The oil, gas, and petrochemical industries are characterized by unstable market conditions with increased regulatory requirements, significantly higher pressures on margins and profitability with commoditized pricing. Currently, oil prices have fallen drastically worldwide on signs that oil storage is filling rapidly, raising concerns that production cuts will not come fast enough to catch the collapse in demand from the coronavirus pandemic. This point of view is an enabler to the acme, how by leveraging the digital platforms, refinery operations, and asset performance monitoring, optimization, Technical Services, advanced analytics for asset and process failure predictions, and logistics planning can be executed remotely and centrally how Digital Refinery Operations Hubs will contribute signi铿乧antly to save cost and improve the productivity, ef铿乧iency, asset life, process safety, and hence the margins.

Current scenario and gap

Refinery unit operations are highly instrumented and are controlled in real-time on a millisecond basis through Distributed Control Systems (DCS) and Programmable Logic Controllers (PLC) and are managed through Manufacturing Execution Systems (MES) and ERP applications that are deployed in each site. Unintegrated IT and OT environments across sites have made work processes disjointed. For example, planning and scheduling activities are done in an IT environment, but the operations team in the OT environment executes those plans and/or schedules. Frequently, changes in plant equipment performance, operation delays, and equipment or process unavailability are unknown to/across sites and headquarters functions in IT environments.

However, most process and asset performance monitoring, failure predictions, optimization, and logistics planning can be executed remotely and centrally across multiple sites more cost-effectively by leveraging digital technologies, data management platforms, advanced analytics, and setting up central digital refinery operations hubs. Doing so breaks silos, instills a culture of collaboration, and brings the best IT practices into the OT domain without compromising any of the characteristics of the OT environment.

Digital Refinery Operations Hubs

To meet the present needs of the oil and gas industry, the bridge between the needs of the business and various technology that supports multiple site operations is essential. Central refinery operations hubs connect across various sites and headquarters systems like ERP, MES, RTDM, and Engineering and Control systems.

This will enable the below possibilities:

• Data Platform: Capture data from multiple sites and headquarters systems like ERP, MES, RTDM, and engineering and control systems and handles structured, semi-structured, and unstructured data.
• Performance Monitoring:聽 Process and asset real-time performance monitoring and analysis, refinery wide performance monitoring (KPI and KOI), refinery mass and energy accounting, emissions monitoring, inventory and movement monitoring, gap analysis - plan vs. schedule vs. actual, and reporting performance gaps across multiple sites.
• Advanced Analytics and Optimization: Asset health and plant efficiency 鈥� Advanced analytics allows operations, maintenance, quality, and engineering service decision-makers to predict when an asset and plant needs maintenance. Data is gathered from installed field devices, control systems, IoT, data historians, and EAM systems and then passed through analytic models to predict asset and process failures through end-of-run date, identify the root causes of failures, and prevent unplanned downtimes.
• Central Reporting: Automates input data from multiple systems for planning cycle and data validation, performing frequent retro analysis, and reporting performance gaps.
• Collaboration: Organizational alignment and operational excellence through orchestrated activities across the stakeholders with improved collaboration will lead to increased production efficiency. Increased organizational resilience will lead to proactive management and reduces the severity of impact due to process upset or critical asset failure and subsequent effects on other units, sites, or business functions.
• Digital Twin: A digital replica of an operational plant. The digital twin runs in parallel with the existing system and is a key factor for enabling and improving preparation, planning, prediction, and performance. The concept is based on the following interrelated, dependent components:

1. The Operational Twin 鈥� The current asset, with existing (often integrated) software layers ranging from sensor to PLC, DCS, SCADA, MES and ERP.
2. The Physical Twin 鈥� Virtual model, based on engineering data, such as 3D drawings.
3. Models and Methods 鈥� Based on original engineering data and actual operational data, they can check the business case (engineered versus actual) and can deduce improvements.

Key Benefits

• Resource optimization across sites
• Reduce unplanned downtime and performance gaps
• Improved productivity, availability, efficiency, and margin
• Optimized levels of asset performance, risk, and expenditure
• Improved levels of compliance and safety
• Better decision making across the organization
• Pro-active and timely reporting of critical issues
• Clear direction and alignment through concise asset management policies, strategies, objectives, and plans
• Minimized human error

Potential Cost Savings

• Operational cost savings up to approximately $2M/year per site based on present unplanned downtime and hours spent on performance monitoring
• Approximately $1M/year per site based on present hours spent on pre-planning activities, performance gaps analysis, and penalties
• Reduction in incidents by 30%鈥�40% due to seamless integration and聽 data transfer
• Improvement in decision making by 30%鈥�40% by the availability of the right data at the right time

Ramesh Srinivasan

Managing Consultant, Energy - Domain and Consulting, 91快活林 Limited.

Ramesh brings 17 years鈥� experience of Oil & Gas Downstream with a core strength in MES applications implementation and integration for refineries, petrochemicals, LNG and lubes plants, and extensive experience in Integrated Manufacturing Operations Management (iMOM) and digital transformation consulting and implementation. He has led the successful delivery of several MES projects in the areas of Planning & Scheduling, Mass Balance and Energy Accounting, Operations Management, Performance Management (KPIs, KOIs), and Real Time Data Management Systems. Ramesh also co-authored 鈥淢ESA Process Industries Guidebook: Adopting Integrated Manufacturing Operations Management (iMOM) in Oil and Gas & Petrochemical Industries, Leveraging Digital Technologies and Industry 4.0 Framework鈥�