TX 75083-3836, U.S.A., fax 01-972-952-9435.
AbstractThe Venezuelan National Oil Company, PDVSA, has dedicated a sustained effort to adapt EOR/IOR technologies to rejuvenate a large number of its mature fields. The first step towards achieving this objective was to select cost-effective technologies suited for conditions of Venezuelan reservoirs. The current strategy for screening EOR/IOR applications is based on the Integrated Field Laboratory philosophy, where a representative pilot area of a number of reservoirs is selected to intensively test EOR/IOR methods, such as WAG injection (water alternating gas) and ASP (alkali polymer surfactant), currently underway. Two problems with this approach are the lack of objective rules to define a reservoir type and the project completion time. In general, the trouble with using expert opinion is that it tends to be too biased by operational experience. It is known that the success of a given EOR/IOR method depends on a large number of variables that characterize a given reservoir. Therefore, the main difficulty for selecting an adequate method is to determine a relationship between reservoir characteristics and the potential of an EOR/IOR method. In this work, data from worldwide field cases have been gathered and data mining was used to extract the experience on those fields. Here, a space reduction method has been used to facilitate the visualization of the needed relationship. Machine learning algorithms have been utilized to draw rules for screening. To illustrate the procedure, several Venezuelan reservoirs have been mapped onto the extracted representation of the international database.