Integrating Big Data to Investigate the Impact of Shut-In on Unconventional Wells

Production shut-in is common for all E&P operators. Well shut-in could be due to planned surface or downhole maintenance, unplanned equipment failures, or in response to offtake constraints. Industry so far has not developed a common understanding on whether frequent/high downtime percentage has a long-term impact on well production. This work has identified a group of wells that experienced high downtime due to unplanned surface constraints and attempted to answer the question whether an operator should shut-in or choke back wells in response to surface constraints. In general, it is assumed that there is no long-term production impact rather than deferred production loss from individual well shut-in. However, this assumption may not be valid. This study analyzes production performance of unconventional wells with different downtime scenarios. A machine learning auto forecast algorithm based on SPEE monograph 4 was developed and used to estimate ultimate recovery (EUR) forecasts for multiple completions vintages of more than 2000+ wells. The change in EUR and key parameters like decline rate are studied to assess whether statistically significant correlation to percentage of downtime exists. This work indicates potential for reduced EUR with long shut-in. In addition, shut-in early in the life of the well appears to be more detrimental than shut-in at later time.