Integrated 3-D Method for Prediction of Mud-Weight Window for Subsalt Well Sections

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Abstract Owing to the complex distribution of stress directions around a salt body, accurate prediction of mud-weight window (MWW) for subsalt wells has presented a challenge to the industry for a long time. This paper presents an integrated 3-D method for the prediction of mud-weight window. An example of the application of the proposed method in the VK field in deep-water Gulf of Mexico (GoM) is presented. In this integrated 3-D method and the example, 3-D stress solutions obtained by the finite-element method are used as input data to the 1-D prediction tool. The field-scale model used in the calculation is a cubic block with a TVD depth of 30,000 ft, a width of 25,000 ft, and a length of 25,000 ft. The salt body's thickness along the wellbore trajectory is 20,000 ft, and its width is 18,000 ft. Based on seismic sectional data, an anti-cline structure is constructed accordingly at its bottom surface. A 3-D, porous, elastoplastic, finite-element calculation was performed first with the field-scale model. The numerical results of effective-stress components were provided for the 1-D prediction of MWW along the given wellbore trajectory. The finite-element model simulates the geometry of the salt body in detail. Comparisons have been made between the results obtained with the integrated 3-D method, conventional 1-D method, and the finite-element submodeling method. Solutions obtained with conventional 1-D method have missed the abnormality of stress distribution at the salt base and have smaller values of MWW compared to the other solutions. The solution of MWW obtained with the integrated 3-D method is very close to the one obtained with the finite-element submodeling method, but at only one-tenth of the time cost. The integrated 3-D method proposed here has proven to be the most effective and cost-efficient method for MWW prediction for subsalt wells. This work also presents an example of best practice for using the proposed 3D method to predict MWW.