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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. © 2016 SETAC.

The impact of sediment coverage on two rhodolith-forming calcareous algae species collected at 100 m 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, ϕ PSIImax ) 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 1–2 weeks 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.

Vision based wildlife monitoring is an important task in the field of environmental monitoring. Wildlife monitoring activities often create large collections of data needing computational approaches to (semi-) automated detection and annotation of objects in the images/video. In this work, we consider the special case of marine wildlife monitoring using camera equipped fixed observatories. In such cases where a-priori knowledge about which species to find is limited, a standard computer vision approach, employing supervised learning, will not be applicable for detecting and classifying species (or events) in the images. In a recently proposed unsupervised learning method, image patches are extracted from a time series of underwater images that feature moving species (like starfish, etc). The patches are automatically grouped into clusters with similar morphology and a so called relevance score is assigned to each of the clusters describing the likeliness that it contains patches showing unusual changes. However, due to the unsupervised fashion (i) the categories don't have labels and (ii) do not reflect the species distribution satisfactory. In this paper, we propose an active learning method that builds upon these results and can be used to assign taxonomic categories to single patches based on a set of human expert annotations making use of the cluster structure and relevance scores. The evaluation shows that compared to traditional sampling strategies our approach uses significantly less manual labels to train a classifier. We are confident that the results are relevant for non-marine contexts as well.

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 Vesteralen ocean observatory, showing the potential to harvest changes of interest and novelties in large image collections.

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.

In recent years, a number of fixed long-term underwater observatories (FUO) have been deployed to monitor marine habitats over time. HD cameras deployed on FUOs enable vision based studies of long-term processes in the monitored habitats. However, in many marine environments there is often only little a-priori knowledge about potential changes that can be expected or where such changes are likely to occur. Therefore, we propose a method to detect regions of potentially relevant changes and to group them into categories. Wavelet analysis is employed to extract features that describe the approximate progression of pixel values over time. Clustering the features using the recently proposed Bi-Domain Feature Clustering (BDFC) achieves feature grouping and a data-driven definition of change categories. Moreover, a relevance score is computed for each change category, to find regions with relevant changes and to illustrate different relevant change categories simultaneously in one image. Our experiments with images from the Lofoten Vesteralen (LoVe) ocean observatory demonstrate the effectiveness of the method to find relevant change patterns and associate them to different regions or biota.

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 - Vesteralen)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.

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. © 2016 SETAC.

Fisheries have been vital to coastal communities around the North Sea for centuries, but this semi-enclosed sea also receives large amounts of waste. It is therefore important to monitor and control inputs of contaminants into the North Sea. Inputs of effluents from offshore oil and gas production platforms (produced water) in the Norwegian sector have been monitored through an integrated chemical and biological effects programme since 2001. The programme has used caged Atlantic cod and blue mussels. PAH tissue residues in blue mussels and PAH bile metabolites in cod have confirmed exposure to effluents, but there was variation between years. Results for a range of biological effects methods reflected exposure gradients and indicated that exposure levels were low and caused minor environmental impact at the deployment locations. There is a need to develop methods that are sufficiently sensitive to components in produced water at levels found in marine ecosystems.

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.

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 Peregrino field, operated by Statoil ASA, is located 80km south of Cabo Frio, in the Campos Basin area, Brazil. The field is operated with two well head platforms drilling production and injection wells and a FPSO located between these. The seabed hosts a calcareous algae (CA) habitat, an area with relatively rich biodiversity. Conventional sediment monitoring has been found not to give sufficient information about potential impact of drill cuttings. A tailor suited, in-situ sensor based monitoring approach was developed for the field including testing and qualification of a number of sensor systems for visual observation, oceanographic parameters, light and turbidity, all placed on a seabed observatory frame. 4, 6-months sampling campaigns were carried out with this system. In addition, three sediment traps were placed at different locations, over totally 7, 4-months campaigns. Data were combined with discharge information and laboratory studies of drill cuttings effects to CA and associated species, to identify potential exposure and impact in the field. Discharge and environmental risk modeling were performed to identify which data gave significant and vital information for the purpose of environmental monitoring of the CA habitat. Combining discharge information with in-situ observations and modeling gives a strongly improved basis for identifying impact of drill cuttings discharges in a field like Peregrino. Technology for this has been developed to a readiness level available for multiple use. A new, cost beneficial monitoring program is proposed as an alternative to traditional sediment monitoring.

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.

(previously submitted to SPE) The International Association of Oil and Gas Producers (OGP) Offshore Environmental Monitoring Task Force has developed a compendium of guidance and best practices for offshore environmental monitoring. Environmental monitoring has increased and become more sophisticated as concerns around the ecological impacts of offshore drilling and production have grown. Environmental monitoring is now integrated into the full life cycle of an offshore oil field, from early seismic evaluations through production to decommissioning. Traditionally, water column analyses, and physical, chemical and biological analyses of sediments have composed the basic elements of an environmental monitoring program. These analyses have often been coupled with dispersion modeling assessments that estimate the spatial footprint of drilling and production discharges both within the water column and on the sea floor. More recently, new technologies are becoming part of the environmental monitoring toolbox. These include subsea chemical and physical sensors attached to subsea landers, remote observational vehicles equipped with video, sensor and sampling capabilities, whole effluent toxicity testing, and biomarkers in organisms caught or caged within the oilfield. This new OGP guidance will enable an operator to define the scope of monitoring appropriate for their operation and stage of development, select from a range of monitoring technologies, and take steps to ensure high quality data is obtained. Extended Abstract: The International Association of Oil and Gas Producers (OGP) Offshore Environmental Monitoring Task Force has developed a compendium of guidance and best practices for offshore environmental monitoring. Environmental monitoring can be driven by regulatory requirements or to meet internal environmental stewardship expectations. The compendium provides guidance to offshore operators on when and how to conduct an environmental monitoring programme. To scope out potential environmental data needs for a monitoring programme, OGP developed the e-SHRIMP1 toolbox to assist offshore operators in the assessment and management of impacts in all project activities throughout the full field development lifecycle. Different project phases can be mapped to the e-SHRIMP1 framework (OGP, 2007).

The term Integrated Environmental Monitoring (IEM) refers to the tight coupling of sensor based real-time environmental monitoring with day-to-day operations. In that perspective IEM extends the traditional meaning of the term environmental monitoring by including discharge control, leak detection and remote sensing. Using offshore operations as an example, environmental monitoring has often been perceived to mean off-line sampling of seabed sediments and the water column during annual and triennial campaigns by ship. Whereas such monitoring has its place in a total monitoring programme, advances in technology and methodology open up for more flexible and real-time sensor based monitoring which can be tied closely to daily operations in the oil and gas industry. A multidisciplinary approach is sought to address these challenges, where input is needed from diverse domains as marine biology, subsea field development, sensor instrumentation and organization development. IEM relies on the presence of several technical and organizational capabilities. Some of these are similar to what has been developed in other areas of the oil & gas industry, for example through increased use of real-time data in drilling and well operations or in the cross-discipline collaboration variously labelled Integrated Operations, e-Field or Digital Oilfield. IEM applies this experience to the environmental monitoring domain. Statoil is currently developing an architecture or a new capability platform to take advantage of these opportunities. This platform has the following layers: intelligent infrastructure, information and collaboration, knowledge sharing & analytics and finally business operations. The paper presents how Statoil sees environmental monitoring over the life-cycle of a field and the overall architecture/platform for Integrated Environmental Monitoring (IEM) will be presented together with some of the focused development areas.

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.

Since the early 1990s focus on the environmental performance in the oil and gas industry has continuously increased. Technology development has resulted in a large variety of mitigation options, both with respect to source reduction, reuse and treatment of different waste streams and discharges. Testing, qualification and implementation of new technology is a result of the increased focus. In parallel, the need for appropriate management systems for decision support in the effort to minimise impacts of E&P activity has become evident. The present paper introduces a system to meet this challenge, by integrating quantitative risk assessment (QRA), mitigation technology/measures and environmental monitoring. QRA methodology has been developed to cover major discharges and emissions to the environment and is used to identify cost effective measures to reduce environmental risk. The QRA is further integrated with monitoring to understand and document ecosystem conditions and effect/benefit of improved performance (discharge and emissions reduction). This integrated approach results in a holistic environmental management system. This paper will mainly focus on the QRA, the environmental monitoring and the integration of these two. Examples of practical application of this integration are given on discharges of produced water and drill cuttings.

In order to achieve the offshore petroleum industries "zero harm" goal to the environment, the environmental impact factor for drilling discharges was developed as a tool to identify and quantify the environmental risks associated with disposal of drilling discharges to the marine environment. As an initial step in this work the main categories of substances associated with drilling discharges and assumed to contribute to toxic or nontoxic stress were identified and evaluated for inclusion in the risk assessment. The selection were based on the known toxicological properties of the substances, or the total amount discharged together with their potential for accumulation in the water column or sediments to levels that could be expected to cause toxic or nontoxic stress to the biota. Based on these criteria 3 categories of chemicals were identified for risk assessment the water column and sediments: Natural organic substances, metals, and drilling fluid chemicals. Several approaches for deriving the environmentally safe threshold concentrations as predicted no effect concentrations were evaluated in the process. For the water column consensus were reached for using the species sensitivity distribution approach for metals and the assessment factor approach for natural organic substances and added drilling chemicals. For the sediments the equilibrium partitioning approach was selected for all three categories of chemicals. The theoretically derived sediment quality criteria were compared to field-derived threshold effect values based on statistical approaches applied on sediment monitoring data from the Norwegian Continental Shelf. The basis for derivation of predicted no effect concentration values for drilling discharges should be consistent with the principles of environmental risk assessment as described in the Technical Guidance Document on Risk Assessment issued by the European Union.

The Lofoten-Versterålen (LoVe) Ocean Observatory is a cabled observatory on the Norwegian continental shelf providing long-term environmental data, including time-lapse imagery of a cold-water coral reef, from a multisensory stationary platform. The high temporal data resolution (frequency and duration) allows new insights into the behavioural responses of reef organisms to environmental variations and interactions between the water column and the seabed community. The dominant current direction in the area is north-west, with the near seabed currents being most consistent (follows the topography). Data analyses clearly indicate increase in polyp activity of Lophelia Pertusa correlates with the spring bloom. The polyp activity increases though the summer, when after the water-column stratification reduces the concentration of phytoplankton after the spring bloom. The reduced phytoplankton concentration and increasing polyp activity seems to correlate with a colour change in L pertusa, i.e. from white to pink and back to white again in the period April – end of November 2015. We preliminary results infer that zooplankton may represent an important food source for the cold-water corals in stratified summer conditions.