Formation Evaluation Using NMR, Mud Gas, and Triple-Combo Data – A Norwegian Logging-While-Drilling Case History

The presence of gas in hydrocarbon reservoirs has a distinct effect on commonly used porosity logging measurements such as density, neutron, and nuclear magnetic resonance (NMR). Although the effect of gas on NMR measurements is well understood, it remains difficult to estimate true formation porosity and hydrocarbon (HC) saturation exclusively from NMR data in gas-bearing formations. The hydrogen index (HI) of gas is much smaller than one and causes an underestimation of the measured NMR porosity. Unless temperature, pressure, HC composition, and saturation in the volume sensed by NMR are known accurately, compensating for the effect of gas is not easily accomplished. This paper compares different approaches for NMR gas zone analysis, and it introduces a new method integrating NMR logging data with a HI curve derived from mud gas surface logging. The results are connected to a full and consistent petrophysical reservoir description. Data and results from two wells drilled in a North Sea clastic reservoir are presented. The reservoir is dominated by thick units of sandstone, deposited in a submarine turbidite fan, with high porosity and high permeability. The wells were logged with triple-combo (i.e., gamma ray, density, neutron, and resistivity), NMR, and formation tester (FT) tools while drilling across gas, oil, and water intervals. Basic mud gas data are available from surface logging. Uncorrected NMR porosity shows an average porosity underestimation of 6 p.u. in the gas zone compared to triple-combo computer-processed interpretation (CPI) processing. The standalone evaluation of NMR data by a T2 cutoff approach and dual wait-time (DTW) data processing reduces the average porosity mismatch but results in zones of under- and overcorrection of the gas effect. Combining NMR DTW data with density improves the results and reduces the average porosity mismatch to less than 2 p.u. Compositional information from the mud gas data validates an inferred trend of fluid property variation in gas and oil zones across the reservoir and is used to derive a continuous HI log for further improving porosity and gas saturation estimation. Complementary to the results of the integrated data evaluation, we show independent results from mud gas data evaluation using a recently implemented method that provides independent porosity, permeability, and saturation indexes. Adverse conditions for the different approaches, including invasion, variations in mineralogy, and the limited vertical resolution of mud gas data, are discussed. Finally, the benefits and potential of combining NMR, mud gas, and triple-combo data are summarized.