A small onshore brownfield in south Oman has low oil recovery because of its heavy oil and high water production, which together with reservoir uncertainties poses development challenges. Petrogas implemented an innovative field development planning approach to quantitatively compare multiple field development scenarios and optimize the operational choices within each. The workflow started with a single history matched model for each of the two geological structures in the field. A set of 14 field development scenarios were defined, on injection rates, well locations, and injection fluids. Identification and quantification of subsurface uncertainties were performed. These uncertainties were included in the geomodel for each scenario, which generated an ensemble of realizations and corresponding production forecasts. Two sets of economic results were produced—a simple, discounted cashflow model and the fiscal terms of the operator's service contract. Each ensemble was run against these models to generate probabilistic performance indicators for each scenario. Using cloud-computing capability, the field development study was drastically accelerated without losing on the quality. Almost 800 simulations were run over 5 days, covering 32 development scenarios in total (for two structures), automatically integrated with the economics workflow, providing in-depth analyses. The scenarios were compared in a series of dashboards that presented the economic metrics and their corresponding cumulative distribution functions. The analysis yielded several important insights: longer wells did not provide enough additional production to offset the increased costs. Moreover, peripheral drive with horizontal wells was more effective than irregular vertical wells. The waterflood scenarios improved production, but the polymer-injection option with short horizontal wells and peripheral infill well pattern was the highest-performing scenario. The study also helped identify areas where more detailed optimization studies should be performed, e.g., to optimize polymer-injection scheduling and polymer design. Traditionally, subsurface uncertainties analysis was restricted to a small number of discrete model realizations. Results were quantified in terms of production ranges only. Here, production forecasts were based on an ensemble of models, capturing the full range of uncertainties. In addition, evaluation criteria included economics.

This paper presents an innovative and practical workflow framework implemented in an Oman southern asset. The asset consists of three isolated accumulations or fields or structures that differ in rock and fluid properties. Each structure has multiple stacked members of Gharif and Alkhlata formations. Oil production started in 1986, with more than 60 commingling wells. The accumulations are not only structurally and stratigraphically complicated but also dynamically complex with numerous input uncertainties. It was impossible to assist the history matching process using a modern optimization-based technique due to the structural complexities of the reservoirs and magnitudes of the uncertain parameters. A structured history-matching approach, Stratigraphic Method (SM), was adopted and guided by suitable subsurface physics by adjusting multi-uncertain parameters simultaneously within the uncertainty envelope to mimic the model response. An essential step in this method is the preliminary analysis, which involved integrating various geological and engineering data to understand the reservoir behavior and the physics controlling the reservoir dynamics. The first step in history-matching these models was to adjust the critical water saturation to correct the numerical water production by honoring the capillary-gravity equilibrium and reservoir fluid flow dynamics. The significance of adjusting the critical water saturation before modifying other parameters and the causes of this numerical water production is discussed. Subsequently, the other major uncertain parameters were identified and modified, while a localized adjustment was avoided except in two wells. This local change was guided by a streamlined technique to ensure minimal model modification and retain geological realism. Overall, acceptable model calibration results were achieved. The history-matching framework's novelty is how the numerical water production was controlled above the transition zone and how the reservoir dynamics were understood from the limited data.