Researchers have developed a machine learning-based workflow to optimize gas lift operations in unconventional oil and gas fields. This system integrates a machine learning model for predicting gas lift performance curves with a Bayesian optimization framework to determine optimal injection rates within facility capacity limits. The workflow has been successfully piloted and deployed across over 200 wells in the Bakken formation, demonstrating an average production uplift of more than 5%. This approach offers an economical solution for fields lacking downhole gauge data or feasible multi-rate testing. AI
IMPACT This workflow demonstrates a practical application of machine learning for optimizing resource extraction, potentially improving efficiency in the energy sector.
RANK_REASON This is a research paper detailing a new machine learning workflow for a specific industrial application. [lever_c_demoted from research: ic=1 ai=0.7]
- arXiv
- Bakken
- Bayesian Optimization Framework
- Gas lift
- Gas Lift Performance Curve
- machine learning
- plunger-assisted gas lift
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