Deepwater drilling contractor, Seadrill, talks to Africa Energy Series – Angola, about its new joint venture with Sonangol with AI technologies at the helm.
In February, Seadrill and Sonangol established a joint venture – Sonadrill – to operate four drillships in Angolan waters. How will this partnership enable Seadrill to gain greater access to the market?
Sonangol built two Drillships at the Korean shipyard Daewoo Shipbuilding Marine Engineering. As a result, it was looking for an international partner with the experience to operate those vessels and have a background in-country. For us, it was good to join forces with a strong partner with a strong reputation in Angola, a very important deepwater market for us. It’s a win-win situation. The first rig, the Sonangol Libongos was delivered in March, the Quenguela in May. The Libongos is on route to Walvis Bay while Quenguela is in Singapore. The highest priority for ourselves and Sonangol is to get two drillships on contract. Our joint venture is essentially a pooling of resources and will have two Seadrill drillships as well as the Libongos and Quenguela. We have yet to confirm which Seadrill drillships these will be as there are various factors to consider in terms of availability, client requirements, and specification but we are proud to have this joint venture with Sonangol and we are excited to get started.
What role does technology play in operating drillships and rigs in demanding environments?
Oil and gas have always been a conventional industry. The principles have remained the same and progress has been slow. However, over the last two and a half years, things started to jump forward. We have developed an artificial intelligence (AI) solution for our rigs, and we will install this technology on the Sonangol rigs. All of the equipment onboard has thousands of sensors that measure and provide information. Previously, not much was done with that information, but today, there is a lot that can be done with it.
We developed a solution in-house called Plato, which analyzes this information. First, we started with the equipment. For example, the draw works may be running fine, but eventually, you see more vibration and you know there is an issue. You can see what is wrong and make decisions about when to make repairs. Now, we are looking at performance. If you have three drillers performing at a certain level and a fourth that is slower or faster, then at the end of the shift, Plato can tell the driller what they need to do differently and how to improve.
We noticed that one of our racking systems, a machine that picks the next drill pipe out of the derrick, rotates it and brings it to the center was slower compared to other rigs. Some of the equipment settings were different and we were able to change them and make improvements to the system. Regarding safety, there are certain zones, called the red zone, that you want to keep people out of as much as possible. Working with the Marsden Group, a global data science company, we developed a tool using AI, LiDAR and Edge computing to monitor this.
As soon as someone walks into the red zone, it gives a warning to the driller indicating that someone is there. We are now looking at connecting this to the equipment so that if someone walks in front of a piece of equipment, the system will shut down.
AI is moving quickly across all industries. We recognized its potential in our industry where previously maintenance was based on a calendar schedule that doesn’t take into account usage. In the future, with the help of AI and Plato, we will move to condition-based maintenance. Condition-based maintenance is more efficient. The customers save time because we don’t have to stop, and we save time because we don’t have to change out the equipment.