Optimisation of Offshore Structures Decommissioning – Cost Considerations

Emmanuel T. Eke, I. Iyalla, J. Andrawus, R. Prabhu
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Abstract

The petroleum industry is currently being faced with a growing number of ageing offshore platforms that are no longer in use and require to be decommissioned. Offshore decommissioning is a complex venture, and such projects are expected to cost the industry billions of dollars in the next two decades. Early knowledge of decommissioning cost is important to platform owners who bear the asset retirement obligation. However, obtaining the cost estimate for decommissioning an offshore platform is a challenging task that requires extensive structural and economic studies. This is further complicated by the existence of several decommissioning options such as complete and partial removal. In this paper, project costs for decommissioning 23 offshore platforms under three different scenarios are estimated using information from a publicly available source which only specified the costs of completely removing the platforms. A novel mathematical model for predicting the decommissioning cost for a platform based on its features is developed. The development included curve-fitting with the aid of generalised reduced gradient tool in Excel® Solver and a training dataset. The developed model predicted, with a very high degree of accuracy, platform decommissioning costs for four (4) different options under the Pacific Outer Continental Shelf conditions. Model performance was evaluated by calculating the Mean Absolute Percentage Error of predictions using a test dataset. This yielded a value of about 6%, implying a 94% chance of correctly predicting decommissioning cost.
海上设施退役的优化-成本考虑
石油行业目前正面临着越来越多的老化海上平台,这些平台不再使用,需要退役。海上退役是一项复杂的冒险,预计在未来20年,这类项目将使该行业损失数十亿美元。对于承担资产退役义务的平台所有者来说,尽早了解退役成本非常重要。然而,获得海上平台退役的成本估算是一项具有挑战性的任务,需要进行广泛的结构和经济研究。由于存在一些退役选择,如完全或部分拆除,情况进一步复杂化。本文利用公开信息估算了三种不同情景下23个海上平台的退役项目成本,这些信息仅指定了完全拆除平台的成本。提出了一种基于平台特点的退役成本预测数学模型。该开发包括借助Excel®Solver中的广义降阶工具和训练数据集进行曲线拟合。所开发的模型以非常高的精度预测了太平洋外大陆架条件下四种不同选择的平台退役成本。通过使用测试数据集计算预测的平均绝对百分比误差来评估模型性能。该方法的预测值约为6%,这意味着正确预测退役成本的概率为94%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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