Integrated Approach for Effective Asphaltene Precipitation and Deposition Detection in Greenfield Wells

Author:

Lee W.1,Hamza M.2,Shim J.2,Mogensen K.3,Grutters M.4

Affiliation:

1. Al Dhafra Petroleum, now with Korea National Oil Corporation

2. Al Dhafra Petroleum

3. ADNOC Upstream

4. ADNOC Onshore

Abstract

Abstract This paper presents a comprehensive approach for assessing asphaltene precipitation and deposition in greenfield wells, which includes asphaltene risk evaluation using stock tank oil and bottomhole oil with and without miscible gas injection, the development of an asphaltene deposition simulation model employing PROSPER, and the interpretation of well operation results. A holistic solution for assessing asphaltene deposition in greenfields is proposed by combining laboratory analysis, simulation models, and field data. The methods involve screening techniques using dead oil, such as SARA plot, De Boer plot, CII index. These preliminary results were complemented with AOP tests using live oil and field intervention outcomes to confirm the risk. A throttle model was developed to estimate asphaltene deposition in wellhead throttle valves, validated using wellhead choke inspection and well test data. The comprehensive approach to managing asphaltene deposition in greenfield proved effective, as demonstrated by the results. The asphaltene risk assessment, well intervention activity interpretation, and simulation model successfully identified high asphaltene risk wells and targeted asphaltene treatment locations. Various screening methods determined different levels of asphaltene risk, with one method selected after comparison with AOP and field observations as the screening technique for new wells and reservoirs. Based on screening, asphaltene envelopes, and the deposition model, eight wells were chosen for asphaltene treatment. This paper offers unique insights by introducing a comprehensive approach for detecting asphaltene deposition in greenfield wells. The novel wellhead choke model, in particular, enables a simple prediction of asphaltene deposition using existing wellhead parameters and well test data. The findings have significant implications for greenfield management and can be applied to other oil-producing regions globally.

Publisher

SPE

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