Abstract
The Niobrara interval in the Denver-Julesberg (DJ) Basin contains several important unconventional hydrocarbon targets. However, the Niobrara is extensively faulted, which poses challenges for accurately landing and steering laterals in zone. Insight into small faulted structures in the Niobrara using traditional manual fault interpretation techniques is challenging because of the tuning thickness in seismic data. Fault throws less than the tuning thickness are difficult to interpret and incorporate into geosteering plans. Consequently, drillers frequently find themselves out of zone after crossing these small faults. Using independent information about fault locations and throws provided from multiple horizontal wells in the DJ Basin, this paper demonstrates the fault likelihood attribute (Hale, 2013) can resolve fault throws as small as 10 ft, allowing seismic-based well plans and unconventional project economics to be significantly improved.
Introduction
Traditional geoscience data interpretation workflows in support of well planning can be tedious and time consuming, requiring manual fault picking on seismic profiles in conjunction with horizon tracing and gridding for structural mapping. The emergence of unconventional resource plays requires both more efficient geoscience workflows to support round-the-clock drilling operations and more detailed structural interpretations to help ensure laterals are steered along sweet spots. Pre-drill mapping of small-scale faults is therefore of particular importance for safe operations and helping ensure that lateral wells stay in zone.
Recent advances in fault-sensitive post-stack seismic attributes are changing the way subsurface professionals think about faults and how to map them in 3D space. In particular, the fault likelihood attribute (Hale, 2013) has provided a breakthrough improvement in the quality of seismic-derived fault attributes. Typically, the fault likelihood attribute is used in exploration settings to rapidly generate a broad-scale structural interpretation, being used both as a guide to manual fault interpretation and as input into automated fault extraction algorithms. This paper demonstrates the value of fault likelihood in development settings for assisting the well planning and geosteering process.
