Abstract

As KOC prepares for production ramp up in a North Kuwait Heavy Oil field, one key for cost efficient development is in optimizing information from observation wells. Given multiple objectives for data collection and restrictions due to dense well spacing and surface infrastructure, it is critical to apply an efficient methodology for the selection of observation well locations. This paper describes the interplay between subsurface needs and urban planning in decisions for placement of observation wells to reduce operational costs, enforce effective field development planning and ensure robust production levels.

The observation well location selection protocol in a heavy-oil steam development requires initial focus on identifying the key subsurface parameters and risks including cap-rock integrity, role of baffles in steam conformance, gas caps, aquifer influx as well as development decisions like adequate well spacing to apply micro-seismic, e.g. Key parameters were mapped and rated with a "traffic-light" approach to distill the diverse datasets into manageable form. Next, different observation well location strategies (evenly distributed, clustered and blended) were assessed and locations (driven by subsurface objectives) were chosen. Finally these locations were reconciled against the surface restrictions, HSSE and operational constraints to determine the final program.

A successful observation well strategy will have:

  • Good subsurface models capturing heterogeneities in order to predict production performance

  • Sufficient well number and thorough reservoir architecture characterization underpinning well placement

  • Robust data collection plan (including production data)

  • Proper staff resources applied to monitor and QC the data

  • Up-to-date and accessible data-base, that can be interactively interrogated

  • Cross-discipline integration, regular data reviews and alignment with urban planning

The inherent value in observation wells is optimized by defining key objectives with broad across discipline engagement, aligning data collection objectives and engaging urban planning and field development with sufficient lead time. This collaborative approach reduces costs for KOC by avoiding rework, missed data collection opportunities or missed value adds if options such as conversion of observers to producers are not considered.

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