Artificial immune systems for classification of petroleum well drilling operations

This paper presents two approaches of Artificial Immune System for Pattern Recognition (CLONALG and Parallel AIRS2) to classify automatically the well drilling operation stages. The classification is carried out through the analysis of some mud-logging parameters. In order to validate the performance of AIS techniques, the results were compared with others classification methods: neural network, support vector machine and lazy learning.

Development of intelligent systems for well drilling and petroleum production

Domains where knowledge representation is too complex to be described analytically and in a deterministic way is very common in the petroleum industry, particularly in the field of exploration and production. In these domains, applications of artificial intelligence techniques are very suitable, especially in cases where the preservation of corporate and technical knowledge is important. The Laboratory for Research on Artificial Intelligence Applied to Petroleum Engineering (LIAP) at Unicamp, has, during the last 10 years, dedicated research efforts to build intelligent systems in well drilling and petroleum production fields. In the following sections, recent advances in intelligent systems, under development in the research laboratory, are described. (C) 2001 Published by Elsevier B.V. B.V.

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. © 2006 IEEE.

Método da histerese por auto-ajuste para minimização de esforço de controle no separador submarino VASPS

Neste trabalho apresenta-se o estudo do desenvolvimento de um controlador não-tradicional baseado em um mecanismo de histerese com auto-ajuste para o controle de nível de líquido de um sistema de separação e bombeio submarino conhecido como VASPS. O controlador desenvolvido gera sinais enviados para a bomba centrífuga submersa para controlar o nível de líquido no tanque do separador, evitando que ele atinja valores muito baixos que poderiam danificar a bomba ou valores muito altos que reduziriam a eficiência da separação líquido/gás. Os sinais de controle gerados pelo controlador visam solicitar a bomba o mínimo possível de modo a evitar o seu desgaste e falhas prematuras. Nas simulações, o controlador desenvolvido foi testado sob grandes variações nas condições de operação, tais como golfadas, produzindo resultados bastante satisfatórios e promissores.

Decision-Making Tool for Knowledge-Based Projects in Offshore Production Systems

The development of an offshore field demands knowledge of many experts to choose the different components of an offshore production system. All the specialized parts of this knowledge are intrinsically related. The aim of this paper is to use Fuzzy Sets and knowledge-based systems to describe and formalize the phases of development of an offshore production system project, in order to share and to manage the required knowledge for carrying out a project, while at the same time proposing alternatives for the oil field configuration.

Entropy variation in isothermal fluid flow considering real gas effects

The present paper concerns on the estimative of the pressure loss and entropy variation in an isothermal fluid flow, considering real gas effects. The 1D formulation is based on the isothermal compressibility module and on the thermal expansion coefficient in order to be applicable for both gas and liquid as pure substances. It is emphasized on the simple methodology description, which establishes a relationship between the formulation adopted for ideal gas and another considering real gas effects. A computational procedure has been developed, which can be used to determine the flow properties in duct with a variable area, where real gas behavior is significant. In order to obtain quantitative results, three virial coefficients for Helium equation of state are employed to determine the percentage difference in pressure and entropy obtained from different formulations. Results are presented graphically in the form of real gas correction factors, which can be applied to perfect gas calculations.

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. (C) 2010 Elsevier Ltd. All rights reserved.

Leak detection in petroleum pipelines using a fuzzy system

A methodology for pipeline leakage detection using a combination of clustering and classification tools for fault detection is presented here. A fuzzy system is used to classify the running mode and identify the operational and process transients. The relationship between these transients and the mass balance deviation are discussed. This strategy allows for better identification of the leakage because the thresholds are adjusted by the fuzzy system as a function of the running mode and the classified transient level. The fuzzy system is initially off-line trained with a modified data set including simulated leakages. The methodology is applied to a small-scale LPG pipeline monitoring case where portability, robustness and reliability are amongst the most important criteria for the detection system. The results are very encouraging with relatively low levels of false alarms, obtaining increased leakage detection with low computational costs. (c) 2005 Elsevier B.V. All rights reserved.

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 cutting's 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. Cutting's 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 cutting's 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 Multi layer 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 one's efficiency.

Periodontal Tissue Engineering After Tooth Replantation

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

PDGF-B gene therapy accelerates bone engineering and oral implant osseointegration

Platelet-derived growth factor-BB (PDGF-BB) stimulates repair of healing-impaired chronic wounds such as diabetic ulcers and periodontal lesions. However, limitations in predictability of tissue regeneration occur due, in part, to transient growth factor bioavailability in vivo. Here, we report that gene delivery of PDGF-B stimulates repair of oral implant extraction socket defects. Alveolar ridge defects were created in rats and were treated at the time of titanium implant installation with a collagen matrix containing an adenoviral (Ad) vector encoding PDGF-B (5.5 x 10(8) or 5.5 x 10(9) pfu ml (1)), Ad encoding luciferase (Ad-Luc; 5.5 x 10(9) pfu ml (1); control) or recombinant human PDGF-BB protein (rhPDGF-BB, 0.3 mg ml (1)). Bone repair and osseointegration were measured through backscattered scanning electron microscopy, histomorphometry, microcomputed tomography and biomechanical assessments. Furthermore, a panel of local and systemic safety assessments was performed. Results indicated that bone repair was accelerated by Ad-PDGF-B and rhPDGF-BB delivery compared with Ad-Luc, with the high dose of Ad-PDGF-B more effective than the low dose. No significant dissemination of the vector construct or alteration of systemic parameters was noted. In summary, gene delivery of Ad-PDGF-B shows regenerative and safety capabilities for bone tissue engineering and osseointegration in alveolar bone defects comparable with rhPDGF-BB protein delivery in vivo. Gene Therapy (2010) 17, 95-104; doi: 10.1038/gt.2009.117; published online 10 September 2009

Characterization and in vitro evaluation of bacterial cellulose membranes functionalized with osteogenic growth peptide for bone tissue engineering

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Bacterial cellulose-collagen nanocomposite for bone tissue engineering

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Action mechanisms of petroleum hydrocarbons present in waters impacted by an oil spill on the genetic material of Allium cepa root cells

Chromosomal aberration (CA) assays have been widely used, not only to assess the genotoxic effects of chemical agents, but also to evaluate their action mechanisms on the genetic material of exposed organisms. This is of particular interest, since such analyses provide a better knowledge related to the action of these agents on DNA. Among test organisms, Allium cepa is an outstanding species due to its sensitivity and suitable chromosomal features, which are essential for studies on chromosomal damage or disturbances in cell cycle. The goal of the present study was to analyze the action mechanisms of chemical agents present in petroleum polluted waters. Therefore, CA assay was carried out in A. cepa meristematic cells exposed to the Guaeca river waters, located in the city of Sao Sebastiao, SP, Brazil, which had its waters impacted by an oil pipeline leak. Analyses of the aberration types showed clastogenic and aneugenic effects for the roots exposed to the polluted waters from Guaeca river, besides the induction of cell death. Probably all the observed effects were induced by the petroleum hydrocarbons derived from the oil leakage. (C) 2008 Elsevier B.V. All rights reserved.

Induction of micronucleus and nuclear alterations in fish (Oreochromis niloticus) by a petroleum

In this study, micronucleus and nuclear alterations tests were performed on erythrocytes of Oreochromis niloticus (Perciformes, Cichlidae) in order to evaluate the water quality of the Atibaia river, in an area that receives effluents discharge of a petroleum refinery and also to evaluate the effectiveness of the treatments used by the refinery. Water samples were collected in five different sites related with a refinery from São Paulo State, Brazil. For the micronucleus and nuclear alterations tests, O. niloticus specimens were exposed for 72 h to the water samples and in pure ground water (negative control). The results herein obtained indicated that the treatments used by the refinery diminished the cytogenetic damage; however they were not fully effective, since the final mill has induced damages in the genetic material of the test organism. (C) 2008 Elsevier B.V. All rights reserved.