Modelling the reservoir-to-tubing pressure drop imposed by multiple autonomous inflow control devices installed in a single completion joint in a horizontal well

Abstract

Horizontal or highly deviated wells with long completions that are open to flow are used to increase the contact between the well and the hydrocarbon-bearing reservoir (Joshi, 1991). However, these wells can suffer from non-uniform inflow rates along the open completion, which results in early breakthrough of unwanted fluid phases such as gas or water. This is due to: (1) the so-called 'heel-toe' effect (Birchenko et al., 2010; Dilib and Jackson, 2013), caused when a significant frictional pressure drop occurs along the production tubing, leading to a lower tubing pressure and, therefore, a larger reservoir pressure drawdown at the heel of the well as compared to the toe, and (2) permeability variations along the well caused by reservoir heterogeneity (Dilib et al., 2012; Dilib and Jackson, 2013). The exact pattern of this permeability heterogeneity around any given well is generally uncertain.

Application of mathematical and machine learning models to predict differential pressure of autonomous downhole inflow control devices

“…After breakthrough, AICDs significantly reduce unwanted fluid production (e. g., water and gas), which increases oil production and also reduces costs of separation and re-injection of unwanted fluids produced along with oil to the surface (Aakre et al, 2013;Andreas et al, 2019;Lei et al, 2020). Cochua et al (2018) and Gurses et al (2019) introduced a cyclone type AICD which is similar to Equiflow AICD and performs based on the viscosity and density of flowing fluids.…”