Achieving Well-Performance Optimization Through Work-Flow Automation

Topics: Intelligent fields/surveillance Marginal/aging fields
Source: SPE 173578
Fig. 1—Data-quality funnel for Samarang IO data-driven work flows.

The Samarang field is located offshore Sabah, Malaysia. The field is undergoing a major redevelopment project with integrated operations (IO). In order to overcome a variety of challenges and to improve field awareness, several work flows were designed and deployed in order to achieve an early milestone of providing real-time well-performance monitoring, surveillance, and optimization. The paper discusses how these work flows were implemented in an integrated way to provide a modern decision-support system for the Samarang field.


Samarang is an old oil field and thus exhibits the characteristics of many mature fields, including declining production. It has been in production for more than 35 years from multiple, now aging, platforms with insufficient metering and monitoring processes. Given this situation, well status and uptime is unknown for many days, causing delays in mitigation and rectification of production issues. Most of the instrument measurements have been carried out on an ad hoc basis; various facility readings were measured manually, which subsequently deferred field review, causing loss of production. The main technique implemented for production optimization has been artificial lift, with more than 80% of wells and strings being gas lifted. It was necessary to find a better way to manage this production flow stream efficiently with an improved asset-management strategy.

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 173578, “Samarang Integrated Operations: Achieving Well-Performance Monitoring, Surveillance, and Optimization Through Data- and Model-Driven Work-Flow Automation,” by M. Zul Izzi Ahmad and Colinus Lajim Sayung, Petronas, and Muzahidin M. Salim, M. Kasim Som, Lee Hin Wong, Shripad Biniwale, Nur Erziyati, Kenneth Soh, Roland Hermann, Vo Tri Nghia, Lau Chong Ee, and Muhammad Firdaus Hassan, Schlumberger, prepared for the 2015 SPE Digital Energy Conference and Exhibition, The Woodlands, Texas, USA, 3–5 March. The paper has not been peer reviewed.
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Achieving Well-Performance Optimization Through Work-Flow Automation

01 March 2016

Volume: 68 | Issue: 3