Gas Oil Ratio Trends In Sedimentary Basins & PVT Behavior

By: Zhiyong He, ZetaWare, Inc.

Gas oil ratios of oil and gas fields plotted against depth show interesting trends as shown below. The figure on the left is from large global datasets, and the one on the right is from an area in the North Sea. What are the reasons we may ask?

We have recently talked about this in several presentations (see references below). We concluded that this is a result of PVT behavior during migration. At shallower depth, the pressure is lower, and oil cannot dissolve as much solution gas as it can at a deeper depth. Likewise, gas can not dissolve much liquid at shallow depth.

This is supported by the relationship between saturation pressure (Psat) and GOR relationship based on some large PVT databases.

In this figure above, the blue shaded area are based on thousands of saturation pressure (bubble point on the left and dew point on the right) measurements. As HC generation windows are typically deeper than the blue band, migrating fluid toward shallow depth will reach saturation pressure at different depth depending on initial GOR of the fluid coming from the source rock. But once the Psat is reached, GOR will be limited by Psat, and follow the trend of the Psat-GOR relationship, resulting the distribution in figure 1.

This process not only changes GOR significantly from the initial fluid expelled from the source rock, it will also change the composition and API gravity. Below is a local example from a gas condensate system.

The initial fluid is found in a deeper reservoir (1) close to the kitchen, it is undersaturated as reservoir pressure is higher than initial Psat. In shallow trap along the migration path, the fluid is separated as a gas cap and an oil leg (2) and (3), with very different GOR and liquid API gravity. The fluid can further fractionate depending on whether migration is vertical or lateral, (4) and (5). Please also note that the saturation pressure itself is also modified by the same process, and becomes lower at shallow depths.

The same happens without a trap, or in between traps, along the migration pathway. In a gas condensate system like the above, the liquid phase that drops out is a) the heaviest fraction of the liquid first, and b) as droplets that are unable to form a continuous phase to migration along with the main gas phase.

Similarly, if the generated fluid is mainly oil, the GOR of the oil will follow the bubble point side of figure 2. Gas bubbles gradually drop out, or trapped in small traps that spill the liquid, reducing GOR along the way. As the gas phase that was dropped out retains the lightest ends extracted from the oil, the API gravity of the remaining oil decreases approaching shallower traps.

In any given trap, the HC fluid composition and therefore properties are not only a function of the initial generated fluid, but also on the pressure history and the complexity of the migration paths. The self regulating process of changing composition and in turn Psat itself, is much too complex to model at this time. Attempt to predict reservoir fluid properties solely based on source rock kinetics, as you may find in recent basin modeling literature, is misdirected in our opinion. A top down approach based on analyzing observed fluid properties in traps and trends in the geological context (Top Down PSA) is recommended.

Select References:

He, Zhiyong, and Andrew Murray, 2019, Top Down Petroleum Systems Analysis and Geospatial Patterns of Petroleum Phase and Properties. Celebrating the life of Chris Cornford (1948-2017): Petroleum Systems Analysis ‘Science or Art?’ The Geological Society, 24 - 25 April 2019

He, Zhiyong, and Andrew Murray, 2019, Top Down Petroleum System Analysis, Exploiting Geospatial Patterns of Petroleum Phase and Properties. AAPG Annual Convention, San Antonio, May 19-21, 2019 Download pdf from search and discovery

Murray, Andrew, and Zhiyong He, 2019, Oil vs. Gas: What are the Limits to Prospect-Level Hydrocarbon Phase Prediction? AAPG Hedberg Conference, The Evolution of Petroleum Systems Analysis, Houston, Texas, March 4-6, 2019 Download pdf from search and discovery

Zhiyong He, and Andrew Murray, 2020.  Migration loss, Lag and fractionation: Implications for fluid property prediction and charge risk. AAPG annual conference, Houston Texas, Sept 28-30, 2020.