A B S T R A C TThis work looks at the application of neural networks in geophysical well-logging problems and specifically their utilization for inversion of nuclear downhole data. Simulated neutron and γ -ray fluxes at a given detector location within a neutron logging tool were inverted to obtain formation properties such as porosity, salinity and oil/water saturation. To achieve this, the forward particle-radiation transport problem was first solved for different energy groups (47 neutron groups and 20 γ -ray groups) using the multigroup code EVENT. A neural network for each of the neutron and γ -ray energy groups was trained to re-produce the detector fluxes using the forward modelling results from 504 scenarios. The networks were subsequently tested on unseen data sets and the unseen input parameters (formation properties) were then predicted using a global search procedure. The results obtained are very encouraging with formation properties being predicted to within 10% average relative error. The examples presented show that neural networks can be applied successfully to nuclear well-logging problems. This enables the implementation of a fast inversion procedure, yielding quick and reliable values for unknown subsurface properties such as porosity, salinity and oil saturation. I N T R O D U C T I O NIn many geophysical problems, the aim is to apply a technique which will enable the determination of subsurface properties (e.g. lithology, porosity, density, hydraulic conductivity, resistivity, salinity and water/oil saturation) through the use of either surface or borehole measurements. This constitutes what is known as a geophysical inverse problem in which a mathematical model is used to relate the measured/observed data to the subsurface model parameters. In order to recover correctly the unknown parameters in the mathematical model an error function, otherwise known as the objective function, is set up. This function measures the discrepancy between the observations and predictions from a forward-modelling calculation. Minimizing this error function leads to the recovery of the unknown parameters, yielding optimal solutions. *

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