Abstract
Abstract
During the initial stages of a new gas-condensate field development planning, studies are performed trying to cover all possible key project factors. Reservoir performance estimations and production forecasts are, by far, the most important aspects influencing any other development requirements and future commitments (number, location and type of wells, capacity/modularity of the production facilities, sales contracts, booked reserves…). Due to the, let’s say, apparent simplicity of the project (homogeneous reservoir model, few development wells, linear gathering pipeline and small compression plant) and the uncertainty of available data, a handcrafted approach was preferred by working all models as separate items but, applying proper contour conditions for each one.
On the other hand, applying defined management methodology, project’s QA/QC explicitly required the development of an integrated subsurface-surface model, no matter the performance of the handcrafted approximation. Integrated Production/Asset Modeling (IPM or IAM) is a powerful asset design, prediction and management tool when properly used but, as with many other emerging or in fashion technologies, care should be taken when dealing with the ‘aspirin’ (good for everything) complex.
To fulfill this requirement, a full asset simulation model was built integrating the complete production cycle: numerical reservoir simulation, steady-state nodal wells, surface gathering/transport network and compression/processing facilities. The process was intensive, troubleshooting and time consuming but, as suspected, obtained results didn’t significantly improve the knowledge already available with the handcrafted initial approximation.
Being able to assess the proper use for each technology should be a basic pillar of our industry. Inefficiently and indiscriminately applying available tools without a suitable engineering criterion, could yield wrong assessments of their actual value and, lead to improper use of valuable time and workforce. Integrated Production Modeling develops real value when dealing with large systems, where the type and number of variables to be controlled lies out of the human capacity; however, simple systems still can be handcraftly managed under proper engineering judgment, without large troubleshooting processes, consuming less time and demanding less workforce. The cobbler should stick to his last.
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