Paleoheat Flux Reconstruction from Thermal Indicators - ACS

Chapter 17

Paleoheat Flux Reconstruction from Thermal Indicators 1

2

He Wei , Malvin Bjorøy , and Elen Roaldset

1

1

Downloaded by UNIV OF IOWA on September 3, 2016 | http://pubs.acs.org Publication Date: November 9, 1994 | doi: 10.1021/bk-1994-0570.ch017

2

Department of Geology and Mineral Resources Engineering, Norwegian Institute of Technology, 7034 Trondheim, Norway Geolab Nos A/S, P.O. Box 5740 Fossegrenda, 7002 Trondheim, Norway

A numerical method is presented for reconstructing palaeoheat flux from thermal maturity indicators. This method utilises kinetic models or empirical expressions of thermal indicators and allows specification of various dependencies of heat flux on time such as exponential, constant, linear, parabolic, polynomial, and even a free variation with geological age, i.e. the variation of heat flux does not follow any time-dependent function. The chemistry-based kinetic models of vitrinite reflectance are valid in controlling palaeoheat flux at a maturity of >0.4 %R . The Heat flux of geologically younger sediments, which makes a large contribution to maturity, may be more accurately reconstructed by using a parallel reaction model of smectite/illite conversion kinetics. Of the kinetic models of thermal indicators, it is considered better to use an activation energy distribution model rather than a single reaction model, since the kinetic parameters used in parallel reactions are less sensitive, besides that the parallel reaction model describes the formation and conversion of thermal indicators under geological conditions more completely. O

Palaeoheat flux and temperature play a very important role in subsurface organic and mineralogical reactions, such as kerogen degradation and the transformation of biomarkers and of smectite to illite. Much attention has therefore been paid to the reconstruction of palaeo-thermal histories of sedimentary basins (7-5). Palaeoheat flux can be estimated by either geophysical or geochemical methods. Geophysical methods, such as the tectonic subsidence approach (7) in rift basins, only provide for an exponential variation of heat flow with geological time. Evaluation of thermal history based on maturity indicators is now an accepted practice since these indicators record the time-temperature events of sediments. The thermal indicators will be widely accepted in coming years due to their specificity of temperature and the increasing ease of obtaining this type of data (2). Modern basin modelling should not use the

0097-6156/94/0570-0269$08.00/0 © 1994 American Chemical Society

Mukhopadhyay and Dow; Vitrinite Reflectance as a Maturity Parameter ACS Symposium Series; American Chemical Society: Washington, DC, 1994.


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VITRINITE REFLECTANCE AS A MATURITY PARAMETER

inaccurate heating parameters such as temperature gradient with depth (°C/km) or heating rate (°C/ma). A "thermal indicator" is defined here as an indicator whose value changes only with time-temperature events, examples of such indicators are the isomerisation and aromatisation of biomarkers, increase in vitrinite reflectance, and the smectite to illite conversion. Basement heat flow is the main thermal energy source which determines formation temperature. "Present-day heat flux", the heat currently flowing from the interior of the earth toward the surface, can be calculated fairly accurately from downhole temperature measurements. However, "palaeoheat flux", the heat which has flowed through sediments in the past, can only be assessed using models. Inverse modelling of thermal indicators to estimate palaeoheat flux has been used by some groups (ex. 5-5). In order to find a more efficient and sophisticated heat flux reconstruction method using the thermal indicators, we performed numerical experiments on classical optimisation algorithms. The results show that, by introducing a minimisation procedure into the palaeoheat flux reconstruction, our method allows specification of various dependencies of heat flux on geological time and even a free variation of heat flux with time. The method and its uncertainties are discussed in detail below and two examples of applications are shown. Method The method assumes constant heat flow with depth, i.e. that the heat flow changes only with geological time. This assumption is a condition of any one-dimensional steady-state heat conduction model. By definition, the alteration and transformation of the thermal indicator (TID) is a function of time-temperature events, and its value directly corresponds to the time-temperature integral (ΤΠ): TID

= F

(

- A

=

(

TTI

F

exp(-E/RT(t,z))

dt

)

)

[1]

In this expression, Ε and A are activation energy and frequency factor respectively for the transformation of the thermal indicator's molecules. The value of TID is usually a function of a transformation ratio that is determined by TTI (ex. 6-5). This method is a one-dimensional heat flux reconstruction based on observed values of the thermal indicator at present-day depths, as illustrated by the diagram of the burial history of one well (Figure 1). The observed TTD's values are indicated by 1, 2, 3, ... in the diagram. The corresponding burial lines were calculated by a decompaction model. Where QO is present-dat heat flux, Q l , Q2, Q3, Q4 are the unknown palaeoheat fluxes at corresponding ages and ΔΤΠΟ, Δ Τ Π 1 , ΔΤΤΙ2, Δ Τ Π 3 , Δ Τ Π 4 are the differences of the time-temperature integrals between the corresponding ages. For a given present-day depth (Figure 1), the geological time duration of equation [1] is fixed. The temperature history for this point, T(t,z) which is a function of time (t) and depth (z), is determined by heat flow history and the thermal



17. WEI ET AL.

Paleoheat Flux ReconstructionfrontThermal Indicators

Figure 1. Method of heat flux reconstruction using thermal indicators.


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VITRINITE REFLECTANCE AS A MATURITY PARAMETER

conductivity of the sediments; and both vary with geological age. If it is possible to estimate the thermal conductivity based on the decompaction model, then by adjusting heat flow values along the burial time, it is possible to let the computed TID value close toward that observed at this point. Fitting single points is usually easy, however the solution is not unique. Two or more different sets of palaeoheat flux values may result in the same computed TTD value (see uniqueness demonstration below). For this reason, more than one observed TID value is required to restrict the palaeoheat flux. In the case of several observed values of the TTD at one well, adjusting heat flow history to approach one point may result in a poor fit of the other points since the temperature histories of all points are determined by the same heat flow history under the assumption of constant heat flow with depth. A best fit for all the points is the intention of this method. The method utilises optimisation algorithms to search for an appropriate set of palaeoheat flux values that minimise the total deviation of computed TTD values from that observed at various present-day depths. The method described aims to determine palaeoheat flux Q(t), which varies with time (t), where the objective function of m 2

I(MtidG]-Ctid[j]) j=l

[2]

takes on a minimum value. This is an optimisation problem where m denotes the number of sampling points. Mtid[j] is the observed value of the thermal indicator at a present-day depth point j , and Ctid[j] is the computed value at the same depth using the palaeoheat flux Q(t). Computation of Ctidjj] (equation [1]) requires knowledge of the temperature history T(t,z) of the corresponding point T(t,z) is determined by heat flux Q(t) and the thermal conductivity of the sediments K(z) that is a function of depth (z): T(t,z)

= TO

+ Q(t)

1/K(z)dz

[3]

Jo Here, TO is the temperature at surface (z=0). K(z) can be estimated from the thermal conductivities of the grain matrix 1^, pore water 1^, and porosity φ(ζ) which is determined by the decompaction model (surface porosity φ , compaction factor c): 0

K(z)

= K (K /K )*

Paleoheat Flux Reconstruction from Thermal Indicators - ACS

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