Asset Margin Optimizer

In the upstream material assets sector, businesses face a range of operational and maintenance challenges. Key issues include commodity price volatility, which affects revenue stability and requires adaptive risk strategies. Operational inefficiencies and asset aging contribute to increased costs and downtime, necessitating ongoing efforts in process optimization and equipment upkeep. Furthermore, market competition demands continual innovation to retain market share, while regulatory compliance imposes additional costs to meet stringent safety and environmental standards.

Additionally, businesses must navigate technological integration complexity due to varied tech stacks like IoT and AI, which can hinder seamless operations. Resource allocation optimization becomes essential for balancing production with cost constraints. Moreover, geopolitical and geographical factors, such as political instability and remote site locations, introduce further complications, increasing both risk and operational unpredictability.

Solution Modular Platform: Integrated Field Work Program AMO

The Asset Margin Optimizer offers a robust solution for maximizing both margin and value extraction across key assets, including reservoirs, wells, pipelines, and surface facilities. By leveraging this application, operators gain the ability to make informed, data-driven decisions. The tool integrates production models, engineering protocols, and business workflows, enabling users to evaluate scenarios and optimize processes efficiently. This comprehensive approach ensures that every operational decision supports both profitability and long-term sustainability, driving greater resilience and competitive advantage.

Key Advanced Features

Optimization Engine

To find the best solution of business objects operating mode, enhance efficiency, and maximize the performance of critical resources

Different approaches

• Data-based – based on statistical data
• Model-based – based on integrated model
• Combined –hybrid approach

Model Catalogue

Ensures standardized data representation, facilitates effective decision-making, and enhances operational reliability across the entire asset lifecycle

Consistency Control

Ensures data integrity, compliance with industry standards, and mitigates operational risks, leading to improved overall performance

AI Engine

Analysis algorithms were built on the base of machine learning which makes it easier to react to specific requirement of a customer

Workflow Management

Pre-defined business and engineering workflows save time and lead to risk mitigation and overall performance

Vendor Neutral

Provides flexibility, interoperability, and cost-effectiveness, enabling seamless integration

Enriched with AI and ML

The integration of AI and ML in operational optimization enhances decision-making and margin maximization. A specialized ML algorithm processes a matrix of multi-sourced operational data, focusing on improving profitability. AI modeling and processing leverage statistical data within a workflow library to support simulation, production forecasting, and planning for maintenance and reconstruction. Historical data calculations incorporate parameters such as time horizon, economic factors, physical limits, and production constraints, ensuring precise optimization. This approach is selectable for various optimization methods, offering flexibility with statistical, AI-based, principal model-based, or hybrid modeling options.

Proven Measurable Benefits

Increased Profitability

Profitability maximization by identification the most efficient operating regime of an asset

Value added time

Quick and rational decisions on unscheduled events and other circumstances