Oil and Gas

Today, contingencies in the refinery planning process do not take historical market data, events, and predictive models of plant condition into account, leading to a massive gap between planned and actual GRM. Excel-based manual monitoring does not predict system performance degradation from high energy costs due to fouling. Additionally, traditional pricing mechanisms hinder revenue optimization, which needs more consumer-centric data.

It’s difficult for the upstream sector to collate the vast volume (e.g., seismic 2D lines and 3D volumes), variety (e.g., petrophysical data), and high frequency of data (e.g., real-time drilling data). In particular, generating presentable and more intuitive formats with minimum lag time can be challenging. AI can aid in digitizing Petro technical data (maps, well logs, and various reports) lying in physical form throughout warehouses or in disparate storage drives, resulting in greater data visibility, faster categorization, and usage of the said data. Present approaches to drilling and production operations to make critical decisions are reactive, often leading to more Non Productive Time (NPT) in drilling and an increase in production deferment. AI can help to break the operational silos and can foster a more predictive and reliable decision-making process.

Today, contingencies in the refinery planning process do not take historical market data, events, and predictive models of plant condition into account, leading to a massive gap between planned and actual GRM. Excel-based manual monitoring does not predict system performance degradation from high energy costs due to fouling. Additionally, traditional pricing mechanisms hinder revenue optimization, which needs more consumer-centric data.