Framework for Prediction of NPT causes using Unstructured Reports

Abstract Drilling capital expenditure represents significant portion of any oil and gas project. Drilling investment accounts to about 40% of the cost of the well. Therefore monitoring the health of equipments and processess associated with rigs are critical in bringing down both the cost of the well and the Non Productive Time (NPT). At the moment there are several Key Performance Indicators (KPIs) to monitor NPT but they are reactive in nature. Proactive measures can be taken to avoid NPT and reduce Invisible Lost Time (ILT) by exploiting the power of big data. The scope of this work is to create a framework to predict NPT and ILT causes from massive degree of unstructured data collected during the drilling operations. KPIs and performance values come from numerical metrics. In addition, the industry has also gathered an enormous amount of unstructured data from drilling and other reports. In many industries, Natural Language Processing (NLP) has been used to create value from unstructured data. Upstream reports are not exactly natural language ready reports. Our approach on generating value from these reports has two major steps (1) transforming NLP challenge to Technology language processing (TLP) in drilling context, and (2) classification of NPT causes. The two-step approach mentioned above results in a framework for mining unstructured reports to detect anomalies like symptoms, events, and actions. A modified NLP, referred here as TLP in the drilling context is used to achieve the first set of data extraction. Furthermore, a predictive model for classification of NPT and ILT causes is performed on the extracted data. The accuracy of these predictive models is an on-going effort, as it is dependent upon the transformation and detection of complex technical language that is inconsistent due to multiple reasons. However, this framework provides a foundation for predicting NPT / ILT during different phases of drilling operations. Use cases that predict attributes related to hole packoff or equipment failure shows the accuracy in the range of 50% to 70% due to a limited amount of extracted technical terms that are relevant. The framework calls for continual improvement of employing deep learning algorithm based on enhanced recovery of correlated technical terms across unstructured reports. While there are many attempts in using NLP for mining data in E&P sector, in this paper we present a framework for mining unstructured reports that leverage contextual Technology Language processing a step ahead NLP. Our framework incorporates the use of various machine learning algorithms to detect multiple attributes necessary to calculate NPT and ILT, and guides on how to enhance the predictive model.