For over fifty years, reservoir development around the world has covered different reservoir types and environments with vast technology, expertise and a growing variety of approaches. However, the predominant challenge from which a myriad of other field development issues arise has been on how to accurately characterise reservoir parameters because the obtained results are largely associated with uncertainties due to subsurface geological complexities.
This paper focuses on the evolving advances and current practices in reservoir uncertainty modelling and gives insight into the future trends. This work critically examines the foremost statistical reservoir uncertainty analysis approaches, the current probabilistic and stochastic uncertainty modelling workflows which are typically based on various numerical models, and the very recent development of embedding some artificial intelligence algorithms (which include genetic algorithms, artificial neural networks, Bayesian networks amongst others) in reservoir uncertainty modelling, which now points to a future of using more sophisticated machine learning systems for achieving reservoir models and parameters with higher confidence.
These evolving trends and approaches are discussed in more detail in this paper; with an in-depth analysis of the associated workflows, fundamental principles, strengths, weaknesses, robustness and economics of each approach. Also, reconciliation between the statistical, probabilistic, stochastic and artificial intelligence methods present a deep insight into the prospects of using artificial intelligence for optimising the modelling of reservoir uncertainties beyond the capabilities of conventional methods. Thus saving time and cost by quantifying the uncertainties in reservoir properties as well as regenerating new best-fit reservoir attributes using the robust uncertainty analysis networks and the pattern-recognition ability of machine learning networks.
Hence, this paper presents a comprehensive review of the various uncertainty analysis methods, and also analyses the confidence of artificial intelligence applications which are increasingly pushing the frontiers to improved uncertainty modelling.