Robust and Automatic Shale Volume Interpretation Using Adaptive Lithological Thresholds Built on Depth Trends, Statistics, and Geological Units
Authors:   Aker BP, Westeng Kjetil , Van Crombrugge Yann ,   Inmeta, Lehre Christian Nilsen ,   Aker BP, Steria Sopra ,   Aker BP, Aursand Peder ,   Aker BP, Fjeldberg Egil ,   Aker BP
Published: November 2024

The volume fraction of shale (VSH) throughout the well is of great importance for formation evaluation, geomechanics, and numerous other workflows. Neither porosity, saturation, nor permeability can be understood if one does not first have a good grasp of the VSH. The objective of this paper is a method for automated VSH calculation which is effective, un-biased, and to lesser degree affected by the log coverage in the well, as well as able to handle the variation in both radioactivity and density-neutron response as a function of depth and formation. The method combines statistics and dynamic filtering with geological context to create adaptive depth trends representing the log response of shale and sand/carbonates. Algorithm main steps: 1)	Calculate dynamic sand and shale trends for each curve within each geological unit 2)	Identify section and tool change – centered rolling quantiles are reset where a tool change is identified 3)	Identify nonshale intervals by using the trends from Step 1 and custom thresholds (these can be adjusted by the user) 4)	For each nonshale window, detect the first inflection point before and after the window and interpolate between these two 5)	Combine the interpolated curve with the rolling quantile curve 6)	Calculate VSH_GR and VSH_DENNEU using the respective final trend curves 7)	Combine these with the final aggregated curve VSH_AUT The method can, e.g., handle variation in matrix radioactivity within a lithostratigraphic system as well as depth trends in shale properties. The efficiency and avoidance of manual parameterization make our method ideal for a fast evaluation of the full wellbore as the starting point for a more detailed manual interpretation or for fast and consistent massive multiwell evaluations. The method solves the normalization issues that often can affect the accuracy and bias of automated solutions, and the result can therefore be useful as an engineered feature in various machine-learning-based well interpretation schemes. This automated VSH interpretation method has been applied to more than 1,000 wells from various areas in the North Sea and the Norwegian Sea. It has been proven to provide a reliable interpretation compared to manual interpretation. In contrast to many other methods for automated VSH interpretation, our proposed method will, by design, be by which intervals are and are not covered by each log. This work will show how the method can be applied to calculate the VSH from both the gamma ray log and from the density-neutron porosity relationship. The method can easily be adapted to other regions and geological settings and has broad applications in traditional well interpretation as well as in machine learning. The method does not rely on training on proprietary data, and we utilize publicly available statistical functions and geological information, making it fully reproducible.