Finding the sweet spot in an unconventional resource play is no simple task. Even with a great deal of local experience, the heterogeneous nature of these rocks can make successful well placement a “hit or miss” proposition from one well to the next.
A closer look at data analytics shows that multivariate analysis (MVA) of geophysical, geological, petrophysical, and geomechanical properties can help explain past successes and failures, as well as facilitate new well placement based on local conditions, even with sparse or poor-quality data.
Demonstrating actual application of MVA for well placement in the Eagle Ford shale play of South Texas, this webinar also shows how production quality can be estimated using MVA, even when available completion data is limited. You will find that the methods presented are practical and applicable to larger, more robust datasets, and can be executed as a machine learning exercise.