The cost-effective development of small fields requires a thorough description and evaluation of the reservoir, with a strong focus on the estimation of associated uncertainty. This paper describes a new, integrated methodology for defining and risking optimum high-angle development wells as applied to a UKSNS basin-margin gas reservoir. Sequential Indicator Simulation (SIS) realisations of genetic unit distributions were generated for each target reservoir zone, conditioned with relevant geological data derived from the reservoir and the analogue outcrop. The models were "pin-cushioned" with a large variety of well trajectories that varied in terms of inclination, azimuth, length and position within the zone. Genetic unit strings are then extracted from each well trajectory and analysed statistically, resulting in ranked well trajectories. This integrated approach produces ranked and risked well planning options, on which risk-conscious management decisions are readily made.


Development decisions are increasingly taken with less data, and the use of minimal facilities requires that optimisation of development wells is more critical than formerly. The planning of development wells in complex reservoir sequences necessitates the detailed integration of data to rank potential well locations and trajectories. Utilisation of hybrid deterministic-stochastic reservoir models, enables this process to be carried out efficiently, with quantification of associated risk. The benefit of optimising the placement of high angle wells is that the risk associated with well placement is minimised and that the maximum volume of the reservoir can be accessed with the least number of wells, which in turn is highly cost effective.

The methodology of this study is outlined in Fig. 1. Detailed quantification of the controls on well performance within the reservoir was followed by the definition of the appropriate genetic units. In parallel, a period of sourcing and appraisal of possible outcrop analogues was followed by a detailed outcrop study which resulted in the derivation of quantitative data of the analogue with relevance to the Frobisher reservoir. Integration of the reservoir specific and outcrop analogue data into a hybrid deterministic-stochastic model provided the basis for determination of the optimum well trajectories and subsequent risking of their success in penetrating pay.

This paper will only briefly introduce the geological analyses of the reservoir and the outcrop analogue but will focus in detail on the generation of the reservoir model and on the determination and risking of the optimum well trajectories.

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