Automated real-time formation evaluation from cuttings and drilling data analysis: State of the art

Abstract

Traditional formation evaluation via laboratory testing and wireline logging of horizontal wells and deep formations face challenges due to several reasons and lead to uncertain results. Real-time cuttings and drilling data analysis of horizontal wells is an actively developing alternative approach to formation evaluation that can overcome several challenges faced by laboratory testing and wireline logging in providing improved estimates of formation parameters relevant to reservoir and completion quality. This study presents a state-of-the-art review of the latest methods and technologies in drill cuttings analysis to enable real-time characterization of the entire suite of formation properties, including chemical composition, densities and porosity, permeability, lithology, geomechanical properties, and characterization of fracture patterns. Specifically, the methods/techniques that enable characterizing drill cuttings in real-time and critically reviewed in this study include Raman spectroscopy for chemical composition, nuclear magnetic resonance for densities and porosity, liquid pressure pulse for permeability, deep learning for rock classification, 7 different methods for geomechanical properties, and mud loss signatures for characterization of fracture patterns. Benchmark comparison of drill cuttings analysis with the measurements from the core samples at similar depths is also reviewed. Key learnings are provided in 4 areas: to address the uncertainties in estimates of specific parameters affected by physical deformations due to drill bits, minimum cutting size for reliable nuclear magnetic resonance data, sweet spot identification, and power and network considerations for real-time analysis, respectively. Cited as: Singh, H., Li, C., Cheng, P., Wang, X., Hao, G., Liu, Q. Automated real-time formation evaluation from cuttings and drilling data analysis: State of the art. Advances in Geo-Energy Research, 2023, 8(1): 19-36. https://doi.org/10.46690/ager.2023.04.03