Physical Sciences and Mathematics Commons^™

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, Tobi Micheal Ore

Graduate Theses, Dissertations, and Problem Reports

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 …