Use of core and drilling data for selective stimulation selection in the Caney Shale

Abstract

Completion of shale plays are costly and require intelligent optimization techniques for an effective cost-saving means of production. An optimization technique is presented that utilizes raw drilling data from existing wells to enhance the completion performance. The objective of this work is to integrate the core and drilling data from existing wells to create petrophysical and geomechanical correlations between unconfined rock strength (UCS), porosity, permeability, Poisson's Ratio, and Young's Modulus to model in a complete geomechanical property log. The proposed method is based on an inverted ROP and torque and drag friction models that have been field-tested and proven with other published data. Raw drilling data that consist of ROP, RPM, mud weight, etc. were collected from two existing wells in the Caney Shale drilled in 2014. The D-Series software was applied with the drag and inverted ROP models to the raw drilling data to obtain the downhole weight on bit (DWOB) and formation UCS for every foot of the well. The ROP model's output data consist of the UCS which is correlated to Young's Modulus, porosity, permeability, and Poisson's Ratio which were developed in this study. These correlations can then be implemented to new wells in the same geographic area to provide the optimal selective perforation criteria that will yield the highest rate of return and hydrocarbon production.