Recently, advanced wireline formation testers are being utilized to obtain clean formation samples more often than in the past. Because obtaining clean samples requires a long pumping operation, knowing the required pump-out volume and time for the given formation condition is very important for an efficient and safe operation. This paper shows an application of a new near wellbore simulator to prejob modeling of sampling jobs to assist selecting sampling depth, pumping rate, and minimum-required time and volume to reach a target contamination level.

Advanced formation testers are equipped with sensors that can distinguish the formation hydrocarbon from mud filtrate. The operation may require extended pumping, which may cause problems like tool sticking, mud weight control, inefficient or prohibitive pumping time, or a slim possibility of good samples due to deep invasion. These problems are more pronounced in deep, extended reach wells offshore where rig costs are high. Therefore, careful planning is required to obtain clean formation samples, either hydrocarbon or water. It is especially important for sampling of formation oil when oil-based mud or synthetic mud is used.

To facilitate optimum sampling operations, a newly developed near wellbore simulator is applied for a prejob modeling sensitivity analysis. The simulator is based on a black oil reservoir simulator and can handle a three dimensional, multi-phase flow in the near-wellbore area by incorporating wellbore and formation tester tool factors. Under the given formation parameters of porosity, capillary pressure, and relative permeability, the effects of permeability, anisotropy, formation thickness, pumping rate, probe depth relative to the formation thickness, and invasion depth are investigated.

Field application results for a gas condensate sampling and a formation water sampling are presented in the paper. The pump-out volume and time under various formation conditions to reach a 5- percent contamination level were estimated during the prejob planning stage, and a near infrared spectrometer sensor was used for fluid identification in the actual pumping operation. The prejob modeling results match very well with the sensor responses and the laboratory analysis results further confirm the excellent sample quality with very low contamination. The success of the sampling operation demonstrated the benefits and importance of the well planned, prejob modeling work.

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