The selection of optimal artificial lift methods using machine learning remains challenging due to complex interactions among reservoir characteristics, fluid properties, and operational constraints. Conventional approaches rely on engineering expertise and static screening criteria, often insufficient to capture multifactorial dependencies. This study presents a framework for classifying the most suitable lift method from four common techniques: ESP, Gas Lift, Rod Pumps, and PCP. A dataset of 990 wells with twelve physically meaningful parameters was compiled, including depth, temperature, GOR, API gravity, reservoir pressure, water cut, production rate, viscosity, sand production, deviatio... More >

Selecting the most suitable enhanced oil recovery (EOR) technique remains challenging due to severe class imbalance in historical datasets and the limitations of traditional screening criteria. To address data imbalance while preserving domain knowledge, this study proposes a novel machine learning framework that incorporates domain-informed synthetic data generation strictly constrained by established EOR screening criteria. An initial dataset of 583 documented EOR projects was compiled from field reports and public databases. After rigorous cleaning, 575 valid samples were retained and subsequently augmented to 760 balanced instances (class sizes ranging from 60–110 samples per class). T... More >