The Advantages of Interactive Fluid Property Modeling
Leon Dzou1 and Zhiyong He2
1BP America, Inc., 200 WestLake Park Boulevard, Houston, Texas 77079, USA
2ZetaWare, Inc., 2299 Lone Star, #403, Sugar Land, Texas 77479, USA
Pre-drill prediction and post-drill evaluation of petroleum quality are critical for prospect ranking, development planning and facilities design under high costs of deepwater exploration, appraisal and field development. BP global field data show a complex distribution of biodegraded oil occurrences in deepwater areas. Some fields, in which present-day reservoir temperature is as high as 80°C, contain biodegraded oils; whereas in other fields, reservoirs which are <50°C contain relatively fresh, unaltered oils. It is important to notice that most literature work looking at predicting biodegradation have focused on the temperature and time effects on API gravity due to biodegradation. However, relatively little attention has been paid to the reality that a lot of the variation in API we see in a basin may be due to variation in maturity and source facies variations, rather than biodegradation. In this paper, we will discuss four major controls on the composition of a petroleum accumulation: type of source, maturity, migration effects and biodegradation processes.
In order to simplify the understanding of crude oil composition, it is useful to classify oils into distinct groups. For oil-source rock correlation purposes, it is necessary to be able to relate the composition of organic matter in source rocks to that in oils. A classification scheme has been devised at BP (Pepper and Corvi 1995) that enables oils to be put into one of five categories - A, B, C, D or E, depending upon their bulk properties. These five groups represent five types of assemblage of organic matter in source rocks. Figure 1 shows the relationships between depositional environment and organic matter type. Basically, organic matter type depends upon primary input (i.e. algal, bacterial or land-plant) and the effects of diagenesis (bacterial reworking, sulphur incorporation).
The variation in properties of oils generated from each class type of organic matter with increasing thermal maturity is considered. This ignores any effect of in-reservoir alteration and instead concentrates on unaltered oils. It can be shown that each class type of organic matter produces oils that have a characteristic range of properties. Within each class type, properties vary as a function of thermal maturity due mainly to the level of maturity at which expulsion from the source rock took place. Clearly source rocks at the onset of generation would expel hydrocarbons of lower thermal maturity than hydrocarbons expelled when the source rock is more deeply buried. Across a basin, oils that represent a range of thermal maturity would be expected. It is natural to believe most oil accumulations are a mixture of varying maturity oil arriving at different time and from different part of the kitchen area. Figure 2 shows how API gravity varies for each class type. Initially expelled oil for class A (clay poor) source rock is around 16 API, in contrast, DE (deltaic) source rocks expel oil in the range of 30 to 55 API.
The effects of migration also influence petroleum composition particularly as a result of migration loss and migration lag. Figure 3 shows how micro traps and first carrier beds may retain petroleum volumes and delay migration until their local seal capacity is reached. Each of these traps may have very small columns due to low effective or small closure, but they may be retaining large petroleum volumes in total. Fluids arriving at shallow reservoir are delayed; less mature than what is currently expelled at the source.
Biodegradation of crude oil in the reservoir is an important alteration process with major economic consequences. The oil may be biodegraded and become heavy and more viscous, greatly reducing the market value of the oil. Net degradation of petroleum fractions in reservoirs is primarily controlled by the reservoir temperature, the chemical compounds being degraded, and relationships between the oil-water contact (OWC) area and oil volume. The relative volumes of water leg to oil leg, prior level of oil biodegradation, and reservoir water salinity act as second-order controls on the process (Larter et al., 2006)
In this talk we will present a workflow to systematically de-risk fluid properties using newly developed toolkit features, which integrates these important factors in a single interactive environment (Figure 4). Firstly, the relationships between API gravity of oil and organo-facies and maturity are empirically established through calibration with hundreds of fluid samples from multiple data sets. The variable thermal histories over the fetch area are then used to model the cumulative GOR and API gravity of expelled fluids through time. Secondly, we established a link between migration loss and fluid property difference between expelled fluids at the source rock and captured fluids in reservoirs. In this step, trap/seal timing can also affect properties of captured fluids. Lastly, the temporally varying charge rate and fluid property are fed into the biodegradation model which includes effects of oil-water-contact area, column height and thermal history of the reservoir to arrive at the final API gravity of the oil.
We applied this new toolkit to unravel the charge history of two fields and explain the observed fluid properties. Such charge history/fluid property calibrations can serve as useful analogs for reducing uncertainties related to predicting fluid properties in the same area. The difficulty involved in pre-drill prediction of fluid properties lies in the fact that each of the determining factors carries significant uncertainties. The interactive toolkit approach allows the petroleum geologist to test different scenarios and calibrate the model against known field data and geological evidence quickly.
In summary, key controls of fluid properties are: (1) source facies and thermal maturity, (2) migration loss/lag, (3) seal effectiveness and residence time, (4) charge history, charge volume and rates, and (5) biodegradation processes (reservoir temperature history, oil water contact area, column height, and oil vs. water volume). A fast fluid property risking toolkit is useful to test different geologic models (Interaction + Integration) and calibration of existing field fluid data is essential.
Pepper, A. S., and Corvi, P. J. (1995) Simple kinetic models of petroleum formation. Part I: Oil and gas generation from kerogen. Marine and Petroleum Geology 12, 291-319.
Larter et al., (2006) The controls on the composition of biodegraded oils in the deep subsurface: Part II Geological controls on subsurface biodegradation fluxes and constraints on reservoir-fluid property prediction. AAPG Bulletin, v. 90, no. 6, pp.921-938.
AAPG Search and Discovery Article #90091©2009 AAPG Hedberg Research Conference, May 3-7, 2009 - Napa, California, U.S.A.