LRLC reservoirs are increasingly at the forefront of the industry's concern in diverse projects ranging from offshore deep-water exploration of turbidites to the development of brown-field secondary objectives. Although LRLC reservoirs have been under production for many years, their identification and the calculation of their reserves and flow properties remains a difficult challenge. This paper compares different petrophysical workflows for clastic reservoirs where thin conductive laminations and high bound water fraction are the source of low resistivity and contrast, with a view to reducing uncertainty in saturations and improving producibility prediction.

When thinly laminated reservoir layers are intercalated with conductive non-reservoir layers, the apparent formation resistivity is dramatically reduced and the apparent clay volume is increased, and the hydrocarbon volume and the permeability calculated from conventional petrophysics are underestimated. We describe new developments in laminated sand analysis and the practical implementation of resistivity anisotropy, including corrections for clay intrinsic anisotropy and thin non-reservoir resistive layers.

Reservoirs with fine grain material, grain-coating clays, or dispersed clays may display high bound water volumes, yet possess significant quantities of producible hydrocarbon. While conventional petrophysical analysis can provide reliable water saturation, it does not distinguish clay- and capillary- bound water from free water. Also, shaly and silty reservoirs often present a complex mineralogy which makes estimates of clay volume and grain density uncertain. We describe the application of nuclear spectroscopy and NMR logs to calculate clay volume, porosity and bound water volume and illustrate their impact on the quality of the resulting evaluation.

Although the petrophysical methods presented were developed for thinly bedded reservoirs, we show that they can improve the analysis of both LRLC and conventional clastic reservoirs. In particular, we propose fit-for-purpose workflows that reduce the uncertainty of fluid volumes and rock flow properties.

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