  The field development optimization (FDO) problem represents a challenging
mixed-integer nonlinear programming (MINLP) problem in which we seek to obtain
the number of wells, their type, location, and drilling sequence that maximizes
an economic metric. Evolutionary optimization algorithms have been effectively
applied to solve the FDO problem, however, these methods provide only a
deterministic (single) solution which are generally not robust towards small
changes in the problem setup. In this work, the goal is to apply convolutional
neural network-based (CNN) deep reinforcement learning (DRL) algorithms to the
field development optimization problem in order to obtain a policy that maps
from different states or representation of the underlying geological model to
optimal decisions. The proximal policy optimization (PPO) algorithm is
considered with two CNN architectures of varying number of layers and
composition. Both networks obtained policies that provide satisfactory results
when compared to a hybrid particle swarm optimization - mesh adaptive direct
search (PSO-MADS) algorithm that has been shown to be effective at solving the
FDO problem.
