Abstract
We study and compare five different combinations of finite element spaces for approximating the coupled flow and solid deformation system, so-called Biot’s equations. The permeability and porosity fields are heterogeneous and depend on solid displacement and fluid pressure. We provide detailed comparisons among the continuous Galerkin, discontinuous Galerkin, enriched Galerkin, and two types of mixed finite element methods. Several advantages and disadvantages for each of the above techniques are investigated by comparing local mass conservation properties, the accuracy of the flux approximation, number of degrees of freedom (DOF), and wall and CPU times. Three-field formulation methods with fluid velocity as an additional primary variable generally require a larger number of DOF, longer wall and CPU times, and a greater number of iterations in the linear solver in order to converge. The two-field formulation, a combination of continuous and enriched Galerkin function space, requires the fewest DOF among the methods that conserve local mass. Moreover, our results illustrate that three out of the five methods conserve local mass and produce similar flux approximations when conductivity alteration is included. These comparisons of the key performance indicators of different combinations of finite element methods can be utilized to choose the preferred method based on the required accuracy and the available computational resources.
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26 April 2021
A Correction to this paper has been published: https://doi.org/10.1007/s11004-021-09942-0
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Acknowledgements
This research has received financial support from the Danish Hydrocarbon Research and Technology Centre under the Advanced Water Flooding program. The computational results in this work were produced by the multiphenics library (Ballarin and Rozza 2019), which is an extension of FEniCS (Alnaes et al. 2015) for multiphysics problems. We acknowledge the developers of and contributors to these libraries. SL is supported by the National Science Foundation under Grant No. NSF DMS-1913016. The authors would like to thank Dr. Solomon Seyum for constructive criticism of the manuscript.
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Appendices
Appendices
In these appendices, we describe the fully discretized formulation of each method listed in Table 1.
Continuous Galerkin (CG) Method
We begin by defining the finite element space for the continuous Galerkin (CG) method for a vector-valued function
where k indicates the order of polynomials, \({\mathbb {C}}^0({\varOmega }{; {\mathbb {R}}^d})\) denotes the space of vector-valued piecewise continuous polynomials, and \({\mathbb {Q}}_{k}(T{; {\mathbb {R}}^d})\) is the space of polynomials of degree at most k over each element T. For our problem, the vector-valued displacement field is approximated using CG space. Next, the CG space for scalar-valued functions is defined as
for the pressure solution.
Thus, we seek the approximated displacement solution (\(\varvec{u_h}\)) by discretizing the linear momentum balance Eq. (6) employing the above CG finite element spaces as done in Choo and Lee (2018), Kadeethum et al. (2019a) and Vik et al. (2018). The fully discretized linear momentum balance Eq. (6) can be defined using the following forms
at each time step \(t^n\), where
and
Here, \(\nabla ^{s}\) is a symmetric gradient operator. We can split
by using
For the approximated pressure solution (\(p_h\)), we solve the mass balance Eq. (10) combined with Eqs. (11) and (25) as follows
for each time step \(t^n\), where
Here, again, we can split
with
We note that the symmetric internal penalty approach is employed to weakly impose the Dirichlet boundary condition (Bazilevs and Hughes 2007; Nitsche 1971; Hansbo 2005). The interior penalty parameter \(\beta \) and \(h_e\) characteristic length are calculated as
The system of equations is nonlinear (the nonlinear variable being reported after a semicolon for the sake of clarity). Thus, Newton’s method is employed in this work. The Jacobian matrix that arises from Eqs. (39) and (45) is
where
and the residual vector is defined as
Here, \(\delta \varvec{u}_{h}\) and \(\delta p_{h}\) are Newton increments of the \(\varvec{u}_h\) and \(p_h\), respectively. The block structure for the CG method arises as follows
Discontinuous Galerkin (DG) Method
For the discontinuous Galerkin (DG) method presented in Table 1, we approximate the \(\varvec{u}_h\) using the CG finite element space, Eqs. (37) and (39), but we utilize the DG finite element space to approximate the \(p_h\). The DG finite element space with the polynomial order k is given as
where \(L^{2}({\varOmega })\) is the space of the square integrable functions. This nonconforming finite element space allows us to consider discontinuous coefficients and preserves the local mass conservation. We then discretize Eqs. (10), (11), and (25) as
for each time step \(t^n\), where
The \({\mathscr {L}}_p\left( \psi _p; \varvec{u}_{h}^{n} \right) \) term is similar to Eq. (47) as presented in the previous section. Next, we split
We utilize the same definition for \(c\left( \varvec{u}_{h}^{n}, \psi _p \right) \) from the CG method, Eq. (49), but we define \(e\left( p_{h}^{n}, \psi _p{; \varvec{u}_{h}^{n}} \right) \) as
Compared to \({{\mathscr {A}}_p\left( (\varvec{u}_{h}^{n}, p_{h}^{n}), \psi _p; \varvec{u}_{h}^{n} \right) }\) from the CG method, we note that there are additional surface integral terms \(\int _{e}\cdot \, d S\) on the interior faces (\(e \in {\mathscr {E}}_h^{I}\)) in \({{\mathscr {B}}_p\left( (\varvec{u}_{h}^{n}, p_{h}^{n}), \psi _p; \varvec{u}_{h}^{n} \right) }\). This results in more demanding computational time when the matrix is assembled.
Again, we note that this system of equations are nonlinear forms in the displacement \(\varvec{u}_{h}^{n}\). We form the Jacobian matrix that arises from Eqs. (39) and (57) as follows
where
and the residual vector is defined as
at each time step \(t^n\). The block structure for the DG method arises as follows
Enriched Galerkin (EG) Method
For the EG method presented in Table 1, we define the EG finite element space with the polynomial order k as
where the CG finite element space is enriched by \({\mathscr {P}}_{h}^{{\mathrm {DG}}_{0}}\left( {\mathscr {T}}_{h}\right) \), the piecewise constant functions. As in the previous section, we approximate the displacement (\(\varvec{u}_h\)) using the CG finite element space Eq. (39), but here the \(p_h\) is approximated by the EG finite element space Eq. (65). Thus, we seek \(p_h\) by solving
We note that the EG method employs the same bilinear \({\mathscr {B}}_p\left( (\varvec{u}_{h}^{n}, p_{h}^{n}), \psi _p; \varvec{u}_{h}^{n} \right) \) and linear \({\mathscr {L}}_p\left( \psi _p; \varvec{u}_{h}^{n} \right) \) forms as the DG method. Since the EG space Eq. (65) only takes piecewise constants (DG\(_0\)) to enforce the discontinuity, several terms involving the derivatives in \({\mathscr {B}}_p\left( (\varvec{u}_{h}^{n}, p_{h}^{n}), \psi _p; \varvec{u}_{h}^{n} \right) \) and \({\mathscr {L}}_p\left( \psi _p; \varvec{u}_{h}^{n} \right) \) become zero. Thus, not only does the EG method have fewer degrees of freedom than DG, but it requires less computational time for assembling the matrix.
We again note that this system of equations are nonlinear forms in the displacement \(\varvec{u}_{h}^{n}\). We employ the same Jacobian matrix, Eqs. (61) and (62), and residual vector, Eq. (63), built for the DG method. The block structure used for the DG method Eq. (64), however, is decomposed into
to satisfy Eq. (66).
Raviart–Thomas Mixed Finite Element (MFE-RT) Method
For the MFE-RT method presented in Table 1, we also approximate the \(\varvec{u}_h\) using the CG finite element space [Eqs. (37) and (39)]. The mass balance Eq. (10) for the MFE-RT method is discretized by using the piecewise constant (i.e., DG finite element space Eq. (56) with \(k=0\)).
Thus, we seek \(p_h\) by solving
for each time step \(t^n\), where
and
Here, we can split Eq. (69) as
where
and
For the velocity approximation (\(\varvec{v}_h\)), we use the RT finite element space, which is defined as
\({\mathbf {q}}(x) =\left( \begin{array}{c}{q_{1}(x)} \\ {q_{2}(x)} \\ {\vdots } \\ {q_{m}(x)} \end{array}\right) +q_{0}(x) \left( \begin{array}{c}{x_{1}} \\ {x_{2}} \\ {\vdots } \\ {x_{m}} \end{array}\right) \quad \forall x:=\left( x_{1}, x_{2}, \ldots , x_{m}\right) ^{{\mathrm {T}}} \in {\varOmega }\), \({\mathbf {q}}(x) \subset {\mathbb {Q}}_{k}(T)^m\), and \(q_{0}(x) \subset {\mathbb {Q}}_{k}(T)\). We then solve the following Darcy velocity Eq. (11) to obtain \(\varvec{v}_{h}^{n}\) by
where
and
Here, we can split
where
and
Similar to the CG, DG, and EG methods, we note that this system of equations are nonlinear forms for the displacement \(\varvec{u}_{h}^{n}\). We then form the Jacobian matrix that arises from Eqs. (39), (68), and (76) as follows
where \(\delta \varvec{v}_{h}\) is the Newton increments of the \(\varvec{v}_h\) and
and the residual vector is defined as
The block structure of the MFE-RT method arises as
Continuous Mixed Finite Element (MFE-P2) Method
For the MFE-P2 method presented in Table 1, we utilize a similar system of equations as employed by the MFE-RT method. The displacement \(\varvec{u}_h\) is approximated by the CG finite element space, which is the same as the CG method Eq. (39). The \(p_h\) in this method is approximated by using the CG finite element space Eq. (38) with \(k=1\)
Here, the approximated velocity \(\varvec{v}_h\) is also discretized by the vector-valued CG finite element space Eq. (37) but with higher-order \(k=2\). Thus, we obtain the following for \(\varvec{v}_h\)
As a result, the block structure of the MFE-RT method Eq. (85) is modified to
Remark 2
For both two- and three-field formulations, the Neumann boundary condition is naturally applied on the boundary faces, \(e \in {\mathscr {E}}_{h}^{N}\). For the two-field formulation, the Dirichlet boundary condition is weakly enforced on \(e \in {\mathscr {E}}_{h}^{D}\), for all CG, EG, and DG methods. On the other hand, the three-field formulation strongly applies the Dirichlet boundary conditions.
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Kadeethum, T., Lee, S. & Nick, H.M. Finite Element Solvers for Biot’s Poroelasticity Equations in Porous Media. Math Geosci 52, 977–1015 (2020). https://doi.org/10.1007/s11004-020-09893-y
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DOI: https://doi.org/10.1007/s11004-020-09893-y