Feasibility study of SQp and SQs attributes application for facies classification

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. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.