A Revolutionary Digital Twin of Autonomous Hydraulic Simulation on Largest and Most Complex Pipeline Network in Indonesia (Case Studies in Rokan Block)
Authors: Kayely Boris Victor , Rimbualam Hans Gunawan , Negara Ari Sukma , Hadi Rizky , Ichsan Chairul , Hidayat Fahmi , Maulidani Oki Ari , Metra Sabrina , Putrawan I Putu Ari 
Published: April 2024

Abstract

Objectives/Scope
Pertamina Hulu Rokan (PHR), the largest oil-producer in Indonesia with &gt;10,000 wells, has been experiencing back pressure issues at its surface facilities due to increased fluid production from an aggressive drilling campaign. PHR aims to drill ~500 new wells annually to contribute to Indonesia's production target of 1 million barrels of oil per day by 2030. In perspective, an automated digital twin solution has been developed to support the campaign and enable effective field intervention.

Methods, Procedures, Process
A digital twin is a virtual model that replicates a physical object or system, and in this case, it relies on pipeline geospatial data and advanced network simulation software. The digital twin solution for PHR developed in three phases. Phase 1 involves network modeling and validation. Phase 2 focuses on network evaluation and optimization. Phase 3 is network auto-calibration set on regular basis and smart monitoring system development through interactive dashboard using the software programming interface feature and a meticulously designed matching algorithm, connecting the model and well/field measurement data seamlessly. Phase 3 is the revolutionary approach of network modelling.

Results, Observations, Conclusions
The project started in early 2023 and has been implemented in major Rokan fields. The automated digital twin can reduce data collection and model validation time by up to 95%, allowing tasks to be completed in hours rather than months. The use of an interactive dashboard provides a comprehensive picture of the actual field situation and valuable insights not only for the operational team but also for management. These insights lay the groundwork in enabling effective field intervention for debottlenecking strategies such as identifying areas with severe erosional risk or high pressure drop and finding the exact section for routine pipeline maintenance. This novel approach eliminates the possibility of human errors, thus hugely enhances work accuracy as well. The emphasis has shifted from data collection and pipeline modelling to decision making and future strategies.
Integration of pipeline geospatial data and automation algorithms into hydraulic simulation helps PHR to reduce the cost by almost a million USD in 2023. As a result of these promising outcomes, the automated digital twin project is being expanded into other PHR fields, with over 2,500 wells expected to be completed by 2024.

Novel/Additive Information
This paper will elaborate in detail on how PHR digital twin of autonomous hydraulic simulation can be a game changer in optimizing cost and debottlenecking process, especially for oilfields with massive number of active wells, large and complex pipeline network. This approach has potential to be adapted in other locations and will ultimately enhance the production optimization process.