Abstract: Various embodiments include apparatus and methods to provide pipe analysis, annulus analysis, or one or more combinations of pipe analysis and annulus analysis with respect to one or more pipes in a wellbore. The analysis can include application of clustering and classification methods with respect to the status and the environment of the one or more pipes in the wellbore. In various embodiments, the clustering and classification can be used in characterizing borehole annular material including cement bond quality evaluation. Additional apparatus, systems, and methods are disclosed.

Abstract: Various embodiments include apparatus and methods to provide pipe analysis, annulus analysis, or one or more combinations of pipe analysis and annulus analysis with respect to one or more pipes in a wellbore. The analysis can include application of clustering and classification methods with respect to the status and the environment of the one or more pipes in the wellbore. In various embodiments, the clustering and classification can be used in characterizing borehole annular material including cement bond quality evaluation. Additional apparatus, systems, and methods are disclosed.

Abstract: A method includes receiving a signal from a sensor that is positioned within a well bore during a hydraulic fracturing operation. A noise canceling operation is performed on the received signal to detect a microseismic event that is caused by the hydraulic fracturing operation.

Abstract: In some embodiments, a method includes receiving a data signal from a sensor that is positioned within a well bore, during a hydraulic fracturing operation. The method further comprising detecting a microseismic event, that is caused by the hydraulic fracturing operation, wherein the detecting comprises performing a noise canceling operation on the data signal.

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.