Perpustakaan Digital ITB

Thermal maturity indices and modelling based on Arrhenius-equation reaction kinetics have played an important role in oil and gas exploration and provided petroleum generation insight for many kerogenrich source rocks. Debate continues concerning how best to integrate the Arrhenius equation and which activation energies (E) and frequency factors (A) values to apply. A case is made for the strong theoretical basis and practical advantages of the time-temperature index (PTTIARR) method, first published in 1998, using a single, carefully selected E-A set (E ¼ 218 kJ/mol (52.1 kcal/mol); A ¼ 5.45Eþ26/my) from the well-established A-E trend for published kerogen kinetics. An updated correlation between PTTIARR and vitrinite reflectance (Ro) is provided in which the PTTIARR scale spans some 18 orders of magnitude. The method is readily calculated in spreadsheets and can be further enhanced by visual basic for application code to provide optimization. Optimization is useful for identifying possible geothermal gradients and erosion intervals covering multiple burial intervals that can match calculated thermal maturities with measured Ro data. A memetic optimizer with firefly and dynamic local search memes is described that flexibly conducts exploration and exploitation of the feasible, multi-dimensional, thermal history solution space to find high-performing solutions to complex burial and thermal histories. A complex deep burial history example, with several periods of uplift and erosion and fluctuating heat flow is used to demonstrate what can be achieved with the memetic optimizer. By carefully layering in constraints to the models specific insights to episodes in their thermal history can be exposed, leading to better characterization of the timing of petroleum generation. The objective function found to be most effective for this type of optimization is the mean square error (MSE) of multiple burial intervals for the difference between calculated and measure Ro. The sensitively-scaled PTTIARR methodology, coupled with the memetic optimizer, is well suited for rapidly conducting basin-wide thermal maturity modelling involving multiple pseudo-wells to provide thermal maturity analysis at fine degrees of granularity