22: Correlation of Seismic Attributes and Geomechanical Properties to the Rate of Penetration in the Mississippian Limestone, Oklahoma
Published:January 01, 2019
Xuan Qi, Joseph Snyder, Tao Zhao, Kurt J. Marfurt, Matthew J. Pranter, 2019. "Correlation of Seismic Attributes and Geomechanical Properties to the Rate of Penetration in the Mississippian Limestone, Oklahoma", Mississippian Reservoirs of the Midcontinent, G. Michael Grammer, Jay M. Gregg, James Puckette, Priyank Jaiswal, S. J. Mazzullo, Matthew J. Pranter, Robert H. Goldstein
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The rate of penetration (ROP) measures drilling speed, which is indicative of the overall time and in general, the cost of the drilling operation process. ROP depends on many engineering factors; however, if these parameters are held constant, ROP is a function of the geology. We examine ROP in the relatively heterogeneous Mississippian limestone reservoir of north-central Oklahoma where hydrocarbon exploration and development have been for over 50 years. A 3-D seismic survey and 51 horizontal wells were used to compute seismic attributes and geomechanical properties in the area of interest. Previous tunnel boring machines (TBM) studies have shown that ROP can be correlated to rock brittleness and natural fractures. We therefore hypothesize that both structural attributes and rock properties should be correlated to ROP in drilling horizontal wells. We use a proximal support vector machine (PSVM) to link rate of penetration to seismic attributes and mechanical rock properties with the objective to better predict the time and cost of the drilling operation process. Our workflow includes three steps: exploratory data analysis, model validation, and classification. Exploratory data analysis using 14 wells indicate high ROP is correlated with low porosity, high lambda–rho , high mu–rho , low curvedness, and high P-impedance. Low ROP was exhibited by wells with high porosity, low , low , high curvedness, and low P-impedance. Validation of the PSVM model using the remaining 37 wells gives an . Using these 5 attributes and 14 training wells, we used PSVM to compute a ROP volume in the target formation. We anticipate that this process can help better predict a budget or even reduce the cost of drilling when an ROP assessment is made in conjunction with reservoir quality and characteristics.