Statistical corrections of fracture sampling bias in boreholes from acoustic televiewer logs

Abstract

Targeting structurally controlled permeability remains a challenge in high temperature geothermal fields, because of the difficulties in characterising faults and fractures and their behaviour within the reservoir. The large-scale structural framework of a reservoir is usually well defined from offsets of key marker stratigraphic units intersected by wells. Some of these large-scale faults significantly contribute to reservoir permeability. Smaller-scale structures, particularly inferred active fractures, are also of major importance for the vertical and lateral flow of fluid within fractured formations. To identify the structures directly within the formations, acoustic televiewer logs are acquired in New Zealand geothermal fields with the advent of the Acoustic Formation Imaging Technology (AFIT) tool, which is rated to 300°C. This wireline logging tool acquires a full 360° acoustic image of the inside of the borehole. Typically, fractures have different acoustic impedances from the wall-rock formation and appear as discordant features on the image, which can be systematically picked during image analysis. Each fracture has its true orientation (dip/dip direction) calculated in-situ taking into account image orientation and well deviation. The detailed analysis of these wireline logs provides insights on the nature, distribution, aperture and orientation of the fractures directly at the borehole wall. This information can be correlated to other logs to identify which structures may be open to fluid flow. However, fractures sub-parallel to the borehole axis will be under-sampled as fewer are intersected by the well. Here we describe a technique which we use to statistically correct for the natural bias involved when counting fractures intersected by a borehole at various angles. We demonstrate the impact that this bias can have on the structural characterisation of a fractured reservoir from acoustic televiewer images, using examples from four AFIT log intervals acquired in the Rotokawa Andesite, Rotokawa Geothermal Field (New Zealand). This correction provides a more accurate representation of the true structural character of the reservoir. The resultant, improved dataset allows for greater confidence in reservoir characterisation, future well targeting, as well as fracture and reservoir modelling.