Authors: Rebecca Nye, Camilo Mejia, and Evgeniya Dontsova, Enovate Upstream



Recent developments in artificial intelligence (AI) have enabled upstream exploration and production companies to make better, faster and accurate decisions at any stage of well construction, while reducing operational expenditure and risk, increasing logistic efficiencies. The achieved optimization through digitization at the wellsite will significantly reduce the carbon emissions per well drilled when fully embraced by the industry. In addition, an industry pushed to drill in more challenging environments, they must embrace safer and more practical methods. An increase in prediction techniques, to generate synthetic formation evaluation wellbore logs, has unlocked the ability to implement a combination of predictive and prescriptive analytics with petrophysical and geochemical workflows in real time. The foundation of the real time automation is based on advanced machine learning (ML) techniques that are deployed via cloud connectivity. Three levels of logging precision are defined in the automated workflow based on the data inputs and machine learning models. The first level is the forecasting ahead of the bit that implements advanced machine learning using historical data, aiding proactive operational decisions. The second level has improved precision by incorporating real time drilling measurements and providing a credible contingency to for wellbore logging program. The last level incorporates petrophysical workflows and geochemical measurements to achieve the highest precision for logging prediction in the industry. Supervised and unsupervised machine learning models are presented to demonstrate the path for automation. Precision above 95% in the real time automated workflows was achieved with a combination of physics and advanced machine learning models. The automation of the workflow has assisted with optimization of logging programs utilizing technology with costly lost in hole charges and high rate of tool failures in offshore operations. The optimization has reduced the requirement for logistics associated with logging and eliminated the need for radioactive sources and lithium batteries. Highest precision in logging prediction has been achieved through an automated workflow for real time operations. In addition, the workflow can also be deployed with robotics technology to automate sample collection, leading to increased efficiencies.