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Enhanced Oil Recovery

Field Planning and Development Strategies

  • 1st Edition - June 2, 2010
  • Latest edition
  • Authors: Vladimir Alvarado, Eduardo Manrique
  • Language: English

Enhanced-Oil Recovery (EOR) evaluations focused on asset acquisition or rejuvenation involve a combination of complex decisions, using different data sources. EOR projects have be… Read more

Description

Enhanced-Oil Recovery (EOR) evaluations focused on asset acquisition or rejuvenation involve a combination of complex decisions, using different data sources. EOR projects have been traditionally associated with high CAPEX and OPEX, as well as high financial risk, which tend to limit the number of EOR projects launched. In this book, the authors propose workflows for EOR evaluations that account for different volumes and quality of information. This flexible workflow has been successfully applied to oil property evaluations and EOR feasibility studies in many oil reservoirs. The methodology associated with the workflow relies on traditional (look-up tables, XY correlations, etc.) and more advanced (data mining for analog reservoir search and geology indicators) screening methods, emphasizing identification of analogues to support decision making. The screening phase is combined with analytical or simplified numerical simulations to estimate full-field performance by using reservoir data-driven segmentation procedures.

Key features

  • Case Studies form Asia, Canada, Mexico, South America and the United States
  • Assets evaluated include reservoir types ranging from oil sands to condensate reservoirs
  • Different stages of development and information availability are discussed

Readership

Petroleum Engineer, Reservoir Engineer, Production Engineer, Drilling Engineer, Completion Engineer, Operations Engineer, Drilling Manager, Operations Manager, Project Production Engineer, Project Reservoir Engineer

Table of contents

Introduction Part One: Methodology Conventional screening, Geological screening, Advanced screening, Evaluation of "soft variables", Performance prediction Part Two: Field Cases Type I: Lack of data and time constraints Case study A, Case study B, Case study CPart Three: Field Cases II: Not enough time to use sufficient data Case study D, Case study E, Case study F

Product details

  • Edition: 1
  • Latest edition
  • Published: June 2, 2010
  • Language: English

About the authors

VA

Vladimir Alvarado

Vladimir Alvarado é professor associado de Química e Engenharia de Petróleo na Universidade de Wyoming. É mestre em exploração e produção (IFP School – Paris) e PhD em Engenharia Química pela Universidade de Minnesota.

EM

Eduardo Manrique

Eduardo Manrique é Diretor da área de Engenharia de Reservatórios na Tiorco Inc., PhD em Química pela Universidad Central de Venezuela.

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