Y. Zee Ma, 2011. "Uncertainty Analysis in Reservoir Characterization and Management: How Much Should We Know About What We Don't Know?", Uncertainty Analysis and Reservoir Modeling: Developing and Managing Assets in an Uncertain World, Y. Zee Ma, Paul R. La Pointe
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A reservoir is the result of geologic processes and is not randomly generated. However, many uncertainties exist in reservoir characterization because of subsurface complexity and limited data. Uncertainties can be mitigated by gaining more information and/or using better science and technology. How much uncertainties should be mitigated depends on the needs of decision analysis for reservoir management and the cost of information. Uncertainty analysis should be conducted for investigational analyses and for decision analysis under uncertainty and risk. To know what needs to be known and what can be known should be the main focal points of uncertainty analysis in reservoir characterization and management.
No uncertainty exists in a reservoir, only uncertainty in our understanding and description of it. A reservoir is not random; it was deposited geologically and evolved into a unique hydrocarbon-bearing entity. Matheron (1989, p. 4) once remarked “Randomness is in no way a uniquely defined or even definable property of the phenomenon itself. It is only a characteristic of the model.” If a reservoir is not random, why should we use a probabilistic method to build a reservoir model? If a reservoir is deterministic in nature, why should we analyze uncertainties in reservoir characterization?
Before answering these questions, it is useful to briefly review the history of uncertainty analysis and two doctrines regarding the course of nature in other scientific fields. Many consider that, historically, probability as a measure of uncertainty was first introduced by monks at the Port-Royal monastery in Paris (Arnauld and Nicole, 1662), although this may not be universally agreed on. The uncertainty notion in their exemplary argument, that fear of harm should be proportional to the probability of an event inspired the development of the probability theory for uncertainty analysis, which in the beginning was applied mostly in games of chance. Uncertainty analysis was largely ignored in the scientific world until the advent of quantum mechanics, which revolutionized physics in the 20th century. Although nearly all physical sciences were traditionally considered deterministic, many phenomena in quantum mechanics could not be explained by Newtonian deterministic laws (Popper, 1979; Hilgevoord and Uffink, 2006). As a result, indeterminism and uncertainty analysis (initially commonly referred to as uncertainty principle) were proposed by several prominent physicists in the late 1920s. The debate centered on whether the world was indeterministic or whether quantum mechanics simply provided an incomplete description of a fully deterministic world. This is now referred to as the “EPR paradox” (Honderich, 2005, p. 237), after the initials of Einstein, Podolsky, and Rosen, who related “randomness” and uncertainty in quantum mechanics to one's ignorance or inability to fully understand or describe some properties of reality (Einstein et al., 1935). This is very similar to reservoir descriptions. Is a reservoir random? Or is that our description cannot be complete while the reservoir itself is deterministic in the underlying course of nature?
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This publicaton presents a comprehensive survey of cutting-edge approaches used by industry to quantify the uncertainty in both conventional and unconventional reservoirs. Balanced between case histories and theory, the chapters in this volume equip experienced practitioners and those just entering the field with the theory, workflows and case history examples needed to make better reservoir management decisions in the uncertain world of the modern oil and gas field.