Next Article in Journal
Multibeam Bathymetric Investigations of the Morphology and Associated Bedforms, Sulina Channel, Danube Delta
Next Article in Special Issue
Diffraction Enhancement Through Pre-Image Processing: Applications to Field Data, Sarawak Basin, East Malaysia
Previous Article in Journal
Flood Hazard Management in Public Mountain Recreation Areas vs. Ungauged Fluvial Basins. Case Study of the Caldera de Taburiente National Park, Canary Islands (Spain)
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Geosciences 2018, 8(1), 10; doi:10.3390/geosciences8010010

Feasibility Study of SQp and SQs Attributes Application for Facies Classification

Department of Geosciences, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Malaysia
*
Author to whom correspondence should be addressed.
Received: 14 November 2017 / Revised: 24 December 2017 / Accepted: 28 December 2017 / Published: 2 January 2018
View Full-Text   |   Download PDF [3305 KB, uploaded 23 January 2018]   |  

Abstract

Formation evaluation is a critical requirement in oil and gas exploration and development projects. Although it may be costly, wireline logs need to be acquired to evaluate and understand the subsurface formation. Gamma ray and resistivity are the two main well-log data used for formation evaluation purposes. However, outside the well, formation evaluation becomes difficult, as these logs are not available. Hence, it is important to have other data equivalent to the gamma ray or resistivity logs, which can be derived from other technique, such as seismic data. As a consequence, the dependency on well-log data can be avoided. Thus, the complexity in formation evaluation outside the well, such as the determination of facies, lithology, and fluid content, as well as petrophysical properties can be solved accurately even without well-log data. The objective of this paper was to demonstrate an application of the SQp and SQs attributes for facies classification. These attributes were derived from attenuation attributes through rock physics approximation by using basic elastic properties: P-wave, S-wave, and density. A series of tests were carried out to show the applicability of these attributes on well-logs and real seismic data from offshore the Malaysia Peninsular. Simultaneous inversion was used in the data sets to produce the three-dimensional (3D) SQp and SQs attributes required as inputs of a neural network engine in defining the facies distribution. The results showed that the SQp attribute was very similar to the gamma ray, while the SQs attribute was similar to the resistivity responses even in different reservoir conditions, including low resistivity low contrast and coal masking environment. In conclusion, the SQp motif, which is similar to the gamma ray motif, can potentially be used for facies classification/identification. Together with the SQs attribute, the SQp attribute can be used as input for the facies classification workflow. The application of the SQp and SQs attributes successfully identified the gas sand distribution and separated it clearly from the brine distribution in an offshore Malaysian field. View Full-Text
Keywords: attenuation; facies; lithology; log responses; porefill attenuation; facies; lithology; log responses; porefill
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Hermana, M.; Ngui, J.Q.; Weng Sum, C.; Prasad Ghosh, D. Feasibility Study of SQp and SQs Attributes Application for Facies Classification. Geosciences 2018, 8, 10.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Geosciences EISSN 2076-3263 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top