White Space Energy Artificial Intelligence Technology for Oil and Gas Projects
White Space Energy (WSE) delivers state-of-the-art artificial intelligence (AI) solutions to redefine complex decision-making in the oil and gas industry.
Most decisions in oil and gas are very complex. Operators often rely on time-consuming, iterative and manual workflows, potentially introducing various types of bias, or run out of time to complete the decision analysis, leaving material value on the table.
Artificial intelligence technology for offshore applications
One of the latest AI developments, Game AI, impersonates the human ability to learn from experience, and as a result, become renowned for achieving super-human performance in challenging games such as chess and poker.
WSE uses Game AI to support faster and better decisions. Applications span across the entire value chain from well trajectory planning, portfolio planning, rig sequence management and maintenance to decommissioning planning.
Our Game AI approach demonstrates that augmented decision-making can add significant business value and transform the ways of working in our industry while humans remain in control.
Game AI for improved ROI for oil and gas projects
WSE has a simple value proposition: immediate return on investment (ROI) through improved and faster decisions in the most complex problem areas currently experienced by the energy industry.
Through better planning, we have seen efficiency in the use of high-cost maintenance and logistics resources, such as vessels and helicopters, improve by up to 40%.
We have constructed decommissioning strategies that have the potential to save tens of millions of dollars compared to existing plans. In addition, we have discovered valuable alternative well trajectories in complex subsurface situations that were not previously considered up to ten times faster than existing work practices.
WSE has a team of industry insiders with a unique combination of AI skills, and global technical and leadership experience within the oil and gas sector. We partner with experts in decision-making and facilitation, which allow us to address complex decisions with an advanced AI toolkit.
AI-assisted maintenance planning for offshore operators
A southern North Sea operator asked WSE to improve its maintenance planning. The operator was facing a heavy workload of preventative and corrective maintenance work. Multiple stakeholders with different opinions were competing for the same resources.
With a predominantly manual planning process, the resulting plans were not optimal and unstable, leading to a year-on-year increase in the maintenance backlog.
We translated the asset’s maintenance planning into a resource allocation game and taught an AI agent to play this game. Through self-play, the AI learns to master the game mechanics and develop strategies that outperform human planning capability.
Using our AI as a planning assistant, we developed maintenance plans with up to 40% more efficient use of the available maintenance resources.
Well trajectory planning with AI technology
Our AI-assisted well trajectory planning solution has been applied to multiple projects, leading to up to ten times faster well proposal delivery times, as well as the unveiling of new and more valuable well trajectories that the team may not have considered before.
WSE works with multiple oil and gas operators worldwide. One of our use cases involves a brownfield infill drilling campaign of approximately 100 wells. The main business challenge is to find well trajectories that do not collide with existing wells while optimising surface locations in a congested infrastructure. Our solution is suited to deal with such complex problems, offering significant CapEx and time savings.
AI-assisted decommissioning planning for oil and gas assets
WSE was asked to use its Game AI approach to optimise the decommissioning sequence for a late-life gas-producing asset. It needed to be decommissioned in the next ten years, involving many production locations and interconnecting pipelines. The resulting large-scale optimisation problem was challenging and made more complicated by competing opinions about business drivers such as cost, safety and production.
We created an AI assistant that develops optimum sequences for different sets of business drivers within the execution constraints. With this solution, we provided a safe plan that maintains production as long as possible, but reduces OpEx spend by millions of dollars compared to the operator’s previous strategy.