Manzoor Ahmad Ali
Department of Chemical Engineering, Ryerson University, 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada.
ACS Omega. 2020 Mar 4;5(10):5258-5269. doi: 10.1021/acsomega.9b04319. eCollection 2020 Mar 17.
Polymer flooding is one of the most incipient chemical-based enhanced oil recovery process that utilizes the injection of polymer solutions into oil reservoirs. The presence of a polymer in water increases the viscosity of the injected fluid, which upon injection reduces the water-to-oil mobility ratio and the permeability of the porous media, thereby improving oil recovery. The objective of this work is to investigate strategies that would help increase oil recovery. For that purpose, we have studied the effect of injection pressure and increasing polymer concentration on flooding performance. This work emphasizes on the development of a detailed mathematical model describing fluid saturations, pressure, and polymer concentration during the injection experiments and predicts oil recovery. The mathematical model developed for simulations is a black oil model consisting of a two-phase flow (aqueous and oleic) of polymeric solutions in one-dimensional porous media as a function of time and -coordinate. The mathematical model consisting of heterogeneous, nonlinear, and simultaneous partial differential equations efficiently describes the physical process and consists of various parameters and variables that are involved in our lab-scale process to quantify and analyze them. A dimensionless numerical solution is achieved using the finite difference method. We implement the second-order high-accuracy central and backward finite-divided-difference formula along the -direction that results in the discretization of the partial differential equations into ordinary differential equations with time as an independent variable. The input parameters such as porosity, permeability, saturation, and pore volume obtained from experimental data by polymer flooding are used in the simulation of the developed mathematical model. The model-predicted and commercial reservoir (CMG)-simulated oil production is in good agreement with experimental oil recoveries with a root-mean-square error (RMSE) in the range of 1.5-2.5 at a maximum constant pressure of 3.44 MPa as well as with temporal variation of the injection pressure between 2.41 and 3.44 MPa.
聚合物驱油是最早基于化学方法的强化采油工艺之一，该工艺通过向油藏注入聚合物溶液来提高采收率。聚合物在水中的存在增加了注入流体的粘度，注入后降低了水油流度比和多孔介质的渗透率，从而提高了原油采收率。本研究的目的是探索有助于提高原油采收率的策略。为此，我们研究了注入压力和提高聚合物浓度对驱油性能的影响。这项工作着重于建立一个详细的数学模型，该模型能够描述注入实验过程中的流体饱和度、压力和聚合物浓度，并预测原油采收率。为模拟开发的数学模型是一个黑油模型，它描述了聚合物溶液在一维多孔介质中的两相流（水相和油相）随时间和坐标的变化。该数学模型由非齐次、非线性和联立偏微分方程组成，有效地描述了物理过程，包含了我们实验室规模过程中涉及的各种参数和变量，以便对其进行量化和分析。使用有限差分法获得了无量纲数值解。我们沿(x)方向采用二阶高精度中心差分和后向有限差分公式，将偏微分方程离散为以时间为自变量的常微分方程。通过聚合物驱油实验数据获得的孔隙度、渗透率、饱和度和孔隙体积等输入参数，被用于所开发数学模型的模拟。在最大恒定压力为(3.44)MPa以及注入压力在(2.41)至(3.44)MPa之间随时间变化的情况下，模型预测的和商业油藏模拟软件（CMG）模拟的产油量与实验原油采收率高度吻合，均方根误差（RMSE）在(1.5 - 2.5)范围内。
