Drilling Automation: Potential for Human Error

Abstract

Mode confusion is an automated system behaving differently than expected and the operator not being aware of (or not properly understanding) what the system is doing. Mode confusion is wellrecognized in the aviation community and has been indicated in a number of high-profile aviation accidents. As an example, a JAS Gripen fighter jet crashed during a test flight in the 1980s because the pilot tried to manually correct instability while the plane's computer was automatically trying to do the same. The potential for the same type of problems, and associated safety hazards, arises in drilling-rig operations as a result of the increasing trend for automation and advisory systems. A simple example could be formation fracturing with an automated downhole pressurecontrol system when displacing to higher mud weight caused by the driller relying on the automated system to maintain sufficiently low flow rate without having reconfigured the system with the new mud properties. This paper describes how the use of a drilling support system in different modes and levels of automation may influence the system operator's performance and risk of human error. The development of a systematic method for detecting mode-confusion problems by model checking is central in this respect. The test cases have been simulated in a virtual test environment created at IRIS specifically for such purposes. The test environment and test cases are described, and results are reported and discussed.

Challenges and Opportunities of Implementing the Operators' Perspective: Experiences from Automated Drilling Projects

“…In simulations of partially automated drilling operations a correlation between the degree of automation and the risk of errors are found (Iversen et al, 2013). This indicates that the design of automated systems should be evaluated with regards to the introduction of new errors, such as those identified in this study; system integration, alarms and training (Flaspöler et al, 2009).…”

“…As offshore drilling is a dynamic activity, continuous situational awareness and cooperation is an important factors, especially when introducing automated operations. As indicated in Iversen et al (2013) there is a correlation between the degree of automation and the risk of errors in training simulations of partially automated drilling operations.…”

A Model for Early Detection of Stuck Pipe Using Random Forest Algorithm

“…Conventionally, torque and drag analysis is an effective method to identify potential drilling problems by comparing the actual torque, hook load readings with the modeled ones (Abbas et al 2018; Adelung et al 1991; Bradley et al 1991;Fisk et al 1998;Iversen et al 2013;Krishan et al 2000). However, this method as well as other analytical models highly relies on the engineer's expertise and experience.…”

Aplicação das Redes Neuronais Artificias para classificação das operações de perfuração: O caso de poços deepwater de Exploração e Produção

“…A institucionalização de tais programas diminui a estrutura de custos substancialmente através do processo sistemático e advento de novas tecnologias. Ao longo da última década, o setor reconheceu o valor de automatizar partes significativas das tarefas de perfuração para manter eficiência, reduzir a sobrecarga e superar o erro humano (Iversen, Gressgård, Thorogood, Balov, & Hepsø, 2013).…”