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Portfolio Development Planning for Operational and Strategic Decisions
SPE 84607
An overview and case study of challenges facing portfolio managers in evaluation and optimization of portfolio value, taking into account relationships between portfolio projects and opportunities. Based on work by BP and Caesar Systems on the Atlantic LNG Plant in Trinidad and Tobago. Dynamic portfolio modeling using PetroVR Toolsuite is presented as an alternative solution.
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Copyright 2007, Society of Petroleum Engineers
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Paper Abstract
BP, a major shareholder of the Atlantic LNG Plant in Trinidad and Tobago, was in search of project modeling and economic forecasting tools to help meet the aggressive commercial gas demands of this asset. This asset consists of 7 production fields that supply oil & gas to the island, with gas being fed through the existing 1, 2 & 3 trains for the next 20 years and projected to feed future LNG trains and downstream gas projects and a proposed gas refinery.
The development & production team collaborated with the resident facilities engineers and the drilling & completions teams to procure facilities cost data, drilling data, and other similarly-structured project planning data. The team was then challenged to formulate a production constraint system that applied strategic and economic analysis of gas supply/demand options, deliverability forecasts, facility capacities, and rig availability.
The team met this challenge through a single decision support tool that accelerated project analysis cycle time by 30%, reduced risk with full-cycle economic options analysis and promoted interdisciplinary teamwork. This tool superseded the use of eight (8) linked Excel workbooks that occupied 17 MB and proved inadequate to meet the team’s performance standards.
In this paper we present an overview of the challenges facing portfolio managers in their attempt to create a dynamic portfolio model for evaluation and optimization of portfolio value taking into account any relationships which may exist between portfolio projects and opportunities. Subsequently, it is demonstrated how the computational toolsuite “PetroVR” (Petroleum Ventures & Risk) managed the scope and complexity of modeling this BP asset. Features that helped search and define scenarios, provided graphic comparisons of model variations and finally helped the development and production team narrow their choice of real options will be discussed.
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