Chromosome aberration and micronucleus frequencies in Allium cepa cells exposed to petroleum polluted water - A case study

In the present study, we applied Chromosome Aberration (CA) and Micronucleus (MN) tests to Allium cepa root cells, in order to evaluate the water quality of Guaeca river. This river, located in the city of Sao Sebastiao, SP, Brazil, had been affected by an oil pipeline leak. Chemical analyses of Total Petroleum Hydrocarbons (TPHs) and Polycyclic Aromatic Hydrocarbons (PAHs) were also carried out in water samples, collected in July 2005 (dry season) and February 2006 (rainy season) in 4 different river sites. The largest CA and MN incidence in the meristematic cells of A. cepa was observed after exposure to water sample collected during the dry season, at the spring of the river, where the oil leak has arisen. The F, cells from roots exposed to such sample (non-merismatic region) were also analyzed for the incidence of MN, showing a larger frequency of irregularities, indicating a possible development of CA into MN. Lastly, our study reveals a direct correlation between water chemical analyses (contamination by TPHs and PAHs) and both genotoxic and mutagenic effects observed in exposed A. cepa cells. (C) 2007 Elsevier B.V. All rights reserved.

BTEX biodegradation by bacteria from effluents of petroleum refinery

Groundwater contamination with benzene, toluene, ethylbenzene and xylene (BTEX) has been increasing, thus requiring an urgent development of methodologies that are able to remove or minimize the damages these compounds can cause to the environment. The biodegradation process using microorganisms has been regarded as an efficient technology to treat places contaminated with hydrocarbons, since they are able to biotransform and/or biodegrade target pollutants. To prove the efficiency of this process, besides chemical analysis, the use of biological assessments has been indicated. This work identified and selected BTEX-biodegrading microorganisms present in effluents from petroleum refinery, and evaluated the efficiency of microorganism biodegradation process for reducing genotoxic and mutagenic BTEX damage through two test-systems: Allium cepa and hepatoma tissue culture (HTC) cells. Five different non-biodegraded BTEX concentrations were evaluated in relation to biodegraded concentrations. The biodegradation process was performed in a BOO Trak Apparatus (HACH) for 20 days, using microorganisms pre-selected through enrichment. Although the biodegradation usually occurs by a consortium of different microorganisms, the consortium in this study was composed exclusively of five bacteria species and the bacteria Pseudomonas putida was held responsible for the BTEX biodegradation. The chemical analyses showed that BTEX was reduced in the biodegraded concentrations. The results obtained with genotoxicity assays, carried out with both A. cepa and HTC cells, showed that the biodegradation process was able to decrease the genotoxic damages of BTEX. By mutagenic tests, we observed a decrease in damage only to the A. cepa organism. Although no decrease in mutagenicity was observed for HTC cells, no increase of this effect after the biodegradation process was observed either. The application of pre-selected bacteria in biodegradation processes can represent a reliable and effective tool in the treatment of water contaminated with BTEX mixture. Therefore, the raw petroleum refinery effluent might be a source of hydrocarbon-biodegrading microorganisms. (c) 2010 Elsevier B.A. All rights reserved.

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.

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.

Determination of labile barium in petroleum-produced formation water using paper-based DGT samplers

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

Prospecting sugarcane genes involved in aluminum tolerance

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

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.

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.

Application of ARX neural networks to model the rate of penetration of petroleum wells drilling

Bit performance prediction has been a challenging problem for the petroleum industry. It is essential in cost reduction associated with well planning and drilling performance prediction, especially when rigs leasing rates tend to follow the projects-demand and barrel-price rises. A methodology to model and predict one of the drilling bit performance evaluator, the Rate of Penetration (ROP), is presented herein. As the parameters affecting the ROP are complex and their relationship not easily modeled, the application of a Neural Network is suggested. In the present work, a dynamic neural network, based on the Auto-Regressive with Extra Input Signals model, or ARX model, is used to approach the ROP modeling problem. The network was applied to a real oil offshore field data set, consisted of information from seven wells drilled with an equal-diameter bit.

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.

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. © 2006 IEEE.

Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm

This paper describes an investigation of the hybrid PSO/ACO algorithm to classify automatically the well drilling operation stages. The method feasibility is demonstrated by its application to real mud-logging dataset. The results are compared with bio-inspired methods, and rule induction and decision tree algorithms for data mining. © 2009 Springer Berlin Heidelberg.

Fast petroleum well drilling monitoring through optimum-path forest

Automatic inspection of petroleum well drilling has became paramount in the last years, mainly because of the crucial importance of saving time and operations during the drilling process in order to avoid some problems, such as the collapse of the well borehole walls. In this paper, we extended another work by proposing a fast petroleum well drilling monitoring through a modified version of the Optimum-Path Forest classifier. Given that the cutting's volume at the vibrating shale shaker can provide several information about drilling, we used computer vision techniques to extract texture informations from cutting images acquired by a digital camera. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and effciency. We used the Optimum-Path Forest (OPF), EOPF (Efficient OPF), Artificial Neural Network using Multilayer Perceptrons (ANN-MLP) Support Vector Machines (SVM), and a Bayesian Classifier (BC) to assess the robustness of our proposed schema for petroleum well drilling monitoring through cutting image analysis.

Biodiversity prospecting: a new panacea for development?

Includes bibliography