Liu, Zhen (Jiangsu University of Science and Technology) | Zhu, Renqing (Jiangsu University of Science and Technology) | Ji, Chunyan (Jiangsu University of Science and Technology) | Chen, Minglu (Jiangsu University of Science and Technology) | Teng, Bin (Dalian University of Technology) | Li, Liangbi (Jiangsu Modern Shipbuilding Technology Co. Ltd, Jiangsu University of Science and Technology)
Streever, Bill (BP) | Ellison, William T. (Marine Acoustics, Inc.) | Frankel, Adam S. (Marine Acoustics, Inc.) | Racca, Roberto (Jasco Applied Sciences) | Angliss, Robyn (Alaska Fisheries Science Center, NMFS/NOAA) | Clark, Christopher (Cornell University) | Fleishman, Erica (University of California) | Guerra, Melania (Cornell University) | Leu, Matthias (The College of William and Mary) | Oliveira, Shirley (North Slope Borough) | Sformo, Todd (SEA, Inc.) | Southall, Brandon (North Slope Borough) | Suydam, Robert
Most assessments of multiple, interacting, and/or repeated anthropogenic underwater sounds (sometimes considered to be an aspect of cumulative effects assessment) rely on narrative descriptions rather than systematic evaluations. In 2010, recognizing the need to better understand the potential effects of multiple sound sources (such as vessels, drilling rigs, pile drivers and seismic operations), British Petroleum (BP) sponsored the University of California to convene an expert committee tasked with advancing a method of systematic evaluation. The method developed by the committee (1) identifies the species, region, and period to be assessed, (2) compiles data on relevant sound sources for that region and period, (3) models the acoustic footprint of those sources, (4) models the movement of simulated marine mammals (animats) through the acoustic footprint, and (5) aggregates data on sound exposure and movements for each of the simulated animals. The method was applied to a test case or trial loosely based on data from the Alaskan Beaufort Sea during a period of seismic exploration and other activities. Substantial additional work is needed to better define output metrics related to degradation of acoustic habitat and to understand the potential effects of multiple sound sources on individuals and populations. Nevertheless, the method provides a starting point that will lead to improved understanding of the implications of multiple underwater sound sources associated with industrial activities.
In May 2011 Shell announced its commitment to the development of a Floating Liquefied Natural Gas (FLNG) concept by taking the Financial Investment Decision on the Prelude FLNG Project. Prelude is located in Australian offshore waters, approximately 475 km north-northeast of Broome and 825 km west of Darwin, and will be Shell's and possibly the world's first FLNG development. FLNG offers a number of environmental advantages over traditional onshore LNG developments. This paper describes some of these and the associated environmental permitting/approval conditions for the project.
Conference review - No abstract available.
Feng, Aichun (Faculty of Engineering and the Environment, University of Southampton) | Chen, Zhimin (Faculty of Engineering and the Environment, University of Southampton) | Xing, Jing Tang (Faculty of Engineering and the Environment, University of Southampton) | You, Yunxiang (School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiaotong University)
Qiao, Dongsheng (Center for Deepwater Engineering, Dalian University of Technology) | Ou, Jinping (Center for Deepwater Engineering, Dalian University of Technology) | Wu, Fei (Luxun Academy of Fine Arts)
Li, Zhigang (Offshore Oil Engineering Co. Ltd.) | He, Ning (Offshore Oil Engineering Co. Ltd.) | Duan, Menglan (Offshore Oil/Gas Research Center, China University of Petroleum) | Wang, Yingying (Offshore Oil/Gas Research Center, China University of Petroleum) | Dong, Yanhui (Offshore Oil/Gas Research Center, China University of Petroleum)
Probabilistic methods for reserves estimation, including uncertainty quantification and probabilistic aggregation, have gained widespread acceptance in the oil and gas industry, since the first comprehensive guidelines were issued by the Society of Petroleum Engineers (SPE) in 2001. The probabilistic methods now used in the oil industry, as proposed in these guidelines, are similar to those also used in portfolio theory and risk management by the finance industry. A significant amount can be learned from the extensive experience with probabilistic methods and quantification of risk with measures [e.g., value-at-risk (VAR)] in financial risk management. Especially, the guidelines issued by the Basel II Accord (Bank for International Settlements 2006) and the discussions since the 2008 financial crisis contain important lessons.
In this paper, we examine a fundamental question: "Is the P90 reserves value an appropriate measure for quantifying the reserves' downside?" For the P90 reserves value to be considered a good measure of the reserves' downside, it needs to possess a number of basic characteristics involving P90 reserves for each field and the probabilistically aggregated P90 reserves for the portfolio of fields. Analogous to the definition of a coherent risk measure used in the finance industry, we define these characteristics for P90 reserves.
The P90 reserves are as good a risk measure as VAR used in the financial industry. However, like VAR, it is not a coherent risk measure. A possible uncertainty scenario, in which one of these necessary characteristics does not hold, is given. An alternative measure of risk for quantifying the reserves' downside, defined as the average reserves over the confidence interval higher than P90, is presented. This is a coherent risk measure.
In this paper, we highlight the appropriateness and limitations of using the P90 reserves estimate as a measure of the reserves' downside. Understanding of the limitations posed by using the P90 reserves value is vital in management of reserves risk.