The ability to detect, evaluate and model thinly laminated formations, as well as to include the sand portion in the reserve calculations, has continuously been a challenging task for oil and gas service and operation companies. This challenge is directly attributed to two reasons:

  • The big difference between the conventional log's vertical resolution (6 inches and above) and the thickness of these beds (sometimes less than 1 inch); and

  • The direct influence of the low resistivity shale laminations within these thin sand laminae on supressing the resistivity reading (thus masking the existing thin sand laminae, which may be oil prone)

Many efforts in the industry to characterise these thinly laminated reservoirs were put together. Yet, all the existing approaches experience some uncertainty in their results. Most of the industries current approaches contain drawbacks due to certain assumptions involving i thin beds analyses, resulting in high uncertainty. Thus, new technology is needed to improve development and production from these reservoirs.

In this research, wells from 4 fields in both Malay Basin and Sabah Basins, with over 4000 feet of core analysis, full sets of conventional logs and over 5000 feet of different types of micro-resistivity image logs were used. New methodology to calculate true resistivity at a vertical resolution of 0.1 inches was introduced in both oil and water-based mud. A modified alpha processing approach solves the mud influence by introducing a new projecting factor.

New methodology to calculate high resolution density and neuron porosity was also introduced to be used with high resolution resistivity log and to conduct full reservoir evaluation using the high-resolution logs. This includes calculating lithology, porosity, permeability and water saturation at the resolution of 0.1 inch. Calibration of the results with core data and core plug tests supported the findings and showed that the new evaluation using the high-resolution logs is better at matching the core tests than the currently available methods. Thus, this novel approach can be used to evaluate thin beds in any environment and using any type of micro-resistivity images.

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