--> Abstract: Comparison of Micro-Techniques Used for Analyzing Oils in Sidewall Cores to Model Viscosity, API Gravity, and Sulfur Content, by J. M. Guthrie, C. C. Walters, and K. E. Peters; #90933 (1998).

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Abstract: 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