A giant gas field that covers 75 km in length and 15 km in width has been producing since 1990 from approximately 1,200 wells which are located in 34 platforms. Deposited within a deltaic environment with enormous multi-layer sand-shale series, the wells undergo commingled production with an average of more than 30 reservoirs per well. With a total of approximately 700 perforation jobs included in more than 4000 well intervention jobs per year, the field is considered as the most complex field in the PSC Block in terms of operations. Prioritization of these perforation jobs are based on the perforation gain of each job. Therefore, properly estimating the perforation gain is crucial in order to efficiently and effectively manage and prioritize well intervention jobs.

Hypothetic approaches, for instance productivity index driven from Darcy's equation, may not be straight-forward due to incomplete and imprecise data measurement. Overwhelming operations workload in the field limits the number of data acquisition jobs performed. Consequently, required data to estimate perforation gain such as skin, pressure and drainage radius becomes limited. An alternative approach using artificial intelligence called fuzzy logic was introduced. Being a soft-computing pattern recognition method that allows imprecise input to yield output, fuzzy logic fits well with the nature of high uncertainty in geosciences data. The one-year study is conducted on reservoir basis using well monitoring results to split well level gas rate into individual reservoir gas rate. In order to ensure that proper data are incorporated in the model training, processes of data filtering must be undertaken. Therefore, implementing fuzzy logic to estimate perforation gain includes 3 main steps: (1) Preparing and Filtering Training Data Set; (2) Building the Fuzzy Model; and (3) Performing Blind Test.

After series of trial and error process, the model has reached its minimum error without compromising sense of engineering and generality. The fuzzy model results in 960 fuzzy rules and 5 input parameters: netpay, porosity, drawdown, mobility and water risk. Afterwards, the blind test shows that the resulting output from fuzzy logic correlates well with the realized gas rate both on reservoir level and well level, with maximum R-squared value of 0.7. The study is limited within the scope of current best practice for unperforated reservoirs and further study would be required to estimate the perforation gain from unconventional perforation methods and re-perforations. This method of estimating perforation gain using fuzzy logic has been implemented on daily basis with the aim to improve the efficiency and effectiveness of managing and prioritizing well intervention jobs in such a complex environment.

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