Tech-enabled asset management holds key for oil & gas upstream industry, says GlobalData
Overview of Report:
- Theme Exposure: Presents the top themes impacting the sector over the last three years compared to other sectors.
- Innovation Map: key real-world innovation use cases of emerging technologies implemented by enterprises and startups in the sector.
- Innovation Insights: innovation examples by each value chain segment of the sector to present key trends.
- Vendor Map: represents a sample list of vendors in each use case highlighted in the report.
Regardless of the asset type, the need of the hour is to implement emerging technologies to optimize oil recovery and maximize output, says GlobalData, a leading data and analytics company.
Abhishek Paul Choudhury, Disruptive Tech Analyst at GlobalData, comments: “Oil & gas companies are increasingly adopting intelligent automation and other digital enablers to synthesize large amounts of data and derive useful insights to ease complex field activities that have defined the upstream value chain. IoT technologies coupled with AI algorithms are in action to screen and discover optimal acreage options, improve subsurface modeling, and enhance drilling performance.”
Convergence of emerging technologies for predictive asset maintenance
US-based energy company KBC co-launched an AI-powered predictive maintenance system with domestic software company OLI that integrates KBC’s Petro-SIM simulation abilities with OLI Alliance Engine. The combined software solution creates a digital twin, which integrates the IoT and AI of entire assets across the system to help with real-time predictions on corrosion, scaling, and fouling for upstream oil & gas players.
IoT-enabled remote oilfield monitoring
American oilfield equipment supplier ‘Sensorfield’ developed IoT-based remote monitoring solutions to provide real-time, round-the-clock operational data of the oil wells. The solutions were developed to withstand the harsh weather conditions and leverage tech advancements to provide real-time data and alarms for tank levels, pressure and flow rates, compressor health, and location security.
AI-augmented production optimization
Equinor developed a machine learning model to analyze mud-gas data to predict the gas to oil ratio of wells as they are drilled. It is written in python and can be embedded into existing commercial petrophysics software. As it happens in real-time, it can act as an alert system when drillers are tapping into uneconomic pay zones.
Mr Paul concludes: “As global oil & gas operators look to 2022 budgets, they must balance investor expectations to grow volumes and revenues. This can only be mapped with judicious upstream technological adoption that can not only keep downtime at bay but also help explore the function’s true potential to improve yield sustainably while avoiding hazards.”
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