Reservoir characterization and volumetric estimation of Orok Field,Niger Delta hydrocarbon province

Geophysical analysis was undertaken on the available suit of well logs, 3D seismic and checkshot data from the field in a view to carrying out reservoir characterization and volumetric estimation of Orok Field Niger Delta Province. Lithology delineation and Petrophysical evaluation including porosity, water saturation and net-to-gross were undertaken. Well to seismic tie, structural interpretation and horizon mapping were carried out. Time and depth maps were generated and volumetric estimation of the reservoirs were carried out. Four gas-bearing reservoir (A001, B001, C001 and D001) of variable thickness and depth from the subsurface were identified. Their effective porosity ranges from 19% for A001, 20% for B001, 20% for C002 and 18% for the basal D001 respectively. Water saturation estimated from the reservoir sands ranges from 23% for A001, B001 20%, C001 12% and 10% for D001. The hydrocarbon saturation of the reservoirs stood at 77%, 80%, 82%, 90% for A001, B001, C001 and D001 respectively. Only sand A001 and sand B001 had good seismic tie and were able to be mapped through the seismic. Three synthetic structural building listric faults were interpreted across the inlines intercepting the top of reservoir Sand A001 and B001. The volumetric estimation reveals that A001 and B001 have an area of 813.708 acres and 813.693 acres and volume of 151?billion cu.ft and 286?billion cu.ft of gas respectively. Three synthetic structural building listric faults (F1, F2 and F3) intercept the top of sandstones A001 and B001. The structural trapping mechanism in both sandstones resulted from synthetic fault assisting structures i.e rollover anticlines. The modeled faults trends approximately northwest to southeast direction and dip in the southwest direction.     

Interactions between linguistic constraints: Procedural vs. declarative approaches

The traditional approach to generation is to derive a surface string from a semantic structure through various intermediate levels using a carefully ordered set of transformation steps. We show by some examples that this approach involves a lot of specific control decisions which cannot be generalized across several languages. We present a constraint-based approach where all levels of linguistic information are represented in a single structure. All levels introduce constraints on the linguistic structure stated as a set of feature type definitions. Relationships between levels are modelled as a set of (partial) relational constraints which apply simultaneously on all levels of the linguistic structure.Research reported in this paper is partly supported by the German Ministry of Research and Technology (BMFT, Bundesminister für Forschung und Technologie), under grant No. 08 B3116 3. The views and conclusions contained herein are those of the authors and should not be interpreted as representing official policies. We would like to thank John Bateman for his helpful comments on a previous version of this paper. The responsibility for all remaining errors resides, of course, with the authors.     

Learning strategies for task delegation in norm-governed environments

How do I choose whom to delegate a task to? This is an important question for an autonomous agent collaborating with others to solve a problem. Were similar proposals accepted from similar agents in similar circumstances? What arguments were most convincing? What are the costs incurred in putting certain arguments forward? Can I exploit domain knowledge to improve the outcome of delegation decisions? In this paper, we present an agent decision-making mechanism where models of other agents are refined through evidence from past dialogues and domain knowledge, and where these models are used to guide future delegation decisions. Our approach combines ontological reasoning, argumentation and machine learning in a novel way, which exploits decision theory for guiding argumentation strategies. Using our approach, intelligent agents can autonomously reason about the restrictions (e.g., policies/norms) that others are operating with, and make informed decisions about whom to delegate a task to. In a set of experiments, we demonstrate the utility of this novel combination of techniques. Our empirical evaluation shows that decision-theory, machine learning and ontology reasoning techniques can significantly improve dialogical outcomes.     

Prevalence and horizontal propagation of gonococcal infections among Nigerian children

The prevalence and mode of spread of gonococcal infections was studied among prepubertal children in Nigeria. Of 16 children with symptoms suggestive of sexually transmissible diseases (STD), 9 (56%) had gonorrhoea, while no causative organism was found in 7. The majority (7; 78%) of the gonococcal isolates produced penicillinase. Three of the cases were by child-to-child transmission, with female peers as the initiators. Prepubertal children should no longer be ignored as propagators of STD.     

How do instructional designers use automated instructional design tool?

The purpose of this study was to investigate the use of an automated design tool by naive, novice, and expert instructional designers. A talk-aloud protocol, attitude survey, performance assessment, and direct observation were used to gather data. While the expert designers used the tool, they used it as a word processor with a rich database of instructional strategies. The novice designers relied on the tool for advice, guidance, and assistance in completing all the design tasks. Non-designers used the tool for learning about design.     

Euphorbia hirta leaves and Musa sapientum fruits in culture media for fungi

Two plant products, Euphorbia hirta leaves and fruits of Musa sapientum, were evaluated as principal ingredients for selective cultivation of fungi. Sapientum glucose agar supported the growth of both dermatophytic, yeast-like, and saprophytic fungi; growth on this medium compared favourably with growth on Sabouraud glucose agar, a standard mycological medium. Sporulation and pigment formation were stronger on sapientum glucose agar than on Sabouraud glucose agar, although fungal growth on the latter was more luxuriant. Addition of Euphorbia extract to mycological media remarkably enhanced fungal growth on the media, and concomitantly suppressed bacterial growth to a similar extent as did antibiotics. The results of this study suggest that Euphorbia sapientum glucose agar can safely be recommended as a cheap and efficient medium for routine isolation of fungi in both clinical and general mycological studies.