A machine learning-driven workflow has been designed to provide drilling engineers and geosteering geologists with a high-resolution earth model populated with elastic, petrophysical or geomechanical properties to assist with safe and efficient drilling. The technology can be deployed pre-drill or in real-time as a well is being actively drilled.
The real-time deployment affords ahead-of-the bit and away-from-the-bit awareness that may help to mitigate pre-drill sub-surface uncertainties. Examples include shallow water flow, shallow gas, depleted formations, and the presence of salt. These phenomena may all cause excessive non-productive time (NPT) or may cause the wellbore's possible loss in extreme circumstances.
A subset of a 3D prestack depth imaged seismic data covering 51 square miles (132 km2) acquired in Midland and Upton Counties, Texas, was used in this study. Wireline log data from seven wells were made available. Five of the seven wells were incorporated in the training model and the two remaining wells were retained as "blind" tests for validation purposes.
Results were achieved in three weeks with a significant increase in resolution compared to seismic data (60 ft to 15 ft). In this study, a suite of pre-drill attributes was generated. In addition, a simulated drilling scenario demonstrating the real-time deployment of the method is also described.