Evaluation of active learning algorithms for formation lithology identification

Learning Algorithms for Active Learning

We introduce a model that learns active learning algorithms via metalearning. For a distribution of related tasks, our model jointly learns: a data representation, an item selection heuristic, and a method for constructing prediction functions from labeled training sets. Our model uses the item selection heuristic to gather labeled training sets from which to construct prediction functions. Usi...

Algorithms for active learning

Neural Networks Based Lithology Identification

In this paper, a novel neural networks based lithology identification approach is proposed. First, the logging curves are squared so that the noise is filtered off. Second, consecutive segments representing similar stratums are merged so that the dimension of inputs is reduced. Third, data sampling from different logging curves are respectively normalized so that the curves of small magnitude a...

Statistical Active Learning Algorithms

We describe a framework for designing efficient active learning algorithms that are tolerant to random classification noise and differentially-private. The framework is based on active learning algorithms that are statistical in the sense that they rely on estimates of expectations of functions of filtered random examples. It builds on the powerful statistical query framework of Kearns [30]. We...

simulation and experimental studies for prediction mineral scale formation in oil field during mixing of injection and formation water

abstract: mineral scaling in oil and gas production equipment is one of the most important problem that occurs while water injection and it has been recognized to be a major operational problem. the incompatibility between injected and formation waters may result in inorganic scale precipitation in the equipment and reservoir and then reduction of oil production rate and water injection rate. ...