Here, we present an approach to identify partners at sea based on fishing track analysis, and describe this behaviour in several fleets: pelagic pair trawlers, large and small bottom otter trawlers, mid-water otter trawlers, all in the North-East Atlantic Ocean, anchovy purse-seiners in the South-East Pacific Ocean, and tuna purse-seiners in the western Indian Ocean. This type of behaviour is known to exist within pair trawlers, since these vessels are in pairs at least during their fishing operations. To identify partners at sea, we used a heuristic approach based on joint-movement metrics computed from vessel monitoring system data and Gaussian mixture models. The models were fitted to joint-movement metrics of the pelagic pair trawlers, and subsequently used to identify partners at sea in other fleets. We found partners at sea in all of the fleets except for the tuna purse-seiners. We then analysed the connections between vessels and identified exclusive partners. Exclusiveness was more common in pelagic pair trawlers and small bottom otter trawlers, with 82% and 74% of the vessels involved in partnerships having exclusive partners. This work shows that there are collective tactics at least at a pairwise level in diverse fisheries in the world.

Graph models are standard for representing mutual relationships between sets of entities. Often, graphs deal with a large number of entities with a small number of connections (e.g. social media relationships, infectious disease spread). The distances or similarities between such large graphs are known to be well established by the Graphlet Correlation Distance (GCD). This paper deals with small graphs (with potentially high densities of connections) that have been somewhat neglected in the literature but that concern important fora like sociology, ecology and fisheries, to mention some examples. First, based on numerical experiments, we study the conditions under which Erdős-Rényi, Fitness Scale-Free, Watts-Strogatz small-world and geometric graphs can be distinguished by a specific GCD measure based on 11 orbits, the GCD11. This is done with respect to the density and the order (i.e. the number of nodes) of the graphs when comparing graphs with the same and different orders. Second, we develop a randomization statistical test based on the GCD11 to compare empirical graphs to the four possible null models used in this analysis and apply it to a fishing case study where graphs represent pairwise proximity between fishing vessels. The statistical test rules out independent pairing within the fleet studied which is a standard assumption in fisheries. It also illustrates the difficulty to identify similarities between real-world small graphs and graph models.

The 2002 to 2003 OtterFieldd evelopment drillingc ampaignu tilized ac ombination ofd etailed trajectory planninga ndi ntegrated geosteeringtechniques. The objectiveofthisw orkwast om aximizeoil recovery,withaminimaln umbero fwells,f rom the complexlyfaulted Otters tructure.Toachievethis,subhorizontalp roduction wells wereplanned to track neart op reservoir,through the structuralculminations,to connectadjacent fault blocks. Otteristhe most northwesterly ofthe Brent Province fieldsofthe Northern North Sea, located inUK blocks210/15a and210/20d, 530 kmnorthofAberdeen,operated byTOTAL withpartners Shell U.K.Exploration andProduction,ExxonMobilandDana.The fieldwasdiscovered bythe Phillips 210/15-2 well in1977 (thencalled Wendy) andappraised byFinawell 210/15a-5 in1997,following3Dseismic acquisition in1994.The decision to proceed withdevelopment wasconfirmed afterthe success ofappraisalwell 210/15a-6 drilled byTotalFinain2000.The Otterstructureisaneasterly dippingtilted panelthatisdivided into four major blocksandseveralminor blocksbyanetworkofsubsidiary faults. The reservoiristhe MiddleJurassic Brent Group,withthe uppermost Tarbert Formation shallow marinesandstonescomprisingthe mainproducingtarget. The oilsource rock isthe LateJurassic KimmeridgeClay,present inthe off-structureareas,though locally absent overthe OtterFieldarea. Top sealisprovided bythe Mid-to LateJurassic HeatherShales. The Otteroilisamedium gravity crude (36.5 8 API)withaGOR of79m 3 /m 3 (443scf/bbl),inanormally pressured reservoiratacrestaldepthof1970 msubsea. Otterwell planningwasconducted usinga3Dgeocellularmodelbased on interpretation ofbothconventionaland acoustic impedance inversion seismic datasets. Apilot study,prior to development drilling, included geochemical andpetrophysicalr eservoiru nitdefinition andthe forwardmodellingofL WD logresponsei ns ub-horizontal wells. The results ofthesestudieswereused to aid geosteering, incorporatingrealtimec hemostratigraphyand LWD dataatthe wellsite.Inaddition,boreholeresistivity imageswhiledrillingwereused to assist instructural interpretation inrealtimeandthus to guide the well trajectory to maximizethe paysection. Akeycomponent in usingthesenewt echnologiesw ast he office-based integration ofa ll the datavia web-based monitoringofthe operations.Three production wells targetthe culminationsatt he extremitiesofthe OtterField, supported byadowndip waterinjector,all drilled from acentrally located subsea template.Followingthe successful drillingofthe first production well,210/215a-T1,production start-up wasinOctober2002,via subsea tieback to the Eiderfacility.

The data centre for French coastal operational oceanography (CDOCO) has been developed in the frame of PREVIMER, the coastal operational system for the French marine environment. The main objective of a coastal oceanography data centre is to provide in a timely fashion the data needed by modellers for assimilation, forcing and validation purposes. The goals of this data centre are:• to provide operational services to the actors and users of coastal operational oceanography,• to develop partnerships with the data producers and the data users,• to develop the operational services for data collection, quality checks, archiving and distribution. The data are received as much as possible in real time or near real time and the data centre delivers it to the modelling processes for several daily runs. In order to feed the models, the data centre collects:• in situ oceanographic measurements, • meteorological data (wind, temperature, atmospheric pressure, rainfalls, radiation),• hydrology information (rivers outflows and nutrient fluxes),• limit conditions at open boundaries (Mercator, MFS, climatologies), • bathymetry. Satellites observations are also taken in consideration but they are processed by the CERSAT data centre and do not circulate through the CDOCO. Two different data flows can be distinguished: a real time data flow which manages the data delivered by the partners (SHOM, Météo-France, DIREN and IUEM) and a delayed mode data flow which manages other data such as reference data (bathymetry); climatologies, analyses.The data centre automatically collects all these data that are received at different frequencies (from several times a day to once a week). Both observed data collected for the models and model results (hindcast and nowcast analyses and forecasts) are archived. It is also of the responsibility of CDOCO to distribute (under agreement) the data and results in a timely fashion.The data are distributed on a timely basis to the actors of the PREVIMER operational prediction system. A password protected access is also provided to other users for downloading data through web interfaces.The data are distributed using standardized specific formats: NetCDF and ASCII.