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West Virginia University

Theses/Dissertations

Unconventional reservoir

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Full-Text Articles in Geology

Application Of 3d Seismic Signal And Geomechanical Attributes For Subsurface Fracture Characterization: A Case Study In Clearfield County, Central Pennsylvania, Iman F. Zulkapeli Jan 2021

Application Of 3d Seismic Signal And Geomechanical Attributes For Subsurface Fracture Characterization: A Case Study In Clearfield County, Central Pennsylvania, Iman F. Zulkapeli

Graduate Theses, Dissertations, and Problem Reports

Hydrocarbon exploration in unconventional reservoirs is highly risky due to the nature of the reservoirs and the variability in fractures and reservoir geomechanical properties in the subsurface. The reservoir needs to be fully characterized to avoid any complication such as frac hit, wellbore failure, blowout, or even a dry hole. The Clearfield reservoir produces an exceptionally low amount of gas, compared to the neighboring region in the proximity, which has been poorly understood. This raises the question as to what causes the reservoir to have low productivity.

This study focuses on the natural fracture characterization using high-quality 3D seismic signal …


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 Jan 2020

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 …