RIASSUNTO
In the current cost-saving and high-tech environment, this paper aims at demonstrating that significant business value can be derived from advanced information technology. The objective was indeed to identify and reduce risk in the Drilling and Wells domains using iterative, multi-disciplinary Big Data analytics and workflows. Examples of operational risk identified in this project include low borehole quality, poor wellbore stability, and stuck pipe.
Subject-matter expertise and advanced analytical capabilities were assembled to mine and analyze large amounts of different data types across drilling parameters, petrophysics and well logs, and geological formation tops for a released data set of approximately 350 oil and gas wells in the UK North Sea. The data set contained information about a large geographical area, which conventional analysis techniques would find difficult, if not impossible, to handle and analyze in its entirety.
Results of this study showed that iterative Big Data “discovery workflows” uncover hidden patterns and unknown correlations in the data and unexpected correlations across the data set are exhibited. It also confirmed the possibility to improve Drilling models using business analytics. In addition the correlations found allow predictive statistics to be computed. Finally advanced visualization capabilities provided an aid to interpret, understand, and make recommendations for Drilling plan and operations.
This novel approach uncovered that patterns and correlations can be detected across a disparate data set, where data types are not traditionally linked, by integrating a large variety and complexity of data in one analytical environment. Furthermore the multi-domain analyses run during the study were all performed ‘on-the-fly’, without preconception or business requirements. As a final point Big Data Analytics can also be used as a Quality Control tool and will certainly be leveraged for further multi-variate analysis in Oil and Gas.