Exploration and production decisions are complex due to several uncertainties: uncertainty in the geologic properties, seismic imaging, repeatability, reservoir structure, rock and fluid properties, etc. Furthermore, several decisions need to be made over the entire life cycle, and often it is not clear how current decisions might affect the future bottom line. In the exploration and characterization stage, more information is acquired about the reservoir structure, reservoir rock and fluid properties, and the spatial distributions of lithology, porosity, and saturations. Exactly how much information needs to be acquired is a critical decision at this stage. Later, decisions have to be made about the technical and economic feasibility of seismically monitoring the reservoir during production. Simple tools like decision trees have become popular (e.g., Newendorp and Schuyler, 2000) but complex situations demand the use of more sophisticated decision-analysis tools and their integration with the existing tools of reservoir geophysics and engineering. Although several case studies show that 4D seismic tests are extremely useful (e.g., Lumley, 2001), in practice it may often be unclear whether they will actually add value to the production process for a particular reservoir. In their seminal paper about assessing the technical risk of a 4D project, Lumley et al. (1997) propose a scorecard method to score a particular reservoir based on reservoir and seismic properties. The scorecard provides a quantitative framework that aids in solving one of the most important decisions: Should one perform a 4D seismic survey? We build on the paper by Lumley et al. (1997) to model the 4D monitoring decision problem using influence diagrams (IDs). After explaining influence diagrams, we build a simple ID model and apply it to two reservoir-monitoring scenarios. Finally, we highlight some of the advantages of using influence diagrams.

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