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Deepwater corals (Lophelia) were identified in the vicinity of the location of the exploration well Pumbaa (NOCS 6407/12-2) on the Norwegian Continental Shelf where a drilling operation was carried out by the GDF SUEZ E&P Norge AS (GDF SUEZ). Det Norske Veritas AS has on behalf of GDF SUEZ conducted an environmental monitoring of drilling activities at Pumbaa (NOCS 6407/12-2).

There is an increasing focus on monitoring of site specific environmental resources and discharges. Systems for collection of Real time and on-line sensor data are evolving as an answer to these challenges. These systems generate large amounts of data, commonly used into planning/documentation and operational decision support. However, there exists no industry standard on how to design and carry out such activities. This paper outlines a guideline for conceptual designs of cost efficient and robust sensor monitoring systems. Based on previous sensor monitoring projects on the Norwegian Continental Shelf, a methodology has been developed to make conceptual designs of cost efficient and robust sensor monitoring systems. This paper proposes in the evaluation process of emerging technologies for possible incorporation in a cost efficient way to use a combination of (1) Existing sampling design strategies, (2) Identified key design variables and (3) Success and acceptance criteria's for implementing new Environmental Monitoring Mapping (EMM) approach. The approach has been tested on two cases; (i) off-line/physical sampling approach and (ii) on-line/modelling approach. A number of driving design variables have been identified for establishing such a system, whereas the most important are: (i) time - frequency and duration of the sampling, (ii) space - spatial resolution and number of sensors, (iii) collection: off/on-line and mobile/stationary sensor platforms (iv) Use: direct or derived use of the data and documentation/mitigation purposes, (v) analysis, and (vi) Presentation: availability and format. By assessing these key design variables one will provide decision support on complexity of the environmental sensor based solution on future environmental monitoring campaigns. In Norway, over the past decade, there has been made substantial efforts in offshore projects to apply use of sensors in different ways where sensitive environmental resources such as cold water corals and deep water sponge aggregations are present. On a general basis most of these projects have been real time data collection using an off-line solution, where the purpose has been documentation. Joint effort R&D projects such as IEM, ELMO and LOVE, and commercial projects like Morvin and Hyme have challenged the off-line application into an on-line setting. Benefits from collection of Real time and on-line sensor data can be to enable operations in compliance with environmental acceptance criteria; meaning more efficient, smarter and greener operations. So far little focus has been on how data from sensor based systems actually can be used for decision support. In order to address the complexity and challenges of setting up different type of sensor based systems, this paper presents a design guideline which is based on a structured approach including the main variables needed to be considered for such a systems. Lessons learned from several previous sensor monitoring projects on the Norwegian Continental Shelf (NCS) are reflected in the guideline. By applying the structured approach it is expected to design more cost efficient and robust monitoring systems.

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Environmental Baseline Study (EBS) in Norway has been carried out as traditional sediment sampling at the planned production site for biological, chemical and physical analysis prior to production drilling according to regulation. The extent of such study should be related to the risk of impact from the planned operations. This baseline can then be used as a reference for future environmental monitoring. Equally important is the contribution to environmental impact assessments defining the measures to eliminate, limit or offsetting the adverse effects that the operation could generate. For the Victoria location, a traditional EBS based on sediment sampling was not considered beneficial due to both anticipated coarse sediment as well as presence of corals at the elevated ridge south west in the area. A comprehensive study including sediment analysis, visual mapping with ROV (Remotely Operated Vehicle), current measurements and interpretation of side scan sonar data and bathymetry data was therefore applied to meet the challenges at Victoria. The video lines covered more than 130 km of sea bed and the interpretation enabled categorisation of the seabed in soft bottom, rocks and reef building corals. Still photos and HD videos of the major corals structures were used to identify the dominant species and assess their health status (dead, poor, live). Results were transferred to a GIS to define the optimal position of anchoring corridors minimising interference with sensitive ecosystems. The combination of the different methods used in the EBS gives a thorough coverage of the area. The amount and quality of data gathered also beyond the location of the appraisal well location, allow a proper assessment of the sensitivity of the area at the field scale. Based on the experiences gained, it is recommended that future monitoring in areas comprising different habitats (soft bottom, rocky habitat etc.) apply different dedicated monitoring methods. For the visual monitoring a recommendation of approaches are:-Turbidity and current should be measured before and during drilling to document the dispersion of particles to secure the monitoring strategies. -Establish photo stations and indicator species to be monitored based on the data from turbidity and current measurements.-The use of a GIS based map system is highly recommended to handle the amount of data collected in an efficient and presentable way. -Collected data from future surveys should be implemented in the already existing GIS based map system of the Victoria site to secure an up to date database.