During the asset management life cycle, one needs to deal with a range of operational problems on a day-to-day basis that are associated with various domains. This consists of handling of massive increment in data streaming per millisecond, real-time data processing, quality control and model feed for proactive problem identification and finally accurate allocation and prediction. The success is still measured by how the investment in real-time systems is leveraged effectively for real-time optimization while minimizing operating expense (OPEX) and improving the profitability of the project. This paper explains with a case study that this cannot be possible just by implementation of integrated data management system, but understanding the business process and streamlining the decisions in fully automated way is the key to success. The case study talks about a mature offshore oilfield, where a lean team was handling around 26 ad-hoc reports manually spending 3-4 hours daily, combining many different data sources, spreadsheets and still prone to human error, inconsistencies and reactive decisions. After thorough business process mapping and key performance indicator (KPI) mapping study, a new streamlined "To-Be" process was designed to deliver analytical production and operational reporting system. Based on full workflow automation, the system is deployed for data acquisition, allocation, reporting and analysis. This has increased accuracy, accountability, and timely availability of quality data, which has helped end users to improve productivity. The comprehensive reporting tool provides access to operational and production reports online, secured approvals and automatic notifications via e-mail for managers. Output reports are available in various formats for nontechnical users without direct access to the core application. The framework allows a streamlined data flow for dynamic updates of well and simulation models, improving process integration and reducing the run-time cycle. A successful deployment of an integrated analytical management system based on application assimilation and workflow automation is helping to improve overall productivity on various levels – Improved data management: Consolidated central database enabling easy data integration and sharing with various processes and applications and reducing the downtime & heightened securityPowerful surveillance: Provides effective KPI tracking, detecting and flagging any data issues and abnormal well behaviour aids entire performance management and decision-making processProactive management: alarms and notifications on operation issues, helping engineers to make proactive operational decisions.Faster cycle times: Business decision cycle times have been reduced from few hours to minutes resulting over 62% efficiency improvementExcellence: Trustworthy and accessible data, streamlined workflows, and application integration have thus provided engineers with faster, better, and confident proactive decision making. Leveraging investment in digital oilfield for quick value delivery by streamlined corporate business process geared towards achieving operational excellence.

This paper discusses a case study of a 42-year-old mature offshore oilfield. For this field, with declining production trend and timeworn equipment and technology, understanding and analyzing the transient flow is not a well-defined process. The Integrated Operations (IO) project was kicked-off in 2012 in order to deploy an Asset Management Decision Support tool. One of the focus areas was ‘Flow Assurance Management’ to overcome challenges of well slugging, liquid surge management and to establish guidelines for Start-Up and Ramp-Up processes. Traditionally in this field, most decisions were based on steady-state well and network modelling without much emphasis on transient behavior. Moreover, lack of instrumentation and manual data processing and model updates made it difficult to estimate current reservoir/operating conditions accurately to support real-time decision making. To overcome these issues, a Dynamic Production Management System (DPMS) was designed and implemented based on a dynamic flow model describing multi-phase flow in the gas lifted wells of the field. This paper describes the system and how it aids in better understanding of flow performanxce issues, collaborative decision making, and improved communication between various operational locations and disciplines. As part of the IO project, Real-Time field measurements (pressure, temperature, flow etc.) were captured at high frequency (seconds) & validated to ensure the desired data quality. These measurements were automatically used as boundary information by the model which calculates pressure, temperature, flows and volumes in real time throughout the field. The model is used in different modes: (1) For real-time surveillance, the online model provides a series of virtual instruments at locations without actual instrumentation in the field. (2) For advance warning, a separate transient model is executed faster than real-time to predict future events for Slug/Surge Management. (3) Finally the model is also used for planning activities, such as Start-up/Ramp-up or pigging and can predict any alarming issues during these operations. Thus DPMS assists production engineers and operators to make proactive decisions for effectively managing flow assurance challenges and adds value in various areas. Surveillance: Continuous, real-time monitoring of operating conditions within the network, along with prediction of future conditions within the inlet separators.Safety (HSE): Prevention and mitigation of facility trips/shut-down due to slug and surge issues during start-up/ramp-upEfficiency: Improved utilization of engineers' time and experience with increased focus on data analysis instead of data manipulation.Production Gain: Proactive field management for improved production, rather than reactive decisions that lead to deferred production.Dynamic PMS: The system will help to fully optimize wells, networks and facilities in order to produce and operate asset to its fullest potential by minimizing unexpected downtimes It's one of the first fields in Asia to implement an integrated DPMS using the online transient model concept as a basis for effective, real-time and proactive decision support.

Samarang is a 35-year-old offshore oilfield in Malaysia, operated by PETRONAS Carigali Sdn Bhd (PCSB). Samarang Redevelopment Project was kicked-off in 2010. Integrated Operations (IO) was planned as an Asset Management Decision Support solution by implementing a real time production, reservoir and process surveillance system. Samarang field was the first field selected for end-to-end asset management IO project. The main objective of IO implementation is to focus at the whole asset operation rather than working in silos. The overall asset optimization is achieved through the levels of monitoring and surveillance, diagnosis, optimization and operations transformation. IO Implementation involves identification of various intelligent asset decision processes referred as workflows in various reservoir, production, and operation domains such as Flow Assurance, Well Performance, Artificial Lift, Production and Facility Planning and Enhanced Oil Recovery (EOR) Optimization. Optimization in Samarang covers a wide spectrum of scenarios from Artificial Lift (Gas Lift, Electric Submersible Pump), separation process, reservoir sweep (Waterflood, GASWAG EOR scheme). The main business driver is to enhance production and improve reserve recovery in order to ramp up the production of Samarang field. Hybrid Steady-State and Transient-State Total Asset Optimization is designed for implementation through Steady-State model based simulation while the Transient-State model continuously predicts short term transient production effects, as a result asset team can better manage the short term production upset to ensure optimization target is achieved. The transient normally manifests as "Flow Assurance" events such as slugging and surges and these manifestations are to be predicted through transient-state modeling and hence, allow corrective action to be taken through Tri-Node Collaborative Working Environment (CWE). The hybrid modeling techniques treat the asset as a whole unit instead of isolated silos, simulating the complex interactions between reservoir, production networks and process facilities where decision taken on the production network will propagate the impact to reservoir performance and vice versa.