Even when geologically based methods are used to determine fault rock permeabilities and thicknesses for input into flow simulators, a wide range of simplifying assumptions regarding fault structure and content are still present. Many of these assumptions are addressed by defining quantitative and flexible methods for realistic parameterization of fault-related uncertainties, and by defining automated methods for including these effects routinely in full-field flow simulation modelling. The fault effects considered include: the two-phase properties of fault rocks; the spatial distributions of naturally variable or uncertain single-phase fault rock properties and fault throws; and the frequencies and properties of sub-resolution fault system or fault zone complexities, including sub-seismic faults, normal drag and damage zones, paired slip surfaces and fault relay zones. Innovative two-phase or geometrical upscaling approaches implemented in a reservoir simulator preprocessor provide transmissibility solutions incorporating the effect, but represented within the geometrical framework of the full-field flow simulation model. The solutions and flexible workflows are applied and discussed within the context of a sensitivity study carried out on two faulted versions of the same full-field flow simulation model. Significant influence of some of these generally neglected fault-related assumptions and uncertainties is revealed.

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Fault representation and scaling in flow models are examined with respect to fault zone properties, the accuracy with which they can be determined, and how these variables and fault geometries can be incorporated realistically in to flow models. Outcrop data show that fault displacement/thickness ratios and permeability vary widely. For simple single fault models, results for numerical models are compared with analytical and statistical methods. Representation of a fault as a transmissibility surface conflates the effects of four variables -fault zone thickness and permeability, grid-block size and matrix (host-rock) cell permeability. Random spatial variation of transmissibility factor values is well represented by a uniform transmissibility factor which is the arithmetic mean of the values representing log-normally distributed permeability and thicknesses. Realistic ranges of fault zone thicknesses can be represented without grid-block refinement by an upscaling method based on simple transformation of transmissibility factor curves derived from a range of coarse grid-block models. Sub-seismic faults have significant effects on effective permeability of model volumes at kilometre scales only when the faults are assigned a permeability less than c. 0.001 of the matrix permeability.

S U M M A R YIt has become increasingly important to develop fast and accurate automatic procedures to process and fully exploit increasing large seismic data sets. Traditionally these data sets are processed manually, which requires significant amounts of both manpower and time with sometimes-variable results. We have developed a cost minimization approach to train three automatic pickers: an Sta/Lta, T pd and the PAI-K picker at each station within a dense temporary network located in northern Chile and southern Bolivia. The optimum picking parameters for each station show regional variability and need to be adjusted individually to achieve the best performance. We developed a weighting scheme that uses four independent predictors of weight calibrated using a handpicked data subset, which mimics the picking by an expert seismologist. We use the fact that each of the three pickers highlights different properties of the observed seismic trace to combine two pickers that work in tandem. The first makes an initial pick before the second picker refines and improves the accuracy of the automatic pick. We find the tandem pickers improve the accuracy of the automatic picks when compared to the single automatic pickers. We demonstrate that following the cost minimization procedure described here the automatic picks have sufficient accuracy that they would be suitable for high-precision earthquake location, focal mechanism determination or high-resolution seismic tomography.

BackgroundMalaria presents public health challenge despite extensive intervention campaigns. A 30-year hindcast of the climatic suitability for malaria transmission in India is presented, using meteorological variables from a state of the art seasonal forecast model to drive a process-based, dynamic disease model.MethodsThe spatial distribution and seasonal cycles of temperature and precipitation from the forecast model are compared to three observationally-based meteorological datasets. These time series are then used to drive the disease model, producing a simulated forecast of malaria and three synthetic malaria time series that are qualitatively compared to contemporary and pre-intervention malaria estimates. The area under the Relative Operator Characteristic (ROC) curve is calculated as a quantitative metric of forecast skill, comparing the forecast to the meteorologically-driven synthetic malaria time series.Results and discussionThe forecast shows probabilistic skill in predicting the spatial distribution of Plasmodium falciparum incidence when compared to the simulated meteorologically-driven malaria time series, particularly where modelled incidence shows high seasonal and interannual variability such as in Orissa, West Bengal, and Jharkhand (North-east India), and Gujarat, Rajastan, Madhya Pradesh and Maharashtra (North-west India). Focusing on these two regions, the malaria forecast is able to distinguish between years of “high”, “above average” and “low” malaria incidence in the peak malaria transmission seasons, with more than 70% sensitivity and a statistically significant area under the ROC curve. These results are encouraging given that the three month forecast lead time used is well in excess of the target for early warning systems adopted by the World Health Organization. This approach could form the basis of an operational system to identify the probability of regional malaria epidemics, allowing advanced and targeted allocation of resources for combatting malaria in India.

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