Reservoir Geology
As against ‘sandstone reservoirs’, ‘carbonate reservoirs’ are usually characterized by ‘extreme’ and more often ‘random’ variations of porosity, permeability and rock-type within a field (associated with the highly-soluble nature of carbonate rocks). Since, the original character of the sedimentary sequence – in a carbonate reservoir - gets ‘greatly’ altered by either solution; cementation; fracturing; or, by mineral replacement; would it remain feasible by ML/AI to deduce any sensible correlation of logical patterns of porosity and permeability distribution of various carbonate reservoirs?
Whether the knowledge of ‘original sedimentary patterns’ (recognition of specific depositional events in a carbonate reservoir that could possibly lead to the major flow paths and to deduce the effect of various types of reservoir energies on ultimate oil recovery) could be of any help – while using ML/AI - towards deducing fruitful insights on characterizing fluid flow through a carbonate reservoir?
The complexity and heterogeneity of carbonate reservoirs make them particularly challenging to model and predict. While machine learning (ML) and artificial intelligence (AI) methods have proven valuable in a range of geoscience and reservoir engineering tasks, their efficacy in deducing sensible correlations of logical patterns of porosity and permeability distribution in carbonate reservoirs may vary depending on the quality and diversity of available data.
Key considerations include:
In summary, while predicting porosity and permeability distribution in carbonate reservoirs using ML/AI methods is challenging due to their highly complex and variable nature, it is not infeasible. The key lies in the quality and diversity of the data, careful feature selection, appropriate model selection, and rigorous validation. However, it's important to remember that ML/AI models are tools to assist with interpretation and decision-making, and their outputs should always be assessed critically in the context of broader geological understanding.