A Data Science Intro
When using data science, labeling events that occur in the field within the system is critical. With accurate labels and comprehensive well data, the raw materials are then available to train AI/ML models. But these raw materials need to be used the right way to create models that are comprehensive enough to handle the dynamic environment and accurate enough to impact performance. The industry has tremendous operators and engineers that understand how to get the most out of their oil fields, but they are busy in the field and lack the understanding of the latest algorithms and approaches to machine learning. Enter data science.
Data science defines the processes, skills, and tools that turn large amounts of data into AI/ML models performing complex and repetitive analyses that can quickly reveal problems and opportunities. Data science enables the automation of tedious and time-consuming analytic processes freeing up operators and engineers to troubleshooting problems and take action.
Data science understands how to train the models, which algorithms to use, which parameters to adjust, how to validate performance, what systems need to scale and so many other esoteric decisions that have to be made. While the algorithms used in data science are not necessarily new, the tools and methods of applying large-scale computing and storage resources (e.g. AWS, Azure) to real-time data streams are advancing at an exponential pace. Data scientists are charged with introducing and supporting AI for the rest of the organization, but they need a great deal of help.