According to McKinsey, advanced analytics, if applied properly, can yield returns as high as 30-50 times the investment within a few months of implementation. Moreover, they can positively transform the organizations and fundamental operating models of oil and gas production systems.
Advanced analytics are powered by ML, which uses statistical methods and computing power to spot patterns among hundreds of variables in continual conditions. The patterns are used to build algorithms that analyze the parameters critical to production, quality and efficiency and alert operators for resolution before the issue becomes critical or causes a failure.
Advanced analytics enable industries to improve production, profitability and margins by leveraging data from several underlying systems – employing ML and AI for smooth operations and maintenance. Critical advanced analytics components include seamless integration of various sources and systems to retrieve sensor data (history and RT), maintenance history, equipment design data, fluid properties, thresholds, alarm history and interlock limits.
Analytics can provide users the required experience, expertise and history of the plant and processes to connect, identify, cleanse, model, predict and monitor various critical assets and processes. By sharing insights along with the underlying data sets and calculations for decision making, asset management improves and so does the operational efficiency of the plant.
Industry 4.0 Improves Decision Making
For midstream oil and gas companies, predictive maintenance has many advantages over reactive or planned maintenance, including increased equipment life, optimized equipment operations, less equipment downtime, and less staff time wasted. These solutions can be developed to address the specific requirements of the customer’s framework by using IR4.0 technologies. These technologies gather plant network data and aggregate it through IIoT platforms. The data platforms can be used to create solutions for many use cases, including asset-predictive maintenance. Data platforms can be realized on cloud platforms, open-source and on-premises. AI solutions should run on live data and include historical data, contextual data and expert input.
Wipro has delivered projects for predictive maintenance models using AI/ML for several plants involving hundreds of assets like compressors, pumps, heat exchangers, etc. The data can be analyzed using spreadsheets, a data historian, or reliability tools, but the required effort and time are excessive, and the results are complex and not real-time. An advanced analytics solution enables process, maintenance and inspection engineers to leverage data to predict failures and take the necessary steps to avoid unplanned shutdowns.