Spatial estimation for 3D formation drillability field: A new modeling framework

Abstract Spatial 3D formation drillability distribution is crucial for the drilling trajectory planning, collision detection, and drilling optimization in the natural gas and petroleum fields. Conventional geostatistical methods are usually used to establish the 3D formation drillability field model. However, few or no machine learning methods, which have a powerful fitting capability, are used to build that model. This paper proposes a new modeling framework for establishing the spatial 3D formation drillability model. Four methods, one geostatistical (Kriging), one non-stochastic (ScatteredInterpolant), and two machine learning methods (random forest and support vector regression) are analyzed in this modeling framework. Well logging data such as acoustic and formation density are introduced as the input parameters. Moreover, the mutual information analysis is introduced to measure the correlations between the 3D coordinates and formation drillability. Finally, comparisons are explored in the 10-fold cross-validation, 3D modeling, and final test experiments using data from Xujiaweizi area, Northeast China. The results indicate that SI has the best 10-fold cross-validation performance while RF achieves the best prediction accuracy in the final test among the four compared methods. The proposed new modeling framework for 3D formation drillability model provides a platform and is applicable to other modeling methods and data.