Hydrate blockage has been a problem in Campos Basin subsea producer wells for a long time. Although hydrate formation was not expected in sea water injection wells there were some occurrences in Campos Basin. The formation of hydrate requires the presence of water and gas at low temperature and high pressure condition. As the subsea sea water injection wells always present water at low temperature and high pressure, the missing link to form hydrate is gas. Thus, to explain the occurrence of hydrate blockage in these wells one has to explain how and when gas enters into the well. A study was carried out to determine the causes of gas inflow into the injection strings and to make recommendations on the proper procedures and equipments to avoid hydrate blockage. The literature survey and the Campos Basin occurrences showed initially that gas segregation, water hammer effects and crossflow were the most probable causes for gas inflow during injection plant shutdowns or long time waiting for injection. The crossflow was ignored because the formations are homogeneous in all the cases. The water hammer effects were analyzed with a numerical simulator developed in the study. The final analyses revealed that gas segregation was the cause of hydrate blockage in the water injection wells studied. Other relevant conclusions are that down hole valves, such as deep subsurface safety valves and backflow valves are useless to prevent gas inflow and that water hammer effects can be managed in the sea water injection plant. This paper presents recommendations for water injection wells startups, regarding hydrate prevention, which can be useful for any subsea water injection project. Introduction Natural gas hydrates are solid structures composed of water and gas. Depending on the thermodynamic conditions, water molecules will form a solid cage entrapping gas molecules (Freitas et al. 2002). Hydrate blockage has been a problem in Campos Basin subsea producer wells since 1994. In Campos Basin the sea temperature decreases with WD showing values around 13°C at 300 m WD, 8.5°C at 500 m WD and varies in the range of 3.5°C to 4.5°C from 1,000 m to 2,000 m WD. In this scenario, hydrate may occur in water depth (WD) beyond 300 m (984 ft), even with small amounts of water and gas. The hydrate blockages often occur during operations such as production shutdown/startup. From 1994 to 2006 hydrate blockage was the main cause of rig intervention in the most prolific field of Campos Basin (Figure 1). Their occurrences in completion and workover have decreased in Campos Basin because of the preventive measures adopted. Besides causing production losses and requiring expensive rig interventions, hydrate blockages can put in risk the production system safety and the environment. This is because they can occur in the wet Christmas tree (WCT) valves, and in the subsurface safety valve control lines (Freitas et al. 2002). Analyses have shown that lack of attention to details in planning the operations and not following program and good practices are by far the main causes of the hydrate problems (Rodrigues et al. 2007). The first occurrence of hydrate blockage in water injection wells (WIW) in Campos Basin was in August 2000. Since then there were eight occurrences in seven wells, all requiring costly rig interventions. At the most penalized field it was decided to install downhole valves to prevent gas migration from the formation open zones up to the WCT valves. The recurrence of hydrate blockage in subsea WIW was the reason for this study. There are no publications about hydrate blockage in WIW to the best of our knowledge. However, there are good references about some probable causes of hydrate blockage that were raised in the early phase of this study. This is the case of water hammer (WH) effects.

Bayesian decision theory is a statistically based theory that is used to assess the degree of certainty and the potential costs when making decisions. This paper presents a methodology, based on the Bayesian decision theory, used to infer subsurface lithofacies and saturation fluid by integrating different data sources, such as well logs data and seismic attributes, which are derived from an elastic seismic inversion. This methodology was applied on a data volume from an offshore Brazilian field to generate, as a final product, a lithofacies model and a fluid indicator for this field. Uncertainty quantification of the models was also analyzed at this work. To infer the subsurface lithofacies, the existing facies were identified from well logs data, using the expectation maximization (EM) algorithm. This step defines the lithofacies behavior in seismic attributes domains through the use of probability density functions (PDF). Next, the subsurface lithofacies were classified by applying the maximum posterior probability (MAP) classification, using the seismic attributes as input and the PDFs computed previously. The environment was divided into cells, then the probability and uncertainty was assessed to infer the lithofacie for each cell. After inferring the subsurface lithofacies, the fluid was inferred for the cells identified as reservoir lithofacies. Assuming an oil-water system, the fluid substitution theory and the Bayes theorem were applied to the well log data to determine the PDFs for each scenario. Following the Bayesian decision theory, the most likely fluid and the associated error was determined for each cell identified as reservoir. Introduction Since the first petroleum exploration studies of seismic reflexion, the seismic sensibility to lithological parameters, such as porosity, lithofacies, fluid properties, and pore pressure is notorious. However, in the 1990s, it became possible to extract these lithological parameters from seismic information. This development occurred as a result of technological progress in seismic processing and rock physics. Since then, the new challenge has been to estimate the uncertainties inherent at the quantitative seismic interpretation process in an attempt to reduce the risk linked to petroleum exploration (Avseth et al., 2001). The methodology of reservoir properties inference, suggested in this work, is presented as a flow of processes that infers lithofacies and reservoir rock saturation fluid from the integration of pre -stack seismic data, petrophysics data, and rock physics relations. The migrated seismic data have been previously processed in an attempt to preserve or restore the relative amplitudes. The petrophysics data are in-situ observations along the wells (log data) and the rock physics models correlate the seismic attributes with the media properties. The central idea of this work is to integrate information from different sources, each one with its own resolution and uncertainty. This work analyzes these uncertainties and its propagation for the final reservoir characterization model. The uncertainty analysis is useful for making decisions that quantify the contribution of each data source (Takahashi, 2000). One successful solution for this kind of problem (reservoir characterization with uncertainties analysis) is the statistical probability theory application. Using the Bayesian methodology, through the Bayes theorem, is possible to develop this kind of model with a proven practical effect (Loures and Moraes, 2002). In this context, the rock physics is used as theoretical base to characterize the seismic signature arising from variations in lithological parameters. Commonly, well log data (and core sample data), are punctual information with good resolution that serve as an information source for the necessary rock physics studies. Conversely, the seismic data represents low resolution information that covers the whole extension of the subsurface volume in study. Figure 1 represents the workflow based on AVO inversion concepts, rock physics, and statistical methods. This work consists of two stages:Lithofacies Inference-This is developed from well data, seismic attributes, and pattern recognition techniques. With the application of the Bayesian decision theory, probability density functions (PDF) are obtained from each facies along the seismic cube. The lithofacies inferences are made from those PDFs. One example of the application of this technique can be found in the work of Braga and Loures (2005).

We develop a Bayesian formulation for joint inference of porosity and clay volume, incorporating multiple data sets, prior information, and rock physics models. The derivation is carried out considering the full uncertainty involved in calculations from unknown hyperparameters required by either rock physics equations (model coefficients) or statistical models (data variances). Eventually, data variances are marginalized in closed form, and the model coefficients are fixed using a calibration procedure. To avoid working with a high-dimension probability density function in the parameter space, our formulation is derived and implemented using a moving window along the data domain. In thisway, we compute a collection of 2D posterior distributions forinterval porosity and clay volume, corresponding to each positionalong the window’s path. We test the methodology on both synthetic and real well logs consisting of gamma-ray, neutron, compressional and shear sonic velocity, and density. Tests demonstrate that integrating the relevant pieces of information about porosity and clay volume reduces the uncertainties associated with the estimates. Error analysis of a synthetic data example shows that neutron and density logs provide more information about porosity, whereas gamma-ray logs and velocities provide more information about clay volume. Additionally, we investigate a change in fluid saturation as a source of systematic error in porosity prediction. A real data example, incorporating porosity measurements on core samples, further demonstrates the consistency of our methodology in reducing the uncertainties associated with our final estimates.

The paper discusses the aspects of the multi-disciplinary well construction process (WCP) that is being applied in the development of Peregrino field located offshore Brazil. Peregrino field is a heterogeneous sandstone reservoir containing a large volume of viscous oil in a complex depositional geological environment. The field development is based on a large number of long horizontal and multilateral wells equipped with high energy-demanding Electrical Submersible Pumps (ESP) for artificial lift.The well construction process covers the feasibility, concept selection, detailed plan and execution phases. The efforts towards a multi-disciplinary process allow cost optimization, risk mitigation and production and reserves maximization. Multi-disciplinary teams include not only the usual subsurface disciplines (geophysics, geology, operations geology, petrophysics, reservoir and production technology) but also drilling, completion, topside and operations from an early stage.In viscous oil fields it is especially important to meet the requirements in reservoir properties, well length and completion design to develop such a field economically. The applied WCP is allowing Peregrino field to achieve its goals in terms of numbers and quality of wells planned and drilled. More than 20 wells have been drilled so far with very good results. Peregrino is now producing at plateau production with a daily oil production potential of 100,000 bbl.