Ingunn Nilssen

Managing the environmental effects of the Norwegian oil and gas industry : From conflict to consensus

Abstract In the early days of exploitation of the oil resources on Norway’s continental shelf there was little control over environmental impacts. The oil companies expected effects of their activities to be found to a 1 km radius round platforms. In the late 1980s data started appearing that suggested that effects were over much larger areas. These findings were disputed but subsequent studies showed that a more realistic figure of the area affected was a 3 km radius giving roughly 10 times the area predicted by the companies. The Norwegian authorities reacted by imposing restrictions on discharges of oil-based drilling cuttings and since 1993 oil-based drilling mud or cuttings have not been intentionally discharged to sea on the Norwegian shelf. In 1996 Norway changed its offshore monitoring from field based monitoring of sediments to a regional monitoring of both sediments and the water column. The change makes it possible to obtain a better overview of the environmental conditions, at the same time it also reduces costs for the operators.

Integrated environmental mapping and monitoring, a methodological approach to optimise knowledge gathering and sampling strategy.

New technology has led to new opportunities for a holistic environmental monitoring approach adjusted to purpose and object of interest. The proposed integrated environmental mapping and monitoring (IEMM) concept, presented in this paper, describes the different steps in such a system from mission of survey to selection of parameters, sensors, sensor platforms, data collection, data storage, analysis and to data interpretation for reliable decision making. The system is generic; it can be used by authorities, industry and academia and is useful for planning- and operational phases. In the planning process the systematic approach is also ideal to identify areas with gap of knowledge. The critical stages of the concept is discussed and exemplified by two case studies, one environmental mapping and one monitoring case. As an operational system, the IEMM concept can contribute to an optimised integrated environmental mapping and monitoring for knowledge generation as basis for decision making.

The effect of sediment mimicking drill cuttings on deep water rhodoliths in a flow-through system: Experimental work and modeling

The impact of sediment coverage on two rhodolith-forming calcareous algae species collected at 100m water depth off the coast of Brazil was studied in an experimental flow-through system. Natural sediment mimicking drill cuttings with respect to size distribution was used. Sediment coverage and photosynthetic efficiency (maximum quantum yield of charge separation in photosystem II, ϕPSIImax) were measured as functions of light intensity, flow rate and added amount of sediment once a week for nine weeks. Statistical experimental design and multivariate data analysis provided statistically significant regression models which subsequently were used to establish exposure-response relationship for photosynthetic efficiency as function of sediment coverage. For example, at 70% sediment coverage the photosynthetic efficiency was reduced 50% after 1-2weeks of exposure, most likely due to reduced gas exchange. The exposure-response relationship can be used to establish threshold levels and impact categories for environmental monitoring.

Integrated environmental monitoring and multivariate data analysis—A case study

The present article describes integration of environmental monitoring and discharge data and interpretation using multivariate statistics, principal component analysis (PCA), and partial least squares (PLS) regression. The monitoring was carried out at the Peregrino oil field off the coast of Brazil. One sensor platform and 3 sediment traps were placed on the seabed. The sensors measured current speed and direction, turbidity, temperature, and conductivity. The sediment trap samples were used to determine suspended particulate matter that was characterized with respect to a number of chemical parameters (26 alkanes, 16 PAHs, N, C, calcium carbonate, and Ba). Data on discharges of drill cuttings and water-based drilling fluid were provided on a daily basis. The monitoring was carried out during 7 campaigns from June 2010 to October 2012, each lasting 2 to 3 months due to the capacity of the sediment traps. The data from the campaigns were preprocessed, combined, and interpreted using multivariate statistics. No systematic difference could be observed between campaigns or traps despite the fact that the first campaign was carried out before drilling, and 1 of 3 sediment traps was located in an area not expected to be influenced by the discharges. There was a strong covariation between suspended particulate matter and total N and organic C suggesting that the majority of the sediment samples had a natural and biogenic origin. Furthermore, the multivariate regression showed no correlation between discharges of drill cuttings and sediment trap or turbidity data taking current speed and direction into consideration. Because of this lack of correlation with discharges from the drilling location, a more detailed evaluation of chemical indicators providing information about origin was carried out in addition to numerical modeling of dispersion and deposition. The chemical indicators and the modeling of dispersion and deposition support the conclusions from the multivariate statistics. Integr Environ Assess Manag 2017;13:387-395.

Computational coral feature monitoring for the fixed underwater observatory LoVe

Fixed underwater observatories (FUOs) equipped with a variety of sensors including HD cameras, allow long-term monitoring with a high temporal resolution of a limited area of interest. FUOs enable in situ monitoring of visual features like size and color of for instance live cold-water corals using imaging techniques. We present a computational workflow to extract coral features from the huge collection of images recorded by the FUO LoVe (Lofoten - Vesterålen)1. The presented approach allows to represent the image-time-series as numerical values of the features. This enables the images to be subject to statistical analysis to gain insight into the short- and long-term relationships between the corals and the environment. The presented automatic extraction of features from digital photos includes a customized pre-processing of the images (spatial and signal alignment), automated segmentation of the living parts of the corals using an unsupervised learning algorithm and extraction of coral specific numerical features which compensate color shifts due to changes of the in-optical properties affecting the whole image. In this initial study, unexpected temporal change patterns could be revealed, by comparing change patterns of live coral areas with change patterns of coral rubble areas using the CIELab color space.

Water Column Monitoring of Offshore Oil and Gas Activities on the Norwegian Continental Shelf: Past, Present and Future

As a result of aging offshore fields discharging higher volumes of produced water, and after the discharge of oil contaminated cuttings was terminated on the Norwegian Continental Shelf (NCS) in 1993, oil originating from produced water has become the dominant contributor of the Norwegian E&P industry to hydrocarbon and chemical input to the marine environment. To reflect the present situation, the environmental monitoring programmes have also changed. The Norwegian requirements related to offshore environmental monitoring are given in the HSE regulations for the petroleum activity. The water column monitoring consists of two parts: the condition monitoring and the effects monitoring. The programmes have developed through dialogue between the authorities, the scientific community, consultants, and the E&P industry, but will still be subjected to revision and improvement in the years to come.

Change detection in marine observatory image streams using Bi-Domain Feature Clustering

Vision based environmental monitoring using fixed cameras generates large image collections, creating a bottleneck in data analysis. In areas with limited background knowledge of the monitored habitat, this bottleneck can often not be overcome by traditional pattern recognition methods. A new change detection method to identify interesting events such as presence and behavior of different species is proposed. The change detection method uses the new Bi-Domain Feature Clustering (BDFC). BDFC integrates the location of a feature vector in the feature space as well as the location in the image into the clustering. Firstly, BDFC is applied to a time dependent representation of the image stream to identify regions of similar change. Secondly it is applied to a time independent representation to group these changes into categories. These categories can rapidly be assessed by a human observer to bypass the time consuming inspection of the whole data set. To make the posterior browsing of detected changes more efficient, a relevance factor computed for each category is proposed. The approach is demonstrated with experimental runs, using images from the Lofoten Vesterålen ocean observatory, showing the potential to harvest changes of interest and novelties in large image collections.

Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation.

This paper presents a machine learning based approach for analyses of photos collected from laboratory experiments conducted to assess the potential impact of water-based drill cuttings on deep-water rhodolith-forming calcareous algae. This pilot study uses imaging technology to quantify and monitor the stress levels of the calcareous algae Mesophyllum engelhartii (Foslie) Adey caused by various degrees of light exposure, flow intensity and amount of sediment. A machine learning based algorithm was applied to assess the temporal variation of the calcareous algae size (∼ mass) and color automatically. Measured size and color were correlated to the photosynthetic efficiency (maximum quantum yield of charge separation in photosystem II, ΦPSIImax) and degree of sediment coverage using multivariate regression. The multivariate regression showed correlations between time and calcareous algae sizes, as well as correlations between fluorescence and calcareous algae colors.

Polyp Activity Estimation and Monitoring for Cold Water Corals with a Deep Learning Approach

Fixed underwater observatories (FUOs) equipped with a variety of sensors including cameras, allow long-term monitoring with a high temporal resolution of a limited area of interest. FUOs equipped with HD cameras enable in situ monitoring of biological activity, such as live cold-water corals on a level of detail down to individual polyps. We present a workflow which allows monitoring the activity of cold water coral polyps automatically from photos recorded at the FUO LoVe (Lofoten - Vesterålen). The workflow consists of three steps: First the manual polyp activity-level identification, carried out by three observers on a region of interest in 13 images to generate a gold standard. Second, the training of a convolutional neural network (CNN) on the gold standard to automate the polyp activity classification. Third, the computational activity classification is integrated into an algorithmic estimation of polyp activity in a region of interest. We present results obtained for an image series from April to November 2015 that shows interesting temporal behavior patterns correlating with other posterior measurements.

Assessing the potential impact of water-based drill cuttings on deep-water calcareous red algae using species specific impact categories and measured oceanographic and discharge data.

The potential impact of drill cuttings on the two deep water calcareous red algae Mesophyllum engelhartii and Lithothamnion sp. from the Peregrino oil field was assessed. Dispersion modelling of drill cuttings was performed for a two year period using measured oceanographic and discharge data with 24 h resolution. The model was also used to assess the impact on the two algae species using four species specific impact categories: No, minor, medium and severe impact. The corresponding intervals for photosynthetic efficiency (ΦPSIImax) and sediment coverage were obtained from exposure-response relationship for photosynthetic efficiency as function of sediment coverage for the two algae species. The temporal resolution enabled more accurate model predictions as short-term changes in discharges and environmental conditions could be detected. The assessment shows that there is a patchy risk for severe impact on the calcareous algae stretching across the transitional zone and into the calcareous algae bed at Peregrino.