  Geosteering workflows are increasingly based on the quantification of
subsurface uncertainties during real-time operations. As a consequence
operational decision making is becoming both better informed and more complex.
This paper presents an experimental web-based decision support system, which
can be used to both aid expert decisions under uncertainty or further develop
decision optimization algorithms in controlled environment. A user of the
system (either human or AI) controls the decisions to steer the well or stop
drilling. Whenever a user drills ahead, the system produces simulated
measurements along the selected well trajectory which are used to update the
uncertainty represented by model realizations using the ensemble Kalman filter.
To enable informed decisions the system is equipped with functionality to
evaluate the value of the selected trajectory under uncertainty with respect to
the objectives of the current experiment.
  To illustrate the utility of the system as a benchmark, we present the
initial experiment, in which we compare the decision skills of geoscientists
with those of a recently published automatic decision support algorithm. The
experiment and the survey after it showed that most participants were able to
use the interface and complete the three test rounds. At the same time, the
automated algorithm outperformed 28 out of 29 qualified human participants.
  Such an experiment is not sufficient to draw conclusions about practical
geosteering, but is nevertheless useful for geoscience. First, this
communication-by-doing made 76% of respondents more curious about and/or
confident in the presented technologies. Second, the system can be further used
as a benchmark for sequential decisions under uncertainty. This can accelerate
development of algorithms and improve the training for decision making.
