Abstract:
Reservoir performance prediction is an iterative process that incorporates various data points, such as production rates, pressure, fluid properties, and geological characteristics. Techniques like decline curve analysis, material balance, and reservoir simulation are commonly used for evaluating a reservoir. The Reserve Performance Indicator (RPI) analyzes parameters such as production rates, pressure data, and fluid properties to evaluate the performance of a reservoir over time. Till now 29 gas fields was discovered in Bangladesh. There isn't any literature or publication that addresses a consistent approach of ranking these reservoirs based on their performance. In this study, an approach is taken to rank the reservoirs according to various indicators used for analyzing the reservoir performance and to identify more prolific and problematic reservoirs. After collecting all the available reports from the public domain (Annual Reports, MIS Reports), reservoirs are ranked by initial reserves, cumulative production (Gas, Condensate), Gas Recovery. The Jalalabad gas field has retrieved more gas than its initial reserve which suggests the necessity of reserve re-estimation. For the majority of discovered fields, the last reserve estimation was completed 14 years ago. Although there is a noticeable reserve in the Kailashtila and Rashidpur fields, just 22.21% and 19.24% of the gas has been recovered, respectively, suggesting that their field development approach is inadequate. Potential gas recovery is possible from these fields. In addition to displaying inadequate development strategies for such fields, only 1 well was drilled in Meghna field during its 26-year production life, while 2 wells were drilled in Narsingdi field over its 27-year production life. Suspended wells of Titas, Habiganj, Bakhrabad, and Kailashtila fields are examined further and the wells of Titas, Bakhrabad, and Kailashtila fields having the potential for workover operation on a priority basis are also identified. Finally, the top 4 fields that are performing well are categorized as Category-I fields, and the 4 fields whose performance was poor and need to change the field development tactics are categorized as Category-II fields.