A constrained machine learning surrogate model to predict the distribution of water-in-oil emulsions in electrostatic fields.

Accurately describing the evolution of water droplet size distribution in crude oil is fundamental for evaluating the water separation efficiency in dehydration systems. Enhancing the separation of an aqueous phase dispersed in a dielectric oil phase, which has a significantly lower dielectric constant than the dispersed phase, can be achieved by increasing the water droplet size through the application of an electrostatic field in the pipeline. Mathematical models, while being accurate, are computationally expensive.

Modelling droplet size distribution in inline electrostatic coalescers for improved crude oil processing.

Water-in-oil emulsions pose significant challenges in the petroleum and chemical industrial processes, necessitating the coalescence enhancement of dispersed water droplets in emulsified oils. This study develops a mathematical model to predict the evolution of water droplet size distribution in inline electrostatic coalescers (IEC) as a promising means to improve the water separation efficiency of current oil processing systems. The proposed model utilizes the population balance modelling approach to effectively simulate the dynamic and complex processes of coalescence and breakage of droplets in crude oil which directly influence the separation efficiency of the process.

Gaussian process regression to determine water content of methane: Application to methane transport modeling.

The uncontrolled release of methane from natural gas wells may pose risks to shallow groundwater resources. Numerical modeling of methane migration from deep hydrocarbon formations towards shallow systems requires knowledge of phase behavior of the water-methane system, usually calculated by classic thermodynamic approaches. This study presents a Gaussian process regression (GPR) model to estimate water content of methane gas using pressure and temperature as input parameters.

Predicting methane solubility in water and seawater by machine learning algorithms: Application to methane transport modeling.

The upward migration of methane from natural gas wells associated with fracking operations may lead to contamination of groundwater resources and surface leakage. Numerical simulations of methane transport in the subsurface environment require knowledge of methane solubility in the aqueous phase. This study employs machine learning (ML) algorithms to predict methane solubility in aquatic systems for temperatures ranging from 273.

Modeling fate and transport of hydraulic fracturing fluid in the presence of abandoned wells.

Hydraulic fracturing in shale/tight gas reservoirs creates fracture network systems that can intersect pre-existing subsurface flow pathways, e.g. fractures, faults or abandoned wells.