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

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.

Determining the likelihood of incidents caused by human error during dynamic positioning drilling operations

“…Procedures for determining the trajectory of the vessel when an excursion takes place were studied by Bhalla and Cao (2005); their results can be used to improve drilling operations by estimating the disconnection times. Sanchez-Varela et al (2021) proposed a model for predicting loss of position for DP drilling operations, where generators and meteorological conditions were the main factors influencing excursions.…”

“…Sanchez-Varela et al. (2021) proposed a model for predicting loss of position for DP drilling operations, where generators and meteorological conditions were the main factors influencing excursions.…”

Key factors influencing multidrug-resistant tuberculosis in patients under anti-tuberculosis treatment in two centres in Burundi: a mixed effect modelling study

“…Then, among recurrent cases from these two countries, the MDR-TB prevalence was 3.9% 17.7% respectively [ 12 ]. In 2019, 23 persons per every 100,000 in the South Africa population were reported to have MDR-TB [ 13 , 14 ]. Regarding risk factors, recent studies conducted in Tanzania and Niger showed TB relapse , history of irregular treatment, cigarette smoking, alcohol abuse, treatment failure, underweight and HIV as leads factors of MDR-TB occurrence [ 15 , 16 ].…”

Assess the Impact of the COVID-19 Pandemic and Propose Solutions for Sustainable Development for Textile Enterprises: An Integrated Data Envelopment Analysis-Binary Logistic Model Approach

“…The researchers followed more than 30 subjects, determined relapse rates, and identified factors that could predict the re-use of addictive substances. Sanchez-Varela et al (2021) used binary logistic models to evaluate position loss predictions on positioning drilling operations. The results demonstrated that position loss is more likely to occur when more generators are used and drilling takes place in shallower water in adverse weather conditions.…”