3D interpretations of outcropping sediments are inhibited by loss of section through erosion or by partial outcrop exposures. This part of my research focuses on translating measured and interpreted outcrop data into 3D models. The purpose is to model spatial uncertainty due to limited exposures and to validate interpretations by 3D spatial modeling. Furthermore, modeling can be used to test interpretations and hypothesis.
Utilizing outcrop analogs to build predictive subsurface modelsWith the introduction and adaptation of multiple-point geostatistics and event-based modeling, more geologic expertise can be included in geostatistical models. I am studying how we can directly transfer information and knowledge acquired from 3D outcrop modeling studies to build better subsurface geomodels for reservoir performance prediction.
2) Seismic reservoir characterizationI have developed a multi-scale, multi-attribute well-to-seismic calibration that facilitates subseismic scale interpretation of seismic attributes within a probabilistic framework. This methodology leverages bed-scale information directly from wellbore data or indirectly from analog data, calibrates this information to seismic attributes and produces a prediction of the probability of seismic pixel containing a sub-seismic scale pattern. For example, a search of “What is the probability of encountering an average sandstone bed thickness greater than 1 meter given my seismic attributes and analog lithofacies patterns?” would yield a 3D probability cube revealing locations where one is likely to encounter this condition.
3) Reservoir characterization to appraise reservoir connectivityThis research focuses on the critical link between geologic heterogeneity and reservoir connectivity. I focus on generating methods to translate the multiple scales of reservoir heterogeneity in geomodels to make more reliable recovery predictions and development scenarios. Studies investigate both static and dynamic measures of connectivity, and emphasize 3D connectivity.