Digitalization and Artificial Intelligence have impacted the oil and gas industry. Drilling of wells, predictive maintenance and digital fields are examples for the use of these technologies. In hydrocarbon production forecasting, numerical reservoir models and "digital twins" of reservoirs have been used for decades. However, increasing computing power and Artificial Intelligence recently enabled oil and gas companies to generate "digital siblings" of reservoirs (model ensembles) covering the uncertainty range in static data (e.g. petrophysics, geological structure), dynamic data (e.g. oil or gas properties) and economics (Capital Expenditures, Operating Expenditures). Machine Learning and Artificial Intelligence are applied to condition the model ensembles to measured data and improve hydrocarbon production forecasting under uncertainty.
The model ensembles can be used for quantitative decision making under uncertainty. This allows companies to shorten the time for field (re-)development planning and to develop into learning organizations for decision making.
These developments require companies to change the way of working in hydrocarbon production forecasting and decision analysis. Additional skills need to be developed in companies to embrace digitalization. Data science - which is considered a key skill in digitalization - has not been identified as crucial in skills development of oil and gas companies in the past. However, for data driven decision making, advanced data analytics skills and data science skills are a pre-requisite. To overcome this skill gap, staff needs to be trained and graduates with data science and profound physical and chemical skills need to be hired.
Furthermore, skills development has to address the challenge of incorrect use of Machine Learning technologies and the risks of Artificial Intelligence leading to erroneous optimizations. In particular interpretability of AI needs to be covered in skills development.