Evaluation of Water Saturation in a Low-Resistivity Pay Carbonate Reservoir Onshore Abu Dhabi: An Integrated Approach

Abstract

Characterization and fluid quantification of Carbonate reservoirs looks more challenging than those of sandstone reservoirs. The determination of accurate hydrocarbon saturation is more tasking due to their complex and heterogeneous pore structures, and mineralogy. Traditionally, resistivity logs are used to identify pay intervals due to the resistivity contrast between oil and water. However, when pay intervals exhibit low resistivity, such logs exhibit low confidence in the precise determination of the hydrocarbon saturation. Few Middle-Eastern reservoirs are categorized as low resistivity pay, where resistivity based log analysis results in high water saturation. However, downhole fluid analysis identifies mobile oil, and the formation flows dry or nearly dry oil during production tests. This makes resistivity based saturation computation questionable. Because of the complexity of low resistivity pay (LRP), its cause should be determined prior to applying a solution. Several reasons were identified to be responsible for this phenomenon- among which are the presence of micro-porosity, fractures, paramagnetic minerals, and deep conductive borehole mud invasion. Integration of Thin section, Nuclear magnetic resonance (NMR) and Mercury injection capillary pressure (MICP) data from the studied formation indicated the presence of micropores network. This paper discusses the observed variations in quantifying water saturation in LRP interval and the related discrepancies between the resistivity and non-resistivity based techniques. The non-resistivity based methods, used in the course of this study, are coined from sigma log measurement and core data, either capillary pressure-based (MICP, Centrifuge, and Porous plate), or direct from Dean-Stark measurements. The interpretation process considered water saturation derived from resistivity measurement and core data combined with production test information. The combination of several water saturation determination approaches captured the uncertainty and improved our understanding of the reservoir properties. This enhanced our capability to develop a robust and reliable saturation model. The integration of data from these various sources added confidence to the estimation of water saturation in the studied field and thus, improved reserves estimation and reservoir simulation for accurate history matching, production forecasting and optimized field development plan.

Experimental Study on the Viscosity Behavior of Silica Nanofluids with Different Ions of Electrolytes

“…The nanofluid, which is a dispersion of NPs in base fluids, exhibits higher thermophysical properties like thermal diffusivity, thermal conductivity, and viscosity than its base fluid . The quantification of these fluid properties in porous materials is highly challenging. , The variation in the properties of nanofluids has increased its application in various sectors of oil and gas. , The injection of NPs into the reservoir does not only improve the fluid–rock interactions but also the fluid–fluid interactions . The interfacial and the rheological properties of fluids are altered by the introduction of NPs to the reservoir. − …”

“…2 The quantification of these fluid properties in porous materials is highly challenging. 3,4 The variation in the properties of nanofluids has increased its application in various sectors of oil and gas. 5,6 The injection of NPs into the reservoir does not only improve the fluid−rock interactions but also the fluid−fluid interactions.…”

Effect of Well Orientation on Oil Recovery from Waterflooding in Shallow Green Reservoirs: A Case Study from Central Africa

“…Figure 5 shows the porosity and permeability distributions of rock types in reservoir. Uchida et al (2015) and Ashqar et al (2016) discussed the factors that should be considered when classifying rock type for reservoir modeling and evaluation [47,48]. It is noted that when classifying different rock types, facies, porosity, and permeability are all considered.…”

A cohesive approach at estimating water saturation in a low-resistivity pay carbonate reservoir and its validation

“…Additionally, the LRP intervals are commonly identified with high water saturation, which makes such intervals of low interest to the extent that they are discarded as attractive to appraise, particularly when oil prices are low. Typically, LRP zones are characterized by formation interval, with moderate to high porosities, showing extremely low resistivity that are often less than 3 X-m and most frequently encountered in areas with saline formation water (Griffiths et al 2006;Obeidi et al 2010;Worthington 2000;Farouk et al 2014;Uchida et al 2015). While Boyd et al (1995) proposed that the resistivity range is between 0.5 and 5 X-m, several other researchers, like Zhao et al (2000), stated that LRP can be identified by the ratio of the pay zone to the water-bearing zone and this ratio is considered to be in the range of 2.…”

“…Typically, in carbonate reservoirs, the macropores hold and produce the moveable hydrocarbons due to their lower entry pressure, which is adjacent to micropores holding the immobile highly saline formation water due to the high entry pressure. Such micropores can be present in different forms as highlighted by Uchida et al (2015) and . Hassan and Kerans (2013) investigated the geological consequences of LRP in carbonate reservoirs and reported that the microporosity is 70% of the total porosity in all facies studied from 14 core plugs.…”