Well clean-up operations are conducted when the well is kicked off after completion to remove the completion/drilling fluids in the wellbore and near wellbore reservoir area. Drilling/completion fluid losses can significantly delay well clean-up thus having an adverse impact on clean-up OPEX and well deliverability. Since clean-up is a transient phenomenon involving temporal changes in hold-up or saturations, a transient simulation tool is required to model, predict, history match and optimize the clean-up operations. This paper proposes a workflow to history match the mud loss into, and recovery from the formation for wells experiencing significant mud losses during drilling/completion. A commercial coupled wellbore-reservoir transient simulator is used in this workflow.

The proposed workflow is demonstrated for a horizontal oil well where more than 30000 barrels of mud was lost during a stuck liner incident in the completion phase. The lost mud made well clean-up difficult as mud backflow from the formation loaded the well requiring coiled tubing assisted nitrogen injection for well unloading. The workflow traces the history of the well by capturing the pressure and saturation transients in the near wellbore reservoir region during the stuck liner incident, well clean-up operation and the production logging test (PLT) using a coupled wellbore-reservoir transient flow model. The workflow starts by arriving at an initial estimate of the skin along the horizontal drain using the PLT data. Sensitivity simulations for mud loss and clean-up are then conducted to identify the parameters having most influence on mud recovery using different combinations of skin, matrix permeability and phase relative permeability to match the mud recovery from the clean-up operation and oil and water flowrates in the PLT after the clean-up.

Mud loss and clean-up simulations using the initially available and estimated reservoir parameters (skin, matrix permeability and phase relative permeability) under predict the mud recovery and produced oil. From the sensitivity analysis, it is revealed that phase relative permeability has the biggest influence on the mud recovery followed by matrix permeability and skin. The optimized combination of the values of the afore mentioned parameters provides not only a good match between the predicted and the measured mud volume recovered during clean-up but also the oil and water rates during the PLT after clean-up.

The proposed workflow not only provides a template for history matching complex clean-up operations involving lost mud recovery but also generates a prototype for using available field or production data in predicting, executing and optimizing similar operations in future thus facilitating better and informed decision making. The workflow also brings forth the value transient simulations can add to understanding complex clean-up operations by giving an insight into the pressure and flow transients in the wellbore and near well reservoir region.