A Monitoring and Analysis Platform for Real Time and Autonomous Formation Evaluation and Machine Learning Deployment of Multiple Concurrent Assets

In upstream organizations, formation evaluation products are required by a multitude of subdisciplines in order to achieve their individual objectives. Moreover, some tasks require these products to be provided in real- time, such as geosteering, drilling management and formation sampling, while others require it in a time- sensitive fashion, such as completion and well testing design and material procurement. While simplified real- time procedural interpretation of logging-while-drilling (LWD) data has been implemented in numerous organizations, we believe this is the first production stage platform that automatizes model parameter selection of complex petrophysical models as targets are drilled, solving for the full non-linear system of equations as customary in modern formation evaluation practice. Additionally, our platform also deploys pre- trained Machine Learning models that augment data acquired prior to formation evaluation, or produces discipline-specific predictions post formation evaluation. The platform is served in a browser and provides a real- time formation evaluation for every wellbore where logging data is being acquired. The petrophysical models utilized by the platform are automatically allocated by a tree-based decision system that consumes data such as the wellbore field, the current geologic unit being drilled and facies identification based on the measured data. The petrophysical interpreter thus honors these models for newly incoming data streamed to a real-time database. Users with special privileges can at any time override the modeling decisions made by the platform and manually prescribe models that will be used over the data already acquired and over the data that is acquired from then on. This supervised-autonomy provides full interpretation flexibility while completely eliminating the need for the well log analyst to repeat any task, empowering specialists to seemingly monitor several wellbores at a time. In a similar fashion, the platform prescribes a number of Machine Learning models to be deployed over specific wellbores across specific geologic units based on a tree-based decision system. The platform maintainer keeps a pool of pre-trained indexed Machine Learning models available to the platform, covering a wide range of objectives, applications and disciplines, thus augmenting the value and usability of the streamed data. In one year, the platform produced real-time formation evaluation analysis for several hundreds of wellbores across multiple fields, serving between 20 to 30 concurrent wellbores at any given time. The platform is monitored by a much smaller group of specialists than would be required when operating over a traditional workflow. Subdisciplines depending on formation evaluation products have access to them 24/7 and in real- time through a web browser, eliminating unnecessary delays. Preliminary results indicate the platform could be responsible for improvements in geosteering outcomes and drilling efficiency. The authors believe this is the first production experience of sophisticated formation evaluation being automated and deployed in real-time over multiple concurrent assets. The integration of Machine Learning models into real-time formation evaluation products clears the path for substantial enhancements in geosteering accuracy and drilling efficiency.