  The paper describes the usage of intelligent approaches for field development
tasks that may assist a decision-making process. We focused on the problem of
wells location optimization and two tasks within it: improving the quality of
oil production estimation and estimation of reservoir characteristics for
appropriate wells allocation and parametrization, using machine learning
methods. For oil production estimation, we implemented and investigated the
quality of forecasting models: physics-based, pure data-driven, and hybrid one.
The CRMIP model was chosen as a physics-based approach. We compare it with the
machine learning and hybrid methods in a frame of oil production forecasting
task. In the investigation of reservoir characteristics for wells location
choice, we automated the seismic analysis using evolutionary identification of
convolutional neural network for the reservoir detection. The Volve oil field
dataset was used as a case study to conduct the experiments. The implemented
approaches can be used to analyze different oil fields or adapted to similar
physics-related problems.
