Implementation of Hybrid Parallel Computation for Complex and Fine Reservoir Model Using Cluster Technology

Penulis

  • IATMI Journal Editor IATMI
  • Budi Darmawan Pertamina Hulu Indonesia
  • Ahmad Reizky Azhar Rock Flow Dynamics

Kata Kunci:

POD, Hybrid Parallel Computation, Complex and Fine Reservoir Model, Cluster Technology, multiple reservoir realizations

Abstrak

Pertamina EP plays an important role in maintaining the oil production supply for national energy stability. However, there is big challenge in the field development plan preparation since they are not fully equipped with the advance computing technology to boost the reservoir modeling and simulation phase. To face the challenge, Pertamina EP evaluated the possibility to create a cluster technology that can accommodate high intensity of simulation numbers and high load of simulation process.

This paper described the success story and innovation of a complex simulation and finer scale reservoir model using the hybrid parallel-computing technology with a set of 8 nodes high performing computer. Three models were tested with satisfying results. This paper discusses the parallel scalability of complex computing systems of multi-CPU clusters. Multi-CPU distributed memory computing system is proven to be able to improve and accelerate the reservoir modeling and simulation time when it is used in combination with a new so called “hybrid” approach. In this approach, the common Message Passing Interface (MPI) synchronization between the cluster nodes is being interleaved with a shared memory system thread-based synchronization at the node level.

The model with the longest simulation time has been accelerated by magnitude of 60%. The most exhausted model with highest number of simulation steps has been accelerated by magnitude of 80%. The model with the greatest number of grid (21.7 million active grids) has finally finished its simulation just in 27 minutes where previously was impossible to have it open and run. The successful study case is then followed by the implementation of the cluster computing technology for two pilot POD projects which led to the very good result.

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2023-05-30

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