In the age of Industry 4.0, Oil & Gas industries (Upstream Production Platforms, Midstream Operations, Refineries, Petrochemicals, Lubes, LNG, etc.) need to rely more than ever on process data and key performance indicators in the production process to stay ahead of the curve, by enabling data-driven decisions at all levels.
The volume of industry data on a real-time basis is enormous. The challenge is combining data from different sources (such as control systems, MES, LIMS, APM, ERP, etc., and multiple geo-separated plants) into a single platform for enterprise-wide data access and visibility. However, getting all data onto a single platform is not the final solution. Data standardization is required to provide context and data modeling to correct the information flow from different sources. Moving the data, the data model and data process applications to the cloud leads to big-data insights that will drive the future of the Oil & Gas industry.
Pulling the Data Together
Data collected from different sources across the value chain feed into a data lake. Next, it is curated by data cleansing, ensuring it is consistent and timely. Data quality also needs to be impeccable from various tools. Finally, the analytical model, which can be equipment- or product-centric, provides context to the data and tags standardization. These processes can run on a single unified data platform on the cloud without limitations on space, scalability and accessibility. After all this, the data is fit for its consumption in various use cases. Bringing all data onto a cloud platform resolves the challenges of having a single repository, data contextualization and standardization at the enterprise level.
Moreover, multiple cloud in-built solutions can help companies make more informed decisions. Various visualization tools (Power BI, Tableau, etc.) can build KPI dashboards for better insights and visibility from a plant asset to enterprise-wide KPIs. Visualizations enable data transparency and minimize dark data pockets. Additionally, there are reductions in time spent searching for data and passing information to the corporate level. AI/ML models built on cloud platforms can predict asset/process failures which will help reduce unplanned downtime.
The cloud provides a single source of data, and application instance consolidation, enabling the complete transformation of data acquisition, historicization and modernization. Digital solutions must address the needs of all stakeholders – operators, process engineers, maintenance engineers, plant site management and the overall enterprise. Some notable digital transformation needs of today include:
- Upgrade existing end-of-life on-premise operational technology systems within the process control network to combat risk and security vulnerabilities
- Future-proof against business disruption
- Real-time management of plant asset data and different production process data
- Real-time data streaming for real-time analytics on the cloud
- Scalable, cost-effective, and highly available digital platforms for continuous monitoring of production operations and performance indicators
Digital Transformation with Cloud Platforms
Digital cloud platforms like AWS, Azure and GCP can be a centralized repository for data from multiple OT systems, ERP, LIMS & APM systems and provide a single operational data source at the enterprise level with unlimited scalability. The data from a cloud platform injected into AI/ML models can provide advanced analytics insights.
The Wipro Energy Consulting team has designed and delivered critical transformations for the Oil & Gas industry using AWS Cloud. These transformations included adopting AWS native architectural capabilities related to manufacturing processes, chemical samples management, asset monitoring for wind turbines, enterprise command centers and asset health care.
Staying with the AWS Cloud example, here are a few solutions built on the platform that leverage data from the underlying systems from different plants across the value chain.
1. Real-time data management platform for refining, petrochemical and renewables manufacturing assets
The migration of data historian infrastructure to AWS improves the scalability of the historian servers globally, making real-time manufacturing process data with associated contextual asset data available to plant supervisory and maintenance staff. It can enable integration with third-party big-data analytics platforms. The AWS migration can reduce operating expenses and achieve zero capex with a strategy of hybrid deployment of a centralized single instance of asset framework in historian, historian server and vision components with the local site-wise deployment of data collectors on AWS.
2. Laboratory information management system (LIMS) for lubricant plants on AWS Cloud using native cloud architecture
In the case of LIMS design and deployment on AWS, global laboratory sites for lubricant plants benefit from better manageability of aggregated historical laboratory datasets over a highly available and fault-resilient platform. Using native cloud architecture can reduce TCO.
3. Asset monitoring for wind turbines
All historian tags for the wind turbines map with correct attributes and elements for a client’s operated and non-operated sites. Asset monitoring helps engineers manage the environment properly and efficiently on a cloud architecture like AWS.
4. Enterprise command center
Companies can achieve best-in-class design standards for high-end visualization and an integrated digital change approach on AWS cloud-based infrastructure. It can develop modular design standards to enable wider scopes within the visualization ecosystem that help define the framework and features list as per business standards, aligned to the client’s digital strategy.
5. Asset healthcare
AI-based predictive analytics and real-time dynamic prescriptive recommendations for various assets such as compressors, pumps, heat exchangers, etc. These solutions sit in a cloud platform and are available across the enterprise, leading to enhanced asset availability, efficiency and reduced reliability OPEX.
The Way Forward for Chief Digital Officers
In working with clients to develop these transformational solutions, several crucial learnings and key observations emerged:
- Digital design decisions should be an outcome not only focused on OpEx and CapEx reduction but also on deployment architecture standards that ease the integration to third-party big-data analytics platforms, reducing network latency and flexibility for the future.
- Work with domain architects and digital leads to address stakeholder apprehensions and speculations.
- The centralized data historians on AWS provide the scalability and ability to provide real-time data access and visualization.
- Ideate and determine the best possible deployment strategies for non-standardized LIMS applications to ensure high availability, resiliency, aggregation of lab historical datasets and automation of lab sample collection.
- Adopting new-age reactive microservices architecture design helps improve the speed to market for critical services used in alternate energy digital platforms.
- Fix the end-of-service life risks and security vulnerabilities with AWS cloud migration and rehosting on upgraded VMs.
The Oil & Gas industry is undergoing great change. Alternate forms of energy will pose new challenges and add more competition. It is more important than ever to leverage enterprise-wide data access. Moving the data, the data model and data process applications to a cloud platform can provide the big-data insights necessary to drive the future of the Oil & Gas industry.