Background:The diagnosis of brucellosis is frequently difficult to establish. This is not only because clinically, the disease can mimic any infectious and noninfectious disease, but also because the established diagnostic methods are not always successful. In this study, we have tried to evaluate PCR techniques in the diagnosis of brucellosis in comparison to conventional techniques. Patients and Methods: Fifty peripheral blood samples from the following groups were collected: patients with brucellosis (17); patients with febrile illnesses due to factors other than brucella etiology (19); symptomatic occupationally exposed persons (9); and healthy volunteers (5). The last three groups were considered controls. Among the 17 Brucella samples, only 14 were obtained before treatment was begun. The samples were tested by serology, using the standard tube agglutination method (STA), blood culture using Bactec machines, and PCR using primer pair to amplify a 223-bp region within a gene coding for a 31-kD Brucella antigen. Diagnosis of brucellosis was based on compatible clinical picture in addition to positive blood culture and/or positive serology. Results: Of the 17 blood samples from patients with brucellosis, eight were culture positive for Brucella species, and all showed high titer antibrucella antibodies. Only 14 of them were positive by PCR, and these were the samples submitted before initiation of therapy, representing 100% sensitivity. Among the 33 controls, blood culture was negative for Brucella in all of them, while one sample showed high-titer antibrucella antibodies. The latter was from the febrile illnesses group. PCR-based assay was able to detect four bands in the controls, all of which were from the occupationally exposed asymptomatic group. Conclusion: In view of the several advantages of PCR over the conventional methods for the diagnosis of brucellosis, such as speed, safety, high sensitivity and specificity, the technique might be considered for laboratory diagnosis of brucellosis. However, for the evaluation of asymptomatic highly exposed persons, PCR might be considered complementary to the traditional methods and followed up by serology and/or culture.

No abstract

Construction of reliable 3-D geological models for reservoir simulation requires a detailed understanding of facies distribution and connectivity within the interval of interest. In particular, it relies on lateral and vertical variabilities in reservoir quality within and between different flow units. Despite thorough data integration and interpretation, several challenges were encountered while integrating geological knowledge into a 3-D stochastic geological model. Some unconventional approaches had to be taken in order to best reflect the geological understanding of the reservoirs while using commercially available software. For example, the 3-D stochastic facies modelling of the reservoir units was based on conceptual geological models. Models were constructed for each reservoir zone through the analysis and integration of core and log data from 50 wells in the study area. However, it was seen that utilising either object modelling or pixel-based modelling methods alone would not generate stochastic models that adequately honoured the conceptual geological model. Consequently, the two modelling methods were used together to generate combined models. A further challenge was to determine adequate facies proportions for each reservoir zone. The direct use of facies statistics from well logs in stochastic modeling lead to unrealistic facies distributions. To overcome this, dummy wells were added to make facies proportions matches the conceptual models rather than basing it on the existing wireline logs alone. Taking these unconventional approaches lead to a greater accuracy of reservoir and porosity distribution around the reservoir. Moreover, the methodology used in this study provides new ideas that can be used in modeling other fields with fluvial depositional settings. Introduction Field development projects involve substantial financial investment and are typically designed around predictions of future reservoir performance. These predictions are generated from a reservoir simulation model, which is based on a geological model. Consequently, the reliability of the simulation model is highly dependent on the accuracy of the geological model (Jian et al., 2002). Several challenges are encountered in the creation of a reliable geological model. One is to build a model with a very limited amount of subsurface information available. Other challenges include defining flow units in the reservoir and identifying reservoir heterogeneities that effect fluid flow (i.e. channels in a fluvial system) (Jian et al., 2002). A flow unit is defined as a volume of reservoir rock that has very similar geological and petrophysical properties, and is distinctly different from the fluid flow properties of the other flow units (Aminian et al., 2002). To overcome these challenges, geoscientists conduct integrated reservoir characterisation studies. The literature contains well documented integrated reservoir studies which have helped in selecting the appropriate workflow for reservoir development by improving the quality of the geological model (Marquez et al., 2001; Tye and Hickey, 2001; Gilman et al., 2002; Meng et al., 2002; Ates et al., 2003). Such studies have demonstrated their importance in reservoir management and future development. One of the important breakthroughs in reservoir characterisation in recent years is the use of high-resolution sequence stratigraphy, 3-D seismic and geological analogues to construct a realistic 3-D conceptual reservoir model which helps in exploration and development (Lang et al., 2002; Strong et al., 2002). Reservoir characterisation studies are usually undertaken to address existing reservoir problems such as unexpected water production or optimisation of future development plans.

Development plans for Oil and gas reservoirs require huge investments. The decision for making these investments is based on many factors among them the reservoir performance predictions. To get reliable reservoir performance predictions, a reliable geological model is need. However, there are a lot of challenges encountered in the process of building a reliable geological. An important challenge is the limited amount of information available to geologists. Another challenge is the non-uniqueness problem, in other words several models can fit the same data and give different future forecasts. Moreover, stochastic modeling methods alone cannot predict accurately lithological distribution. New advances have been made in building and ranking geological models to over come all of these problems. This paper reviews the current status of integrated reservoir modeling in general and present advances in integrated geological modeling for fluvial reservoir. It covers advances in three important areas: reservoir characterisation of fluvial reservoirs, stochastic modeling and geological models ranking. These new advances have made it possible to generate the best possible geological model to be used in reservoir simulation. The first part focuses on advance in building a fully integrated reservoir characterisation study and exploring the different possible reservoir depositional settings for fluvial settings. In the second part covers advance in the utilization of stochastic modeling such as the use of object oriented algorithms to generate multi realizations that match modern analogs. The third part covers ranking geological models by static and dynamic methods. Static methods include the match of input and output statistics, while the dynamic methods include streamline simulation methods. Introduction Development plans for oil and gas reservoirs require huge financial investment. The decision for making these investments is based on many factors, among them the reservoir performance predictions. These predictions answer questions such as how much this reservoir can produce and for how long and they are generated from a simulation model, which is based on a geological model. Therefore to get reliable reservoir performance predictions, a reliable geological model is needed. However, there are many challenges encountered in the process of building a reliable geological model. An important challenge is the limited amount of information available to geologists. Another challenge is the non-uniqueness problem, where several models can fit the same data and give different future forecasts. To overcome these challenges, methods have been developed that integrate different data such as well logs, 3-D seismic and conceptual geological models into a comprehensive geological model. Noticeably, the use of conceptual geological models is increasing in integrated geological modeling, due to the recognition of their role in adding geological knowledge in geological models. Also, it is well known that stochastic modeling methods alone cannot accurately predict lithological distribution. The conceptual geological model is based on the geologist's knowledge, which results from interpretations of well data using principle geological rules. Unfortunately, due to integrated geological methods alone are not enough to solve the non-uniqueness problem. For these reasons new methods have been developed to rank geological models to help in selecting the best possible one. Advances in Reservoir Characterisation Studies Field development projects require huge financial investment and they are typically based on predictions of future reservoir performance. These predictions are generated from a reservoir simulation model, which is based on a geological model. Consequently, the reliability of the simulation model is highly dependent on the accuracy of the geological model (Jian et al. 2002).

No abstract