Uncertainty mitigation through the integration with production history matching

Orientadores: Denis Jose Schiozer, Celio MaschioDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecanica e Instituto de GeocienciasResumo: A escassez de informações de qualidade introduz risco ao processo de previsão da produção de petróleo tornando imprescindível o ajuste de histórico de produção, que é a calibração do modelo a partir da resposta produtiva registrada. O ajuste é um problema inverso, em que diferentes combinações dos valores dos parâmetros do reservatório podem conduzir a respostas aceitáveis, especialmente quando o grau de incerteza desses parâmetros é elevado. A integração do ajuste de histórico com a análise probabilística dos cenários representativos conduz à obtenção de uma metodologia para detecção dos modelos calibrados dentro de uma faixa de aceitaçãodefinida. O tratamento de atributos interdependentes de influência global e local e o avanço por etapas são necessários. Desta forma, o objetivo deste trabalho é apresentar uma metodologia que integra a análise de incertezas com o ajuste de histórico em modelos de reservatórios complexos. Este procedimento auxilia a detectar os atributos incertos críticos e sua possível variação com o intuito de estimar a faixa representativa das reservas a desenvolver. Não é alvo obter o melhor ajuste determinístico, mas refletir como o histórico possibilita uma mitigação das incertezas. Assim, a meta é usar modelos mais complexos e aprimorar a metodologia iniciada por Moura Filho (2006), desenvolvida para um modelo teórico simples. São utilizados dois casos de estudo de complexidade similar. Um deles referente ao reservatório do Campo de Namorado, utilizado para verificar e validar, em nível global, a aplicação da metodologia. Na etapa de aplicação, é usado um modelo sintético construído a partir de dados de afloramentos reais no Brasil e compreendendo informações de campos análogos com sistemas turbidíticos depositados em águas profundas. Os métodos aplicados, mediante a redefinição das probabilidades associadas e níveis dos atributos incertos, permitem: (1) reduzir a faixa de ajustes possíveis e obter modelos mais confiáveis; (2) identificar e condicionar à incerteza presente em função dos dados registrados; (3) diminuir os intervalos de incerteza dos parâmetros críticos identificados; (4) demarcar os limites seguros do desempenho futuro do reservatório. A conseqüência é um aumento da confiança no uso da simulação como ferramenta auxiliar do processo decisório. Além disso, procura-se fornecer à equipe multidisciplinar uma metodologia para reduzir o tempo empregado no gerenciamento de múltiplos atributos incertos na etapa de ajuste do modelo.Abstract: The lack of reliable data or with high degree of uncertainty yields risk to the process of production prediction making the history matching, the model calibration from the registered field production indispensable. History matching is an inverse problem and, in general, different combinations of reservoir attributes can lead acceptable solutions, especially whit high degree of uncertainty of these attributes. The integration of history matching with a probabilistic analysis of representative models yields a way to detect matched models inside an acceptance interval, providing more efficient framework for predictions. It is necessary to consider dependences between global and local attributes. The scope of this work is to present a methodology that integrates the uncertainty analysis with the history matching process in complex models. This procedure helps to detect critical subsurface attributes and their possible variation, in order to estimate a representative range of the additional reserves to be developed. . It is not an objective to obtain the best deterministic model, but to mitigate uncertainties by using observed data. The objective is to improve the methodology initiated by Moura Filho (2006), applied to a simple model. The methodology presented in this work is applied in two study cases with similar complexity. Firstly, the methodology is verified and validated, on global scale, in Namorado Field. Then, at the application stage, it is chosen a synthetic reservoir model made from real outcrop data of Brazil and involving information from analog fields with turbiditic systems deposited in deep waters. The methodology allows the redefinition of the probability and levels of the dynamic and static attributes in order: (1) to reduce the group of possible history matching obtaining more realistic models; (2) to identify the existent uncertainty as a function of observed data; (3) to decrease the uncertainty range of critical reservoir parameters; (4) to increase the confidence in production forecast. One contribution of this work is to present a quantitative approach to increase the reliability on the use of reservoir simulation as an auxiliary tool in decision processes. Another purpose of this work is to provide a procedure to reduce the consumed time to handle multiples uncertainty attributes during the history matching.MestradoReservatórios e GestãoMestre em Ciências e Engenharia de Petróle

“FACTORES DE RIESGO PARA EL DESARROLLO DE DISPLASIA BRONCOPULMONAR EN EL HOSPITAL MATERNO PERINATAL MÓNICA PRETELINI SÁENZ DEL PERÍODO DEL 1º DE DICIEMBRE 2015 AL 1º DE OCTUBRE 2016”

RESUMEN Introducción: La Displasia Broncopulmonar (DBP), es una de las secuelas más frecuentes que afectan a los recién nacidos prematuros, de peso muy bajo al nacer. Posterior a la introducción de la ventilación con presión positiva y el uso de surfactante exógeno para el tratamiento de los recién nacidos con síndrome de dificultad respiratoria (SDR) se llevo a un incremento de esta patología por aumento de la sobrevida. Objetivos: Determinar los factores de riesgo asociados con la DBP en los recién nacidos de 26 a 36 semanas de gestación en la Unidad de Cuidados Intensivos Neonatales del Hospital Materno Perinatal “Mónica Pretelini Sáenz” en el periodo del 1º de diciembre 2015 al 1º de octubre de 2016. Metodología: Se realizó un estudio descriptivo, observacional y ambispectivo de casos y controles en recién nacidos prematuros menores de 36 semanas de gestación ingresados a la Unidad de Cuidados Intensivos Neonatales del Hospital Materno Perinatal “Mónica Pretelini Sáenz” Con una muestra no probabilística de 66 pacientes en el periodo del 1º de diciembre del 2015 al 1º de octubre del 2016. Resultados: Los principales factores de riesgo observados para el desarrollo de DBP fueron: edad gestacional encontrando el 60.6% de los recién nacidos eran menores de 32 semanas de gestación; peso al nacimiento que oscilo entre 1000 a 1499 gramos en un 69.7%. El sexo femenino con un 51.5%, neumonía en el 63.3%, sepsis en el 96.9%, empleo PIP arriba de 18 cmH2O en el 78.8% y uso de FiO2 elevado durante tiempo prolongado. Conclusiones: En nuestro estudio encontramos que el 4% de la población estudiada desarrollo DBP siendo el 54.4%(N=18) leve. Con predominio en el sexo femenino 51.5% siendo la sepsis, la neumonía y el empleo de ventilación mecánica con presiones inspiratorias pico elevadas en el 78.7% y el uso de FiO2 mayores al 40% por más de 4 días los más representativos, consideramos necesario mayor población para mejorar confiabilida

Petroleum reservoir uncertainty mitigation through the integration with production history matching

This paper presents a new methodology to deal with uncertainty mitigation using observed data, integrating the uncertainty analysis and the history matching processes. The proposed method is robust and easy to use, offering an alternative way to traditional history matching methodologies. The main characteristic of the methodology is the use of observed data as constraints to reduce the uncertainty of the reservoir parameters. The integration of uncertainty analysis with history matching naturally yields prediction under uncertainty. The workflow permits to establish a target range of uncertainty that characterize a confidence interval of the probabilistic distribution curves around the observed data. A complete workflow of the proposed methodology was carried out in a realistic model based on outcrop data and the impact of the uncertainty reduction in the production forecasting was evaluated. It was demonstrated that for complex cases, with a high number of uncertain attributes and several objective-function, the methodology can be applied in steps, beginning with a field analysis followed by regional and local (well level) analyses. The main contribution of this work is to provide an interesting way to quantify and to reduce uncertainties with the objective to generate reliable scenario-based models for consistent production prediction.147158Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

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