Levels of Automation Baseline Supports Surface Well Testing Operations, Enables Process Safety, Reduces Operational Risk, and Environmental Impact

Abstract The objective of a well testing is to deliver accurate, reliable data. The means to obtain this data can often be very challenging, & in many well testing Operations today we see personnel performing repetitive, physical, isolated, & dangerous work tasks, directly in the line of fire! An example of these manual work tasks, operators are required to physically operate Sand Management Systems, Choke Manifolds, Three-Phase Test Separators, Surge Tanks and Transfer Pumps, which more often than not require multiple operators to monitor, manage, and operate this equipment for the whole operation, often placing them directly in the line of fire (red zone) – a risk that has been traditionally considered to be acceptable based on the ALARP principle, "as low as reasonably practicable" and is a term often used in safety management and often utilized as guidance in well testing application risk assessments, mainly due to the temporary nature of surface well testing operations and a lack of available technology. The well testing service industry operates on tight budget margins and at times requires quick critical decision-making. An outdated operating / detection system can place assets at risk (with the added hazard towards the safety of the entire crew). Much of the well testing industry was built on machinery designed for the previous century. While they were reliable, these aging systems lack the connectivity and visibility needed to detect and adapt to today's increasingly dynamic conditions. Outdated monitoring puts safety at risk if threats go unnoticed. Digital transformation can improve outcomes across the well testing sector, helping to provide solutions to mitigate these risks, improve operational efficiency, and provide better end-user experiences. Digital transformation of well testing applications is the integration of emerging technologies like automation, IoT, and analytics across multiple functions / platforms. The goals of digitalization typically revolve around improving operational efficiency, driving crew productivity, promoting sustainability, mitigating risks, and providing better end-user experiences – especially taking into consideration a generation that has grown up with the digital world, that is always connected and tuned into the digital economy, the result is also a stronger workforce engagement. New opportunities for automation in the well testing Service Industry are being made available by technologies such as big data and analytics, sensors, and control systems. Utilizing data to improve performance is not a new concept (Bohannon, 1984), but what is new is the quicker pace of change in capturing the benefits of automation and process know-how. This paper reviews the available literature related to digitalization and automation and discusses how to categorize each application based on levels of automation (LoA). As part of this paper, a new baseline for comparing automated well testing systems is proposed – with the aim to facilitate understanding and standardization between all stakeholders involved in the execution of digital and automated well testing systems, combining the definitions from Sheridan-Verplank (1999), Endsley and Kaber (1999), Parasuraman (2000), Lorenz et al. (2001), and the SAE J3016™ (2021) standard. Automation is defined as the technique of making a product, process, or system operate automatically. The degree to which a task is automated is referred to as levels of automation (LOA) As mentioned, such review was necessary to standardize a baseline for comparison among different automated systems, due to the legacy surface Well Testing operations function as a "Level 0" LOA, and with the "adding together" of sensors and simple functions (for example, "basic remote-control" capabilities) could cause further uncertainty and confusion with regards to the assessment and analysis of automated systems within the Well Testing applications.