Proceedings of the Workshop on Data Mining for Oil and Gas

05/09/2017
by   Alípio Jorge, et al.
0

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.

READ FULL TEXT
05/12/2017

An Overview of Data Mining Applications in Oil and Gas Exploration: Structural Geology and Reservoir Property-Issues

Low oil prices have motivated energy executives to look into cost reduct...
10/27/2017

Audiovisual Analytics Vocabulary and Ontology (AAVO): initial core and example expansion

Visual Analytics might be defined as data mining assisted by interactive...
07/28/2020

Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing

The implementation of robust, stable, and user-centered data analytics a...
03/01/2020

The Data Science Fire Next Time: Innovative strategies for mentoring in data science

As data mining research and applications continue to expand in to a vari...
10/19/2010

Mining Knowledge in Astrophysical Massive Data Sets

Modern scientific data mainly consist of huge datasets gathered by a ver...
10/17/2020

Using machine learning to reduce ensembles of geological models for oil and gas exploration

Exploration using borehole drilling is a key activity in determining the...
02/07/2018

Game Data Mining Competition on Churn Prediction and Survival Analysis using Commercial Game Log Data

Usually, game companies avoid sharing their game data with external rese...