What follows provides biomarker and non-biomarker relations from which the depo-sitional environment of petroleum samples can be estimated. The ratio of dibenzoth-iophene to phenanthrene (DBT/P) can distinguish between a carbonate (DBT/P >
1) and a shale (DBT/P < 1) source (Hughes et al., 1995). When used together with Pr/Ph, the DBT/P has a high specificity towards a source rock’s depositional environment and lithology, this is known as a Hughes plot (Hughes et al., 1995), Figure 5.1(a).
The C31R/C30 hopane ratio can be used to indicate a marine vs lacustrine source deposition (Peters et al., 2005h). A value of > 0.25 indicates a marine shale, car-bonate and marl source rocks. For better specificity, the ratio should be used to-gether with C26/C25 tricyclic terpanes, as shown in Figure 5.1(b), although areas of overlap are still prominent. Redox conditions can be inferred from the 30nor -hopane/regular hopane ratio, whereby values > 1 would indicate an anoxic carbon-ate or marl source (Peters et al., 2005h). The C27-C28-C29 sterane ternary diagram provides very good specificity towards distinguishing different source rocks (Peters et al., 2000) or different organic facies of the same source rock (Grantham et al., 1988). This sterane relation provides limited specificity to distinguish marine from non-marine depositional environments (Figure 5.1(c)), except plants with a strong input from higher-plant organic matter (Moldowan et al., 1985), thus should be used in conjunction with other source indicators.
Figure 5.1: Relations used in sample source assessment. References for plots (a) and (b) are Hughes et al. (1995) and (Peters et al., 2005h, Figure 13.77 (GeoMark Research Inc., Zumberge 2000, personal communication)) respectively. Plot (c) adapted from Moldowan et al. (1985).
Two versions of the gammacerane (Ga) index (GI),
GI1 = Ga
Ga+C3017α, 21β-hopane× 10 and
GI2 = Ga
C3117α, 21β-30-homo-hopane 22R hopane,
provide a high specificity for water-column stratification during source rock depo-sition, which may indicate hypersalinity at depth (Sinninghe Damst´e et al., 1995).
The interpretation should be made with caution, however, as hypersalinity can also result from temperature gradients (Peters et al., 2005h). When in low quantities, gammacerane can coelute with other peaks thus should be used carefully (the m/z
412 ion fragmentogram can be assessed as well).
The pristane/phytane ratio is extensively used in thermal maturity assessment (thus is affected by it) and provides some specificity towards redox conditions of the source rock (Peters et al., 2005h). According to Didyk et al. (1978), Pr/Ph < 1 can be related to source rock anoxia, and oxic conditions otherwise. The ratio is used to differentiate between marine carbonate/marine shale/lascustrine source input also, as is illustrated in the Hughes plot above (Figure 5.1 (a)). However, for petroleum samples within the oil generation window, Ph/Ph can only by used at extreme val-ues, as it is otherwise weakly correlated with redox conditions. The ratio values
> 3 indicates an oxic terrigenous organic matter deposition and anoxic, hyper-saline/carbonate deposition for Pr/Ph < 0.8 (Peters et al., 2005h). For better specificity, Pr/(Pr+Ph) can be plotted against the C27 Dia/(Dia+Regular) sterane ratio which has a strong positive correlation (Moldowan et al., 1994b, 1986a). The C30/(C27-C30) regular sterane index has a high specificity to marine organic matter input (Moldowan et al., 1985; Peters et al., 1986). The C35pentahomo-hopane index
C35 17α, 21β-30,31,32,33,34-pentahomo-hopane 22S +R C31+32+ . . . +35 17α, 21β-homo-hopanes 22S +R
indicates high anoxic conditions where the index value is above ca. 20% (Peters &
Moldowan, 1991; Peters et al., 1995). McKirdy et al. (1983) also used a C34/C3522S homo-hopane index to illustrate that carbonate source rocks have a ratio of > ca.
0.9, whereas Peters et al. (2005h) noted that lower values would indicate a shale source.
6.1 Introduction
Particle molecular aggregation and/or particle flocculation can be detected by a variety of methods, such as optical microscopy, confocal microscopy, near-infrared microscopy (Miller, 1996; Cosgrove & Zasadzinski, 1998; Habdas & Weeks, 2002;
Challis et al., 2005), calorimetry (Anderson & Birdi, 1991; Bury et al., 1991; Loh et al., 2004; Smirnovas et al., 2005; Sun et al., 2005) and nuclear magnetic res-onance (Fabre et al., 1980; Zajac et al., 1994; Freed et al., 2009), to name a few.
Ultrasonic characterisation is another of such techniques, widely used for commercial and academic research purposes due to its high sensitivity to molecular rearrange-ment, invariance to sample opacity and the availability of non-destructive on-line sampling (Zaman et al., 2004; Xiaobo et al., 2016). The power levels that are typi-cally deployed during ultrasonic measurements are ca. 10 kW m-2(Puskar, 1982) and it is assumed that displacements induced by ultrasonic pressure are elastic (Povey, 1997b). The method allows to detect changes in volumetric/elastic properties of sam-ples, such as the particle size distribution (Challis et al., 2005; Povey, 2013, 2017), contrasts in viscous/density properties (Challis et al., 2005), aggregation (Zielinski et al., 1986; Andreatta et al., 2005a; Ray et al., 2005; Abbott & Povey, 2012; Sval-ova et al., 2017), particle stability (creaming/flocculation) (Shukla et al., 2007) and crystallisation (Dickinson et al., 1996; Povey, 2017). Advantages of ultrasound over light spectroscopic techniques include the phase sensitivity of acoustic transducers, a higher frequency range (10−1 to 1013 Hz vs 3-6×1016 Hz), coherence between pulses and non-polarization of the sound pulse (Povey, 1997b).
Ultrasound has been applied to study phase behaviour properties of e.g. proteins, fats, ionic solutions and surfactants. For example, Taulier & Chalikian (2001) used ultrasonic velocity along with density, fluorescence anisotropy and circular dichro-ism measurements to understand conformational transitions in β-lactoglobulin. The
temperature-induced aggregation and denaturation of the same protein was charac-terised using ultrasonic velocity and absorption in heat-catalysed conditions (Ochen-duszko & Buckin, 2010). Airborne ultrasound measurements have been used to monitor the volume and size distributions of air bubbles in chocolate (Watson et al., 2014). Low-intensity ultrasound has also been successfully used to promote peri-odontal alveolar bone regeneration in dogs (Wang et al., 2018). For present pur-poses, of particular interest is ultrasonic characterisation of colloidal mixtures and solutions of surfactants. Ultrasound has been widely used to study aggregation of surfactant solutions. Zielinski et al. (1986) proposed an ultrasonic velocity model determining the critical micelle concentration of alkyltrimethylammonium bromide surfactants in water. Ray et al. (2005) have demonstrated secondary micellarisa-tion of the same type of compound using ultrasound, as well as tensiometric, con-ductometric, fluorimetric and calorimetric methods. Assuming asphaltenes behave similarly to surfactants, Andreatta et al. (2005a) used ultrasound to estimate the critical nanoaggregate concentration of asphaltenes in toluene as well as aggregation of Tween 80 and sodium dodecyl sulphate surfactants. What follows provides an introduction into sound mechanics and defines mathematical apparatus in order to detect micellarisation and nanoaggregation in solutions using ultrasonic characteri-sation.
6.2 Acoustic Propagation in Homogeneous Fluids and Colloids