Introduction: Oil and gas is a complex and critical industry
For years, oil and gas companies have leaned into digital transformation initiatives in an effort to deliver their products more efficiently and cost-effectively, ensure regulatory compliance, improve worker safety, and achieve sustainability goals.
But the exponential growth of data flowing from industrial physical assets can quickly become overwhelming. For example, a single centrifugal compressor streaming temperature, pressure, flow, vibration, and power information can generate as much as 2.5MB/sec of data, or 216GB/day. Multiply this by hundreds or even thousands of interconnected assets and processes generating similar amounts of data, and it’s no wonder many oil and gas companies today struggle to extract actionable value from their data.
More data should lead to better business intelligence that empowers teams and improves profitability. Too frequently, though, it instead leads to information overload and alert fatigue, clouding the picture of what’s really happening across interrelated systems and processes. Huge risks and missed opportunities are hanging in the balance. Converting your operating environment’s deluge of data into real-time insights requires capabilities only artificial intelligence (AI) can deliver.
Operating an increasingly complex fleet of petroleum assets is an immense challenge
Upstream
Efficient and uninterrupted oil and gas upstream operations are critical to company performance as they directly drive profitability and competitive advantage by controlling the capital and recurring costs needed to discover and extract hydrocarbons. The upstream segment is the single greatest driver of value in oil and gas, and the wide-ranging capabilities of AI improve cost efficiency and maximize product output, translating to greater profitability, enabling success in volatile commodity and retail markets.
Midstream
Safe and economical transportation, storage, and initial processing of hydrocarbons is the essential bridge between exploration/production (E&P) and refining for oil and gas companies. Employing AI across the full range of day-to-day midstream processes ensures efficient product movement, enabling upstream operators to move products to market and downstream companies to receive necessary feedstocks, optimizing the end-to-end value chain.
Downstream
Downstream/refining asset operations are crucial to profitable delivery of oil and gas products to market. Degraded operations or unplanned failure of compressors, pumps, and other refining equipment contributes directly to lost revenue, increased capital and operating cost, environmental noncompliance, and reduced worker safety. AI-enabled predictive identification of pending failures and prescriptive recommendations mitigates these failures, saving cost and ensuring continuing production.
Avathon’s Autonomy Platform drives optimal oil and gas performance
Avathon Autonomy empowers a wide range of performance-enhancing applications, all critical to achieving oil and gas performance excellence.
Predictive/prescriptive equipment maintenance: Uses normal behavior modeling (NBM) to analyze real-time and historical performance data from sensors, visual inspection, and acoustic monitoring to identify equipment and systems that are trending toward failure; forecasting failures in ESPs, pumps, and rigs in advance of failure; and enabling proactive maintenance that reduces non-productive time and extends asset lifetimes.
Computational knowledge graph (CKG) technology: Defines relationships and interdependencies between wells, equipment, and operational workflows to improve risk prediction, decision-making, and process efficiency. Using proprietary CKG technology to integrate feedstocks, chemistry, and equipment maximizes refinery throughput, balances energy loads, ensures product quality, and minimizes emissions.
Generative AI guidance for drilling and production: Enhances real-time analysis of drilling logs, sensor data, and rig performance with contextual recommendations to optimize exploration effectiveness, production efficiency, and operational profitability.
Onshore rig logistics optimization: Minimizes rig move times and ensures that logistics scheduling, routing, crew, and equipment utilization are optimized, reducing costs and maximizing product output.
Supply chain integration: Uses predictive models to align maintenance schedules with part availability, ensuring critical spares are available when and where they’re needed at remote drilling sites; Automatically aligns spares availability with maintenance schedules, worker skillsets, and tool availability; Optimizes inventory levels across depots, warehouses, and field locations; Forecasts demand for refining and distribution equipment, automates procurement, and balances inventory across global networks of warehouses, refineries, and storage facilities; Provides contextual views of asset lifecycles, part dependencies, and vendor networks for better decision-making.
Logistics and routing models: Optimizes oil and gas supply chains by mapping pipelines, tanks, valves, and fleets for a comprehensive view of operating interdependencies; Manages shipping and logistics using CKGs and machine reinforcement learning to optimize fleet effectiveness, enhance profitability, and reduce emissions.
Pipeline and tank integrity monitoring: Uses data from existing camera infrastructure to monitor assets, ensuring more reliable production operations; Detects leaks, corrosion, and flow anomalies early using multi-modal data from sensors, visual inspection, and acoustic monitoring, enabling automatic shutoffs or other corrective actions to minimize equipment damage, product losses, and safety/environmental risk.
Computer vision for worker safety: Monitors personal protective equipment (PPE) compliance, safe zone adherence, and incident detection in real time, providing proactive worker warnings and regular reports of current or impending risks; Uses video and sensor fusion to detect hazardous conditions such as leaks, spills, flare activity, or gas releases; Automatically escalates problems to predict and mitigate risks before they become incidents.
Retail operations optimization: Ensures fuel distribution and sales happen efficiently and profitably using automated monitoring to ensure product availability/placement, customer service, and health/safety compliance.
Conclusion: The future of oil and gas performance excellence is autonomous
The oil and gas industry is experiencing a new operating environment in which equipment is outfitted with a myriad of sensors and communication devices. While all the data collected by this smart equipment offers great opportunities—better systematic visibility, equipment efficiency, and insight into operations—it also creates plenty of implementation and analytical challenges.
The Avathon Autonomy Platform uses the power of AI to significantly reduce operating and capital costs while improving exploration and production efficiency for oil and gas operators. Avathon Autonomy is the only autonomous energy management platform that optimizes day-to-day operations through agentic management of assets and supply chain processes.
To learn more about Avathon’s Autonomy Platform for the Oil and Gas industry, visit our website.
