  The process of exploring and exploiting Oil and Gas (O&G) generates a lot of
data that can bring more efficiency to the industry. The opportunities for
using data mining techniques in the "digital oil-field" remain largely
unexplored or uncharted. With the high rate of data expansion, companies are
scrambling to develop ways to develop near-real-time predictive analytics, data
mining and machine learning capabilities, and are expanding their data storage
infrastructure and resources. With these new goals, come the challenges of
managing data growth, integrating intelligence tools, and analyzing the data to
glean useful insights. Oil and Gas companies need data solutions to
economically extract value from very large volumes of a wide variety of data
generated from exploration, well drilling and production devices and sensors.
  Data mining for oil and gas industry throughout the lifecycle of the
reservoir includes the following roles: locating hydrocarbons, managing
geological data, drilling and formation evaluation, well construction, well
completion, and optimizing production through the life of the oil field. For
each of these phases during the lifecycle of oil field, data mining play a
significant role. Based on which phase were talking about, knowledge creation
through scientific models, data analytics and machine learning, a effective,
productive, and on demand data insight is critical for decision making within
the organization.
  The significant challenges posed by this complex and economically vital field
justify a meeting of data scientists that are willing to share their experience
and knowledge. Thus, the Worskhop on Data Mining for Oil and Gas (DM4OG) aims
to provide a quality forum for researchers that work on the significant
challenges arising from the synergy between data science, machine learning, and
the modeling and optimization problems in the O&G industry.
