Halliburton Energy Services, Inc.

Jeffery L Grable

Harry D Smith, Jr

Dingding Chen

John Andrew Quirein

Syed Hamid

Mahdi Parak

Matthew Edwin Wise

Nishant Raizada

Moray Lamond Laing

Jacky Muri Wiener

Aspects of the subject technology relate to systems and methods for predicting dysfunctions in physical systems. A dysfunction codex can be provided that includes a plurality of dysfunction models for predicting one or more dysfunctions in a physical system based on one or more specific contexts of the physical system. The dysfunction codex can be applied by selecting a dysfunction model of the plurality of dysfunction models within the dysfunction codex to apply based on the one or more specific contexts of the physical system. Further, a dysfunction of the physical system can be predicted by applying the dysfunction model to input system data of the physical system to predict the dysfunction of the physical system.

Dynamic drilling dysfunction codex

A method of drilling a borehole can include chopping drill cuttings with a drilling tool connected in a drill string, the tool receiving the drill cuttings from a drill bit, and then forcing the chopped cuttings against a borehole wall. A well system can include a drilling tool which chops drill cuttings, and another drilling tool which forces the chopped drill cuttings against a borehole wall. Another method of drilling a borehole can include chopping drill cuttings with a drilling tool connected in a drill string, and then forcing the chopped drill cuttings against a wall of the borehole with another drilling tool connected in the drill string. Another drilling system can include a drilling tool connected in a drill string, the tool including at least one device which decreases an average size of drill cuttings between an outer housing and an inner mandrel.

Enhanced plastering effect in borehole drilling

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.

Neural network training data selection using memory reduced cluster analysis for field model development

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.

Ensembles of neural networks with different input sets

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.

Growing community of inventors

Houston, TX, United States of America

Jeffery L Grable