Safely responding to critical events in industrial processes

钻井是一个复杂且具有潜在危险的过程，其中设备故障、对地下环境的不了解造成的意外环境影响以及人为错误都可能导致潜在的灾难性后果。为了降低此类事故发生的可能性以及钻井成本，斯伦贝谢正在开发能够在复杂、危险和不确定的环境中自主或半自主操作的人机团队系统。这样的系统必须能够对意外事件做出适当的响应，部署它们的一个重大挑战是验证系统是安全的，并且它将适当地检测和响应关键事件。这在钻井中尤其具有挑战性，因为系统的传感器往往很差，即使系统处于安全状态，也经常涉及到一系列重要的活动。在这个项目中，我们的目标是提供自动化支持，帮助作业者在作业过程中发生意外事件时做出决策。我们将探索在危急情况下支持决策的技术。特别是，我们将重点验证一旦检测到意外事件，将导致安全状态的拟议动作序列实际上是安全的。我们的方法将利用基于模型的测试生成的最新发展，特别是将多智能体系统与机器学习相结合用于测试生成和复杂人机交互系统的在线测试。

Drilling wells is a complex and potentially dangerous process in which equipment failures, unexpected environmental effects due to the poorly understood subsurface, and human errors can lead to potentially catastrophic outcomes. In an effort to reduce the likelihood of these as well as the cost of drilling, Schlumberger is developing systems that can operate autonomously or semi-autonomously as part of human machine teams in complex, dangerous and uncertain environments. Such systems must be able to respond appropriately to unexpected events and one significant challenge in deploying them is in verifying that the system is safe, and that it will detect and respond to critical events appropriately. This is particularly challenging in drilling as the systems tend to be sensor poor and even leaving the system in a safe state in response to an event often involves a non-trivial sequence of activities. In this project we aim to provide automated support that helps operators to make decisions when unexpected events occur during operations. We will explore techniques to support decision making in critical situations. In particular, we will focus on verifying that proposed sequences of actions that would lead to a safe state once unexpected events have been detected are in fact safe. Our approach will exploit recent developments in model-based test generation, especially the use of multi-agent systems combined with machine learning for test generation and for online testing of complex human-interactive systems.