Integration of Resistivity Imaging and Enhanced Vertical Resolution Nuclear Magnetic Resonance Data for Improved Quantification of Reserves in Thinly Bedded Reservoirs

Abstract

Abstract There is little that is more important to an operating company than the accurate assessment of its oil and gas reserves. In thick, clean formations that are relatively homogeneous laterally and vertically, reserves calculations are usually straightforward. The level of uncertainty in these calculations can increase substantially, however, with increasing reservoir complexity. One such source of complexity is the challenge presented by thinly laminated formations. The challenge is that thinly bedded formations are, by definition, below the resolution of the most commonly used logging tools. As such, many thin bed formations may not even be detected, let alone properly evaluated and accounted for in reserves estimates. Two wireline formation evaluation technologies are available that, when used in concert with conventional macro wireline logs, can substantially reduce reserves uncertainty in thin bed formations. One of these is a resistivity imager tool such as the X-tended Range Micro Imager (XRMI). With vertical resolution down to 0.10 inches, resistivity imager tools provide a reliable means of thin bed detection. The other technology is nuclear magnetic resonance (NMR). NMR tools such as the MRIL Prime can provide estimates of formation porosity, permeability and hydrocarbon properties in thin bed formations, albeit with relatively coarse vertical resolution which can be enhanced by post-processing technique. This paper explores a new method for enhancing the vertical resolution of the MRIL data that leads to integrates the best attributes of both NMR and resistivity imaging tools to bring about improved reserves estimates. A case study is presented from the Nile delta in which a field - comprised of a thick, clean, high porosity deltaic sedimentary layer overlain by a thinly laminated sand-shale sequence – is quantitatively evaluated for fluid saturation and productivity with a new post-processing technique. Recommendations for future data integration methods and applications are also proposed.