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Summary It is common during the exploration phase or at the early stage of a field development to work with a few of or even no well data, with uncertain Time or Velocity maps and with doubts about fault location. Lack of data and uncertain data decrease the robustness Depth-converted structural maps used in estimation of the field economic potential. Geostatistical methods developed in the framework of the UncerTZ RD Stochastic conditional simulations allow integrating uncertainty on Time or Velocity maps and on fault location. They also allow quantifying the global uncertainty resulting from all the uncertainties on data. With these methods, it is possible to get usable results from which the uncertainty on extension and volumes of petroleum traps can be estimated, allowing stakeholders to take the most appropriate decisions about field development.

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Summary The structurally lowest point in a hydrocarbon trap that can retain hydrocarbons is called a Spill Point and characterizing these locations over a depth horizon is a common approach in trap analysis. However, a horizon is an uncertain object typically produced through a time to depth conversion procedure which might involve several different variables like time, velocity, and fault position. Each of those variables brings its own uncertainty. By using geostatistical simulations, we produce different realizations of the depth horizons and further process them individually to determine the probability of presence of reservoirs and spill points associated to highly probable reservoirs. This paper presents a methodology to achieve such results including our analysis algorithm for trap and spill point characterization. By using a case-study we demonstrate that only proper characterization of all relevant realizations in the uncertainty space show us the possible scenarios, and their impact on traps volume.

Summary Faults are objects interpreted from seismic data that are often difficult to characterize. Not only the type of a fault can appear different depending on the domain the modeler is working on (time or depth), but accurate shape mapping often extends below the level of coherent seismic data. This proves especially impactful when we consider that simple errors in the fault-network characterization can render a subsurface model useless in terms of its flow properties and provide erroneous values for volumes of reserves. Nevertheless, in absence of proper technology, depth conversion workflows do not account for fault uncertainties, instead relying on a single deterministic interpretation. This paper presents a new methodology that considers positional fault uncertainty in time-to-depth conversion workflows. By considering different realizations of a fault, the target surface is dynamically adapted to this new position and can be used to update further volumetric computations. The proposed methodology can be fully automated and results in a more complete exploration of the uncertainty space regarding fault interpretation.

To optimize hydrocarbons recovery and quantify future production risks, it is necessary to accurately characterize traps geometry. This geometry is estimated from both seismic and well data, using Time-Depth conversion methods. This characterization is a critical issue in reservoirs with thin beds, as the uncertainty on horizons depth can lead to large variations of beds thickness. The paper presents an application on real data of a new Time-Depth conversion integrated workflow which is based on advanced geostatistical estimation and simulation multivariate algorithms and on automatic Spill Point recognition. It enhances horizons depth estimation and minimizes the uncertainty on the consecutive horizons. This workflow is extremely efficient in faulted layer-cake deposits, especially when reservoirs are thin. First, the theoretical background of the geostatistical multivariate algorithm is briefly summarized, including its version in the Bayesian framework. Then, the application example is used to highlight the differences between the simultaneous conversion of consecutive horizons and the standard conversion where horizons are considered as independent to each other. Focus is put on the vertical evolution of uncertainty and on the Geophysicist input allowed by the Bayesian framework. The workflow is versatile enough to convert directly Time in Depth or to compute intermediate enhanced velocity models when required. Practical examples are presented to illustrate the characteristics of each method. The impact of the different conversion options on Spill Points location and on the reservoirs Gross Rock Volumes are highlighted. One of the most original and useful features of the workflow is its ability to include faults location uncertainty in the global Gross Rock Volumes uncertainty quantification. The implementation of this capability is explained and illustrated from its application to real data. In the end, the impact on ultimate recovery uncertainty is analyzed and illustrated from a practical case study.

In immunocompromised patient, parapoxvirus infection can be extensively necrotic and recurrent evolution. We describe a case of Orf nodule in a liver transplanted woman. We will consider the therapeutic options in case of infections by parapox in immunosuppressive patients, as described in the medical literature. In our specific case, local application of cidofovir (concentration of 1 %) together with local antiseptic solution, povidone iodine, led to complete remission of the lesion without any sign of toxicity. Finally, we will consider the therapeutic use of local cidofovir.

Factorial kriging or kriging with filtering (Matheron, 1982) is used on post-stack or pre-stack seismic dataset to filter out unwanted components from the seismic signal. To account for non-stationarity that is often encountered within seismic data sets, kriging parameters can be locally set using Local Geo-Statistics (LGS) or M-GS (Moving-GeoStatistics) (Magneron, 2009). There are several approaches to compute the optimized parameters; local variogram parameters in adjacent areas, automatic cross-validation techniques and morphological analysis. The paper focuses on the latter approach. The idea is to determine some interesting characteristics of a seismic image that should then be transformed to local kriging parameters for the variogram model and the neighbourhood extension. Mathematical morphology techniques provide a set of tools to analyse the image, however they are not well known by geophysicists who are more familiar with seismic attributes. A seismic attribute is a quantity extracted or derived from seismic data that can be analysed in order to enhance information of a seismic image. The advantage of using seismic attributes is that they are available on common geophysical interpretation software packages. Surprisingly there is no much reference of the application of such attributes to derive the local parameters of the geostatistical filters.