The problems of the engineering often are characterized to have many objectives that are in conflict and these they concern to diverse knowledge of the engineering. In this item, it is looked for to design the functions objectives for the engineering problem that is presented.

Defining the criteria of design in a multi-dominion system combines different engineering fields, this a complex process that involves many variables that must be analyzed from the petroleum engineering and optimization point of view. The additional recovery processes are an example of multi-dominion for simulation. The design of a function for the optimization is in many, cases a complex procedure that implies a great amount of experience in multiple engineering fields.

The production of hydrocarbons is either originated by recovery mechanisms by energy characteristic of the location (primary) or for the injection of fluids and the maintenance of pressure (secondary) which are not enough to recover all the volume of hydrocarbons of the locations. Due to the above-mentioned they are volumes feasible considerable remainders of recovering by means of well-known techniques as of improved recovery (Tertiary), which are more complex and they require of the application of new methodologies to support the taking of decisions as for the projects of additional recovery in the reservoirs.

Taber, Chaumet and Cottin have defined criteria for the processes of additional oil recovery but these investigations are generic and they have been based on the experience of the authors. In this item a revision of those investigations and an analysis of behaviors of 550 fields with additional oil recovery processes (secondary and tertiary) that includes more than 80 years of experiences are made.

Also, a factorial analysis based on artificial intelligence with method QFD (Quality Function Deployment) is made. The QFD method is used in this study to clarify the relation between the characteristic of the system and the system parameters.

The approach used for this study is divided in the following phases:

  • the knowledge integration of world-wide cases;

  • statistical multivariant analyses (data mining) and

  • matrix criteria designs definition (fuzzy neural networks) and

  • optimization objective design functions.

In conclusion, criteria of designing additional oil recovery processes, as well as processes for each reservoir, type of fluid, and type of rock were determined by artificial intelligence. The objective functions for every reservoir type and type of fluids that are essential for the process simulation were also defined.


The Additional Oil Recovery. Oil obtaining in surface is associate to the physical mechanisms of operation, as well as to the engineering of handling and operation in producting/injectors wells. In recovery terms, the one is the reservoir that affects the volumetric values of extraction and is for that reason, that the recovery processes are defined by the analysis of the mechanisms that control the movement and retention of the fluids in porous means.

From the historical point of view of production of a deposit, the recovery of petroleum is subdivided in three phases: primary, secondary and tertiary, this one to make the association to the chronology of the application of some process of recovery implemented during the productive life of the reservoir. However, from the point of view of recovery process, to the stage of initial of production, result of the flow of the fluids towards wells due to the existing natural energy in the deposit, this first period of production is called primary recovery.

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