Modelling of petroleum generation requires kinetic parameters as an input to the modelling programs. Whereas some standard kinetics are provided by commercial software and in the literature (e.g. Pepper & Corvi 1995; Pepper & Dodd 1995), the kerogen within a particular study area may not be well represented, in which case it can be useful to obtain kinetic parameters for some representative samples.
Kinetic parameters comprise a range of activation energies (Eact) and associated frequency factor(s), also known as Arrhenius constant (A), which represent the amount of energy required to break bonds within kerogen to produce hydrocarbons, and the frequency with which such reactions occur.
Even common marine source rocks can exhibit substantial heterogeneities throughout their geographical and temporal ranges – e.g. Draupne Fm (Keym et al. 2006) – which can have profound influences on generation modelling if only a single or just a few samples are taken.
A variety of methods for determining kinetic parameters are available, but they fall into two basic types: open and closed system pyrolysis. APT offer both open system (bulk kinetics) and closed system (compositional kinetics) methods via Rock-Eval/HAWK and microscale sealed vessels (MSSV), respectively.
Open system is based on the Rock-Eval method, whereby petroleum products are immediately swept from the system, allowing little opportunity for chemical interaction with the residual kerogen. This method is convenient and relatively quick, but one deficiency is that alkenes are abundant among the products, whereas under natural, slower reaction rates, the structure of the residual kerogen becomes more condensed and aromatic, so liberating sufficient H to quench C=C bonds, yielding dominantly n-alkanes.
Closed-system pyrolysis is performed in sealed tubes, commonly MSSV, which can be heated isothermally for different periods. This method also permits detailed examination of products at various stages of generation by GC. Water can be added to the tubes as a source of hydrogen (hydrous pyrolysis), so that a more natural distribution of hydrocarbons is generated, without anomalous n-alkenes (Lewan 1997; Burnham 1998).
At its simplest, the compositional kinetics can represent just oil and gas generation, but greater complexity is possible, depending upon the selected groupings of products resolved by GC.
MSSV is the preferred method when PVT characteristics need to be modelled accurately. The basis of the phase kinetics approach (di Primio & Horsfield 2006) is to acquire open system bulk kinetic data for the source rock sample together with MSSV compositional data for various transformation ratios (typically 10, 30, 50, 70 and 90%), quantifying the amounts of 14 compositional groups, 7 for gas (C1, C2, C3, iC4, nC4, iC5, nC5) and 7 for oil (nC6, C7–C15, C16–C25, C26–C35, C36–C45, C46–C55, C56–C80), for each transformation ratio interval.
Source rocks behave as closed systems up to the onset of expulsion, providing the opportunity for generation products to undergo secondary reactions (Ungerer 1990; Ritter et al. 1995), but thereafter become effectively open systems, so neither pyrolysis method precisely represents natural maturation.
However, comparison with natural systems suggests that both methods provide similar, reasonably accurate modelling of types I and II kerogen (Burnham 1998; Schenk & Horsfield 1993, 1998), but coals and type III kerogen in general are not so well represented by open system pyrolysis kinetic parameters.
Source rock samples containing type I/II should be of a maturity just prior to the onset of hydrocarbon generation (typically vitrinite reflectance 0.5%, Tmax <425–430°C). This is in order that the complete generation capability can be investigated, but the kinetic parameters are not adversely affected by changes to the kerogen structure that precede the evolution of hydrocarbons.
The same amount of material is required as for Rock-Eval analysis in triplicate. Care should be taken to cover the range of heterogeneity demonstrated by the source rock unit.
Open system bulk kinetics via Rock-Eval
The simplest approach is to consider the transformation of kerogen as a single, homogeneous reactant into petroleum, which provides bulk transformation kinetics. It does not provide distinction between oil and gas on its own.
A Rock-Eval 6 or HAWK instrument can be used, but the HAWK is recommended. After an initial thermal desorption (10 min at 300°C), to remove residual bitumen, the samples are pyrolysed at heating rates of 1, 5 and 15°C to a maximum temperature of 650°C (the maximum reliable heating rate for kinetic work on Rock-Eval 6 is 10°C/min).
The data are then pre-processed before kinetic optimization is performed. The first 10 min (600 data points) are discarded, because they correspond to S1, and so are the points above 600°C. The remaining points are ‘thinned’ to ~500 using Kinetics05 (LLNL) software and the baseline corrected (between lowest signals before and after S2). Curve smoothing is applied, with a 3-point moving average for reaction data and temperature.
The optimization software then calculates an Eact distribution and single Arrhenius constant. Routinely, the analysis method is set at discrete with fixed Eact distribution spacing, kinetics based on reaction rate using relative reaction rate data type.
Closed system compositional kinetics via MSSV
Compositional kinetics is performed at APT using the closed system method of micro-scale sealed vessel (MSSV). Three different heating rate experiments are required, with five samples for each heating rate, so that a sample can be withdrawn at ~10, 30, 50, 70 and 90% conversion in order for the kinetic parameters to be determined. The timing of removal is determined from an open system pyrolysis experiment.
Normally, the client is involved in deciding on heating temperatures and performs the calculations to determine compositional kinetics. However, studies can be performed in cooperation with GeoS4 (Brian Horsfield) if help is required.
Finely crushed material is loaded into small MSSV tubes and weighed. Small glass beads are added and the tubes sealed by melting. Normally 5 tubes are loaded with the same sample and heated to 5 different temperatures (from 250°C) at a rate 0.7°C/min. Each sealed tube is loaded into a GC injector and, when the injector pressure has stabilized, the tube is crushed and the GC program started. For the first 10 min the products are collected in a cold trap with liquid nitrogen. The cooling is then removed, the trap heated to 300°C and the following GC temperature progamme is used: 40°C for 13 min (from breaking of sample tube), 5°C/min to 300°C, and after 25 min at this temperature, 5°C/min to 320°C, with a final isothermal period of 10 min. Quantification involves integrating the area from one n-alkane to the next, for both total area and resolved peaks.
Whether the kerogen in samples from exploration wells usually positioned on structural highs and/or basin peripheries accurately represents the original composition in the mature depocentre kitchen is conjectural. The inorganic mineral matrix of a source rock can also affect the kinetics of petroleum generation where the kerogen is finely dispersed (Dembicki 1991).
Variability in source rock quality throughout a basin can be accommodated by determining kinetic parameters for several samples, and using an average activation energy distribution with an appropriate standard deviation. The average activation energy distribution can be weighted according to the S2 yield of each sample in order that undue weight is not given to the leaner samples, which would be expected to generate less petroleum (Peters et al. 2006; Dieckmann & Keym 2006).
There are unavoidable potential errors related to the fact that there is no unique solution to the kinetic equations, and that laboratory conditions and heating rates are being extrapolated to those in natural geological settings. The restructuring that occurs in coals/type III kerogen is an example, which cannot be readily represented in the laboratory (Schenk & Horsfield 1993).
Kinetic optimisation software uses a series of iterations to arrive at best fit to the laboratory experiments, and care is required that the fitting criteria selected are appropriate and that the optimization has not stuck in a local minimum rather than the global minimum that is sought. Even so, it is possible to observe a compensation effect, whereby Eact and A values exhibit a positive correlation (higher Eact range is offset by higher A value).
Various factors have been discussed by Stainforth (2009). Kinetic data are potentially the largest source of error in modelling, so best and worst case scenarios are advisable t bracket the potential variation.
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