Multi-component resistivity and borehole image logs are commonly used for evaluating low-resistivity laminated sand-shale reservoirs. Stoneley permeability may also be added to help with testing and completion decisions. However, acquiring a comprehensive wireline dataset in a complex J-shape well is extremely challenging. This case study, from the Nam Con Son basin of Vietnam, describes a workflow to effectively use a dataset acquired while drilling to identify and evaluate these low- resistivity reservoirs.
A complete suite of logs, comprising gamma-ray-density-neutron-resistivity-acoustic-resistivity image, was acquired while drilling. The propagation resistivity was processed to obtain horizontal (Rh) and vertical (Rv) resistivities. Laminated Sand Shale Analysis (LSSA), using these inputs, was used to determine the hydrocarbon saturation over the thin beds, and to calculate net pay. A high-definition image log was used to identify the laminated section, and net sand was calculated from this using a threshold technique. The result was compared with that from the Thomas-Stieber method, which also provided shale distribution. Acoustic permeability was also derived using the attenuation of Stoneley wave amplitude.
The conventional resistivity of the thin-bed reservoirs in this basin is generally lower because of the presence of conductive shale layers. Consequently, the conventional formation evaluation approach using these resistivity values resulted in high-water saturation and a small net pay, which did not provide enough justification for a detailed testing and completion plan. The propagation resistivity data was therefore processed using an array inversion technique to provide vertical resistivity (Rv) which is less affected by the laminated shale (Meyer 1998). A high anisotropy (Rv/Rh) was observed against the hydrocarbon-bearing laminated intervals. Using this elevated resistivity in the LSSA method resulted in 52% of additional net pay. The laminar sand volume from the borehole image provided a similar result to that of the Thomas–Steiber and tensor methods, boosting the confidence of the calculation. Permeability calculated from the Stoneley wave, and additional net pay from LSSA, provided adequate information to develop the testing plan which was later pursued. The high-definition image log helped to select the best possible sand layers for testing and sampling.
A comprehensive dataset is required for thorough evaluation of thin-bed, low-resistivity reservoirs, but sometimes the complex well geometry hinders the acquisition process. This case study shows that the suitable data set can be acquired while drilling, and using the resistivity anisotropy along with acoustic permeability and image log can provide detailed information for effective evaluation of these formations.