Print this pageAbstract: Comparison of Micro-Techniques Used for Analyzing Oils in Sidewall Cores to Model Viscosity, API Gravity, and Sulfur Content
Guthrie, J. M.; C. C. Walters; and K. E. Peters - Mobil Technology Company
Proper field management of reservoirs with biodegraded oil requires knowledge of the oil properties within producing and non-producing zones. It is often too costly and impractical to conduct downhole fluid testing to develop a vertical profile of oil quality. Sidewall cores are more easily obtained, but may not produce a sufficient quantity of oil to permit direct measurement of oil properties. In recent years, several published studies have shown the utility of micro-techniques for predicting the bulk physical and chemical properties of oil in sidewall core samples. In addition, the distributions of various saturated biomarker compounds that reflect the degree of biodegradation have been used to model viscosity. In all these studies, chemical properties were calibrated with a set of produced oils of known physical properties and related to sidewall core samples.
For this study, twenty-three oils from Venezuela
were analyzed by C15+ chemical group-type separation using high
pressure liquid chromatography (HPLC), saturated biomarker analysis, programmed
pyrolysis-FID (py-FID), and Fourier transform infrared spectroscopy (FT-IR).
Results from these analyses were used to develop a 
predictive
model of
oil quality that was applied to sidewall core samples from a Venezuelan
well. Our results show that a combined data set using HPLC, py-FID, and
saturated biomarker ratios yields an excellent model for predicting the
physical and chemical properties of the oils (Fig. 1). The application
of any one single data set, such as the py-FID or saturated biomarker ratios,
does not always result in a good predictive model. However, Figure 1 shows
that there is good agreement between the predictive model generated only
from HPLC data and the predictive model using the combined data sets.
Figure 2 shows the vertical profiles of oil quality
for a Venezuelan well generated from the predictive
model using the combined
data set and only the HPLC data. Both models predict similar ranges for
API gravity, log viscosity, and sulfur content. In general, there are stratigraphic
variations in oil quality in the well (Fig. 2). Log viscosity can range
as much as 3.5 to 4.5 cp and API gravity from -3 to 12°. Sulfur contents
average around 2% and do not vary significantly with depth in the well.
A model based on FT-IR data was fairly accurate for predicting the properties
of the oils but proved to be less reliable when predicting the properties
of the heavy oil in the sidewall core samples.
The application of micro-techniques to predict the physical and chemical properties of oils is reliable and accurate when using a combined data set of HPLC, py-FID, and saturated biomarker ratios and also when using only HPLC data. A sample set of produced oils from Venezuela has allowed us to generate a model for predicting viscosity, API gravity, and sulfur content in oil-stained sidewall cores where these properties cannot be measured directly. The vertical profiles of oil quality generated from these models have many applications, but may be most useful to optimize the placement of new wells and completion intervals, and to monitor relative amounts of production from discrete intervals in the well.
AAPG Search and Discovery Article #90933©1998 ABGP/AAPG International Conference and Exhibition, Rio de Janeiro, Brazil