A New Workflow for Improved Resistivity-Based Water Saturation Assessment in Organic-Rich Mudrocks: Application to Haynesville, Eagle Ford, and Woodford Formations

Abstract

Summary Reliable fluid saturation assessment in organic-rich mudrocks has been a challenge for the oil and gas industry. The composition and spatial distribution of rock components have a significant impact on electrical resistivity and, thus, on hydrocarbon reserves estimates. Clays are typically considered, in resistivity models, to be distributed in laminated or dispersed forms. Additionally, conventional resistivity models do not incorporate conductive components other than brine. Such assumptions can lead to uncertainty in fluid saturation assessment in organic-rich mudrocks. We introduce a well-log-based workflow that quantitatively assimilates the type and spatial distribution of all conductive components to improve reserves evaluation in organic-rich mudrocks and demonstrate its field application in the Eagle Ford, the Woodford, and the Haynesville formations. The introduced workflow consists of an inversion algorithm to estimate geometry-dependent parameters (depolarization factors or geometric model parameters) and water saturation. Inputs to the inversion algorithms include volume concentrations of minerals, estimated from the multimineral analysis. Other inputs are conductivity of rock components and porosity obtained from laboratory experiments and interpretation of well logs. The petrophysical model considers that brine forms the conductive background to which conductive (e.g., clay, pyrite, and kerogen) and nonconductive (e.g., grains and hydrocarbon) components are incorporated. The assumed/estimated petrophysical properties have an impact on the effective conductivity of the rock and thereby can impact the performance of the new resistivity-based method. We applied the new method to different organic-rich mudrock formations to test the universal nature of the method and its efficacy in organic-rich mudrock reservoirs with varying volumetric concentrations of minerals within the rock. We successfully applied the workflow to four wells in the Eagle Ford, the Woodford, and the Haynesville formations. The formation-by-formation inversion showed a variation in geometric model parameters in different petrophysical zones, resulting in improved water saturation estimates. A comparison of the results obtained from the new workflow against those from the Waxman-Smits and Archie models indicated a relative improvement in saturation estimates of 9.5 and 26.3% in the Eagle Ford formation. Similar improvements were noted in the Woodford and the Haynesville formations as well. The improvement can be enhanced in formations with larger fractions of conductive components. The results confirmed that the new workflow improves the reliability of water saturation estimates in organic-rich mudrocks, which has been a challenge for the oil and gas industry. In contrast to conventional techniques, the new method does not need water saturation obtained from core measurements for calibration efforts. All the parameters in the new workflow are geometry- or physics-based. We verified that formation-based geometric model parameters in the Eagle Ford formation were consistent in both wells, which is promising for calibration-free assessment of water/hydrocarbon saturation in the field-scale domain using electrical resistivity measurements. The new method minimizes the need for expensive and time-consuming core measurements of water saturation, which is a unique contribution of this work. Finally, the new workflow is physics-based and incorporates the volumetric concentration and electrical conductivity of all rock components. This enables the introduced workflow to be applied to different formations with ease for improved assessment of water saturation.