Experimental Design as a Framework for Multiple Realisation History Matching: F6 Further Development Studies

Alessio, Laurent Didier (Sarawak Shell Berhad) | Bourdon, Laurent Marc (Sarawak Shell Berhad) | Coca, Spencer (Sarawak Shell Berhad)

OnePetro 

History matching is traditionally complex and time-consuming: multiple parameters influence the match and their inter-dependency produces effects that are difficult to predict. Defining the match itself can be challenging, since various indexes or responses can be used: water breakthrough timing, pressures, layer contributions etc… Consequently, whilst multiple realisations methodologies are routinely applied for "green" field development planning, most of the time incremental activity screening on "brown" fields is done on a single matching realisation -"the" matched model - with little confidence that the full range of uncertainties is captured.

Experimental design provides a well-suited framework to tackle the challenge of multi-realisation history matching, following these key steps:

• Selection of key parameters with variance analysis,

• Reduction of dimensionality by creating hybrid parameters, using techniques related to principle component analysis,

• Predicting matching domains: combination of parameters levels (once discretised) that are likely to generate a match. This greatly helps the likelihood of finding multiple matching realisations, covering the range of parameter variation.

This methodology was successfully applied in the F6 subsurface studies, aimed at screening field redevelopment opportunities. F6 is the largest gas field in the Central Luconia carbonate province, offshore Sarawak (Borneo), having a GIIP of more than 7 Tscf. With over half the reserves produced, well capacity is now threatened by the rising aquifer. In order to safeguard and possibly increase the reserves, a field review was undertaken to identify further development opportunities, and a multi-realisation approach was chosen to capture the effect of key subsurface uncertainties on those activities.

A total of 28 matching realisations were generated, covering the variation range of the identified key seven parameters whilst optimising the number of runs performed, thus saving time. Key to the success of the method lies in the integration of disciplines to allow the upfront identification of parameters and their ranges.

The screening of redevelopment options against those realisations allowed to establish the range of expected incremental reserves, assess risks, and form a sound basis for business decisions.