Earth Science Analytics attended ECIM 2017 in Haugesund this week. ECIM hosts the main E&P Data and IM Conference in Europe. For us, as data/geo scientists, this years theme; 'Delivering Value - Applying Methods and Technology', was most relevant, and we really appreciated the program and the many discussions on data science in the E&P industry. Thanks to @DavidHolmesUK from Dell EMC for inviting us. We will definitely be back next year.
Here's an abstract on our contribution to the event;
Machine Learning Assisted Petroleum Geoscience
Petroleum geoscience is hard. Particularly when it comes to predicting properties away from known measurements. It is hard because it is so complex. It is hard because there are no simple rules, like Newton's laws of motion, that help us predict the spatial distribution of, for instance, reservoir properties, or where to look for the next big commercial discovery. It is basically hard to "codify" and "formalize" what we do as petroleum geoscientists. Until today we have attacked these kinds of "hard to codify" problems by assigning teams of human experts to solve them. The combined experience of these experts from multiple disciplines helps us extract knowledge and insights from the data available. What if we could replicate this method with computers? Can we have the computer learn relationships directly from the data, from all relevant sources? This is exactly what machine learning is for, and it works remarkably well when there is enough structured, and labelled, data to train on.
The incredibly rich subsurface data and metadata on the NCS seems to be perfect for machine learning. We will soon be able to use this technology to build incredibly detailed, high-dimensional models using all our data. When machine-learning models, today, are trained on smaller data sets they enable petroleum geoscientists to better understand the spatial distribution of reservoir properties and hydrocarbons. This technology is available, and being used today. It is not solely a technology of the future. Workflow efficiency is being improved by orders of magnitude, today. Prediction accuracy is exceeding that of traditional "best practice", today. Imagine what it will be like tomorrow when really large data sets are available for training models.
This talk illustrates what is being done today with workflow examples and case studies. We discuss how machine learning can be applied to both reservoir characterization and exploration on a regional scale and on prospect level. Machine Learning technology and data science is exposing to geoscientists hidden relationships in measured data; it removes biases and provides metrics for predictions and estimations. We discuss the potential of value creation by applying machine learning on very large data sets, and the value to society that can be created by making data sources openly accessible.
The currently applied machine-learning technology shows that interdisciplinary approaches lead to deeper understanding of our prediction problems and provide a framework for creative solutions and better decisions. The future for decision making technology for exploration and production is here, today, and we should integrate this technology into our workflows to enable data driven and cost effective decisions.
Does machine learning have the power to transform petroleum geoscience today, like Newton's calculus transformed physics more than 300 years ago?