A Practical Approach to Determine Low-Resistivity Pay in Clastic Reservoirs

Abstract

Low-resistivity pay (LRP) has been a challenging problem in formation evaluation for many years. This is because conventional petrophysical interpretations are unable to identify pay intervals in low-resistivity reservoirs. This paper lays out a robust workflow for identifying LRP in thinly laminated sands with silty and/or shaly layers.The workflow is essentially a two-step process which integrates data from gas while drilling (GWD), conventional logs and nuclear magnetic resonance (NMR) logs which identify potential pay intervals for further examination using wireline formation tester (WFT). This approach allows one not only to identify pay intervals but also their phase and their flow characteristics without the need of a conventional drill-stem-test (DST).It is common for LRP to have high water saturation (60-70%) computed by conventional petrophysical interpretation while producing sustained water-free oil for few years. A petrophysical study was undertaken to integrate core data analyses including conventional, NMR and mercury injection capillary pressure (MICP). Data from over 100 sidewall cores (SWC) were examined. This novel approach for estimating irreducible water saturation (S wi ) was developed based on 1. A good relationship was recognized between S wi from capillary pressure data as compared to that estimated from NMR de-saturated core. 2. A correlation was established between S wi of de-saturated core and T 2, LM . 3. A validation was performed for zones producing water-free oil to link between NMR-MICP core analysis and NMR logs, using the following two methods for estimating S wi from a. capillary pressure data that is computed with knowledge of the height above free water level (FWL), b. T 2, LM of NMR logs using the correlation developed in Step 2. The benefits of this methodology are that it improves decision in well completion, predicts well performance accurately and reduces uncertainty in reserve estimation. In addition, it allows the user not only to identify zones of pay that would have been missed using conventional analysis, but also to estimate FWL elevations with higher accuracy from a saturation height model (SHM). Saturation profiles derived by this approach and those ones modeled from saturation height equations based on MICP capillary pressure data can be fitted better due to substantial reduction in uncertainties of log derived saturation data. Consequently the initialization of in-place volumes for hydrocarbons will be enhanced.

A correction factor for the resistivity log in gas reservoirs with low resistivity pay zone in the Poti Formation, Parnaiba Basin, Brazil

“…According to Sneider (2003), the presence of minerals that increase conductivity, such as expansive argillominerals, pyrite, glauconite and others, is common in many basins worlwide. This problem demands a lithological characterization of the reservoir and, once the problem is identified, special evaluation for the correct estimation of the hydrocarbon saturation (Chu et. al, 2011).…”

“…A common problem in petroleum reservoirs, both gas and oil, are low resistivity pay zones (LRPZ) related to the presence of clay minerals and other conductive minerals (Sneider, 2003), which affect well log evaluation, directly impacting the water saturation (SW) calculations and, therefore, the hydrocarbon volumes available in the reservoir. The conventional petrophysical interpretation is unable to identify pay intervals in low-resistivity reservoir (Chu et. al, 2011).…”

Reservoir characteristics and control mechanism of resistivity low-contrast oil pays in Chang 8 tight sandstone of Longdong West area, Ordos Basin

“…(2) The resistivity increase rate of the oil layer is generally between 1.0 and 3.0 (Xie et al 2002;Ouyang et al 2009;Zhang et al 2018). This phenomenon makes it difficult to distinguish oil pays from water layers by using resistivity logging response (Chu and Steckhan 2011;Xiao et al 2012).…”

Using the Modified Resistivity–Porosity Cross Plot Method to Identify Formation Fluid Types in Tight Sandstone with Variable Water Salinity

“…Fluid identification is the basis for reservoir evaluation and research. For tight sandstone, complex pore structure and minerals weaken the fluid identification ability of conventional logs [1]. Additionally, the great difference in formation water salinity also reduces the contrast between the oil layer and the water layer to some extent, which brings great difficulties to fluid identification [2].…”