DATA ANALYTICS

Off-the-shelf data analytics neglects the spatial correlation of subsurface data. Neglecting the spatial context imposes biased models and yield unrealistic/overoptimistic predictions. Also, the constant presence of geological trends is an opportunity to develop an automatic and reliable workflow for modeling trends. We address these problems by modifying hypothesis testing and automating the modeling of geological trends while honoring spatial data. The results provide support for decision making for future drilling and subsurface modeling. We recommend applying these methods to provide reliable, accurate estimates and robust models in the presence of data paucity.

Spatial and Multivariate Data Imputation

  • Methods that integrate spatial and multivariate  data for imputation of missing data with uncertainty

Spatial Hypothesis Testing

  • Robust methods to evaluate difference in well sets
  • Methods to test trend models

Truth model and well pads.

DPMDA01

DPMDA02

Work by Salazar (supervised by Pyrcz and Lake) and Liu (supervised by Pyrcz).