A Global-Convergent Newton Optimization Algorithm for the Phase Behavior Calculations with Capillary Pressure Effect for Tight Reservoir Fluids

Hongbin Jing, Jianqiao Liu, Huanquan Pan, Tie Kuang, Z. Yin, Bensheng Li
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Abstract

The thermodynamic behavior of a fluid in a tight reservoir differs from that in the conventional environment. A new phase equilibrium algorithm with capillary pressure is presented and formulated using the laws of thermodynamics. At a given temperature, volume, and moles with capillary pressure, this new algorithm is based on the Newton iteration and line search, which guarantees a global convergence. We obtain the Newton direction by utilizing the modified Cholesky factorization to ensure a descending direction and combine line search to facilitate the iterations in the feasible domain. The initial values of the new algorithm originate from Michelsen's two-sided method. All relevant derivatives are computed analytically and automatically through the Automatically Differentiable Expression Templates Library (ADETL), developed at Stanford University. The new algorithm is based on the Helmholtz free energy, and the corresponding energy surface will not be influenced by the pressure inequality between the liquid and vapor phases. We tested our algorithm on several fluids with different pore radii over a wide range of temperatures and total volumes, and no single calculation breakdown occurred. Meanwhile, the new algorithm can also determine the system phase status at a given temperature and pressure. We compared the results between the given temperature and volume and the given temperature and pressure. There is a dispute in effect of the derivatives of capillary pressure with respect to compositions on the phase equilibrium calculation in literature. We compared the results with and without the derivatives at a given temperature and volume and a given temperature and pressure. These results show that our new algorithm exhibits a good convergent performance and a robust solution even if the pore radius decreases to one nanometer, which indicates the potential of our algorithm for simulating the shale reservoir production process.
考虑毛细压力影响的致密储层流体相行为全局收敛牛顿优化算法
致密储层中流体的热力学行为与常规环境中的流体不同。提出了一种新的毛细管压力相平衡算法,并利用热力学定律推导了该算法。在给定的温度、体积和摩尔、毛细管压力条件下,该算法基于牛顿迭代和直线搜索,保证了算法的全局收敛性。利用改进的Cholesky分解法获得牛顿方向,保证了牛顿方向的下降,并结合直线搜索,方便了可行域内的迭代。新算法的初始值来源于Michelsen的双面法。所有相关的导数都是通过斯坦福大学开发的自动可微分表达式模板库(addl)进行分析和自动计算的。新算法基于亥姆霍兹自由能,相应的能量面不受液、气两相压力不均等的影响。我们对几种不同孔隙半径的流体在很宽的温度和体积范围内测试了我们的算法,没有出现任何计算故障。同时,该算法还可以确定给定温度和压力下系统的相位状态。我们比较了给定温度和体积与给定温度和压强的结果。文献中对于毛细管压力对组分的导数对相平衡计算的影响存在争议。我们比较了在给定的温度和体积,以及给定的温度和压强下,有导数和没有导数的结果。这些结果表明,即使孔隙半径减小到1纳米,新算法也具有良好的收敛性能和鲁棒性,表明了该算法在模拟页岩储层生产过程中的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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