Date of Graduation
Eberly College of Arts and Sciences
Geology and Geography
In unconventional reservoir sweet-spot identification, brittleness is an important parameter that is used as an easiness measure of production from low permeability reservoirs. In shaly reservoirs, production is realized from hydraulic fracturing, which depends on how brittle the rock is–as it opens natural fractures and also creates new fractures. A measure of brittleness, brittleness index, is obtained through elastic properties of the rock. In practice, problems arise using this method to predict brittleness because of the limited availability of elastic logs.
To address this issue, machine learning techniques are adopted to predict brittleness at well locations from readily available geophysical logs and spatially using 3D seismic data. The geophysical logs available as input are gamma ray, neutron, sonic, photoelectric factor, and density logs while the seismic is a post-stack time migrated data of high quality.
Support Vector Regression, Gradient Boosting, and Artificial Neural Network are used to predict the brittleness from the geophysical logs and Texture Model Regression to invert the brittleness from the seismic data. The Gradient Boosting outperformed the other algorithms in predicting brittleness. The result of this research further demonstrates the application of machine learning, and how these tools can be leveraged to create data-driven solutions to geophysical problems. Also, the seismic inversion of brittleness shows promising results that will be further investigated in the future.
Ore, Tobi Micheal, "A Machine Learning and Data-Driven Prediction and Inversion of Reservoir Brittleness from Geophysical Logs and Seismic Signals: A Case Study in Southwest Pennsylvania, Central Appalachian Basin" (2020). Graduate Theses, Dissertations, and Problem Reports. 7714.