Modelo de campo de gás não convencional do tipo indireto: caso Elsinore, Oeste do Texas, EUA

Pós-graduação em Geologia Regional - IGCE

Caracterização do sistema petrolífero das águas rasas na Bacia do Espírito Santos com a aplicação de técnicas geofísicas

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

Fácies e associação de fácies da formação Pirambóia na região de Descalvado/SP

The Pirambóia Formation is a lithostratigraphic unit of the Paraná Basin, positioned between the Corumbataí (lower) and Botucatu (upper) Formations on the eastern edge of the basin. This unit is focused by many studies due to its great importance as an essential component in the Guarani Aquifer System (SAG) and the petroleum system “Irati-Pirambóia”, as excellent reservoirs. The Pirambóia Formation is historically the subject of several controversies on issues like age, contact relationships with the upper unit and depositional paleoenvironment. Despite these aspects, the Pirambóia Formation is commonly taken to be of Triassic age and is considered a product of wet aeolian systems, with plenty of wet interdunes and subordinate fluvial facies. In this work, by using techniques such as facies analysis, depositional architecture and facies association, facies of this unit were characterized and their depositional paleoenvironment was inferred particularly in Jundu Mining, region of Descalvado in northeastern São Paulo. Techniques such as grain size and petrographic analyses, aimed to characterize this unit as a potential reservoir rock. Five facies were described for the Pirambóia Formation in the studied region: St, Sh, Sm, Sr and Gt facies, generated by sedimentary processes of the bottom load type, mostly under low flow regime (with exception for the Sh facies, which is formed by upper flow regime processes). In addition to that, four facies associations were recognized from the architectural elements, primarily contained within the main channel: complex channel bars, composed by foreset macroforms (FM), sandy bedforms (SB) and gravel bars and bedforms (GB); flood deposits, constituted by laminated sand sheets (LS); deposits of hyperconcentrated flows and eolian deposits. It was interpreted that the Pirambóia Formation in Descalvado (SP) is the record of the sedimentation of braided rivers, with dunes and interdunes deposits...

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.

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.

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.

Modelling an expert GIS system based on knowledge to evaluate oil spill environmental sensitivity

Oil spills cause great damage to coastal habitats, especially when rapid and suitable response measures are not taken. Establishing high priority areas is fundamental for the operation of response teams. Under this context and considering the need for keeping all geographical information up-to-date for emergencial use, the present study proposes employing a decision tree coupled with a knowledge-based approach using GIS to assign oil sensitivity indices to Brazilian coastal habitats. The modelled system works based on rules set by the official standards of Brazilian Federal Environment Organ. We tested it on one of the littoral regions of Brazil where transportation of petroleum is most intense: the coast of the municipalities of Sao Sebastiao and Caraguatatuba in the northern littoral of São Paulo state, Brazil. The system automatically ranked the littoral sensitivity index of the study area habitats according to geographical conditions during summer and winter; since index ranks of some habitats varied between these seasons because of sediment alterations. The obtained results illustrate the great potential of the proposed system in generating ESI maps and in aiding response teams during emergency operations. (C) 2009 Elsevier Ltd. All rights reserved.

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.

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 handle continuous processes like those in chemical and petroleum industries In its architeture, 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 to managing an off-shore installation for petroleum production onto a submarine separation process is described. © 2008 IEEE.

An intelligent system to detect drilling problems through drilled cuttings return analysis

Cuttings return analysis is an important tool to detect and prevent problems during the petroleum well drilling process. Several measurements and tools have been developed for drilling problems detection, including mud logging, PWD and downhole torque information. Cuttings flow meters were developed in the past to provide information regarding cuttings return at the shale shakers. Their use, however, significantly impact the operation including rig space issues, interferences in geological analysis besides, additional personel required. This article proposes a non intrusive system to analyze the cuttings concentration at the shale shakers, which can indicate problems during drilling process, such as landslide, the collapse of the well borehole walls. Cuttings images are acquired by a high definition camera installed above the shakers and sent to a computer coupled with a data analysis system which aims the quantification and closure of a cuttings material balance in the well surface system domain. No additional people at the rigsite are required to operate the system. Modern Artificial intelligence techniques are used for pattern recognition and data analysis. Techniques include the Optimum-Path Forest (OPF), Artificial Neural Network using Multilayer Perceptrons (ANN-MLP), Support Vector Machines (SVM) and a Bayesian Classifier (BC). Field test results conducted on offshore floating vessels are presented. Results show the robustness of the proposed system, which can be also integrated with other data to improve the efficiency of drilling problems detection. Copyright 2010, IADC/SPE Drilling Conference and Exhibition.

Cellular damages in the Allium cepa test system, caused by BTEX mixture prior and after biodegradation process

Petroleum and derivatives have been considered one of the main environmental contaminants. Among petroleum derivatives, the volatile organic compounds benzene, toluene, ethylbenzene and xylene (BTEX) represent a major concern due to their toxicity and easy accumulation in groundwater. Biodegradation methods seem to be suitable tools for the clean-up of BTEX contaminants from groundwater. Genotoxic and mutagenic potential of BTEX prior and after biodegradation process was evaluated through analyses of chromosomal aberrations and MN test in meristematic and F 1 root cells using the Allium cepa test system. Seeds of A. cepa were germinated into five concentrations of BTEX, non-biodegraded and biodegraded, in ultra-pure water (negative control), in MMS 4×10 -4M (positive control) and in culture medium used in the biodegradation (blank biodegradation control). Results showed a significant frequency of both chromosomal and nuclear aberrations. The micronucleus (MN) frequency in meristematic cells was significant for most of tested samples. However, MN was not present in significant levels in the F 1 cells, suggesting that there was no permanent damage for the meristematic cell. The BTEX effects were significantly reduced in the biodegraded samples when compared to the respective non-biodegraded concentrations. Therefore, in this study, the biodegradation process showed to be a reliable and effective alternative to treat BTEX-contaminated waters. Based on our results and available data, the BTEX toxicity could also be related to a synergistic effect of its compounds. © 2011 Elsevier Ltd.