Production Metric Analytics in the Wolfcamp Formation

Abstract

Successfully discriminating future lateral wells with above type-curve performance from those with below-type performance is important for financial planning and field development. To address this, a predictive analytics approach is utilized, that will leverage an asset’s financial position through infill drilling prioritization.

While most lateral wells drilled into unconventional shale reservoirs or benches in the Permian Basin will be economical, some wells will have higher yields or better performance than others due to variations in shale mineralogy. Geophysically, we can systematically identify variations in silica content, effective porosity and total organic carbon content; however, determining the best combination of these mineralogies in terms of higher yields can be problematic. The complexity and high variability of unconventional reservoirs require a more generalized solution to classify asset areas with higher production potential, beyond simple cross-plotting of wells and one or two seismic attributes and or simultaneous prestack inversions and transforms. To that end, a predictive analytic production model is constructed using multi-attribute seismic data and normalized production metrics from lateral wells.

Predictive analytics is a proactive and forward-looking methodology to better anticipate or prognosticate future outcomes (i.e. good, better and best lateral locations) using multiple weighted data sets on an asset scale, encompassing 10s of square miles. This predictive analytics approach incorporates multi-attribute 3D data with production monitoring of lateral wells to build a production metric model, which objectively determines which intervals or benches will better perform or underperform within an unconventional shale reservoir.

AAPG Datapages/Search and Discovery Article #90323 ©2018 AAPG Annual Convention and Exhibition, Salt Lake City, Utah, May 20-23, 2018