Density and neutron well log processing algorithms designed for conventional oil and gas reservoirs are not optimum for coal bed methane evaluation. In particular the corrections applied to measured electron density values (to derive bulk density) assume a calcium carbonate rock matrix, and quantitative analysis of neutron porosity logs is hindered by low count rates in coal and a lack of published information regarding the sensitivity of the measurement to variations in coal composition. The thinly-bedded nature of many coals is an additional challenge. This paper describes a new log processing method that simultaneously enhances statistical precision and vertical resolution whilst seeking to avoid additional sensitivity to the borehole environment. It then describes a fast nuclear rock properties modelling application developed to study the sensitivity of density, photo-electric cross-section (Pe) and neutron porosity measurements to variations in coal chemistry. The model has been validated using an accurate (but slow) Monte Carlo particle transport code which has been extensively benchmarked in independently characterized test blocks. The findings are applied to high resolution log data acquired in wells drilled for the evaluation of coal bed methane reservoirs. The key parameter used in the transformation of electron to bulk density is investigated and optimum values suggested. The sensitivity of density and neutron porosity measurements to variations in the volumes and chemistry of organic material, mineral matter and moisture is determined, and it is shown that appropriately processed neutron porosity logs have usable sensitivity to such compositional variations. The inclusion of neutron porosity improves our ability to differentiate coal types from logs, and addresses an important source of uncertainty in the reconciliation of log and core density values; in so doing it helps improve estimates of in-situ coal properties and associated quality attributes including gas-in-place.

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