Malgorzata Peszynska

Coupled fluid flow and geomechanical deformation modeling

Accurate prediction of reservoir production in structurally weak geologic areas requires both mechanical deformation and fluid flow modeling. Loose staggered-in-time coupling of two independent flow and mechanics simulators captures much of the complex physics at a substantially reduced cost. Two 3-D finite element simulators—Integrated Parallel Accurate Reservoir Simulator (IPARS) for flow and JAS3D for mechanics—together model multiphase fluid flow in reservoir rocks undergoing deformation ranging from linear elasticity to large, nonlinear inelastic compaction. The loose coupling algorithm uses a highlevel driver to call the flow simulator for a set of time steps with fixed reservoir properties. Pore pressures from flow are used as loads for the geomechanics code in the determination of stresses, strains, and displacements. The mechanics-derived strain is used to calculate changes to the reservoir parameters (porosity and permeability) for the next set of flow time steps. Mass is conserved in the coupled code despite dynamically changing reservoir parameters via a modification to the Newton system for the flow equations, and an approximate rock compressibility becomes a useful preconditioner to help with convergence of the modified flow equations. Two numerical experiments illustrate the accuracy of the coupled code. The first example is a quarterfive-spot waterflood undergoing poroelastic deformation, which is validated against a fully coupled simulator. Vertical displacements at the well locations match to within 10%. Moreover, experimentation shows that 13 mechanics time steps (taken over the course of 5 years of simulation time) were sufficient to achieve this result (a substantial cost savings over full coupling in which both the mechanics and flow equations must be solved at each time step). The second numerical example is based on real data from the Belridge Field in California, which illustrates one of the complex plastic constitutive relationships available in the coupled code. The results mimic behavior which was observed in the field. The coupled code serves as a prototype for loosely coupling together any two preexisting simulators modeling diverse physics. This technique produces a coupled code relatively quickly and inexpensively and has the advantage of accurately modeling complex nonlinear phenomena often

Coupled geomechanics and flow simulation for time‐lapse seismic modeling

To accurately predict production in compactible reservoirs, we must use coupled models of fluid flow and mechanical deformation. Staggered‐in‐time loose coupling of flow and deformation via a high‐level numerical interface that repeatedly calls first flow and then mechanics allows us to leverage the decades of work put into individual flow and mechanics simulators while still capturing realistic coupled physics. These two processes are often naturally modeled using different time stepping schemes and different spatial grids—flow should only model the reservoir, whereas mechanics requires a grid that extends to the earths surface for overburden loading and may extend further than the reservoir in the lateral directions. Although spatial and temporal variability between flow and mechanics can be difficult to accommodate with full coupling, it is easily handled via loose coupling. We calculate the total stress by adding pore pressures to the effective rock stress. In turn, changes in volume strain induce up...

Computational engineering and science methodologies for modeling and simulation of subsurface applications

We discuss computational engineering and science (CES) methodologies and tools applicable to a variety of subsurface models and their couplings. First we overview both basic and widely recognized multiphase and multicomponent models. In the CES methodologies area we focus on accurate and robust numerical algorithms and linear and nonlinear solvers with parallel scalability. In the CES tools area, we discuss a few representative programming tools and technologies. We present several simulation examples which reflect the experiences of the research group at the Center for Subsurface Modeling at The University of Texas at Austin.

A Parallel Multiblock/Multidomain Approach for Reservoir Simulation

Our approach for parallel multiphysics and multiscale simulation uses two levels of domain decomposition: physical and computational. First, the physical domain is decomposed into subdomains or blocks according to the geometry, geology, and physics/chemistry/biology. Each subdomain represents a single physical system, on a reasonable range of scales, such as a black oil region, a compositional region, a region to one side of a fault, or a near-wellbore region. Second, the computations are decomposed on a parallel machine for efficiency. That is, we use a multiblock or macro-hybrid approach, in which we describe a domain as a union of regions or blocks, and employ an appropriate hierarchical model on each block. This approach allows one to define grids and computations independently on each block. This local grid structure has many advantages. It allows the most efficient and accurate discretization techniques to be employed in each block. The multiblock structure of the algebraic systems allows for the design and use of efficient domain decomposition solvers and preconditioners. Decomposition into independent blocks offers great flexibility in accommodating the shape of the external boundary, the presence of internal features such as faults and wells, and the need to refine a region of the domain in space or time (by treating it as a distinct block); interfacing structured and unstructured grids; and accommodating various models of multiscale and multiphysical phenomena. The resulting grid is not suited to direct application of discretization methods. We use mortar space techniques to impose physically meaningful, mass conservative, fluxmatching conditions on the interfaces between blocks. We present numerical simulations to illustrate several of these decomposition strategies, including the coupling of IMPES and fully implicit models and upscaling by varying the number of degrees of freedom on the block interfaces.

Autonomic oil reservoir optimization on the Grid

The emerging Grid infrastructure and its support for seamless and secure interactions is enabling a new generation of autonomic applications where the application components, Grid services, resources, and data interact as peers to manage, adapt and optimize themselves and the overall application. In this paper we describe the design, development and operation of a prototype of such an application that uses peer‐to‐peer interactions between distributed services and data on the Grid to enable the autonomic optimization of an oil reservoir. Copyright

Biofilm growth in porous media: Experiments, computational modeling at the porescale, and upscaling

Abstract Biofilm growth changes many physical properties of porous media such as porosity, permeability and mass transport parameters. The growth depends on various environmental conditions, and in particular, on flow rates. Modeling the evolution of such properties is difficult both at the porescale where the phase morphology can be distinguished, as well as during upscaling to the corescale effective properties. Experimental data on biofilm growth is also limited because its collection can interfere with the growth, while imaging itself presents challenges. In this paper we combine insight from imaging, experiments, and numerical simulations and visualization. The experimental dataset is based on glass beads domain inoculated by biomass which is subjected to various flow conditions promoting the growth of biomass and the appearance of a biofilm phase. The domain is imaged and the imaging data is used directly by a computational model for flow and transport. The results of the computational flow model are upscaled to produce conductivities which compare well with the experimentally obtained hydraulic properties of the medium. The flow model is also coupled to a newly developed biomass–nutrient growth model, and the model reproduces morphologies qualitatively similar to those observed in the experiment.

Finite element approximation of diffusion equations with convolution terms

Approximation of solutions to diffusion equations with memory represented by convolution integral terms is considered. Such problems arise from modeling of flows in fissured media. Convergence of the method is proved and results of numerical experiments confirming the theoretical results are presented. The advantages of implementation of the algorithm in a multiprocessing environment are discussed.

A simulation and data analysis system for large‐scale, data‐driven oil reservoir simulation studies

The main goal of oil reservoir management is to provide more efficient, cost‐effective and environmentally safer production of oil from reservoirs. Numerical simulations can aid in the design and implementation of optimal production strategies. However, traditional simulation‐based approaches to optimizing reservoir management are rapidly overwhelmed by data volume when large numbers of realizations are sought using detailed geologic descriptions. In this paper, we describe a software architecture to facilitate large‐scale simulation studies, involving ensembles of long‐running simulations and analysis of vast volumes of output data. Copyright

Driving scientific applications by data in distributed environments

Traditional simulation-based applications for exploring a parameter space to understand a physical phenomenon or to optimize a design are rapidly overwhelmed by data volume when large numbers of simulations of different parameters are carried out. Optimizing reservoir management through simulation-based studies, in which large numbers of realizations are sought using detailed geologic descriptions, is an example of such applications. In this paper, we describe a software architecture to facilitate large scale simulation studies, involving ensembles of long-running simulations and analysis of vast volumes of output data. This architecture is built on top of two frameworks we have developed: IPARS and DataCutter. These frameworks make it possible to implement tools and applications to run large-scale simulatios, and generate and investigate terabyte-scale datasets efficiently.

A Parallel Multiblock Black-Oil Model in Multimodel Implementation

In this paper we discuss the multiblock algorithm for an implicit black-oil model as implemented in the multiphase simulator framework of IPARS (Integrated Parallel Accurate Reservoir Simulator). The multiblock algorithm decomposes the simulation domain into multiple nonoverlapping subdomains, or blocks, according to the geometric, geological, and physical/chemical properties, and well distribution. Each block can have its own grid system, and the grids of the neighboring blocks can be nonmatching on the interface, which allows for local grid refinement, or discrete fault or fracture modeling. Adjacent blocks are coupled across the interface by a set of conditions imposing a continuity of both primary variables and component mass fluxes that is realized through the use of special interface mortar variables. The resulting system is solved by an interface Newton procedure. Regularization techniques and preconditioners are proposed to improve the performance of the solver. The multiblock technique is effective and scalable, as shown by our numerical experiments. In addition, we present how the multiblock black-oil model has been used in the coupling of different physical models.