Well Services Geophysical Well Log Analysis (GWLA®) - Integrated Services
Well logs need to be carefully conditioned or pre-processed prior to their use in a modeling workflow. We term this step Geophysical Well Log Analysis or GWLA. The specifics of each project or job can be varied to suit the needs of the client and the characteristics of the data available.
A representative GWLA display
Well log analysis for geophysics differs in several important ways from standard log analysis. In most cases well logs are obtained for the purpose of estimating recoverable hydrocarbon volumes. Therefore the zone of interest is mainly the producing interval(s). For geophysics, well logs form the basis for relating seismic properties to the reservoir. While we are still concerned about producing intervals, we also need good information about all of the rock through which the seismic waves have passed. Therefore our zone of interest is much larger, encompassing everything from the surface to TD. This means we have to take great care to correctly treat the log data through shales, across drilling breaks, casing points, and washouts.
In all cases the log data will require some editing, normalization, and interpretation before they can be used in a reservoir study. Several specific analysis steps are followed:
De-spike and filter to remove or correct anomalous data points
Normalize logs from all of the selected wells to determine the appropriate ranges and cutoffs for porosity, clay content, water resistivity, etc.
Compute the volumetric curves such as total porosity, Vclay, and Sw
Calibrate the volumetric curves to core data if available
Correct sonic and density logs for mud filtrate invasion if needed
Compute Vshear on all wells
In wells where important log curves are missing, we reconstruct those curves synthetically. There are two ways this is done. The first is through application of modern rock physics principles. For example, several deterministic methods exist for obtaining density from sonic logs or sonic logs from resistivity. The other approach is to use neural network technology. This is often required when no direct physical relationship is available.