Online Journal for E&P Geoscientists

Ariwodo, Izu ^*1; Saldungaray, Pablo ²
(1) Reservoir Description, Saudi Aramco, Dhahran, Saudi Arabia. (2) Wireline, Schlumberger, Dhahran, Saudi Arabia.

Reservoir productivity is often influenced by the various lithologies present in the drilled wells. This is because lithology plays a major part in determining the rocks physical and chemical properties such as permeability, porosity, capillarity, rock strength, reactivity, precipitation, etc. A good knowledge of the reservoir rock lithology is vital in defining an efficient, effective and economic development strategy for the reservoir.

Various methods have been employed in the petroleum industry to characterize rocks into lithology groups and types. One of the earliest rock classifications methods involved the splitting of clastic rocks into reservoir and non-reservoir with the use of a simple electrical potential measuring device called the spontaneous potential tool (SP). The advancements in technology introduced other improved ways of identifying and classifying rock lithologies. Some of the subsequent methods used widely in the petroleum industry included the natural gamma ray (standard and spectral), the porosity cross-plots (density - neutron, neutron - sonic), and more recently neutron-induced gamma-ray spectroscopy using pulsed neutron or chemical sources.

In these days of high development cost for hydrocarbon resources, the geologists and petrophysicists are expected to produce a more realistic static reservoir model. This starts with an accurate lithology analysis. Unfortunately, the widely used porosity-lithology cross-plot models or methods using the photo-electric factor are often impaired by the type of fluids present in the well or in the reservoir. This problem demands to search for a better way of identifying and classifying the rock lithology with downhole measurements.

This paper describes how a more realistic lithology evaluation can be achieved through data integration. This method is based on the assumption that most of the data acquired in wells, would have a piece of the genetic make-up of the lithological sequences traversed during the logging. By integrating these data sources with their lithological coding, a more accurate lithological expression of the rock can be extracted.

A few examples will be used to show how the data integration has helped in lithology identification and classification process. Further examples will be used to show how the addition of the gamma ray spectroscopy can further refine the lithological identification and classification of reservoir rocks.