Simulation and Analysis of Unconventional Reservoirs Using Fast Marching Method and Transient Drainage Volume

Unconventional tight/shale reservoirs have become an important component of the world’s energy map in the recent decade and have been attracting a lot of interests in both academia and industry. However, the industry today still faces significant challenges in understanding the fundamental mechanisms. Unconventional tight/shale reservoirs are characterized by low or ultra-low permeability, such that the transient pressure behavior might last throughout the production lifetime. Recent research has proposed a novel approach for unconventional reservoir analysis based on the high-frequency asymptotic approximation of diffusivity equation. By solving the Eikonal equation with the Fast Marching Method (FMM), one can rapidly obtain the diffusive time of flight (DToF) which depicts the pressure transient propagation process. A fast DToF-based forward simulation is further proposed to solve the fluid flow equation in a 1D equivalent coordinate system, with the DToF as the spatial coordinate. In this study, we first adopt the DToF-based simulation as a rapid forward simulator to formulate an efficient hydraulic fracture design and optimization workflow. The DToF-based simulation can be orders of magnitude faster than the conventional finite difference/volume based simulation, and is ideal for optimization process where hundreds or thousands of simulations are necessary. Our workflow focuses on optimizing the number of hydraulic fracture stages, their spacing, and the allocation of proppant. The workflow also accounts for the geologic uncertainty, which given by different natural fracture distributions. Next, we extend this DToF-based simulation from Cartesian and corner point grid system to unstructured grids to better characterize the complex fracture geometry induced by hydraulic fracturing job. Two different constructions of the local Eikonal equation solver, based on Fermat’s principle and Eulerian discretization, are investigated and compared. Numerical examples are presented to illustrate the power and validity of this extended DToF-based simulation workflow. Finally, we propose a model-free production data analysis method to analyze the performance of unconventional reservoirs when a full simulation model is not available. The transient drainage volume is derived directly based on bottom-hole pressure and production rate. We further define the drainage volume derivative and instantaneous recovery ratio, which can measure how effectively the hydraulic fractures have stimulated the reservoir. This technique is then applied to select candidate wells for refracturing

Center for Aeronautics and Space Information Sciences

This report summarizes the research done during 1991/92 under the Center for Aeronautics and Space Information Science (CASIS) program. The topics covered are computer architecture, networking, and neural nets

Quarc: an architecture for efficient on-chip communication

The exponential downscaling of the feature size has enforced a paradigm shift from computation-based design to communication-based design in system on chip development. Buses, the traditional communication architecture in systems on chip, are incapable of addressing the increasing bandwidth requirements of future large systems. Networks on chip have emerged as an interconnection architecture offering unique solutions to the technological and design issues related to communication in future systems on chip. The transition from buses as a shared medium to networks on chip as a segmented medium has given rise to new challenges in system on chip realm. By leveraging the shared nature of the communication medium, buses have been highly efficient in delivering multicast communication. The segmented nature of networks, however, inhibits the multicast messages to be delivered as efficiently by networks on chip. Relying on extensive research on multicast communication in parallel computers, several network on chip architectures have offered mechanisms to perform the operation, while conforming to resource constraints of the network on chip paradigm. Multicast communication in majority of these networks on chip is implemented by establishing a connection between source and all multicast destinations before the message transmission commences. Establishing the connections incurs an overhead and, therefore, is not desirable; in particular in latency sensitive services such as cache coherence. To address high performance multicast communication, this research presents Quarc, a novel network on chip architecture. The Quarc architecture targets an area-efficient, low power, high performance implementation. The thesis covers a detailed representation of the building blocks of the architecture, including topology, router and network interface. The cost and performance comparison of the Quarc architecture against other network on chip architectures reveals that the Quarc architecture is a highly efficient architecture. Moreover, the thesis introduces novel performance models of complex traffic patterns, including multicast and quality of service-aware communication

An Evolutionary Approach to Multistage Portfolio Optimization

Portfolio optimization is an important problem in quantitative finance due to its application in asset management and corporate financial decision making. This involves quantitatively selecting the optimal portfolio for an investor given their asset return distribution assumptions, investment objectives and constraints. Analytical portfolio optimization methods suffer from limitations in terms of the problem specification and modelling assumptions that can be used. Therefore, a heuristic approach is taken where Monte Carlo simulations generate the investment scenarios and' a problem specific evolutionary algorithm is used to find the optimal portfolio asset allocations. Asset allocation is known to be the most important determinant of a portfolio's investment performance and also affects its risk/return characteristics. The inclusion of equity options in an equity portfolio should enable an investor to improve their efficient frontier due to options having a nonlinear payoff. Therefore, a research area of significant importance to equity investors, in which little research has been carried out, is the optimal asset allocation in equity options for an equity investor. A purpose of my thesis is to carry out an original analysis of the impact of allowing the purchase of put options and/or sale of call options for an equity investor. An investigation is also carried out into the effect ofchanging the investor's risk measure on the optimal asset allocation. A dynamic investment strategy obtained through multistage portfolio optimization has the potential to result in a superior investment strategy to that obtained from a single period portfolio optimization. Therefore, a novel analysis of the degree of the benefits of a dynamic investment strategy for an equity portfolio is performed. In particular, the ability of a dynamic investment strategy to mimic the effects ofthe inclusion ofequity options in an equity portfolio is investigated. The portfolio optimization problem is solved using evolutionary algorithms, due to their ability incorporate methods from a wide range of heuristic algorithms. Initially, it is shown how the problem specific parts ofmy evolutionary algorithm have been designed to solve my original portfolio optimization problem. Due to developments in evolutionary algorithms and the variety of design structures possible, a purpose of my thesis is to investigate the suitability of alternative algorithm design structures. A comparison is made of the performance of two existing algorithms, firstly the single objective stepping stone island model, where each island represents a different risk aversion parameter, and secondly the multi-objective Non-Dominated Sorting Genetic Algorithm2. Innovative hybrids of these algorithms which also incorporate features from multi-objective evolutionary algorithms, multiple population models and local search heuristics are then proposed. . A novel way is developed for solving the portfolio optimization by dividing my problem solution into two parts and then applying a multi-objective cooperative coevolution evolutionary algorithm. The first solution part consists of the asset allocation weights within the equity portfolio while the second solution part consists 'ofthe asset allocation weights within the equity options and the asset allocation weights between the different asset classes. An original portfolio optimization multiobjective evolutionary algorithm that uses an island model to represent different risk measures is also proposed.Imperial Users onl

Towards Optimal Application Mapping for Energy-Efficient Many-Core Platforms

Siirretty Doriast

A Scalable Multi-Stage Packet-Switch for Data Center Networks

The growing trends of data centers over last decades including social networking, cloud-based applications and storage technologies enabled many advances to take place in the networking area. Recent changes imply continuous demand for bandwidth to manage the large amount of packetized traffic. Cluster switches and routers make the switching fabric in a Data Center Network (DCN) environment and provide interconnectivity between elements of the same DC and inter DCs. To handle the constantly variable loads, switches need deliver outstanding throughput along with resiliency and scalability for DCN requirements. Conventional DCN switches adopt crossbars or/and blocks of memories mounted in a multistage fashion (commonly 2-Tiers or 3-Tiers). However, current multistage switches, with their space-memory variants, are either too complex to implement, have poor performance, or not cost effective. We propose a novel and highly scalable multistage switch based on Networkson- Chip (NoC) fabrics for DCNs. In particular, we describe a three-stage Clos packet-switch with a Round Robin packets dispatching scheme where each central stage module is based on a Unidirectional NoC (UDN), instead of the conventional singlehop crossbar. The design, referred to as Clos-UDN, overcomes shortcomings of traditional multistage architectures as it (i) Obviates the need for a complex and costly input modules, by means of few, yet simple, input FIFO queues. (ii) Avoids the need for a complex and synchronized scheduling process over a high number of input-output modules and/or port pairs. (iii) Provides speedup, load balancing and path-diversity thanks to a dynamic dispatching scheme as well as the NoC based fabric nature. Simulations show that the Clos-UDN outperforms some common multistage switches under a range of input traffics, making it highly appealing for ultra-high capacity DC networks

Multistage Packet-Switching Fabrics for Data Center Networks

Recent applications have imposed stringent requirements within the Data Center Network (DCN) switches in terms of scalability, throughput and latency. In this thesis, the architectural design of the packet-switches is tackled in different ways to enable the expansion in both the number of connected endpoints and traffic volume. A cost-effective Clos-network switch with partially buffered units is proposed and two packet scheduling algorithms are described. The first algorithm adopts many simple and distributed arbiters, while the second approach relies on a central arbiter to guarantee an ordered packet delivery. For an improved scalability, the Clos switch is build using a Network-on-Chip (NoC) fabric instead of the common crossbar units. The Clos-UDN architecture made with Input-Queued (IQ) Uni-Directional NoC modules (UDNs) simplifies the input line cards and obviates the need for the costly Virtual Output Queues (VOQs). It also avoids the need for complex, and synchronized scheduling processes, and offers speedup, load balancing, and good path diversity. Under skewed traffic, a reliable micro load-balancing contributes to boosting the overall network performance. Taking advantage of the NoC paradigm, a wrapped-around multistage switch with fully interconnected Central Modules (CMs) is proposed. The architecture operates with a congestion-aware routing algorithm that proactively distributes the traffic load across the switching modules, and enhances the switch performance under critical packet arrivals. The implementation of small on-chip buffers has been made perfectly feasible using the current technology. This motivated the implementation of a large switching architecture with an Output-Queued (OQ) NoC fabric. The design merges assets of the output queuing, and NoCs to provide high throughput, and smooth latency variations. An approximate analytical model of the switch performance is also proposed. To further exploit the potential of the NoC fabrics and their modularity features, a high capacity Clos switch with Multi-Directional NoC (MDN) modules is presented. The Clos-MDN switching architecture exhibits a more compact layout than the Clos-UDN switch. It scales better and faster in port count and traffic load. Results achieved in this thesis demonstrate the high performance, expandability and programmability features of the proposed packet-switches which makes them promising candidates for the next-generation data center networking infrastructure