This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 29815, “Discovering Patterns Within Drilling Reports by Use of Artificial Intelligence for Operation Monitoring,” by Danilo Colombo, Petrobras, and Daniel Carlos Guimarães Pedronette and Ivan Rizzo Guilherme, UNESP, et al., prepared for the 2019 Offshore Technology Conference Brasil, Rio de Janeiro, 29-31 October. The paper has not been peer reviewed. Copyright 2020 Offshore Technology Conference. Reproduced by permission.
In well-drilling activities, successful execution of a sequence of operations defined in a well project is critical. To provide proper monitoring, operations executed during drilling procedures are reported in daily drilling reports (DDRs). The complete paper provides an approach using machine-learning and sequence-mining algorithms for predicting and classifying the next operation based on textual descriptions. The general goal is to exploit the rich source of information represented by the DDRs to derive methodologies and tools capable of performing automatic data-analysis procedures and assisting human operators in time-consuming tasks.
Methods
Classification Tasks. fastText.This is a library discussed in the literature designed to learn word embeddings and text classification. The technique implements a simple linear model with rank constraint, and the text representation is a hidden state that is used to feed classifiers. A softmax function computes the probability distribution over pre-defined classes.
Conditional Random Fields (CRFs). CRFs are a category of undirected graphical models that allow combination of features from each timestep of the sequence, with the ability to transit between labels for each episode in the input sequence. They were proposed to overcome the problem of bias that existed in techniques such as hidden Markov models and maximum-entropy Markov models.
Recurrent Models. Despite achieving good results in several scenarios and learning word embeddings as a byproduct of its training, the fastText classifier does not properly consider word-ordering information that can be useful for several classification tasks. Such a shortcoming can be addressed by a recurrent neural network (RNN), which considers the fact that a fragment of text is formed by an ordered sequence of words. The authors consider the gated recurrent unit variant, which is easier to train than traditional RNNs and achieves results comparable with those of the long short-term memory unit, while figuring fewer parameters to learn. The methodology of these classifiers is detailed mathematically in the complete paper.
Sequence Prediction.Sequential pattern mining can be defined broadly by the task of discovering interesting subsequences in a set of sequences, where the level of interest can be measured in terms of various criteria such as occurrence frequency, length, and profit, according to the application. The authors focus in this paper on the specific task of sequence prediction.
