Alexandre Emerick
Technical Consultant Petrobras Research CenterMr. Emerick has previously held positions of Reservoir Engineer and Senior Petroleum Engineer with Petrobras Research Center.
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Most history-matching studies have fixed resources—that is, the team of engineers and geoscientists is predetermined. Moreover, the deadlines are always very strict. This constrained scenario often leads to an unfortunate result: The quality of the study suffers. -
Most history-matching studies have fixed resources—that is, the team of engineers and geoscientists is predetermined. Moreover, the deadlines are always very strict. This constrained scenario often leads to an unfortunate result: The quality of the study suffers.
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Machine-learning methods have gained tremendous attention in the last decade. The underlying idea behind machine learning is that computers can identify patterns and learn from data with minimal human intervention. This is not very different from the notion of automatic history matching.
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One unfortunate consequence of a base-case model, however, is the risk of an anchoring effect, in which case we may underestimate uncertainty. Essentially, the anchoring effect refers to our tendency to rely too heavily on the information offered, introducing a bias in the model-construction process
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I believe the main development in history matching is the recognition that it can be formulated as a Bayesian inference problem. History matching is no longer a searching process to find the best model. Instead, history matching is a process of mitigating uncertainty in light of new information.
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History matching is only one part of something more comprehensive—reservoir modeling.
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Nowadays, there is a wealth of history-matching methods. As a result, we observe that it is becoming a common industrial practice to generate multiple history-matched models, even for fairly complex problems with thousands of uncertainty variables and a large amount of data.
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In the past decade, there has been a great deal of research and progress in the development of computational methods to assist reservoir engineers in the arduous task of history matching their models.
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Reservoir simulation is essential in the decision-making process for the development and management of petroleum reservoirs. A simulation model can predict the reservoir behavior under various operating conditions.
