Baghewala Field in Rajasthan produces heavy crude oil (17–19° API) from the Jodhpur Sandstone reservoir. The reservoir is characterized by High crude viscosity, High asphaltene content, Low reservoir pressure, Low reservoir temperature (46–48°C) and Poor oil mobility under primary recovery. Consequently, artificial lift and thermal enhanced oil recovery are critical for sustained production. At present, CSS cycle design and SRP operation are optimized separately using historical experience. As r
Current operations face the following challenges: • CSS parameters (steam volume, injection pressure, soak time and production cut-off) are largely based on historical practices.
Baghewala Field in Rajasthan produces heavy crude oil (17–19° API) from the Jodhpur Sandstone reservoir Consequently, artificial lift and thermal enhanced oil recovery are critical for sustained production At present, CSS cycle design and SRP operation are optimized separately using historical experience
Current operations face the following challenges: • CSS parameters (steam volume, injection pressure, soak time and production cut-off) are largely based on historical practices. • SRP operating parameters (stroke length, SPM and VFD settings) are adjusted manually and reactively. • Heavy crude causes rod floating, effect loading, frequent pump unsetting, rod failures and increased maintenance. • Reservoir behaviour, wellbore conditions and SRP performance are not optimized together. • Lack of predictive analytics results in higher Steam-Oil Ratio (SOR), increased energy consumption and reduced production efficiency. • Expected Outcome / Solution Develop an AI-enabled Well-to-Surface Digital Twin that integrates reservoir, wellbore and surface production systems to provide real-time monito