Linking High Resolution Geomechanical Datasets to Log Scale via Machine Learning
Authors: Gragg Evan , Fluckiger Samuel , Borchardt Shannon 
Published: September 2023

Multiscale datasets have long been difficult to integrate within the geosciences. We showcase workflows that leverage machine learning methods to integrate data from multiple scales including high resolution core scans, core plugs and log measurements. The trained models generate continuous elastic and rock strength curves at high resolution that are then retrained for log scale application. The result of this nested machine learning approach is the creation of log scale predictions of geomechanical properties. The data are based on four pilot wells with advanced open hole log suites, core, fine scale compositional, and high-resolution impulse hammer data. The mechanical parameters considered include unconfined compressive strength, friction angle, toughness, Young's modulus, Poisson ratio, and Biot coefficient with most measurements considering transverse isotropy. Predictions were tested against core data sets where regression scores, normalized prediction error distributions, exploratory data analysis, and blind expert interpretation were used to decide the final groupings of models and methods. We found this workflow allowed circumvention of the classical need to conduct dynamic to static elastic property conversions. Utilizing the data through this unique workflow can help operators improve their geomechanical models in unconventional reservoirs to make key landing zone, drilling and hydraulic fracture design decisions.

INTRODUCTION

In this paper, we aim to showcase workflows, methods, and practices for tying high resolution mechanical and compositional data sets to the log scale. To accomplish this, we utilize open hole log suites including - sonic, gamma ray, neutron porosity, resistivity, and bulk density measurements paired with AutoScan and mechanical core test data collected over four pilot wells in Texas. The AutoScan data are generated by New England Research, Inc. and contains high resolution acoustic, mechanical and mineralogical information scanned over core butts at the mm-cm scale (Ahmadov and Boitnott, 2019). Many researchers have highlighted multi-scale heterogeneities and the important impact they can have on hydrocarbon resource assessments in unconventional plays (Ferrill et al., 2017, Abell and Sassen, 2020, Hyrb et al., 2020, Gonzalez et al., 2021). The evaluation and incorporation of sub-log scale geologic variations into earth models and simulators has been a computational challenge (Dontsov, 2017, Hyrb et al., 2020). Upscaling workflows typically start at the log scale and do not capture information below the individual resolutions of sonic, density, gamma ray, and resistivity measurements. Whilst some high tier-high resolution logging tools like image and dielectric produce data at the cm scale, the data are often expensive to collect and spatially scarce at reservoir/basin scales. It is therefore useful to develop models using machine learning (ML) techniques that can leverage well logs with a preponderance of data spatially while incorporating information from high resolution core scans and core plug data sets. For this analysis, we are particularly interested in mechanical and rock strength heterogeneities including unconfined compressive strength, friction angle, fracture toughness, Young's modulus, Poisson ratio, and Biot coefficient. The parameters in this study are measured both vertical and horizontal to bedding via laboratory core testing and allow us to account for vertically transverse isotropic (VTI) media. VTI is commonly present in shale plays and important to account for (Sayers and Boer, 2016). Mechanical heterogeneities are believed to impact both the stimulated rock volumes (SRVs), fracture conductivity and hence drained rock volumes (DRVs) within reservoirs (Abell et al., 2021 and Zhai et al., 2022). Therefore, understanding the spatial variations of these geomechanical properties is vital to many reservoir characterization workflows. To accomplish this, we evaluated and applied nested ML techniques to predict properties, analyzed hundreds of sensitivities, and benchmarked outputs to select the most robust models.