Integrated Approach in Lithofacies Prediction & Stratigraphic Reservoir Characterization

Rp200,000.00

ABSTRACT

Stratigraphic traps are produced when the seal or barrier lithology is formed as a result of lateral and vertical variations in the thickness, texture, porosity or impermeable lithology of the reservoir rock during the deposition of the reservoir beds. This lithological variation may be caused by depositional process, as in the case of channels, sand bars, fan or even reef. One important thing in stratigraphy reservoir is the trap could appear in many layers of rock change, sometimes over short distances, even within the same rock layer. As an example, it is possible that a layer of rock which is sandstone at one location is a siltstone or shale at another location. In between, the rock grades between the two rock types.
Sandstones make a good reservoir because their many pore spaces content. On the other hand, shale, made up of clay particles, does not make a good reservoir, because it does not contain large effective pore spaces. Therefore, if oil migrates into the sandstone, it will flow along this rock layer until it meets low-porosity shale. Therefore, to characterize stratigraphic reservoir need extremely careful handling during lithological variation characterization, and also when performing other reservoir properties prediction such as porosity and pore fluid type distribution.
A robust method for stratigraphic reservoir characterization is proposed by integrating all the information gathered from cores, direct seismic rock physics measurement for core samples, wells, and seismic waves using a hybrid statistical rock physics and artificial intelligence approach. In addition that algorithm must be processed in sequential workflow, which means that it is started by lithological variation prediction, and followed by their reservoir porosity prediction then pore fluid prediction. Each prediction process must consider the reservoir parameter which is already produced from previous process. For example, during porosity prediction, we must consider lithology distribution. And during pore fluid prediction processing done, the porosity distribution must be identified and considered while processing is done. Results showed by many case studies, the sandstone distribution is almost different with structure orientation.

Keywords: stratigraphic reservoir, lithology, porosity, statistical seismic rock physics, statistical artificial intelligence

Description

Authors : B. E. B Nurhandoko, Y. Hariman, R. Kurniadi, Susilowati, K. Triyoso, S. Widowati