A Statistical Workflow for Mud Weight Prediction and Improved Drilling Decisions

Abstract

Summary We study a drilling situation based on real data, where the high-level problem concerns mud weight prediction and a decision about casing in a section of a well plan. Sensitivity analysis is done to select the most relevant input parameters for the mud weight window. In doing so, we study how the uncertainties in the inputs affects uncertainties in the mud weight window. Our approach for this is based on distance-based generalized sensitivity analysis, and we discover that the pore pressure and unconfined compressive strength are the most important input parameters. Building on this insight, a statistical model is fitted for the mud weight window and the two main input parameters, keeping in mind their geostatistical trends and dependencies. Finally we use the fitted model to the decision situation concerning casing, in a trade-off between drilling risks and costs. We conduct value of information analysis to determine the optimal data gathering scheme at a given depth, for making better decisions about casing or not. In spite of being case specific, we aim to develop a workflow that could be applied in other drilling contexts.