A Breakthrough in Accurate Downhole Fluid Sample Contamination Prediction in Real Time
- Julian Y. Zuo (Schlumberger) | Adriaan Gisolf (Schlumberger) | Hadrien Dumont (Schlumberger) | Francois Dubost (Schlumberger) | Thomas Pfeiffer (Schlumberger Oilfield Services) | Kang Wang (Schlumberger) | Vinay K. Mishra (Schlumberger) | Li Chen (Schlumberger) | Oliver C. Mullins (Schlumberger-Doll Research) | Mario Biagi (Eni Congo) | Serafino Gemelli (Eni Congo)
- Document ID
- Society of Petrophysicists and Well-Log Analysts
- Publication Date
- June 2015
- Document Type
- Journal Paper
- 251 - 265
- 2015. Society of Petrophysicists & Well Log Analysts
- 1 in the last 30 days
- 229 since 2007
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Accurate quantification of oil-based mud (OBM) filtrate contamination of hydrocarbon samples is still one of the biggest challenges in fluid sampling with formation testers. Existing techniques apply only to a very limited combination of probe type and formation fluid type. The methods can be technique sensitive, and lack a confident level of quality control. The technology and variety of downhole fluid analysis (DFA) sensors has evolved greatly over the recent years. However, the methods used to predict contamination have not kept up with the change in technology—until now. The response of multiple sensors has been combined in new methods, new techniques, and new algorithms to significantly improve the prediction accuracy of a fluid sample’s contamination downhole in real time. Results of these new approaches from field studies have been validated against laboratory measurements with good agreement.
Any downhole quantification of hydrocarbon-filtrate content requires knowledge of the properties of a virgin reservoir fluid and pure OBM filtrate. These properties, here referred to as endpoints, cannot be measured directly in most cases. This is the core challenge of real-time OBM contamination monitoring (OCM). Accurate native fluid characterization is the gateway, not only to obtain clean samples, but also to understand fluid-property distributions with confidence in a single well and across the entire reservoir. It is also an enabler for the emerging DFA workflow to use downhole sensor data to characterize the reservoir and the slow dynamic processes that give rise to its fluid distribution.
The mixing rule for optical density has been used for OCM in the past two decades. The assumption has been made that the crude oil is “dark” in an optical color channel where the mud filtrate is assumed “colorless” (coloration is associated with asphaltene content). However, this method is valid only for oil with sufficient asphaltene content in solution (enough optical density in color channels), and it may be erroneous in cases in which the OBM filtrate is not colorless in specified channels or the reservoir fluid does not exhibit color. Moreover, the accuracy of the endpoint characterization is limited if there is no or minimal optical density contrast between the oil and the filtrate. Such lack of contrast in optical density is frequently observed, typically when mud systems absorb color due to well-to-well reuse or if the native fluid lacks color. New physical chemistry-based mixing rules and algorithms have been developed for mass density, optical density, and gas-oil ratio (GOR), and the results are confirmed by laboratory measurements. This novel methodology enables accurate quantification of both the OBM filtrate and the pure virgin formation fluid. The self-consistency of using multiple independent sensors provides confidence and greatly improves the robustness and quality control of OBM-filtrate contamination monitoring downhole. Finally, contamination results can be expressed in volume or weight percent and as live-fluid or stock-tank liquid (oil) fraction for easy comparison to laboratory results.Three field case studies demonstrate the requirements and effectiveness of the new method. A brief description of the formation-testing objectives sets the scene, not only for the contamination monitoring accuracy requirement but also for demonstrating the need to obtain uncontaminated native fluid for determining composition, GOR, live-fluid density, and optical density. Case studies include gas condensate, light oil, and black oil examples, and all results are in good agreement with the results of the laboratory analysis.
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