The hydrostatic pressure of the mud column in the wellbore is usually greater than the formation pressure causing mud filtrate to invade the formation in the vicinity of the wellbore. When oil-based mud (OBM) is used, unlike water-based mud (WBM), OBM is miscible with the formation fluid and alters its fluid properties and phase behavior. To be able to sufficiently correct for the contamination in the fluid sample, it is required that the contamination level be sufficiently low. This means that the engineer will continue to pump the contaminated fluid around the probe area until it is sufficiently clean. It is important to be able to measure the contamination level of the reservoir fluid as accurately as possible in real time before taking the sample, as taking additional downhole samples after the well is completed may be difficult if previous samples are not useable. Cleanup time depends on multiple parameters, including formation permeability, fluid viscosity, depth of invasion, and wellbore mud column overbalance pressure.

Most current methods for predicting contamination rely on curve fitting to a single property such as fluid density, gas content, and color. Curve fitting relies on the assumption that when the properties being monitored do not change significantly as the pumping continues, the contamination level is low. However, this can also be because of a steady-state effect even at high contamination levels. Also, contamination value from curve fitting method is sensitive to data selection and also depends on the endmember filtrate and formation fluid properties which cannot measured directly either downhole or in the laboratory.

In this work, we present a technique for predicting contamination using pumpout density and volume and formation properties such as drawdown mobility, overbalance, formation pressure, and drawdown pressure. By combining multiple parameters such as fluid density, drawdown mobility, formation pressure, overbalance, and drawdown pressures, predicted contamination values are better constrained. Moreover, this technique does not depend on end member properties. The technique is based on constraining pumpout data with formation properties from pressure testing data. In this technique, a large dataset of pump-out volume, density, and formation properties data acquired from wells from different regions of the world are used to develop a predictive model using a machine learning approach. The pumpout density is represented as optimized parameters of an inverse power law model.

The estimated contamination value at the endpoint in the field case examples presented in this paper is fairly close the reported laboratory value of the sample taken at the end of each pumpout.

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