In this paper, we illustrate and discuss an a priori analysis on information valuation, known as the Value-of-Information (VOI) analysis. Although the cost required to collect such data, and conduct the HM, is significant, few companies conduct an a priori analysis of the information value from the data.Īlthough some studies have demonstrated the post-hoc value of HM data, few have demonstrated its a priori value i.e., the assessment required to determine whether it is worthwhile investing in gathering the data and conducting the HM. Downhole rate and pressure data are commonly collected for the purpose of uncertainty reduction through the HM process. HM approaches now include robust and efficient numerical algorithms with the ability to account for geological and petrophysical uncertainties. With the rapid increase in computing power over the past several decades, automatic or semi-automatic approaches to history matching (HM) have become viable replacements for the traditional manual HM approach.
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