Prediction of Loss of Position during Dynamic Positioning Drilling Operations Using Binary Logistic Regression Modeling

Sanchez-Varela, Zaloa; Boullosa-Falces, David; Larrabe Barrena, Juan Luis; Gomez-Solaeche, Miguel A.

doi:10.3390/jmse9020139

Open AccessArticle

Prediction of Loss of Position during Dynamic Positioning Drilling Operations Using Binary Logistic Regression Modeling

by

Zaloa Sanchez-Varela

^1,2,*

,

David Boullosa-Falces

²,

Juan Luis Larrabe Barrena

²

and

Miguel A. Gomez-Solaeche

²

¹

Faculty of Maritime Studies, University of Split, 21000 Split, Croatia

²

Faculty of of Engineering in Bilbao, University of the Basque Country UPV/EHU, 48920 Portugalete, Spain

^*

Author to whom correspondence should be addressed.

J. Mar. Sci. Eng. 2021, 9(2), 139; https://doi.org/10.3390/jmse9020139

Submission received: 15 January 2021 / Revised: 22 January 2021 / Accepted: 25 January 2021 / Published: 29 January 2021

(This article belongs to the Section Ocean Engineering)

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Abstract

The prediction of loss of position in the offshore industry would allow optimization of dynamic positioning drilling operations, reducing the number and severity of potential accidents. In this paper, the probability of an excursion is determined by developing binary logistic regression models based on a database of 42 incidents which took place between 2011 and 2015. For each case, variables describing the configuration of the dynamic positioning system, weather conditions, and water depth are considered. We demonstrate that loss of position is significantly more likely to occur when there is a higher usage of generators, and the drilling takes place in shallower waters along with adverse weather conditions; this model has very good results when applied to the sample. The same method is then applied for obtaining a binary regression model for incidents not attributable to human error, showing that it is a function of the percentage of generators in use, wind force, and wave height. Applying these results to the risk management of drilling operations may help focus our attention on the factors that most strongly affect loss of position, thereby improving safety during these operations.

Keywords: dynamic positioning; offshore; risk analysis; drilling

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MDPI and ACS Style

Sanchez-Varela, Z.; Boullosa-Falces, D.; Larrabe Barrena, J.L.; Gomez-Solaeche, M.A. Prediction of Loss of Position during Dynamic Positioning Drilling Operations Using Binary Logistic Regression Modeling. J. Mar. Sci. Eng. 2021, 9, 139. https://doi.org/10.3390/jmse9020139

AMA Style

Sanchez-Varela Z, Boullosa-Falces D, Larrabe Barrena JL, Gomez-Solaeche MA. Prediction of Loss of Position during Dynamic Positioning Drilling Operations Using Binary Logistic Regression Modeling. Journal of Marine Science and Engineering. 2021; 9(2):139. https://doi.org/10.3390/jmse9020139

Chicago/Turabian Style

Sanchez-Varela, Zaloa, David Boullosa-Falces, Juan Luis Larrabe Barrena, and Miguel A. Gomez-Solaeche. 2021. "Prediction of Loss of Position during Dynamic Positioning Drilling Operations Using Binary Logistic Regression Modeling" Journal of Marine Science and Engineering 9, no. 2: 139. https://doi.org/10.3390/jmse9020139

APA Style

Sanchez-Varela, Z., Boullosa-Falces, D., Larrabe Barrena, J. L., & Gomez-Solaeche, M. A. (2021). Prediction of Loss of Position during Dynamic Positioning Drilling Operations Using Binary Logistic Regression Modeling. Journal of Marine Science and Engineering, 9(2), 139. https://doi.org/10.3390/jmse9020139

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Prediction of Loss of Position during Dynamic Positioning Drilling Operations Using Binary Logistic Regression Modeling

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