The role of geological uncertainty in developing combined geological and potential field inversions

Abstract

Recently, implicit model building techniques have been developed which use, for example, geo-statistical methods to interpolate boundary orientations as a scalar field. Boundaries are implicitly formulated as iso-values of that field. Using more than one potential allows modelling for intrusion and unconformities. This technique is attractive because it makes 3D geological modeling a repeatable task and model uncertainty can be estimated from the geostatistical estimators. However, this uncertainty does not take into account the error on field measurements, nor estimates of how relevant are the measurements to the modelled structures. Lastly, this uncertainty does not take into account possible variation in the knowledge based information which is very often interpretative, such as structural evolution history, fault network, and overprinting relationships with themselves and the different formations. We present an innovative method that will simulate numerous (millions of) geological models from a single initial structural dataset, taking into account these variables. These models are used to estimate geological uncertainty, highlighting future areas of research or data collection. A series of best models are then assessed against potential field inversions and modified to better fit potential field data. In the end, the models that both better fit geological input data and potential field data will be retained. A best probable model will be proposed that will satisfy geophysical data as well as geological data. To assess the models against the initial geological data input, we develop geological objective functions based on (e.g.) locations and gradients of boundaries. It is our intent to combine these objectives functions with classical geophysical objective functions to provide a new method for combined geological and potential field inversions.