Guan Qin

An Approximation to Miscible Fluid Flows in Porous Media with Point Sources and Sinks by an Eulerian--Lagrangian Localized Adjoint Method and Mixed Finite Element Methods

We develop an Eulerian--Lagrangian localized adjoint method (ELLAM)-mixed finite element method (MFEM) solution technique for accurate numerical simulation of coupled systems of partial differential equations (PDEs), which describe complex fluid flow processes in porous media. An ELLAM, which was shown previously to outperform many widely used methods in the context of linear convection-diffusion PDEs, is presented to solve the transport equation for concentration. Since accurate fluid velocities are crucial in numerical simulations, an MFEM is used to solve the pressure equation for the pressure and Darcy velocity. This minimizes the numerical difficulties occurring in standard methods for approximating velocities caused by differentiation of the pressure and then multiplication by rough coefficients. The ELLAM-MFEM solution technique significantly reduces temporal errors, symmetrizes the governing transport equation, eliminates nonphysical oscillation and/or excessive numerical dispersion in many simulators, conserves mass, and treats boundary conditions accurately. Numerical experiments show that the ELLAM-MFEM solution technique simulates miscible displacements of incompressible fluid flows in porous media accurately with fairly coarse spatial grids and very large time steps, which are one or two orders of magnitude larger than the time steps used in many methods. Moreover, the ELLAM-MFEM solution technique can treat large mobility ratios, discontinuous permeabilities and porosities, anisotropic dispersion in tensor form, and point sources and sinks.

Analysis of a compositional model for fluid flow in porous media

In this paper we consider a compositional model for three-phase multicomponent fluid flow in porous media. This model consists of Darcys law for volumetric flow velocities, mass conservation for hydrocarbon components, thermodynamic equilibrium for mass interchange between phases, and an equation of state for saturations. These differential equations and algebraic constraints are rewritten in terms of various formulations of the pressure and component-conservation equations. Phase, weighted fluid, global, and pseudoglobal pressure and component-conservation formulations are analyzed. A numerical scheme based on the mixed finite element method for the pressure equation and the Eulerian--Lagrangian localized adjoint method for the component-conservation equations is developed. Numerical results are reported to show the behavior of the solution to the compositional model and to investigate the performance of the proposed numerical scheme.

An Efficient Upscaling Procedure Based on Stokes-Brinkman Model and Discrete Fracture Network Method for Naturally Fractured Carbonate Karst Reservoirs

This publication is partly based on work supported by Award No. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). The authors also gratefully acknowledge the support from Research Inst. of Petroleum Exploration and Development of Sinopec Corp.

An Efficient Upscaling Process Based on a Unified Fine-scale Multi-Physics Model for Flow Simulation in Naturally Fracture Carbonate Karst Reservoirs

This publication is partly based on work supported by Award No. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). The authors also gratefully acknowledge the support from Research Inst. of Petroleum Exploration and Development of Sinopec Corp.

Data-driven Monte Carlo Simulations in Estimating the Stimulated Reservoir Volume (SRV) by Hydraulic Fracturing Treatments

Hydraulic fracturing treatment has been proven to be the key factor for shale gas to flow at economic rate. Micro-seismic mapping has shown the extreme complexity of the hydraulic fracture network after the stimulation due to the geological complexity of shale formations. It becomes vitally important to understand the impact of the hydraulic fracture treatment, especially the massive multistage, multi-cluster hydraulic fracturing stimulations, to optimize stimulation and development plans of shale gas reservoirs. Recent advances in micro-seismic mapping enable realistic modeling of hydraulic fracture network, though with significant uncertainty. Consequently, it is possible, to certain extent, to represent actual large-scale fracture distribution in reservoir modeling and simulation of shale gas development. In this paper, we propose a simulation method that is able to generate highly likely realizations of fracture network based on micro-seismic data, taking into account of data and shale formation uncertainty. The simulated realizations are then used to construct highly constrained unstructured gridding and a connection list of all neighboring cells (SPE 143590), using the Discrete Fracture Modeling (DFM) approach. DFM enables the prediction of production yield curve. With real production data, statistical analysis is done to calibrate and refine the simulation attributes. Based on a well calibrated simulation system, and linking initial hydraulic stimulation, induced fracture network and production data, we predict future stimulated reservoir volume and production yield curve, hence enabling the optimization of stimulation and development. The proposed approach is extremely computational intensive. Approximations, efficient implementation and parallelization are used to make the approach practical. The approach was tested with success on real field experiments and data and the numerical results have shown great potential of the proposed approach to better understand the impact of hydraulic fracturing treatment.

A Lattice Boltzmann model for multi-component vapor-liquid two phase flow

Abstract In traditional multi-phase multi-component Lattice Boltzmann (LB) models, the phase properties especially the vapor-liquid equilibrium calculations failed to model the actual flow realistically. A new multi-component vapor-liquid two phase LB model that involves a four-parameter (critical temperature, critical pressure, acentric factor and volume shift factor) equation of state (EOS) is proposed. The new model employs a four-parameter EOS to calculate the force on each lattice and then to determine the force between different fluid phases accurately. An accurate difference method is proposed to integrate the interaction force into the evolution functions of each component. Moreover, the Lorentz-Bray-Clark viscosity model is implemented to calculate the viscosity of lattice fluid according to the component density. The new model is validated and applied to forecast the phase behavior of methane, ethane, and propane mixture at multiple temperatures and pressures. The simulation results show a good agreement with experimental data and theoretical predictions.

Intelligent fracture creation for shale gas development

Shale gas represents a major fraction of the proven reserves of natural gas in the United States and a collection of other countries. Higher gas prices and the need for cleaner fuels provides motivation for commercializing shale gas deposits even though the cost is substantially higher than traditional gas deposits. Recent advances in horizontal drilling and multistage hydraulic fracturing, which dramatically lower costs of developing shale gas fields, are key to renewed interest in shale gas deposits. Hydraulically induced fractures are quite complex in shale gas reservoirs. Massive, multistage, multiple cluster treatments lead to fractures that interact with existing fractures (whether natural or induced earlier). A dynamic approach to the fracturing process so that the resulting network of reservoirs is known during the drilling and fracturing process is economically enticing. The process needs to be automatic and done in faster than real-time in order to be useful to the drilling crews.

Advantages of multiscale detection of defective pills during manufacturing

We explore methods to automatically detect the quality in individual or batches of pharmaceutical products as they are manufactured. The goal is to detect 100% of the defects, not just statistically sample a small percentage of the products and draw conclusions that may not be 100% accurate. Removing all of the defective products, or halting production in extreme cases, will reduce costs and eliminate embarrassing and expensive recalls. We use the knowledge that experts have accumulated over many years, dynamic data derived from networks of smart sensors using both audio and chemical spectral signatures, multiple scales to look at individual products and larger quantities of products, and finally adaptive models and algorithms.