Palynology and reservoir models
This recent paper discussed the lateral and vertical variation in palynology within individual argillaceous units, as well as lateral variation between different argillaceous units in the same sedimentary complex at similar stratigraphic levels. It aims at a tool for understanding more about why mudstone heterogeneity happens and ways of predicting heterogeneity in the subsurface. In this new article for LinkedIn, I look at ways that this initial published work could be extended as part of a reservoir model workflow, by statistically assigning levels of likelihood that mudstone units could act as reservoir baffles.
The value of the work in Jordan, detailed in the recent paper is the excellent exposure which is close to 100% in places, and the good preservation of palynological assemblages recovered from the exposed rocks. In a highly concentrated survey of a ~2-km2 outcrop area around a major meandering channel (Fig. 1) great variation was revealed within and between argillaceous units associated directly with the channel and independent of the channel (see Stephenson et al. 2024).
Fig. 1 Part of the survey of different argillaceous deposits in the Upper Permian Umm Irna Formation. See Stephenson et al. 2024 for more details.
Statistical clustering
In further work beyond the published paper, some basic statistical clustering work was also carried out on the mudstone units using Stratabugs software which uses the Grimm (1987) clustering method. The software is part of the palynology workflow, which can easily be extended to carry out clustering of assemblages. The software can cluster assemblages (1) within sections, (2) the sections themselves (groups of palynological assemblages from a single section) and (3) individual mudstone units (groups of sections) and shows cluster membership using a simple colour code but also the background clustering values. It therefore provides a very quick relatively objective and repeatable numerical assessment of internal palynological heterogeneity within individual argillaceous units and the palynological differences between argillaceous units.
Fig. 2. Clustering, using Stratabugs, of assemblages in a laterally accreting unit associated with a bend on the major meandering channel
An example is the clustering for the ‘Dyke Plateau’ argillaceous unit sampled in 4 separate short sections of a metre or so in thickness (Fig. 2). This is a laterally accreting unit associated with a bend on the major meandering channel shown in Fig. 1. The colour coding for cluster membership shows that the palynology of the unit is quite internally homogenous (similar colours). Other mudstone units in the 2km2 area show much greater internal variability of palynology.
An overview of many sections as part of a series of argillaceous units across the 2km2 area reveals different levels of internal palynological heterogeneity (revealed by an array of colours), and differences between units (Fig. 3).
Fig. 3. Clustering of assemblages across several mudstone units in the 2km2 survey area
What do these statistical clustering measures mean for reservoir heterogeneity?
One of the chief causes of reservoir heterogeneity is the presence and lateral persistence of mudstone units (e.g. Thompson et al 2019). Stephenson et al. (2024) tentatively suggested that the palynological character and diversity of each unit results from an interplay between connectedness to the major channel and influence of the local floral sub-environments of the floodplain. Those mudstone units with highest palynological diversity and highest internal homogeneity probably result from deposition in the major channels and most faithfully represent the flora of the basin, like a ‘baseline’ palynoflora. They tend to have the largest lateral extents because of their association with large active channels (see Stephenson et al. 2024), so they are more likely to be baffles. Those that are lower diversity and contain elements that differ markedly from the baseline flora, are more likely to be smaller units unconnected to the main channel and thus less likely to be baffles. They would cluster away from the baseline values. Thus the cluster values of units could relate to their propensity to form baffles and perhaps could be built into the workflow for static modelling of reservoirs.
Although this kind of work is in its infancy, some basic statistical methods to add to the tools of the palynologist attempting to fingerprint different argillaceous units could be useful because they offer more objectivity than the human operator, and more repeatability. Using techniques like this, classification systems of mudstone units based on numerical values could contribute more reliably to numerical models of complex mixed reservoir environments, both marine and continental.
Mike Stephenson is available for consulting
References
Grimm, Eric C. 1987. CONISS: a FORTRAN 77 program for stratigraphically constrained cluster analysis by the method of incremental sum of squares, Computers & Geosciences, Volume 13, Issue 1, 13-35,
Stephenson, M. H., Bomfleur, B., Jardine, P., Kerp, H., Blomenkemper, P., Bäumer, R., … Schneider, J. W. (2024). Palynological variability within the Permian (Changhsingian) Umm Irna Formation (Jordan): implications for biostratigraphy and fluid-flow character in alluvial formations. Palynology. https://doi.org/10.1080/01916122.2024.2388134
Thompson J, Parkes D, Hough E, Wakefield O. 2019. Using core and outcrop analogues to predict flow pathways in the subsurface: examples from the Triassic sandstones of north Cheshire, UK. Adv. Geosci. 49: 121–127,