Currently, there is a growing worldwide interest for the treatment of wastes, and especially farm wastes, by anaerobic digestion. Biochemical methane potential is a key parameter for the design, optimisation and monitoring of the anaerobic digestion process, but it is also time consuming (4-7 weeks). Near infrared reflectance spectroscopy seems a promising method to predict the biochemical methane potential of a wide range of organic substrates. This study compares a 'global' predictive model mainly built with biogas plant feedstocks, and a more 'agricultural' specific one built with farm wastes only (e.g. manures and crop residues). The global model was calibrated with 245 samples and the specific one with 171 samples. In parallel, validation sets composed of 36 farm wastes and eight other wastes (sludge, fruit residues and vegetables) were used to evaluate and compare both models. Satisfying results were obtained on the validation sets considering, respectively for the global and the specific models, a root mean square error of prediction of 44 and 34 NL CH kg volatile solid, a coefficient of determination of 0.76 and 0.83, and a ratio of performance to deviation of 2.0 and 2.4. In general rules, the specific model was better than the global one in the prediction of farm wastes methane potential. However, thanks to its larger sample variability, the global one was more robust, especially towards the 'other' wastes, which can be introduced punctually in agricultural biogas plant.

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Log derived permeability averages in homogeneous clastic reservoirs most often matches the reservoir scale permeability determined from well testing. However, when it comes to heterolithic, anisotropic reservoirs such as observed in successions interpreted as turbidites, there can be significant differences between reservoir scale estimates and traditional techniques such as arithmetic and geometric averaging. This mismatch is often overlooked, although it can be critical when it comes to history matching production data. The fundamental flaw of the log-only approach is that it assumes permeability to be isotropic, and therefore ignores the three-dimensional nature of permeability. In heterogeneous clastic reservoirs, the anisotropies originating from various small scale sedimentary structures may have an influence on fluid flow at larger scales, impacting the observed reservoir-scale permeability.This paper demonstrates how data acquired from wireline logs and cores from very heterogeneous successions like turbidites may be used to consistently predict the reservoir scale permeability. The workflow consists in processing and integrating the data into a lamina-scale near-wellbore model containing structural as well as petrophysical properties; a flowbased upscaling method is then applied to provide a geologically consistent input into a single-well model, from which a typical well test scenario will be simulated; the resulting pressure transient is then analyzed to determine a simulated permeability-thickness product.Presented in this paper is an application to two wells intersecting a turbidite reservoir. This shows that the permeability obtained from this workflow is closer to the well test permeability when compared to log derived estimates from traditional averaging techniques.This method can be applied to obtain valid reservoir-scale permeability values that can then be compared to the actual well test result. However, it can also be applied in cases where the value of information processes prove well testing to be uneconomical; then, a global reservoir permeability value can still be obtained using the workflow described.