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Technical Paper: Multilateral-Complex Well Optimization

Society: SPE
Paper Number: 140882
Presentation Date: 2011
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Abstract

Reservoir simulation is used to optimize the design of multilateral wells, where the parameters include the well path, well position, the completions design (e.g. ICD configuration), the production constraints and uncertainty from multiple realizations of a geological model.

The target application is the design of new multi-lateral complex wells within an established large study. The optimized well is included in the prediction scenario of a history matched model. The target full field models are very large (millions of cells) and have large amounts of wells (hundreds).

The parameterization of the well path and position is performed by constraining the well path to surfaces, and geometric translation. Laterals are independently parameterized allowing the tie-in depth, angle and length to be parameterized. Completion design is parameterized by compartment length, number of valves per compartment and valve inflow diameter. The complex completion design relies on advanced well modeling features of the reservoir simulator. Production constraints, such as target oil rates and water limits are considered within the optimization. Finally, the uncertainty from multiple geological realizations is accounted for; this is achieved by introduction of a risk aversion factor which penalizes the objective function by a scaled standard deviation.

Examples of optimization demonstrate long multi-lateral wells being optimized within a Full field study. The sensitivity of each parameter is studied, i.e. the effect of oil production on the well position, effect of modifying ICD inflow area, the ICD compartment length, the water handling limits, and the geological uncertainty.

The major significance of this work is the provision of a system to enable engineers to produce an optimal well configuration given geological, production, well placement and completion (ICD) uncertainties in a timely fashion. The use of optimization assists the engineer in designing an optimal solution given many variables.

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