Title: Toward investigating the application of reservoir opportunity index in facilitating well placement optimization under geological uncertainty Abstract There have been several attempts to reduce the computational costs associated with well placement optimization under geological uncertainty by selecting a representative subset of realizations or by reducing the number of function evaluations to speed up the optimization...

Title: Introduction to Geological Uncertainty Management in Reservoir Characterization and Optimization Chapter 1: Uncertainty Management in Reservoir Engineering Almost all activities in real life entail different kinds of uncertainty. From daily decisions to complicated problems, such as petroleum reservoir characterization, suffer from uncertainties. Uncertainty can have different roots, including incomplete observation of the system, incomplete modeling of...

Title: Application of fast marching method and quality map to well trajectory optimization with a novel well parametrization Abstract This paper proposes a novel parametrization technique for deviated and horizontal wells and investigates the potential of combining the Fast Marching Method (FMM) with Particle Swarm Optimization (PSO) to facilitate optimization of the trajectory of vertical, deviated, and horizontal wells...

Title: An Insight into the Prediction of Scale Precipitation in Harsh Conditions Using Different Machine Learning Algorithms Abstract Scale precipitation in petroleum equipment is known as an important problem that causes damages in injection and production wells. Scale precipitation causes equipment corrosion and flow restriction and consequently a reduction in oil production. Due to this...

Title: Determination of optimal oil well placement using deep learning under geological uncertainty Abstract Optimal placement of wells is a time-consuming task, which is further made problematic when dealing with geological uncertainties. Although many researchers have attempted to address the intricacies of the problem, they have all taken the single-time optimization of all wells, while...

Title: Fast marching method assisted permeability upscaling using a hybrid deep learning method coupled with particle swarm optimization Abstract Geo-cellar models contain millions of gridblocks and are very time-consuming to simulate. This challenge necessitates upscaling geo-cellular models to obtain fit-for-purpose models for simulation. Simulation-based upscaling methods are computationally demanding, especially if there is a large...

Title: Improved history matching of channelized reservoirs using a novel deep learning-based parametrization method Abstract Most of the geological parametrization techniques used in history matching of sub-surface formations including the deep learning-based methods could not capture the non-linear and non-Gaussian dependencies and were limited to facies realizations. This paper proposes a novel method for parametrization of permeability...

Title: CO2-brine interfacial tension correlation based on the classical orthogonal polynomials: monovalent salts with common anion Abstract Accurate estimation of CO2-brine Interfacial Tension (IFT) is of great importance since it is required to model the residual/capillary trapping of CO2 in geological formations. In this paper, a novel correlation is proposed based on the classical orthogonal polynomials...