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Challenges
Upstream
Midstream
Downstream
HSE
Logistics Optimization
Case Studies

Avathon Autonomy
for Oil and Gas Operations

The Avathon Autonomy Platform uses the power of AI to significantly reduce operating and capital costs while improving decision making and exploration and production efficiency for oil and gas operators. Avathon Autonomy is the only autonomous energy management platform that optimizes your day-to-day operations through agentic management of assets and supply chain processes.

Challenges

The oil and gas industry faces a large number of significant operating and strategic challenges that must be proactively handled in order to ensure efficient, profitable operations.

Economic and Market Volatility

  • Market volatility of oil and natural gas prices
  • Rising production costs due to depletion of easily accessible reserves
  • Capital investment scrutiny by investors

Geopolitical and Operational

  • Geopolitical instability in major producing or transit regions
  • Cybersecurity threats that increase infrastructure vulnerability

Technological and Workforce

  • Technological advancement requiring investment in digital transformation
  • Talent shortages due to industry workforce aging

Upstream

Upstream operating efficiency is critically important as it directly determines operating profitability and competitive advantage by controlling the cost to explore and produce hydrocarbons. Since the upstream segment is the source of greatest value in the oil and gas industry, leveraging the wide-ranging capabilities of AI to improve cost efficiency and maximize product output translates directly to lower final pricing, a critical enabler of achieving success in volatile commodity and retail markets.

Predictive equipment monitoring

Uses normal behavior modeling (NBM) to detect and prioritize operating abnormalities and forecast failures in ESPs, pumps, and rigs in advance, enabling proactive maintenance to reduce non-productive time.

Computational knowledge graphs of reservoirs and assets

Define relationships and interdependencies between wells, equipment, and operational workflows to improve risk prediction and decision-making.

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 and production efficiency.

Onshore rig logistics optimization

Minimizes rig move times and ensures logistics scheduling, routing, crew, and equipment utilization are optimized, reducing costs and maximizing product output.

Spare parts supply chain integration

Uses predictive models to align maintenance schedules with part availability, ensuring critical spares are in place when needed at remote drilling sites.

Autonomous field monitoring

Uses data from existing camera infrastructure to monitor assets and the health and safety of workers, ensuring safer, more reliable production operations.

Midstream

The safe and efficient transportation, storage, and initial processing of hydrocarbons is the essential bridge between production and refining for oil and gas operators. Building AI into day-to-day processes ensures efficient movement of product, allowing upstream companies to get products to market and downstream companies to receive necessary feedstocks, thereby optimizing the entire value chain.

Logistics and routing models

Optimize oil and gas supply chains by mapping pipelines, tanks, valves, and fleets for a comprehensive view of operating interdependencies. Managing shipping and logistics using tools like computational knowledge graphs and machine reinforcement learning optimize fleet effectiveness, enhance profitability, and reduce emissions.

Pipeline and tank integrity monitoring

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.

Autonomous predictive maintenance

Analyzes real-time and historical performance data from sensors, visual inspection, and acoustic monitoring to identify equipment and systems that are trending toward failure, enabling proactive mitigating actions.

Downstream

Efficient, cost-effective operation of downstream/refining assets is crucial to the profitable delivery of hydrocarbon products to market. The unplanned failure of pumps, compressors, and other refinery equipment leads directly to lost production revenue, increased capital and operating cost, environmental noncompliance, and increased safety risk for workers. AI-enabled predictive identification of pending failures and prescriptive recommendations mitigates these failures, saving cost and ensuring continuing product outputs.

Process optimization

Using proprietary computational knowledge graph technology integrates feedstocks, chemistry, and equipment to maximize refinery throughput, balance energy loads, ensure product quality, and minimize emissions.

Predictive/prescriptive maintenance of refining assets

Proactively identifies pump, turbine, and compressor anomalies and disruptions, prescribes failure mitigating actions, and autonomously acts to avoid costly downtime.

Supply chain optimization

Forecasts demand for refining and distribution equipment, automates procurement, and balances inventory across global networks of warehouses, refineries, and storage facilities.

Retail operations optimization

Ensures fuel distribution and sales happen efficiently and profitably using automated monitoring to ensure product availability, customer service, and health/safety compliance.

Health, Safety, and Environmental Compliance

Whether upstream, midstream, or downstream, the oil and gas industry has always been inherently risky and environmentally challenging. Many worker hours are lost each year to injury and health issues, making safety a primary focus of the industry. In addition, the continuing imperative to reduce industrial carbon footprint demands proactive steps that identify emissions and actively manage them.

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.

Operational safety monitoring

Using video and sensor fusion detects hazardous conditions such as leaks, spills, flare activity, or gas releases.

Compliance tracking

Leverages the power of computational knowledge graph technology to link safety policies, incidents, and assets to ensure adherence to regulatory standards.

Autonomous alerts and responses

Automatically escalate problems while AI-driven workflows predict and mitigate risks before they become incidents.

Logistics Optimization

Oil and gas operators are continually challenged not only to identify and extract new economically viable sources of hydrocarbons, but also to deliver their products to market efficiently and safely while keeping enormous fleets of capital equipment and transportation assets operating 24/7.

Computational knowledge graphs of assets and supply chains

Provide contextual views of asset lifecycles, part dependencies, and vendor networks for better decision-making.

Predictive spare parts demand planning

Forecasts part failures and automatically aligns spares availability with maintenance schedules, worker skillsets, and tool availability.

End-to-end equipment logistics planning

Optimizes inventory across depots, warehouses, and field locations to minimize downtime and ensure that parts are where they are needed, when they are needed.

Generative AI-driven recommendations

Simulate supply chain scenarios and recommend the most efficient distribution of equipment and spares.

Case Studies

Upstream
A supermajor with high-volume offshore platforms implemented the Avathon Autonomy Platform on critical systems to model asset degradation and process instability. Model deployment required only 1 month per platform, enabling an 80% reduction in predictive alert triaging time and a 4% production improvement, delivering $30M in annual value per platform.
Midstream
A large midstream LNG operator implemented Avathon’s Autonomy Platform to proactively schedule maintenance and reduce work hours by providing an average of 8 days lead time before failure events. The solution delivered a 94% detection rate for high-risk degradation events and reduced alerts from hundreds per week to only one false alert every three months per asset, enabling staff to intervene, minimizing asset deterioration and improving performance. The solution extended the life of four compressors by an additional 8 years for an estimated total savings of $1.5M+ annually.
Downstream
A large international refining firm implemented Avathon Autonomy to predict events on a centrifugal compressor that experienced frequent vibration issues due to power surges, reducing the lifespan of the asset. The implementation resulted in 86% event detection, increasing visibility into asset operation and improving the company’s event lead time goal from 15 minutes to 100 minutes.

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