Abstract: Detecting a breakover event during a friction test includes obtaining a hookload measurement for each of a series of time windows and generating a linear model and a nonlinear model from the plurality of hookload measurements. During run time, from the nonlinear model, an inflection point is identified from the nonlinear model, where the inflection point is determined to have occurred at a particular time window. A hookload value associated with the linear model, and a hookload value associated with the nonlinear model is determined for the particular time window. A breakover event is determined to have occurred at the particular time when the hookload value associated with the linear model at the particular time window exceeds the hookload value associated with the nonlinear model at the particular time window.

Abstract: Detecting a breakover event during a friction test includes obtaining a hookload measurement for each of a series of time windows and generating a linear model and a nonlinear model from the plurality of hookload measurements. During run time, from the nonlinear model, an inflection point is identified from the nonlinear model, where the inflection point is determined to have occurred at a particular time window. A hookload value associated with the linear model, and a hookload value associated with the nonlinear model is determined for the particular time window. A breakover event is determined to have occurred at the particular time when the hookload value associated with the linear model at the particular time window exceeds the hookload value associated with the nonlinear model at the particular time window.

Abstract: Predicting a drill string stuck pipe event. At least some of the illustrative embodiments are methods including: receiving a plurality of drilling parameters from a drilling operation; applying the plurality of drilling parameters to an ensemble prediction model comprising at least three machine-learning algorithms operated in parallel, each machine-learning algorithm predicting a probability of occurrence of a future stuck pipe event based on at least one of the plurality of drilling parameters, the ensemble prediction model creates a combined probability based on the probability of occurrence of the future stuck pipe event of each machine-learning algorithm; and providing an indication of a likelihood of a future stuck pipe event to a drilling operator, the indication based on the combined probability.

Abstract: Predicting a drill string stuck pipe event. At least some of the illustrative embodiments are methods including: receiving a plurality of drilling parameters from a drilling operation; applying the plurality of drilling parameters to an ensemble prediction model comprising at least three machine-learning algorithms operated in parallel, each machine-learning algorithm predicting a probability of occurrence of a future stuck pipe event based on at least one of the plurality of drilling parameters, the ensemble prediction model creates a combined probability based on the probability of occurrence of the future stuck pipe event of each machine-learning algorithm; and providing an indication of a likelihood of a future stuck pipe event to a drilling operator, the indication based on the combined probability.

Abstract: A data classification method and apparatus are disclosed for labeling unknown objects. The disclosed data classification system employs a learning algorithm that adapts through experience. The present invention classifies objects in domain datasets using data classification models having a corresponding bias and evaluates the performance of the data classification. The performance values for each domain dataset and corresponding model bias are processed to identify or modify one or more rules of experience. The rules of experience are subsequently used to generate a model for data classification. Each rule of experience specifies one or more characteristics for a domain dataset and a corresponding bias that should be utilized for a data classification model if the rule is satisfied.

Abstract: Euclidean analysis is used to define queries in terms of a multi-axis query space where each of the keywords T1, T2, . . . Ti, . . . Tn is assigned an axis in that space. Sets of test queries St for each one from one of a plurality of server sources, are plotted in the query space. Clusters of the search terms are identified based on the proximity of the plotted query vectors to one another. Predominant servers are identified for each of the clusters. When a search query Ss is received, the location of its vector is determined and the servers accessed by the search query Ss are those that are predominant in the cluster which its vector may fall or is in closest proximity to.

Abstract: A data classification method and apparatus are disclosed for labeling unknown objects. The disclosed data classification system employs a model selection technique that characterizes domains and identifies the degree of match between the domain meta-features and the learning bias of the algorithm under analysis. An improved concept variation meta-feature or an average weighted distance meta-feature, or both, are used to fully discriminate learning performance, as well as conventional meta-features. The “concept variation” meta-feature measures the amount of concept variation or the degree of lack of structure of a concept. The present invention extends conventional notions of concept variation to allow for numeric and categorical features, and estimates the variation of the whole example population through a training sample. The “average weighted distance” meta-feature of the present invention measures the density of the distribution in the training set.

Abstract: A data classification method and apparatus are disclosed for labeling unknown objects. The disclosed data classification system employs a learning algorithm that adapts through experience. The present invention classifies objects in domain datasets using data classification models having a corresponding bias and evaluates the performance of the data classification. The performance values for each domain dataset and corresponding model bias are processed to identify or modify one or more rules of experience. The rules of experience are subsequently used to generate a model for data classification. Each rule of experience specifies one or more characteristics for a domain dataset and a corresponding bias that should be utilized for a data classification model if the rule is satisfied.

Abstract: Euclidean analysis is used to define queries in terms of a multi-axis query space where each of the keywords T1, T2, . . . Ti, . . . Tn is assigned an axis in that space. Sets of test queries St each one from one of a plurality of server sources are plotted in the query space. Clusters of the search terms are identified based on the proximity of the plotted query vectors to one another. Predominant servers are identified for each of the clusters. When a search query Ss is received, the location of its vector is determined and the servers accessed by the search query Ss are those that are predominant in the cluster which its vector may fall or is in closest proximity to.

Abstract: A unified framework is disclosed for representing and generating evaluation functions for a classification system. The disclosed unified framework provides evaluation functions having characteristics of both traditional or purity-based evaluation functions (class uniformity) and discrimination-based evaluation functions (discrimination power). The disclosed framework is based on a set of configurable parameters and is a function of the distance between examples. By varying the choice of parameters and the distance function, more emphasis is placed on either the class uniformity or the discrimination power of the induced example subsets. A user-configurable function is used to score each of the features based on the class uniformity and discrimination power measures and thereby select the feature having a highest score to partition the data (e.g., using a decision tree or rule-base). This process is recursively applied until all of the examples are partitioned.