  Well known oil recovery factor estimation techniques such as analogy,
volumetric calculations, material balance, decline curve analysis, hydrodynamic
simulations have certain limitations. Those techniques are time-consuming,
require specific data and expert knowledge. Besides, though uncertainty
estimation is highly desirable for this problem, the methods above do not
include this by default. In this work, we present a data-driven technique for
oil recovery factor estimation using reservoir parameters and representative
statistics. We apply advanced machine learning methods to historical worldwide
oilfields datasets (more than 2000 oil reservoirs). The data-driven model might
be used as a general tool for rapid and completely objective estimation of the
oil recovery factor. In addition, it includes the ability to work with partial
input data and to estimate the prediction interval of the oil recovery factor.
We perform the evaluation in terms of accuracy and prediction intervals
coverage for several tree-based machine learning techniques in application to
the following two cases: (1) using parameters only related to geometry,
geology, transport, storage and fluid properties, (2) using an extended set of
parameters including development and production data. For both cases model
proved itself to be robust and reliable. We conclude that the proposed
data-driven approach overcomes several limitations of the traditional methods
and is suitable for rapid, reliable and objective estimation of oil recovery
factor for hydrocarbon reservoir.
