 1 : Received: 26 May 2021 / Approved: 27 May 2021 / Online: 27 May 2021 (08:04:06 CEST)

How to cite: Islamov, S.; Grigoriev, A.; Beloglazov, I.; Savchenkov, S.; Gudmestad, O.T. Risk Factors Evaluation for Monitoring of Well Drilling. Preprints 2021, 2021050657 (doi: 10.20944/preprints202105.0657.v1). Islamov, S.; Grigoriev, A.; Beloglazov, I.; Savchenkov, S.; Gudmestad, O.T. Risk Factors Evaluation for Monitoring of Well Drilling. Preprints 2021, 2021050657 (doi: 10.20944/preprints202105.0657.v1).

Abstract

Drilling of wells for oil and gas production is a highly complex and expensive part of reservoir development. Thus, together with injury prevention, there is a goal to save cost expenditures on downtime and repair of drilling equipment. Nowadays companies have begun to look for ways to improve the efficiency of drilling and minimize non-production time with the help of new technologies. To support decisions in a narrow time frame, it is valuable to have an early warning system. Such a decision support system will help an engineer to intervene in the drilling process and prevent high expenses of unproductive time and equipment repair due to a problem. This work is describing a comparison of machine learning algorithms for anomaly detection during well drilling. Tested models classify drilling problems based on historical data from previously drilled wells. To validate anomaly detection algorithms, we use historical logs of drilling problems for 67 wells at a large brownfield in Siberia, Russia. Wells with problems were selected and analyzed. It should be noted that out of the 67 wells, 20 wells were drilled without expenses for unproductive time. Experiential results illustrated that a model based on gradient boosting can classify the complications in the drilling process best of all.

Subject Areas

machine learning; drilling problems; artificial intelligence; risk factors evaluation; gradient boosting

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