Reshaping Reservoir Economics with ShaleIQ

Unleashing a new era of efficiency and innovation

The energy industry is at a critical point where there is a constant juggling act to meet demand, maintain capital efficiency and debt reduction, while moving towards sustainability. This leaves organizations with shale assets facing a particular challenge given the high decline rate and short life span of many of their unconventional wells.

To mitigate these issues and ensure optimal recovery, companies often explore infill (or child) wells. This strategy utilizes existing facilities, reduces surface disruption, and can improve production by accessing reserves that were otherwise inaccessible. However, infill wells can also interfere with existing (or parent) wells, resulting in parent-child interference, potentially impacting the production from both infill and existing parent wells. Being able to model and predict optimal well locations and density becomes critical and is where predictive modelling is necessary. Organizations need a method that is both accurate and fast, two items that are often mutually exclusive in the modelling world. Until now.