Vast amounts of information are collected during subsurface exploration and development on a regular basis but at present, not all of this data is used efficiently to support decisions made throughout all stages of subsurface reservoir lifetime.
Subsurface monitoring for Prediction and Decision-making primarily involves using and interpreting data measured during drilling or logging such as from wire-line or image logs, cores, formation microscanners and pressure measurements. These data are then integrated with data from other technologies such as remote sensing or seismic interpretation to give a multi-faceted approach to data analysis. Such large quantities of data are often difficult to handle and manual data interpretation and integration remains a common practice, which limits the capability for whole data screening and exploration.
There are therefore challenges associated with how to make the most efficient use of the accumulated and streamed information and identify the data of particular value for prediction and operating decisions. These could be addressed in the Lyell Centre using techniques of data exploration and knowledge discovery that have been developed in the last decades.
Research into these techniques could explore novel methods of data mining and Geomatics for tackling the problem of intelligent computer screening of acquired data. This could result in identifying critical events in the data domain that may affect modelling decisions. The research, which integrates with EGIS’s current expertise in innovative mapping and risk prediction, could also link with BGS themes related to data acquisition and particularly NERC’s emphasis on Big Data.