Downstream Oil and Gas: Refining & Petrochemicals

The use of digital in the oil refining sector will be integral in achieving cost reductions, increased safety, and faster optimization. Applications include AI for production process optimization, as well as materials management automation and digital asset management. For instance, companies can improve asset reliability through advanced maintenance management, using big data and machine-learning algorithms to optimize maintenance cycles and predict critical equipment failures.

“Digital twin” technology—in which a virtual replica is created and constantly updated—will also serve as a key strategic tool in supporting and creating efficiency in all three phases of life-cycle management: the planning phase, to estimate budgets and plan projects; the project development phase, moving from concept to construction; and the operations phase, when owners and operators have a final asset in use and can work toward business goals.

Refineries today are facing increasing regulations regarding product specifications and refinery emissions—changes that require big investments that won’t bring short-term returns. To succeed in this environment, refiners need to focus on operations excellence by developing a decision support system that aligns top management economic incentives and experience at the operational level:

• Prioritized plant KPIs that will drive economic performance in real time
• Challenging plant operation targets, according to optimized performance and real limits
• Powerful predictive algorithms, allowing for a full understanding of the downstream oil and gas process flow at each point of the plant
• Greater automation through advanced control of operations with an economic perspective
• Change management that includes upgrading operators’ accountability and engagement

Current refinery scheduling processes use a trial-and-error approach based on the schedulers’ experience. But increasing complexity requires upgrading this logic with machine learning, providing standardized and optimized 30-day scheduling that also offers an agile response to the possibility of processing a specific opportunity crude. Primary benefits include:

• Scheduling optimization based on economics
• Higher stability of refinery operations
• Quick reaction to unexpected events
• Increase in the number of high-margin crudes processed

Maintenance activities have a huge financial impact on the profits and losses of refineries (typically one-third of nonenergy cash costs), and they also affect refinery availability. When integrated into an industrialized ecosystem, big data and advanced analytics allow for optimized decision making, as well as better reliability, by providing:

• Predictive maintenance based on operational data and static characteristics
• Connected plant and maintenance workforce digitization
• Asset health systems

MARPOL implementation will result in market disruption for 2019 through 2022. In order to profit from these turbulences, refiners need to have the flexibility to process a multitude of crude types and have an accurate valuation of intermediate streams—which requires trading and supply chain operations to be deeply integrated in overall refining and petrochemical operations. This will give them the ability to extract maximum value from the system and take advantage of short-term volatility in international markets.