Prediction of Flow Units and Permeability Using Artificial Neural Networks

Abstract

Abstract An accurate description of the reservoir is necessary for predicting the reservoir performance. The flow unit model is most practical approach for reservoir simulation purposes. The flow units are defined according to geological and petrophysical properties that influence the flow of fluids in the reservoir. Identification of flow units requires permeability data. Predicting permeability distribution is a difficult problem in heterogeneous reservoirs due to insufficient permeability measurements. The geophysical well logs are the most abundant source of data in a reservoir. Therefore, a well log-based methodology for predicting permeability can enhance prediction of the flow unit distribution. In this study, in a complex reservoir was successfully characterized using flow unit modeling. Statistical techniques were employed to identify flow units based on limited data obtained from core analysis supplemented by mini-permeameter measurements and geological interpretations. Permeability was then predicted from geophysical well log and preliminary flow units using artificial neural network (ANN). An innovative approach for training and testing of the ANN was developed which provided consistent and reliable predictions. The neural network predictions were then verified for the wells with core data. Well log data, available on substantial number of wells in the reservoir, were then utilized to predict the distribution of flow units, permeability, and porosity in the reservoir. This approach led to development of a reliable reservoir model. The accuracy of model was verified by successful simulation of the production performance. The methodology presented in this paper can serve as a new guideline for the characterization of reservoirs with limited core data. Introduction Accurate model of the reservoir is necessary to reliably predict the production performance. Initially, stratigraphic and petrographic analysis of cores, correlation of logs, and major rock types are combined to describe various sedimentary bodies or "facies" that have distinct physical, chemical and biological attributes within the formation (Monroe, 1992). Within a given facies the reservoir properties can vary the significantly. This variation has lead to a further subdivision known as flow units. Flow units are regions in the sedimentary sequence that control the flow of fluids within the reservoir (Hearn, 1984). Flow units are defined on the basis of not only their geologic characteristics and position in the vertical sequence but also on their petrophysical properties, especially porosity and permeability. Certain ranges of porosity and permeability are used to subdivide the reservoir into various Flow units (Hearn, 1984). A flow unit is a volume of the total reservoir rock within which geological and petrophysical properties that affect fluid flow are internally consistent and predictably different from properties of other rock volumes (Ebanks 1987). Studies in the subsurface and in surface outcrops have shown that flow units do not always coincide with geological lithofacies. The flow unit approach provides a means of uniquely subdividing reservoirs into volumes that approximate the architecture of a reservoir at a scale consistent with reservoir simulations. The flow unit model provides the most complete reservoir description since it allows for the interpretation of many of the geological and petrophysical properties into the reservoir description leading to improved recovery and reservoir management (Slatt and Hopkins 1988). A number of techniques have been proposed in the literature for identification of the flow units (Amaefule, et al 1993 and Gunter, et al 1997). The techniques for flow unit identification rely upon availability of permeability and porosity from core analysis. The core analyses are usually available for only few wells in a reservoir. Porosity can be also evaluated from the well log data, which are available for most wells in the reservoir. However, the need for permeability data significantly limits the identification of flow units in reservoirs by statistical techniques. The goal of this study was to develop a methodology for reservoir characterization with limited permeability data.