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Ph.D de

Ph.D
Group : Learning and Optimization

Optimization of non-conventional wells: type, location and trajectory...

Starts on 01/12/2008
Advisor : SCHOENAUER, Marc
[AUGER, Anne (LRI), DING didier Yu (IFP)]

Funding : Convention industrielle de formation par la recherche
Affiliation : Université Paris-Sud
Laboratory : IFP / LRI

Defended on 03/04/2012, committee :

Research activities :

Abstract :
Non-conventional wells (multibranched well with arbitrary trajectory) allow to increase considerably hydrocarbon recovery. The productivity of this kind of wells depends on various factors which make the optimum well implementation difficult. The main factors to be considered are the well configurations (depth, orientation of main drain, number of branches, length and orientation of each branch, trajectory ...), reservoir heterogeneity or nature of fluid in place (oil, water, gas). By considering the high drilling cost and the potential earn in well productivity, optimum implementation of non-conventional wells is an important issue in petroleum industry.
Optimum determination of type, position and trajectory of a well can be achieved by numerical simulations. The problem can be formulated as a non-linear optimisation problem with oil production or NPV (Net Present Value) as objective function. However, this optimisation problem set several difficulties illustrated as follows:
- well productivity depends usually on a large number of parameters (10 – 200) according to number of wells and their configurations;
- the objective function is strongly non-linear due to reservoir heterogeneity;
- using reservoir simulator for calculation of objective function is very expensive in CPU time.
In this thesis, we propose to study new methodologies to overcome above difficulties for optimum well implementation. In particular, we will study the following points:
- Optimisation method for large number of parameters and strongly non-linear problem - The approach "evolution strategy" and in particular a variant called CMA-ES (Covariance Matrix Adaptation – Evolution Strategy) seems promising. This method, used in other domains, will be evaluated, tested and adapted to the problem of optimum implementation for non-conventional wells.
- Search and analysis of attributes - The previous optimisation method requires a large number of reservoir simulations. To reduce optimisation cost, simple attributes, correlated with well production, will be defined to substitute reservoir simulations. It is necessary to find a compromise between the quality of the solution of optimisation and the number of reservoir simulations.
- Applications - New algorithms for optimum well implementation, will be developed, based on the above studies. These new algorithms will be tested on real or synthetic cases.

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