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Modelling at a Provincial Scale: Challenges and Solutions

Abstract

The Alberta Geological Survey (AGS) branch of the Alberta Energy Regulator provides geological information and expertise to government, industry and the public about Alberta's resources and geological processes. The AGS is building a 3D digital framework of the bedrock geological units in Alberta from surface down to the Precambrian basement. The province of Alberta has an area of over 660,000 km2 (255,000 sq mi); building a geological model of this size has distinct challenges that must be overcome. One challenge is the presence of an overall trend of increasing depth (decreasing elevation) of geological horizons towards Rocky Mountains to the southwest. Over hundreds of kilometres, this trend dominates local-scale structures. The trend can be modelled explicitly and removed from the data using methods including local polynomial interpolation or a distance-weighting scheme. The trend is reincorporated after the residuals are modelled using a stationary method such as kriging. Alternatively, a modelling method could be used that implicitly accounts for the trend, for example the convergent interpolation algorithm in the Petrel software. There are hundreds of thousands of oil and gas wells in Alberta that can be used to interpret the subsurface geology. The age of some of these wells and the state of some legacy data is such that filtering out the poor-quality data in favour of more modern and/or higher-quality data is not straightforward. Screening for local errors and accounting for data sources must be built into the modelling process in an iterative workflow. The history of geological interpretations in Alberta has varied the nomenclature between different areas. Varying names for the same units across regions of the province makes gathering and merging disparate datasets difficult, as there can be inconsistencies in the naming conventions and features that have been interpreted as formation tops. The lower Banff/upper Exshaw/lower Exshaw units are sometimes called the upper/middle/lower Bakken, for example. Deciphering whether a unit has a different name or does not exist at all in a different area adds a layer of complexity not seen in smaller models. Point data such as geological horizon tops picked from wells represent a snapshot at one location. For a large-scale model, this can create challenges in the differing variance between areas of high and low data density. This fact must be noted and accounted for in areas of very high or low data coverage.