Abstract
Abstract
Reservoir description for simulation studies requires good knowledge of the permeabilities. Unfortunately, reliable permeability is only available from laboratory tests on cores, which are usually taken from a small percentage of the wells. Frequently, this information is extrapolated to calculate permeabilities all over the field, but the lack of enough data points usually causes unreliable predictions. We propose a method to estimate formation permeabilities from standard well logs and core data. The analysis includes a first step consisting of the interpretation of the petrophysics and a characterization in lithofacies, electrofacies and hydraulic flow units. This step involves the use of modern mathematical tools to rationally classify each reservoir region into a given (discrete) hydraulic flow unit. As a second step the core permeability data is mapped with the well log data using neural networks and the restrictions found on the first step of the analysis. This approach allows the use of continuous hydraulic flow unit values and reduces the error arising from the discrete zonation technique. Also, it overcomes the error coming from the mapping between log data and hydraulic flow units. The method should be applicable to any kind of reservoir as long as sufficient core and log data are available. The method assumes that the Carman-Kozeny equation holds for the reservoir rocks, which is a fairly reasonable assumption, and that the well logs available contain intrinsic information on tortuosity, sand size distribution, cementing characteristics, etc., which ultimately determine the flow performance of the rock. This hypothesis is usually strong because the available logs are not able to fully read the physical phenomena that cover the complex dynamics of the flow on the reservoir rocks. The method was tested using available core and log data in a sandstone formation in Chihuido de la Salina, Neuquen Basin, Argentina. Some core data points were not used to train the neural network and therefore useful for validation and comparison. In spite of the cited drawbacks, the method has shown to outperform both the standard regression techniques and the hydraulic flow units approach.
Introduction
Rock permeability is an extremely important parameter in reservoir characterization and simulation, because it influences the hydrocarbon rate of production, ultimate recovery, optimal placement of wells, pressure and fluid contacts evolution, etc. Thus, the proper determination of the permeability is of paramount importance because it affects the economy of the whole venture of development and operation of a field.
The most reliable data of local permeability are taken from laboratory analysis of cores. Extensive coring is very expensive and this expense becomes reasonable in very limited cases. In general, these core data are available only from some wells in the field, and for some intervals in each cored well. Then the permeability of the whole field is estimated from this sparse information. This frequently results in a lack of statistics that causes poor prediction capabilities.
We pursue an approach already explored by several researchers1,2, consistent of finding a relationship between diverse well log data and the core permeabilities. Typical classic methodologies are the log k vs. f transform3 or, more recently, the use of the Flow Zone Indicator (FZI) technique4. There are also correlations to predict the permeability based on the porosity and the lithologic volumes5, thus allowing a permeability profile to be obtained. These predicted permeability "logs" can be calibrated to fit pressure transient analysis tests6, usually the most widely accepted integral permeability measurements.
In this paper a methodology to obtain permeabilities from all available well logs is presented. The method takes advantage of modern mathematical tools that have proved to be effective in other fields of science and engineering, including neural networks, cluster analysis, principal components, etc. The method is tested against independent core data and is compared to correlations and the FZI method, for Chihuido de la Salina field at the Neuquén Basin in Argentina.