Oil Field Data Mining / Shahab D. Mohaghegh, Emre Artun.

Format:

Book

Author/Creator:

Mohaghegh, Shahab D., author.

Artun, Emre, author.

Contributor:

American Society for Testing and Materials.

ASTM International.

Series:

Journal of ASTM International. Manual Series ; MNL7320140004.

ASTM manual series ; MNL7320140004

Language:

English

Subjects (All):

Seismic.

Data mining.

Neural networks.

Artificial intelligence.

Reservoir characterization.

Petroleum engineering.

Gas engineering.

Petroleum--Prospecting.

Petroleum.

Natural gas--Prospecting.

Natural gas.

Physical Description:

1 online resource (28 pages) : illustrations, figures, tables.

Volume 2016, Issue 73 (September 2016)

Other Title:

ASTM digital library.

Place of Publication:

West Conshohocken, Pa. : ASTM International, 2016.

System Details:

Mode of access: World Wide Web.

text file PDF

Summary:

Today, large volumes of data are collected in the oil and gas industry. Data collection starts even before drilling for oil and gas. Seismic survey used to be the process during which the largest volume of data in the exploration and production industry is collected. With the invention and continuous use of sensors and such processes as measurement while drilling, logging while drilling, and permanent downhole gauges for smart fields, large volumes of data are collected and stored during drilling and operation of the wells. The large percentage of the data that currently is collected in the oil field is never used for decision making. Oil field data mining is set to change this. Oil field data mining provides the means for making the most of the investment that has gone into collection and storage of the data. Oil field data mining is a set of tools and technologies used to extract information and knowledge from the collected data in the exploration and production industry. The main characteristics of oil field data mining is its use of data-driven solutions as opposed to deterministic approaches that have dominated the exploration and production industry for the past five decades. One of the most significant attributes of oil field data mining that distinguishes it from conventional statistical approaches (that recently have became popular) is its use of artificial intelligence. Artificial intelligence has been called by different names. It has been referred to as virtual intelligence, computational intelligence, and soft computing. Among these names, artificial intelligence is now used most often as an umbrella term. Artificial intelligence may be defined as a collection of analytic tools that attempts to imitate life. Artificial intelligence techniques exhibit an ability to learn and deal with new situations. Artificial neural networks, evolutionary programming, and fuzzy logic are among the technologies that are classified as artificial intelligence. These techniques possess one or more attributes of reason, such as generalization, discovery, association, and abstraction. In the past decade, artificial intelligence has matured to include a set of analytic tools that facilitate solving problems that were previously difficult or impossible to solve. The current trend is the integration of these tools as well as the use conventional statistics and deterministic solutions to build sophisticated systems that can solve challenging problems. Artificial intelligence is used in areas such as medical diagnosis, credit card fraud detection, bank loan approval, smart household appliances, subway systems, automatic transmissions, financial portfolio management, robot navigation systems, and many more. In the oil and gas industry, these tools have been used to solve problems related to pressure transient analysis, well log interpretation, reservoir characterization, and candidate well selection for stimulation, among other things.

Notes:

"ASTM Stock Number: MNL7320140004".

Includes bibliographical references 31.

Electronic reproduction. W. Conshohocken, Pa. : ASTM International, 2016. Mode of access: World Wide Web. System requirements: Web browser. Access may be restricted to users at subscribing institutions.

Description based on publisher's website, viewed May 03, 2017.

ISBN:

9780803170698 (e-ISBN)

9780803170681

Access Restriction:

Restricted for use by site license.