Automatic hydraulic fracturing design for low permeability reservoirs using artificial intelligence

The hydraulic fracturing technique is one of the major developments in petroleum engineering in the last two decades. Today, nearly all the wells completed in low permeability gas reservoirs require a hydraulic fracturing treatment in order to produce at an economical level. This study presents a new methodology, applicable to tight gas reservoirs, for designing hydraulic fractures.;This study is intended to develop an automatic hydraulic fracture design tool to help users design fracture jobs without being an expert in the art and science of hydraulic fracturing. This process is composed entirely of an integration of several artificial intelligence techniques.;The methodology consists of three modules: formation stress determination, optimum treatment design and net treatment pressure prediction. The first module combines the classic approach of stress calculations with a fuzzy lithology identification system to better characterize the reservoir and estimate the stress profile. The result of this module is essential for the fracture treatment design. The second module incorporates an optimization system composed of neural networks and a genetic algorithm to search for the optimum treatment design. The third, and final, module is designed to predict the net treating pressure expected during fracturing. A one-dimensional vector quantization technique samples and extracts the main characteristic of the pressure profile. The net treatment pressure neural network generates the main features of the pressure profile and then reconstructs the entire signal.;The methodology was integrated in a computer program aimed to help petroleum engineers design optimum treatment schedules and predict net treatment pressure for hydraulic fracturing. This tool is designed to reduce the engineering time for designing optimum treatment schedules

Design Optimization Utilizing Dynamic Substructuring and Artificial Intelligence Techniques

In mechanical and structural systems, resonance may cause large strains and stresses which can lead to the failure of the system. Since it is often not possible to change the frequency content of the external load excitation, the phenomenon can only be avoided by updating the design of the structure. In this paper, a design optimization strategy based on the integration of the Component Mode Synthesis (CMS) method with numerical optimization techniques is presented. For reasons of numerical efficiency, a Finite Element (FE) model is represented by a surrogate model which is a function of the design parameters. The surrogate model is obtained in four steps: First, the reduced FE models of the components are derived using the CMS method. Then the components are aassembled to obtain the entire structural response. Afterwards the dynamic behavior is determined for a number of design parameter settings. Finally, the surrogate model representing the dynamic behavior is obtained. In this research, the surrogate model is determined using the Backpropagation Neural Networks which is then optimized using the Genetic Algorithms and Sequential Quadratic Programming method. The application of the introduced techniques is demonstrated on a simple test problem

Optimum Selection of DNN Model and Framework for Edge Inference

This paper describes a methodology to select the optimum combination of deep neuralnetwork and software framework for visual inference on embedded systems. As a first step, benchmarkingis required. In particular, we have benchmarked six popular network models running on four deep learningframeworks implemented on a low-cost embedded platform. Three key performance metrics have beenmeasured and compared with the resulting 24 combinations: accuracy, throughput, and power consumption.Then, application-level specifications come into play. We propose a figure of merit enabling the evaluationof each network/framework pair in terms of relative importance of the aforementioned metrics for a targetedapplication. We prove through numerical analysis and meaningful graphical representations that only areduced subset of the combinations must actually be considered for real deployment. Our approach can beextended to other networks, frameworks, and performance parameters, thus supporting system-level designdecisions in the ever-changing ecosystem of embedded deep learning technology.Ministerio de Economía y Competitividad (TEC2015-66878-C3-1-R)Junta de Andalucía (TIC 2338-2013)European Union Horizon 2020 (Grant 765866

Multiclass scheduling algorithms for the DAVID metro network

Abstract—The data and voice integration over dense wavelength-division-multiplexing (DAVID) project proposes a metro network architecture based on several wavelength-division-multiplexing (WDM) rings interconnected via a bufferless optical switch called Hub. The Hub provides a programmable interconnection among rings on the basis of the outcome of a scheduling algorithm. Nodes connected to rings groom traffic from Internet protocol routers and Ethernet switches and share ring resources. In this paper, we address the problem of designing efficient centralized scheduling algorithms for supporting multiclass traffic services in the DAVID metro network. Two traffic classes are considered: a best-effort class, and a high-priority class with bandwidth guarantees. We define the multiclass scheduling problem at the Hub considering two different node architectures: a simpler one that relies on a complete separation between transmission and reception resources (i.e., WDM channels) and a more complex one in which nodes fully share transmission and reception channels using an erasure stage to drop received packets, thereby allowing wavelength reuse. We propose both optimum and heuristic solutions, and evaluate their performance by simulation, showing that heuristic solutions exhibit a behavior very close to the optimum solution. Index Terms—Data and voice integration over dense wavelength-division multiplexing (DAVID), metropolitan area network, multiclass scheduling, optical ring, wavelength-division multiplexing (WDM). I

New insights on neutral binary representations for evolutionary optimization

This paper studies a family of redundant binary representations NNg(l, k), which are based on the mathematical formulation of error control codes, in particular, on linear block codes, which are used to add redundancy and neutrality to the representations. The analysis of the properties of uniformity, connectivity, synonymity, locality and topology of the NNg(l, k) representations is presented, as well as the way an (1+1)-ES can be modeled using Markov chains and applied to NK fitness landscapes with adjacent neighborhood.The results show that it is possible to design synonymously redundant representations that allow an increase of the connectivity between phenotypes. For easy problems, synonymously NNg(l, k) representations, with high locality, and where it is not necessary to present high values of connectivity are the most suitable for an efficient evolutionary search. On the contrary, for difficult problems, NNg(l, k) representations with low locality, which present connectivity between intermediate to high and with intermediate values of synonymity are the best ones. These results allow to conclude that NNg(l, k) representations with better performance in NK fitness landscapes with adjacent neighborhood do not exhibit extreme values of any of the properties commonly considered in the literature of evolutionary computation. This conclusion is contrary to what one would expect when taking into account the literature recommendations. This may help understand the current difficulty to formulate redundant representations, which are proven to be successful in evolutionary computation. (C) 2016 Elsevier B.V. All rights reserved

Self-Organized Routing For Wireless Micro-Sensor Networks

In this paper we develop an energy-aware self-organized routing algorithm for the networking of simple battery-powered wireless micro-sensors (as found, for example, in security or environmental monitoring applications). In these networks, the battery life of individual sensors is typically limited by the power required to transmit their data to a receiver or sink. Thus effective network routing algorithms allow us to reduce this power and extend both the lifetime and the coverage of the sensor network as a whole. However, implementing such routing algorithms with a centralized controller is undesirable due to the physical distribution of the sensors, their limited localization ability and the dynamic nature of such networks (given that sensors may fail, move or be added at any time and the communication links between sensors are subject to noise and interference). Against this background, we present a distributed mechanism that enables individual sensors to follow locally selfish strategies, which, in turn, result in the self-organization of a routing network with desirable global properties. We show that our mechanism performs close to the optimal solution (as computed by a centralized optimizer), it deals adaptively with changing sensor numbers and topology, and it extends the useful life of the network by a factor of three over the traditional approach