Digitally Distanced Inspection & Maintenance at Drilling Rigs : Applied Augmented Reality

For drilling contractors, the moment of truth is the operations at the site. If the technician at the site encounters a problem he can't solve, then everything stops. The team has to wait for a subject matter expert (SME) to arrive at the site to diagnose rectify the problem. Such process of SME mobilization and till that time Non-Productive Time (NPT) results in loss of hundreds of thousands of dollars. Hence the key challenge is converting the Sparse to Adequate availability of Right Knowledge at Right Time at Right Place, for the support of technicians. This paper is focused on the approach of moving from Hand Held devices to Hands-Free environment at sites and connecting local/global support to site support systems, to reduce cost, improve HSE and enhance operational performance. The augmented reality technology-enabled, smart glass laced headsets are rugged, zone 1 certified, and are voice-operated which are better than smart tablets which were considered during Technology Qualification Process. Evaluation criteria were: 1. Availability and follow up of the digital work instruction while operating. Moreover, not missing a single step of work instruction while inspection or maintenance continues was noted carefully. 2. Reduced travel/accommodation cost : Normally at the time of shutdown, the rig crew contacts subject matter experts (SME) and (at times) in turn the SME contacts the OEM support team to mobilize service engineers globally. 3. Response time improvement-Availability of support by SME right at the time of need results from better response time to diagnose and fix the issue at hand. Call logging till final resolution process improvement is considered an important metric. Travel restrictions imposed by Covid-19, are also being addressed through the distanced inspection. A hands-free environment is compared vis a vis handheld device. Better training and knowledge transfer are achieved through better communication methods and this goes better with learning by doing. Subsequent text (NLP-speech to text) analysis is planned through deep learning models to derive related predictions. Sparse to Adequate availability of support to rig staff with Right Knowledge at Right Place at Right Time is the key outcome of this Proof of Value project.