Figure 11 - uploaded by Kwame Sarkodie
Content may be subject to copyright.
Source publication
Production and NPV forecast from oil fields are affected by different forms of uncertainties; geological (static and dynamic) and economical. This study investigates the impacts of these uncertainties to quantify them and to prove the fact that intelligent well (IW) technology under an appropriate control strategy reduces the impacts of these uncer...
Similar publications
In this paper, we develop a stochastic Asymptotic-Preserving (sAP) scheme for the kinetic chemotaxis system with random inputs, which will converge to the modified Keller-Segel model with random inputs in the diffusive regime. Based on the generalized Polynomial Chaos (gPC) approach, we design a high order stochastic Galerkin method using implicit-...
Intrusive Uncertainty Quantification methods such as stochastic Galerkin are gaining popularity, whereas the classical stochastic Galerkin approach is not ensured to preserve hyperbolicity of the underlying hyperbolic system. We present a modification of this method that uses a slope limiter to retain admissible solutions of the system, while provi...
Vulnerability and robustness are major concerns for future power grids. Malicious attacks and extreme weather conditions have the potential to trigger multiple components outages, cascading failures and large blackouts. Robust contingency identification procedures are necessary to improve power grids resilience and identify critical scenarios. This...
Lower-bounds on uncertainties in oceanic data and a model are calculated for the 20-year time means and their temporal evolution for oceanic temperature, salinity, and sea surface height, during the data-dense interval 1994–2013. The essential step of separating stochastic from systematic or deterministic elements of the fields is explored by suppr...
Keywords: Uncertainty Quantification, Effective Elastic Modulus, Midpoint Approximation, Random Field Theory, Stochastic Finite Element Method
