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Uncertainty Management: a structured Approach towards Recognizing, Quantifying and Managing Sampling Biases in Subsurface Unknowns

Proceedings Title : Proc. Indon. Petrol. Assoc., 34th Ann. Conv., 2010

This paper illustrates, through field studies examples, why and how a structured approach towards managing uncertainties, and especially sampling biases, delivers valuable insights through the successive early asset life stages - exploration, appraisal and field development phases. In doing so, we respond to three fundamental questions. Firstly, What are the key uncertainties - those that matter? Field studies should begin with a comprehensive upfront assessment of uncertainties impact on historical and future well and field performance. However, often major factors are overlooked, leading to under-prediction of true outcome ranges and the inability to reconcile historical production. Our illustration is a large producing carbonate field, where after 15 years of production, large scale Karstification was finally evidenced to be the explanation for the field performance that couldnt be history matched with the measured matrix porosity and permeability ranges. Secondly What are realistic ranges for these uncertainties? Known Industry best practices include intensive expert-assist, integration of drilling, mud-logging and other traditional sources of data from the field, resorting to analogue benchmarking. Despite these, we often fail to understand and correct for sampling bias, which we show often leads to over-optimism. The paper will highlight why such biases are present and propose simple and practical methods to remove them. The case study is the volumetric assessment of a gas discoveries portfolio, where geophysical techniques were instrumental in exploration and appraisal drilling. Finally How these uncertainties will evolve with time? This is an important question for assessing Value of Information: the impact that additional data may have on the uncertainty range of uncertainties and the base case. Unconventional fractured plays, often characterized by data abundance but extreme variability, provide surprising insights on how uncertainties ranges evolve. This paper presents methods to develop confidence curves for important parameters.

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