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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.

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The probability of a human-caused incident occurring during dynamic positioning (DP) drilling operations is determined in this paper using binary logistic regression models built with data on 42 incidents that took place during the period 2011–2015. For each case, a range of variables characterising the configuration of the DP system, weather conditions and water depth are taken into account. These variables are taken into account to develop a logistic regression model that shows the likelihood of an incident being caused by human error. The results obtained show that human-based incidents are significantly more likely to occur when there is a lower usage of thrusters. These results are useful for focusing our attention on variables that may be associated with incidents attributable to human error, as well as for setting operational limits that could help to prevent these incidents and improve safety during these operations.

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This paper aims to present a method to determine the type of dynamic positioning (DP) incidents that have a more significant risk during drilling operations in the period 2007-2015, according to the element or the type of failure that causes the DP system to fail. Two different classifications are made: 1) according to the element that produces the incident (which has been the traditional classification in the industry) and 2) according to the type of error that arises, the latter being an alternative classification proposed in this paper. The predictable financial losses for each level of severity are used to define the resulting consequences for each case. A risk analysis is performed with the data obtained, showing the potentially more dangerous incidents, either because of their higher number of occurrences or because their consequences are remarkable. According to the classification proposed, the main causes with the higher risk results were power and environmental, according to the traditional classification, and fault/failure. Thus, the power segment’s combination of failures is the riskiest cause during the DP drilling operations.

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