Permeability Estimation Using Machine Learning Techniques for a Heterogeneous Mud Dominated Carbonate Reservoir, Offshore UAE

Abstract

The implementation of Artificial Intelligence and Machine Learning algorithms has introduced the opportunity for alternative techniques to estimate permeability within uncored intervals/wells. Different combinations of input data and different approaches for training values were evaluated to select a consistent predictive model able to produce permeability logs while honoring the geological concept and available core permeability measurements. The approach presented here is deployed in a low porosity, low permeability hydrocarbon bearing carbonate reservoir with a limited dataset, aiming to estimate a permeability log from available wireline log and core data. The geological description from core in Reservoir 3 indicates the presence of oil saturation in mud-dominated carbonate rocks with a moderate degree of calcite cementation. After a rigorous quality check of the available permeability measurements from conventional core analysis data, a high confidence database of porosity and permeability measurements were combined with existing wireline logs (e.g. GR, Resistivity, Neutron Porosity and Archie Water Saturation). The resulting structured dataset was used for permeability prediction using Random Forest regression (from scikit-learn in python). Three cases of permeability logs were generated from this methodology at well log resolution to be used in static reservoir modeling and saturation-height-modeling with J-functions. The permeability and saturation height models are key inputs for dynamic modelling to generate production forecasts in this undeveloped reservoir. Three different permeability models were trained using 144 high quality core plug measurements from six cored wells. Even though the number of available samples can be considered low for a machine learning workflow, an oversampling approach with point repetition was adopted to overcome data insufficiency for this dataset. From an E & P point of view, accurately predicting reservoir permeability in hydrocarbon reservoirs has been one of the major challenges facing the industry for decades. The approach outlined here reduces uncertainty in permeability prediction in the uncored interval. Furthermore, since permeability is part of the estimation of water saturation, this approach reduces uncertainty in water saturation interpretation, the potential deliverability of flow units and the volumetrics.