Over the last 20 years of Dolphin field gas production, it has always been a challenge to estimate inplace volumes, history match pressure and water productions, and generate a reasonable range of production forecasts. Given the unconsolidated nature of the Greater Dolphin Area (GDA) reservoirs, it has not been possible to acquire representative cores and perform successful core plug tests. As a result, estimates of not only static parameters like porosity and saturation, but also dynamic parameters such as compressibility and permeability have been challenging. Historically this led to smaller static volume estimates than dynamic volume estimates from material balance. A new set of static and dynamic GDA reservoir models have been built that integrates and incorporates reservoir engineering techniques and revised petrophysical/geological properties to resolve the mismatch between static and dynamic volumes. Key inputs of this new model are in-depth reviews of dynamic datasets ranging from compressibility, Production Logging Analysis (PLA) results, and both core and well test derived permeability. Revised interpretation of these datasets, along with an iterative approach between static and dynamic QC has resulted in a range of deterministic and probabilistic history matched reservoir models that are used for forecasting and project planning decisions. The main approach was initially focused on reviewing the material balance studies and evaluating the effect of compaction in these unconsolidated sands. This highlighted the impact of compressibility in partially inflating the historical estimates of dynamic volumes. Additionally the study was focused on de-convolving field-wide material balance of comingled production from multiple reservoirs with heterolithic formations and a varied range of static properties and pressure depletion trends. Later the level of communications amongst various compartments and with nearby fields were investigated and taken into account. This assisted with an improved understanding of volume distributions amongst different reservoirs/compartments and helped to constrain the connected volumes, while building the dynamic models. In addition, a new methodology was developed to model permeability based on the kh derived from pressure transient analysis and the layer contributions observed in initial PLA results. This novel permeability modelling technique also helped to match PLA results in wells and individual reservoir layers gas contributions and water productions. These new approaches along with an alternative petrophysical methodology to better estimate water saturation within thin bedded intervals have been incorporated into an integrated workflow to account for both static and dynamic uncertainties. This set of probabilistic simulation models achieved a range of history matched results with a better understanding of dynamic reservoir behavior and also helped to overcome the historical shortage of static volumes required to match observed pressure data. This in itself brought more confidence towards generated production forecasts and future project's decision making processes.
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