Artículos de revistas
Impacts Of Polymer Properties On Field Indicators Of Reservoir Development Projects
Registro en:
Journal Of Petroleum Science And Engineering. Elsevier Science Bv, v. 147, p. 346 - 355, 2016.
0920-4105
1873-4715
WOS:000388630900032
10.1016/j.petrol.2016.05.020
Autor
Lamas de Oliveira
Luis Fernando; Schiozer
Denis Jose; Delshad
Mojdeh
Institución
Resumen
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Polymer flooding is a mature EOR technique implemented in many successful field projects. Understanding its mechanisms is very important to managing field operations. Generally, more oil is recovered when polymer solution is injected for a longer time. For a real project, optimization of field operations usually requires consideration of economic indicators, such as net present value (NPV) as objective function so polymer and other costs are included in the decision 3process. Several commercial and in-house simulators have the capability to model polymer flooding for the optimization purposes. In this paper, we present the numerical modeling of polymer retention and the reversibility, inaccessible pore volume, shape of the curve of viscosity vs. polymer concentration, salinity, permeability reduction, non Newtonian behavior and degradation, etc. We first tested these models in simple benchmark cases, and then applied them to two synthetic field cases. The goal is to understand the impact of each polymer property on field performance indicators, especially NPV. The synthetic fields represent target Brazil oil fields which have high average permeability with heavy oil (similar to 14 degrees API). Retention of polymer, salinity, polymer rheology and degradation can decrease NPV by up to 25%. Permeability reduction, adsorption reversibility, inaccessible pore volume and selection of a correlation curve for the function of viscosity vs. concentration can influence NPV by less than 3%. Identification of the crucial polymer properties/simulation parameters helps with the decision for key laboratory tests and reducing uncertainties associated with each. This information also helps on the selection of simulation models and key sensitivity parameters where unimportant parameters can be neglected in simulation studies. (C) 2016 Elsevier B.V. All rights reserved. 147 346 355 CAPES Foundation [BEX 10004/14-9] University of Campinas - UNICAMP Center of Petroleum Studies - CEPETRO Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)