TY  - JOUR
Y1  - 2020///
A1  - Ahmed, Q.A.
A1  - Nimir, H.B.
A1  - Ayoub, M.A.
A1  - Mohyaldinn, M.E.
JF  - Journal of Petroleum Exploration and Production Technology
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073778316&doi=10.1007%2fs13202-019-00771-w&partnerID=40&md5=9952b9cbb4b2294c2bb9de0635b639a9
VL  - 10
N2  - Well intervention performed on oil or gas well often involves the injection of different stimulating fluids or chemical solutions that aims to increase the production rate. The main objective of this paper is to identify the effect of uncertainty in different variables and parameters used to quantify well productivity and injectivity. Monte Carlo simulation technique is used to develop probabilistic models for radial Darcyâ??s inflow on the one hand and near wellbore water-based chemical injection on the other hand. The probabilistic model is based on assigning probability density function for all variables and parameters used in the governing formulas. Variance-based sensitivity analysis (VBSA) was performed to quantify the contribution and the correlation between different modelâ??s inputs and outputs. Results indicate that some rough assumptions for about 60 of injectivity modelâ??s parameters and factors, i.e., value with considerable error/uncertainty, can still result in output with small standard deviation in comparison with other parameters. In Darcyâ??s law, the uncertainty in reservoir pressure value affects the calculated flow rate two times higher than the effect of the formation of permeability or produced fluid viscosity. At low drawdown condition, about 50 of Darcyâ??s flow variance is caused by the uncertainty in reservoir pressure input value. Throughout VBSA, it is also found that data accuracy of variables and parameters used in the injectivity model is not of importance as for formation permeability, injected fluid viscosity, pressure, and temperature of the injected fluid. Application of this methodology will focus on the cost of information needed by the decision makers and will save a lot of efforts and resources needed to apply confirmation tests or to validate different data sets. © 2019, The Author(s).
IS  - 2
ID  - scholars13527
KW  - Boreholes; Chemical analysis; Decision making; Flow of fluids; Injection (oil wells); Intelligent systems; Monte Carlo methods; Natural gas well production; Oil field development; Oil wells; Petroleum reservoir engineering; Probability density function; Productivity; Scattering parameters; Viscosity; Water levels
KW  -  Formation permeability; Injectivity; Monte carlo simulation technique; Probabilistic modeling; Probabilistic models; Value of information; Variance-based sensitivity analysis; Well productivity
KW  -  Sensitivity analysis
PB  - Springer
SN  - 21900558
EP  - 738
AV  - none
N1  - cited By 5
SP  - 729
TI  - Application of variance-based sensitivity analysis in modeling oil well productivity and injectivity
ER  -