AbstractThis work is focused on exploring the applicability of intelligent methods in assessing porosity and permeability in the context of reservoir characterization. The main motivation underlying our study is that appropriate estimation of reservoir petrophysical parameters such as porosity and/or permeability is a key step for in-situ hydrocarbon reservoir evaluation. We ground our analysis on information on log-depth, caliper, conductivity, sonic logging, natural gamma, density and neutron porosity, water saturation, percentage of shale volume, and type of lithology collected from well loggings in an oil field in the middle-east (a total number of 11 exploratory wells are considered). Data also include porosities and permeabilities evaluated on core samples from the same wells. All these data are embedded in a neural network-based approach which enables us to establish input-output relationships in terms of an optimized number of input variables. Three diverse intelligent techniques are tested. These include: (i) classical artificial neural networks; (ii) artificial neural networks based on principal component analysis (PCA) transformation; and (iii) statistical neural networks based on a bagging approach. Our results suggest that the statistical neural network is most effective for the field setting considered. The application of this neural network with 9 input parameters provides reliable performances in 94% and 81% of the cases, respectively in the training and validation phases, for the estimation of porosity. A trained network with 10 input parameters leads to successfull reproduction of permeability values in 85% and 79.5% of the cases, respectively during training and validation of the network. Results from this study are expected to be transferable to applications involving evaluation of petrophysical properties of a target reservoir in the presence of incomplete well log datasets.
Carbonate reservoirs have petrophysical property distributions largely controlled by a combination of the depositional, diagenetic, and structural (burial/uplift) histories of the reservoir itself and also of the basins that contain them. Carbonates are very prone to diagenetic alteration; porosity and permeability can be strongly affected by the thermal state, fluid-pressure and pore fluid chemistry through their geological history. We use a novel workflow, adapted from basin modelling, to investigate how the burial/uplift history of an offshore carbonate reservoir and its basin, taken as a system, can have controlled the fluid and heat movement within, into and out of the reservoir. The reservoir rock properties and diagenetic history are assessed, as is the local and regional geological evolution for potential contributory factors to the diagenesis. A model of the potential basin system is developed, observed reservoir diagenetic history being added to the normal basin modelling constraints. This model provides good estimates of geometry and property evolution, and of fluid transport, through geological time. Since fluid and heat fluxes are important in the diagenetic evolution of the carbonate pore system, these results are complemented by simulating the movement of heat and brine in the reservoir using finite element-finite volume simulations. These simulations capture the complex geological structures, especially fault-fracture systems, and better represent the flow physics and chemistry that control reservoir diagenesis. Results from these simulations will later be returned to the basin model to improve the calibration of the timing, depth, and rates of diagenetic events.
This new workflow is applied to a Lower Eocene offshore carbonate reservoir with a complex diagenetic history which seems to have a strong basin evolution influence. Importantly this workflow is generic and can be applied to any carbonate reservoir to enhance the link between geological models at the basin scale and reservoir scale models.