To efficiently produce from unconventional reservoirs, it is imperative to determine and understand the geomechanical properties of the formation. Successful wellbore stability, drilling design and hydraulic fracturing techniques are some of the key results of fully understanding the geomechanical properties of the formation. These geomechanical properties include, Young's Modulus, Poisson's Ratio, Bulk Modulus, Shear Modulus, and Minimum Horizontal Stress and these can be obtained from geomechanical well logs. Unfortunately, these logs are not widely used because of the high cost involved. There is a need for an alternate and cost-effective way to obtain the geomechanical properties of rocks.
This leads to the idea behind this study. This study focuses on using Artificial Intelligence and Data Mining to establish a relationship between conventional logs and geomechanical logs, and predict geomechanical properties of the formation in a cost-effective way. The Upper Bakken Shale in North Dakota is the focus area of this study. 112 wells from five of the main producing counties in North Dakota; namely Burke, Mountrail, McKenzie, Dunn, and Williams, are analyzed. Shear wave velocity is first predicted by linear methods and neural networks. Shear wave velocity is crucial in making reliable calculations, especially the calculation of dynamic geomechanical properties of the formation. The geomechanical properties of the Upper Bakken Shale are then predicted from conventional well logs such as gamma ray and bulk density logs by using neural networks. Ultimately, a data-driven model is developed from neural networks that predicts geomechanical properties of future wells with an accuracy of at least 90% for the Upper Bakken Shale. Thus, obtaining these logs by generating it from statistical methods and artificial intelligent methods is a preferred way to understanding the pivotal fundamentals of shale rock properties. This study shows a potential for significant improvements in performance, efficiency, and profitability.
