Ivan Rizzo Guilherme

Classification of Petroleum Well Drilling Operations Using Support Vector Machine (SVM)

During the petroleum well drilling operation many mechanical and hydraulic parameters are monitored by an instrumentation system installed in the rig called a mud-logging system. These sensors, distributed in the rig, monitor different operation parameters such as weight on the hook and drillstring rotation. These measurements are known as mud-logging records and allow the online following of all the drilling process with well monitoring purposes. However, in most of the cases, these data are stored without taking advantage of all their potential. On the other hand, to make use of the mud-logging data, an analysis and interpretationt is required. That is not an easy task because of the large volume of information involved. This paper presents a support vector machine (SVM) used to automatically classify the drilling operation stages through the analysis of some mud-logging parameters. In order to validate the results of SVM technique, it was compared to a classification elaborated by a Petroleum Engineering expert.

Petroleum well drilling monitoring through cutting image analysis and artificial intelligence techniques

Petroleum well drilling monitoring has become an important tool for detecting and preventing problems during the well drilling process. In this paper, we propose to assist the drilling process by analyzing the cutting images at the vibrating shake shaker, in which different concentrations of cuttings can indicate possible problems, such as the collapse of the well borehole walls. In such a way, we present here an innovative computer vision system composed by a real time cutting volume estimator addressed by support vector regression. As far we know, we are the first to propose the petroleum well drilling monitoring by cutting image analysis. We also applied a collection of supervised classifiers for cutting volume classification.

A Genetic Neuro-Model Reference Adaptive Controller for Petroleum Wells Drilling Operations

Motivated by rising drilling operation costs, the oil industry has shown a trend towards real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated to parameters modeling. One of the drill-bit performance evaluators, the rate of penetration (ROP), has been used in the literature as a drilling control parameter. However, the relationships between the operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on the auto-regressive with extra input signals model, or ARX model, to accomplish the system identification and on a genetic algorithm (GA) to provide a robust control for the ROP. Results of simulations run over a real offshore oil field data, consisted of seven wells drilled with equal diameter bits, are provided.

An Intelligent System for Petroleum Well Drilling Cutting Analysis

Cutting analysis is a important and crucial task task to detect and prevent problems during the petroleum well drilling process. Several studies have been developed for drilling inspection, but none of them takes care about analysing the generated cutting at the vibrating shale shakers. Here we proposed a system to analyse the cuttings concentration at the vibrating shale shakers, which can indicate problems during the petroleum well drilling process, such that the collapse of the well borehole walls. Cuttings images are acquired and sent to the data analysis module, which has as the main goal to extract features and to classify frames according to one of three previously classes of cuttings volume. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and efficiency. We used the Optimum-Path Forest (OPF), Artificial Neural Network using Multilayer Perceptrons (ANN-MLP), Support Vector Machines (SVM) and a Bayesian Classifier (BC) for this task. The first one outperformed all the remaining classifiers. Recall that we are also the first to introduce the OPF classifier in this field of knowledge. Very good results show the robustness of the proposed system, which can be also integrated with other commonly system (Mud-Logging) in order to improve the last ones efficiency.

Unsupervised manifold learning through reciprocal kNN graph and Connected Components for image retrieval tasks

Abstract Performing effective image retrieval tasks, capable of exploiting the underlying structure of datasets still constitutes a challenge research scenario. This paper proposes a novel manifold learning approach that exploits the intrinsic dataset geometry for improving the effectiveness of image retrieval tasks. The underlying dataset manifold is modeled and analyzed in terms of a Reciprocal kNN Graph and its Connected Components. The method computes the new retrieval results on an unsupervised way, without the need of any user intervention. A large experimental evaluation was conducted, considering different image retrieval tasks, various datasets and features. The proposed method yields better effectiveness results than various methods recently proposed, achieving effectiveness gains up to +40.75%.

Identification and frequency of transposable elements in Eucalyptus

Transposable elements (TE) are major components of eukaryotic genomes and involved in cell regulation and organism evolution. We have analyzed 123,889 expressed sequence tags of the Eucalyptus Genome Project database and found 124 sequences representing 76 TE in 9 groups, of which copia, MuDR and FAR1 groups were the most abundant. The low amount of sequences of TE may reflect the high efficiency of repression of these elements, a process that is called TE silencing. Frequency of groups of TE in Eucalyptus libraries which were prepared with different tissues or physiologic conditions from seedlings or adult plants indicated that developing plants experience the expression of a much wider spectrum of TE groups than that seen in adult plants. These are preliminary results that identify the most relevant TE groups involved with Eucalyptus development, which is important for industrial wood production.

A Multiagent Architecture for Supervisory and Control System

Supervising and controlling the many processes involved in petroleum production is both dangerous and complex. Herein, we propose a multiagent supervisory and control system for managing an off-shore installation for petroleum production. In its architecture, there are agents responsible for managing data production and analysis, and also the production equipments. Fuzzy controllers were used as control agents. The application of a fuzzy control system onto a submarine separation process is described.

Uma arquitetura multiagente para sistemas Web semântico para gestão de conteúdos educacionais

A utilizacao da Web como plataforma para a educacao a distância (e-learning), tem sido uma das grandes alternativas para a educacao em sala de aula tradicional. Embora esses sistemas sejam amplamente utilizados, existem limitacoes quanto a dificuldade de busca, integracao e reuso dos materiais existentes. Neste contexto, neste trabalho e apresentada uma arquitetura multiagente para o desenvolvimento de sistemas Web semânticos para a gestao de conteudos educacionais. Como parte da arquitetura foi especificado um conjunto de ontologias e agentes inteligentes, responsaveis por recuperar e integrar conteudos educacionais, para as atividades de busca, autoria de metadados de Objetos de Aprendizagem (OAs) e autoria de cursos.

FTMOntology: an ontology to fill the semantic gap between music, mood, personality, and human physiology

This paper presents a domain ontology, the Feeling The Music Ontology - FTMOntology. FTMOntology is designed to represent the complex domain of music and how it relates to other domains like mood, personality and physiology. This includes representing the main concepts and relations of music domain with each of the above-mentioned domains. The concepts and relations between music, mood, personality and physiology. The main contribution of this work is to model and relate these different domains in a consistent ontology.

Swarm Control Designs applied to a non-ideal load transportation system

In this paper, a load transport system in platforms is considered. It is a transport device and is modelled as an inverted pendulum built on a car driven by a DC motor. The motion equations were obtained by Lagranges equations. The mathematical model considers the interaction between the DC motor and the dynamic system. The dynamic system was analysed and a Swarm Control Design was developed to stabilize the model of this load transport system.