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Development Of Reduced Order Models Using Reservoir Simulation And Physics Informed Machine Learning Techniques, Mark V. Behl Jr
Development Of Reduced Order Models Using Reservoir Simulation And Physics Informed Machine Learning Techniques, Mark V. Behl Jr
LSU Master's Theses
Reservoir simulation is the industry standard for prediction and characterization of processes in the subsurface. However, simulation is computationally expensive and time consuming. This study explores reduced order models (ROMs) as an appropriate alternative. ROMs that use neural networks effectively capture nonlinear dependencies, and only require available operational data as inputs. Neural networks are a black box and difficult to interpret, however. Physics informed neural networks (PINNs) provide a potential solution to these shortcomings, but have not yet been applied extensively in petroleum engineering.
A mature black-oil simulation model from Volve public data release was used to generate training data …
Mechanistic Modeling Of Nanoparticle-Stabilized Supercritical Co2 Foams And Its Implication In Field-Scale Eor Applications, Doris Patricia Ortiz Maestre
Mechanistic Modeling Of Nanoparticle-Stabilized Supercritical Co2 Foams And Its Implication In Field-Scale Eor Applications, Doris Patricia Ortiz Maestre
LSU Master's Theses
Previous experimental studies show that nanoparticle-stabilized supercritical CO2 foams (or, NP CO2 foams) can be applied as an alternative to surfactant foams, in order to reduce CO2 mobility in gas injection enhanced oil recovery (EOR). These nanoparticles, if chosen correctly, can be an effective foam stabilizer attached at the fluid interface in a wide range of physicochemical conditions.
By using NP CO2 foam experiments available in the literature, this study performs two tasks: (i) presenting how a mechanistic foam model can be used to fit experimental data and determine required model parameters, and (ii) investigating the …