--> Virtual Kerogen Kinetics for Maturity Mapping — A Fast and Powerful Workflow

AAPG Annual Convention and Exhibition

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Virtual Kerogen Kinetics for Maturity Mapping — A Fast and Powerful Workflow

Abstract

Mapping thermal maturity across a basin of interest is one of the most basic tasks a geoscientist undertakes when exploring for hydrocarbons, particularly in unconventional shale plays. Some difficulties can arise with traditional thermal maturation assessment tools, however. For example, optical evaluation of cuttings with little to no vitrinite, or cuttings which have been dried at excessively high heat, can be misleading. In addition, the correlation of Rock-Eval Tmax to %Ro varies, depending on the kerogen type and/or the source rock's bitumen content. Data from produced fluids are often not available in areas of frontier exploration. The source organic facies frequently varies greatly through the sediment column, making maturity assessments based on Hydrogen Index (HI) or Transformation Ratio (TR) difficult, since each organic facies will have a different original HI. A very effective way around this difficulty is to use the activation-energy (Ea) distribution for kerogens, because while the original HI of the various organic facies may vary due to changes in preservation, their Ea distributions will not, if the organic matter input remains relatively uniform during deposition. One-run kinetic analyses using a fixed A is a technology well suited to this approach. An alternative approach is to model the transformation of the various organic facies using a kinetic profile reasonable for the particular kerogen type, based on knowledge of the depositional environment. This approach allows one to fit the modeled kinetic transforms to Hydrogen Index data and more confidently assess the Transformation Ratio of the organic matter within a sediment column at a single well. It also permits the Tmax-%Ro relationship to be adjusted to fit the pyrolysis data and the modeled kinetic transform. Thus, maturity over a basin can be mapped quickly and often with excellent accuracy if one has enough pyrolysis data over the basin. If produced fluid data are available, the model can be calibrated to those data. This workflow will be demonstrated in an example from a North American basin.