Modelos de regresión logística aplicados a incidentes de posicionamiento dinámico ocurridos durante operaciones de perforación mar adentro.

156 p.La prevención de incidentes en la industria offshore es una parte crucial del proceso de análisis y gestiónde riesgos, ya que permite optimizar las operaciones de perforación de posicionamiento dinámico, y asíreducir las consecuencias de un posible incidente.En esta disertación, aplicaremos modelos de regresión logística binaria sobre una base de datos de 42incidentes ocurridos durante el período 2011-2015. Para cada caso, se consideran las variables quedescriben las diferentes configuraciones del sistema de posicionamiento dinámico, las condicionesclimáticas y la profundidad del agua.El primer objetivo trata de determinar la probabilidad de tener una excursión durante un incidente. Enesta investigación se comprueba que las pérdidas de posición tienen mayor probabilidad de ocurrircuando hay un mayor uso de generadores, y la perforación se realiza en aguas menos profundas,obteniendo este modelo muy buenos resultados cuando se aplica a la muestra. Las variablesclimatológicas también son consideradas, obteniendo un modelo que combina las variables antesmencionadas con la fuerza del viento.Los incidentes causados por factores humanos son cada vez más numerosos e importantes. Laprobabilidad de que ocurra un incidente de origen humano durante las operaciones de perforación deposicionamiento dinámico se determina utilizando modelos de regresión logística binaria sobre la mismabase de datos. Los resultados obtenidos mostraron que es mucho más probable que ocurran incidentes deorigen humano cuando hay un menor uso de los propulsores.Estos resultados, aplicados a la gestión de riesgos de las operaciones de perforación, pueden ayudar acentrar la atención en los elementos que afectan más fuertemente las pérdidas de posición, mejorando asíla seguridad durante estas operaciones. Asimismo, estos resultados son útiles para enfocar nuestraatención en variables que pueden estar asociadas a incidentes atribuibles a error humano, así como paraestablecer límites operacionales que podrían ayudar a prevenir estos incidentes y mejorar la seguridaddurante estas operaciones

Human error analysis in dynamic positioning incidents according to the nature of the operations in progress

Human errors are known to contribute to incidents in the maritime industry. Although the dynamic positioning operator has to undergo a standard training schedule before becoming a full operator, human errors contribute to 20% of the incidents in dynamic positioning operations. This research aims to investigate which dynamic positioning operations have a more considerable percentage of human errors. With a 266 dynamic positioning incidents database, different offshore operations are classified and then cross-tabulated with the human causes, classified as either primary or secondary cause as described in the incident report. The results and discussion present that drilling and diving operations are significantly correlated with human causes. This study's results could help provide better directions for the training schedule, proposing simulator exercises based on these scenarios.Peer Reviewe

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

[EN] 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.This research received no specific grant from any funding agency, commercial or not-for-profit sectors

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

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

Analysis of human-related incidents during dynamic positioning operations

Dynamic positioning systems ensure additional position accuracy in different operations in the maritime industry, such as shuttling, drilling, diving, pipe laying, and others. Although they are known to be robust systems, they are not exempt from failures leading to incidents, among which we can find human-related incidents. This research analyzes 62 human-related dynamic-positioning incidents and aims to determine which segment in the dynamic positioning system influences these occurrences by applying binary logistic regression modeling techniques in the test sample and validating the results using a control sample. The results indicate that thrusters have the most significant influence on human-related incidents; however, not all DP operations are affected in the same measure. By stratifying the database and considering the different operations in progress, it is noted that human-related incidents while drilling operations are in progress are effectively influenced using a higher percentage of thrusters online.This work was partially supported by the Research Group of the Basque Government (IT1514-22)