CSEM anomaly identification
Neville Barker and Daniel Baltar
Journal name: First Break
Issue: Vol 34, No 4, April 2016 pp. 47 - 50
Info: Article, PDF ( 1010.49Kb )
Price: € 30
In a sedimentary basin, everything typically present is highly resistive, except brine. A localized region of higher resistivity, whether identified on well logs or from controlled-source electromagnetic (CSEM) data, is therefore indicative of a local reduction in interconnected brine content. This may be due to the presence of fresh water, low-porosity lithologies (including salt, volcanics and some types of carbonate), or hydrocarbons. It is this first-order sensitivity to fluid presence and properties that makes CSEM information of high potential value in an exploration environment (Baltar et al., 2015; Fanavoll et al., 2014; Zweidler et al., 2015). In its simplest form, the use of CSEM for hydrocarbon detection can be considered a two-stage process. First, localized regions of higher resistivity need to be identified from the CSEM data. Second, these ‘anomalous’ regions must be interpreted in terms of their potential for being indicative of hydrocarbon presence. One might reasonably expect that the greater challenge is the latter: successfully predicting the geological cause of an anomalous resistivity. However, in our experience, the initial task of reliably identifying the anomalous features can prove equally challenging without an appropriate process. Early CSEM interpretation workflows, focusing on measurement interpretation, tended to use a ‘threshold normalized amplitude response’ (NAR) rule such as 15% (Hesthammer, 2010). This proved useful in the most simple geologies, but of less value in more complex settings, and also failed to account for relative data quality. Today, the starting-point for CSEM interpretation is subsurface resistivity images. With these, there still exists plenty of leeway for the choice of colour scale to have a large effect on the apparent sizes of any ‘red blobs’ in the study area. We detail here a simple and clear criterion to qualify a feature as anomalous with respect to its surroundings, which is analogous to that followed when qualifying the significance of seismic amplitude anomalies (Roden et al., 2014). We expand on an approach first proposed in Baltar and Roth, 2013, as part of a quantitative interpretation workflow for CSEM, providing a more practical guide to its application and implications. The concept is first illustrated with well data, before the method is detailed with CSEM examples.