Fully Coupled Multiblock Wells in Oil Simulation

1985 ◽  
Vol 25 (04) ◽  
pp. 535-542 ◽  
Author(s):  
A. Behie ◽  
D. Collins ◽  
P.A. Forsyth ◽  
P.H. Sammon

Abstract A fully coupled treatment of oil wells that are completed in more than one zone results in a bordered matrix. This paper develops solution algorithms that incorporate paper develops solution algorithms that incorporate existing direct and iterative (incomplete LU) solutions in a straightforward manner. Timings in scalar and vector modes on the Cray for a typical reservoir simulation problem are presented. problem are presented. Introduction Numerical simulation of oil reservoirs requires the solution of coupled sets of highly nonlinear partial differential equations. These equations represent the conservation of oil, gas, water, and energy. It usually is necessary to solve from 3 to 10 coupled equations per finite-difference cell. The equations usually are discretized by use of a nearest-neighbor coupling in space and a fully implicit timestep scheme. The resulting set of nonlinear algebraic equations then is solved by Newtonian iteration., Clearly, simulation of large systems requires effective solution of the Jacobian matrix. Many practical reservoir simulation problems involve multiblock wells or fractures. These situations arise when a well is completed in several layers, and consequently the wellbore penetrates several finite-difference cells. Each conservation equation in a cell penetrated by a well will have a source term of the form .....................................(1) where qjt is the mass influx of component k (resulting from the well), Xk is the mobility of component k, 1 pi is the pressure in cell i, and pi, is the unknown wellbore pressure in well j. pressure in well j. To specify the wellbore pressure, pi, an additional equation is required. This extra equation as generally a constraint op the total flow into the well - This constraint is of the form .....................................(2) where qJt. is the total specified fluid flow into well j, Nc, is the total number of components, and is the set of cell numbers penetrated by well j. Because several cells are connected to the same well, there is now an extra degree of coupling between these cells through the well-bore pressure. This coupling generally will not be consistent with the coupling produced by the usual finite-difference molecule. If the well pressures, pjw, are treated explicitly, or are lagged one iteration, convergence difficulties or stability limitations often result. 7 Fully coupled treatment of multiblock wells gives rise to a bordered matrix. We develop various methods to solve these systems. These methods are specifically designed for the block-banded systems arising from fully implicit thermal problems, although similar methods can be used for single-component systems The iterative methods are extensions of the incomplete factorization techniques (ILU), and a direct method is presented for comparison. Existing solution routines can be modified easily to solve the bordered system. Solution of the Bordered Matrix The standard approach to solving fully implicit, fully coupled multiblock wells (or fractures) is to order the unknowns so that those connected with flow in the reservoir (cell pressures, saturations, etc.) appear first in the solution vector. The unknowns connected with the well (well pressures) are placed last in the solution vector. This produces a bordered Jacobian matrix (see Fig. 1). The upper left portion of the matrix has the usual incidence matrix for the Jacobian of nearest-neighbor finite-difference discretization. The incidence matrix for the Jacobian is a matrix with entries zero if the Jacobian elements are zero, and with entries one if the Jacobian elements are nonzero. The border of columns on the upper right of Fig. 1 contains derivatives of the source terms (Eq. 1) with respect to the wellbore pressure The border of rows on the lower left contains derivatives of the constraint equations (Eq. 2) with respect to reservoir variables (i.e., cell pressures). The block on the lower right contains derivatives of the constraint equations with respect to the wellbore pressures and is diagonal. The number of extra columns and rows is proportional to the number of fully coupled wells (or fractures). Although the incidence matrix of the reservoir flow portion of the matrix is symmetric, the incidence matrix of portion of the matrix is symmetric, the incidence matrix of the borders is not necessarily symmetric. George discusses three possible block factorizations of sparse, linear systems. The algorithm used here is based on his second factorization. SPEJ P. 535

2021 ◽  
Vol 137 (3) ◽  
pp. 739-767
Author(s):  
Abdul Salam Abd ◽  
Na Zhang ◽  
Ahmad S. Abushaikha

AbstractCapillary dominated flow or imbibition—whether spontaneous or forced—is an important physical phenomena in understanding the behavior of naturally fractured water-driven reservoirs (NFR’s). When the water flows through the fractures, it imbibes into the matrix and pushes the oil out of the pores due to the difference in the capillary pressure. In this paper, we focus on modeling and quantifying the oil recovered from NFR’s through the imbibition processes using a novel fully implicit mimetic finite difference (MFD) approach coupled with discrete fracture/discrete matrix (DFDM) technique. The investigation is carried out in the light of different wetting states of the porous media (i.e., varying capillary pressure curves) and a full tensor representation of the permeability. The produced results proved the MFD to be robust in preserving the physics of the problem, and accurately mapping the flow path in the investigated domains. The wetting state of the rock affects greatly the oil recovery factors along with the orientation of the fractures and the principal direction of the permeability tensor. We can conclude that our novel MFD method can handle the fluid flow problems in discrete-fractured reservoirs. Future works will be focused on the extension of MFD method to more complex multi-physics simulations.


2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Muhammad Asim Khan ◽  
Norhashidah Hj. Mohd Ali ◽  
Nur Nadiah Abd Hamid

Abstract In this article, a new explicit group iterative scheme is developed for the solution of two-dimensional fractional Rayleigh–Stokes problem for a heated generalized second-grade fluid. The proposed scheme is based on the high-order compact Crank–Nicolson finite difference method. The resulting scheme consists of three-level finite difference approximations. The stability and convergence of the proposed method are studied using the matrix energy method. Finally, some numerical examples are provided to show the accuracy of the proposed method.


Author(s):  
Armando Félix Quiñonez ◽  
Guillermo E Morales Espejel

This work investigates the transient effects of a single subsurface inclusion over the pressure, film thickness, and von Mises stress in a line elastohydrodynamic lubrication contact. Results are obtained with a fully-coupled finite element model for either a stiff or a soft inclusion moving at the speed of the surface. Two cases analyzed consider the inclusion moving either at the same speed as the mean velocity of the lubricant or moving slower. Two additional cases investigate reducing either the size of the inclusion or its stiffness differential with respect to the matrix. It is shown that the well-known two-wave elastohydrodynamic lubrication mechanism induced by surface features is also applicable to the inclusions. Also, that the effects of the inclusion become weaker both when its size is reduced and when its stiffness approaches that of the matrix. A direct comparison with predictions by the semi-analytical model of Morales-Espejel et al. ( Proc IMechE, Part J: J Engineering Tribology 2017; 231) shows reasonable qualitative agreement. Quantitatively some differences are observed which, after accounting for the semi-analytical model's simplicity, physical agreement, and computational efficiency, may then be considered as reasonable for engineering applications.


2014 ◽  
Vol 555 ◽  
pp. 530-540
Author(s):  
Honoriu Vălean ◽  
Mihail Abrudean ◽  
Mihaela Ligia Ungureşan ◽  
Iulia Clitan ◽  
Vlad Mureşan

In this paper an original solution for the modeling of distributed parameter processes using neural networks is presented. The proposed method represents a particular alternative to a very accurate modeling-simulation method for this kind of processes, the method based on the matrix of partial derivatives of the state vector (Mpdx), associated with Taylor series. In order to compare the performances generated by the two methods, a distributed parameter thermal process associated to a rotary hearth furnace (R.H.F) from the technological flow of producing seamless steel pipes is considered. The main similarities and differences between the two methods are highlighted in the paper. The treated solution represents a premise for the usage of the neural networks in the automatic control of the distributed parameter processes domain.


Author(s):  
T. N. Krishnamurti ◽  
H. S. Bedi ◽  
V. M. Hardiker

This chapter on finite differencing appears oddly placed in the early part of a text on spectral modeling. Finite differences are still traditionally used for vertical differencing and for time differencing. Therefore, we feel that an introduction to finite-differencing methods is quite useful. Furthermore, the student reading this chapter has the opportunity to compare these methods with the spectral method which will be developed in later chapters. One may use Taylor’s expansion of a given function about a single point to approximate the derivative(s) at that point. Derivatives in the equation involving a function are replaced by finite difference approximations. The values of the function are known at discrete points in both space and time. The resulting equation is then solved algebraically with appropriate restrictions. Suppose u is a function of x possessing derivatives of all orders in the interval (x — n∆x, x + n∆x). Then we can obtain the values of u at points x ± n∆ x, where n is any integer, in terms of the value of the function and its derivatives at point x, that is, u(x) and its higher derivatives.


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