Predicting reservoir wettability via well logs. (5th April 2016)

• Record Type: Journal Article
• Authors: Feng, Cheng; Fu, Jinhua; Shi, Yujiang; Li, Gaoren; Mao, Zhiqiang
• Is Part Of: Journal of geophysics and engineering. Volume 13:Number 3(2016:Jun.)
• Page Start: 234
• Page End: 241
• Publication Date: 2016-04-05
• Subjects: wettability -- NMR T2 spectrum -- capillary pressure curve -- median radius ratio -- contact angle -- low-permeability; Geophysics -- Periodicals; Prospecting -- Geophysical methods -- Periodicals; Engineering -- Periodicals; 622.1505
• DOI: 10.1088/1742-2132/13/3/234
• Languages: English
• ISSNs: 1742-2132

Abstract

Abstract: Wettability is an important factor in controlling the distribution of oil and water. However, its evaluation has so far been a difficult problem because no log data can directly indicate it. In this paper, a new method is proposed for quantitatively predicting reservoir wettability via well log analysis. Specifically, based on the J function, diagenetic facies classification and the piecewise power functions, capillary pressure curves are constructed from conventional logs and a nuclear magnetic resonance (NMR) log respectively. Under the influence of wettability, the latter is distorted while the former remains unaffected. Therefore, the ratio of the median radius obtained from the two kinds of capillary pressure curve is calculated to reflect wettability, a quantitative relationship between the ratio and reservoir wettability is then established. According to the low-permeability core sample capillary pressure curve, NMR T 2 spectrum and contact angle experimental data from the bottom of the Upper Triassic reservoirs in western Ordos Basin, China, two kinds of constructing capillary pressure curve models and a predictive wettability model are calibrated. The wettability model is verified through the Amott wettability index and saturation exponent from resistivity measurement and their determined wettability levels are comparable, indicating that the proposed model is quite reliable. In addition, the model's good application effect is exhibited in the field study. Thus, the quantitatively predicting reservoir wettability model proposed in this paper provides an effective tool for formation evaluation, field development and the improvement of oil recovery.