Abstract: Processing gamma count rate decay curves using neural networks. At least some of the illustrative embodiments are methods comprising obtaining a gamma count rate decay curve one each for a plurality of gamma detectors of a nuclear logging tool (the gamma count rate decay curves recorded at a particular borehole depth), applying the gamma count rate decay curves to input nodes of a neural network, predicting by the neural network a geophysical parameter of the formation surrounding the borehole, repeating the obtaining, applying and predicting for a plurality of borehole depths, and producing a plot of the geophysical parameter of the formation as a function of borehole depth.

Abstract: A system and method for selecting a training data set from a set of multidimensional geophysical input data samples for training a model to predict target data. The input data may be data sets produced by a pulsed neutron logging tool at multiple depth points in a cases well. Target data may be responses of an open hole logging tool. The input data is divided into clusters. Actual target data from the training well is linked to the clusters. The linked clusters are analyzed for variance, etc. and fuzzy inference is used to select a portion of each cluster to include in a training set. The reduced set is used to train a model, such as an artificial neural network. The trained model may then be used to produce synthetic open hole logs in response to inputs of cased hole log data.

Abstract: Processing gamma count rate decay curves using neural networks. At least some of the illustrative embodiments are methods comprising obtaining a gamma count rate decay curve one each for a plurality of gamma detectors of a nuclear logging tool (the gamma count rate decay curves recorded at a particular borehole depth), applying the gamma count rate decay curves to input nodes of a neural network, predicting by the neural network a geophysical parameter of the formation surrounding the borehole, repeating the obtaining, applying and predicting for a plurality of borehole depths, and producing a plot of the geophysical parameter of the formation as a function of borehole depth.

Abstract: Methods of creating and using robust neural network ensembles are disclosed. Some embodiments take the form of computer-based methods that comprise receiving a set of available inputs; receiving training data; training at least one neural network for each of at least two different subsets of the set of available inputs; and providing at least two trained neural networks having different subsets of the available inputs as components of a neural network ensemble configured to transform the available inputs into at least one output. The neural network ensemble may be applied as a log synthesis method that comprises: receiving a set of downhole logs; applying a first subset of downhole logs to a first neural network to obtain an estimated log; applying a second, different subset of the downhole logs to a second neural network to obtain an estimated log; and combining the estimated logs to obtain a synthetic log.

Abstract: Logging systems and methods are disclosed to reduce usage of radioisotopic sources. Some embodiments comprise collecting at least one output log of a training well bore from measurements with a radioisotopic source; collecting at least one input log of the training well bore from measurements by a non-radioisotopic logging tool; training a neural network to predict the output log from the at least one input log; collecting at least one input log of a development well bore from measurements by the non-radioisotopic logging tool; and processing the at least one input log of the development well bore to synthesize at least one output log of the development well bore. The output logs may include formation density and neutron porosity logs.

Abstract: A system and method for selecting a training data set from a set of multidimensional geophysical input data samples for training a model to predict target data. The input data may be data sets produced by a pulsed neutron logging tool at multiple depth points in a cases well. Target data may be responses of an open hole logging tool. The input data is divided into clusters. Actual target data from the training well is linked to the clusters. The linked clusters are analyzed for variance, etc. and fuzzy inference is used to select a portion of each cluster to include in a training set. The reduced set is used to train a model, such as an artificial neural network. The trained model may then be used to produce synthetic open hole logs in response to inputs of cased hole log data.