Technical Paper: Selection of Infill Drilling Locations Using Customized Type Curves

Society: SPE
Paper Number: 122186
Presentation Date: 2009
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This paper discusses a new workflow to stochastically estimate the performance of infill locations in a mature oil or gas field. Usually performance evaluations for infill wells are conducted using either much generalized statistical methods or numerical simulation. Both approaches have a significant drawback; the prior being quick however very often lacking in accuracy, the latter being very accurate however usually very complex in setup and computation.

The presented workflow is a new approach to infill well performance prediction that combines speed and reasonable accuracy. The workflow generates a set of key performance indicators of existing wells derived from historic dynamic data (fluid production rates, pressures, etc.), static data (reservoir properties, etc.) and predicted data (simplified production forecasts). The wells are then grouped according to the similarity of their KPIs. The production profiles of the wells within the same group are combined to a type curve that is described by the most likely production profile and an associated uncertainty range.

A data-driven expert system is used to identify and capture the correlations of the parameters such as geographic locations, well spacing, reservoir properties and the group membership (equivalent to type curve). This expert system can then be applied to any location in the field in order to determine the most likely group membership of a potential infill well. The classification of an infill well to a group is hereby not necessarily unique; the expert system might classify an infill well into several groups and assign a probability of occurrence for each of the groups. A Monte Carlo routine is then applied to forecast the performance of the infill locations honoring the respective probability of occurrence of each type curve.

The presented approach has been successfully applied for infill well selection in a statistical field development study for YPF in the Argentinean San Jorge Basin.

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