| Abstract: |
In remote upstream oil and gas facilities such as well pads, energy companies frequently have various service contractors bringing in items, performing service, and removing items from the site. These facilities do not have permanent staff on location and facility owners have to rely on contractors and service providers to perform routine work at the facility. The facilities are industrial and workers on site must wear appropriate personal protection equipment (PPE), such as a hard hat. This case study uses custom machine learning models created by and deployed on with Amazon Web Services tools to 1) identify the PPE-presence or absence of properly worn hard hats; and 2) identify and track service provider trucks accessing the facility by license plate number and any markings on the side of the truck. An edge video camera runs machine learning models and only transmits logs and alerts when vendor trucks and personnel are present at the facility.
Computer vision is a different way to approach the limited monitoring available at remote oil and gas facilities. The approach described here allows operators to have “eyes” on their facilities all the time. Importantly, while the service is always on, customers only incur costs when the system detects someone at the facility. The system’s operational costs are recovered if the well is able to produce a single incremental barrel. Developing, deploying, and managing computer vision models for well pads has previously been cost prohibitive. Our solution costs $2 per facility per year. Cloud-based ML solutions simplify this process so that anyone can build, deploy, and maintain sophisticated models that improve safety and logistics for remote facilities. AWS customers can quickly add new training to the model without dedicated data scientists or AWS personnel. Operators can iterate on their models and push updates to the edge for processing.
Looking towards the future, we expect to see more artificial intelligence and machine learning solutions applied at industrial facilities, including oil and gas production facilities. Automating work previously done by well lease operators and production engineers can increase production, reduce maintenance issues, and reduce safety events. This demonstration is one example of how artificial intelligence can be deployed at a facility with minimal capital investment. |