Accurate prediction of annular flow is of primary importance in slimhole drilling. A model was developed that better represents actual amular configurations. It accounts for axial variations of drillpipe eccentricity and for effeets arising from drillpipe rotation. The model is validated with data obtained from 3 industrial slimhole wells (4''3/4 and 3"3/8 phases). Based on model predictions, guidelines are given to define flow parameters and to adapt fluid rheology in order to obtain optimal hole cleaning under safe drilling conditions. introduction Flow in the reduced annular geometries of slimhole wells may give pronounced pressure losses at moderate or even low flow rates, generating thus substantially higher equivalent circulating densities than in conventioml wells. Accurate control of wellbore hydraulics and adequate choice of fluid rheology and flow parameters are therefore imponant issues of the slimhole teehrwlogy, as far as avoiding excessive pressure against the formation and efficient hole cleaning are comerned.Early field practice ] and more recent laboratory studies2 showed that the usual hydraulic models are not able to correctly predict pressure losses in slimhole armuli. These models assume a concentric annular geometry and ignore drillpipe rotation. However, the flow is extremely sensitive to the precise boundary conditions, so drillpipe actual configuration and rotation must be accounted for3.

As both the concept of ‘landscape’ and ‘beauty’ have very perceptual definitions, they must be assessed by large groups to be appropriately addressed. Therefore, amid a review of AONB designation, cultural perceptions of what is valuable within these designated areas is of paramount importance. Ecosystem services have gained traction as a way to assign social value to the non-physical benefits landscape can provide. AONB landscapes have a list of ‘special qualities’ (SQs) which are the features and characteristics of this area that warrant its protection. This study looks at the extent to which SQs reflect public values. Multiple methods including photo elicitation, participatory mapping, in-person surveys and social media data analysis have been used. The study suggests that when presented with the same landscape there is huge diversity as to what should be considered ‘special’. There is a general preference of landscape heterogeneity, therefore no one SQ could be more important than any of the others if considered in isolation. SQs that have a tangible link to the cultural ecosystem services they provide, were most appreciated. All methods used had their own benefits and flaws, hence future research should use a combination of methods to address perception issues.

Résumé -Surveillance intelligente du forage à l'aide du diagnostic temps réel -La surveillance du processus de forage pendant la réalisation d'un puits est fondamentale pour assurer le bon déroulement d'un chantier. À cet effet, des mesures appelées mesures de mud logging, pour la plupart effectuées en cours de forage à l'aide de capteurs installés sur le rig, sont stockées dans des bases de données. Cependant, sans interprétation, elles ne permettent pas de connaître précisément l'état du forage.Depuis quelques années, l'IFP et Geoservices ont collaboré à un programme de recherche dont l'objectif est de réaliser un système capable d'informer le foreur sur les problèmes qu'il rencontre en cours de forage, en se basant essentiellement sur ces mesures de mud logging. L'objet de cet article est de présenter ce système, appelé GetSMART, qui à ce jour, aborde la détection en cours de forage des principales vibrations anormales et des dysfonctionnements hydrauliques. Ce système est fondé sur la méthodologie des arbres de diagnostic, dont la richesse est de prendre en compte la connaissance « métier » du foreur pour analyser les signaux de forage et générer des alarmes. Des résultats d'application, déjà obtenus à partir de cas concrets de dysfonctionnements constatés sur chantier, montrent l'intérêt et la pertinence du système GetSMART. D'autres alarmes seront introduites dans le système dans le futur (i.e. stabilité de paroi). Abstract -Intelligent Drilling Surveillance through Real Time Diagnosis -Drilling a well is a complex process which needs to be monitored continuously to ensure that the well will reach its goals. For this purpose mud logging is performed with sensors installed

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