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Previous HitGeologicNext Hit Modeling and History Matching

 

Proett, Brian A.1, John J. Lawrence1, Colin J. Lyttle1, David J. Fenter2, Robert C. Szafranski1, Lester H. Landis1, Nancy S. Huang2, Kenneth A. Frederick2, David E. Courtnage2, Steve L. Lyons1, Adrian J. Coker1, and James K. Miller1 (1) ExxonMobil Upstream Research Co, Houston, TX (2)ExxonMobil Production Company, Houston, TX

 

The Reservoir Evaluation Time Reduction (RETR) methodology was developed to improve the process of building reservoir models, both Previous HitgeologicNext Hit and simulation. This improvement is achieved by using an iterative approach that measures the effect of increasing amount of detail with a corresponding simulation response. Effective proper­ties are generated using patented in-house software and used to populate Previous HitgeologicNext Hit mod­els built at a similar scale to the simulation grid, thereby streamlining the model building and history matching processes.

The RETR process was tested on six producing fields of varying zonal complexity, number of wells, and depositional environment. All fields had significant production his­tory that had previously been history matched using our conventional approaches. This previous work provided a reference for comparison of the RETR methodology with the tra­ditional approach to model building and reservoir history matching.

The following are the major conclusions of our study.

· Improved ability to integrate dynamic data into the Previous HitgeologicNext Hit model

· Enhanced ability to maintain synchronization of Previous HitgeologicNext Hit and simulation models

· Ability to build easily updateable Previous HitgeologicNext Hit and simulation models

· Increased collaboration of geophysical, Previous HitgeologicNext Hit, and engineering disciplines

· Enhanced ability to create models representing alternate Previous HitgeologicTop scenarios

· Improved ability to produce a history match in a timely manner

· Reduced calendar time required to build and update reservoir models.