Joint 53rd South-Central/53rd North-Central/71st Rocky Mtn Section Meeting - 2019

Paper No. 34-4
Presentation Time: 8:30 AM-11:45 AM

CORRELATING REBOUND HARDNESS TO MINERALOGY AND POROSITY OF CARBONATE RESERVOIRS- APPLICATION TO GROUNDWATER AQUIFERS AND PETROLEUM RESERVOIRS


KNEEDY, Sheyanne, Oklahoma State University, Boone Pickens School of Geology, Stillwater, OK 74074, WANG, Yulun, Boone Pickens School of Geology, Boone Pickens School of Geology, Noble Research Center 103, Stillwater, OK 74078 and GRAMMER, G. Michael, Boone Pickens School of Geology, Oklahoma State University, Noble Research Center, Stillwater, OK 74078

Measuring rebound hardness (RHN) via cores can be an important asset to the evaluation of both groundwater and petroleum reservoirs due to the large amount of data that be analyzed in a time and cost efficient manner. RHN is measured by using a handheld device that hits the testing surface and automatically calculates a ratio of rebound and impact velocities. Previous studies on carbonate mudrock reservoirs show a positive correlation with the increase of harder minerals such as quartz and calcite content yielding higher RHN values, and a negative correlation with softer intervals containing abundant authigenic or detrital clay minerals. In addition, a negative correlation is observed between RHN and porosity across different reservoir types. In this study, RHN data will be collected to further test these statistical relationships in a “conventional carbonate reservoir” consisting of sucrosic dolomites within a cyclically stacked Devonian tidal flat system. Rebound hardness can help understand the rock mechanical properties of different lithologies and facies, and help estimate mineralogy and reservoir quality in a cheaper and faster way as compared to conventional laboratory testing. Understanding rock brittleness, clay content and porosity, as detected by physical measurements of rebound hardness, yield valuable insight into the development and management of natural resource reservoirs, and can ultimately improve reservoir performance and well economics in oil and gas reservoirs, as well as groundwater aquifers, where core data is available.