The objective of this study is to find out a novel way of overcoming uncertainties associated with successfully locating best sites for drilling infill development wells. These uncertainties are usually associated with the non-uniqueness of responses from such petrophysical properties as porosity, permeability, and water saturation created through the static reservoir modeling process, leading, in many cases, to misleading allocation of proposed infill well locations.
In order to achieve this objective, a normalized combined super-property, composed of selected petrophysical properties, was created. This new property was calibrated using certain scalar factors that were given to each property in order to bring the real distribution and weightage of each of the component properties into effect.
The main observation from the present study is that the novel combined super-property has proven to give more realistic and accurate allocation of proposed infill well sites. This observation was documented through practical validation of negative (wet) wells, giving misleading positive responses from conventional petrophysical properties, while giving correct accurate negative response, and vice versa, using the novel combined super-property. Incorporating this novel technique into the high-resolution stratigraphic model resulted in maximizing property distribution accuracy per reservoir layer. More accurate prediction of lateral and vertical distribution of reservoir facies and petrophysical properties in three dimensions was then achieved and resulted in outlining a number of new well locations based on this technique.
The value of this novel technique lies in significantly reducing the risk of drilling negative boreholes based on static reservoir models. This is because each individual value of a property, such as water saturation, can be the result of a multitude of factors, including natural factors and/or data acquisition, processing, & interpretation pitfalls, therefore, not necessarily indicating presence of hydrocarbons. The present study reduces non-uniqueness of each individual property response using the scaled combined super-property.