Large volumes of hydrocarbons (c. 2.9 GSm3 rec. o.e.; NPD 2016) remain to be found on the NCS. Finding and extracting these hydrocarbons is difficult (i.e. 187 exploration wells resulted in <10 commercial discoveries during the last five years) and expensive (i.e. 143 BNOK was invested in the 187 exploration wells that discovered only 224 MSm3 rec. o.e. in the last five years). We believe that under-utilization of data, and of the existing subsurface knowledge base, are at least partly responsible for the disappointing exploration performance. Furthermore, we argue that the incredibly rich subsurface data set available on the NCS can be used much more efficiently to deliver much more precise predictions, and to thus support more profitable investment decisions during hydrocarbon exploration and production.
We argue that Artificial Intelligence (AI), i.e. Machine Learning-based technology, which leverages algorithms that can learn and make predictions directly from data, represents one way to contribute to exploration and production success on the NCS. One key advantage of AI is the technology’s ability to efficiently handle very large volumes of multidimensional data, thus saving time and cost and, therefore, allowing human resources to be deployed to other, perhaps more creative tasks. Another advantage is AI’s ability to detect complex, multidimensional patterns that are not readily detectable by humans.
AI-assisted geoscience applications and workflows will enable petroleum geoscientists to better understand the tectono-stratigraphic development of sedimentary basins in general, and more accurately and quickly predict the nature and occurrence of hydrocarbons in sedimentary basins in particular. More specifically, these applications will enable geoscientists to apply more quantitative techniques to very large subsurface data sets, thereby facilitating a better understanding of the multidimensional and nonlinear relationships existing between some of the key geological properties (e.g. lithology distribution and properties such as porosity, fluid saturation, source-rock maturity and sealing capacity).
In areas where geophysical methods are associated with low resolution and reliability, for example at significant burial depths, we need alternative methods for predicting rock and fluid properties. AI techniques help identify and map relationships between rock properties and the broader geological context in which they occur. When the ML algorithms have learned these relationships directly from data, they can be used to predict (quantitatively and probabilistically) rock properties based on regional geological data.
To tackle this challenge, we need to quantify the multidimensional relationships between the extracted rock- and fluid-properties, and regional data such as structural setting, stratigraphic setting, and other map-based data such as sub-crop-, isopach-, depth-, provenance-, temperature- and pressure-maps. Successful predictions based on regional geological data must be based on an understanding of relationships between multiple geological parameters and their interactions.
AI-assisted petroleum geoscience will enable efficient use of large amounts of hitherto under-utilized subsurface data, and handling of multidimensional parameter sets in a purely data-driven way; this is currently not possible with the technology and workflows available under the current paradigm. Machine learning-based technology will, reduce human bias, which currently is pervasive in petroleum geosciences, and enable much more data-driven analytics and investment-decisions in the E&P industry.