Application of seismic attributes and neural network for sand probability prediction — A case study in the North Malay Basin


Author : Jianyong Hou, Toshihiro Takahashi, Arata Katoh, Suwit Jaroonsitha, Khun Puvanat Chumsena & Kazuo NakayamaPublication : Bulletin of the Geological Society of MalaysiaVolume : 54Page : 115-121Year : 2008


Description

Bulletin of the Geological Society of Malaysia, Volume 54, November 2008, pp.115 - 121

 

Application of seismic attributes and neural network for sand probability prediction - A case study in the North Malay Basin

Jianyong Hou1*, Toshihiro Takahashi1, Arata Katoh1, SuwiJaroonsitha2, Khun PuvanaChumsena& Kazuo Nakayama3

1JGI, Inc., 1-5-21 Otsuka Bunkyo-ku, Tokyo, 112-0012, Japan

*Tel: +81-3-5978-8038, Fax: +81-3-5978-8050,

E-mail address:  hou@jgi.co.jp

2CPOC, PETRONAS Twin Towers, Kuala Lumpur City Centre, 50088 Kuala Lumpur, Malaysia

3JAPEX Co, Ltd., 1-7-12, Marunouchi Chiyoda-ku, Tokyo, 100-0005, Japan

 

Abstract— The application of seismic data has greatly increased our capability to correctly understand the distributions of reservoir layers and geological structures of interest. Seismic attributes analysis is a popular and important method to predict the distributions of reservoir properties such as lithology, porosity and thickness. In the North Malay Basin, some sandstone layers related to reservoirs are found between A and B horizons. Especially, the gas reservoirs have been confirmed in the sandstone layers around Z horizon developed between A and B. Although 3D seismic survey has been executed, the distributions of the sandstone layers, e.g. thickness, porosity, still remain unclear across the area because there are only few wells drilled. To understand the distributions of sandstone layers and lithologic change across the whole 3D seismic survey area, we utilized Artificial Neural Network (ANN) method supported by Geology Driven Integration (GDI) technique. This process established the relationship between sandstone lithology and some seismic attributes. We selected Z horizon as calculating datum plane to predict the sandstone distribution and lithologic change within the range from 150 ms above the horizon to 120 ms below it, that is from A horizon to B horizon.

Keywords: Artificial Neural Network, seismic attributes, rock properties, pseudo well, relationship